From ab672d451d96f3df8e6db664173d515f0a01c83f Mon Sep 17 00:00:00 2001 From: Reinier Koops Date: Thu, 11 Apr 2024 12:39:18 +0200 Subject: [PATCH] Explicit support for Regression, performed major refactoring of tests, removed unused code and updated notebooks to work (again). (#248) This PR depends on the PR to be accepted: https://github.com/ing-bank/probatus/pull/242 ______ The PR fixes the following: - Removed unused code and so partially fixed: https://github.com/ing-bank/probatus/issues/245 - Updates all notebooks so they are all working: https://github.com/ing-bank/probatus/issues/246 - Add explicit support for regressors and multiclass: : https://github.com/ing-bank/probatus/issues/241 & : https://github.com/ing-bank/probatus/issues/169 - Updated the yaml files for github actions. Now we have a weekly cronjob (instead of daily) to test the notebooks. - Enable tests which were previously failing or only enabled in certain circumstances (much has changed the last 3 years with regards of adoption of the Mac ARM architecture support) - Removed most (if not all except one on purpose) of the copyright notice in code. --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/cronjob_unit_tests.yml | 14 +- .github/workflows/publish_to_pypi.yml | 2 - .github/workflows/unit_tests.yml | 10 +- CONTRIBUTING.md | 2 +- README.md | 2 +- docs/discussion/nb_rfecv_vs_shaprfecv.ipynb | 14616 ++++++++++++- docs/howto/grouped_data.ipynb | 163 +- docs/howto/reproducibility.ipynb | 62 +- .../nb_automatic_best_num_features.ipynb | 23 +- docs/tutorials/nb_custom_scoring.ipynb | 30 +- docs/tutorials/nb_sample_similarity.ipynb | 129 +- docs/tutorials/nb_shap_dependence.ipynb | 6675 +----- .../nb_shap_feature_elimination.ipynb | 17614 +++++++++++++++- .../tutorials/nb_shap_model_interpreter.ipynb | 291 +- ...iance_penalty_and_results_comparison.ipynb | 1413 +- mkdocs.yml | 2 +- probatus/__init__.py | 2 +- probatus/feature_elimination/__init__.py | 20 - .../feature_elimination.py | 119 +- probatus/interpret/__init__.py | 20 - probatus/interpret/model_interpret.py | 64 +- probatus/interpret/shap_dependence.py | 38 +- probatus/sample_similarity/__init__.py | 20 - .../sample_similarity/resemblance_model.py | 64 +- probatus/utils/__init__.py | 41 +- probatus/utils/_utils.py | 36 - probatus/utils/arrayfuncs.py | 107 +- probatus/utils/base_class_interface.py | 20 - probatus/utils/exceptions.py | 85 +- probatus/utils/missing_helpers.py | 42 - probatus/utils/plots.py | 91 - probatus/utils/scoring.py | 28 +- probatus/utils/shap_helpers.py | 24 +- probatus/utils/warnings.py | 42 - pyproject.toml | 4 +- tests/docs/test_notebooks.py | 18 +- .../test_feature_elimination.py | 77 +- tests/interpret/test_model_interpret.py | 3 - tests/interpret/test_shap_dependence.py | 37 +- .../test_resemblance_model.py | 3 - tests/utils/test_utils_array_funcs.py | 140 +- 41 files changed, 33296 insertions(+), 8897 deletions(-) delete mode 100644 probatus/utils/missing_helpers.py delete mode 100644 probatus/utils/plots.py delete mode 100644 probatus/utils/warnings.py diff --git a/.github/workflows/cronjob_unit_tests.yml b/.github/workflows/cronjob_unit_tests.yml index c9652be9..58ac25fa 100644 --- a/.github/workflows/cronjob_unit_tests.yml +++ b/.github/workflows/cronjob_unit_tests.yml @@ -1,11 +1,11 @@ name: Cron Test Dependencies # Controls when the action will run. -# Everyday at 4:05 -# See https://crontab.guru/#5_4_*_*_* +# Every sunday at 4:05 +# See https://crontab.guru/#5 4 * * 0 on: schedule: - - cron: "5 4 * * *" + - cron: "5 4 * * 0" jobs: run: @@ -17,13 +17,10 @@ jobs: include: - build: macos os: macos-latest - SKIP_LIGHTGBM: True - build: ubuntu os: ubuntu-latest - SKIP_LIGHTGBM: False - build: windows os: windows-latest - SKIP_LIGHTGBM: False python-version: [3.8, 3.9, "3.10", "3.11", "3.12"] steps: - uses: actions/checkout@master @@ -40,22 +37,17 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install Python dependencies - env: - SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }} run: | pip3 install --upgrade setuptools pip pip3 install ".[all]" - name: Run linters - env: - SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }} run: | pre-commit install pre-commit run --all-files - name: Run (unit) tests env: - SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }} TEST_NOTEBOOKS: 1 run: | pytest --cov=probatus/binning --cov=probatus/metric_volatility --cov=probatus/missing_values --cov=probatus/sample_similarity --cov=probatus/stat_tests --cov=probatus/utils --cov=probatus/interpret/ --ignore==tests/interpret/test_inspector.py --cov-report=xml diff --git a/.github/workflows/publish_to_pypi.yml b/.github/workflows/publish_to_pypi.yml index e8e9247c..0cb7c734 100644 --- a/.github/workflows/publish_to_pypi.yml +++ b/.github/workflows/publish_to_pypi.yml @@ -18,8 +18,6 @@ jobs: pip3 install --upgrade setuptools pip pip3 install ".[all]" - name: Run unit tests and linters - env: - SKIP_LIGHTGBM: False run: | pytest - name: Build package & publish to PyPi diff --git a/.github/workflows/unit_tests.yml b/.github/workflows/unit_tests.yml index 65bf2c51..40664ef6 100644 --- a/.github/workflows/unit_tests.yml +++ b/.github/workflows/unit_tests.yml @@ -16,13 +16,10 @@ jobs: include: - build: macos os: macos-latest - SKIP_LIGHTGBM: True - build: ubuntu os: ubuntu-latest - SKIP_LIGHTGBM: False - build: windows os: windows-latest - SKIP_LIGHTGBM: False python-version: [3.8, 3.9, "3.10", "3.11", "3.12"] steps: - uses: actions/checkout@master @@ -39,23 +36,18 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install Python dependencies - env: - SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }} run: | pip3 install --upgrade setuptools pip pip3 install ".[all]" - name: Run linters - env: - SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }} run: | pre-commit install pre-commit run --all-files - name: Run (unit) tests env: - SKIP_LIGHTGBM: ${{ matrix.SKIP_LIGHTGBM }} - TEST_NOTEBOOKS: 1 + TEST_NOTEBOOKS: 0 run: | pytest --cov=probatus/binning --cov=probatus/metric_volatility --cov=probatus/missing_values --cov=probatus/sample_similarity --cov=probatus/stat_tests --cov=probatus/utils --cov=probatus/interpret/ --ignore==tests/interpret/test_inspector.py --cov-report=xml pyflakes probatus diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5d9d750f..9625c151 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,6 +1,6 @@ # Contributing guide -`Probatus` aims to provide a set of tools that can speed up common workflows around validating binary classifiers and the data used to train them. +`Probatus` aims to provide a set of tools that can speed up common workflows around validating regressors & classifiers and the data used to train them. We're very much open to contributions but there are some things to keep in mind: - Discuss the feature and implementation you want to add on Github before you write a PR for it. On disagreements, maintainer(s) will have the final word. diff --git a/README.md b/README.md index 49c70ef9..b502eb65 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ ## Overview -**Probatus** is a python package that helps validate binary classification models and the data used to develop them. Main features: +**Probatus** is a python package that helps validate regression & (multiclass) classification models and the data used to develop them. Main features: - [probatus.interpret](https://ing-bank.github.io/probatus/api/model_interpret.html) provides shap-based model interpretation tools - [probatus.sample_similarity](https://ing-bank.github.io/probatus/api/sample_similarity.html) to compare two datasets using resemblance modelling, f.e. `train` with out-of-time `test`. diff --git a/docs/discussion/nb_rfecv_vs_shaprfecv.ipynb b/docs/discussion/nb_rfecv_vs_shaprfecv.ipynb index aec5982b..0902e928 100644 --- a/docs/discussion/nb_rfecv_vs_shaprfecv.ipynb +++ b/docs/discussion/nb_rfecv_vs_shaprfecv.ipynb @@ -1,45 +1,32 @@ { - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8-final" - }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.8 64-bit ('conda': virtualenv)", - "metadata": { - "interpreter": { - "hash": "9919f76640f8d69c95119de6d10b189fddd758802ef5abb02d09a410e646625a" - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 2, "cells": [ { + "cell_type": "markdown", + "metadata": {}, "source": [ "# ShapRFECV vs sklearn RFECV\n", "\n", + "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ing-bank/probatus/blob/master/docs/discussion/nb_rfecv_vs_shaprfecv.ipynb)\n", + "\n", "In this section we will compare the performance of the model trained on the features selected using the probatus [ShapRFECV](https://ing-bank.github.io/probatus/api/feature_elimination.html) and the [sklearn RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html).\n", "\n", "In order to compare them let's first prepare a dataset, and a model that will be applied:" - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install probatus\n", + "!pip install lightgbm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -61,43 +48,14390 @@ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n", "\n", "# Set up the model:\n", - "clf = lightgbm.LGBMClassifier(n_estimators=10, num_leaves=7)" + "model = lightgbm.LGBMClassifier(n_estimators=10, num_leaves=7, random_state=0)" ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Now, we can run ShapRFECV and RFECV with the same parameters, to extract the optimal feature sets:" - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { - "output_type": "display_data", + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000838 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001032 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000940 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000915 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000906 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000902 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000882 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000969 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001013 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000805 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001116 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000886 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000970 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001410 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001092 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001650 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001065 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002925 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000933 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000740 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001145 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000738 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002417 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001140 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001589 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000820 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000719 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001476 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000826 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001368 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000758 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001101 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001061 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000744 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000796 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000741 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000693 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000771 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000663 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000914 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000837 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000762 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000652 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000953 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000638 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001017 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000763 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000928 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000634 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000919 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000811 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000767 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000651 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000616 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001152 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000742 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000655 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000741 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000759 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001001 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001129 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 5355[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000662 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000703 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000565 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000704 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000601 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000575 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000619 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000820 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000149 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000937 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001021 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000895 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001064 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001713 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000833 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001018 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000896 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000934 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001791 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000929 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000987 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000709 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000812 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001391 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000871 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000861 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000978 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000753 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001019 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001132 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000773 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000754 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000823 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000774 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001024 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001015 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000966 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000866 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000837 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000804 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000861 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000889 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001031 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000770 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001033 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000755 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000709 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000976 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001023 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Total Bins 9180\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000851 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000951 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000706 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000927 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001165 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000741 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000730 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000964 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001337 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000705 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000725 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000767 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000763 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140[LightGBM] [Info] Total Bins 7140\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000626 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000645 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000811 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 5610[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000461 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000557 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000675 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000695 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000543 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000664 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000533 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000798 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000911 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000869 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000811 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001022 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000773 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000855 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001027 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001878 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002549 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000901 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000971 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000914 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000832 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Total Bins 11730\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000983 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000722 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000983 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000801 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000893 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000804 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000951 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000907 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001408 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000849 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000876 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000956 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001019 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000918 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000954 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000988 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000851 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000762 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000908 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000740 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000768 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000751 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000543 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000968 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000661 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002441 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001430 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000769 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000661 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000620 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000787 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000765 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000725 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000910 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000715 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000431 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000687 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000603 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000685 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001460 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000984 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000780 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000678 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000633 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000730 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001072 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000766 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000640 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000687 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000590 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000459 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000995 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000428 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000976 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000956 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000854 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000981 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000920 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001306 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000863 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000855 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002509 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000995 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000788 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000770 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000951 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000893 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000892 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000863 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000843 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000740 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001001 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000916 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000840 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000745 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001078 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000804 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001134 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000819 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000818 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000692 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000813 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000798 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000910 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000790 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000736 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000958 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000832 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000835 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000713 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000743 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000801 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000709 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000714 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000993 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000712 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000956 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000723 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000538 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000734 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000644 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000792 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000714 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000693 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000868 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000706 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000645 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000504 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000621 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000661 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000669 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000922 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000425 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 1530[LightGBM] [Info] Total Bins 1275\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000813 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000900 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001097 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000941 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000895 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000866 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000925 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000950 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000815 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000865 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000817 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000890 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000756 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000809 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000788 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000863 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000795 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000900 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000971 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000804 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001398 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000905 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001114 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001046 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000631 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000785 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000887 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000795 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000838 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000684 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000715 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000830 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001378 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000789 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000798 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000958 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001040 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001056 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Total Bins 7905[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000830 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001342 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000657 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000833 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000678 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000950 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000748 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000626 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000724 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000937 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000762 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000774 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000789 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000518 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000559 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000627 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000767 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805[LightGBM] [Info] Total Bins 3315\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000757 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000802 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000802 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000643 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000559 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000670 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000864 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000987 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000922 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000833 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000956 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000952 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000820 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000954 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000961 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000757 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001030 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000924 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001759 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000985 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000959 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11985[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000794 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000880 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000835 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000782 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000767 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000977 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000917 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000808 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000735 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000886 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000791 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000750 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000697 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000907 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000793 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001027 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000911 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000766 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002575 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000761 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001577 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000760 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000728 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000749 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000883 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001877 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000961 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000939 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000507 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000739 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000667 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000813 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000543 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001136 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000622 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000664 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001098 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000638 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000829 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000716 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000957 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000687 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000807 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000708 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000518 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000827 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000454 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000662 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Total Bins 6630\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Total Bins 5610\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000626 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000641 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000564 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000470 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000801 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000564 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000692 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000950 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001715 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000885 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000110 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000880 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000871 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000841 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000974 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001340 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000826 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000914 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000644 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000829 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000741 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000762 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000851 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000752 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000808 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000659 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000736 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000746 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000851 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000605 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000709 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000670 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000590 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35[LightGBM] [Info] Start training from score 0.001684\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000662 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000644 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000611 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000484 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315[LightGBM] [Info] Total Bins 3570\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Start training from score 0.001684\n", + "\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000759 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000754 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000715 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000593 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000640 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000896 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001125 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000858 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000943 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000782 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000995 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001858 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002042 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001591 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001118 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001139 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000932 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000954 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001077 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001122 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001114 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001157 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000990 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001010 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000831 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001035 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001052 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001032 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001778 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000966 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000897 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000921 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000936 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000922 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002404 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000870 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001091 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001023 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001356 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001079 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000790 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000947 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000917 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.001684[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000736 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000772 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Total Bins 12495\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 49\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000983 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000995 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000849 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000935 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000951 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001752 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000918 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001061 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000824 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000944 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001010 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000768 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001573 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000908 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001012 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000702 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000812 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 12240\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 48\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000806 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000860 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000858 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001020 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000935 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001040 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001105 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001324 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000962 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001142 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000983 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000938 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000793 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001065 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001023 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001080 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001028 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001020 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000785 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000791 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11985\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 47[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001006 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001135 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001095 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002085 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001126 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001082 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000969 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001000 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000890 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000909 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000891 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000855 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001452 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000944 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000992 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000932 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11730\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 46\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000937 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000867 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000925 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001075 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001112 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001116 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000711 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000913 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000909 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001003 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000943 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001062 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000818 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000906 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000873 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000929 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.001684[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000904 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000773 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000753 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 11475\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 45\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000987 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000866 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000981 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000719 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000795 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001437 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000886 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001070 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000918 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000954 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000805 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000814 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001093 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000915 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000874 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000729 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000754 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Total Bins 11220\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 44[LightGBM] [Info] Start training from score 0.001684\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001088 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001953 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001081 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000956 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000978 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000987 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001015 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001131 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000926 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000856 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000740 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000929 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001033 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000815 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000969 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000809 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000840 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10965\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 43\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000938 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000917 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000983 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000795 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001003 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000575 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000948 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000801 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Total Bins 10710\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000725 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001847 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000771 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001128 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000927 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001014 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000770 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000858 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000824 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10710\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 42\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000905 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000779 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001030 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000855 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000793 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000855 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000945 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000898 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000964 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000814 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000841 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000649 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000837 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000899 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10455\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 41\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000570 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000751 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000794 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000725 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000834 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000720 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000879 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001080 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001815 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000869 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000934 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000937 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000799 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001029 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000944 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000722 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000704 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 10200\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 40\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000763 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000814 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001155 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000911 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000823 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000723 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000837 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000868 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000900 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001083 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000706 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000762 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000764 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] Total Bins 9945\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 39\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000861 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000945 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000909 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000831 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001440 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001340 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000715 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000728 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002314 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001034 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001010 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001148 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001238 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001011 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000926 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000630 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9690\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 38\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000973 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000943 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000754 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000755 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000753 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000798 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000845 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000921 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000928 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000807 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000749 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000707 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000782 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001737 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000817 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000693 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9435\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 37\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001027 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001055 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000903 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000665 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000698 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000689 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000774 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000782 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001002 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001151 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000634 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001654 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001170 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000878 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001103 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000698 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 9180\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 36\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000841 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000753 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001094 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000749 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000749 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000760 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000709 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000848 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000981 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000736 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000685 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000812 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 8925\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 35\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000838 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000840 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8670[LightGBM] [Info] Total Bins 8670\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000926 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001409 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001560 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000805 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000817 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000771 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000918 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 8670\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 34\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001459 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000977 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001322 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000768 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000906 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001097 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000659 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000831 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000565 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 8415\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 33\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001002 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000941 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000667 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000811 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000724 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000843 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000928 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000772 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] Total Bins 8160\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 32\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000755 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000664 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Total Bins 7905\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000857 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000892 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905[LightGBM] [Info] Total Bins 7905\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000747 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000735 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000721 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000708 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000657 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7905\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 31\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000926 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000984 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000808 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000498 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000691 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000761 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000652 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000688 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000741 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001025 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000663 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000695 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7650\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001053 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001129 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000645 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Total Bins 7395\n", + "\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000873 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000906 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000791 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000724 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000665 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000724 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000695 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000530 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7395\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684[LightGBM] [Info] Total Bins 7395\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 29\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000913 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000945 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000791 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000743 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000759 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000506 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000871 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000680 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000859 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000657 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000662 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000990 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000667 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000667 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Total Bins 7140\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 28\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001012 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000970 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000812 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000772 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000949 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000905 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000772 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001103 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001458 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000575 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Total Bins 6885\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 27\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000618 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000848 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000700 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001000 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000668 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000954 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000685 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000742 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001899 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000704 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6630\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 26\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000781 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000598 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000725 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000570 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000562 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000640 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Total Bins 6375\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000564 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001487 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000789 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000934 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6375\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 25\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000870 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000955 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000611 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000621 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Total Bins 6120\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Total Bins 6120\n", + "\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000716 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000601 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 6120\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 24\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000685 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000497 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001073 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Total Bins 5865\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 23\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000878 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000700 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000747 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000934 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000711 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000682 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000663 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000805 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000640 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001010 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001044 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000959 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 5610\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 22\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000748 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000638 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000685 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000744 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000601 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000979 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000661 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000622 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] Total Bins 5355\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 21\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000621 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000603 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000980 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000799 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000865 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000479 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000817 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 5100\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 20\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000668 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000962 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000971 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000643 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000758 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000965 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000712 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000591 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000958 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000532 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Total Bins 4590\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 18\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000605 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000562 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001147 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000768 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000756 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000619 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Total Bins 3825[LightGBM] [Info] Total Bins 3825\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000799 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000887 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000731 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000620 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000619 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000832 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000543 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3570\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 14\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000875 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000668 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000460 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000519 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000533 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3060\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 12\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000770 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000396 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001117 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000688 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000504 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000655 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000460 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000315 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001317 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001641 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Total Bins 1530\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000611 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000698 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684[LightGBM] [Info] Start training from score 0.001684\n", + "\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000706 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000570 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765[LightGBM] [Info] Total Bins 765\n", + "\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 3\n", + "\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000774 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Total Bins 510[LightGBM] [Info] Total Bins 510\n", + "\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526[LightGBM] [Info] Start training from score 0.002526\n", + "\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2378, number of negative: 2372\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500632 -> initscore=0.002526\n", + "[LightGBM] [Info] Start training from score 0.002526\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n", + "[LightGBM] [Info] Number of positive: 2377, number of negative: 2373\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 4750, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500421 -> initscore=0.001684\n", + "[LightGBM] [Info] Start training from score 0.001684\n" + ] + }, + { "data": { - "text/plain": "
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\n" 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", + "text/plain": [ + "
" + ] }, - "metadata": { - "needs_background": "light" - } + "metadata": {}, + "output_type": "display_data" } ], "source": [ "# Run RFECV and ShapRFECV with the same parameters\n", - "rfe = RFECV(clf, step=1, cv=20, scoring=\"roc_auc\", n_jobs=3).fit(X_train, y_train)\n", - "shap_elimination = ShapRFECV(clf=clf, step=1, cv=20, scoring=\"roc_auc\", n_jobs=3)\n", + "rfe = RFECV(model, step=1, cv=20, scoring=\"roc_auc\", n_jobs=3).fit(X_train, y_train)\n", + "shap_elimination = ShapRFECV(model=model, step=1, cv=20, scoring=\"roc_auc\", n_jobs=3, random_state=0)\n", "shap_report = shap_elimination.fit_compute(X_train, y_train)\n", "\n", "# Compare the CV Validation AUC for different number of features in each method.\n", "ax = pd.DataFrame(\n", " {\n", - " \"RFECV Validation AUC\": list(reversed(rfe.grid_scores_)),\n", + " \"RFECV Validation AUC\": list(reversed(rfe.cv_results_[\"mean_test_score\"])),\n", " \"ShapRFECV Validation AUC\": shap_report[\"val_metric_mean\"].values.tolist(),\n", " },\n", " index=shap_report[\"num_features\"].values.tolist(),\n", @@ -109,54 +14443,160 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ - "The plot above presents the averaged CV Validation AUC of model performance for each round of the RFE process in both ShapRFECV and RFECV. The optimal number of features is 21 for the former, and 13 for the latter.\n", + "The plot above presents the averaged CV Validation AUC of model performance for each round of the RFE process in both ShapRFECV and RFECV. The optimal number of features is 16 (based on the highest validation metric mean) for the former, and 15 for the latter.\n", "\n", "Now we will compare the performance of the model trained on:\n", "\n", "- All 50 available features (baseline),\n", - "- 13 features selected by RFECV (final),\n", - "- 21 features selected by ShapRFECV (final),\n", - "- 13 feature selected by ShapRFECV (baseline)." - ], - "cell_type": "markdown", - "metadata": {} + "- 15 features selected by RFECV (final),\n", + "- 16 features selected by ShapRFECV (final),\n", + "- 15 feature selected by ShapRFECV (baseline)." + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { - "output_type": "display_data", + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000759 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2253, number of negative: 2247\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500667 -> initscore=0.002667\n", + "[LightGBM] [Info] Start training from score 0.002667\n", + "[LightGBM] [Info] Number of positive: 2253, number of negative: 2247\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000692 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500667 -> initscore=0.002667\n", + "[LightGBM] [Info] Start training from score 0.002667\n", + "[LightGBM] [Info] Number of positive: 2253, number of negative: 2247\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500667 -> initscore=0.002667\n", + "[LightGBM] [Info] Start training from score 0.002667\n", + "[LightGBM] [Info] Number of positive: 2253, number of negative: 2247\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500667 -> initscore=0.002667\n", + "[LightGBM] [Info] Start training from score 0.002667\n", + "[LightGBM] [Info] Number of positive: 2253, number of negative: 2247\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500667 -> initscore=0.002667\n", + "[LightGBM] [Info] Start training from score 0.002667\n", + "[LightGBM] [Info] Number of positive: 2253, number of negative: 2247\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000769 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500667 -> initscore=0.002667\n", + "[LightGBM] [Info] Start training from score 0.002667\n", + "[LightGBM] [Info] Number of positive: 2253, number of negative: 2247\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000725 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500667 -> initscore=0.002667\n", + "[LightGBM] [Info] Start training from score 0.002667\n", + "[LightGBM] [Info] Number of positive: 2252, number of negative: 2248\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500444 -> initscore=0.001778\n", + "[LightGBM] [Info] Start training from score 0.001778\n", + "[LightGBM] [Info] Number of positive: 2252, number of negative: 2248\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000726 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500444 -> initscore=0.001778\n", + "[LightGBM] [Info] Start training from score 0.001778\n", + "[LightGBM] [Info] Number of positive: 2252, number of negative: 2248\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 12750\n", + "[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 50\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500444 -> initscore=0.001778\n", + "[LightGBM] [Info] Start training from score 0.001778\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n", + "[LightGBM] [Info] Number of positive: 2503, number of negative: 2497\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 5000, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500600 -> initscore=0.002400\n", + "[LightGBM] [Info] Start training from score 0.002400\n" + ] + }, + { "data": { - "text/plain": "
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uWCGjNnToUMaNG8ekSZNYvHgxt99+O8cffzz9+vXjL3/5CxMnTmT99dev337WrFmcffbZPPDAA0ycOJFx48Zx6623AvDmm2/Sv39/nnzySQ444ACuuuqqBsszfPhwBg8ezODBgxk+fHgj3+JyjX2fZmZmawsHZ61o8ODBjBgxgiVLlnDrrbfymc98ZrW2u/TSS+szVDNmzGDatGmrfK+xY8cyePBg2rVrx9Zbb83BBx9cv+7BBx9k7733Ztddd+WBBx5g8uTJjZZ73LhxDBgwgC5dutC+fXtOOukkxo4dC0DHjh054ogjAOjbty91dXUr7f/qq68ybdo09ttvP3bccUc6dOjApEmTgIY787uDv5mZtSUOzmpol112YcKECatcP2jQIG666Sbuv/9+dtttt1X2m2pouzFjxnD//ffz6KOP8uSTT7L77ru/r+kj3n77bb7yla8watQonn76aU499dQPNA1Fhw4d6oOpdu3asWTJkpW2uemmm5g3bx7du3enW7du1NXV1WfPNttsM+bNm1e/7euvv07nzp2Bpr9PMzOztYGDsxo6+OCDeeedd1boD/bUU0/xj3/8A4AddtiBzp07M2zYsAabNEsa2m7BggVsuummfOhDH+LZZ5/lsccea7QsBxxwACNHjmTp0qW8/PLLPPjggwD1gVjnzp1ZtGjRCiMlN9xwQxYuXLjSa+2111489NBDvPbaayxdupThw4dz4IEHVvmtpCbNu+++m7q6Ourq6pgwYUJ9v7MBAwYwcuRI3n33XSANSjjooIMAOPHEE3nkkUe4447lAzvGjh1bn3UzMzNbGzg4qyFJ3HLLLdx///3ssMMO7LLLLpxzzjlsueWW9dsMHjyYZ599lmOPPbbR16rc7tBDD2XJkiXstNNODBs2bKUpKSodc8wx9OzZk5133pmTTz6ZffbZB4BNNtmEU089lV69evGpT32KPffcs36fIUOGcMYZZ9QPCCjZaqutuOiiizjooIPo3bs3ffv25eijj67qO6mrq+PFF19cobzdu3dn44035vHHH+eII45g//33p2/fvvTp04eHH364ftqQ9ddfn9tvv53LLrus/rNcccUVdOnSpar3NjMzWxOs1Tc+f+aZZ9hpp51aqUTWmvzbm5lZkTV243NnzszMzMwKxMGZmZmZWYE4ODMzMzMrEAdnZmZmZgXi4MzMzMysQBycmZmZmRWIg7Mamjt3Ln369KFPnz5sueWWbLPNNvXPS5OsNmbMmDEr3AOzIQMHDlxpjrMhQ4asMJksLL8BOsDUqVM57LDD6NmzJ3vssQcnnHDCKm9QbmZmZi2rfWsXoEWdv3Ezv96CRldvttlmTJw4MW16/vl06tSJs846q+qXHzNmDJ06dWLfffdtcP38+fOZMGECnTp1Yvr06fTo0aPJ13z77bc5/PDDueSSSzjyyCPr32fOnDmrvH2UmZmZtRxnzlrYhAkTOPDAA+nbty+f+tSnePnll4F0E/Odd96Z3XbbjUGDBlFXV8fvf/97fv3rX9OnT5/6Wz6Vu/nmmznyyCMZNGhQ/e2PmnLjjTeyzz771AdmkG6Z1KtXr+b5gGZmZvaBtK3MWSuLCL72ta/x97//nS5dujBy5EjOPfdcrr32Wi666CJeeOEF1l13XebPn88mm2zCGWec0Wi2bfjw4fzgBz9giy224LjjjuN73/tek2WYNGkSffv2be6PZmZmZs3EwVkLeuedd5g0aRKHHHIIAEuXLmWrrbYCYLfdduOkk05i4MCBDBw4sMnXevXVV5k2bRr77bcfkujQoQOTJk2iV69eSFpp+4aWmZmZWfG4WbMFRQS77LILEydOZOLEiTz99NPce++9ANxxxx189atf5YknnmDPPfdkyZIljb7WTTfdxLx58+jevTvdunWjrq6O4cOHA6mv27x58+q3ff311+ncuTMAu+yyCxMmTKjRJzQzM7MPysFZC1p33XWZM2cOjz76KADvvfcekydPZtmyZcyYMYODDjqIiy++mAULFrBo0SI23HBDFi5c2OBrDR8+nLvvvpu6ujrq6uqYMGFCfb+zAQMGMHLkyPoRoddddx0HHXQQACeeeCKPPPIId9xxR/1rjR07lkmTJtXyo5uZmVmVHJy1oHXWWYdRo0Zx9tln07t3b/r06cMjjzzC0qVL+dznPseuu+7K7rvvzplnnskmm2zCkUceyS233LLSgIC6ujpefPHFFabQ6N69OxtvvDGPP/44RxxxBPvvvz99+/alT58+PPzww1x88cUArL/++tx+++1cdtll9OzZk5133pkrrriCLl26tPj3YWZmZitTRLR2GZpFv379Yvz48Ssse+aZZ9hpp51aqUTWmvzbm1lb123YHU1v1ELqLjq8tYtQOJImRES/htY5c2ZmZmZWIA7OzMzMzArEwZmZmZlZgaz1wdna0qfOquff3MzM1mRrdXC23nrrMXfuXJ+s25CIYO7cuay33nqtXRQzM7P3Za2+Q0DXrl2ZOXMmc+bMae2iWAtab7316Nq1a2sXw8zM7H1Zq4OzDh060L1799YuhpmZmVnV1upmTTMzM7M1jYMzMzMzswJxcGZmZmZWIA7OzMzMzArEwZmZmZlZgTg4MzMzMysQB2dmZmZmBeLgzMzMzKxAHJyZmZmZFYiDMzMzM7MCcXBmZmZmViA1Dc4kHSrpOUnPSxrWwPohkuZImpgfXypb93NJkyU9I+lSSaplWc3MzMyKoGY3PpfUDrgcOASYCYyTNDoiplRsOjIihlbsuy/w/4Dd8qJ/AgcCY2pVXjMzM7MiqGXmbC/g+YiYHhHvAiOAo6vcN4D1gI7AukAH4NWalNLMzMysQGoZnG0DzCh7PjMvq3ScpKckjZK0LUBEPAo8CLycH/dExDOVO0o6TdJ4SePnzJnT/J/AzMzMrIW19oCA24BuEbEbcB9wPYCkjwA7AV1JAd3Bkvav3DkiroyIfhHRr0uXLi1YbDMzM7PaqGVw9hKwbdnzrnlZvYiYGxHv5KdXA33z38cAj0XEoohYBNwF7FPDspqZmZkVQi2Ds3FAT0ndJXUEBgGjyzeQtFXZ06OAUtPlf4EDJbWX1IE0GGClZk0zMzOztU3NRmtGxBJJQ4F7gHbAtRExWdKFwPiIGA2cKekoYAnwOjAk7z4KOBh4mjQ44O6IuK1WZTUzMzMripoFZwARcSdwZ8WyH5T9fQ5wTgP7LQVOr2XZzMzMzIqotQcEmJmZmVkZB2dmZmZmBVLTZk0zM2s+3Ybd0dpFqFd30eGtXQSztZYzZ2ZmZmYF4uDMzMzMrEAcnJmZmZkViIMzMzMzswJxcGZmZmZWIA7OzMzMzArEwZmZmZlZgTg4MzMzMysQB2dmZmZmBeLgzMzMzKxAHJyZmZmZFYiDMzMzM7MCcXBmZmZmViAOzszMzMwKxMGZmZmZWYE4ODMzMzMrEAdnZmZmZgXi4MzMzMysQNq3dgHMzMxsLXf+xq1dguXOX9DaJWiSM2dmZmZmBeLgzMzMzKxAHJyZmZmZFYiDMzMzM7MCcXBmZmZmViAerWnWyroNu6O1i1Cv7qLDW7sIZmZtnjNnZmZmZgXSZHAmaQtJ10i6Kz/fWdIXa180MzMzs7anmszZdcA9wNb5+VTgGzUqj5mZmVmbVk1w1jkibgKWAUTEEmBpTUtlZmZm1kZVE5y9KWkzIAAk9QeKf+8DMzMzszVQNaM1vwWMBnaQ9DDQBTi+pqUyMzMza6OaDM4i4glJBwIfBQQ8FxHv1bxkZmZmZm1QNaM1vwp0iojJETEJ6CTpK7UvmpmZmVnbU02fs1MjYn7pSUTMA06tWYnMzMzM2rBqgrN2klR6Iqkd0LF2RTIzMzNru6oZEHA3MFLSH/Lz0/MyMzMzM2tm1QRnZ5MCsi/n5/cBV9esRGZmZmZtWDWjNZcB/5sfZmZmZlZDTQZnkv4fcD6wfd5eQEREj9oWzczMzKztqaZZ8xrgm8AEfNsmMzMzs5qqJjhbEBF31bwkZmZmZlZVcPagpF8ANwPvlBZGxBM1K5WZmRXb+Ru3dgmWO9+3e7a1SzXB2d75335lywI4uKkdJR0K/BZoB1wdERdVrB8C/AJ4KS/6XURcnddtRxoVum1+v8Mioq6K8pqZmZmtsaoZrXnQ+3nhPFnt5cAhwExgnKTRETGlYtORETG0gZe4AfhJRNwnqROw7P2Uw8zMzGxNUk3mDEmHA7sA65WWRcSFTey2F/B8REzPrzECOBqoDM4aer+dgfYRcV9+r0XVlNMa4KYHMzOzNUo1Nz7/PfBZ4GukaTQ+Q5pWoynbADPKns/MyyodJ+kpSaMkbZuX7QjMl3SzpH9L+kXOxFWW7TRJ4yWNnzNnThVFMjMzMyu2au6tuW9EnAzMi4gLgH1IwVNzuA3oFhG7ke48cH1e3h7YHzgL2BPoAQyp3DkiroyIfhHRr0uXLs1UJDMzM7PWU01wtjj/+5akrYH3gK2q2O8lUmf+kq4s7/gPQETMjYjSCNCrgb7575nAxIiYHhFLgFuBPap4TzMzM7M1WjXB2e2SNiGNqnwCqAOGV7HfOKCnpO6SOgKDgNHlG0gqD/KOAp4p23cTSaV02MFU0VfNzMzMbE1XzWjNH+U//ybpdmC9iGiyZ3dELJE0FLiHNJXGtRExWdKFwPiIGA2cKekoYAnwOrnpMiKWSjoL+D9JIt2d4KrV/3hmZmZma5Zq7q3ZDjgc6FbaXhIRcUlT+0bEncCdFct+UPb3OcA5q9j3PmC3pt7DzMzMbG1SzVQatwFvA0/jucbMzMzMaqqa4KxrHk1pZmZmZjVWzYCAuyR9suYlMTMzM7OqMmePAbdIWoc0jYaAiIiNaloyMzMzszaomuDsEtLEs09HRNS4PGZmZmZtWjXNmjOASQ7MzMzMzGqvmszZdGCMpLuA0mz+VU2lYWZmZmarp5rg7IX86JgfZmZmZlYjjQZneQLaHSPipBYqj5mZmVmb1mifs4hYCmyf741pZmZmZjVWbZ+zhyWNBt4sLXSfMzMzM7PmV01w9p/8WAfYsLbFMTMzM2vbmgzOIuICAEmd8vNFtS6UmZmZWVvV5DxnknpJ+jcwGZgsaYKkXWpfNDMzM7O2p5pJaK8EvhUR20fE9sC3gatqWywzMzOztqma4GyDiHiw9CQixgAb1KxEZmZmZm1YVaM1JX0f+FN+/jnSCE4zMzMza2bVZM6+AHQBbgb+BnTOy8zMzMysma0ycybpTxHxP8DJEXFmC5bJzMzMrM1qLHPWV9LWwBckbSrpw+WPliqgmZmZWVvSWJ+z3wP/B/QAJgAqWxd5uZmZmZk1o1VmziLi0ojYCbg2InpERPeyhwMzMzMzsxpodECApHbAQS1UFjMzM7M2r9HgLCKWAs9J2q6FymNmZmbWplUzz9mmpNs2/Qt4s7QwIo6qWanMzMzM2qhqgrPv17wUZmZmZgZUEZxFxEOStgd6RsT9kj4EtKt90czMzMzaniaDM0mnAqcBHwZ2ALYhTbPx8doWzcxa3Pkbt3YJljt/QWuXwMysVVRz+6avAv8PeAMgIqYBm9eyUGZmZmZtVTXB2TsR8W7piaT2pElozczMzKyZVROcPSTpXGB9SYcAfwVuq22xzMzMzNqmaoKzYcBs4GngdOBO4LxaFsrMzMysrVrlgABJmwPfAz5CCsy+GBFvtFTBzMzMzNqixjJnN5Amnb0M6ARc2iIlMjMzM2vDGptKY6uIODf/fY+kJ1qiQGZmZmZtWaPznEnaFFB+2q78eUS8XuOymZmZmbU5jQVnGwMTWB6cAZSyZwH0qFWhzMzMzNqqVQZnEdGtBcthZmZmZlQ3lYaZmZmZtRAHZ2ZmZmYF4uDMzMzMrEAam4T2w43t6NGaZmZmZs2vsdGaE0ijMtXAOo/WNDMzM6uBxkZrdm/JgpiZmZlZFX3OlHxO0vfz8+0k7VX7opmZmZm1PdUMCLgC2Ac4MT9fCFxezYtLOlTSc5KelzSsgfVDJM2RNDE/vlSxfiNJMyX9rpr3MzMzM1vTNXr7pmzviNhD0r8BImKepI5N7SSpHSmIOwSYCYyTNDoiplRsOjIihq7iZX4EjK2ijGZmZmZrhWoyZ+/lQCsAJHUBllWx317A8xExPSLeBUYAR1dbMEl9gS2Ae6vdx8zMzGxNV01wdilwC7C5pJ8A/wR+WsV+2wAzyp7PzMsqHSfpKUmjJG0LIGkd4FfAWVW8j5mZmdlao8lmzYj4i6QJwMdJ02oMjIhnmun9bwOGR8Q7kk4HrgcOBr4C3BkRM6WGZvJIJJ0GnAaw3XbbNVORzMzMzFpPtZPQzgaGl6+rYhLal4Bty553zcvqRcTcsqdXAz/Pf+8D7C/pK0AnoKOkRRExrGL/K4ErAfr16xdNlKfFdBt2R2sXoV7deq1dAjMzM1sd1U5Cux0wL/+9CfBfoKl50MYBPSV1JwVlg1g+4hMASVtFxMv56VHAMwARcVLZNkOAfpWBmZmZmdnaqMlJaCVdBdwSEXfm558GBjb1whGxRNJQ4B6gHXBtREyWdCEwPiJGA2dKOgpYArwODPlgH8fMzMxszVbNVBr9I+LU0pOIuEvSzxvboWzbO4E7K5b9oOzvc4BzmniN64Drqnk/MzMzszVdNcHZLEnnAX/Oz08CZtWuSGZmZmZtVzVTaQwGupCm07gF2DwvMzMzM7NmVs1UGq8DX5e0YXoai2pfLDMzM7O2qZobn++ab900CZgsaYKkXrUvmpmZmVnbU02z5h+Ab0XE9hGxPfBt8txiZmZmZta8qgnONoiIB0tPImIMsEHNSmRmZmbWhlUzWnO6pO8Df8rPPwdMr12RzMzMzNquajJnXyCN1rw5P7rkZWZmZmbWzKoZrTkPOLMFymJmZmbW5jV24/PRje0YEUc1f3HMzMzM2rbGMmf7ADOA4cDjpJuem5mZmVkNNRacbQkcQrobwInAHcDwiJjcEgUzMzMza4tWOSAgIpZGxN0RcQrQH3geGCNpaIuVzszMzKyNaXRAgKR1gcNJ2bNuwKWk+2uamZmZWQ00NiDgBqAXcCdwQURMarFSmZmZmbVRjWXOPge8CXwdOFOqHw8g0g3QN6px2czMzMzanFUGZxFRzQS1ZmZmZtaMHICZmZmZFYiDMzMzM7MCcXBmZmZmViAOzszMzMwKxMGZmZmZWYE4ODMzMzMrEAdnZmZmZgXi4MzMzMysQBycmZmZmRWIgzMzMzOzAnFwZmZmZlYgDs7MzMzMCsTBmZmZmVmBODgzMzMzKxAHZ2ZmZmYF4uDMzMzMrEAcnJmZmZkViIMzMzMzswJxcGZmZmZWIA7OzMzMzArEwZmZmZlZgTg4MzMzMysQB2dmZmZmBeLgzMzMzKxAHJyZmZmZFYiDMzMzM7MCcXBmZmZmViAOzszMzMwKxMGZmZmZWYHUNDiTdKik5yQ9L2lYA+uHSJojaWJ+fCkv7yPpUUmTJT0l6bO1LKeZmZlZUbSv1QtLagdcDhwCzATGSRodEVMqNh0ZEUMrlr0FnBwR0yRtDUyQdE9EzK9Vec3MzMyKoJaZs72A5yNiekS8C4wAjq5mx4iYGhHT8t+zgNlAl5qV1MzMzKwgahmcbQPMKHs+My+rdFxuuhwladvKlZL2AjoC/2lg3WmSxksaP2fOnOYqt5mZmVmrae0BAbcB3SJiN+A+4PrylZK2Av4EfD4illXuHBFXRkS/iOjXpYsTa2ZmZrbmq2Vw9hJQngnrmpfVi4i5EfFOfno10Le0TtJGwB3AuRHxWA3LaWZmZlYYtQzOxgE9JXWX1BEYBIwu3yBnxkqOAp7JyzsCtwA3RMSoGpbRzMzMrFBqNlozIpZIGgrcA7QDro2IyZIuBMZHxGjgTElHAUuA14EhefcTgAOAzSSVlg2JiIm1Kq+ZmZlZEdQsOAOIiDuBOyuW/aDs73OAcxrY78/An2tZNjMzM7Miau0BAWZmZmZWxsGZmZmZWYE4ODMzMzMrEAdnZmZmZgXi4MzMzMysQBycmZmZmRWIgzMzMzOzAnFwZmZmZlYgDs7MzMzMCsTBmZmZmVmBODgzMzMzKxAHZ2ZmZmYF4uDMzMzMrEAcnJmZmZkViIMzMzMzswJxcGZmZmZWIA7OzMzMzArEwZmZmZlZgTg4MzMzMysQB2dmZmZmBeLgzMzMzKxAHJyZmZmZFYiDMzMzM7MCcXBmZmZmViAOzszMzMwKxMGZmZmZWYE4ODMzMzMrEAdnZmZmZgXi4MzMzMysQBycmZmZmRWIgzMzMzOzAnFwZmZmZlYgDs7MzMzMCsTBmZmZmVmBODgzMzMzKxAHZ2ZmZmYF4uDMzMzMrEAcnJmZmZkViIMzMzMzswJxcGZmZmZWIA7OzMzMzArEwZmZmZlZgTg4MzMzMysQB2dmZmZmBeLgzMzMzKxAahqcSTpU0nOSnpc0rIH1QyTNkTQxP75Utu4USdPy45RaltPMzMysKNrX6oUltQMuBw4BZgLjJI2OiCkVm46MiKEV+34Y+CHQDwhgQt53Xq3Ka2ZmZlYENQvOgL2A5yNiOoCkEcDRQGVw1pBPAfdFxOt53/uAQ4Hhq9ph+pw3+ewfHv3AhV7bfPad81q7CMv59yk81xerluuKrQ7Xl9VTy+BsG2BG2fOZwN4NbHecpAOAqcA3I2LGKvbdpnJHSacBp+Wni246Y9/nmqPga5MXoTPwWmuXw9YMri9WLdcVWx2uLw3aflUrahmcVeM2YHhEvCPpdOB64OBqd46IK4Era1W4tYGk8RHRr7XLYWsG1xerluuKrQ7Xl9VTywEBLwHblj3vmpfVi4i5EfFOfno10Lfafc3MzMzWRrUMzsYBPSV1l9QRGASMLt9A0lZlT48Cnsl/3wN8UtKmkjYFPpmXmZmZma3VatasGRFLJA0lBVXtgGsjYrKkC4HxETEaOFPSUcAS4HVgSN73dUk/IgV4ABeWBgfYanOzr60O1xerluuKrQ7Xl9WgiGjtMpiZmZlZ5jsEmJmZmRWIgzMzMzOzAmntqTSsDZCkcPu5NQNJ6wACiIilrVwcW8vk+kVELGvtslix1bquOHNmNefAzN4PSevk28DVi4hlEbHUgZm9H5JU9vdK579cv5bl9Zu3ZNmsWFq7rjg4s/dFSZP1J59gd5G0X2m/2pfO1jS5nqxQn0qBWMV2u0v6uaQHJH2jRQtpa5R8jFrheFN+oVh2Yt2oVPck9ZY0VtJk4HJJG7Vooa1VFLGueLSmVaVUcRvLgknqCmwTEY/n558Gfg3MBp6qvMG9tS3V1KG83YdI0+98nnTHkGeB3wGvABcC7wL3A3XAKxGxpHaltiKT1AEgIt5rqn5Jag/sDGwVEfdIuhzoDywA/hwR10o6D5gF/NEZ/7XLmlZX3OfMAMjNRx8G3gDeraxs5c8lrUu61+knSP1/rgf+BqwPzJB0SET8GDgFGBIRj7XMp7AikbQd6e4eT0XEoso6lSen3pJ0H92BpPrUn1SXHgI2A74P7EYKzk7LrzeDdI++l9282XbkE2rppLpM0ibAYaS68lID9WtD0vHnMkn/A5wIvAM8n7Mf/wa+AawHzJI0hhT4fxHYWtLDwNMR4ftBrmHWhrrizFkbVdaxellERE7JfhP4cemEJ2mdsnRuN2BoRJwl6Sekk+hTwIPAHsA/82NnYDwpePsf4FjgEeB54O6ImN5yn9JaSkODPiT1JtWBG0hXmB1JAdjfI2KBpEHAdyKir6TLSFmyXYEPkQL+F4A3gYOALqQ7hXQHPgu8DewPfDcixtT+E1pLqqazdc5ujAQmAvNIQf1PgSsiYpykTqRs61bAAOAPwB4R8Yqkq4HepBNwR2AxcCbwH2Av0kXDt4AxwEUO0Iprba0rzpyt5SqDsNLyBiryUmAwsGW+ZdYpwNOSjo6IZ0hXDAPyumdJJ8oLI2KepC/n7V8l3enht/n1fgWMIAVqFwMfkXR+RLxRu09szSlfgfYGugH/iohZFUG7IsvPtwY6RcRUYEfgy8BxwFjgF6SD2KOk5oF/k+oVwCRSE8IySW8BHwH+mx/fIF2VvkfKmo3N7/UT4EBJ4yLizdp+E1YL+aQ5BDgA+EtE3AMrH59yZr8XsCfQCbgc6AH0AXYAHifNQL850F3ShIhYlPsD9QReJJ08tyCdhNuTsigrnExzfX4g//0W8P9YXketFbW1uuIBAWuohjrWS/qQpKskPSJpX1hhdFt5s+S2kk6TdJ2kX+U+PjsBy0gZirvyDelfJZ1gITUjzSRlLl4BHiZdZQAsIrW794+IwyLimxExm1S/FkTEo8DfgbdImRBbc/QBbgK2I1/M5QBqI0k75qxrR0mnS/o36er0B5J2JWVM7wW+HRGnRMQkUp3qkl+7DlgvNznMBOZK6pwPtnXAkxFxTUQ8AXSV1ElSL0lnS7oC+DjwiAOzYstZiVVZCpxMyq6Wgu6uko6VdJakLfJ2hwLfAbYnHYPOIGVWLyMde06PiHdJF449WJ54mE7K7L9MuiDompf/g3Ty7Zbf89OSDgI2zcfQJ0lN6s9FxMwP+BVYlfJxZS9JDQU5baquODhbg0jqIukQSQesogPixsCGwKCIeETJEZJ+nSvRR/J2fUn9ea4hp2gjYjzpxPpQRFyft5sC7J7/fgt4CdiFdGXRHuic1/0d2D2/106Svqt0z9TNgBGSngb2Be53H6FikLSepC9KukbSw5JGq2LaimxrUvbqEWC2pG0kPQDcDfxM0hH5QPd/wF4RsT8pe/qViHgJmAp0yX06AOYD20rqkC8AFrD8ahVSlhXgl8B+kkZKuge4ndTkWQrE/gV8PiLua67vxD4YpRG37cuen5L/778haZtV7HYUKYPxJWBPSZsB5wEHko5lX5G0S0TcAXwduBnYCDiBdOKdTsrIfyy/3rOkk3KpHAuBj0XEnPz39nn5TaTj1sU5Y/J9YPtI93C+FPhURPSNiKs/2LdiDalMLkj6cQ5y/o/0f3+LBnZrU3XFzZoFJGl9UiZrG+DeiHgnNxf9BQggJP2S1IcrSk1LwFBgb+A+SacBc0jt5/eTKvuXJN0UEbdKepHUl+fTpIPncNKJ9BOSuuQKOgE4JBfrPVKw1Q24FejA8hPpX0nNTeeQ0rrPkIK8VyQd78xG68lXoMeRmgIej4hr86p1SFeXN5IOMFMqA+ec8fgxsC3pgPR54GxSP6/xkk4AfiTpGVJwdZWk3UkB1xuStiLVi61YfiH4LOni4CZJO5EuKPoBo0gH2O6kjNk/JP0X2Id0UfB0RMzPr3Fxc30/9sHkbMUwUh/UjYE/SbomZ87nkE6oo0hNTC+prEk860PqdrEoIsZKOp90YvwtqW/h6aRMxmTgB6QM7uS8757AOFK/1nfzsidIGdVDJc0i1b098ro3SIOWiIiFwChJY3NZ60XE02Wfr7K89j7lgGwYKZP1b0mnR8SreXV3UlPlz8u3r0hC9KEN1RUHZwWSMxd3Ax8lZa3+DTxGOsh9F7gnIi6S9BnSQe81YFwO0NqTOl1PBUZHxEOSLiYFU+sDR5KaKB+X9BIpkHqUdJVyIKkj/yTgJFJ2A1LT5VGSvkaa2mB9YO+IWCjpBVKGhJwBeSg/VuDArHYqD16S+gJLI2Ji2WYfIgXgnYGOkv4UEe9FxFuSngOGR8S0hl4/98M4nhSgfSciXpe0B3CkpNmkbOqDpODpG8AbEdFbafDIJaRmgeeAj5GudJ8kXWCcQWq2HAPcR+oPOUfSBaTmzdL7v8jyjJq1MknHkDo//zmfsCCN8H4KuIoUeN8HLCH1L3w40sCPf5OC7FvJI+jKXEiqR+/k5+8C3yZd+M0incxvlXQA8MmI2CmXZTTLs7oLge9Kmg5cS+rMfRnpWHg16UIS4GeVFyClk61y39wG1jswex8q60rpWCVpGqkPWDdSQFYKziYDHXKGvQswOx9/yo9xbaquODhrQZL6kAKtucBFwN9yhmCdUt8wSfOAH0bEHyt2X0qO5Ent7buRRquNk9Q+IpZI+hOwCcubft4jBWQ9SCfFxyNihqQDgf0i4oQcEJ5Huop4gHTlcYukxaSrkR/l9aV29QUAEXFRc343Vh2l/llvAl8FrmD5VSCkTq4/Io1IKnmD1Cm/D2l4+FakgxSkenitpKWkg9OluV9YeeC3IL/f/sBw0kH03oi4sKJcHUn1DOAIUr3rH2loeh/gj3n/s4GfkZrUny0/wEXEc+/jK7FmkI8DHyUNxHiclFWdEmk+p/aR5pI7nBSAL5TULv92M0kn4Pfy6zxAutCDdCKFdIE5uPRWFW/dgdRntdR9YiowNiJOLCvbRqR6t27O1u6c9+sTEYsl/S+p1WAp8HZEPEjqEL6CxrpUOAir3vutK/mYMkpSd9LvtR2pbkBKDFxKOufMBsZKujQi5pcdi9pUXXFwVgM5fXs8sDgibi9b9X3SiLeLc5PRx0idDcsPWBNJWYbRwI6ROtNDyjSUOufPJzUXlSpp6cpiKelk3T0/f4bUhl6eKt4BeBp4RdJvSCfrqaSrnHdJw4sHANNI05s9Tsq6WQsqv2KUdAbpoLURaRj4GaQU/iJJN0Tq8wUpYzGnfP98oFyoNJoI0gGxFJwNJx1kHyDNAfR9Sb+KiH+RmiGXkvokziCNcoKUzR2k1CF/U2A/0oH1SqCbpJmkbNqFeT+AX0XEL8o+3pxSOa3lKc0/ty4wPZ80vwCcRQrWXyFlJhaS+vyNj4in8q5zWJ61CKhv8im97vqkrg/j86LSCe5fwA8bKktEvJuz8AfnRXeTBpQMJWXr9wDG5yD/q8CppJPvd0gncSJiCvCVBj5nO9IgJzn4en+aua6UzlML8mPbsrcaAxyfu0t0JjVVfpF07Cjt36bqioOz2tgSuIB0UqwrZSNIzYSlTvn/YfkJr9w0UjPSTqQO2EdExLmkE93H8zZB+o+xLiyP8iPiTUkvk4K7dUh9wb6Qg7DOpBPx7yLieklfJ3XSvwt4LCIW5Nd+IT+shShN6vtx0lDsHqSpK84DbpZ0BKkf4UWkoGc7UsB0LimgepHUx7A079xiaHDm63mkq87tSfPRQRqgcV8uwxxSn6+9SSfT0v7vkLIjn8zPb8zL7iJlZqeRDohzJH0tIt6u/HxFOdi1NUrT3qxPOjGdyfKJNT8M3BARP8tNQCcBn87NyEjamHRh93lS1vXLkrYl9VsdCSv+pmUXEruRTpzfqdhmEqluEQ3fzWEuaTTulpH6qX6ClGFtRzpm3pX3vav0dwOfdaWmprK/PZlnE1qqrmSLSL95qcM9kaZXGp//fk1pUtceWrkfV5upKw7OamMr0pXBX0kdE0/Iy58hNflAyl4clv8ur3x3A5vnCtoZ+KekEaT08c8kfShSf6G9gSfLmhdKFpOCw60i4iVJA0n/0RaSTvCTASLiH6SsnbUgpX5hh5D6XOxPyoLNIQXkU0h9d2bE8n5gxwIvRJ7ThzSZL6TArR9p3p/7SH0qNo807URDFpKaOLfL5ajsbPsW6crzl7D8gJqby58nN1nmg+gfc8auss/FSoGZtSxJHydlvxeTfuurgN+QRsT+KyIqswYfBV6MiBeVbm+zNF+oLZD0F2CMpO+SsvUHkZqEVlBWj64mjaBdqS+OpGWSvkO68LsvUl+0Uh18IZf5zbz9bFJfooY+X7XzNloTWqOuQH0GbC7Lm78rWwo6k46R1zTwu7aZuuLgrDaWktq5rwZOl9QtIupIWYbS9BMvAhsrTSnwXmnH3EywMP/9mlIftK4R8bSkqcA3lUbH9QfOaaBt/B+kUZYvl73eH2r0Oa0Buf9Vb1ImoQdwS6SpSiBlvD5GOqD8MSIel7QjcCcwKso680vanjQT/sT8fJ18oisF5H8FfpyzZvPIw88buNokZ7aeZXn/ig65+f2npECxG6n/RkODOlYa7NFYnwyrLUmbk+7msTXpRPoPUj/Vt0nHl82BnqUsldI8hpPIE2RK6pL3G0fK3j8J6Z6DeX3ptjfPS7qZVFf/TOp0vT7L+5LVn1SVJgQeCdTlDMsrETFV0rGkOv8WaWDKrVQ0c0Wa2PO2is8oUtN6lNflNeXEWhRFqit5+9Kx60XgCElfBB6NiCmSfg4cQ7qIvJeGB5i1mbri4Kw29gT+FBFvSPoVcJqkn5KaJpfmk+4M0kFqO1ITJ1Dfb+Nd0oiV3Ulp5pfy6iGkGdbPIP0HqB/GWxIVQ32tVZyXHxeQAqZvS/pdRDxMmi9sck67l8whXeH9Mvep6Es6QJ5JarpuBytks0qB0SRSgPZ90sHsjvLtykk6i9T00EPSmcBRkQajPEOaauXp8GSbhVN29R+kk0+Qpqw4m5SdeJPUB+g7pMEgT5HuObokN5dvm0+cr5MC+ZNIzdFvkjL37wLrS9ogUreIykz8ZaQ7fXyUdAGxghyY7UC6Tdf6wKdI55WfkPqy3g/c2VRWtTKTm//2BcBqKHpdyb/xUknfJA0Kmk061v2E1GpwCSlwXNzE52wTdcXBWW0cDCyW9CNSX6LdSHOSjVWaT2Vn0mR780lZlP+UVfTNSSf1vqSmz6sid7KMiHmkE7EV21OkaQQuUBoafh7p4Pgw6SD0v5I2IE3o+4+I+LGk10gd/u8ALomIZ/LBdjZp8tVbyrIU6+RtF0TEdflgdw7wucqClGXRlpIOfo8AU2N5P0VPslkA5SfWpq7+I2KapHmlLKekO4G98oXdDNKNmJ8k/eZPKE2F8zIpA/vjiPh32ftOJvUn/AhpfrnSfXV3AObm93qcdDKfHhFzyzK4pZPkItKN61+MiLkVZX0jv55IFxnRUNa1/GRrjVsT60rpJUiB419JU/i8UlHWV/LrlTJhyxqqF22lrjg4q42lpI7VN5FS+kcAn8lZiv8AAyLiLkkLWT6yslThZgJnNHWlaYU2nnT1SKSh5JuQbzdCyiZ0IvU/HE7KfkE6ID4cEbeWXiSfAEcC1yiNhCpdjQ4kdYx9lHQ1+wdSPVvpnqVlJ9FfN+cHtNUnaUvKRqnGih2SG8p2bkIK4A8m9cF5hBS4zyaN1O0ZqW9ib9KgjMV5v2mkk9+1Za/1JqmOrV/+HhExUtL+pJG655KC/k/ncl6TNxtBGpk7rrysZc2Sr7J8vqpS8LDCZ8rbNjQYwBqwttWV8r/LM/TKdyWp+HxrZSZsdfn2Tc1MaZ6VxcBpEXFhpHlUfkcKvg4hjXYrze1yVkT8DlZssnJgtmaL1L9wPUknKd3JYVdS1gzSSWw6MDIixpWl8F8D2mv5bY6QtHGkPmiXkoL78bnf2KmkaVpKU2hcGRHbRMS/Sn1AKsrTJq40i0JSB0n7SvqqpP+VtFdeNRHokf+PLy3bvr2kT0r6qaS/5T5bkAZoXEYaYPQl0lQ6Z+Z1j5PusvAAacDPorIiTCOdIEt3iIB0An2DfF9TSbsr3SOwPSmwv5N0z8JfkOZKHF+qXxHxn4g4K9KdRVaqX5Uizdm4Rvf3aSmuKyt+PlvOmbPa+DDwQr6CDFK6/8zKjSLirZX2tLXFC8BppANZAGdL+nVEzFaaXf9g0mzWpQEhc8mDByS9QQroRkj6eaSpTx4ANiA1SVZ29l9S9rcDsdZ3OmmU7ZOk0bWl3+dp4AClEbvHAtdGxN2kfkG9yZNqkgLxD5Eyo2+QRs5NlfR70t0ZdiB1lt45v84gYGiuS8NJM/X3y+9Zeu/5pOks/iRpSl7+GOkOI6+RZkuvz55UKjU1+UTa7FxXrEEOzppZ7mNxfOXi1iiLtao64PaIuErSLaSOsqeRpsx4iTR5661l2/83P1+XdKX7XKm/DkBEzMAKL58oDwXOjohxeVnpODsROJk02mwK8Nl8krxN0h9JAfvxpAz7bNIUKeOAjnn/F0n9tjbI+78VaQb1K0nB/Y8l3UdqMv9y3qd0glxA6nP464iY3kj5V9WfyU1Nzcx1xRrjZk2z2hhPuuE4ETGVNIdQP0m9SCNtH87r3sv/zo2IqyLid7m5c6X+Y7ZGaE/qN3psbn7qz/KL4CdJmY8rIuJ80omxNLH08flxM2k+wo/nzMNilt+hYx5p4ugXKMt45BPjzaQbjO9E6m90dF5XP21FRLxQOtlKWqfU36dcbpJc6mbJFuG6YqvkzJlZbYxhxTtA3A7c5mbHtVuk6XOGkzIP/UhzFm4k6Suk+9euE2kS6fakk+be+cT3GeC7ETFBadqTLfJLvggcI+k60tQ6t0SeC1HSUOV7GeaT87llRZnaRDl9Qm1lrivWGPlcYWbW/CR1JXXMPoR0H8GTSR2w++Ympj6kE/PHgZ+TTrJBanYaTMps9CdlNu4FJkTEf/NrrzTRcKkDti8A1jyuK1bJzZpmZjUQETMj4gFSE3fHWD7fXOm2NfNJfYR6AMNITVmPkG6h1S0i5pOaqjYlDQT5b9lJdVnu81P+fuGT7ZrJdcUquVnTzKwZKc1RtTMpu9GNNGv+JXn1/aRbu0GaBPTQiChNbfCrBl7uP6TpdxbAipkONzet+VxXbFXcrGlm1oyU7v5wLqmp6SlShuOhqOIWRngKgjbFdcVWxcGZmVkLaqgPkFlDXFfaLgdnZmY1UDb9QPgEa41xXbFKDs7MzMzMCsSjNc3MzMwKxMGZmZmZWYE4ODMzMzMrEAdnZmZmZgXi4MzMzMysQBycmZmZmRXI/wd/d4gmcmUNpwAAAABJRU5ErkJggg==\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, - "metadata": { - "needs_background": "light" - } + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "n_features_shap = 21\n", - "n_features_rfecv = rfe.n_features_\n", + "n_features_shap = len(shap_elimination.get_reduced_features_set(\"best\")) # 16\n", + "n_features_rfecv = int(rfe.n_features_) # 15\n", "\n", - "# Calculate the AUC for the models with different feature sets\n", - "test_auc_full = clf.fit(X_train, y_train).score(X_test, y_test)\n", - "val_auc_full = np.mean(cross_val_score(clf, X_train, y_train, cv=10))\n", + "# Calculate the AUC using all features.\n", + "test_auc_full = model.fit(X_train, y_train).score(X_test, y_test)\n", + "val_auc_full = np.mean(cross_val_score(model, X_train, y_train, cv=10))\n", "\n", + "# Optimal set for RFECV\n", "rfe_features_set = X_train.columns[rfe.support_]\n", - "test_auc_rfe = clf.fit(X_train[rfe_features_set], y_train).score(X_test[rfe_features_set], y_test)\n", - "val_auc_rfe = rfe.grid_scores_[n_features_rfecv]\n", + "test_auc_rfe = model.fit(X_train[rfe_features_set], y_train).score(X_test[rfe_features_set], y_test)\n", + "val_auc_rfe = rfe.cv_results_[\"mean_test_score\"][n_features_rfecv]\n", "\n", + "# Optimal set for SHAP\n", "shap_feature_set = X_train.columns[shap_elimination.get_reduced_features_set(n_features_shap)]\n", - "test_auc_shap = clf.fit(X_train[shap_feature_set], y_train).score(X_test[shap_feature_set], y_test)\n", + "test_auc_shap = model.fit(X_train[shap_feature_set], y_train).score(X_test[shap_feature_set], y_test)\n", "val_auc_shap = shap_report[shap_report.num_features == n_features_shap][\"val_metric_mean\"].values[0]\n", "\n", + "# Same nr of features as RFECV\n", "shap_feature_set_size_rfe = X_train.columns[shap_elimination.get_reduced_features_set(n_features_rfecv)]\n", - "test_auc_shap_size_rfe = clf.fit(X_train[shap_feature_set_size_rfe], y_train).score(\n", + "test_auc_shap_size_rfe = model.fit(X_train[shap_feature_set_size_rfe], y_train).score(\n", " X_test[shap_feature_set_size_rfe], y_test\n", ")\n", "val_auc_shap_size_rfe = shap_report[shap_report.num_features == n_features_rfecv][\"val_metric_mean\"].values[0]\n", @@ -180,13 +14620,39 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "As shown in the plot, ShapRFECV provides superior results for both: CV Validation and Test AUC, compared to RFECV and the baseline model with all the available features. Not only the introduced method allows to eliminate features without the loss in performance, but also it may increase the performance of the model.\n", "\n", - "When it comes to time required to perform the feature selection in the experiment above, RFECV takes 6.11 s ± 33.7 ms, while ShapRFECV takes 10.1 s ± 72.8 mss, which shows that the latter is more computation expensive, due to SHAP values calculation." - ], - "cell_type": "markdown", - "metadata": {} + "When it comes to time required to perform the feature selection in the experiment above, RFECV takes 6.11 s ± 33.7 ms, while ShapRFECV takes 10.1 s ± 72.8 ms, which shows that the latter is more computation expensive, due to SHAP values calculation." + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.8 64-bit ('conda': virtualenv)", + "metadata": { + "interpreter": { + "hash": "9919f76640f8d69c95119de6d10b189fddd758802ef5abb02d09a410e646625a" + } + }, + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "orig_nbformat": 2 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/howto/grouped_data.ipynb b/docs/howto/grouped_data.ipynb index 60ab2e1a..9f1ff3f3 100644 --- a/docs/howto/grouped_data.ipynb +++ b/docs/howto/grouped_data.ipynb @@ -6,6 +6,8 @@ "source": [ "# How to work with grouped data\n", "\n", + "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ing-bank/probatus/blob/master/docs/howto/grouped_data.ipynb)\n", + "\n", "One of the often appearing properties of the Data Science problems is the natural grouping of the data. You could for instance have multiple samples for the same customer. In such case, you need to make sure that all samples from a given group are in the same fold e.g. in Cross-Validation.\n", "\n", "Let's prepare a dataset with groups." @@ -13,7 +15,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install probatus" + ] + }, + { + "cell_type": "code", + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -22,7 +34,7 @@ "[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]" ] }, - "execution_count": 6, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -46,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -73,21 +85,21 @@ "\n", "from probatus.feature_elimination import ShapRFECV\n", "\n", - "clf = RandomForestClassifier(random_state=42)\n", + "model = RandomForestClassifier(random_state=42)\n", "\n", "param_grid = {\n", " \"n_estimators\": [5, 7, 10],\n", " \"max_leaf_nodes\": [3, 5, 7, 10],\n", "}\n", - "search = RandomizedSearchCV(clf, param_grid, cv=cv, n_iter=1, random_state=42)\n", + "search = RandomizedSearchCV(model, param_grid, cv=cv, n_iter=1, random_state=42)\n", "\n", - "shap_elimination = ShapRFECV(clf=search, step=0.2, cv=cv, scoring=\"roc_auc\", n_jobs=3, random_state=42)\n", + "shap_elimination = ShapRFECV(model=search, step=0.2, cv=cv, scoring=\"roc_auc\", n_jobs=3, random_state=42)\n", "report = shap_elimination.fit_compute(X, y)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -125,91 +137,91 @@ " 1\n", " 10\n", " [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", - " [8, 7]\n", - " 1.000\n", - " 0.001\n", - " 0.957\n", - " 0.086\n", + " [6, 7]\n", + " 0.999562\n", + " 0.000876\n", + " 0.954945\n", + " 0.090110\n", " \n", " \n", " 2\n", " 8\n", - " [0, 1, 2, 3, 4, 5, 6, 9]\n", + " [0, 1, 2, 3, 4, 5, 8, 9]\n", " [5]\n", - " 0.999\n", - " 0.001\n", - " 0.966\n", - " 0.055\n", + " 0.999118\n", + " 0.001081\n", + " 0.945513\n", + " 0.089606\n", " \n", " \n", " 3\n", " 7\n", - " [0, 1, 2, 3, 4, 6, 9]\n", + " [0, 1, 2, 3, 4, 8, 9]\n", " [4]\n", - " 1.000\n", - " 0.000\n", - " 0.942\n", - " 0.114\n", + " 0.999559\n", + " 0.000548\n", + " 0.928749\n", + " 0.137507\n", " \n", " \n", " 4\n", " 6\n", - " [0, 1, 2, 3, 6, 9]\n", - " [9]\n", - " 0.999\n", - " 0.001\n", - " 0.980\n", - " 0.032\n", + " [0, 1, 2, 3, 8, 9]\n", + " [8]\n", + " 0.999179\n", + " 0.001051\n", + " 0.969288\n", + " 0.058854\n", " \n", " \n", " 5\n", " 5\n", - " [0, 1, 2, 3, 6]\n", - " [6]\n", - " 1.000\n", - " 0.000\n", - " 0.960\n", - " 0.073\n", + " [0, 1, 2, 3, 9]\n", + " [9]\n", + " 0.999748\n", + " 0.000237\n", + " 0.961767\n", + " 0.066540\n", " \n", " \n", " 6\n", " 4\n", " [0, 1, 2, 3]\n", " [1]\n", - " 0.999\n", - " 0.001\n", - " 0.951\n", - " 0.091\n", + " 0.999433\n", + " 0.000700\n", + " 0.950816\n", + " 0.090982\n", " \n", " \n", " 7\n", " 3\n", " [0, 2, 3]\n", - " [3]\n", - " 0.999\n", - " 0.001\n", - " 0.971\n", - " 0.052\n", + " [0]\n", + " 0.999120\n", + " 0.000729\n", + " 0.970596\n", + " 0.051567\n", " \n", " \n", " 8\n", " 2\n", - " [0, 2]\n", - " [0]\n", - " 0.998\n", - " 0.002\n", - " 0.925\n", - " 0.122\n", + " [2, 3]\n", + " [3]\n", + " 0.999496\n", + " 0.000617\n", + " 0.938639\n", + " 0.117736\n", " \n", " \n", " 9\n", " 1\n", " [2]\n", " []\n", - " 0.998\n", - " 0.002\n", - " 0.938\n", - " 0.098\n", + " 0.998424\n", + " 0.001819\n", + " 0.938339\n", + " 0.097936\n", " \n", " \n", "\n", @@ -217,29 +229,29 @@ ], "text/plain": [ " num_features features_set eliminated_features \\\n", - "1 10 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [8, 7] \n", - "2 8 [0, 1, 2, 3, 4, 5, 6, 9] [5] \n", - "3 7 [0, 1, 2, 3, 4, 6, 9] [4] \n", - "4 6 [0, 1, 2, 3, 6, 9] [9] \n", - "5 5 [0, 1, 2, 3, 6] [6] \n", + "1 10 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [6, 7] \n", + "2 8 [0, 1, 2, 3, 4, 5, 8, 9] [5] \n", + "3 7 [0, 1, 2, 3, 4, 8, 9] [4] \n", + "4 6 [0, 1, 2, 3, 8, 9] [8] \n", + "5 5 [0, 1, 2, 3, 9] [9] \n", "6 4 [0, 1, 2, 3] [1] \n", - "7 3 [0, 2, 3] [3] \n", - "8 2 [0, 2] [0] \n", + "7 3 [0, 2, 3] [0] \n", + "8 2 [2, 3] [3] \n", "9 1 [2] [] \n", "\n", " train_metric_mean train_metric_std val_metric_mean val_metric_std \n", - "1 1.000 0.001 0.957 0.086 \n", - "2 0.999 0.001 0.966 0.055 \n", - "3 1.000 0.000 0.942 0.114 \n", - "4 0.999 0.001 0.980 0.032 \n", - "5 1.000 0.000 0.960 0.073 \n", - "6 0.999 0.001 0.951 0.091 \n", - "7 0.999 0.001 0.971 0.052 \n", - "8 0.998 0.002 0.925 0.122 \n", - "9 0.998 0.002 0.938 0.098 " + "1 0.999562 0.000876 0.954945 0.090110 \n", + "2 0.999118 0.001081 0.945513 0.089606 \n", + "3 0.999559 0.000548 0.928749 0.137507 \n", + "4 0.999179 0.001051 0.969288 0.058854 \n", + "5 0.999748 0.000237 0.961767 0.066540 \n", + "6 0.999433 0.000700 0.950816 0.090982 \n", + "7 0.999120 0.000729 0.970596 0.051567 \n", + "8 0.999496 0.000617 0.938639 0.117736 \n", + "9 0.998424 0.001819 0.938339 0.097936 " ] }, - "execution_count": 25, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -247,13 +259,6 @@ "source": [ "report" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -272,7 +277,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/howto/reproducibility.ipynb b/docs/howto/reproducibility.ipynb index 8d3a3bfd..e1eee449 100644 --- a/docs/howto/reproducibility.ipynb +++ b/docs/howto/reproducibility.ipynb @@ -11,6 +11,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ing-bank/probatus/blob/master/docs/howto/reproducibility.ipynb)\n", + "\n", "This page describes how to make sure that the analysis that you perform using `probatus` is fully reproducible.\n", "\n", "There are two factors that influence reproducibility of the results:\n", @@ -33,7 +35,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install probatus" + ] + }, + { + "cell_type": "code", + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -84,13 +96,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", - "clf = RandomForestClassifier(random_state=42)" + "model = RandomForestClassifier(random_state=42)" ] }, { @@ -104,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +126,7 @@ " \"n_estimators\": [5, 7, 10],\n", " \"max_leaf_nodes\": [3, 5, 7, 10],\n", "}\n", - "search = RandomizedSearchCV(clf, param_grid, n_iter=1, random_state=42)" + "search = RandomizedSearchCV(model, param_grid, n_iter=1, random_state=42)" ] }, { @@ -133,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -154,19 +166,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from probatus.feature_elimination import ShapRFECV\n", "\n", - "shap_elimination = ShapRFECV(clf=search, step=0.2, cv=cv2, scoring=\"roc_auc\", n_jobs=3, random_state=42)\n", + "shap_elimination = ShapRFECV(model=search, step=0.2, cv=cv2, scoring=\"roc_auc\", n_jobs=3, random_state=42)\n", "report = shap_elimination.fit_compute(X, y)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -217,32 +229,32 @@ " \n", " 4\n", " 6\n", - " [1]\n", + " [6]\n", " 0.979\n", " \n", " \n", " 5\n", " 5\n", " [4]\n", - " 0.978\n", + " 0.983\n", " \n", " \n", " 6\n", " 4\n", - " [6]\n", - " 0.989\n", + " [1]\n", + " 0.987\n", " \n", " \n", " 7\n", " 3\n", - " [3]\n", + " [0]\n", " 0.991\n", " \n", " \n", " 8\n", " 2\n", - " [0]\n", - " 0.956\n", + " [3]\n", + " 0.991\n", " \n", " \n", " 9\n", @@ -259,15 +271,15 @@ "1 10 [8, 9] 0.983\n", "2 8 [5] 0.969\n", "3 7 [7] 0.984\n", - "4 6 [1] 0.979\n", - "5 5 [4] 0.978\n", - "6 4 [6] 0.989\n", - "7 3 [3] 0.991\n", - "8 2 [0] 0.956\n", + "4 6 [6] 0.979\n", + "5 5 [4] 0.983\n", + "6 4 [1] 0.987\n", + "7 3 [0] 0.991\n", + "8 2 [3] 0.991\n", "9 1 [] 0.969" ] }, - "execution_count": 19, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -293,9 +305,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/nb_automatic_best_num_features.ipynb b/docs/tutorials/nb_automatic_best_num_features.ipynb index 00172178..5bb68086 100644 --- a/docs/tutorials/nb_automatic_best_num_features.ipynb +++ b/docs/tutorials/nb_automatic_best_num_features.ipynb @@ -5,7 +5,23 @@ "id": "ee10c7aa", "metadata": {}, "source": [ - "# Automatic num_feature selection techniques" + "# Automatic Feature selection techniques\n", + "\n", + "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ing-bank/probatus/blob/master/docs/tutorials/nb_automatic_best_num_features.ipynb)\n", + "\n", + "Below we'll walk you through how to change selecting the best `feature_set` manually to automatically by using `get_reduced_features_set`. This function has many ways of selecting the most optimal feature selection for your use-case. Let's show you first how a simple 'manaul' probatus feature elimination works before we'll show you how to automate it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f9bd53c", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install probatus\n", + "!pip install catboost" ] }, { @@ -17,7 +33,6 @@ "source": [ "from probatus.feature_elimination import ShapRFECV\n", "from sklearn.datasets import make_classification\n", - "from sklearn.model_selection import train_test_split\n", "from catboost import CatBoostClassifier" ] }, @@ -38,8 +53,8 @@ "source": [ "# Simple ShapRFECV example\n", "X, y = make_classification(n_samples=500, n_informative=20, n_features=50)\n", - "clf = CatBoostClassifier(n_estimators=100, verbose=0)\n", - "shap_elimination = ShapRFECV(clf, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\")\n", + "model = CatBoostClassifier(n_estimators=100, verbose=0)\n", + "shap_elimination = ShapRFECV(model, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\")\n", "report = shap_elimination.fit_compute(X, y)" ] }, diff --git a/docs/tutorials/nb_custom_scoring.ipynb b/docs/tutorials/nb_custom_scoring.ipynb index 95fd68a8..a60a85af 100644 --- a/docs/tutorials/nb_custom_scoring.ipynb +++ b/docs/tutorials/nb_custom_scoring.ipynb @@ -53,7 +53,7 @@ ")\n", "\n", "# Prepare model\n", - "clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)" + "model = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)" ] }, { @@ -74,13 +74,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train Accuracy: 0.714,\n", - "Test Accuracy: 0.706.\n" + "Train Accuracy: 0.708,\n", + "Test Accuracy: 0.714.\n" ] } ], "source": [ - "rm = SHAPImportanceResemblance(clf, scoring=\"accuracy\")\n", + "rm = SHAPImportanceResemblance(model, scoring=\"accuracy\")\n", "feature_importance, train_score, test_score = rm.fit_compute(X1, X2, column_names=feature_names, return_scores=True)\n", "\n", "print(f\"Train Accuracy: {np.round(train_score, 3)},\\n\" f\"Test Accuracy: {np.round(test_score, 3)}.\")" @@ -102,15 +102,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train Score: 0.714,\n", - "Test Score: 0.706.\n" + "Train custom_metric: 0.725,\n", + "Test custom_metric: 0.72.\n" ] } ], @@ -121,7 +121,7 @@ "\n", "scorer = Scorer(\"custom_metric\", custom_scorer=make_scorer(custom_metric))\n", "\n", - "rm2 = SHAPImportanceResemblance(clf, scoring=scorer)\n", + "rm2 = SHAPImportanceResemblance(model, scoring=scorer)\n", "feature_importance2, train_score2, test_score2 = rm2.fit_compute(X1, X2, column_names=feature_names, return_scores=True)\n", "\n", "print(f\"Train custom_metric: {np.round(train_score2, 3)},\\n\" f\"Test custom_metric: {np.round(test_score2, 3)}.\")" @@ -129,19 +129,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -166,9 +164,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/nb_sample_similarity.ipynb b/docs/tutorials/nb_sample_similarity.ipynb index fad288d8..51f73315 100644 --- a/docs/tutorials/nb_sample_similarity.ipynb +++ b/docs/tutorials/nb_sample_similarity.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ ")\n", "\n", "# Prepare model\n", - "clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)" + "model = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)" ] }, { @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -112,23 +112,23 @@ " \n", " \n", " f1\n", - " 0.074271\n", - " 0.007526\n", + " 0.085214\n", + " 0.004823\n", " \n", " \n", " f3\n", - " 0.057329\n", - " -0.001271\n", + " 0.057195\n", + " -0.007281\n", " \n", " \n", " f4\n", - " 0.027532\n", - " 0.000174\n", + " 0.024985\n", + " 0.002262\n", " \n", " \n", " f2\n", - " 0.022310\n", - " 0.000958\n", + " 0.022478\n", + " -0.000453\n", " \n", " \n", "\n", @@ -136,10 +136,10 @@ ], "text/plain": [ " mean_abs_shap_value mean_shap_value\n", - "f1 0.074271 0.007526\n", - "f3 0.057329 -0.001271\n", - "f4 0.027532 0.000174\n", - "f2 0.022310 0.000958" + "f1 0.085214 0.004823\n", + "f3 0.057195 -0.007281\n", + "f4 0.024985 0.002262\n", + "f2 0.022478 -0.000453" ] }, "metadata": {}, @@ -151,7 +151,7 @@ "\n", "from probatus.sample_similarity import SHAPImportanceResemblance\n", "\n", - "rm = SHAPImportanceResemblance(clf)\n", + "rm = SHAPImportanceResemblance(model)\n", "feature_importance, train_auc, test_auc = rm.fit_compute(X1, X2, column_names=feature_names, return_scores=True)\n", "\n", "display(feature_importance)" @@ -175,19 +175,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -245,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -276,23 +272,23 @@ " \n", " \n", " f3\n", - " 0.123734\n", - " 0.011815\n", + " 0.144109\n", + " 0.012946\n", " \n", " \n", " f1\n", - " 0.100127\n", - " 0.015754\n", + " 0.104887\n", + " 0.015780\n", " \n", " \n", " f2\n", - " 0.038549\n", - " 0.011208\n", + " 0.046489\n", + " 0.009168\n", " \n", " \n", " f4\n", - " 0.018376\n", - " 0.007511\n", + " 0.009776\n", + " 0.006557\n", " \n", " \n", "\n", @@ -300,10 +296,10 @@ ], "text/plain": [ " mean_importance std_importance\n", - "f3 0.123734 0.011815\n", - "f1 0.100127 0.015754\n", - "f2 0.038549 0.011208\n", - "f4 0.018376 0.007511" + "f3 0.144109 0.012946\n", + "f1 0.104887 0.015780\n", + "f2 0.046489 0.009168\n", + "f4 0.009776 0.006557" ] }, "metadata": {}, @@ -313,7 +309,7 @@ "source": [ "from probatus.sample_similarity import PermutationImportanceResemblance\n", "\n", - "perm = PermutationImportanceResemblance(clf)\n", + "perm = PermutationImportanceResemblance(model)\n", "\n", "feature_importance, train_auc, test_auc = perm.fit_compute(X1, X2, column_names=feature_names, return_scores=True)\n", "display(feature_importance)" @@ -329,60 +325,23 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = perm.plot()" ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize the difference in the most important feature\n", - "\n", - "We can also use the utils to provide more insights into the feature distribution difference in the two samples." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from probatus.utils.plots import plot_distributions_of_feature\n", - "\n", - "feature_distributions = [X1[\"f3\"], X2[\"f3\"]]\n", - "plot_distributions_of_feature(feature_distributions, plot_perc_outliers_removed=0.01)" - ] } ], "metadata": { @@ -401,7 +360,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/tutorials/nb_shap_dependence.ipynb b/docs/tutorials/nb_shap_dependence.ipynb index 38a96cd5..707664a2 100644 --- a/docs/tutorials/nb_shap_dependence.ipynb +++ b/docs/tutorials/nb_shap_dependence.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -135,7 +135,7 @@ "4 5.0 3.6 1.4 0.2 1" ] }, - "execution_count": 15, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -166,12 +166,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "clf = RandomForestClassifier()\n", - "clf = clf.fit(X, y)" + "model = RandomForestClassifier()\n", + "model = model.fit(X, y)" ] }, { @@ -184,11 +184,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "tdp = DependencePlotter(clf).fit(X, y)" + "tdp = DependencePlotter(model).fit(X, y)" ] }, { @@ -200,1855 +200,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3647,8 +249,8 @@ "X.at[0, \"sepal_length\"] = 25\n", "\n", "# Retrain the classifier and fit the plotter\n", - "clf = RandomForestClassifier().fit(X, y)\n", - "tdp = DependencePlotter(clf).fit(X, y)\n", + "model = RandomForestClassifier().fit(X, y)\n", + "tdp = DependencePlotter(model).fit(X, y)\n", "\n", "# Plot the dependence plot.\n", "fig = tdp.plot(feature=\"sepal_length\", figsize=(7, 4))" @@ -3666,3252 +268,23 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = tdp.plot(feature=\"sepal_length\", figsize=(7, 4), max_q=0.99)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Working with skewed distributions\n", - "The binning functionality of `probatus` can be used to plot a sensible histogram under different distributions of feature data. For example, using the `'quantile'` setting (without removing the outlier, produces the following histogram." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = tdp.plot(feature=\"sepal_length\", figsize=(7, 4), type_binning=\"agglomerative\")" - ] } ], "metadata": { @@ -6935,7 +308,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/tutorials/nb_shap_feature_elimination.ipynb b/docs/tutorials/nb_shap_feature_elimination.ipynb index b1be1d62..e6c93323 100644 --- a/docs/tutorials/nb_shap_feature_elimination.ipynb +++ b/docs/tutorials/nb_shap_feature_elimination.ipynb @@ -13,14 +13,14 @@ "\n", " While any features left, iterate:\n", " 1. (Optional) Tune hyperparameters, in case sklearn compatible search CV e.g. `GridSearchCV` or\n", - " `RandomizedSearchCV` or `BayesSearchCV`are passed as clf,\n", + " `RandomizedSearchCV` or `BayesSearchCV`are passed as model,\n", " 2. Calculate SHAP feature importance using Cross-Validation,\n", " 3. Remove `step` lowest importance features.\n", "\n", "\n", - "The functionality is similar to [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html), yet it removes the lowest importance features, based on SHAP features importance. It also supports the use of any hyperparameter search schema that is consistent with sklearn API e.g. [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html), [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html) and [BayesSearchCV](https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html#skopt.BayesSearchCV) passed as a `clf`, thanks to which you can perform hyperparameter optimization at each step of the search.\n", + "The functionality is similar to [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html), yet it removes the lowest importance features, based on SHAP features importance. It also supports the use of any hyperparameter search schema that is consistent with sklearn API e.g. [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html), [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html) and [BayesSearchCV](https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html#skopt.BayesSearchCV) passed as a `model`, thanks to which you can perform hyperparameter optimization at each step of the search.\n", "hyperparameters of the model at each round, to tune the model for each features set. Lastly, it supports categorical features (`object` and `category` dtype) and missing values in the data, as long as the model supports them.\n", - " \n", + "\n", "The main advantages of using this routine are:\n", "\n", "- It uses a tree-based or a linear model to detect the complex relations between features and the target.\n", @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -177,7 +177,7 @@ " \n", " 4\n", " -1.028435\n", - " 1.505766\n", + " NaN\n", " 0\n", " -0.576209\n", " -0.790525\n", @@ -192,10 +192,10 @@ "1 -2.480698 0.772855 0 0.302824 0.729950\n", "2 -0.690014 1.350847 0 1.837895 -0.745689\n", "3 -5.291164 4.559465 0 -1.277930 3.688404\n", - "4 -1.028435 1.505766 0 -0.576209 -0.790525" + "4 -1.028435 NaN 0 -0.576209 -0.790525" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -219,19 +219,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [], "source": [ - "clf = lightgbm.LGBMClassifier(max_depth=5, class_weight=\"balanced\")\n", + "model = lightgbm.LGBMClassifier(max_depth=5, class_weight=\"balanced\")\n", "\n", "param_grid = {\n", " \"n_estimators\": [5, 7, 10],\n", " \"num_leaves\": [3, 5, 7, 10],\n", "}\n", - "search = RandomizedSearchCV(clf, param_grid)" + "search = RandomizedSearchCV(model, param_grid)" ] }, { @@ -245,11 +245,5576 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000806 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4830\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000543 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4065\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000306 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000704 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000880 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000593 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000370 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000791 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000354 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000428 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000147 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000144 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000139 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000117 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000147 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000158 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000140 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000155 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000113 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000122 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000122 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000118 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000134 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000159 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000142 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000134 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000142 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000123 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000155 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000126 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000139 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n" + ] + } + ], "source": [ - "shap_elimination = ShapRFECV(clf=search, step=0.2, cv=10, scoring=\"roc_auc\", n_jobs=3)\n", + "shap_elimination = ShapRFECV(model=search, step=0.2, cv=10, scoring=\"roc_auc\", n_jobs=3)\n", "report = shap_elimination.fit_compute(X, y)" ] }, @@ -262,7 +5827,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -296,79 +5861,79 @@ " 1\n", " 20\n", " [f1, f2_missing, f3_static, f4, f5, f6, f7, f8...\n", - " 0.922\n", + " 0.923000\n", " \n", " \n", " 2\n", " 16\n", - " [f17, f19, f20, f8, f1, f9, f4, f18, f16, f14,...\n", - " 0.922\n", + " [f1, f2_missing, f4, f5, f8, f9, f10, f11, f12...\n", + " 0.923040\n", " \n", " \n", " 3\n", " 13\n", - " [f20, f19, f8, f1, f9, f18, f16, f14, f11, f5,...\n", - " 0.923\n", + " [f1, f5, f8, f9, f10, f11, f12, f14, f15, f16,...\n", + " 0.923200\n", " \n", " \n", " 4\n", " 11\n", - " [f18, f20, f19, f16, f8, f14, f11, f5, f9, f10...\n", - " 0.923\n", + " [f5, f8, f9, f10, f11, f14, f15, f16, f18, f19...\n", + " 0.923381\n", " \n", " \n", " 5\n", " 9\n", - " [f20, f19, f16, f8, f14, f11, f5, f9, f15]\n", - " 0.904\n", + " [f5, f8, f9, f11, f14, f15, f16, f19, f20]\n", + " 0.903575\n", " \n", " \n", " 6\n", " 8\n", - " [f20, f19, f16, f8, f14, f5, f9, f15]\n", - " 0.910\n", + " [f5, f8, f9, f14, f15, f16, f19, f20]\n", + " 0.910296\n", " \n", " \n", " 7\n", " 7\n", - " [f20, f19, f16, f8, f14, f9, f15]\n", - " 0.905\n", + " [f5, f8, f9, f14, f15, f16, f19]\n", + " 0.919259\n", " \n", " \n", " 8\n", " 6\n", - " [f20, f19, f16, f14, f9, f15]\n", - " 0.917\n", + " [f5, f9, f14, f15, f16, f19]\n", + " 0.889271\n", " \n", " \n", " 9\n", " 5\n", - " [f19, f16, f14, f9, f15]\n", - " 0.889\n", + " [f9, f14, f15, f16, f19]\n", + " 0.888632\n", " \n", " \n", " 10\n", " 4\n", - " [f19, f9, f16, f14]\n", - " 0.877\n", + " [f9, f14, f16, f19]\n", + " 0.879369\n", " \n", " \n", " 11\n", " 3\n", - " [f19, f16, f9]\n", - " 0.867\n", + " [f9, f16, f19]\n", + " 0.869466\n", " \n", " \n", " 12\n", " 2\n", - " [f19, f16]\n", - " 0.818\n", + " [f16, f19]\n", + " 0.817814\n", " \n", " \n", " 13\n", " 1\n", " [f16]\n", - " 0.720\n", + " 0.720609\n", " \n", " \n", "\n", @@ -377,36 +5942,36 @@ "text/plain": [ " num_features features_set \\\n", "1 20 [f1, f2_missing, f3_static, f4, f5, f6, f7, f8... \n", - "2 16 [f17, f19, f20, f8, f1, f9, f4, f18, f16, f14,... \n", - "3 13 [f20, f19, f8, f1, f9, f18, f16, f14, f11, f5,... \n", - "4 11 [f18, f20, f19, f16, f8, f14, f11, f5, f9, f10... \n", - "5 9 [f20, f19, f16, f8, f14, f11, f5, f9, f15] \n", - "6 8 [f20, f19, f16, f8, f14, f5, f9, f15] \n", - "7 7 [f20, f19, f16, f8, f14, f9, f15] \n", - "8 6 [f20, f19, f16, f14, f9, f15] \n", - "9 5 [f19, f16, f14, f9, f15] \n", - "10 4 [f19, f9, f16, f14] \n", - "11 3 [f19, f16, f9] \n", - "12 2 [f19, f16] \n", + "2 16 [f1, f2_missing, f4, f5, f8, f9, f10, f11, f12... \n", + "3 13 [f1, f5, f8, f9, f10, f11, f12, f14, f15, f16,... \n", + "4 11 [f5, f8, f9, f10, f11, f14, f15, f16, f18, f19... \n", + "5 9 [f5, f8, f9, f11, f14, f15, f16, f19, f20] \n", + "6 8 [f5, f8, f9, f14, f15, f16, f19, f20] \n", + "7 7 [f5, f8, f9, f14, f15, f16, f19] \n", + "8 6 [f5, f9, f14, f15, f16, f19] \n", + "9 5 [f9, f14, f15, f16, f19] \n", + "10 4 [f9, f14, f16, f19] \n", + "11 3 [f9, f16, f19] \n", + "12 2 [f16, f19] \n", "13 1 [f16] \n", "\n", " val_metric_mean \n", - "1 0.922 \n", - "2 0.922 \n", - "3 0.923 \n", - "4 0.923 \n", - "5 0.904 \n", - "6 0.910 \n", - "7 0.905 \n", - "8 0.917 \n", - "9 0.889 \n", - "10 0.877 \n", - "11 0.867 \n", - "12 0.818 \n", - "13 0.720 " + "1 0.923000 \n", + "2 0.923040 \n", + "3 0.923200 \n", + "4 0.923381 \n", + "5 0.903575 \n", + "6 0.910296 \n", + "7 0.919259 \n", + "8 0.889271 \n", + "9 0.888632 \n", + "10 0.879369 \n", + "11 0.869466 \n", + "12 0.817814 \n", + "13 0.720609 " ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -427,19 +5992,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -456,16 +6019,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['f20', 'f19', 'f16', 'f14', 'f9', 'f15']" + "array(['f5', 'f9', 'f14', 'f15', 'f16', 'f19'], dtype=object)" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -487,22 +6050,5155 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4830\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000576 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4293\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4296\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4335\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4321\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4320\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4322\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4321\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4322\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4321\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4321\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4322\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4322\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4322\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3783\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3786\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3825\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3811\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3810\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3812\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3811\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3812\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3811\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3811\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3812\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3812\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3812\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001090 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001531 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000158 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2550\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000158 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000361 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000165 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000129 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000148 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000117 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000124 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000155 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000133 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000121 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001105 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n" + ] + }, { "data": { - "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "shap_elimination = ShapRFECV(clf=search, step=0.2, cv=10, scoring=\"roc_auc\", n_jobs=3, min_features_to_select=4)\n", + "shap_elimination = ShapRFECV(model=search, step=0.2, cv=10, scoring=\"roc_auc\", n_jobs=3, min_features_to_select=4)\n", "report = shap_elimination.fit_compute(X, y, columns_to_keep=[\"f10\", \"f15\", \"f19\"])\n", "\n", "performance_plot = shap_elimination.plot()" @@ -523,7 +11219,7 @@ { "data": { "text/plain": [ - "['f15', 'f16', 'f10', 'f19']" + "array(['f10', 'f15', 'f16', 'f19'], dtype=object)" ] }, "execution_count": 10, @@ -552,15 +11248,6754 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000669 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001122 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000670 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4803\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4806\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4845\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000562 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4830\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000530 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.465827\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.454783\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.454006\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.513013\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.468752\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.452854\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4831\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.476738\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.483996\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.534713\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4832\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.505899\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4038\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4041\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4080\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.473584\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Total Bins 4065\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.490241\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.484878\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.528974\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4066\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.498454\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.470442\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.513266\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4067\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.556948\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.515432\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.515026\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.454296\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.454006\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.465827\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.513013\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.468719\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.452793\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.476738\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.534713\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 3315\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.483996\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.506466\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.478752\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.478448\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.482626\n", + "Evaluated only: binary_logloss[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.528974\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.471477\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.485633\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.500004\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.562179\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.506202\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2805\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.506634\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.505061\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.506587\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.50443\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.547321\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.536243\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.498161\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.539676\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.568482\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.524184\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2295\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.531564\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000148 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 8\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.468522\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.485702\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.491121\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.526667\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.501151\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.472235\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.498667\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.544275\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.507936\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 2040\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.505284\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001181 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.462813\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.481659\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.499456\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.515423\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.504663\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.479116\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.50173\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1785\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.550578\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.514864\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.511735\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000123 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000504 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.454962\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.464957\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.479176\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.513415\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.48979\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.479703\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.499557\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.541592\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.487624\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1530\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.496412\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000144 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 5\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.445933\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.480537\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.491074\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.529248\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.498087\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.468703\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.489092\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1275\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.533967\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.498007\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.506948\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000148 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000170 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000137 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000148 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 4\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.469823\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.484669\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.494342\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.540004\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.516157\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.471291\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.518385\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.534377\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.512096\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1020\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.502945\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000144 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000140 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000873 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000147 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000155 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 3\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.460488\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.497209\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.497021\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.543129\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.52572\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.485141\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000665 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.523529\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.535525\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 765\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.527338\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.514554\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000116 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000144 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000133 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000120 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000593 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001470 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000278 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000892 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000136 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000139 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 2\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000102 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.514657\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.571934\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.525647\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.586075\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.565491\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.528234\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.558795\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 510\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.589879\n", + "Evaluated only: binary_logloss\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.569968\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[7]\tvalid_0's binary_logloss: 0.566081\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000165 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000134 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000120 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000127 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000155 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000148 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 395, number of negative: 405\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "[LightGBM] [Info] Number of positive: 396, number of negative: 404\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000155 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Info] Number of positive: 494, number of negative: 506\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000122 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.56876\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 444, number of negative: 456\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002583 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", + "[LightGBM] [Info] Start training from score 0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.612354\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000281 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.623429\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.609415\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.649635\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.580063\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.592034\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Training until validation scores don't improve for 5 rounds\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.619967\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] Number of positive: 445, number of negative: 455\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 255\n", + "[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", + "[LightGBM] [Info] Start training from score -0.000000\n", + "Training until validation scores don't improve for 5 rounds\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.606731\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "Did not meet early stopping. Best iteration is:\n", + "[10]\tvalid_0's binary_logloss: 0.622506\n", + "Evaluated only: binary_logloss\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n", + "[LightGBM] [Warning] Unknown parameter: eval_metric\n" + ] + } + ], "source": [ "from probatus.feature_elimination import EarlyStoppingShapRFECV\n", "\n", - "clf = lightgbm.LGBMClassifier(n_estimators=200, max_depth=3)\n", + "model = lightgbm.LGBMClassifier(n_estimators=200, max_depth=3)\n", "\n", "# Run feature elimination\n", "shap_elimination = EarlyStoppingShapRFECV(\n", - " clf=search, step=0.2, cv=10, scoring=\"roc_auc\", eval_metric=\"auc\", early_stopping_rounds=5, n_jobs=3\n", + " model=search, step=0.2, cv=10, scoring=\"roc_auc\", eval_metric=\"auc\", early_stopping_rounds=5, n_jobs=3\n", ")\n", "report = shap_elimination.fit_compute(X, y)" ] @@ -572,15 +18007,12 @@ "outputs": [ { "data": { - "image/png": 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mean_abs_shap_value_testmean_abs_shap_value_trainmean_shap_value_testmean_shap_value_train
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\n" + "image/png": 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rxMbGvnaENm3MzMxw5MgRxMXFYffu3di7dy82b96M1q1bY//+/TA0NNTbfk9E/y4GFkSkRvXxrnv37hW7jJiYGDg4OOCbb77RmBYbG4vt27dj+fLlsi+WdOHu7o6DBw+iWbNmhS7v8OHDePDgAWJjY9GyZUspXduoVAUFEKo74mlpaWrp+e/Uv66+Fy5cQJs2bQpcTmnIy8vDjRs3pKcUAPDXX38BgPTysouLCw4ePIjMzEy1pxaqbmSqEZIKU9A6P3r0CD///DMiIiIwZcoUKV11J7443N3dkZeXhz///LPAi23Vi8EODg4lFvja29vD3NwcV65c0ZiWkJAAAwODYl9IF7T9VOthbW2t03o4Ojpi2LBhGDZsGJKTk9GoUSPMnDlTCiyKum8aGBigTZs2aNOmDRYsWIBZs2Zh0qRJiIuLQ9u2bYu035el44KI1PEdC6K3VFxcnNY7tz/99BMAaO2moYtnz54hNjYWgYGB6Nmzp8a/ESNGIDMzU+t7HPrQu3dv5ObmYvr06RrTcnJypCBAdXf71W2SnZ2NyMhIjfksLCy0dr1RXbwdOXJESsvNzcWKFSuKVN+7d+9i5cqVGtOePXtW4IhA/4alS5dK/y+EwNKlS2FsbIw2bdoAADp37ozc3Fy1fACwcOFCKBQKne52q77jkT8409Y+AGR9/bl79+4wMDDAtGnTNJ54qJbToUMHWFtbY9asWXjx4oVGGarvVBSFoaEh2rdvjx07dqgNTfzPP/9g48aNaN68ebG6WAGQug/m337e3t5wd3fHvHnz8PjxY435VOuRm5ursW87ODjAyclJbdjXgo4BbR4+fKiRpgrkVGUWZb+3sLDQWD8iKhv4xILoLfW///0PT58+RVBQEGrWrIns7GwcP34cmzdvhqurq8ZLxbr68ccfkZmZiW7dummd7ufnB3t7e8TExKBPnz5yVkEn/v7+GDJkCGbPno3z58+jffv2MDY2xtWrV7F161YsXrwYPXv2RNOmTWFnZ4eQkBCMHDkSCoUC69ev1xp8eXt7Y/Pmzfjkk0/QuHFjWFpaomvXrvDy8oKfnx8mTpyIhw8foly5cti0aRNycnJ0ru+HH36ILVu24OOPP0ZcXByaNWuG3NxcJCQkYMuWLdi3b5/0VOnfZGpqir179yIkJAS+vr7Ys2cPdu/ejc8//1zqTta1a1cEBARg0qRJSEpKQv369bF//37s2LEDo0eP1mlYUDMzM9SuXRubN2+Gp6cnypUrhzp16qBOnTpo2bIl5syZgxcvXqBy5crYv3+/rO+ceHh4YNKkSZg+fTpatGiB4OBgmJiYID4+Hk5OTpg9ezasra2xbNkyfPjhh2jUqBHee+892Nvb49atW9i9ezeaNWumEUjpYsaMGdK3HYYNGwYjIyN8++23yMrKwpw5c4q9Tu7u7rC1tcXy5cthZWUFCwsL+Pr6ws3NDatWrUKnTp3g5eWFsLAwVK5cGXfv3kVcXBysra2xc+dOZGZmwtnZGT179kT9+vVhaWmJgwcPIj4+XhoQASj4GNBm2rRpOHLkCLp06QIXFxckJycjMjISzs7OaN68OYCi7ffe3t44ePAgFixYACcnJ7i5uWkMz0tEpaTUxqMiolK1Z88eMWDAAFGzZk1haWkplEql8PDwEP/73//EP//8o5bXxcVFdOnSRWs5qqFUVcPNdu3aVZiamoonT54UuOzQ0FBhbGwsUlNThRAvhx593fCWBdE2vKo2K1asEN7e3sLMzExYWVmJunXrigkTJoi///5bynPs2DHh5+cnzMzMhJOTkzQEL/INv/r48WPx/vvvC1tbWwFAbdjN69evi7Zt2woTExNRsWJF8fnnn4sDBw5oHW7Wy8tLa12zs7PFV199Jby8vISJiYmws7MT3t7eIiIiQqSnpxd5exTUftq2u7bhUVVlXr9+XbRv316Ym5uLihUriqlTp2oM05qZmSnGjBkjnJychLGxsahevbqYO3eu2hCnBS1b5fjx48Lb21solUq1oWfv3LkjgoKChK2trbCxsRG9evUSf//9t8bwtKrhZvMPb1rQcMBr1qwRDRs2lLa1v7+/NEyySlxcnOjQoYOwsbERpqamwt3dXYSGhorTp09rXYfCtqfK2bNnRYcOHYSlpaUwNzcXAQEBakMCv1rn+Pj4Qpfzqh07dojatWsLIyMjjaFnz507J4KDg0X58uWFiYmJcHFxEb179xY///yzEEKIrKwsMX78eFG/fn1hZWUlLCwsRP369UVkZKTaMgo7BvL7+eefxbvvviucnJyEUqkUTk5Oom/fvuKvv/5Sy6frfp+QkCBatmwpzMzMBAAOPUtUhiiE+Bfe0CMiojdWaGgotm3bprULDRERkQrfsSAiIiIiItkYWBARERERkWwMLIiIiIiISDa+Y0FERERERLLxiQUREREREcnGwIKIiIiIiGRjYEFERERERLKVycBixYoVePHiRWlXg4iIiIiIdFQmAwsiIiIiInqzMLAgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2BhZERERERCQbAwsiIiIiIpKNgQUREREREcnGwIKIiIiIiGRjYEFERERERLIxsCAiIiIiItkYWBARERERkWwMLIiIiIiISDYGFkREREREJBsDCyIiIiIiko2BBRERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJxsCCiIiIiIhkY2BBRERERESyMbAgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2BhZERERERCSbQgghSrsS+Snm5ZR2FYiIiIiISo0YZ1TaVSgyPrEgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2BhZERERERCQbAwsiIiIiIpKNgQUREREREcnGwIKIiIiIiGRjYEFERERERLIxsCAiIiIiItmKHFjcvXsXY8eORdu2beHj44Pw8HA9VIuIiIiIiN4kRf5WeEREBK5evYoBAwagfPnycHZ2xoEDB3D8+HEkJCTgxo0byM3NxY8//ggnJyd91JmIiIiIiMqYIgUW2dnZOHfuHHr37o0PP/xQSh88eDAuXbqE6tWrw9nZGTdv3izxihIRERERUdlVpMDi4cOHEELA2tpaLX3atGmoUKECjIyM8NVXXzGwICIiIiJ6y+j8jkV4eDgCAwMBACtXroSPjw98fHxw+vRpVKpUCUZGRe5VRURERERE/xE6RwPBwcHw9PTEggULEBAQgICAAACAm5ub3ipHRERERERvBp0Di3r16qFChQpYsGABPDw80LlzZ33Wi4iIiIiI3iD8jgUREREREcnGwIKIiIiIiGRjYEFERERERLIxsCAiIiIiItkYWBARERERkWwl8vGJs2fP4uzZswCAy5cvAwC2bNkCS0tLAMBHH31UEoshIiIiIqIyqkQCi/j4eKxcuVItbcOGDdL/M7AgIiIiIvpvUwghRGlXIj/FvJzSrgIRERERUakR40rk/v+/iu9YEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJxsCCiIiIiIhkY2BBRERERESylckBcr+1XoOwsDAYGxuXdlWIiIiIiEgHfGJBRERERESyMbAgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2BhZERERERCQbAwsiIiIiIpKNgQUREREREcnGwIKIiIiIiGRjYEFERERERLIxsCAiIiIiItkYWBARERERkWwMLIiIiIiISDYGFkREREREJJtCCCFKuxL5KebllHYViMoUMc6otKtAREREVCg+sSAiIiIiItkYWBARERERkWwMLIiIiIiISDYGFkREREREJBsDCyIiIiIiko2BBRERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2YocWNy9exdjx45F27Zt4ePjg/DwcD1Ui4iIiIiI3iRGRZ0hIiICV69exYABA1C+fHk4Oztr5Jk4cSIOHDiAatWqYcuWLSVSUSIiIiIiKruKFFhkZ2fj3Llz6N27Nz788EOteX799Vf8/PPPMDExKZEKEhERERFR2VekrlAPHz6EEALW1tZapz99+hRffvklevXqhXLlypVIBYmIiIiIqOzTObAIDw9HYGAgAGDlypXw8fGBj48PTp8+LeWJjIxEXl4ehg4dWvI1JSIiIiKiMkvnrlDBwcHw9PTEggULEBAQgICAAACAm5sbAODixYvYsmULZs6cCUtLS/3UloiIiIiIyiSdA4t69eqhQoUKWLBgATw8PNC5c2dpWk5ODmbMmAE/Pz+0a9dOLxUlIiIiIqKyq8ijQmmzfv163L59G/PmzSuJ4oiIiIiI6A0j+wN5t2/fxqpVqzBgwACtQ88SEREREdF/n+wnFgsXLoS1tTUCAgJw+/ZtKT03Nxc5OTm4ffs2zMzMUKFCBbmLIiIiIiKiMkp2YHH//n2kpKSgd+/eWqcHBQWhefPmWLRokdxFERERERFRGSU7sBg1ahQyMzM10r/66isolUqMGTOGTyuIiIiIiP7jZAcWvr6+WtMXL14MMzMztG3bVu4iiIiIiIiojJP98jYREREREZFCCCFKuxL5KebllHYViMoUMa5ERoYmIiIi0hs+sSAiIiIiItkYWBARERERkWwMLIiIiIiISDYGFkREREREJBsDCyIiIiIiko2BBRERERERycbAgoiIiIiIZCuTg+N/a70GYWFhMDY2Lu2qEBERERGRDvjEgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJxsCCiIiIiIhkY2BBRERERESyMbAgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2hRBClHYl8lPMyyntKlABxDij0q4CEREREZVBfGJBRERERESyMbAgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2BhZERERERCQbAwsiIiIiIpKNgQUREREREcnGwIKIiIiIiGRjYEFERERERLIVObC4e/cuxo4di7Zt28LHxwfh4eF6qBYREREREb1JjIo6Q0REBK5evYoBAwagfPnycHR0xKxZs/Dnn3/i3r17ePr0Kezt7eHl5YWQkBDUrFlTH/UmIiIiIqIyRCGEELpmzs7ORrNmzdC7d2+MHz8eAPDs2TMMHjwY9erVQ+XKlWFubo5//vkHP/74Ix48eIAlS5agcePGRavUvJyirQX9a8S4IseiRERERPQWKNJV4sOHDyGEgLW1tZRmZmaG9evXa+Tt0aMHunTpgvXr1xc5sCAiIiIiojeLzu9YhIeHIzAwEACwcuVK+Pj4wMfHB6dPn9aa387ODiYmJsjMzCyZmhIRERERUZml8xOL4OBgeHp6YsGCBQgICEBAQAAAwM3NDQCQm5uLzMxM5OTk4J9//sGGDRvw9OlTNGvWTD81JyIiIiKiMkPnwKJevXqoUKECFixYAA8PD3Tu3FltemJiIt577z3pt6WlJcLCwhAaGlpilSUiIiIiorKpxN7ErVy5Mr755hu8ePECd+7cwU8//YTHjx/jxYsXMDLiC79ERERERP9lJXbFb2ZmBl9fX+l3t27d0K9fP0yYMAFLliwpqcUQEREREVEZpLcvb5ubmyMgIAAnTpzAnTt39LUYIiIiIiIqA/QWWABAVlYWACA9PV2fiyEiIiIiolImO7B49OgR8vLyNNJTU1Nx8OBBmJubw93dXe5iiIiIiIioDJP9jsWePXvw3XffoVWrVqhcuTKMjIxw69Yt7N69GxkZGZg8eTJMTU1Loq5ERERERFRGyQ4sGjZsiMuXL+Po0aNITU3FixcvUL58eTRp0gTvvfce6tevXxL1JCIiIiKiMkwhhBClXYn8FPNySrsKVAAxjkMHExEREZEmvb68TUREREREbwcGFkREREREJBsDCyIiIiIiko2BBRERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbGXya2ffWq9BWFgYjI2NS7sqRERERESkAz6xICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJxsCCiIiIiIhkY2BBRERERESyMbAgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2BhZERERERCQbAwsiIiIiIpJNIYQQpV2J/BTzcoo1nxhnVMI1ISIiIiIiXfCJBRERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJVuTA4u7duxg7dizatm0LHx8fhIeH66FaRERERET0JjEq6gwRERG4evUqBgwYgPLly8PZ2Rnfffcdjh49iqSkJKSlpaFixYrw9vbGwIEDUalSJX3Um4iIiIiIypAiBRbZ2dk4d+4cevfujQ8//BAAcPz4cSxatAiNGzdGr169YGtri+vXryM2NhYHDhzAmjVrUK1aNb1UnoiIiIiIyoYiBRYPHz6EEALW1tZSmqurK77//ns4Ozur5W3evDmGDx+O5cuXY86cOSVTWyIiIiIiKpN0fsciPDwcgYGBAICVK1fCx8cHPj4++PvvvzWCCgDw9fWFjY0Nrl+/XnK1JSIiIiKiMknnJxbBwcHw9PTEggULEBAQgICAAACAm5ub1vyPHz/GkydP4O7uXjI1JSIiIiKiMkvnwKJevXqoUKECFixYAA8PD3Tu3LnQ/KtXr0ZOTg66dOkiu5JERERERFS26eU7FgcPHsSGDRvQtGlTdOvWTR+LICIiIiKiMqTEA4ujR4/iiy++QK1atTBr1iwoFIqSXgQREREREZUxJRpYHD9+HBMmTEC1atWwdOlSWFpalmTxRERERERURpVYYHH8+HGMGzcOrq6uiIyMVBuSloiIiIiI/ttKJLA4efIkxo8fDxcXF0RGRsLGxqYkiiUiIiIiojdEkT6Qp82ff/6JsWPHQgiBrl274vjx4xp5XjeCFBERERERvdlkBxbXr19HVlYWAGDBggVa8zCwICIiIiL6b1MIIURpVyI/xbycYs0nxsmOk4iIiIiIqBj08h0LIiIiIiJ6uzCwICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJViY//PCt9RqEhYXB2Ni4tKtCREREREQ64BMLIiIiIiKSjYEFERERERHJxsCCiIiIiIhkY2BBRERERESyMbAgIiIiIiLZGFgQEREREZFsDCyIiIiIiEg2BhZERERERCQbAwsiIiIiIpKNgQUREREREcnGwIKIiIiIiGRjYEFERERERLIxsCAiIiIiItkYWBARERERkWwMLIiIiIiISDYGFkREREREJBsDCyIiIiIiko2BBRERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJxsCCiIiIiIhkY2BBRERERESyGZV2BfITQuDZs2fIyMiAsbFxaVeHiIiIiOitZ2VlBYVCUWgehRBC/Ev10Ulqairs7e1LuxpERERERPT/paenw9rautA8Ze6JhYmJCRo0aIDdu3fD0tKytKtDxfD48WN06dKFbfiGYvu92dh+bza235uPbfhmY/sVzMrK6rV5ylxgoVAoYGhoCGtrazboG8rAwIBt+AZj+73Z2H5vNrbfm49t+GZj+8nDl7eJiIiIiEg2BhZERERERCRbmQsslEolBg0aBKVSWdpVoWJiG77Z2H5vNrbfm43t9+ZjG77Z2H7ylLlRoYiIiIiI6M1T5p5YEBERERHRm4eBBRERERERycbAgoiIiIiIZGNgQUREREREsun1A3lJSUmYM2cOfv/9d1hYWKBz584YNmwYjI2NC51PCIG1a9di69atSEtLg6enJz755BPUrVtXLV9KSgrmzJmDU6dOwcjICAEBARgzZgw/aFJC9Nl+p0+fxscff6wxb7t27TB79uwSX5e3UXHbb+vWrTh27BguXryItLQ0fPnll2jbtq1GPh5/+qXP9uPxp3/Fab/U1FTExMTg1KlTuHPnDiwtLdGwYUOMGDECjo6Oanl5/OmfPtuQx6D+Ffcc+sUXX+DixYtISUmBsbExPDw8MHDgQPj5+anle/z4MRYsWIDDhw8jJycHfn5+mDBhAipUqKDP1Srz9BZYZGRk4OOPP0bVqlUxd+5cJCcnY+HChXj+/Dk+/fTTQuddu3Ytvv32W4wYMQLVq1fH1q1bMWLECMTExMDZ2RkAkJOTgxEjRgAAZsyYgefPn2Px4sWYPHkyFi1apK/Vemvou/1Upk6dCldXV+m3ra2tHtbm7SOn/Xbv3g0AaNasmfT/+fH40y99t58Kjz/9KG77Xb58GXFxcejWrRvq1q2LtLQ0rFq1CiEhIdi8eTPs7OwA8Pj7N+i7DVV4DOqHnHPoixcv8MEHH6BKlSrIzs7Gjh07MGrUKCxfvhwNGzaU8k2cOBE3btzAxIkToVQqERkZiZEjR2LdunUwMtLrffuyTejJmjVrRPPmzUVaWpqU9v3334smTZqI5OTkAud7/vy5aNmypVi6dKmUlp2dLQIDA8Xs2bOltD179ggfHx+RmJgopZ04cUJ4e3uLP/74o2RX5i2k7/aLj48X3t7e4tKlS/pZgbdccdtPCCFyc3OFEELcvXtXeHt7iwMHDmjk4fGnX/puPx5/+lXc9svIyBAvXrxQS7t//77w8fER69evl9J4/OmfvtuQx6B+yTmH5peTkyM6d+4sZsyYIaVduHBBeHt7ixMnTkhpiYmJwsfHR+zfv1/+CrzB9PaOxfHjx9GkSRPY2NhIae3atUNeXh5OnjxZ4Hy///47njx5ovbo3tjYGAEBATh27Jha+dWrV1eL9H19fWFjY6OWj4pH3+1H+lXc9gMAA4PXnxZ4/OmXvtuP9Ku47WdlZaVxp7NixYqws7NDSkqKWvk8/vRL321I+iXnHJqfoaEhrKys8OLFC7Xyrays4OvrK6W5urrC09PzrT8G9fYXKCkpSe2kB7w84CpUqICkpKRC5wOgMa+bmxvu37+P58+fS/lcXFzU8igUCri4uBRaPulG3+2nMmrUKDRp0gSdO3fG4sWLNaZT8RS3/YpSPo8//dF3+6nw+NOPkmy/mzdv4uHDh3Bzc1Mrn8effum7DVV4DOqH3PYTQiAnJwdpaWlYv349bt++jeDgYLXyXVxcoFAo1OZzc3N7649Bvb5jYWVlpZFuZWWFjIyMQudTKpUwMTHRmE8IgczMTJiamiIzM1Nr+dbW1oWWT7rRd/tZWlqif//+aNSoEUxMTBAfH48NGzYgMTGRfYRLQHHbT1c8/vRL3+3H40+/Sqr9hBCYN28e7O3t0aFDBymdx5/+6bsNeQzql9z227FjB2bMmAEAMDc3x6xZs1CvXr0SK/+/7C1+u4RKU82aNVGzZk3pd+PGjVGhQgXMmTMHFy9eRJ06dUqxdkT/bTz+3gwrVqzAb7/9hiVLlsDMzKy0q0PFUFAb8hgs21q1agVPT0+kpaXh4MGDmDhxIubOnYtmzZqVdtXKPL11hbK2tsbjx4810jMzM2FtbV3ofNnZ2cjKytKYT6FQSBGilZWV1vIzMjIKLZ90o+/206Zdu3YAgISEhGLWmlSK23664vGnX/puP214/JWckmi/7du3Y+XKlfj888/RpEkTtWk8/vRP322oDY/BkiO3/WxtbVG7dm00bdoUU6ZMQdOmTbF48eISK/+/TG+Bhaurq0Y/s8ePHyM1NVWj31v++YCXfRJflZSUhEqVKsHU1LTA8oUQuHnzZqHlk2703X6kX8VtPznl8/grOfpuP9Ivue0XFxeHL7/8Eh9//DHeffddncrn8Vey9N2GpF8lfQ6tWbMm7ty5o1b+zZs3IYRQy6ft3Y63jd4Ci6ZNm+K3335DZmamlHbw4EEYGBhofGTkVfXq1YOFhQUOHjwopeXk5CAuLk7tEVTTpk1x9epV3Lp1S0r77bffkJ6ezkdVJUDf7afNvn37AAC1a9eWWXsqbvsVpXwef/qj7/bThsdfyZHTfqdPn8akSZPQvXt3fPTRRwWWz+NPv/TdhtrwGCw5JX0OvXDhAipXrqxWfkZGBn777Tcp7ebNm7hy5cpbfwzq7R2LHj16YPPmzRg7diwGDBiA5ORkLF68GMHBwbC3t5fyDR06FPfu3cMPP/wAADAxMUFYWBhWrFgBOzs7eHh4YOvWrUhPT0e/fv2k+dq2bYuoqChMmDABw4cPx/Pnz7Fo0SI0b96cfRNLgL7b74svvoCzszNq1qwpvbi2ceNGtGrViifVElDc9gOAP//8E3///TfS0tIAABcvXgQA2NnZwdvbGwCPP33Td/vx+NOv4rZfYmIixo0bhypVqqBz5874448/pLx2dnbSB0Z5/OmfvtuQx6B+Fbf9jh49it27d6N58+aoWLEiMjIysHfvXpw4cQIzZ86U5qtXrx7eeecdTJs2DWPGjJE+kFe9enUEBAT826tbpihE/uc4JSgxMRFz587FhQsXYGFhgS5dumh8Tn3w4MG4d+8edu7cKaUJIRAdHY1t27bh0aNH8PT0xCeffKL2Rj4AJCcnY+7cuTh16hQMDQ0REBCATz75BJaWlvpapbeKPtsvKioKe/bswf3795GdnQ0nJyd07NgRYWFhauVT8RW3/cLDw7Fr1y6N8ho1aoQVK1ZIv3n86Zc+24/Hn/4Vp/127tyJiIgIreUFBgYiPDxc+s3jT//02YY8BvWvOO2XlJSEJUuW4M8//0RaWhpsbW1RvXp1hISESDdmVB4/fowFCxYgLi4Oubm58PX1xYQJE9QCl7eRXgMLIiIiIiJ6O/ATrUREREREJBsDCyIiIiIiko2BBRERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbAwsqMxITk6GjY0NVq5cqZYeGhoKV1fX0qnUf0R4eDgUCgWSkpL+leVFR0drLO/Zs2dwcnIq8ONRhSlo36DiU7XR4cOHS7sqVMrknh+4L729kpKSoFAo1D7e+G84fPgwFAoFoqOjizX/+fPnYWBggF9++aVkK0YMLKjsmDx5Muzt7REWFqZT/vv372PcuHGoU6cOrKysYG1tjerVq+O9995DbGysWt5WrVoV+kVa1R/W06dPa53+6NEjmJmZQaFQYP369QWW4+rqCoVCIf1TKpVwdXXFRx99hNu3b+u0Xv9VZmZm+OyzzzB37lzcu3evSPMWdd+gt9v58+cRHh7+rwXSVPqSkpIQHh6O8+fP/6vL5b6mKS0tDeHh4WU60GzQoAG6d++OsWPHgt+JLlkMLKhMuHPnDtasWYP//e9/MDIyem3+mzdvon79+vjmm2/g5+eHL7/8ErNnz0ZgYCASEhIQFRVVovWLiYlBVlYW3NzcsGbNmkLzOjs7Y/369Vi/fj0WL14MX19frFmzBr6+vkhNTS3Rer1pBg4cCIVCgQULFug8T1H3DdLNhx9+iGfPnqFly5alXZUSd/78eURERPBi7y2SlJSEiIiIUgks3uZ9zcXFBc+ePcPkyZOltLS0NERERJTpwAIARo8ejTNnzuCnn34q7ar8p/CvNJUJ3377LRQKBfr27atT/nnz5iE5ORk//PAD3n33XY3p9+/fL9H6rV69GgEBAXj33XcxevRo3LhxA9WqVdOa18bGBv369ZN+Dx06FA4ODli6dCmioqIwfvz4Eq3bm8TCwgLBwcGIjo7GjBkzYGJi8tp5irpvlLbc3FxkZWXB3Ny8tKtSKENDQxgaGpZ2NYjoDaZQKGBqalra1SiWFi1awNXVFcuXL0eXLl1Kuzr/GXxi8YZS9Wn9+eefMW3aNLi4uMDMzAy+vr44efIkAOCXX35B8+bNYWFhAUdHR0yfPl1rWadPn0ZQUBAqVKgAExMT1KhRAzNnzkROTo5avt9++w2hoaHw9PSEubk5rKys0KxZM2zfvl2jzNDQUCgUCqSnp0sX1qampmjWrBlOnTqlkX/r1q3w8fGBg4ODTut/9epVAECbNm20Tq9UqZJO5eji7NmzOH/+PEJCQvD+++/DyMjotU8t8uvQoQMA4Nq1awXm2bNnDxQKBb7++mut09955x3Y29vjxYsXAIrWHtqo2kgbhUKB0NBQjfTNmzejefPmsLKygrm5OXx9fbFt2zadlqfSqVMnpKamIi4uTqf8Be0beXl5mDlzJlq2bIlKlSpBqVSiatWqGDp0KB48eCDlS0tLg6mpKYKDg7WWP3HiRCgUCrU7nenp6fj000/h4eEBExMT2Nvbo2/fvrhx44bavKrj8ODBg5g+fTrc3d1hamqKLVu2AAD279+PPn36oFq1ajAzM4OtrS3at29fYL/e77//HvXr14epqSmqVq2KiIgIHDx4UGtf4qysLMyaNQteXl4wNTWFra0tunbtinPnzum0XbX1iy+p84qrqytatWqFs2fPonXr1rC0tES5cuUQEhKC5ORktbyZmZmYPHkyfH19pXOQh4cHPvvsMzx9+lSjbCEEVq5cCV9fX1haWsLS0hJ169bFlClTALzs1qjqMhcQECB1S9S2P+f3+++/IygoCOXLl4epqSlq166NOXPmIDc3Vy1fUc9v2qi6X/75558YPXo0HB0dYW5ujjZt2uDKlSsAgNjYWDRq1AhmZmZwdXXFihUrtJa1atUqKZ+NjQ3at2+Po0ePauTLy8vD7Nmz4ebmBlNTU9SpUwcxMTEF1vHevXsYOnQoqlatCqVSCScnJwwePFijDYtK1+3cqlUrre/X5e/XHx0djYCAAABAWFiY1OatWrUCoN4ff8mSJfD09ISpqSk8PT2xZMkSjfJV+29++fv1F3dfU+0/Dx48QGhoKCpUqAArKyt0795duim2YsUK1KpVC6ampqhZsyZ27NihUU5kZCTat2+PypUrQ6lUwtHREf369dP69CQ3NxfTp0+Hi4sLTE1NUa9ePWzevFnr+zVF2b/zt8Xhw4fh5uYGAIiIiJC2iaodC3s3oqC/STt27EDDhg1hamqKKlWq4IsvvpD+DuZXlPOiQqFAhw4dsHfvXjx+/FhreVR0fGLxhvvss8+Qm5uLUaNGITs7G/Pnz0f79u2xbt06DBw4EIMHD8YHH3yALVu2YMqUKXBzc1O7m757924EBwfDw8MDY8eORbly5XDixAlMmTIF58+fx9atW6W827dvR0JCAnr37g0XFxc8ePAAa9euRXBwMGJiYvD+++9r1K9Dhw6wt7fHlClT8ODBAyxYsABdunRBYmIirKysAAD//PMPrly5gpEjR+q83u7u7gCAlStXYvTo0QVeIOdXUFckbRcwKqtXr4alpSV69OgBCwsLBAYGYu3atZg2bRoMDHSLzVWBUIUKFQrM0759e1SqVAnr1q3T2BZXr17FyZMnMXLkSBgbGwMoXnvIMXnyZMycORMdO3bE9OnTYWBggO3bt6NXr15YunQphg8frlM577zzDoCXf2A6duxYaN7C9o3s7GzMnTsXPXr0wLvvvgsLCwvEx8dj9erVOHr0KM6cOQOlUglbW1t069YNO3bswMOHD1GuXDmpjLy8PMTExKBevXpo0KABgJdBRdOmTXHr1i0MGDAAXl5euHfvHiIjI+Hr64vTp0/DxcVFrS7jxo3DixcvMGjQIFhbW6NGjRoAXl7wPHz4EP3794ezszPu3r2LVatWoU2bNoiLi0OLFi2kMjZv3oy+ffvC3d0dU6dOhZGREdauXYudO3dqrPuLFy/QsWNHHD9+HB9++CFGjBiB9PR0rFy5Es2aNcORI0fg4+OjU3toI/e8ArzswtamTRv06NEDPXv2xNmzZ7FmzRqcPn0a8fHx0hMd1Tbp0aOHFLj/8ssvmDNnDs6dO4d9+/aplfvhhx8iJiYGvr6+mDRpEmxtbZGQkIBt27Zh2rRpCA4Oxr1797BixQp8/vnnqFWrFoD/O2cU5PTp0/D394exsTGGDx+OSpUqYefOnfj0009x4cIFrRfgupzfXickJASWlpb4/PPPkZKSgvnz56NDhw6YPn06JkyYgKFDh2LAgAFYvXo1hgwZgtq1a6N58+bS/J9++inmzJmDJk2aYNasWcjMzMSKFSsQEBCAHTt2oHPnzlLeTz75BIsXL0bLli0xZswYJCcnY/jw4Vqfvt66dQvvvPMOsrOzMXDgQLi7u+PatWtYtmwZ4uLicPr0adjY2Oi0jnK38+u0bNkSn3/+OWbNmoXBgwdLx1XFihXV8i1ZsgT379/HkCFDYGVlhe+++w4jR47Ew4cPMXXq1CIvt7j7mkrHjh3h7OyMadOm4dq1a/j6668RFBSE4OBgrFixAgMHDoSpqSm+/vpr9OzZE3/99Zd00Q68fHLv5+eHkSNHoly5crh48SJWrVqFQ4cO4Y8//kD58uWlvCNGjMDy5csREBCAcePGISUlBcOGDVMrL7/i7N+1atXCwoULMWbMGGldABT6jmNhtm/fjh49esDV1RVTpkyBkZERoqKisHv3bo28xTkvvvPOO/j2229x9OjR1/49Ih0JeiNFRUUJAKJhw4YiKytLSt+xY4cAIIyMjER8fLyUnpWVJSpVqiT8/PyktGfPnomKFSuKFi1aiBcvXqiVv2DBAgFAxMXFSWmPHz/WqMeTJ0+Ep6enqFWrllp6SEiIACCGDh2qlr5lyxYBQCxfvlxKO3TokAAgFi9erHVdQ0JChIuLi1ra9evXhbW1tQAgqlSpIt5//32xcOFCcfr0aa1l+Pv7CwCv/ffqNlNtI1tbWxESEiKl/fDDDwKA+OmnnzSW4+LiImrWrClSUlJESkqKuHHjhlizZo2wsbERRkZG4o8//tBaP5Vx48YJAOLSpUtq6ZMnTxYAxJkzZ6S0orTH1KlTBQCRmJgopanaSBsAaut85swZAUBMnDhRI++7774rrKysREZGhpSm2j9fXd6rjIyMRGBgoNZpryps38jLyxNPnz7VSF+1apUAIDZv3iyl7dq1SwAQ33zzjVregwcPCgBi/vz5UtrIkSOFqampOH/+vFrepKQkYWVlpbZdVOvp6ekpnjx5olEXbW10//59Ub58edGpUycp7cWLF8LJyUk4ODiIhw8fSumZmZnCzc1NABBRUVFSuur43Lt3r1rZ6enpokqVKsLf319jufmp6v7qMV4S5xUhXh4HAMTChQvV0lX1nj17tloZ2dnZGvVT7fOnTp2S0jZv3iwAiH79+onc3Fy1/K/+1rZur9O0aVNhaGgoLly4IKXl5eWJXr16CQDi4MGDUnpRzm8FUR2TgYGBIi8vT0pfvHixACCsrKzErVu3pPTk5GRhYmIi3nvvPSktISFBKBQK0axZM7X2unv3rrCxsREuLi4iJydHLW/r1q2lNCFeHtsKhULjeO3WrZuwt7cXt2/fVqt3fHy8MDQ0FFOnTpXSirK9i7Kd/f39Nc79QgiRmJgoAKjVIS4uTuM4yT/N0tJSbX2ysrJE48aNhZGRkVq6i4uL1mNI2zKKs6+p9p9hw4appY8ZM0b6m5aeni6lX7hwQQAQn332mVp+becX1Tntq6++ktIuXrwoAIgOHTqoHSe///67MDAwKPBvgy77t7a20JamUlg75f+blJOTI6pUqSLKly8vUlJSpPS0tDRRtWrVEjkv/vrrrwKAmDdvnsY0Kh52hXrDDR06FEqlUvqtulPj6+urFpkrlUo0adJEunMOAAcOHMA///yDsLAwpKWlITU1Vfqnusu1f/9+Kb+FhYX0/0+fPsWDBw/w9OlTtG7dGpcvX0ZGRoZG/caMGaP2u3Xr1gCgVo+UlBQAULuT/DrVqlXDhQsXpLvkGzduxJgxY+Dj44N69erhzJkzGvOYmpriwIEDWv99+OGHWpcTGxuLtLQ0hISESGmdO3eGvb19gd2hEhISYG9vD3t7e1SrVg0DBgxAhQoVsGPHDtSpU6fQ9VItZ926dVKaEAIbNmxAnTp10KhRIym9OO1RXDExMVAoFAgJCVHbT1JTU9GtWzdkZmbixIkTOpdXrlw5nbpTFLZvKBQKmJmZAXj5mF+1D6v2sVcf2Xfo0AEVK1ZU267Ay+1sZGSEDz74AMDLbR0TE4OWLVuicuXKautpYWEBPz8/tWNCZejQoVrfqXi1jR4/fowHDx7A0NAQvr6+avU7c+YM/v77b4SGhsLOzk5Kt7S0xMcff6xR7oYNG1CzZk14e3ur1TE7Oxvt2rXD0aNH8ezZMy1bVDdyzisq1tbWGDZsmFrasGHDYG1trdZdT6lUSk/hcnJy8OjRI6SmpqJt27YA1NtRdTd73rx5Gk8LdX16qE1ycjKOHz+Obt26oV69elK6QqHApEmTAEBrF0Ndzm+vM3LkSLUnrqpt3a1bN1SpUkVKt7e3R40aNdTK3rFjB4QQmDBhglp7OTk5ISwsDDdv3pS6gKjyfvLJJ2rv1jRq1Ajt2rVTq1N6ejp27dqFbt26wdTUVG0fc3V1hYeHh9bj4HWKu51LygcffABnZ2fpt1KpxJgxY5CTk6P1yaC+jR49Wu23qu379+8Pa2trKb1evXqwtrbW2K9U55e8vDykp6cjNTUV9evXh42Njdpxs2vXLgDAqFGj1I6TunXrSt10tSmJ/VuOM2fO4Pbt2wgLC1N72m9jY1Ni50XVUx253fvo/7Ar1Bsu/yNs1UWJtsebdnZ2an3PL1++DAAYMGBAgeX/888/0v8nJydj8uTJ2LFjh9aDMC0tTe1kqK1+qoP41Xqo/qiKIg755urqiqVLl2Lp0qW4d+8ejh49ivXr12Pnzp0IDAzEpUuX1C5IDQ0NpYuV/LT1RwZedoOyt7eHs7Oz2vsR7du3x9atW5GamqrRvcnV1VX63oKqX7KHh4dO66QKHmJiYjBr1iwYGBjgyJEjSEpKwpw5c9TyFqc9iuvy5csQQqBmzZoF5nl1X3kdIYRO3ddet29s2bIF8+fPx7lz5zT63D569Ej6f1XwsGDBAvz111/w9PTEkydPEBsbi/bt20tdJlJSUvDgwQPs378f9vb2Wpep7QLW09NTa97r169j0qRJ2LdvH9LS0rSuGwAkJiYCgNSF6lXa0i5fvoxnz54VWEfgZbe/Vy9Mi0LOeeXVMl692AUAExMTVKtWTeNdlcjISCxfvhyXLl1CXl6e2rRX2/Hq1atwdHTU6OIil2r7e3l5aUyrVasWDAwMNOoM6HZ+e52ibuubN2/qVG9V2o0bN+Dj4yPVX9sxXLt2bbVA4cqVK8jLy8Pq1auxevVqneqti+Ju55Ki6qr0qtq1awOAXpdbELnH2aFDhzBt2jScOnUKz58/V5v26nHzuvPLnj17dKpfcfZvOV63z+ZXnPOi6m+Lrt2p6fUYWLzhChrVRZfRXlQH1Ny5c6X+5fk5OTlJedu3b4/Lly9j1KhR8PHxgY2NDQwNDREVFYWNGzdqXBAUVo9XLxRVJ4GHDx++ts4FcXR0RK9evdCrVy988MEH2LhxI3766SeNft9FkZiYiLi4OAghCrxw3LBhg8ZdJwsLiwIDGF30798fo0ePxqFDh9C2bVusW7cOhoaGautS3PZ4VUEn0vwv7auWp1AosGfPngLbVNvFQkEePXpU6MlfpbB9IzY2Fn369EGTJk2wePFiVKlSBaampsjNzUXHjh011r9///5YsGAB1q1bhxkzZiA2NhaPHz9Wexql2i/btm2LTz/9VOf10fa04vHjx2jZsiWePHmC0aNHo27durCysoKBgQFmz56NQ4cO6Vx+fkII1K1bt9Bhe3XZvgWRc14pqgULFmDs2LFo3749Ro4cCScnJyiVSty9exehoaGv3Y9Lky7nt+KWURJlF5dqGf369VM7Pl6lelqoT0U5R72Jy5XT9vHx8Wjfvj08PDzw5Zdfws3NTfrW0nvvvVcix40+9sHCLuDlbt/inBdVf1vknC9JHQOLt1j16tUB6HYh/Pvvv+PChQuYMmWKxpeTV61aJaseqgvSknq86ufnh40bN+Lu3buyyomKipJGoLG1tdWYPnnyZKxZs0YjsJDr/fffx/jx47Fu3To0a9YM27ZtQ7t27eDo6CjlKYn2UD3Nyf9Cs7Y7d9WrV8fevXtRtWpVrXf9iiIpKQk5OTmv7RYGFL5vrF+/HqampoiLi1O7sE9ISNBaVv369VG/fn1s2LAB06dPx7p166QXu1Xs7e1ha2uLjIwMWcEhAPz888/4+++/sWbNGo0P+7065jsAacQU1WhAr9KWVr16daSkpKB169ayugDp040bN5Cdna321CIrKws3btxQuwO5fv16uLq6Ys+ePWrrsnfvXo0yPT09sWPHDvzzzz+FPrUo6t1H1R3iS5cuaUxLSEhAXl5ese7Q65uqTpcuXdJ4YfjPP/9Uy6P6b0JCQoF5VTw8PKBQKJCdnS37OHhVUbdzuXLltHZr1XaO0qXNVU/pX5V/O6mWq+1mRnGXqw8bN25Ebm4u9uzZo/aE48mTJ2pPKwD180v+/Vjb+UWuwrbJq3938su/fV/dZ/PLv88CxTsvqnoi6PL3iHRTNv8i0b+iQ4cOcHBwwJdffqn1IH/27BkyMzMB/N+di/x3Ki5evCi7T6y9vT28vLyk4Sx1cfjwYa19yPPy8qS+stoeleoqLy8P0dHRqFu3Lj766CP07NlT41/fvn3xxx9/ID4+vtjL0cbe3h6dOnVCbGwsYmJikJGRoXHXsCTaQ/UU5uDBg2rp8+fP18iregfl888/1xgSEihaNyhVO/v7+782b2H7hqGhIRQKhdqdOSEEZsyYUWB5ISEhuHnzJjZu3IhDhw6hT58+amOwGxgY4IMPPsBvv/1W4DC6uvbFLaiN9u/frzFko4+PDxwdHREdHa12UfD48WMsX75co+z+/fvj/v37Bd6ZK0p76EtGRgYiIyPV0iIjI5GRkYHu3btLaap2fHU75eTk4Msvv9QoU/UuzIQJEzTuyL46v2oEGl2fgjo4OKBp06bYuXMnLl68qFbm7NmzAQBBQUE6lfVv6tatGxQKBebOnavWFfDevXuIioqCi4sLGjZsqJZ3wYIFasfw2bNnNc4B5cuXR+fOnREbG6v12BNCSO8/FUVRt7OnpycyMzPx22+/SWl5eXlYuHChRtm6tHlMTAzu3Lkj/c7OzsbChQthaGiIwMBAteUmJCSo3ZzKysrCN998U6zl6kNB55dZs2ZpHBtdu3YFACxevFht2h9//KEx6lpJKGybuLm5wcjISGOfO378uMa+5u3tDWdnZ0RFRamN6JiRkVFi58WTJ0/CyMgIzZo1e/2KkU74xOItZmFhgXXr1qF79+6oUaMGBgwYAA8PD6SlpSEhIQGxsbHYvn07WrVqhVq1asHLywtz5szB06dPUaNGDfz111/49ttvUbduXa13lYqiV69emD59Ou7du6d2Z74g8+bNw7Fjx9C1a1c0atQINjY2uH//Pr7//nucOXMGAQEBsj54s3//fty+fRsDBw4sME+PHj0QHh6O1atXo3HjxsVeljYhISH48ccfMXbsWNjY2KhdiAEokfbo27cvPv/8cwwePBgJCQkoV64c9u7dq3VI3saNGyM8PBzh4eFo0KABevXqBScnJ9y7d0/6cml2drZO6/bTTz+hQoUK0rjzr1PQvtGzZ098//33aN26Nfr3748XL17ghx9+KHTo4A8++AATJkzAsGHDkJeXp7Wbx8yZM3Hs2DH07t0bvXv3hp+fH5RKJW7evImffvoJ3t7eWsdgz6958+aoVKkSxo4di6SkJDg7O+P8+fNYv3496tatiz/++EPKa2RkhHnz5uGDDz5AkyZNMHDgQBgZGSE6Ohrly5dHYmKi2l3AUaNG4cCBAxg/fjwOHTqE1q1bw9raGrdu3cLPP/8sPckpTe7u7oiIiMDFixfh7e2NM2fOYM2aNahZs6ba8ME9e/bExIkT0alTJwQHByMjIwMbN26UXuh+Va9evdCnTx+sW7cOV69eRbdu3WBnZ4e//voL+/btky5WGzduDAMDA8ycOROPHj2ChYUF3Nzc4OvrW2B9Fy9eDH9/f7Ro0UIaBnXXrl3Yt28f3n///QK/mVOaatSogfHjx2POnDlo2bIl+vTpIw03+/jxY8TExEgXoDVr1sTw4cOxdOlStG7dGj169EBycjKWLl2K+vXra4zzv2zZMjRv3hwtW7ZE//790bBhQ+Tl5eHGjRvYsWMH+vfvL327oCiKsp0HDx6M+fPnIygoCKNGjYJSqcS2bdu0dpmpXbs2rKysEBkZCXNzc9ja2sLBwUF64Rh4GTD4+vri448/hpWVFTZu3Ij4+Hh88cUXav3uR4wYgU2bNqFt27b4+OOPkZ2djfXr12vt8licfa0kBAUFYeHChejcuTMGDx4MpVKJAwcO4Pfff9d478/LywuDBw/GihUr0LZtWwQFBSElJQXffPMNGjZsiDNnzpTok5fy5cvDw8MDmzZtgru7OypWrAgLCwt07doVlpaWCA0NxapVq9C3b1+0atUKV69eRVRUFOrVq4cLFy5I5RgaGmLhwoXo3bs3mjRpgkGDBknfkSpfvjxu3bqlttyinheFENi7dy86duxY7OFwSQs9jzpFelLYEHfIN1SoSkHDi/7xxx/igw8+EE5OTsLY2Fg4ODiId955R0ybNk08ePBAypeUlCR69uwpKlSoIMzMzETjxo1FbGys7KFMhXg5PKKRkZHWId+0DTd74sQJ8cknnwgfHx/h4OAgjIyMhI2NjfDz8xPz588Xz58/V8vv7+8vLCwstNZHiP8b+lE1lGbPnj0FAPH7778XOI8QQnh6egobGxtp2FMXFxfh5eVV6Dy6yMrKEuXKlRMAxEcffaQ1T1HaQ1uaEEKcPHlSNG3aVJiYmIjy5cuLQYMGiUePHhW4D+3atUu0b99e2NnZCaVSKZydnUXHjh3FsmXL1PIVNNzs48ePhYWFhRg3bpzO26KwfWPFihWiVq1awsTERFSqVEkMGjRIPHjwoMD6CyFEYGCgACCqV69e4DKfPHkipk2bJurUqSNMTU2FpaWlqFmzpvjoo4/EyZMnNdazoKEmL1y4IDp06CBsbW2FpaWl8Pf3F0eOHCnw+NiyZYuoW7euUCqVokqVKiI8PFzExsZqDJ8rxMshahcvXix8fHyEubm5MDc3Fx4eHuL9998X+/btK3DdCqt7SZ1XVMN1njlzRgQEBAhzc3Nha2sr+vXrJ+7fv6+WNycnR8yaNUu4u7sLpVIpqlatKsaPHy/+/PNPrUNW5ubmiqVLl4qGDRsKMzMzYWlpKerWrSvCw8PV8kVHR4tatWoJY2PjQveHV50/f168++670v5ds2ZN8dVXX6kNz1rQOr9uO+VX0DFZ2FCdBQ2/umLFCtGgQQNhYmIirKysRNu2bcWRI0c08uXm5ooZM2aIqlWrCqVSKby8vMSGDRsKrEtKSooYN26cqF69ujAxMRE2NjaiTp06YuTIkWpDYhd1yFVdt7MQQuzevVvUr19fKJVK4ejoKCZMmCASEhK0bqPdu3eLhg0bChMTEwFAGl701SFOFy9eLDw8PIRSqRQeHh5i0aJFWusYHR0tPD09hbGxsXB1dRVfffWV+Pnnn7UOlVrUfa2g/aewoVi1DYG7fft20ahRI2Fubi7Kly8v+vTpI27evKk1b05OjggPDxdVqlQRSqVS1K1bV2zevFmMHTtWABD//PPPa+snhOb+XdD+eurUKdG0aVNhbm4uAKjtt5mZmWLgwIGiXLlywszMTDRv3lwcO3aswOV+//330j7g7OwsJk+eLPbv3691WxXlvHj48GEBQOzatUvrulLxKIT4F94EI9LBxx9/jP379+PKlStqdytDQ0Nx+PBhrV8TpbIpOjoaYWFhSExMVPty7uLFizFp0iRpdB9dFbRvvA3mz5+PcePG4cSJE/Dz8yvt6ujE1dUVrq6ual/1Jiothw8fRkBAAKKionT6AvvbpGvXrjh06BAyMjL0MjhDWRYUFITbt28jPj6eo0KVIL5jQWXGtGnT8ODBA0RFRZV2VUgPnj17hi+//BLjx48vUlABvB37RnZ2tsb7K48fP8Y333yD8uXLq33DhIioKLS9k/j7779jz549aN269VsXVJw7dw47duzA/PnzGVSUML5jQWWGg4MD0tPTS7sapCdmZma4d+9eseZ9G/aNGzduoFOnTnjvvffg5uaGe/fuYe3atUhMTMSyZcs0vglBRKSrtWvXYt26dejSpQvs7e2RkJCAFStWQKlUYtq0aaVdvX+d6p0hKnkMLIiIygB7e3v4+fkhJiYGycnJMDIyQt26dfHll1+id+/epV09InqDNWrUCNu3b8fXX3+Nhw8fwsrKCq1bt8bUqVOlkcOISgLfsSAiIiIiItn4jgUREREREcnGwIKIiIiIiGRjYEFERERERLIxsCAiIiIiItkYWBARERERkWwMLIiIiIiISDYGFkREREREJBsDCyIiIiIiko2BBRERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQkGwMLIiIiIiKSjYEFERERERHJxsCCiIiIiIhkY2BBRERERESyMbAgIiIiIiLZGFgQEREREZFsb3VgERoaCldX19KuBhERERHRG69MBhYKhUKnf4cPHy7tqpIMWVlZ+PTTT+Hk5AQzMzP4+vriwIEDOs+/adMmNGrUCKamprC3t8fAgQORmpqqkS89PR0TJkxA9erVYWZmBhcXFwwcOBC3bt0qdplEREREpE4hhBClXYn8NmzYoPZ73bp1OHDgANavX6+W3q5dO1SsWLHYy3nx4gXy8vJgYmJS7DKo+Pr27Ytt27Zh9OjRqF69OqKjoxEfH4+4uDg0b9680HmXLVuGYcOGoU2bNggODsadO3ewePFieHh44NSpUzA1NQUA5OXlwc/PD3/++SeGDRsGT09PXLt2DZGRkbC2tsbly5dhZWVVpDKJiIiISAvxBhg+fLjQpapPnjz5F2pTNI8fPy7tKpRJp06dEgDE3LlzpbRnz54Jd3d38c477xQ6b1ZWlrC1tRUtW7YUeXl5UvrOnTsFAPH1119LaceOHRMAxNKlS9XKWLNmjQAgYmNji1wmEREREWkqk12hdNGqVSvUqVMHZ86cQcuWLWFubo7PP/8cALBjxw506dIFTk5OMDExgbu7O6ZPn47c3Fy1MvK/Y5GUlASFQoF58+ZhxYoVcHd3h4mJCRo3boz4+PjX1ik6OhoKhQK//PILhg0bBgcHBzg7O0vTIyMj4eXlBRMTEzg5OWH48OFIS0vTKOfUqVPo3Lkz7OzsYGFhgXr16mHx4sU6b5vs7GxMmTIF3t7esLGxgYWFBVq0aIG4uDi1fIcPH9bapUy1HaKjo9XSExIS0Lt3b9jb28PMzAw1atTApEmTNPJo62KU37Zt22BoaIjBgwdLaaamphg4cCBOnDiB27dvFzjvxYsXkZaWhj59+kChUEjpgYGBsLS0xKZNm6S0jIwMANB4suXo6AgAMDMzK3KZRERERKTJqLQrIMeDBw/QqVMnvPfee+jXr5908RgdHQ1LS0t88sknsLS0xKFDhzBlyhRkZGRg7ty5ry1348aNyMzMxJAhQ6BQKDBnzhwEBwfjxo0bMDY2fu38w4YNg729PaZMmYInT54AAMLDwxEREYG2bdti6NChuHLlCpYtW4b4+HgcO3ZMKvfAgQMIDAyEo6MjRo0ahUqVKuHy5cvYtWsXRo0apdN2ycjIwKpVq9C3b18MGjQImZmZWL16NTp06IDffvsNDRo00KmcV/3+++9o0aIFjI2NMXjwYLi6uuL69evYuXMnZs6cKeWrVasW/P39X/v+y7lz5+Dp6Qlra2u19CZNmgAAzp8/jypVqmidNysrC8D/BQWvMjMzw7lz55CXlwcDAwP4+PjAwsICX3zxBcqVK4caNWrg2rVrmDBhAho3boy2bdsWuUwiIiIi0qK0H5noQltXKH9/fwFALF++XCP/06dPNdKGDBkizM3NxfPnz6W0kJAQ4eLiIv1OTEwUAET58uXFw4cPpfQdO3YIAGLnzp2F1jMqKkoAEM2bNxc5OTlSenJyslAqlaJ9+/YiNzdXSl+6dKkAINasWSOEECInJ0e4ubkJFxcX8ejRI7WyX+2e8zo5OTkiKytLLe3Ro0eiYsWKYsCAAVJaXFycACDi4uLU8qq2Q1RUlJTWsmVLYWVlJW7evFlovQAIf3//19bRy8tLtG7dWiP90qVLBbarSkpKilAoFGLgwIFq6QkJCQKAACBSU1Ol9F27dglHR0dpGgDRoUMHkZmZWewyiYiIiEjdG3371cTEBGFhYRrpr951zszMRGpqKlq0aIGnT58iISHhteX26dMHdnZ20u8WLVoAAG7cuKFTvQYNGgRDQ0Pp98GDB5GdnY3Ro0er3fEeNGgQrK2tsXv3bgAv7+InJiZi9OjRsLW1VSvz1e45r2NoaAilUgng5cvLDx8+RE5ODnx8fHD27Fmdy1FJSUnBkSNHMGDAAFStWrXQegkhdBqt69mzZ1pfmle9IP3s2bMC561QoQJ69+6NtWvXYv78+bhx4wZ+/fVX9OnTR3ry8+r89vb2aNiwIWbOnIkffvgB4eHh+PXXX9X2naKWSURERETq3uiuUJUrV5YuoF916dIlTJ48GYcOHZL62Kukp6e/ttz8F8+qIOPRo0c61cvNzU3t982bNwEANWrUUEtXKpWoVq2aNP369esAgDp16ui0nMKoLpATEhLw4sWLAuumC1VAVRL1UjEzM5O6H73q+fPn0vTCfPvtt3j27BnGjRuHcePGAQD69esHd3d3xMbGwtLSUqp7QEAA1q1bhx49egAA3n33Xbi6uiI0NBR79uxBp06dilQmEREREWl6owMLbRefaWlp8Pf3h7W1NaZNmwZ3d3eYmpri7Nmz+PTTT5GXl/facl992vAqoePIvK+7KNa3DRs2IDQ0FN27d8f48ePh4OAAQ0NDzJ49WwpegIKfguR/yV0fHB0dcffuXY30e/fuAQCcnJwKnd/GxgY7duzArVu3kJSUBBcXF7i4uKBp06awt7eXnvhER0fj+fPnCAwMVJu/W7duAIBjx45JgYWuZRIRERGRpjc6sNDm8OHDePDgAWJjY9GyZUspPTExsdTq5OLiAgC4cuUKqlWrJqVnZ2cjMTFReoHY3d0dwMsRilRpxbFt2zZUq1YNsbGxasHD1KlT1fKpnsTkH5lK9QRFRVXnixcvFrtO+TVo0ABxcXHIyMhQe4H71KlT0nRdVK1aVXrClJaWhjNnzkhPJgDgn3/+gRBCI1hSPcXJyckpcplEREREpOmNfsdCG9XThlefLmRnZyMyMrK0qoS2bdtCqVTi66+/VqvX6tWrkZ6eji5dugAAGjVqBDc3NyxatEjjYl/XpyWA9m1w6tQpnDhxQi2fi4sLDA0NceTIEbX0/NvK3t4eLVu2xJo1azSGks1fL12Hm+3Zsydyc3OxYsUKKS0rKwtRUVHw9fVVGxHq1q1bOr0bM3HiROTk5GDMmDFSmqenJ4QQ2LJli1re7777DgDQsGHDIpdJRERERJr+c08smjZtCjs7O4SEhGDkyJFQKBRYv359kS7MS5q9vT0mTpyIiIgIdOzYEd26dcOVK1cQGRmJxo0bo1+/fgAAAwMDLFu2DF27dkWDBg0QFhYGR0dHJCQk4NKlS9i3b59OywsMDERsbCyCgoLQpUsXJCYmYvny5ahduzYeP34s5bOxsUGvXr2wZMkSKBQKuLu7Y9euXUhOTtYo8+uvv0bz5s3RqFEjDB48GG5ubkhKSsLu3btx/vx5KZ+uw836+vqiV69emDhxIpKTk+Hh4YG1a9ciKSkJq1evVsvbv39//PLLL2pt+OWXX+LixYvw9fWFkZERfvjhB+zfvx8zZsxA48aNpXyhoaGYN28ehgwZgnPnzsHLywtnz57FqlWr4OXlhaCgoCKXCbz8jkr+OhERERG91UpnMKqiKWi4WS8vL635jx07Jvz8/ISZmZlwcnISEyZMEPv27dMYWrWg4WZf/Rq0CgAxderUQuupGm42Pj5e6/SlS5eKmjVrCmNjY1GxYkUxdOhQjWFlhRDi6NGjol27dsLKykpYWFiIevXqiSVLlhS67Ffl5eWJWbNmCRcXF2FiYiIaNmwodu3apbG+QrwcZrVHjx7C3Nxc2NnZiSFDhoiLFy9qDDcrhBAXL14UQUFBwtbWVpiamooaNWqIL774Qi0PdBxuVoiXX9oeN26cqFSpkjAxMRGNGzcWe/fu1cinGlr4Vbt27RJNmjQRVlZWwtzcXPj5+YktW7ZoXc6dO3fEgAEDhJubm1AqlcLR0VEMGjRIpKSkFLtMb29vUalSJZ3Wk4iIiOhtoBCCt1yJiiIzMxPlypXDokWLMHz48NKuDhEREVGZ8J97x4JI344cOYLKlStj0KBBpV0VIiIiojKDTyzeINnZ2Xj48GGheWxsbEp9uFsiIiIievv8517e/i87fvw4AgICCs0TFRWF0NDQf6dCRERERET/H59YvEEePXqEM2fOFJrHy8sLjo6O/1KNiIiIiIheYmBBRERERESy8eVtIiIiIiKSjYEFERERERHJViYDC4VCodO/133dWRdPnz5FeHh4iZRFmrKysvDpp5/CyckJZmZm8PX1xYEDB3Sef9OmTWjUqBFMTU1hb2+PgQMHIjU1VSNfeno6JkyYgOrVq8PMzAwuLi4YOHAgbt26VewyiYiIiEh3ZfIdiw0bNqj9XrduHQ4cOID169erpbdr1w4VK1aUtazU1FTY29tj6tSpCA8Pl1UWaerbty+2bduG0aNHo3r16oiOjkZ8fDzi4uLQvHnzQuddtmwZhg0bhjZt2iA4OBh37tzB4sWL4eHhgVOnTsHU1BQAkJeXBz8/P/z5558YNmwYPD09ce3aNURGRsLa2hqXL1+GlZVVkcokIiIioiIqrU9+F8Xw4cOFvqqakpIiAIipU6cWu4zHjx+XXIX+Q06dOiUAiLlz50ppz549E+7u7uKdd94pdN6srCxha2srWrZsKfLy8qT0nTt3CgDi66+/ltKOHTsmAIilS5eqlbFmzRoBQMTGxha5TCIiIiIqmjLZFUoXeXl5WLRoEby8vGBqaoqKFStiyJAhePTokVq+06dPo0OHDqhQoQLMzMzg5uaGAQMGAACSkpJgb28PAIiIiJC6WBX25CI6OhoKhQK//PILhg0bBgcHBzg7O0vTIyMj4eXlBRMTEzg5OWH48OFIS0vTKOfUqVPo3Lkz7OzsYGFhgXr16mHx4sU6r392djamTJkCb29v2NjYwMLCAi1atEBcXJxavsOHD2vtNpaUlASFQoHo6Gi19ISEBPTu3Rv29vYwMzNDjRo1MGnSJI082roY5bdt2zYYGhpi8ODBUpqpqSkGDhyIEydO4Pbt2wXOe/HiRaSlpaFPnz5QKBRSemBgICwtLbFp0yYpLSMjAwA0nl6pht1VfTCwKGUSERERUdG8sR/IGzJkCKKjoxEWFoaRI0ciMTERS5cuxblz53Ds2DEYGxsjOTkZ7du3h729PT777DPY2toiKSkJsbGxAAB7e3ssW7YMQ4cORVBQEIKDgwEA9erVe+3yhw0bBnt7e0yZMgVPnjwBAISHhyMiIgJt27bF0KFDceXKFSxbtgzx8fFSnQDgwIEDCAwMhKOjI0aNGoVKlSrh8uXL2LVrF0aNGqXT+mdkZGDVqlXo27cvBg0ahMzMTKxevRodOnTAb7/9hgYNGhR5m/7+++9o0aIFjI2NMXjwYLi6uuL69evYuXMnZs6cKeWrVasW/P39X/teyrlz5+Dp6Qlra2u19CZNmgAAzp8/jypVqmidNysrCwC0fkXczMwM586dQ15eHgwMDODj4wMLCwt88cUXKFeuHGrUqIFr165hwoQJaNy4Mdq2bVvkMomIiIioiEr7kYku8neF+vXXXwUAERMTo5Zv7969aunbt28XAER8fHyBZRe1K1RUVJQAIJo3by5ycnKk9OTkZKFUKkX79u1Fbm6ulL506VIBQKxZs0YIIUROTo5wc3MTLi4u4tGjR2plv9o953VycnJEVlaWWtqjR49ExYoVxYABA6S0uLg4AUDExcWp5U1MTBQARFRUlJTWsmVLYWVlJW7evFlovQAIf3//19bRy8tLtG7dWiP90qVLAoBYvnx5gfOmpKQIhUIhBg4cqJaekJAgAAgAIjU1VUrftWuXcHR0lKYBEB06dBCZmZnFLpOIiIiIdPdG3prdunUrbGxs0K5dO6Smpkr/vL29YWlpKXUHsrW1BQDs2rULL168KNE6DBo0CIaGhtLvgwcPIjs7G6NHj1a74z1o0CBYW1tj9+7dAF7exU9MTMTo0aOl+qm82j3ndQwNDaFUKgG87Bb28OFD5OTkwMfHB2fPni3y+qSkpODIkSMYMGAAqlatWmi9hBA6jaL17NkzmJiYaKSrXpB+9uxZgfNWqFABvXv3xtq1azF//nzcuHEDv/76K/r06SM9+Xl1fnt7ezRs2BAzZ87EDz/8gPDwcPz6668ICwsrdplEREREpLs3sivU1atXkZ6eDgcHB63Tk5OTAQD+/v7o0aMHIiIisHDhQrRq1Qrdu3fH+++/r/WCtyjc3NzUft+8eRMAUKNGDbV0pVKJatWqSdOvX78OAKhTp46s5QOQLpATEhLUAqf8ddPFjRs3SqxeKmZmZlL3o1c9f/5cml6Yb7/9Fs+ePcO4ceMwbtw4AEC/fv3g7u6O2NhYWFpaSnUPCAjAunXr0KNHDwDAu+++C1dXV4SGhmLPnj3o1KlTkcokIiIioqJ5IwOLvLw8ODg4ICYmRut01QvZCoUC27Ztw8mTJ7Fz507s27cPAwYMwPz583Hy5ElZF5GvuyjWtw0bNiA0NBTdu3fH+PHj4eDgAENDQ8yePVsKXoCCn4Lk5ubqvY6Ojo64e/euRvq9e/cAAE5OToXOb2Njgx07duDWrVtISkqCi4sLXFxc0LRpU9jb20tPfKKjo/H8+XMEBgaqzd+tWzcAwLFjx6TAQtcyiYiIiKho3sjAwt3dHQcPHkSzZs10usD38/ODn58fZs6ciY0bN+KDDz7Apk2b8NFHHxWp+1FhXFxcAABXrlxBtWrVpPTs7GwkJiZKLxC7u7sDeDlCkSqtOLZt24Zq1aohNjZWbR2mTp2qls/Ozg4ANEamUj1BUVHV+eLFi8WuU34NGjRAXFwcMjIy1F7gPnXqlDRdF1WrVpW6Z6WlpeHMmTPSkwkA+OeffyCE0AiWVE9xcnJyilwmERERERXNG/mORe/evZGbm4vp06drTMvJyZEuoh89egSR7/t/qotZVRcdc3NzAJoX3kXVtm1bKJVKfP3112rLXL16NdLT09GlSxcAQKNGjeDm5oZFixZpLDN/XQujer/j1XlOnTqFEydOqOVzcXGBoaEhjhw5opYeGRmp9tve3h4tW7bEmjVrNIaSzV8vXYeb7dmzJ3Jzc7FixQopLSsrC1FRUfD19VUbEerWrVtISEh4bZkTJ05ETk4OxowZI6V5enpCCIEtW7ao5f3uu+8AAA0bNixymYDu60lEREREb+gTC39/fwwZMgSzZ8/G+fPn0b59exgbG+Pq1avYunUrFi9ejJ49e2Lt2rWIjIxEUFAQ3N3dkZmZiZUrV8La2hqdO3cG8LJLU+3atbF582Z4enqiXLlyqFOnTpHfNbC3t8fEiRMRERGBjh07olu3brhy5QoiIyPRuHFj9OvXDwBgYGCAZcuWoWvXrmjQoAHCwsLg6OiIhIQEXLp0Cfv27dNpeYGBgYiNjUVQUBC6dOmCxMRELF++HLVr18bjx4+lfDY2NujVqxeWLFkChUIBd3d37Nq1S3oP5VVff/01mjdvjkaNGmHw4MFwc3NDUlISdu/ejfPnz0v5dB1u1tfXF7169cLEiRORnJwMDw8PrF27FklJSVi9erVa3v79++OXX35RC2K+/PJLXLx4Eb6+vjAyMsIPP/yA/fv3Y8aMGWjcuLGULzQ0FPPmzcOQIUNw7tw5eHl54ezZs1i1ahW8vLwQFBRU5DKLsp5EREREhDdzuFmVFStWCG9vb2FmZiasrKxE3bp1xYQJE8Tff/8thBDi7Nmzom/fvqJq1arCxMREODg4iMDAQHH69Gm1co4fPy68vb2FUql87dCzquFmCxrCdunSpaJmzZrC2NhYVKxYUQwdOlRjWFkhhDh69Kho166dsLKyEhYWFqJevXpiyZIlOm+TvLw8MWvWLOHi4iJMTExEw4YNxa5du0RISIhwcXFRy5uSkiJ69OghzM3NhZ2dnRgyZIi4ePGixnCzQghx8eJFERQUJGxtbYWpqamoUaOG+OKLL9TyQMfhZoV4+aXtcePGiUqVKgkTExPRuHFjsXfvXo18/v7+Gm28a9cu0aRJE2FlZSXMzc2Fn5+f2LJli9bl3LlzRwwYMEC4ubkJpVIpHB0dxaBBg0RKSkqxyyzKehIRERG97RRCFKH/DRERERERkRZv5DsWRERERERUtryR71j8l2VnZ+Phw4eF5rGxsSn14W6JiIiIiF7FwKKMOX78OAICAgrNExUVhdDQ0H+nQkREREREOuA7FmXMo0ePcObMmULzeHl5wdHR8V+qERERERHR6zGwICIiIiIi2fjyNhERERERycbAgoiIiIiIZGNgQUREREREsjGwICIiIiIi2RhYEBERERGRbAwsiIiIiIhINgYWREREREQk2/8DAG8dBB4VjfEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] }, - "metadata": { - "needs_background": "light" - } + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -165,19 +221,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "
", - "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-06-16T15:51:32.055692\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n" + "image/png": 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\n" + "image/png": 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", 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\n" 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", 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\n" 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mean_abs_shap_value_testmean_abs_shap_value_trainmean_shap_value_testmean_shap_value_train
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f20.009400.0065140.0001680.000425
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" + ], "text/plain": [ " mean_abs_shap_value_test mean_abs_shap_value_train mean_shap_value_test \\\n", - "f3 0.041311 0.024811 -0.039222 \n", - "f2 0.007557 0.009072 0.006605 \n", - "f1 0.000000 0.329813 0.000000 \n", - "f4 0.000000 0.126920 0.000000 \n", + "f3 0.05316 0.044163 -0.002152 \n", + "f2 0.00940 0.006514 0.000168 \n", + "f1 0.00000 0.316807 0.000000 \n", + "f4 0.00000 0.087430 0.000000 \n", "\n", " mean_shap_value_train \n", - "f3 -0.016373 \n", - "f2 0.009072 \n", - "f1 0.329813 \n", - "f4 0.126920 " - ], - "text/html": "
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mean_abs_shap_value_testmean_abs_shap_value_trainmean_shap_value_testmean_shap_value_train
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f20.0075570.0090720.0066050.009072
f10.0000000.3298130.0000000.329813
f40.0000000.1269200.0000000.126920
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" + "f3 -0.003084 \n", + "f2 0.000425 \n", + "f1 -0.055661 \n", + "f4 -0.010959 " + ] }, + "execution_count": 9, "metadata": {}, - "execution_count": 8 + "output_type": "execute_result" } ], "source": [ - "shap_interpreter = ShapModelInterpreter(clf)\n", + "shap_interpreter = ShapModelInterpreter(model)\n", "feature_importance = shap_interpreter.fit_compute(X_train, X_test, y_train, y_test, approximate=False)\n", "feature_importance" ] @@ -323,30 +436,28 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "
", - "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-06-16T15:51:42.100679\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n" + "image/png": 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PNXHX1YtIdgUcf+Gu7PuWCcOWdjEETy0ndcpPYF0zkatOJvLptwCwfnEbd37heVrXd7PPW3Zh08IltLam6G6oJh6kOW7zUp7bXEGzVFORTFM/r543/fEYKsdXsPKMv9By1+vUahPRVIAQkKyvxWtuYyybkNoKKo6ZTOSv/yU1fxb31h/N5hUdVHZ1s+ea9URROonSTDXpufUsbqgm3R0QCZJ0xSqIBGnmb1jKnhuTjI808Up8Bsvu3kjd9VfxSu08vNYpVEuSiKZJE8EjoILk1mNuYwxNzCEgRsvlj/Pkd15jcvtmZqXWQ8xj3JMfpeWxtbR/+E48lMDzmLjsImLTG4r1MeVULvdzc+n3yBKJxELgbODsRCLRmkgkrvB9/zrf9/8KNBckhyYv4zpTVHckiSfTVHUmqelKkUyBioeKB+IRVaVtc4rHH2oinVKWvdbJg3dtzCv9zvY0d9ywhu7OgKaNSRd0AURQEYI03P6jlXmldfdPV9KyKYWqK6ArsLmxO+e6/7hpNS0b3Y9Kqlu58/vLtlvn+Yc38fK/NxOklKXPtvDEPWu3W+fe65fS1R6ACJ1tAbd//TWevncd6aSyZlEb//7d6u3SfPW/W0DdYw2LntjC8w9u6POY1ixq47Hb1pBOKuuXtPPEHY2kupXNqzr5xw3Le63btqmbf/xoMamugObGLh784WLWLW0Ng65rubn0qSaSnQEtG7q5//oldLamuO/HywjaU2hHiiCtrHimiafuWJ07Q0P0l+sW07Y5SXd7mj9f8zpBWgfeqISlP30bLNsIHUnSl9yOrmkC4JEfL2bLqk5S3QHP/Hkdzeu7aI/ESXYEzFq8ki1LWtlQUUtldxpRaHmlmZeveZHm2xbR8sdFaDJFNBUAghJBmjsZx0Y8FGntJLlwEXSnWLEoyeYVHQBMbm4lToCHUk2SSrpZToR0t2uPkfZiSDqgMpnkDatfZmxXK1PbN3HIupdJRuIsqZtKWyxOJA1JiTCeZsbRwmTWM4nXqKIRjw5aGQvE8ID6oJN4OmBt1Ti6JYqXTNP0nj/Q9LH78MLqMS8I2PTuPxbh09l59Rt4fd9fANwM3Oj7fq3v+18pTLb61tLSYtM5plsiHm1VMbpjUToqY3RkdyEZ8rJ+R5PJbQFvoH1lVvzk+jmOhNWjA+a5nxqk7PWzS5iRqPSZfg/xtl8nGuudjpc1eET2echVs9jV1dlnPvurZRfpvX5rW2uv9UXA2y6BbWdYg3Sf56znVv5wfZdUMxrlZeyzVL7ng57udaKBnu9GP5+XAhHd/hsuHnR0doZJbb8813OnFUHX1mmP3g0eBd3uH0mAWJDeGhQB4ukklelOJA2IEHhub+NYh+IxjZdpYCNx2qhmPZk/6wJENCAQj45ITzWzQlavdkFs2/vBnueRUs6tmkfdUdTV1dl0jmkvGun1IyNehMxbTRL+kBxwaC2HHTeGikqP3eZXc8JpU/JKv7I6wrsumEpVbYTxk+OMmxhzPw2qeKpUVHmc/plZeeX5nR+ZwZiJMcTb9mTeLtMrcq5/zPumMGl2JQhU10V49yW7brfOvseMZ++jxhGr9NjtoAYOftvE7dZZ8Mldqd8lBkDDhDjvvWIPEm+fRLzKY/petRxz9q691t/n6PHMf+NYxHO/UXsePpbESdP7PK7Jc2s44vSpxKsjTN69hkNPm0pFdYQJs6s49sOzeq0/acY43nzhblTURhg7vYo3X7gbu8yqYfpedbhqAGXuoWOprIsyZkolx1+8O5U1UU66cDaxhhheTYxohcfsg8dy4Dun5XXO851+26d3p35CnKr6KAsumbv1XnOpfM8HOx353mkwbxI0VBH53mnIpHrq6uo4+mO7MX52NfGaCAe9YzJjplVRS4qK2igr5s2kfv4Y5nRuJlUdgYgw5oBx7Hnx3kw6az/qz9gDqakkVRXHNQFKw4QaNjGeNB46pprYu/aCuiqm7l/PHsdNIJ5M0xyrIh2G7E6idHoV7FoJ0Up3W0Q0IPA8miuqeWG8+1/q9iIsqx/P+K6NzG1dS0QD2uuiTNPlKEqSKDEyg3uKGEl6InpTtJL2SJxIkKY61UEQi9Bw23sZe8s76QnvQWWcSX85c8jneeSU73O8ojmu7DIlEolfA6nsRlR9zS+A0V33NUKmXrSedVWVpMMr2fHdSdZ+cwz3/H49kbgwdWqchrExdps/fI8YpNO6tZQ7VB2tKTSA6np7sg0gSCkSYUgN1UzhaSoNkbCRVCpA+qhpAgiSabxYhOS6dryaGJGa2NZl6WRAJKyVSXakSLYmidzxBA//eAmNyWqmHbELR15zBJGqKI/M/wMHLHqAdVXj6UjuwtjUemawCAHaaaCV6SiCkObhyXsx9+2zmXPRXtTtEkMm1PT6bmkyjcR2uGHViHxZG+UrOX/rJ+sVo/6fw37tysT09k6md6dYWV1JbTrN7ptbiUTGcvIZE0dsnzsadAGqau0rmCm7CtyUtszWwP0FXQAvDHCxidtf/EYyboXEqqLEqqJw/pG89fwjt1t37x8dyZYTnmR2x3JSrGczM1jkHUBLJMak5FrqaGRTZAovjZnLPp85gPmf3r7R4tY873jQHTHl3GXkoH/1EolEHFdFHQE0kUhUAoHv+7lbx5iCqFDlmbG1BCJslDiT2jsH3sgYM+qs/+Or7JFaQoSAOJ2uujiYjb5lH6bf9zkAqpuTzIx7RKpG74WtjU7U29+AozLenwM8DBw9DPkxQ/RKbTUT2rqpSAcEwAsNhbgHY4wpNF26iUhGQ60ISQRl6vm7b23nEWvo/7n40aCc7ykOGHh93z8n6/3RI5UZM3QqQkXK/TN6QFU63f8GxphRacbX3sTq+//B1GAVitDGBBSl5sTdip21YVXOJd7yPbKdTBAV1yI2FB+g0ZwxZnSqP2gCVQ99kaeqjmQZ+9HCOCovOgyvIjbwxqOIIjlf5WD03gAwvdQHSl0Q0CVCVBWxT9aYsjX2TZMZ2/7JYmdjRO3sfTWbUaChs5vKQKkQV/KN2HC8xphRrFxKt7lY4C0T6QCqg4C0CJ4qGV23GmPMqFPOgdfu8ZaJFfWVtFZEiamiIiwaV1PsLBljzJDZPV5T8qaOifCf8eOp70zSEY8QdAcDb2SMMSWqVPtlFpG3AO8FJqrqAhFJAPWq+mC+aZTmkZlBe+FzdcS7U2ypitGNcNsp9tEaY0Yv7eNVTCJyIfBjYBHQ061YB/DVwaRjJd4yISJ0XFbFnXffQ0SUBfssKHaWjDFmyEr0Od5PAm9W1aUicmk472Vgj8EkYoG3zESk2NeExhiz40r0caI6YEU43fNjGwMG1WVySV5SGGOM2bmVaOOqR4DPZc37BPCPwSRiJV5jjDElp0RLvBcCC0XkPKBORF4BmoFB3duzwGuMMabklEDpdjuqukZEDgYOAWbiqp0fV9VBPUZigbeMrNqY4g8vzWL++C2Du/wyxpgSU6IlXlRVgf+GryGxwFsmFq/t5oDvddBeOw9vZZoXfraFmz88ptjZMsaYISnFEq+IrKCPp5pUdWa+6VjgLRNvuq6VlnF1IEK6Isqdq611szFm9CrREu/7s95PAS4CfjeYRCzwlolk2ts6CDZAZWl+aY0xJi+lWOJV1Yez54nIQ8BfgO/nm44F3jJRlUpT1ZGkIxJBRKnrtFESjDGjV4l2oJFLF7DrYDawwFsmNlTG6FCBtKJAY2W82FkyxpghK8WbZSJyZdasauBE4L7BpGOBt0x00ruquSsoxa+tMcbkp0Tv8c7Iet8GXAP8djCJWOAtE7H2LrqjETTigSrRDqtqNsaMXqUYeFX1A8ORjgXeMhFLK9LWRXdljEgqIN6dLnaWdkrrj/sl3gMvko7GGPPCp4jPm1DsLBkzKpVK4BWRY/NZbzDDAlrgLRNjUFbWVgBCEI1QE1jgLbSmn/vUPODTQRVVqTY27XENk/XqYmfLmFGphFo1/yKPdRSYk2+CFnjLREdVDDK+qJ3WuKrg1l38D5rZg82MwSNgFsuLnSVjRq1SKfGq6qBaLOdjwMCbSCTGAbcChwKv+b5/0HBnwuw4BTfWVNhjqI6alviF0XXz47x83sO0RyuY/Kbx7Prn9+W/7fpO1i9chi7ezIRz9qRy7hgAgmSaLSddT8XrjcS+eQqrOmtJUe2W4bGGyew2EgdjzE6ghEq8wy6fEu8FQC0w3vf9VM/MRCKxH+ADj/i+f9wI5c/kaYvnucAbdY2rurvg+efauPYbq0kDgecRrRBmTPBoqPV460ljqa6N0tUZ0NqS5vlnWjno0Dr2O7CO//nNLH65g8PePIaJUypY/noHXZ0B4yfGeOnJVurHRZgxp4pff20J61d3MWFmJYeesAvjJsRpa07y2J/W4qmw4CPTePLGZTTft4LqZJL6Qyfy2DIlSCvV4+PUbWhnz7HKlEv34d4rXqG6qY09dqtk6bIkY+fXcdg5s6jcfQxLn22m8ZUWVj++keY2ZdK8Wk750jzWPbuJ/37rJbpTStWKZogIs945C5q7ePa+1aTiUeZUdrN0XUDS85g5vprdNi7jpafiPLLPHSTOn0fNXuPReITIkk0s/csSxixZQSpeSderLTQ0drChuobOVAVTks3UaCfrvvZX6qo2EUu2E0slqSPO5ngdSz78EJsaJhHrErxAiXemUS3fHw5jRlqplHgziUg9cDlwFLALGdWMg+kyUlx/z31LJBI3APi+f27GvCjwGNAEaIEDrz0nk4Nc1gIS6TXvrNdXIV7vom8AVCVTWx9NDwAVAXEn9oB9K3n56TYE8Dw46m1j+cfdmwHwRPFSrkgtQZqI4h5hUiUQSEZjxJNJKlLu/rKkA3ZdtJqWsTVsmDgGVIloQHc0SjoS2bpONJUiFY8hQUB9azt4Ls26zW1URKBxbD2RVJpUPIrg8hlPp9j1f6uo7kihQPOYajZPrGfXRY0smj8RAfZZtJKNDTU0ThizNZ/Hv/Q4U1o38/L46fhTd6erIobGYoxta+aU5x7GQ3i5djIVrRVEwq/a2poaWqoqGZNqYb+mxUQ0jUeaClpp92p4Yvx8GivHU9eU2vpfGEulmdTexIF64XB9xMaUqhGJkP+QX+X8rT9GP1C0iCwiNwHTgWuBm3BdSF4C3K6q1+abTr8VkolEYiFwNnB2IpFoTSQSV4SLPg88AfxzCHk3I6Aye1AqAfW2/3g9ev+XCNtmCPDyc+1blwcB/OfvTVvXDVS2XvV4PUEX99dTkCAgmt6WEY14xLuTLuiG66XFI52RryDikYzHAIik0y7ohuu2NlRTt2YL0Yyg25PPIAXVHamt7+NdSSau2UJbXYV7n0wxYXMrTXXVIMK49k2c9uIdTO98hSjt7LlxJRENiIWtv/dYvwIPIUBYGZu0Nei2xWKsq6+nIxZnTdV4XqudiiAoERQhGgiHr3+ZYxufIaLbGrQFInRi99mNGaog/H/MfhXZW4FTVfUuIB3+fQ9w5mAS6Tfw+r6/ALgZuNH3/Vrf97+SSCT2Bc4BLh1StndQS0uLTeeYTsG2oAWgW2/39jLQoJFVNb2/EhMmxzLS3HYBqpnvM+anI16v9RUlktoWkATFy65lCd8HntcrrWgqTVdlnJTn4aWDrUFfARUIMg43iHi011RQ29yJAqlIhK5YlLHN7QAct+QfTGlbR02qjSrW0elFSItHEOa3NV61NX9d8Qg9dQLprIuXpES3rhew7WJgTLKNeKRza/6qupK9fiJK5Xti0zY93NMjRZGcryLzcDW9AK0iMgZYA8wdTCL5VDX/Gkj5vn9uRhXzFb7vL0wkEpcDb7Sq5uKTL7VSJUJHNEI0UEgm+dYurfhPdSLqupEUIB4EVESUubtVUlsXARGWLumkuTnNxEkxvvj1mfzq2tU0ruzksGPHcNixY7j39xvo6gyorRNefKKF2voo+76hlvt+uWbr/qvHxZg6r4alL7YRbOnCAw44pIbV/9xAbE0LbXVVpOMROiqiBEAyHgcRqtq7mDKvinUvtVLV3kV9eztNDbVERTlonwoqjpzOy8+20vLiFjo2dtFVEUfjHgvOn0Hjza/S9shaCEvbGyc3sEt7F7GOLlZMqsdTZebqDazbpYF3LLmThm73Y6HAfVOPY+PUmdTsNQ71BG/pZvZ/8j+MaW+jsbKelckZ1Hd3k8Zjw7hqOirieBqwR+tSpreupJI0EVIEYWOqlHj8ae4RdHlxZi7fTDQIqO7q5HD9aKG/CsYU2ohEw79GfpPzt/7/pc8qZlXzA8DXVfUBEbkVV5ZpBQ5S1US+6Qz2caLPAot83184yO3MCGtIp4lWROmojuKl0kzrSHPxxVNZvz7JunXd7LlnFSKC5w38nT3vkum93r/73Mlbp99x9pSt08ecPDGPnM3r9a69KYlEhaqa/L96bzi9jwXvnJbX9p1NSWTun9ENYTV1JMaJq3IFxBMBGAfsBTQ9spr45Gq6BB498xGksYOq49/A+F99Hk2l0ccXkTrq22hKWD9mIlUt3VAhrJ9Qw9gNnVTRnfcxGmN6K9G2ieex7ULjE8DVwBjgrMEkMtjA+1bgwEQisSF8Xw1Ew/fzfN/fNMj0zDBJRzyaaipci+ZohE3V7v7ihAkxJkyIDbB14VQ3FD4vlQ0xWH8D/O0pWNtC5P1H5rVdw5FTAagC3vbY23stk2gEOXxP4kn3bP0soDX6LVbJBAg8IoHiDVixb4zpSxAtyci7TNU15lDV9cC5A6yf02AD77uBioz3nwIOBk4HtgwlA2Z4dABz17UwsbmTjniEFyfUFTtLpeetB45o8rXv3IMxf1xDNxE8lFq6RnR/xpQzjZRk4G0UkduAW1T10aEmMqjA6/v++sz3iUSiGejyfX/lUDNghseY7hSTm13jnpruNNO3dODKaqZQZt12Ms3xHxBJKkLAhM/nfcvHGJMlXZol3rfiCpq3iEiA61zqFlX932ASGbBxVQkadRkuhIkXb2CPjBGJmmIez/1gUhFztPNKbuwgUhfHi0cGXtmY0W9EIuRdY2/J+Vt/8uYzSiIii8hRuCB8CtCoqvvlu6311VwmIoEg4HqpEiGWtPuLxRIbbzUNxuyooDSrmjO9ArwErAB2H8yGFnjLRFdM+OfYBkgGEBGmdKUG3sgYY0pUugQDb/jc7qnAGbjxC/4GfBO4ezDpWOAtE53xCLRtq2peF7NqTmPM6FWijxOtBv4N3AKcoqpNA6yfkwXeMqHJrBkpG4/XGDN6pfPoc6AIdlPVNQOv1j8bPK5MjEv3rlquDuwerzFm9FJPcr6KmqdhCLpggbdsfOLIGNNSaWIi1KPMTNs9XmPM6BV4kvNVDqyquUxc+o56Fq/dwn//10EsnuJf35oy8EbGGFOigvKIsTlZ4C0jPz1/DAsXum6047EFRc6NMcYMXbmUbnOxqmZjjDElJxDJ+Somcc4TkQdF5Llw3pEictpg0rHAa4wxpuSU6D3eK4EPAT8DZobzVjLI8emtqtkYY0zJ0SKXbvtwDvB/qrpBRH4czlsCzBlMIhZ4jTHGlJwS7TIyghv4HraNG1CbMS8vVtVcRu478180n9dB04WdJFP2HK8xZvRSkZyvIrsPuEZEKsDd8wWuAhYOJhELvGXi4U89znOLk7y+30yWzpnGb+fdVewsGWPMkGnEy/kqsouBKUAT0IAr6c7C7vHunF74yzq6pu+CABr12DBlbLGzZIwxQ1bsXqqyiUgEeBduKMB6XMBdoaqNg03LAm+Z6K6IM3nFJqas3EJ7bQXL5uxS7CwZY8yQBV5pDfSiqmkRuUZVfwl0AuuGmpYF3jJRs7Gd3VZsRoDa1i5iHdmjJhhjzOhRaiXe0EIRWaCqg7qnm80Cb7kQj8yvaSRpjauMMaNXiQbeSuCPIvIfYAXbWjajqmflm4gF3jKxfGoDc1ZswlNFgZfmTOLkYmfKGGOGqARaMOfyfPjaIRZ4y8S6uiqe3GcKEzZ10F4V48l5NkiCMWb0Corfgnk7qnrFcKRjgbdM7LZmE9OaWsCDeFeSg19bAexV7GwZY8yQlGKJV0SO7WuZqj6YbzoWeMtETVeKpdMnsnzqLjS0dbDniyuKnSVjjBmywCu9Ei/wi6z3E4A4rr/mvLuNHDDwJhKJccCtwKHAa77vHzSITJoCWTZtF9JRYf4ry2iuqeKpfWfzwWJnyhhjhqgUS7yqumvm+/DZ3suAlsGkk0+J9wJcX5TjgfpEIvEIsCeuddd64FfA13zf176TKC8vrEsxodpjYu32V2RNXQFVUSGeo5/RIFC8QbTUG/v1NrZ0CHhK42cqmFTX98cV7+rmjc++hqrH5uoq0v3cH9H2LvRb9yCH7Yb8vwOgsxteXAH7zoKYVYIYY4qvFO/xZguf7f0arsR7Tb7b5fMrOwd4yff9VCKRaAM+Arzq+34ykUjsCtyLe5D4Z0PId8G8skn5ybMBYyuVe16HpU1w+p7C9471EBGWNwW8809pXtwIXUnXMpiIuMbireEzsZ6AAJ4HpAGlLq5EIh5z6gKeWq0Q8UADSAYgAoFCPOKmox4EAZXpNNURZW6D4iFMGyfc/YoSeMKB4wOeWZUmKRGXVhBACiZ/tR3S4SNCPVeCqTSCEguUG5cuJxnECcSjpj3NnkvW0ChfQlCEFDW0uWOa0kB8zRoCoggBUTYTYdujRyoxQEi97XB00nikoxPv2ZfxVq5BOrrdvk88EH7xUdjvU9C4BY7eG/52eSE+RmPMTqIUS7x9eAswqOc3+w28iURiIXB8OP1e4Lu+738la7UA2GMwOy201m7l6N+naWzrPf+6p5XpdQGXHBLhkN+mWduOC5QBEBMXZASoibngGyjEevem0tIJVHo8tVmAFKQCt01thVshFUB3GirD7TyPToXOpk4e3xImsowwmMMTGoXuFNR6bl7Uc/vtTLm/EuYrcJ+zInR7QmNNDbt2dGzN14TmNpJUhM/2BsToppJuWLOWgIpw2wgBlURo37qdaAqIEln4H5LU49FOhKwTd8d/4d6noDO8ILn/Ofjcb+AbeT/GZowx/SrFe7wi0uvZXaAaV/v7scGk0++R+b6/ALgZuNH3/dqeoJtIJO5JJBIdwGKgDvjpYHa6I1paWgY9vbKF7YJuj3+vVlpaWtjQE7MUFzgzr7Z6zlL2BZgIpHXbdM82mcE56rmScyZPen90mTQjvVhYUo544XRGvrK2v3/eLCTYdtHV0N2ekV2PJJU5D8EjnZ2BcH4AYWk5p87ePWOl73tq6/RQPiObtmmbHp3TIyXwvJyvIns/cGbG63hgqqreOJhERLX/W7OJROLXQMr3/XOz5keAg4EFuJLwpsHseAcM+l5yMq0cdkuaJ9duv+z+dwnHzY5w+E1J/rMaF/hSuBKvJ+59WqEt5aJWRWRbpUJEIJmGyqirBu5Ou2VRz1UvgyuldiTdOj1BM5mGtm63rEfPFyom0JFyJeaKjAqJ7pTbxvNcOqqQDIOiKm9auZ7P3/U4GnHV27XayZxUT1eiSi2bqCBJQIooihIB0sTZGAbZTDHSxElRQ4QOIrRsF7CZPRGWZnRVeuelcPIbBvoojDHlZ0TqhL/6lsdy/tZfdv+hRauDFpHPqOp3csz/lKoO6z3enHzfTwOPJRKJI4Ef4kZsKEmxiPDQeyIsfF2ZWiv8eXGaZ9fBFYcLh05zAfLf749xzeNp7l0csLZVWbEloCnlAlw8nSaoFIgIqQCIhyXWIM1pe3m0pgLet7fwoYXQGbjqaS+VQhHGRgNi9R6bOwO6FVA4boYCMY6aKbR0wfQGj+sfT5EO4DOHC399TbjztcBVU4eBlGQKz4OAAAJBgoCoKJPqhQqFk+5dTCwVIGEs3lRXy5yWVQhChE5iJEl7EbyPH4t33d24YnySXrcmIkIwYzJ0pNBvnk3E85BUgC5bA88uQlZtdBcAF78dzj4GLrsF/vIUXPZuC7rGmGEVlGaXkV8Gtgu8uJbNIx94s9LYfRjSGVG1ceH0+e6DPGpG7sP+1CERPnXI0EfEOGO/+JC3vfCNFVunLzjc/T39D53c9lLA1Dph2ZfqkH4aG/z2hg7WT6mhaWwlFZ0pqlpSTG/uo5OV75+CdiahIrpdml7W33599Qz3MsaYYVZKjasyOs6IiMgx9C7lz2EEHifaKpFIHArUAP8GuoEjgIso8RbNo9Wtp1Vya57rdsWhpb4agI7aOAPVyEtlbMcyZ4wxIygtRb+fm6mn44xK4JcZ8xVoBC4cTGKDLfHGgW8B88IdrgKuA74xyHTMMNs8ro6a1LYGTxsmNBQxN8YYs2NKqcTb03GGiPxmMKMQ9WXAwOv7/jkZ048A1nNVCeqqiJOuilPX0kZ3PMbGMRZ4jTGjVwm0YN7OcARdsL6ay0a6K83aXScTnzCOQIRUdx+PARljzCigpVPg3UpE6oHLgaOAXci416uqM/NNp/QuKcyQeAG83lDH8vo6Xh9TT6TdAq8xZvRKe17OV5H9CDgQuBIYh7u3uxy4djCJWIm3TLRVxVgfi5L0PFJATcoCrzFm9Cqle7wZ3grMV9WNIpJW1btExAcWMojga4G3TPgzJrEqHmdV+H76+Pqi5scYY3ZEujSf4/WApnC6VUTGAGuAuYNJxAJvmajNvKeryqzGDcXLjDHG7CAdmQ6xdtSzuPu7DwD/xHUe1Qq8OphELPCWicSaRuJd3aypr+WQpatpSCUH3sgYY0pUCdzPzeU8tjWo+gRwNTAGGFRrZwu8ZWJB9RZau2o45tlVNNVUI/tPKnaWjDFmyErxHq+qLs6YXg+c28/qfSrJSwozePs/eCpnn1BJ1+xKxrwlxWd+sW+xs2SMMUOW9iTnq5jEOU9EHhSR58J5R4rIaYNJxwJvGZn/mQPY7aMdTDu8q9hZMcaYHRIgOV9FdiXwIVw3yT3P7a4ELh1MIlbVbIwxpuQUu3Tbh3OA/1PVDSLy43DeEtxACXmzwGuMMabkBCV4jxeI4Foxw7aRaGoz5uXFqpqNMcaUnFK8xwvcC1wjIhXg7vkCV+E60MibBV5jjDElR5GcryL7FDAV14lGA66kOwu7x7vzOu7DS5m0fhYbairZY34H8+ZWFTtLxhgzJKkSeo5XRCaraqOqNgPvEJGJuIC7QlUbB5te6RyZ2SEf/uIyjnlxKRNSAQev2cgXvrB44I2MMaZEBZL7VSTZPVP9RFWfGErQBSvxlo22V1t4bPc5iCcoysz1m4qdJWOMGbK0lFS5MDvkH70jiVngLROd0VgYdEEQXhs/tthZMsaYISti6TYXHXiV/FngLRPrq+JUeELgeYgqrZGSulo0xphBSZVWiTcqIsewreSb/R5VfTDvxIY5c6ZIKtJKEHNfVBWhJmmDJBhjRq90aZV41wG/zHi/Meu9MohONCzwlolAg17vx3Zat5HGmNErVfxndrdS1dnDmZ4F3jLRVBEj8IS6QOkWwSuhpvjGGDNYydKqah5WFnjLRFI8JnZ1E4hHVRCwuaqy2FkyxpghK7HGVcPKAm+ZGNOdYnFtzdaHzme1d/RavuG7j9H5UhNTrj2GSF28GFk0xpi8dZdmX83DYsDAm0gkxgG3AocCr/m+f9CI58oMnie9enrp8Dye2/s66EwSX6+0tlSjeGz+xfPs3fEJnt/jN0SXryOYP4V9XjynePk2xpgcSnSQhGGRT4n3AtzoC+OBOYlE4o/AYUA9sBy41vf9G0YuiyYftd3dTG1rZ1ZrO1sqYhy65Hk6l3TR5tUyo3s5E2klThdNNLCk6ivswVIq6KTrpTiv7tXJvBcvKPYhGGPMVl1lHHjzuXs9B3jJ9/0UMBb4B3AwLvCeD3wnkUicMnJZNPmo6Uzy1lWNzG9q5rB1G5m3pos1FdNojjXwYtVeeBIQwaOBNuKkqaATgAq6mfPS36HqPdsnesdjsOeFcOil8NJKuPQ3MOcj8J7vQLu1mjbGjJy0SM5XOei3xJtIJBYCx4fT7wW+6/v+VzJWeTSRSNwPHAXcMWK5NANqrqvAa23b+n5LXQM1GzcCsHf7MmrVBUoPjzi9n/GNEEBnAF+5Fa443c1s64QzroWucN13fRteXOGml6yFfWfBZe8e2YMyxuy0OsskyObSb4nX9/0FwM3Ajb7v12YFXRKJRDWu2vm5kctiby0tLTadY3rJuHraohHAPck9fW0jqIIq41Lb1vUIEFJk6hlqq2vp2m1pbtyyLegCtHb22oaWjpI5dpu2aZsu3vRISUruVzkQ1f67oEwkEr8GUr7vn5s1PwL8AZgIHOv7fqG6ShrWPjPLxWEfWs7aygqmtnewJR5jwYtPc86/nqXdq2RWuhEvvMZKEmUTEebyIhEC0kR4nfnM43no/B1UZLR4/upt8OXfQU0F3HIxXPdn+PtzMG8qPHgFTBtfpKM1xpSQEQmHUz+xIedv/errdhn14XdIjxMlEokYriQ8BTihgEHX9KFbAzojERbX1QLw+PRdOSf+JJNSa+nGI0k14NFJBc3E+S/HEiVNJ1VUVyWh9XLI7nTjsnfDxQsgFoF4DBYcDE1tUF8NZVwNZIwpvvYy/o0ZdOBNJBKVwB9xLZ3f6vt+67DnygxaW2WMlEA0cBeJL02ZyPOTZ5MSYf7yDdQF3YC7NJ3/8vt5dq8/0RXEiMbgoNYLoK/u2WqyOuJoqBnBozDGGKfJAq+TSCRqgYVAElfS7RhgE1Mg9UGSV6ZOgK4UxCLM3NzCqUvPBkAD5bX/u5n0ymam/u4kavfYhSPS5xU5x8YY04/yjbuDLvGeihsAuANYn0gkeubf5Pu+PQhaRLXNXczb2MSrE8dS19nN3is3ALsAIJ6w+7PvL24GjTFmMMq4xDtg46oSNOoyXAgHn7OEt61aS1c8RiQIeLa2mrtv27vY2TLGlL8RiZDy6S05f+v1u2NGfUS2vprLRK0oXZUVAKQ9jwlBMMAWxhhTwsq4xGuBt0xMa23v9X5clzU0N8aMYuUbd/PqMtKMAsl0mniXa7nspQOq2toH2MIYY0qYSO5XGbASb5moTUNdWztBeweiSlvMPlpjzChWHjE2JyvxlonGMbUo4KnrAPLVSeOKnSVjjBk6K/GaUlcxuYLf188jsXwtjfXVvGyB1xgzmpVHjM3JSrxl4varp9FaVcHPjtiHf86dzg9OqRx4I2OMKVVW4jWlTjzhpW/twp23L8SLwXGHLyh2lowxZujKJMjmYoG3zETiA69jjDElr3zjrgVeY4wxpah8I68FXmOMMaWnjFsgWeA1xhhTesr4Hm8ZX1MYY4wxpcdKvMYYY0qPZyVeY4wxxgwDK/EaY4wpPWV8j9cCrzHGmNJTvnHXAq8xxpgSVMaB1+7xGmOMMQVkJV5jjDGlx1o1G2OMMWY4WInXGGNM6bFWzcYYY0wBlW/ctcBrjDGmBJVx4LV7vMYYY0YtEVkqIvsUOx+DYSVeY4wxpcdKvMYYY0wBieR+5bWpnCUi/xOR50TkTyIyMZz/HxE5OJz+kYi8EE5HRWSDiNSM2PFkGHUlXhH5K7BLsfNRyqLR6C6pVGpDsfMx2th5Gxo7b0NTRuftL6p6/HAnqpdEh1TmDaudvwEcpKprROQq4AfAe4AHgDcDTwBvBDpEZAowG3hJVduGI+8DGXWBdyQ+4HKTSCR83/cTxc7HaGPnbWjsvA2NnbcRcwxwr6quCd//FHg2nH4Q+IKI3AxsBB7GBeJdcUG5IKyq2RhjTDkRQLPm9bz/F3AgcBIu0PaUgN+MC8oFYYHXGGNMOXkAOFFEJofvzwP+DqCqXcBTwOfCeY8BRwD7hdMFMeqqmk1eflbsDIxSdt6Gxs7b0Nh5Gz5/F5FUxvsvAPeLiAKLgfMzlj0AHAz4qpoSkdeAJaraXajMimp2idwYY4wxI8Wqmo0xxpgCssBrjDHGFJDd4x2lEonEPOBGYDyuWfxZvu8vylonAlwHHI9r1fcN3/dvKHReS0me5+1LwHuBVPj6gu/7fy10XktJPuctY909gKeBH/m+/5nC5bL05HveEonEacCX2NYi9zjf99cWMq+mcKzEO3r9BPih7/vzgB/inlXL9j5gLrA7cBhweSKRmF2wHJamfM7b48DBvu/vD3wQ+H0ikagqYB5LUT7nredi76fAnYXLWkkb8LwlEokEcDnwFt/398F17NBUyEyawrLAOwolEomJuGfRbg1n3QocmEgkJmSt+h7g577vB77vr8f9GL67YBktMfmeN9/3/+r7fnv49jlcKWR8wTJaYgbxfQP3mMY9wKsFyl7JGsR5uxj4ju/7jQC+7zf5vt9ZuJyaQrPAOzrNAFb5vp8GCP+uDudnmgksy3i/PMc6O5N8z1ums4DXfd9fWYD8laq8zlsikdgP+H/AtQXPYWnK9/u2FzAnkUg8kkgknkokEpclEokyHiLAWOA1pg+JROIo4Crg9GLnpdQlEokY8HPggp5AY/IWxXXg8BbgKOAE4Myi5siMKAu8o9MKYFp4P63nvtrUcH6m5cCsjPczc6yzM8n3vJFIJA4DbgLe4fv+KwXNZenJ57xNAXYD7k0kEkuBTwLnJRKJnbmTiHy/b8uAP/q+3+X7fgtwF3BIQXNqCsoC7yjk+/464Bm2lcROB54O7+Nmug334+eF95XeAdxeqHyWmnzPWyKROBj4PfAu3/efKmgmS1A+5833/eW+7+/i+/5s3/dnA9/DtS/4cIGzWzIG8X96C/DWRCIhYc3Bm9nWqb8pQxZ4R68LgAsTicSrwIXhexKJxL1hK0mA3+K6S1uE64f0St/3FxcjsyUkn/P2I6AK+GkikXgmfO1bnOyWjHzOm9lePuftd8A64EVcoH4B+EXhs2oKxbqMNMYYYwrISrzGGGNMAVngNcYYYwrIAq8xxhhTQBZ4jTHGmAKywGuMMcYUkAVeUxAiMltEVESmj/B+LhCR32a8v09EPjuS+zS5ichrInJOnusW5PtRCCJSISKLRGTPYufFlCYLvCVGROaIyG0i0igirSKyQkT+JCLxcPk5IvJaju36mv/+8AftyzmWPSQiXeF+mkTkaRE5dWSObOSJSA1wJW6kFwBU9QRV/VbRMjWA8LN5Y7HzsTMYiXMtIkeLSCpznqp2Ad8Bvj2c+zLlwwJv6bkXWAPsAdThhvP7K26EnKH4MLAJOFdEIjmWX6WqtbjRd24Ffi8i84a4r2J7P/A/VX292BkxO71bgWNFZG6xM2JKjwXeEiIi43EB9yeq2qTOSlX9SXgVPdj05gNvAs7G9aV7Ql/rqmoK12NTBNiulyYR+biIPJ01b1cRSYvI7PD9r8ISeouIvCgiZ/STt8tF5O9Z8x4Skcsy3u8jIn8VkQ0islxErhaRWD+H/A7g/r7SzKjOPDvMX5uI3CsiY0XkGyKyLqxp+FjG9ueEVaaXisiacJ3vZuZjoOMWkf1E5C8isl5ENonI/eH8nm4B/xbWOtzQx7mqFpHvh/vYICJ3isjMrGP8rojcHubhdRE5ua+TlHFMF4vIynCb74jI+DCNZhF5ObN0KCJREfmyiCwOj+EBEdknY3lMRK7JOIeX5tjvm0Tk0XD710Xk0yKS9wWliJwqIs+GtTPPisg7s48pa/1f95zTvs61iCwNj+vRcL4vIgfnSiNj3lJxNUlTgfuASLhtq4icDaCqzcATwNvzPT6z87DAW0JUdSOuu7gbROQsEdlrMD9MOZyPKwHegytJ99lvrriq7I8BSXL3E3szMF9EDsiYdw7wkKouDd8/ChwAjMFV+f5aRPYaSsZFZCLwMHAHrmP5w3Cjt3y+n80OxHW7N5BTcYONzwRmA/8FXg/38wHge5mBDTfQxExgTpiPBcBnMpb3edwiMiU8jofDfU0GvgmgqvuH279VVWtV9dw+8nstcGj4mgVsABZK7xqMs4FrgAbgeuBGEanu5xzMCvM7JzwXF+KCyLeBsbjz/quM9S/BDZF4Iu4i7p/A/SJSHy7/HPA24HBg1/BYtw7QISJ7476D3wYmACcBHyfPUXhE5DDcd/BzuNqZLwC3isgb8tl+gHN9AXARMA74I3BvxnH1l+Zq3MVsOkyzVlVvzFjlf7jvpDG9WOAtPUcDD+FGd3kGWCsiX8oKwLuKyJbMF660upWIVOJ+1H4ZzvoFcKJs33jli+H2K4GTgVNVdbt7xaq6GTdqygfC9AX3Y//LjHV+oaobVTWtqr/DDSJ/9CCPv8dZwLOq+lNV7VbVVcDV4fy+jAWa80j7KlXdFF7o3AMkVfXnqppS1fuAzcD/ZawfAJeoakdYjf0twvMAAx73mcBrqnq1qraFx9KrpN8fEfFwx3yZqq5S1Tbcd2M+vUew+b2q/ktVA+BnuAC8ez9JdwBXhPl5Fnex9YSqPqaqadzITHNFpCFc/wPAN1X15bD25UogjQughHn8pqq+pqoduAuTzP5oPwLcpqp3hefpZdwFQn+fZ6YPALer6n3h5/Rn4E/AB/Pcvj+/UNUnVbUbd1HUgbuI2FHNuGBuTC8WeEuMqm5Q1S+o6oG4EslngS+T8UMPLFHVMZkv4KNZSb0bqMX9gIIrbawDsktVXwvTmKiqh6vqwn6y9yvgfWHp+Ngwf3eACxAicqWIvBJWBW4B9seVboZiV+CIrIuLX+JKjH3ZDAxYUsHdQ+/RnvW+Z15dxvt1qtqe8X4pMB3yOu7ZwKt55KkvE4BK3GAXAKhqK+6zzBxQfU3G8rZwMvMYsq0Lg3SP7PPQc7w9aczIykOAOw89eZgevs/Mw7qM9HYFTs/6PL+CKz3no9f+Q6+z/aDyQ7G0Z0Jd5/XLCT/fHVSPa19hTC8WeEuYqrar6q9xJagDBrn5+bj7tc+LSCOuRDsO+JDkbmSVj78BnbjSwDnA78LSDbghz87FVeOODS8GnqXvRmGtQE3WvKkZ08uAv2ddYDSEDcH68jQwpKrtAUzMqradjTufMPBxL6X/kudAo5SsB7pwgQsAEakFJlLYsZVXZOXBw52HnjysCt/3LK/B5bHHMuCXWZ9nvaruPZT9h+Zk7H+g7xP0fa4z8y242wo9n2+vdEUkSu/jyrx4ybYP7jtpTC8WeEuIuEY+V4trVBQLG7ScivsH/ucg0tkLOAJ4Jy5g97wOwZUYTxxK/sJSzm+ATwCnkFHNjLu6T+EChSciH8SV/PriAweKyEHhcX6c3j+svwESIvJBEakMS5ZzROT4ftK8Ezhu0Ac2MA/4hohUicgcXDVqz728gY77JmAPcY2zqsPP9c0ZyxvpJzBnnPOrRGRqeAHwXeBl4PFhOr58/Br4rIjMC2s8vghEgT+Hy38LXCIiu4lIFa46PvOi60fAe0VkQcZ3ey8ROWoQ+z9VRP6fiERE5ATcd7DnPvTTuAukt4XflXcCR2al0de5/qCIHCiuwdwlQHXGcfnAm8U1JKwAvgZkNvBrxDWu6nVRICJ1uP+3u/M8PrMTscBbWrpxV9N34Kqo1gOXAReq6m2DSOd84ClVXaiqjRmv54DbwuVD9SvgKFx1d+YP/424Rkqv4Uo/e9HPxYKqPoQLIH/BVXFOAv6VsbwROAbXUnkprhr5T7hSTl9+C+wfBsfhtAx3TEtwx/gXXGCBAY47bIBzNK5h2EpgLZDZ4veLwJUisllEftrH/i/GBYAncNWgU4C3h/diC+XbuEdk/oY7hmNxDZV67qlfjXvs7THceVqOO28AqOrzuJqST+I+73W4YJrXrQhV/TeuTcF3cN+FbwHvV9XHwuWv4xpI/Qz3v3M8cHtWMn2d658B14Xpvgc4SVWbwmU344LnU7iq7eW4z7knX6/iLioeD6vQexqLnQ78Q1UX5XN8Zudi4/GasiIiFwBHqGperWXzSO8cXMMmex6zDInIUtzne9NA6w4izQrgedzF0UvDla4pH9FiZ8CY4aSqPwF+Uux8mJ1X2Oq7v/v6ZidnVc3GGGNMAVlVszHGGFNAVuI1xhhjCsgCrzHGGFNAFniNMcaYArLAa4wxxhSQBV5jjDGmgCzwGmOMMQVkgdcYY4wpIAu8xhhjTAFZ4DXGGGMKyAKvMcYYU0AWeI0xxpgCssBrjDHGFJAFXmOMMaaALPAaY4wxBWSB1xhjjCkgC7zGGGNMAe3UgVdE7hORs4udD2OMMTuPURd4RaQ14xWISEfG+/cNJi1VPUFVbxypvI5GIjJORP4kIm0iskxEzuhn3QoRuVZEVovIZhH5kYjEMpbPFpF7w2WNInK9iEQzlp8rIq+Fn91fRGTqSB+fMcYU26gLvKpa2/MClgMLMubd3LNe5g/8SCvkvgrgh0A3MAl4H/BjEdm7j3U/BySAfYB5wIHAZRnLfwSsA6YABwBHAR8FEJGjgK8DJwPjgCXArcN7KMYYU3pGXeDti4gcLSIrReRSEWkEfiUiY0XkHhFZH5a67hGR6RnbPCQi54bT54jIoyLynXDdJSJyQj/7Wxru6zmgTUSiIvJ2EXlBRLaEac/PWH+GiNwR5mWjiFw/wPHsJiIPhutuEJGbRWRMxnIVkbkZ738tIl/NeH+yiDwjIs0i8rqIHJ/HOawBTgW+pKqtqvoocDdwZh+bLACuU9VNqroeuA74YMbyXYE/qGqnqjYCfwH2ztj2NlV9QVW7gauAI0Vkt4HyaYwxo1nZBN7QZFzpaRbwYdzx/Sp8PxPoAPoLeG8AXgF2Ab4F/EJEpJ/1TwdOAsYAc3Altk8CE4B7gYUiEheRCHAPsAyYDUwDfjfAsQhwNTAVmA/MAC4fYBu3ocghwG+AS8K8HQksDZd9TkTu6WPTeUBaVV/NmPcs24JlrjxK1vvpItIQvv8+8F4RqRaRacAJuODb17bgSs/GGFO2yi3wBsBXVLVLVTtUdaOq3q6q7araAnwNV93Zl2Wq+nNVTQM34qpIJ/Wz/nWqukJVO4D3AH9W1ftVNQl8B6gCDgcOwQXQS1S1LSwBPtrfgajqa2FaXWFp8poB8p7pQ8Avw+0DVV2lqi+H6X5DVd/Wx3a1QFPWvCagro/17wMuEpEJIjIZ+EQ4vzr8+zAuaDcDKwEfuDNcdi9wmojsJyJVwJcBzdjWGGPKUrkF3vWq2tnzJixp/TRsJNQMPAKMCUuguTT2TKhqezhZ28/+VmRMT8WVaHu2D8Ll03Cl1WWqmsr3QERkooj8TkRWhXm/CVcSz8cM4PV895WhFajPmlcPtPSx/teAp4FngH/jgmoSWCciHvBX4A6gBpf3scA3AVT1AeArwO2487Y03M/KIeTbGGNGjXILvJr1/tPAHsAbVLUeV+UKvas4h2t/q3FV2m4Hrop6BrAKF4BnDrIR1tVh+vuFeX8/vfPdTu/S4eSM6RXAUO6VvgpERWT3jHn7Ay/kWjmsVfi4qk5T1TnARuDJsMZgHO74rw9L7Rtx1f4nZmz/Q1XdXVUn4gJwFHh+CPk2xphRo9wCb7Y63H3dLSIyDlfCGil/AE4SkTeHj9R8GujClQQfB9YA3xCRGhGpFJEj8sh7a5j3abj7tZmeAc4QkUjYcCqzGvoXwAfCvHgiMk1E9hzoAFS1DVdCvTLM5xG4Vse/zbV+mO5UcQ4FvkR4jlV1A66l8kfChmdjgLNx94wJz8E+4bYzgZ8B31fVzeHyc0Rk6UB5NsaY0abcA+/3cPdZNwCPsa1hz7BT1VdwpdIfhPtbgHvUqTssAS4A5uIegVqJuyfcnytwj+c0AX/GBcRMF4VpbsE99nNnRl4eBz4AXBtu/zBhaVxEviAi9/Wz34/iztk6XGOxj6jqC+G2M8NnbmeG6+6Gu7Bow90T/5yq/i0jrVOA44H1wGtACrg4XFYJ3IK7uHgc+A8ucPeYAfyrn3waY8yoJKrZtbPGFJ+I/A24SFVfKnZejDFmOFngNcYYYwqo3KuaS5qI/ER6d4HZ8/pJsfNmjDFmZFiJ1xhjjCkgK/EaY4wxBWSB1xhjjCmgURd4ZRiHBQzT2zpQws5GRA4QkSdFpD38e8AA6x8nIk+JGzJwhYiclrHs2HBZs4gsFpEPZyzLvpfdJSJ99YZljDFlbdQF3nyHBRwpg+x9qmSJSBy4C9cV5Vjcc7h3hfNzrb8X7rnbLwINuGH+ngyXxYA/AT8Nl70HuEZE9gdQ1QuyPrdbgdtG7uiMMaZ0jbrA25ewh6bPhUPgbRSRP4S9VfX0knRTOH+LiDwhIpNE5GvAm4Drw5LYdiMXiRvMXUXkQyKyHHgw3NdlYR/Q60TkN7JtRB5E5I0i8u9wXytE5JwB8n6SiDwdlhZXiMjlGcuOFpGVWesvFZHjwulI2CnG6yLSEpZcZ+Rxyo7GddH4vbBLx+twXVIe28f6lwE/VdX7VDUVDkDR0x/0OFyfzr9V5wngJWCvHMfaM/TgjXnk0Rhjyk7ZBF7cyDjvwHWdOBXYjBvUHVxXhQ243pDGAxcAHar6ReCfwMfD0tjH+0n/KNzwfP8POCd8HYMbDrCWcLjBsFen+3A9WE3AlQyfGSDvbcBZuCH8TsJ1s/iOAY/Y+RRueMITccHvg7h+nBE3/vDn+thub+A57d2s/Tn6HgLw0DDN/4nImvBCZhyAqq7FlWI/EF4IHIbrKSvXCEyn4nqyeiTP4zPGmLJSToH3fOCLqrpSVbtwY9e+K6waTuIC7lxVTavqk6raPMj0Lw+H9OvAddF4jaouVtVW4PO4cWej4bK/q+qtqpoMS4bP9Jewqj6kqv8Lh/B7DhfE8h0C8FzgMlV9JSxtPhsOSICqvk1Vv9HHdoMdAnA6cCYucO6O61byBxnLb8UN7deFu5j5oqquyE4EdxH0m6yAb4wxO41yCryzgD+F1btbcFWdadx4ur/FDVH3OxFZLSLfCu9LDkafQwCG09FwX4Mekk9E3iAi/xCR9SLShCuRl9oQgB3Ar1T11fBi4+uEIw2JG4Dh97hSexxXav6siJyUmUBYBX4U8Jsh5NcYY8pCOQXeFcAJqjom41UZDgKfVNUrVHUv3MD0b8MFCdh+KMG+9DkEIDATNwDAWoY2JN8twN3ADFVtAH7CtiEA28gY/k/cWMITMrYd6hCALwD7iUjmUIP70ccQgLhq6L7O1T7AK6r617DU/gpuYIcTstY7C/i3qi4eQn6NMaYslFPg/QnwNRHpGYVngoicHE4fIyL7hkGrGVf1nA63W4u7TzsYtwIXi8iuIlKLK/39Phzo/mbgOBE5TdxweOMHekwHV727SVU7ReQQ4IyMZa8ClWEDrBiukVNFxvIbgKtEZHdx9hOR8Xkcw0O4c/AJEakQkZ772w/2sf6vcPdw54hINXApcE+47Glg9/CRIhGR3XAXN89mpXEW8Os88maMMWWrnALv93Glxr+Fz4g+BrwhXDYZ+CMu6L6EGybvpozt3iUim0Xkujz39Utc9fUjuDFnO4ELAVR1Oa4K9tPAJlzDqv0HSO+juDFwW3D3Sf/Qs0BVm8LlNwCrcCXgzFbO14Tr/y08vl/g7r8iIveJyBdy7VBVu3GN0c7CDS34QeAd4XxE5H0i8kLG+r/EVRH/F1e13oVr0EbYuvmDwHVhHh7GDWz/i57twwZX08nxGFF/+TTGmHJjfTUbY4wxBVROJV5jjDGm5FngLRAReUFyDwE46G4ujTHGjF5W1WyMMcYUkJV4jTHGmAKywGuMMcYUkAVeY4wxpoAs8BpjjDEF9P8BUGY/t/d9wQcAAAAASUVORK5CYII=\n" 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" + ] }, - "metadata": { - "needs_background": "light" - } + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -370,9 +481,12 @@ } ], "metadata": { + "interpreter": { + "hash": "5a908063083e2f8d262c3bd819ca1cf94ab387c2c9dc63b290040a76bd958100" + }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.6.12 64-bit ('myenv': conda)" + "display_name": "Python 3.6.12 64-bit ('myenv': conda)", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -384,12 +498,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.12" - }, - "interpreter": { - "hash": "5a908063083e2f8d262c3bd819ca1cf94ab387c2c9dc63b290040a76bd958100" + "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/nb_shap_variance_penalty_and_results_comparison.ipynb b/docs/tutorials/nb_shap_variance_penalty_and_results_comparison.ipynb index 2e380751..1d0d4995 100644 --- a/docs/tutorials/nb_shap_variance_penalty_and_results_comparison.ipynb +++ b/docs/tutorials/nb_shap_variance_penalty_and_results_comparison.ipynb @@ -1,20 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a4037e00", - "metadata": {}, - "outputs": [], - "source": [ - "from probatus.feature_elimination import ShapRFECV\n", - "from sklearn.datasets import make_classification\n", - "from sklearn.model_selection import train_test_split\n", - "from catboost import CatBoostClassifier\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, { "cell_type": "markdown", "id": "d76b55e4", @@ -22,6 +7,8 @@ "source": [ "# Shap variance penalty\n", "\n", + "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ing-bank/probatus/blob/master/docs/tutorials/nb_shap_variance_penalty_and_results_comparison.ipynb)\n", + "\n", "When ShapRFECV is computing feature importance and subsequently eliminating features, it computes the average of shap values to get an estimate of that feature's overall importance. In some situations, the variance of these shap values might be high - which might indicate a lack of agreement regarding that feature's importance. Catering to this situation, probatus allows you to penalize features that have a higher variance of shap values.\n", "\n", "By setting `shap_variance_penalty_factor` param within `fit_compute()` method, the averaging of shap values is computed by:\n", @@ -30,32 +17,50 @@ "See example below:" ] }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c5fbf6b4", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install probatus\n", + "!pip install catboost" + ] + }, { "cell_type": "code", "execution_count": 2, + "id": "a4037e00", + "metadata": {}, + "outputs": [], + "source": [ + "from probatus.feature_elimination import ShapRFECV\n", + "from sklearn.datasets import make_classification\n", + "from sklearn.model_selection import train_test_split\n", + "from catboost import CatBoostClassifier\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "id": "f50b0d6a", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 99%|===================| 991/1000 [00:27<00:00] The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - " 98%|===================| 975/1000 [00:27<00:00] The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n" - ] - } - ], + "outputs": [], "source": [ - "X, y = make_classification(n_samples=5000, n_informative=20, n_features=100)\n", - "clf = CatBoostClassifier(n_estimators=1000, verbose=0)\n", - "shap_elimination = ShapRFECV(clf=clf, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1)\n", + "X, y = make_classification(n_samples=500, n_informative=20, n_features=100)\n", + "model = CatBoostClassifier(n_estimators=100, verbose=0)\n", + "shap_elimination = ShapRFECV(model=model, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1)\n", "report_with_penalty = shap_elimination.fit_compute(X, y, shap_variance_penalty_factor=1.0)\n", "report_without_penalty = shap_elimination.fit_compute(X, y, shap_variance_penalty_factor=0)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "41bf725e", "metadata": {}, "outputs": [ @@ -94,171 +99,171 @@ " 1\n", " 100\n", " [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...\n", - 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"2 80 [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15... \n", - "3 64 [0, 1, 2, 3, 4, 8, 9, 11, 12, 13, 15, 16, 17, ... \n", - "4 52 [0, 1, 2, 3, 4, 9, 11, 12, 13, 15, 16, 17, 18,... \n", - "5 42 [0, 1, 2, 3, 4, 11, 12, 13, 16, 17, 18, 21, 24... \n", - "6 34 [0, 1, 2, 4, 11, 12, 13, 16, 17, 18, 24, 25, 2... \n", - "7 28 [0, 1, 4, 12, 13, 16, 17, 18, 24, 25, 28, 29, ... \n", - "8 23 [0, 1, 4, 12, 13, 16, 17, 25, 28, 29, 35, 38, ... \n", - "9 19 [0, 1, 12, 13, 16, 17, 25, 29, 35, 38, 43, 46,... \n", - "10 16 [0, 1, 12, 13, 17, 29, 35, 43, 46, 66, 67, 71,... \n", - "11 13 [12, 13, 17, 29, 35, 43, 46, 66, 67, 71, 75, 7... \n", - "12 11 [12, 13, 17, 29, 35, 43, 66, 71, 75, 79, 92] \n", - "13 9 [12, 13, 17, 29, 35, 43, 75, 79, 92] \n", - "14 8 [12, 13, 17, 29, 35, 43, 79, 92] \n", - "15 7 [12, 13, 17, 29, 35, 79, 92] \n", - "16 6 [13, 17, 29, 35, 79, 92] \n", - "17 5 [13, 17, 29, 35, 79] \n", + "2 80 [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14... \n", + "3 64 [0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 1... \n", + "4 52 [0, 2, 3, 4, 5, 9, 10, 11, 13, 14, 15, 18, 19,... \n", + "5 42 [0, 2, 5, 9, 10, 11, 13, 15, 19, 20, 21, 22, 3... \n", + "6 34 [0, 5, 9, 10, 11, 13, 15, 19, 20, 21, 30, 35, ... \n", + "7 28 [0, 5, 9, 10, 11, 13, 20, 21, 35, 40, 43, 45, ... \n", + "8 23 [5, 9, 10, 11, 13, 20, 35, 40, 45, 46, 47, 51,... \n", + "9 19 [5, 9, 10, 11, 13, 35, 40, 46, 47, 51, 54, 58,... \n", + "10 16 [5, 9, 10, 11, 35, 40, 46, 47, 51, 54, 58, 65,... \n", + "11 13 [5, 9, 10, 11, 40, 46, 54, 58, 65, 73, 81, 85,... \n", + "12 11 [5, 9, 10, 11, 40, 54, 65, 73, 81, 85, 91] \n", + "13 9 [5, 10, 11, 40, 54, 65, 73, 81, 85] \n", + "14 8 [5, 11, 40, 54, 65, 73, 81, 85] \n", + "15 7 [11, 40, 54, 65, 73, 81, 85] \n", + "16 6 [11, 40, 54, 65, 73, 85] \n", + "17 5 [11, 40, 65, 73, 85] \n", "\n", " eliminated_features train_metric_mean \\\n", - "1 [94, 57, 78, 41, 30, 47, 10, 88, 22, 95, 36, 8... 0.999 \n", - "2 [45, 40, 83, 6, 14, 86, 26, 80, 42, 23, 99, 89... 0.999 \n", - "3 [53, 69, 44, 60, 33, 34, 65, 93, 8, 73, 72, 32] 0.999 \n", - "4 [15, 85, 98, 64, 52, 9, 49, 63, 74, 58] 0.999 \n", - "5 [27, 31, 48, 54, 3, 50, 81, 21] 0.999 \n", - "6 [39, 11, 84, 37, 68, 2] 0.998 \n", - "7 [91, 18, 97, 24, 90] 0.998 \n", - "8 [77, 82, 28, 4] 0.998 \n", - "9 [16, 25, 38] 0.997 \n", - "10 [0, 1, 76] 0.994 \n", - "11 [67, 46] 0.989 \n", - "12 [71, 66] 0.978 \n", - "13 [75] 0.960 \n", - "14 [43] 0.944 \n", - "15 [12] 0.929 \n", - "16 [92] 0.904 \n", - "17 [] 0.875 \n", + "1 [48, 23, 25, 17, 41, 79, 70, 67, 96, 95, 52, 5... 1.000000 \n", + "2 [29, 44, 31, 80, 34, 42, 60, 87, 77, 75, 64, 7... 1.000000 \n", + "3 [63, 59, 83, 12, 38, 90, 93, 16, 49, 94, 8, 1] 1.000000 \n", + "4 [62, 14, 36, 18, 3, 4, 24, 74, 82, 89] 1.000000 \n", + "5 [2, 66, 68, 39, 71, 72, 22, 99] 1.000000 \n", + "6 [19, 69, 53, 30, 37, 15] 1.000000 \n", + "7 [21, 43, 98, 57, 0] 1.000000 \n", + "8 [45, 20, 84, 88] 1.000000 \n", + "9 [13, 76, 92] 0.972956 \n", + "10 [47, 51, 35] 0.969608 \n", + "11 [46, 58] 0.962777 \n", + "12 [9, 91] 0.956023 \n", + "13 [10] 0.951823 \n", + "14 [5] 0.930154 \n", + "15 [81] 0.906913 \n", + "16 [54] 0.894709 \n", + "17 [] 0.875344 \n", "\n", " train_metric_std val_metric_mean val_metric_std \n", - "1 0.000 0.943 0.006 \n", - "2 0.000 0.947 0.006 \n", - "3 0.000 0.947 0.006 \n", - "4 0.000 0.947 0.006 \n", - "5 0.000 0.949 0.006 \n", - "6 0.000 0.950 0.007 \n", - "7 0.000 0.954 0.010 \n", - "8 0.000 0.952 0.008 \n", - "9 0.000 0.946 0.008 \n", - "10 0.001 0.930 0.007 \n", - "11 0.001 0.917 0.008 \n", - "12 0.002 0.903 0.003 \n", - "13 0.002 0.880 0.006 \n", - "14 0.003 0.861 0.006 \n", - "15 0.002 0.841 0.007 \n", - "16 0.003 0.822 0.010 \n", - "17 0.002 0.784 0.010 " + "1 0.000000 0.783734 0.036136 \n", + "2 0.000000 0.818636 0.027409 \n", + "3 0.000000 0.809475 0.040263 \n", + "4 0.000000 0.825634 0.027513 \n", + "5 0.000000 0.858765 0.031187 \n", + "6 0.000000 0.845318 0.034718 \n", + "7 0.000000 0.847304 0.029020 \n", + "8 0.000000 0.863716 0.027382 \n", + "9 0.005839 0.815100 0.035161 \n", + "10 0.003283 0.823234 0.055277 \n", + "11 0.011050 0.800052 0.048493 \n", + "12 0.008971 0.814270 0.051047 \n", + "13 0.009062 0.804158 0.079721 \n", + "14 0.008260 0.770472 0.048131 \n", + "15 0.008914 0.762450 0.029873 \n", + "16 0.009730 0.743937 0.029733 \n", + "17 0.012642 0.725548 0.026652 " ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -334,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "7bc62391", "metadata": { "scrolled": false @@ -375,341 +380,341 @@ " 1\n", " 100\n", " [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...\n", - " [94, 57, 78, 41, 30, 47, 10, 88, 22, 95, 36, 8...\n", - " 0.999\n", - " 0.000\n", - " 0.943\n", - " 0.006\n", + " [48, 23, 25, 17, 41, 79, 70, 67, 96, 95, 52, 5...\n", + " 1.000000\n", + " 0.000000\n", + " 0.783734\n", + " 0.036136\n", " \n", " \n", " 2\n", " 80\n", - " [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15...\n", - " [45, 40, 83, 6, 14, 86, 26, 80, 42, 23, 99, 89...\n", - " 0.999\n", - " 0.000\n", - " 0.947\n", - " 0.006\n", + " [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14...\n", + " [29, 44, 31, 80, 34, 42, 60, 87, 77, 75, 64, 7...\n", + " 1.000000\n", + " 0.000000\n", + " 0.818636\n", + " 0.027409\n", " \n", " \n", " 3\n", " 64\n", - " [0, 1, 2, 3, 4, 8, 9, 11, 12, 13, 15, 16, 17, ...\n", - 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"2 80 [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14... \n", - "3 64 [0, 1, 2, 3, 4, 6, 7, 10, 11, 12, 13, 14, 16, ... \n", - "4 52 [0, 1, 2, 4, 6, 7, 11, 12, 13, 14, 16, 17, 18,... \n", - "5 42 [0, 1, 4, 7, 11, 12, 13, 16, 17, 18, 24, 25, 2... \n", - "6 34 [0, 1, 4, 11, 12, 13, 16, 17, 18, 24, 25, 27, ... \n", - "7 28 [0, 1, 4, 12, 13, 16, 17, 18, 24, 25, 28, 29, ... \n", - "8 23 [0, 1, 4, 12, 13, 16, 17, 25, 28, 29, 35, 38, ... \n", - "9 19 [1, 12, 13, 16, 17, 25, 28, 29, 35, 38, 43, 46... \n", - "10 16 [1, 12, 13, 16, 17, 25, 29, 35, 43, 46, 66, 67... \n", - "11 13 [12, 13, 17, 29, 35, 43, 46, 66, 67, 71, 75, 7... \n", - "12 11 [12, 13, 17, 29, 35, 43, 66, 71, 75, 79, 92] \n", - "13 9 [12, 13, 17, 29, 35, 43, 75, 79, 92] \n", - "14 8 [12, 13, 29, 35, 43, 75, 79, 92] \n", - "15 7 [13, 29, 35, 43, 75, 79, 92] \n", - "16 6 [29, 35, 43, 75, 79, 92] \n", - "17 5 [29, 35, 43, 75, 79] \n", + "2 80 [0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 1... \n", + "3 64 [0, 1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, ... \n", + "4 52 [0, 1, 3, 5, 7, 9, 10, 11, 13, 16, 17, 20, 21,... \n", + "5 42 [0, 5, 7, 9, 10, 11, 13, 16, 20, 21, 22, 27, 2... \n", + "6 34 [5, 9, 10, 11, 13, 16, 20, 21, 22, 27, 28, 32,... \n", + "7 28 [5, 9, 10, 11, 13, 20, 22, 28, 32, 35, 40, 45,... \n", + "8 23 [5, 9, 10, 11, 13, 22, 32, 35, 40, 45, 47, 51,... \n", + "9 19 [5, 9, 10, 11, 22, 35, 40, 45, 47, 51, 54, 58,... \n", + "10 16 [5, 9, 10, 11, 22, 40, 45, 47, 51, 54, 65, 73,... \n", + "11 13 [5, 9, 10, 11, 22, 40, 45, 51, 54, 65, 73, 81,... \n", + "12 11 [5, 10, 11, 22, 40, 51, 54, 65, 73, 81, 85] \n", + "13 9 [5, 10, 22, 40, 51, 54, 65, 73, 85] \n", + "14 8 [5, 10, 22, 40, 54, 65, 73, 85] \n", + "15 7 [5, 10, 40, 54, 65, 73, 85] \n", + "16 6 [10, 40, 54, 65, 73, 85] \n", + "17 5 [10, 40, 65, 73, 85] \n", "\n", " eliminated_features train_metric_mean \\\n", - "1 [94, 57, 78, 41, 30, 47, 10, 88, 22, 95, 36, 8... 0.999 \n", - "2 [45, 40, 83, 6, 14, 86, 26, 80, 42, 23, 99, 89... 0.999 \n", - "3 [53, 69, 44, 60, 33, 34, 65, 93, 8, 73, 72, 32] 0.999 \n", - "4 [15, 85, 98, 64, 52, 9, 49, 63, 74, 58] 0.999 \n", - "5 [27, 31, 48, 54, 3, 50, 81, 21] 0.999 \n", - "6 [39, 11, 84, 37, 68, 2] 0.998 \n", - "7 [91, 18, 97, 24, 90] 0.998 \n", - "8 [77, 82, 28, 4] 0.998 \n", - "9 [16, 25, 38] 0.997 \n", - "10 [0, 1, 76] 0.994 \n", - "11 [67, 46] 0.989 \n", - "12 [71, 66] 0.978 \n", - "13 [75] 0.960 \n", - "14 [43] 0.944 \n", - "15 [12] 0.929 \n", - "16 [92] 0.904 \n", - "17 [] 0.875 \n", - "1 [33, 49, 69, 53, 45, 78, 85, 57, 80, 34, 74, 6... 0.999 \n", - "2 [15, 70, 5, 98, 63, 64, 9, 59, 88, 65, 22, 32,... 0.999 \n", - "3 [52, 10, 37, 36, 51, 87, 96, 72, 3, 26, 93, 44] 0.999 \n", - "4 [23, 14, 48, 39, 6, 21, 50, 2, 89, 62] 0.999 \n", - "5 [54, 31, 84, 73, 55, 58, 7, 30] 0.999 \n", - "6 [68, 61, 47, 11, 81, 27] 0.998 \n", - "7 [91, 90, 18, 97, 24] 0.998 \n", - "8 [0, 77, 82, 4] 0.998 \n", - "9 [38, 28, 76] 0.997 \n", - "10 [16, 25, 1] 0.994 \n", - "11 [67, 46] 0.989 \n", - "12 [71, 66] 0.978 \n", - "13 [17] 0.960 \n", - "14 [12] 0.953 \n", - "15 [13] 0.932 \n", - "16 [92] 0.903 \n", - "17 [] 0.870 \n", + "1 [48, 23, 25, 17, 41, 79, 70, 67, 96, 95, 52, 5... 1.000000 \n", + "2 [29, 44, 31, 80, 34, 42, 60, 87, 77, 75, 64, 7... 1.000000 \n", + "3 [63, 59, 83, 12, 38, 90, 93, 16, 49, 94, 8, 1] 1.000000 \n", + "4 [62, 14, 36, 18, 3, 4, 24, 74, 82, 89] 1.000000 \n", + "5 [2, 66, 68, 39, 71, 72, 22, 99] 1.000000 \n", + "6 [19, 69, 53, 30, 37, 15] 1.000000 \n", + "7 [21, 43, 98, 57, 0] 1.000000 \n", + "8 [45, 20, 84, 88] 1.000000 \n", + "9 [13, 76, 92] 0.972956 \n", + "10 [47, 51, 35] 0.969608 \n", + "11 [46, 58] 0.962777 \n", + "12 [9, 91] 0.956023 \n", + "13 [10] 0.951823 \n", + "14 [5] 0.930154 \n", + "15 [81] 0.906913 \n", + "16 [54] 0.894709 \n", + "17 [] 0.875344 \n", + "1 [80, 4, 64, 33, 14, 87, 48, 36, 56, 6, 18, 29,... 1.000000 \n", + "2 [77, 19, 2, 31, 57, 86, 26, 37, 59, 68, 72, 63... 1.000000 \n", + "3 [34, 15, 12, 83, 55, 8, 38, 75, 44, 53, 78, 69] 1.000000 \n", + "4 [17, 89, 93, 3, 23, 62, 60, 1, 49, 96] 1.000000 \n", + "5 [43, 71, 0, 84, 7, 97, 98, 88] 1.000000 \n", + "6 [90, 21, 16, 70, 27, 95] 1.000000 \n", + "7 [28, 46, 61, 20, 94] 1.000000 \n", + "8 [82, 32, 74, 13] 1.000000 \n", + "9 [92, 58, 35] 0.978937 \n", + "10 [47, 76, 91] 0.973436 \n", + "11 [9, 45] 0.966317 \n", + "12 [11, 81] 0.958512 \n", + "13 [51] 0.946004 \n", + "14 [22] 0.932403 \n", + "15 [5] 0.918967 \n", + "16 [54] 0.905771 \n", + "17 [] 0.876856 \n", "\n", " train_metric_std val_metric_mean val_metric_std \n", - "1 0.000 0.943 0.006 \n", - "2 0.000 0.947 0.006 \n", - "3 0.000 0.947 0.006 \n", - "4 0.000 0.947 0.006 \n", - "5 0.000 0.949 0.006 \n", - "6 0.000 0.950 0.007 \n", - "7 0.000 0.954 0.010 \n", - "8 0.000 0.952 0.008 \n", - "9 0.000 0.946 0.008 \n", - "10 0.001 0.930 0.007 \n", - "11 0.001 0.917 0.008 \n", - "12 0.002 0.903 0.003 \n", - "13 0.002 0.880 0.006 \n", - "14 0.003 0.861 0.006 \n", - "15 0.002 0.841 0.007 \n", - "16 0.003 0.822 0.010 \n", - "17 0.002 0.784 0.010 \n", - "1 0.000 0.943 0.006 \n", - "2 0.000 0.944 0.005 \n", - "3 0.000 0.945 0.006 \n", - "4 0.000 0.950 0.005 \n", - "5 0.000 0.950 0.008 \n", - "6 0.000 0.948 0.006 \n", - "7 0.000 0.954 0.010 \n", - "8 0.000 0.952 0.008 \n", - "9 0.000 0.945 0.005 \n", - "10 0.001 0.936 0.004 \n", - "11 0.001 0.917 0.008 \n", - "12 0.002 0.903 0.003 \n", - "13 0.002 0.880 0.006 \n", - "14 0.003 0.875 0.005 \n", - "15 0.003 0.850 0.012 \n", - "16 0.003 0.816 0.005 \n", - "17 0.003 0.778 0.004 " + "1 0.000000 0.783734 0.036136 \n", + "2 0.000000 0.818636 0.027409 \n", + "3 0.000000 0.809475 0.040263 \n", + "4 0.000000 0.825634 0.027513 \n", + "5 0.000000 0.858765 0.031187 \n", + "6 0.000000 0.845318 0.034718 \n", + "7 0.000000 0.847304 0.029020 \n", + "8 0.000000 0.863716 0.027382 \n", + "9 0.005839 0.815100 0.035161 \n", + "10 0.003283 0.823234 0.055277 \n", + "11 0.011050 0.800052 0.048493 \n", + "12 0.008971 0.814270 0.051047 \n", + "13 0.009062 0.804158 0.079721 \n", + "14 0.008260 0.770472 0.048131 \n", + "15 0.008914 0.762450 0.029873 \n", + "16 0.009730 0.743937 0.029733 \n", + "17 0.012642 0.725548 0.026652 \n", + "1 0.000000 0.783734 0.036136 \n", + "2 0.000000 0.826010 0.033883 \n", + "3 0.000000 0.840406 0.004867 \n", + "4 0.000000 0.834353 0.023681 \n", + "5 0.000000 0.845624 0.017201 \n", + "6 0.000000 0.863096 0.030650 \n", + "7 0.000000 0.856743 0.036105 \n", + "8 0.000000 0.858418 0.031434 \n", + "9 0.004357 0.857832 0.040339 \n", + "10 0.004632 0.856597 0.044714 \n", + "11 0.006283 0.836464 0.069308 \n", + "12 0.007243 0.826117 0.055609 \n", + "13 0.011688 0.807256 0.066020 \n", + "14 0.010371 0.796027 0.049649 \n", + "15 0.005895 0.797665 0.054531 \n", + "16 0.006757 0.785010 0.050255 \n", + "17 0.006382 0.725792 0.058888 " ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -846,36 +851,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "a7e41b2e", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n" - ] - }, { "data": { "text/html": [ @@ -911,155 +890,85 @@ " \n", " \n", " 0\n", - " 0.783\n", - " 0.015\n", - " 67\n", - " 0.787\n", - " 0.022\n", - " 103\n", - " 930\n", + " 0.892217\n", + " 0.028543\n", + " 8\n", + " 0.894579\n", + " 0.013364\n", + " 20\n", + " 188\n", " 200\n", - " 138\n", + " 13\n", " \n", " \n", " 1\n", - " 0.844\n", - " 0.025\n", - " 44\n", - " 0.843\n", - " 0.026\n", - " 67\n", - " 1224\n", + " 0.797291\n", + " 0.036200\n", + " 24\n", + " 0.801302\n", + " 0.040278\n", + " 36\n", + " 404\n", " 200\n", - " 105\n", + " 141\n", " \n", " \n", " 2\n", - " 0.776\n", - " 0.030\n", - " 44\n", - " 0.775\n", - " 0.042\n", + " 0.741265\n", + " 0.021340\n", " 29\n", - " 1154\n", + " 0.701914\n", + " 0.028287\n", + " 54\n", + " 499\n", " 200\n", " 180\n", " \n", " \n", " 3\n", - " 0.923\n", - " 0.003\n", - " 200\n", - " 0.923\n", - " 0.003\n", - " 200\n", - " 3608\n", + " 0.786747\n", + " 0.087275\n", + " 8\n", + " 0.787802\n", + " 0.065435\n", + " 24\n", + " 179\n", " 200\n", - 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" best_score_a std_a num_features_a best_score_b std_b num_features_b \\\n", - "0 0.783 0.015 67 0.787 0.022 103 \n", - "1 0.844 0.025 44 0.843 0.026 67 \n", - "2 0.776 0.030 44 0.775 0.042 29 \n", - "3 0.923 0.003 200 0.923 0.003 200 \n", - "4 0.786 0.026 67 0.786 0.013 44 \n", - "5 0.916 0.011 103 0.914 0.017 83 \n", - "6 0.820 0.020 128 0.825 0.017 54 \n", - "7 0.844 0.029 67 0.851 0.028 160 \n", - "8 0.843 0.014 67 0.841 0.019 83 \n", - "9 0.892 0.013 200 0.896 0.020 54 \n", + " best_score_a std_a num_features_a best_score_b std_b \\\n", + "0 0.892217 0.028543 8 0.894579 0.013364 \n", + "1 0.797291 0.036200 24 0.801302 0.040278 \n", + "2 0.741265 0.021340 29 0.701914 0.028287 \n", + "3 0.786747 0.087275 8 0.787802 0.065435 \n", + "4 0.834444 0.067287 7 0.770303 0.091079 \n", "\n", - " n_samples n_features n_informative \n", - "0 930 200 138 \n", - "1 1224 200 105 \n", - "2 1154 200 180 \n", - "3 3608 200 27 \n", - "4 744 200 105 \n", - "5 3272 200 52 \n", - "6 1388 200 159 \n", - "7 2391 200 104 \n", - "8 1141 200 94 \n", - "9 2704 200 39 " + " num_features_b n_samples n_features n_informative \n", + "0 20 188 200 13 \n", + "1 36 404 200 141 \n", + "2 54 499 200 180 \n", + "3 24 179 200 183 \n", + "4 5 176 200 198 " ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1067,7 +976,7 @@ "source": [ "# Compare A: shap_variance_penalty_factor=0.5 & approximate=True\n", "# vs B: shap_variance_penalty_factor=0 (disabled) & approximate=True\n", - "num_simulations = 10\n", + "num_simulations = 5\n", "results = []\n", "\n", "\n", @@ -1080,7 +989,7 @@ "\n", "for i in range(num_simulations):\n", " # Params\n", - " n_samples = np.random.randint(500, 5000)\n", + " n_samples = np.random.randint(100, 500)\n", " n_features = 200\n", " n_informative = np.random.randint(10, 200)\n", " test_size = np.random.uniform(0.05, 0.5)\n", @@ -1092,10 +1001,12 @@ " X_test = pd.DataFrame(X_test)\n", "\n", " # Model\n", - " clf = CatBoostClassifier(n_estimators=1000, verbose=0)\n", + " model = CatBoostClassifier(n_estimators=100, verbose=0)\n", "\n", " # Best score from ShapRFECV WITHOUT penalization\n", - " shap_elimination = ShapRFECV(clf=clf, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1)\n", + " shap_elimination = ShapRFECV(\n", + " model=model, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1\n", + " )\n", " report_a = shap_elimination.fit_compute(\n", " X_train, y_train, shap_variance_penalty_factor=0, approximate=True, check_additivity=False\n", " )\n", @@ -1105,7 +1016,9 @@ " num_features_a = report_a[\"num_features\"].iloc[best_idx_a]\n", "\n", " # Best score from ShapRFECV WITH penalization\n", - " shap_elimination = ShapRFECV(clf=clf, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1)\n", + " shap_elimination = ShapRFECV(\n", + " model=model, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1\n", + " )\n", " report_b = shap_elimination.fit_compute(\n", " X_train, y_train, shap_variance_penalty_factor=0.5, approximate=True, check_additivity=False\n", " )\n", @@ -1139,36 +1052,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "fe9fb4d3", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. 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" best_score_a std_a num_features_a best_score_b std_b num_features_b \\\n", - "0 0.891 0.019 24 0.891 0.019 24 \n", - "1 0.819 0.025 128 0.821 0.019 83 \n", - "2 0.840 0.032 160 0.843 0.020 128 \n", - "3 0.927 0.011 16 0.927 0.011 16 \n", - "4 0.811 0.034 128 0.812 0.029 67 \n", - "5 0.904 0.011 83 0.899 0.010 103 \n", - "6 0.854 0.011 83 0.862 0.014 103 \n", - "7 0.839 0.013 128 0.828 0.019 103 \n", - "8 0.957 0.003 36 0.957 0.007 67 \n", - "9 0.854 0.010 128 0.855 0.016 160 \n", + " best_score_a std_a num_features_a best_score_b std_b \\\n", + "0 0.742664 0.091943 11 0.773073 0.095611 \n", + "1 0.829656 0.052365 20 0.798127 0.053808 \n", + "2 0.724558 0.043146 83 0.746103 0.022388 \n", + "3 0.822537 0.044845 36 0.825153 0.038366 \n", + "4 0.729214 0.038897 83 0.731563 0.024997 \n", "\n", - " n_samples n_features n_informative \n", - "0 1059 200 34 \n", - "1 1357 200 129 \n", - "2 2898 200 175 \n", - "3 2656 200 18 \n", - "4 1574 200 129 \n", - "5 4039 200 89 \n", - "6 1769 200 107 \n", - "7 2080 200 162 \n", - "8 4916 200 31 \n", - "9 4139 200 164 " + " num_features_b n_samples n_features n_informative \n", + "0 13 250 200 43 \n", + "1 29 327 200 24 \n", + "2 20 394 200 179 \n", + "3 29 479 200 60 \n", + "4 54 485 200 176 " ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1360,12 +1177,12 @@ "source": [ "# Compare A: shap_variance_penalty_factor=0.5 & approximate=False\n", "# vs B: shap_variance_penalty_factor=0 (disabled) & approximate=False\n", - "num_simulations = 10\n", + "num_simulations = 5\n", "results = []\n", "\n", "for i in range(num_simulations):\n", " # Params\n", - " n_samples = np.random.randint(500, 5000)\n", + " n_samples = np.random.randint(100, 500)\n", " n_features = 200\n", " n_informative = np.random.randint(10, 200)\n", " test_size = np.random.uniform(0.05, 0.5)\n", @@ -1377,10 +1194,12 @@ " X_test = pd.DataFrame(X_test)\n", "\n", " # Model\n", - " clf = CatBoostClassifier(n_estimators=1000, verbose=0)\n", + " model = CatBoostClassifier(n_estimators=100, verbose=0)\n", "\n", " # Best score from ShapRFECV WITHOUT penalization\n", - " shap_elimination = ShapRFECV(clf=clf, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1)\n", + " shap_elimination = ShapRFECV(\n", + " model=model, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1\n", + " )\n", " report_a = shap_elimination.fit_compute(X_train, y_train, shap_variance_penalty_factor=0, approximate=False)\n", " best_idx_a = get_best_idx(report_a)\n", " best_score_a = report_a[\"val_metric_mean\"].iloc[best_idx_a]\n", @@ -1388,7 +1207,9 @@ " num_features_a = report_a[\"num_features\"].iloc[best_idx_a]\n", "\n", " # Best score from ShapRFECV WITH penalization\n", - " shap_elimination = ShapRFECV(clf=clf, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1)\n", + " shap_elimination = ShapRFECV(\n", + " model=model, step=0.2, min_features_to_select=5, cv=5, scoring=\"f1\", n_jobs=5, verbose=1\n", + " )\n", " report_b = shap_elimination.fit_compute(X_train, y_train, shap_variance_penalty_factor=0.5, approximate=False)\n", " best_idx_b = get_best_idx(report_b)\n", " best_score_b = report_b[\"val_metric_mean\"].iloc[best_idx_b]\n", @@ -1417,21 +1238,13 @@ "# Show results\n", "results_df" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d353500a", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": ".env", + "display_name": "probatus", "language": "python", - "name": ".env" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1443,7 +1256,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/mkdocs.yml b/mkdocs.yml index 28210ab4..3fa8fc3d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -2,7 +2,7 @@ site_name: Probatus repo_url: https://github.com/ing-bank/probatus/ site_url: https://ing-bank.github.io/probatus/ -site_description: Validation of binary classifiers and data used to develop them +site_description: Validation of regressors and classifiers and data used to develop them site_author: ING Bank N. V. use_directory_urls: false diff --git a/probatus/__init__.py b/probatus/__init__.py index 09e25e55..397b3565 100644 --- a/probatus/__init__.py +++ b/probatus/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2020 ING Bank N.V. +# Copyright (c) ING Bank N.V. # # Permission is hereby granted, free of charge, to any person obtaining a copy of # this software and associated documentation files (the "Software"), to deal in diff --git a/probatus/feature_elimination/__init__.py b/probatus/feature_elimination/__init__.py index 491e05d4..0dc708da 100644 --- a/probatus/feature_elimination/__init__.py +++ b/probatus/feature_elimination/__init__.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - from .feature_elimination import ShapRFECV, EarlyStoppingShapRFECV __all__ = ["ShapRFECV", "EarlyStoppingShapRFECV"] diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index 5812c5d4..318cc8e2 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -4,10 +4,10 @@ import numpy as np import pandas as pd from joblib import Parallel, delayed +from loguru import logger from sklearn.base import clone, is_classifier, is_regressor from sklearn.model_selection import check_cv from sklearn.model_selection._search import BaseSearchCV -from loguru import logger from probatus.utils import ( BaseFitComputePlotClass, @@ -46,7 +46,7 @@ class ShapRFECV(BaseFitComputePlotClass): [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html), [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html?highlight=randomized#sklearn.model_selection.RandomizedSearchCV), or [BayesSearchCV](https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html), which - needs to be passed as the `clf`. Thanks to this you can perform hyperparameter optimization at each step of + needs to be passed as the `model`. Thanks to this you can perform hyperparameter optimization at each step of the feature elimination. Lastly, it supports categorical features (object and category dtype) and missing values in the data, as long as the model supports them. @@ -77,18 +77,18 @@ class ShapRFECV(BaseFitComputePlotClass): # Prepare model and parameter search space - clf = RandomForestClassifier(max_depth=5, class_weight='balanced') + model = RandomForestClassifier(max_depth=5, class_weight='balanced') param_grid = { 'n_estimators': [5, 7, 10], 'min_samples_leaf': [3, 5, 7, 10], } - search = RandomizedSearchCV(clf, param_grid) + search = RandomizedSearchCV(model, param_grid) # Run feature elimination shap_elimination = ShapRFECV( - clf=search, step=0.2, cv=10, scoring='roc_auc', n_jobs=3) + model=search, step=0.2, cv=10, scoring='roc_auc', n_jobs=3) report = shap_elimination.fit_compute(X, y) # Make plots @@ -103,7 +103,7 @@ class ShapRFECV(BaseFitComputePlotClass): def __init__( self, - clf, + model, step=1, min_features_to_select=1, cv=None, @@ -116,7 +116,7 @@ def __init__( This method initializes the class. Args: - clf (classifier, sklearn compatible search CV e.g. GridSearchCV, RandomizedSearchCV or BayesSearchCV): + model (classifier or regressor, sklearn compatible search CV e.g. GridSearchCV, RandomizedSearchCV or BayesSearchCV): A model that will be optimized and trained at each round of feature elimination. The recommended model is [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html), because it by default handles the missing values and categorical variables. This parameter also supports @@ -165,11 +165,12 @@ def __init__( reproducible and in random search at each iteration a different hyperparameters might be tested. For reproducible results set it to an integer. """ # noqa - self.clf = clf - if isinstance(self.clf, BaseSearchCV): - self.search_clf = True + # TODO: Add a check for models which are not supported. + self.model = model + if isinstance(self.model, BaseSearchCV): + self.search_model = True else: - self.search_clf = False + self.search_model = False if (isinstance(step, int) or isinstance(step, float)) and step > 0: self.step = step @@ -340,7 +341,7 @@ def _get_feature_shap_values_per_fold( self, X, y, - clf, + model, train_index, val_index, sample_weight=None, @@ -356,7 +357,7 @@ def _get_feature_shap_values_per_fold( y (pd.Series): Labels for X. - clf (classifier): + model (classifier or regressor): Model to be fitted on the train folds. train_index (np.array): @@ -386,16 +387,16 @@ def _get_feature_shap_values_per_fold( y_train, y_val = y.iloc[train_index], y.iloc[val_index] if sample_weight is not None: - clf = clf.fit(X_train, y_train, sample_weight=sample_weight.iloc[train_index]) + model = model.fit(X_train, y_train, sample_weight=sample_weight.iloc[train_index]) else: - clf = clf.fit(X_train, y_train) + model = model.fit(X_train, y_train) # Score the model - score_train = self.scorer.scorer(clf, X_train, y_train) - score_val = self.scorer.scorer(clf, X_val, y_val) + score_train = self.scorer.scorer(model, X_train, y_train) + score_val = self.scorer.scorer(model, X_val, y_val) # Compute SHAP values - shap_values = shap_calc(clf, X_val, verbose=self.verbose, random_state=self.random_state, **shap_kwargs) + shap_values = shap_calc(model, X_val, verbose=self.verbose, random_state=self.random_state, **shap_kwargs) return shap_values, score_train, score_val def fit( @@ -413,7 +414,7 @@ def fit( Fits the object with the provided data. The algorithm starts with the entire dataset, and then sequentially - eliminates features. If sklearn compatible search CV is passed as clf e.g. + eliminates features. If sklearn compatible search CV is passed as model e.g. [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html), [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html) or [BayesSearchCV](https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html), @@ -522,7 +523,7 @@ def fit( "sample_weight is passed only to the fit method of the model, not the evaluation metrics." ) sample_weight = assure_pandas_series(sample_weight, index=self.X.index) - self.cv = check_cv(self.cv, self.y, classifier=is_classifier(self.clf)) + self.cv = check_cv(self.cv, self.y, classifier=is_classifier(self.model)) remaining_features = current_features_set = self.column_names round_number = 0 @@ -559,18 +560,18 @@ def fit( np.random.seed(self.random_state) # Optimize parameters - if self.search_clf: - current_search_clf = clone(self.clf).fit(current_X, self.y) - current_clf = current_search_clf.estimator.set_params(**current_search_clf.best_params_) + if self.search_model: + current_search_model = clone(self.model).fit(current_X, self.y) + current_model = current_search_model.estimator.set_params(**current_search_model.best_params_) else: - current_clf = clone(self.clf) + current_model = clone(self.model) # Perform CV to estimate feature importance with SHAP results_per_fold = Parallel(n_jobs=self.n_jobs)( delayed(self._get_feature_shap_values_per_fold)( X=current_X, y=self.y, - clf=current_clf, + model=current_model, train_index=train_index, val_index=val_index, sample_weight=sample_weight, @@ -579,7 +580,7 @@ def fit( for train_index, val_index in self.cv.split(current_X, self.y, groups) ) - if self.y.nunique() == 2 or is_regressor(current_clf): + if self.y.nunique() == 2 or is_regressor(current_model): shap_values = np.vstack([current_result[0] for current_result in results_per_fold]) else: # multi-class case shap_values = np.hstack([current_result[0] for current_result in results_per_fold]) @@ -656,7 +657,7 @@ def fit_compute( Fits the object with the provided data. The algorithm starts with the entire dataset, and then sequentially - eliminates features. If sklearn compatible search CV is passed as clf e.g. + eliminates features. If sklearn compatible search CV is passed as model e.g. [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html), [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html) or [BayesSearchCV](https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html), @@ -768,7 +769,7 @@ def get_reduced_features_set(self, num_features, standard_error_threshold=1.0, r else: raise ValueError( - "Parameter num_features can be of type int, or of type str with" + "Parameter num_features can be of type int, or of type str with " "possible values of 'best', 'best_coherent' or 'best_parsimonious'" ) @@ -984,7 +985,7 @@ class EarlyStoppingShapRFECV(ShapRFECV): the training speed, by skipping the training of trees that do not improve the scoring metric any further, which is particularly useful when the training dataset is large. - Note that if the classifier is a hyperparameter search model is used, the early stopping parameter is passed only + Note that if the regressor or classifier is a hyperparameter search model is used, the early stopping parameter is passed only to the fit method of the model duiring the Shapley values estimation step, and not for the hyperparameter search step. Early stopping can be seen as a type of regularization of the optimal number of trees. Therefore you can use @@ -1030,11 +1031,11 @@ class EarlyStoppingShapRFECV(ShapRFECV): X = pd.DataFrame(X, columns=feature_names) # Prepare model - clf = LGBMClassifier(n_estimators=200, max_depth=3) + model = LGBMClassifier(n_estimators=200, max_depth=3) # Run feature elimination shap_elimination = EarlyStoppingShapRFECV( - clf=clf, step=0.2, cv=10, scoring='roc_auc', early_stopping_rounds=10, n_jobs=3) + model=model, step=0.2, cv=10, scoring='roc_auc', early_stopping_rounds=10, n_jobs=3) report = shap_elimination.fit_compute(X, y) # Make plots @@ -1049,7 +1050,7 @@ class EarlyStoppingShapRFECV(ShapRFECV): def __init__( self, - clf, + model, step=1, min_features_to_select=1, cv=None, @@ -1064,7 +1065,7 @@ def __init__( This method initializes the class. Args: - clf (sklearn compatible classifier or regressor, sklearn compatible search CV e.g. GridSearchCV, RandomizedSearchCV or BayesSearchCV): + model (sklearn compatible classifier or regressor, sklearn compatible search CV e.g. GridSearchCV, RandomizedSearchCV or BayesSearchCV): A model that will be optimized and trained at each round of features elimination. The model must support early stopping of training, which is the case for XGBoost and LightGBM, for example. The recommended model is [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html), @@ -1132,7 +1133,7 @@ def __init__( Note that `eval_metric` is an argument of the model's fit method and it is different from `scoring`. """ # noqa super().__init__( - clf, + model, step=step, min_features_to_select=min_features_to_select, cv=cv, @@ -1142,7 +1143,7 @@ def __init__( random_state=random_state, ) - if self.search_clf: + if self.search_model: if self.verbose > 0: warnings.warn( "Early stopping will be used only during Shapley value" @@ -1196,7 +1197,7 @@ def _get_fit_params_lightGBM( Positions of validation fold samples. Raises: - ValueError: if the clf is not supported. + ValueError: if the model is not supported. Returns: dict: fit parameters @@ -1252,7 +1253,7 @@ def _get_fit_params_XGBoost( Positions of validation fold samples. Raises: - ValueError: if the clf is not supported. + ValueError: if the model is not supported. Returns: dict: fit parameters @@ -1300,7 +1301,7 @@ def _get_fit_params_CatBoost( Positions of validation fold samples. Raises: - ValueError: if the clf is not supported. + ValueError: if the model is not supported. Returns: dict: fit parameters @@ -1319,12 +1320,12 @@ def _get_fit_params_CatBoost( return fit_params def _get_fit_params( - self, clf, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None + self, model, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None ): - """Get the fit parameters for the specified classifier. + """Get the fit parameters for the specified classifier or regressor. Args: - clf (classifier): + model (classifier or regressor): Model to be fitted on the train folds. X_train (pd.DataFrame): @@ -1353,7 +1354,7 @@ def _get_fit_params( Positions of validation fold samples. Raises: - ValueError: if the clf is not supported. + ValueError: if the model is not supported. Returns: dict: fit parameters @@ -1364,7 +1365,7 @@ def _get_fit_params( try: from lightgbm import LGBMModel - if isinstance(clf, LGBMModel): + if isinstance(model, LGBMModel): return self._get_fit_params_lightGBM( X_train=X_train, y_train=y_train, @@ -1380,7 +1381,7 @@ def _get_fit_params( try: from xgboost.sklearn import XGBModel - if isinstance(clf, XGBModel): + if isinstance(model, XGBModel): return self._get_fit_params_XGBoost( X_train=X_train, y_train=y_train, @@ -1396,7 +1397,7 @@ def _get_fit_params( try: from catboost import CatBoost - if isinstance(clf, CatBoost): + if isinstance(model, CatBoost): return self._get_fit_params_CatBoost( X_train=X_train, y_train=y_train, @@ -1415,7 +1416,7 @@ def _get_feature_shap_values_per_fold( self, X, y, - clf, + model, train_index, val_index, sample_weight=None, @@ -1438,8 +1439,8 @@ def _get_feature_shap_values_per_fold( Note that they're only used for fitting of the model, not during evaluation of metrics. If not provided, then each sample is given unit weight. - clf: - Classifier to be fitted on the train folds. + model: + Classifier or regressor to be fitted on the train folds. train_index (np.array): Positions of train folds samples. @@ -1461,7 +1462,7 @@ def _get_feature_shap_values_per_fold( y_train, y_val = y.iloc[train_index], y.iloc[val_index] fit_params = self._get_fit_params( - clf=clf, + model=model, X_train=X_train, y_train=y_train, X_val=X_val, @@ -1476,34 +1477,34 @@ def _get_feature_shap_values_per_fold( try: from lightgbm import LGBMModel - if isinstance(clf, LGBMModel): - clf.set_params(eval_metric=self.eval_metric) + if isinstance(model, LGBMModel): + model.set_params(eval_metric=self.eval_metric) except ImportError: pass try: from xgboost.sklearn import XGBModel - if isinstance(clf, XGBModel): - clf.set_params(eval_metric=self.eval_metric, early_stopping_rounds=self.early_stopping_rounds) + if isinstance(model, XGBModel): + model.set_params(eval_metric=self.eval_metric, early_stopping_rounds=self.early_stopping_rounds) except ImportError: pass try: from catboost import CatBoost - if isinstance(clf, CatBoost): - clf.set_params(early_stopping_rounds=self.early_stopping_rounds) + if isinstance(model, CatBoost): + model.set_params(early_stopping_rounds=self.early_stopping_rounds) except ImportError: pass # Train the model - clf = clf.fit(**fit_params) + model = model.fit(**fit_params) # Score the model - score_train = self.scorer.scorer(clf, X_train, y_train) - score_val = self.scorer.scorer(clf, X_val, y_val) + score_train = self.scorer.scorer(model, X_train, y_train) + score_val = self.scorer.scorer(model, X_val, y_val) # Compute SHAP values - shap_values = shap_calc(clf, X_val, verbose=self.verbose, random_state=self.random_state, **shap_kwargs) + shap_values = shap_calc(model, X_val, verbose=self.verbose, random_state=self.random_state, **shap_kwargs) return shap_values, score_train, score_val diff --git a/probatus/interpret/__init__.py b/probatus/interpret/__init__.py index a1f0eceb..d58fa368 100644 --- a/probatus/interpret/__init__.py +++ b/probatus/interpret/__init__.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - from .shap_dependence import DependencePlotter from .model_interpret import ShapModelInterpreter diff --git a/probatus/interpret/model_interpret.py b/probatus/interpret/model_interpret.py index c1d8a4a1..ace8256f 100644 --- a/probatus/interpret/model_interpret.py +++ b/probatus/interpret/model_interpret.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - import matplotlib.pyplot as plt import numpy as np import pandas as pd @@ -60,11 +40,11 @@ class ShapModelInterpreter(BaseFitComputePlotClass): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Prepare and fit model. Remember about class_weight="balanced" or an equivalent. - clf = RandomForestClassifier(class_weight='balanced', n_estimators = 100, max_depth=2, random_state=0) - clf.fit(X_train, y_train) + model = RandomForestClassifier(class_weight='balanced', n_estimators = 100, max_depth=2, random_state=0) + model.fit(X_train, y_train) # Train ShapModelInterpreter - shap_interpreter = ShapModelInterpreter(clf) + shap_interpreter = ShapModelInterpreter(model) feature_importance = shap_interpreter.fit_compute(X_train, X_test, y_train, y_test) # Make plots @@ -80,12 +60,12 @@ class ShapModelInterpreter(BaseFitComputePlotClass): """ - def __init__(self, clf, scoring="roc_auc", verbose=0, random_state=None): + def __init__(self, model, scoring="roc_auc", verbose=0, random_state=None): """ Initializes the class. Args: - clf (binary classifier): + model (classifier or regressor): Model fitted on X_train. scoring (string or probatus.utils.Scorer, optional): @@ -105,7 +85,7 @@ def __init__(self, clf, scoring="roc_auc", verbose=0, random_state=None): Random state set for the nr of samples. If it is None, the results will not be reproducible. For reproducible results set it to an integer. """ - self.clf = clf + self.model = model self.scorer = get_single_scorer(scoring) self.verbose = verbose self.random_state = random_state @@ -118,7 +98,6 @@ def fit( y_test, column_names=None, class_names=None, - shap_variance_penalty_factor=None, **shap_kwargs, ): """ @@ -132,10 +111,10 @@ def fit( Dataframe containing test data. y_train (pd.Series): - Series of binary labels for train data. + Series of labels for train data. y_test (pd.Series): - Series of binary labels for test data. + Series of labels for test data. column_names (None, or list of str, optional): List of feature names for the dataset. If None, then column names from the X_train dataframe are used. @@ -144,12 +123,6 @@ def fit( List of class names e.g. ['neg', 'pos']. If none, the default ['Negative Class', 'Positive Class'] are used. - shap_variance_penalty_factor (int or float, optional): - Apply aggregation penalty when computing average of shap values for a given feature. - Results in a preference for features that have smaller standard deviation of shap - values (more coherent shap importance). Recommend value 0.5 - 1.0. - Formula: penalized_shap_mean = (mean_shap - (std_shap * shap_variance_penalty_factor)) - **shap_kwargs: keyword arguments passed to [shap.Explainer](https://shap.readthedocs.io/en/latest/generated/shap.Explainer.html#shap.Explainer). @@ -171,8 +144,8 @@ def fit( self.class_names = ["Negative Class", "Positive Class"] # Calculate Metrics - self.train_score = self.scorer.score(self.clf, self.X_train, self.y_train) - self.test_score = self.scorer.score(self.clf, self.X_test, self.y_test) + self.train_score = self.scorer.score(self.model, self.X_train, self.y_train) + self.test_score = self.scorer.score(self.model, self.X_test, self.y_test) self.results_text = ( f"Train {self.scorer.metric_name}: {np.round(self.train_score, 3)},\n" @@ -184,7 +157,7 @@ def fit( self.expected_value_train, self.tdp_train, ) = self._prep_shap_related_variables( - clf=self.clf, + model=self.model, X=self.X_train, y=self.y_train, column_names=self.column_names, @@ -199,7 +172,7 @@ def fit( self.expected_value_test, self.tdp_test, ) = self._prep_shap_related_variables( - clf=self.clf, + model=self.model, X=self.X_test, y=self.y_test, column_names=self.column_names, @@ -213,7 +186,7 @@ def fit( @staticmethod def _prep_shap_related_variables( - clf, + model, X, y, approximate=False, @@ -231,7 +204,7 @@ def _prep_shap_related_variables( Shap values, expected value of the explainer, and fitted TreeDependencePlotter for a given dataset. """ shap_values, explainer = shap_calc( - clf, + model, X, approximate=approximate, verbose=verbose, @@ -247,7 +220,7 @@ def _prep_shap_related_variables( expected_value = expected_value[1] # Initialize tree dependence plotter - tdp = DependencePlotter(clf, verbose=verbose).fit( + tdp = DependencePlotter(model, verbose=verbose).fit( X, y, column_names=column_names, @@ -334,10 +307,10 @@ def fit_compute( Dataframe containing test data. y_train (pd.Series): - Series of binary labels for train data. + Series of labels for train data. y_test (pd.Series): - Series of binary labels for test data. + Series of labels for test data. column_names (None, or list of str, optional): List of feature names for the dataset. @@ -380,10 +353,9 @@ def fit_compute( y_test=y_test, column_names=column_names, class_names=class_names, - shap_variance_penalty_factor=shap_variance_penalty_factor, **shap_kwargs, ) - return self.compute(shap_variance_penalty_factor=shap_variance_penalty_factor) + return self.compute(return_scores=return_scores, shap_variance_penalty_factor=shap_variance_penalty_factor) def plot(self, plot_type, target_set="test", target_columns=None, samples_index=None, show=True, **plot_kwargs): """ diff --git a/probatus/interpret/shap_dependence.py b/probatus/interpret/shap_dependence.py index 5e947e2b..0ad3fea3 100644 --- a/probatus/interpret/shap_dependence.py +++ b/probatus/interpret/shap_dependence.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - import matplotlib.pyplot as plt import numpy as np import pandas as pd @@ -42,8 +22,8 @@ class DependencePlotter(BaseFitComputePlotClass): from probatus.interpret import DependencePlotter X, y = make_classification(n_samples=15, n_features=3, n_informative=3, n_redundant=0, random_state=42) - clf = RandomForestClassifier().fit(X, y) - bdp = DependencePlotter(clf) + model = RandomForestClassifier().fit(X, y) + bdp = DependencePlotter(model) shap_values = bdp.fit_compute(X, y) bdp.plot(feature=2) @@ -52,13 +32,13 @@ class DependencePlotter(BaseFitComputePlotClass): """ - def __init__(self, clf, verbose=0, random_state=None): + def __init__(self, model, verbose=0, random_state=None): """ Initializes the class. Args: - clf (model object): - Binary classification model or pipeline. + model (model object): + regression or classification model or pipeline. verbose (int, optional): Controls verbosity of the output: @@ -71,7 +51,7 @@ def __init__(self, clf, verbose=0, random_state=None): Random state set for the nr of samples. If it is None, the results will not be reproducible. For reproducible results set it to an integer. """ - self.clf = clf + self.model = model self.verbose = verbose self.random_state = random_state @@ -79,7 +59,7 @@ def __repr__(self): """ Represent string method. """ - return f"Shap dependence plotter for {self.clf.__class__.__name__}" + return f"Shap dependence plotter for {self.model.__class__.__name__}" def fit(self, X, y, column_names=None, class_names=None, precalc_shap=None, **shap_kwargs): """ @@ -118,7 +98,7 @@ def fit(self, X, y, column_names=None, class_names=None, precalc_shap=None, **sh self.class_names = ["Negative Class", "Positive Class"] self.shap_vals_df = shap_to_df( - self.clf, + self.model, self.X, precalc_shap=precalc_shap, verbose=self.verbose, @@ -151,7 +131,7 @@ def fit_compute(self, X, y, column_names=None, class_names=None, precalc_shap=No Provided dataset. y (pd.Series): - Binary labels for X. + Labels for X. column_names (None, or list of str, optional): List of feature names for the dataset. If None, then column names from the X_train dataframe are used. diff --git a/probatus/sample_similarity/__init__.py b/probatus/sample_similarity/__init__.py index 026c0200..cc9a5770 100644 --- a/probatus/sample_similarity/__init__.py +++ b/probatus/sample_similarity/__init__.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - from .resemblance_model import ( BaseResemblanceModel, PermutationImportanceResemblance, diff --git a/probatus/sample_similarity/resemblance_model.py b/probatus/sample_similarity/resemblance_model.py index dcc268e1..ebeda0ae 100644 --- a/probatus/sample_similarity/resemblance_model.py +++ b/probatus/sample_similarity/resemblance_model.py @@ -1,29 +1,9 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - import warnings import matplotlib.pyplot as plt -from loguru import logger import numpy as np import pandas as pd +from loguru import logger from shap import summary_plot from sklearn.inspection import permutation_importance from sklearn.model_selection import train_test_split @@ -46,7 +26,7 @@ class BaseResemblanceModel(BaseFitComputePlotClass): def __init__( self, - clf, + model, scoring="roc_auc", test_prc=0.25, n_jobs=1, @@ -57,8 +37,8 @@ def __init__( Initializes the class. Args: - clf (model object): - Binary classification model or pipeline. + model (model object): + Regression or classification model or pipeline. scoring (string or probatus.utils.Scorer, optional): Metric for which the model performance is calculated. It can be either a metric name aligned with @@ -85,7 +65,7 @@ class is 'roc_auc'. reproducible and in random search at each iteration a different hyperparameters might be tested. For reproducible results set it to an integer. """ # noqa - self.clf = clf + self.model = model self.test_prc = test_prc self.n_jobs = n_jobs self.random_state = random_state @@ -169,10 +149,10 @@ def fit(self, X1, X2, column_names=None, class_names=None): shuffle=True, stratify=self.y, ) - self.clf.fit(self.X_train, self.y_train) + self.model.fit(self.X_train, self.y_train) - self.train_score = np.round(self.scorer.score(self.clf, self.X_train, self.y_train), 3) - self.test_score = np.round(self.scorer.score(self.clf, self.X_test, self.y_test), 3) + self.train_score = np.round(self.scorer.score(self.model, self.X_train, self.y_train), 3) + self.test_score = np.round(self.scorer.score(self.model, self.X_test, self.y_test), 3) self.results_text = ( f"Train {self.scorer.metric_name}: {np.round(self.train_score, 3)},\n" @@ -297,8 +277,8 @@ class PermutationImportanceResemblance(BaseResemblanceModel): from probatus.sample_similarity import PermutationImportanceResemblance X1, _ = make_classification(n_samples=100, n_features=5) X2, _ = make_classification(n_samples=100, n_features=5, shift=0.5) - clf = RandomForestClassifier(max_depth=2) - perm = PermutationImportanceResemblance(clf) + model = RandomForestClassifier(max_depth=2) + perm = PermutationImportanceResemblance(model) feature_importance = perm.fit_compute(X1, X2) perm.plot() ``` @@ -307,7 +287,7 @@ class PermutationImportanceResemblance(BaseResemblanceModel): def __init__( self, - clf, + model, iterations=100, scoring="roc_auc", test_prc=0.25, @@ -319,8 +299,8 @@ def __init__( Initializes the class. Args: - clf (model object): - Binary classification model or pipeline. + model (model object): + Regression or classification model or pipeline. iterations (int, optional): Number of iterations performed to calculate permutation importance. By default 100 iterations per @@ -352,7 +332,7 @@ class is 'roc_auc'. reproducible results set it to integer. """ # noqa super().__init__( - clf=clf, + model=model, scoring=scoring, test_prc=test_prc, n_jobs=n_jobs, @@ -401,7 +381,7 @@ def fit(self, X1, X2, column_names=None, class_names=None): super().fit(X1=X1, X2=X2, column_names=column_names, class_names=class_names) permutation_result = permutation_importance( - self.clf, + self.model, self.X_test, self.y_test, scoring=self.scorer.scorer, @@ -528,8 +508,8 @@ class SHAPImportanceResemblance(BaseResemblanceModel): from probatus.sample_similarity import SHAPImportanceResemblance X1, _ = make_classification(n_samples=100, n_features=5) X2, _ = make_classification(n_samples=100, n_features=5, shift=0.5) - clf = RandomForestClassifier(max_depth=2) - rm = SHAPImportanceResemblance(clf) + model = RandomForestClassifier(max_depth=2) + rm = SHAPImportanceResemblance(model) feature_importance = rm.fit_compute(X1, X2) rm.plot() ``` @@ -540,7 +520,7 @@ class SHAPImportanceResemblance(BaseResemblanceModel): def __init__( self, - clf, + model, scoring="roc_auc", test_prc=0.25, n_jobs=1, @@ -551,8 +531,8 @@ def __init__( Initializes the class. Args: - clf (model object): - Binary classification model or pipeline. + model (model object): + Regression or classification model or pipeline. scoring (string or probatus.utils.Scorer, optional): Metric for which the model performance is calculated. It can be either a metric name aligned with @@ -580,7 +560,7 @@ class is 'roc_auc'. reproducible results set it to integer. """ # noqa super().__init__( - clf=clf, + model=model, scoring=scoring, test_prc=test_prc, n_jobs=n_jobs, @@ -629,7 +609,7 @@ def fit(self, X1, X2, column_names=None, class_names=None, **shap_kwargs): super().fit(X1=X1, X2=X2, column_names=column_names, class_names=class_names) self.shap_values_test = shap_calc( - self.clf, self.X_test, verbose=self.verbose, random_state=self.random_state, **shap_kwargs + self.model, self.X_test, verbose=self.verbose, random_state=self.random_state, **shap_kwargs ) self.report = calculate_shap_importance(self.shap_values_test, self.column_names) return self diff --git a/probatus/utils/__init__.py b/probatus/utils/__init__.py index 18c31ba1..c1f2994c 100644 --- a/probatus/utils/__init__.py +++ b/probatus/utils/__init__.py @@ -1,59 +1,22 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - -from .exceptions import NotFittedError, DimensionalityError, UnsupportedModelError, NotInstalledError +from .exceptions import NotFittedError, UnsupportedModelError from .scoring import Scorer, get_scorers, get_single_scorer from .arrayfuncs import ( - assure_numpy_array, assure_pandas_df, - check_1d, - check_numeric_dtypes, assure_pandas_series, preprocess_data, preprocess_labels, ) from .shap_helpers import shap_calc, shap_to_df, calculate_shap_importance -from .warnings import ApproximationWarning -from ._utils import ( - class_name_from_object, - assure_list_of_strings, - assure_list_values_allowed, -) -from .plots import plot_distributions_of_feature +from ._utils import assure_list_of_strings from .base_class_interface import BaseFitComputeClass, BaseFitComputePlotClass __all__ = [ "NotFittedError", - "DimensionalityError", "UnsupportedModelError", - "NotInstalledError", "Scorer", - "assure_numpy_array", "assure_pandas_df", - "check_1d", - "ApproximationWarning", - "class_name_from_object", "get_scorers", "assure_list_of_strings", - "assure_list_values_allowed", - "check_numeric_dtypes", - "plot_distributions_of_feature", "shap_calc", "shap_to_df", "calculate_shap_importance", diff --git a/probatus/utils/_utils.py b/probatus/utils/_utils.py index 1efcb693..844cf0f7 100644 --- a/probatus/utils/_utils.py +++ b/probatus/utils/_utils.py @@ -1,30 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - -def class_name_from_object(obj): - """ - Helper to quickly retrieve a class name from an object. - """ - return obj.__class__.__name__ - - def assure_list_of_strings(variable, variable_name): """ Make sure object is a list of strings. @@ -35,12 +8,3 @@ def assure_list_of_strings(variable, variable_name): return [variable] else: raise (ValueError("{} needs to be either a string or list of strings.").format(variable_name)) - - -def assure_list_values_allowed(variable, variable_name, allowed_values): - """ - Assert list. - """ - for value in variable: - if value not in allowed_values: - raise (ValueError("Value {} in variable {} is not allowed").format(value, variable_name)) diff --git a/probatus/utils/arrayfuncs.py b/probatus/utils/arrayfuncs.py index fd3377a6..3b319869 100644 --- a/probatus/utils/arrayfuncs.py +++ b/probatus/utils/arrayfuncs.py @@ -1,91 +1,8 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - -import numbers import warnings import numpy as np import pandas as pd -from probatus.utils import DimensionalityError - - -def check_1d(x): - """ - Checks whether or not a list, numpy array, pandas dataframe, pandas series are one-dimensional. - - Returns True when check is ok, otherwise throws a `DimensionalityError` - - Args: - x: list, numpy array, pandas dataframe, pandas series - - Returns: True or throws `DimensionalityError` - - """ - if isinstance(x, list): - if any([isinstance(el, list) for el in x]): - raise DimensionalityError("The input is not 1D") - else: - return True - if isinstance(x, np.ndarray): - if x.ndim == 1 and all([isinstance(el, numbers.Number) for el in x]): - return True - else: - raise DimensionalityError("The input is not 1D") - if isinstance(x, pd.core.frame.DataFrame): - if len(x.columns) == 1 and pd.api.types.is_numeric_dtype(x[x.columns[0]]): - return True - else: - raise DimensionalityError("The input is not 1D") - if isinstance(x, pd.core.series.Series): - if x.ndim == 1 and pd.api.types.is_numeric_dtype(x): - return True - else: - raise DimensionalityError("The input is not 1D") - - -def assure_numpy_array(x, assure_1d=False): - """ - Returns x as numpy array. X can be a list, numpy array, pandas dataframe, pandas series. - - Args: - x: list, numpy array, pandas dataframe, pandas series - assure_1d: whether or not to assure that the input x is one-dimensional - - Returns: numpy array - - """ - if assure_1d: - _ = check_1d(x) - if isinstance(x, list): - return np.array(x) - if isinstance(x, np.ndarray): - return x - if isinstance(x, pd.core.frame.DataFrame): - if len(x.columns) == 1: - return x.values.flatten() - else: - return x.values - if isinstance(x, pd.core.series.Series): - return x.values - def assure_pandas_df(x, column_names=None): """ @@ -144,28 +61,6 @@ def assure_pandas_series(x, index=None): raise TypeError("Please supply a list, numpy array, pandas Series") -def check_numeric_dtypes(x): - """ - Checks if all entries in an array are of a data type that can be interpreted as numeric (int, float or bool). - - Args: - x (np.ndarray or pd.Series, list): array to be checked - - Returns: - x: unchanged input array - - Raises: - TypeError: if not all elements are of numeric dtypes - """ - x = assure_numpy_array(x) - allowed_types = [bool, int, float] - - for element in np.nditer(x): - if type(element.item()) not in allowed_types: - raise TypeError("Please supply an array with only floats, ints or booleans") - return x - - def preprocess_data(X, X_name=None, column_names=None, verbose=0): """ Preprocess data. @@ -236,7 +131,7 @@ def preprocess_data(X, X_name=None, column_names=None, verbose=0): def preprocess_labels(y, y_name=None, index=None, verbose=0): """ - Does basic preparation of the labels. Turns them into Series, and wars in case the target is not binary. + Does basic preparation of the labels. Turns them into Series, and WARS in case the target is not binary. Args: y (pd.Series, list, np.array): diff --git a/probatus/utils/base_class_interface.py b/probatus/utils/base_class_interface.py index cf615ac7..ffac9035 100644 --- a/probatus/utils/base_class_interface.py +++ b/probatus/utils/base_class_interface.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - from abc import ABC, abstractmethod from probatus.utils import NotFittedError diff --git a/probatus/utils/exceptions.py b/probatus/utils/exceptions.py index dbd0ce83..484e2543 100644 --- a/probatus/utils/exceptions.py +++ b/probatus/utils/exceptions.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - class NotFittedError(Exception): """ Error. @@ -30,77 +10,14 @@ def __init__(self, message): self.message = message -class DimensionalityError(Exception): - """ - Error. - """ - - def __init__(self, message): - """ - Init error. - """ - self.message = message - - class UnsupportedModelError(Exception): """ Error. """ def __init__(self, message): + # TODO: Add this check for unsupported models to our implementations. """ Init error. """ self.message = message - - -class NotInstalledError: - """ - Raise error when a dependency is not installed. - - This object is used for optional dependencies. - This allows us to give a friendly message to the user that they need to install extra dependencies as well as a link - to our documentation page. - - Adapted from: https://github.com/RasaHQ/whatlies/blob/master/whatlies/error.py - - Example usage: - - ```python - from probatus.utils import NotInstalledError - try: - import dash_core_components as dcc - except ModuleNotFoundError as e: - dcc = NotInstalledError("dash_core_components", "dashboard") - dcc.Markdown() # Will raise friendly error with instructions how to solve - ``` - Note that installing optional dependencies in a package are defined in setup.py. - """ - - def __init__(self, tool, dep=None): - """ - Initialize error with missing package and reference to conditional install package. - - Args: - tool (str): The name of the pypi package that is missing - dep (str): The name of the extra_imports set (defined in setup.py) where the package is present. (optional) - """ - self.tool = tool - self.dep = dep - - msg = f"In order to use {self.tool} you'll need to install via;\n\n" - if self.dep is None: - msg += f"pip install {self.tool}\n\n" - else: - msg += f"pip install probatus[{self.dep}]\n\n" - - msg += "See probatus installation guide here: https://ing-bank.github.io/probatus/index.html" - self.msg = msg - - def __getattr__(self, *args, **kwargs): - """Raise when accessing an attribute.""" - raise ModuleNotFoundError(self.msg) - - def __call__(self, *args, **kwargs): - """Raise when accessing a method.""" - raise ModuleNotFoundError(self.msg) diff --git a/probatus/utils/missing_helpers.py b/probatus/utils/missing_helpers.py deleted file mode 100644 index 7e231f7e..00000000 --- a/probatus/utils/missing_helpers.py +++ /dev/null @@ -1,42 +0,0 @@ -import numpy as np - - -def generate_MCAR(df, missing): - """ - Generate missing values completely at random for dataframe df. - - Args: - df: input dataframe where some values will be masked - missing: (float or dict) - - float ( must be a fraction between 0 and 1 - both inclusive), then it will apply this - fraction of missing values on the whole dataset. - - dict: - - keys: column names to mask values - - values: fraction of missing values for this column - - Returns: - pd.DataFrame: same as the input dataframe, but with some values masked based on the missing variable - - Examples: - - # Apply 20% missing values over all the columns - miss_rand = generate_MCAR(data, missing=0.2) - - # Use the dictionary - missing_vals = {"PAY_0":0.3,"PAY_5": 0.5} - miss_rand = generate_MCAR(data, missing=missing_vals) - - """ - - df = df.copy() - - if isinstance(missing, float) and missing <= 1 and missing >= 0: - df = df.mask(np.random.random(df.shape) < missing) - elif isinstance(missing, dict): - for k, v in missing.items(): - df[k] = df[k].mask(np.random.random(df.shape[0]) < v) - - else: - raise ValueError("missing must be float within range [0.1] or dict") - - return df diff --git a/probatus/utils/plots.py b/probatus/utils/plots.py deleted file mode 100644 index 93ce590f..00000000 --- a/probatus/utils/plots.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - -import matplotlib.pyplot as plt -from matplotlib.pyplot import figure - - -def plot_distributions_of_feature( - feature_distributions, - feature_name=None, - sample_names=None, - plot_bw_method=0.05, - plot_perc_outliers_removed=0.01, - plot_figsize=(15, 6), -): - """ - This function plots multiple distributions of the same feature. - - It is e.g. useful to compare - distribution between train and test. - - For categorical feature the plot bar is plotted, and for numeric the density plot. - - Args: - feature_distributions (list of pd.Series): List of distributions of the same feature, - e.g. values of feature 'f1' for Train, Validation and Test. - - feature_name (Optional, str): Name of the feature plotted. - - sample_names (Optional, list of str): List of names of samples e.g. ['Train', 'Validation', 'Test']. - - plot_bw_method (Optional, float): Estimator bandwidth in density plot. - - plot_perc_outliers_removed (Optional, float): Percentage of outliers removed from each side before plotting. - - plot_figsize (Optional, tuple): Size of the figure. - - """ - figure(figsize=plot_figsize) - - if feature_name is None: - feature_name = feature_distributions[0].name - - if sample_names is None: - sample_names = [f"sample_{i}" for i in range(len(feature_distributions))] - - if feature_distributions[0].dtype.name == "category": - data_dict = {} - - for feature_distribution_index in range(len(feature_distributions)): - data_dict[sample_names[feature_distribution_index]] = feature_distributions[ - feature_distribution_index - ].value_counts(normalize=True) - - plt.ylabel("Relative frequencies of values in feature.") - else: - for feature_distribution_index in range(len(feature_distributions)): - current_feature = feature_distributions[feature_distribution_index] - - # Remove outliers in each feature - current_feature = current_feature[ - current_feature.between( - current_feature.quantile(0 + plot_perc_outliers_removed), - current_feature.quantile(1 - plot_perc_outliers_removed), - ) - ] - - # Plot density plot - current_feature.plot.density(bw_method=plot_bw_method) - - plt.title(f"Distribution of {feature_name}") - plt.xlabel("Feature Distribution") - plt.legend(sample_names, loc="upper right") - plt.show() diff --git a/probatus/utils/scoring.py b/probatus/utils/scoring.py index 3f25cfe0..23bbbee1 100644 --- a/probatus/utils/scoring.py +++ b/probatus/utils/scoring.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - from sklearn.metrics import get_scorer @@ -97,12 +77,12 @@ def custom_metric(y_true, y_pred): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Prepare and fit model. Remember about class_weight="balanced" or an equivalent. - clf = RandomForestClassifier(class_weight='balanced', n_estimators = 100, max_depth=2, random_state=0) - clf = clf.fit(X_train, y_train) + model = RandomForestClassifier(class_weight='balanced', n_estimators = 100, max_depth=2, random_state=0) + model = model.fit(X_train, y_train) # Score model - score_test_scorer1 = scorer1.score(clf, X_test, y_test) - score_test_scorer2 = scorer2.score(clf, X_test, y_test) + score_test_scorer1 = scorer1.score(model, X_test, y_test) + score_test_scorer2 = scorer2.score(model, X_test, y_test) print(f'Test ROC AUC is {score_test_scorer1}, Test {scorer2.metric_name} is {score_test_scorer2}') ``` diff --git a/probatus/utils/shap_helpers.py b/probatus/utils/shap_helpers.py index c14f16fa..70f3f26a 100644 --- a/probatus/utils/shap_helpers.py +++ b/probatus/utils/shap_helpers.py @@ -1,23 +1,3 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - import warnings import numpy as np @@ -43,7 +23,7 @@ def shap_calc( Helper function to calculate the shapley values for a given model. Args: - model (binary model): + model (model): Trained model. X (pd.DataFrame or np.ndarray): @@ -135,7 +115,7 @@ def shap_to_df(model, X, precalc_shap=None, **kwargs): Calculates the shap values and return the pandas DataFrame with the columns and the index of the original. Args: - model (binary model): + model (model): Pretrained model (Random Forest of XGBoost at the moment). X (pd.DataFrame or np.ndarray): diff --git a/probatus/utils/warnings.py b/probatus/utils/warnings.py deleted file mode 100644 index 54226600..00000000 --- a/probatus/utils/warnings.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (c) 2020 ING Bank N.V. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy of -# this software and associated documentation files (the "Software"), to deal in -# the Software without restriction, including without limitation the rights to -# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of -# the Software, and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS -# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR -# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER -# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - -class ApproximationWarning(Warning): - """ - Warning for approx. - """ - - def __init__(self, message): - """ - Init. - """ - self.message = message - - -class NotIntendedUseWarning(Warning): - """ - Warning for not intended use. - """ - - def __init__(self, message): - """ - Init. - """ - self.message = message diff --git a/pyproject.toml b/pyproject.toml index 7b76aa8f..2d0661c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,9 +4,9 @@ build-backend = "setuptools.build_meta" [project] name = "probatus" -version = "3.0.1" +version = "3.1.0" requires-python= ">=3.8" -description = "Validation of binary classifiers and data used to develop them" +description = "Validation of regression & classifiers and data used to develop them" readme = { file = "README.md", content-type = "text/markdown" } authors = [ { name = "ING Bank N.V.", email = "reinier.koops@ing.com" } diff --git a/tests/docs/test_notebooks.py b/tests/docs/test_notebooks.py index ed1e111f..3cee9262 100644 --- a/tests/docs/test_notebooks.py +++ b/tests/docs/test_notebooks.py @@ -5,26 +5,16 @@ import nbformat from nbconvert.preprocessors import ExecutePreprocessor -# TODO Fix the breaking notebooks or make them run faster than timeout, then remove the fixed one from the list -FAILING_NOTEBOOKS = { - "nb_shap_dependence.ipynb", - "nb_shap_variance_penalty_and_results_comparison.ipynb", - "nb_rfecv_vs_shaprfecv.ipynb", -} - -TIMEOUT_SECONDS = 180 +TIMEOUT_SECONDS = 1800 PATH_NOTEBOOKS = [str(path) for path in Path("docs").glob("*/*.ipynb")] -TEST_NOTEBOOKS = bool(os.environ.get("TEST_NOTEBOOKS")) # Turn on tests by setting TEST_NOTEBOOKS = 1 -SKIP_ALL_NB_TESTS = not TEST_NOTEBOOKS +NB_FLAG = os.environ.get("TEST_NOTEBOOKS") # Turn on tests by setting TEST_NOTEBOOKS = 1 +TEST_NOTEBOOKS = False if NB_FLAG == "1" else True @pytest.mark.parametrize("notebook_path", PATH_NOTEBOOKS) -@pytest.mark.skipif(SKIP_ALL_NB_TESTS, reason="Skip notebook tests if TEST_NOTEBOOK isn't set") +@pytest.mark.skipif(TEST_NOTEBOOKS, reason="Skip notebook tests if TEST_NOTEBOOK isn't set") def test_notebook(notebook_path: str) -> None: """Run a notebook and check no exception is raised.""" - if Path(notebook_path).name in FAILING_NOTEBOOKS: - pytest.skip(f"NEEDS FIXING! - Notebook {notebook_path} is either failing or taking too long to run.") - with open(notebook_path) as f: nb = nbformat.read(f, as_version=4) diff --git a/tests/feature_elimination/test_feature_elimination.py b/tests/feature_elimination/test_feature_elimination.py index 0e2cab5b..fe8f3d86 100644 --- a/tests/feature_elimination/test_feature_elimination.py +++ b/tests/feature_elimination/test_feature_elimination.py @@ -1,16 +1,14 @@ -import os - import pandas as pd import pytest from lightgbm import LGBMClassifier -from sklearn.datasets import make_classification +from sklearn.datasets import load_diabetes, make_classification from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.model_selection import RandomizedSearchCV, StratifiedGroupKFold, StratifiedKFold from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC -from xgboost import XGBClassifier +from xgboost import XGBClassifier, XGBRegressor from probatus.feature_elimination import EarlyStoppingShapRFECV, ShapRFECV from probatus.utils import preprocess_labels @@ -62,6 +60,30 @@ def XGBoost_classifier(random_state): return model +@pytest.fixture(scope="function") +def XGBoost_regressor(random_state): + """This fixture allows to reuse the import of the XGBRegressor class across different tests.""" + model = XGBRegressor(n_estimators=200, max_depth=3, random_state=random_state) + return model + + +def test_shap_rfe_regressor(XGBoost_regressor, random_state): + """ + Test with a Regressor. + """ + diabetes = load_diabetes() + X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names) + y = diabetes.target + + shap_elimination = ShapRFECV(XGBoost_regressor, step=0.8, cv=2, scoring="r2", n_jobs=4, random_state=random_state) + report = shap_elimination.fit_compute(X, y) + + assert report.shape[0] == 3 + assert shap_elimination.get_reduced_features_set(1) == ["bmi"] + + _ = shap_elimination.plot(show=False) + + def test_shap_rfe_randomized_search(X, y, randomized_search_decision_tree_classifier, random_state): """ Test with RandomizedSearchCV. @@ -133,7 +155,7 @@ def test_shap_pipeline_error(X, y, decision_tree_classifier, random_state): """ Test with ShapRFECV for pipelines. """ - clf = Pipeline( + model = Pipeline( [ ("scaler", StandardScaler()), ("dt", decision_tree_classifier), @@ -141,7 +163,7 @@ def test_shap_pipeline_error(X, y, decision_tree_classifier, random_state): ) with pytest.raises(TypeError): shap_elimination = ShapRFECV( - clf, + model, random_state=random_state, step=1, cv=2, @@ -155,8 +177,8 @@ def test_shap_rfe_linear_model(X, y, random_state): """ Test ShapRFECV with linear model. """ - clf = LogisticRegression(C=1, random_state=random_state) - shap_elimination = ShapRFECV(clf, random_state=random_state, step=1, cv=2, scoring="roc_auc", n_jobs=4) + model = LogisticRegression(C=1, random_state=random_state) + shap_elimination = ShapRFECV(model, random_state=random_state, step=1, cv=2, scoring="roc_auc", n_jobs=4) report = shap_elimination.fit_compute(X, y) assert report.shape[0] == 3 @@ -167,8 +189,8 @@ def test_shap_rfe_svm(X, y, random_state): """ Test with ShapRFECV with SVM. """ - clf = SVC(C=1, kernel="linear", probability=True, random_state=random_state) - shap_elimination = ShapRFECV(clf, random_state=random_state, step=1, cv=2, scoring="roc_auc", n_jobs=4) + model = SVC(C=1, kernel="linear", probability=True, random_state=random_state) + shap_elimination = ShapRFECV(model, random_state=random_state, step=1, cv=2, scoring="roc_auc", n_jobs=4) shap_elimination = shap_elimination.fit(X, y) report = shap_elimination.compute() @@ -366,7 +388,6 @@ def test_shap_rfe_penalty_factor(X, y, decision_tree_classifier, random_state): assert shap_elimination.get_reduced_features_set(1) == ["col_1"] -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_complex_dataset(complex_data, complex_lightgbm, random_state_1): """ Test on complex dataset. @@ -380,7 +401,7 @@ def test_complex_dataset(complex_data, complex_lightgbm, random_state_1): search = RandomizedSearchCV(complex_lightgbm, param_grid, n_iter=1, random_state=random_state_1) shap_elimination = ShapRFECV( - clf=search, step=1, cv=10, scoring="roc_auc", n_jobs=3, verbose=1, random_state=random_state_1 + model=search, step=1, cv=10, scoring="roc_auc", n_jobs=3, verbose=1, random_state=random_state_1 ) report = shap_elimination.fit_compute(X, y) @@ -388,16 +409,15 @@ def test_complex_dataset(complex_data, complex_lightgbm, random_state_1): assert report.shape[0] == X.shape[1] -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_shap_rfe_early_stopping_lightGBM(complex_data, random_state): """ Test EarlyStoppingShapRFECV with a LGBMClassifier. """ - clf = LGBMClassifier(n_estimators=200, max_depth=3, random_state=random_state) + model = LGBMClassifier(n_estimators=200, max_depth=3, random_state=random_state) X, y = complex_data shap_elimination = EarlyStoppingShapRFECV( - clf, + model, random_state=random_state, step=1, cv=10, @@ -412,7 +432,6 @@ def test_shap_rfe_early_stopping_lightGBM(complex_data, random_state): assert shap_elimination.get_reduced_features_set(1) == ["f5"] -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_shap_rfe_early_stopping_XGBoost(XGBoost_classifier, complex_data, random_state): """ Test EarlyStoppingShapRFECV with a LGBMClassifier. @@ -436,7 +455,7 @@ def test_shap_rfe_early_stopping_XGBoost(XGBoost_classifier, complex_data, rando assert shap_elimination.get_reduced_features_set(1) == ["f4"] -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") +# def test_shap_rfe_early_stopping_CatBoost(complex_data_with_categorical, catboost_classifier, random_state): """ Test EarlyStoppingShapRFECV with a CatBoostClassifier. @@ -459,18 +478,17 @@ def test_shap_rfe_early_stopping_CatBoost(complex_data_with_categorical, catboos assert shap_elimination.get_reduced_features_set(1)[0] in ["f4", "f5"] -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_shap_rfe_randomized_search_early_stopping_lightGBM(complex_data, random_state): """ Test EarlyStoppingShapRFECV with RandomizedSearchCV and a LGBMClassifier on complex dataset. """ - clf = LGBMClassifier(n_estimators=200, random_state=random_state) + model = LGBMClassifier(n_estimators=200, random_state=random_state) X, y = complex_data param_grid = { "max_depth": [3, 4, 5], } - search = RandomizedSearchCV(clf, param_grid, cv=2, n_iter=2, random_state=random_state) + search = RandomizedSearchCV(model, param_grid, cv=2, n_iter=2, random_state=random_state) shap_elimination = EarlyStoppingShapRFECV( search, step=1, @@ -490,17 +508,16 @@ def test_shap_rfe_randomized_search_early_stopping_lightGBM(complex_data, random _ = shap_elimination.plot(show=False) -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_get_feature_shap_values_per_fold_early_stopping_lightGBM(complex_data, random_state): """ Test with ShapRFECV with features per fold. """ - clf = LGBMClassifier(n_estimators=200, max_depth=3, random_state=random_state) + model = LGBMClassifier(n_estimators=200, max_depth=3, random_state=random_state) X, y = complex_data y = preprocess_labels(y, y_name="y", index=X.index) shap_elimination = EarlyStoppingShapRFECV( - clf, early_stopping_rounds=5, scoring="roc_auc", random_state=random_state + model, early_stopping_rounds=5, scoring="roc_auc", random_state=random_state ) ( shap_values, @@ -509,7 +526,7 @@ def test_get_feature_shap_values_per_fold_early_stopping_lightGBM(complex_data, ) = shap_elimination._get_feature_shap_values_per_fold( X, y, - clf, + model, train_index=list(range(5, 50)), val_index=[0, 1, 2, 3, 4], ) @@ -518,7 +535,6 @@ def test_get_feature_shap_values_per_fold_early_stopping_lightGBM(complex_data, assert shap_values.shape == (5, 5) -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_get_feature_shap_values_per_fold_early_stopping_CatBoost( complex_data_with_categorical, catboost_classifier, random_state ): @@ -547,7 +563,6 @@ def test_get_feature_shap_values_per_fold_early_stopping_CatBoost( assert shap_values.shape == (5, 5) -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_get_feature_shap_values_per_fold_early_stopping_XGBoost(XGBoost_classifier, complex_data, random_state): """ Test with ShapRFECV with features per fold. @@ -574,13 +589,12 @@ def test_get_feature_shap_values_per_fold_early_stopping_XGBoost(XGBoost_classif assert shap_values.shape == (5, 5) -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_EarlyStoppingShapRFECV_no_categorical(complex_data, random_state): """Test EarlyStoppingShapRFECV when no categorical features are present.""" - clf = LGBMClassifier(n_estimators=50, max_depth=3, num_leaves=3, random_state=random_state) + model = LGBMClassifier(n_estimators=50, max_depth=3, num_leaves=3, random_state=random_state) shap_elimination = EarlyStoppingShapRFECV( - clf=clf, + model=model, step=0.33, cv=5, scoring="accuracy", @@ -598,7 +612,6 @@ def test_EarlyStoppingShapRFECV_no_categorical(complex_data, random_state): _ = shap_elimination.plot(show=False) -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_LightGBM_stratified_kfold(random_state): """ Test added to check for https://github.com/ing-bank/probatus/issues/170. @@ -615,14 +628,14 @@ def test_LightGBM_stratified_kfold(random_state): X[0] = X[0].astype("float") y = [0] * 5 + [1] * 5 - clf = LGBMClassifier(random_state=random_state) + model = LGBMClassifier(random_state=random_state) n_iter = 2 n_folds = 3 for _ in range(n_iter): skf = StratifiedKFold(n_folds, shuffle=True, random_state=random_state) shap_elimination = EarlyStoppingShapRFECV( - clf=clf, + model=model, step=1 / (n_iter + 1), cv=skf, scoring="accuracy", diff --git a/tests/interpret/test_model_interpret.py b/tests/interpret/test_model_interpret.py index a42f5725..7cb352ff 100644 --- a/tests/interpret/test_model_interpret.py +++ b/tests/interpret/test_model_interpret.py @@ -1,5 +1,3 @@ -import os - import numpy as np import pandas as pd import pytest @@ -172,7 +170,6 @@ def test_shap_interpret_fit_compute( pd.testing.assert_frame_equal(expected_feature_importance, importance_df) -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_shap_interpret_complex_data(complex_data_split_with_categorical, complex_fitted_lightgbm, random_state): """ Test lightgbm. diff --git a/tests/interpret/test_shap_dependence.py b/tests/interpret/test_shap_dependence.py index 2717705c..f147e2cd 100644 --- a/tests/interpret/test_shap_dependence.py +++ b/tests/interpret/test_shap_dependence.py @@ -1,5 +1,3 @@ -import os - import matplotlib import matplotlib.pyplot as plt import numpy as np @@ -71,7 +69,7 @@ def expected_shap_vals(): @pytest.fixture(scope="function") -def clf(X_y, random_state): +def model(X_y, random_state): """ Fixture. """ @@ -97,15 +95,14 @@ def expected_feat_importances(): ) -def test_not_fitted(clf, random_state): +def test_not_fitted(model, random_state): """ Test. """ - plotter = DependencePlotter(clf, random_state) + plotter = DependencePlotter(model, random_state) assert plotter.fitted is False -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_fit_complex(complex_data_split, complex_fitted_lightgbm, random_state): """ Test. @@ -124,13 +121,13 @@ def test_fit_complex(complex_data_split, complex_fitted_lightgbm, random_state): _ = plotter.plot(feature="f2_missing", show=False) -def test_get_X_y_shap_with_q_cut_normal(X_y, clf, random_state): +def test_get_X_y_shap_with_q_cut_normal(X_y, model, random_state): """ Test. """ X, y = X_y - plotter = DependencePlotter(clf, random_state).fit(X, y) + plotter = DependencePlotter(model, random_state).fit(X, y) plotter.min_q, plotter.max_q = 0, 1 X_cut, y_cut, _ = plotter._get_X_y_shap_with_q_cut(0) @@ -158,46 +155,46 @@ def test_get_X_y_shap_with_q_cut_normal(X_y, clf, random_state): assert np.equal(y_cut.values, [1, 0, 0, 1, 1, 0, 0, 0, 0]).all() -def test_get_X_y_shap_with_q_cut_unfitted(clf, random_state): +def test_get_X_y_shap_with_q_cut_unfitted(model, random_state): """ Test. """ - plotter = DependencePlotter(clf, random_state) + plotter = DependencePlotter(model, random_state) with pytest.raises(NotFittedError): plotter._get_X_y_shap_with_q_cut(0) -def test_get_X_y_shap_with_q_cut_input(X_y, clf, random_state): +def test_get_X_y_shap_with_q_cut_input(X_y, model, random_state): """ Test. """ - plotter = DependencePlotter(clf, random_state).fit(X_y[0], X_y[1]) + plotter = DependencePlotter(model, random_state).fit(X_y[0], X_y[1]) with pytest.raises(ValueError): plotter._get_X_y_shap_with_q_cut("not a feature") -def test_plot_normal(X_y, clf, random_state): +def test_plot_normal(X_y, model, random_state): """ Test. """ - plotter = DependencePlotter(clf, random_state).fit(X_y[0], X_y[1]) + plotter = DependencePlotter(model, random_state).fit(X_y[0], X_y[1]) _ = plotter.plot(feature=0) -def test_plot_class_names(X_y, clf, random_state): +def test_plot_class_names(X_y, model, random_state): """ Test. """ - plotter = DependencePlotter(clf, random_state).fit(X_y[0], X_y[1], class_names=["a", "b"]) + plotter = DependencePlotter(model, random_state).fit(X_y[0], X_y[1], class_names=["a", "b"]) _ = plotter.plot(feature=0) assert plotter.class_names == ["a", "b"] -def test_plot_input(X_y, clf, random_state): +def test_plot_input(X_y, model, random_state): """ Test. """ - plotter = DependencePlotter(clf, random_state).fit(X_y[0], X_y[1]) + plotter = DependencePlotter(model, random_state).fit(X_y[0], X_y[1]) with pytest.raises(ValueError): plotter.plot(feature="not a feature") with pytest.raises(TypeError): @@ -206,9 +203,9 @@ def test_plot_input(X_y, clf, random_state): plotter.plot(feature=0, min_q=1, max_q=0) -def test__repr__(clf, random_state): +def test__repr__(model, random_state): """ Test string representation. """ - plotter = DependencePlotter(clf, random_state) + plotter = DependencePlotter(model, random_state) assert str(plotter) == "Shap dependence plotter for RandomForestClassifier" diff --git a/tests/sample_similarity/test_resemblance_model.py b/tests/sample_similarity/test_resemblance_model.py index 7d81fb47..ad75111e 100644 --- a/tests/sample_similarity/test_resemblance_model.py +++ b/tests/sample_similarity/test_resemblance_model.py @@ -1,5 +1,3 @@ -import os - import matplotlib import matplotlib.pyplot as plt import numpy as np @@ -160,7 +158,6 @@ def test_shap_resemblance_class_lin_models(X1, X2, logistic_regression, random_s rm.plot(plot_type="dot") -@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_shap_resemblance_class2(complex_data_with_categorical, complex_lightgbm, random_state): """ Test. diff --git a/tests/utils/test_utils_array_funcs.py b/tests/utils/test_utils_array_funcs.py index 1cf0232a..b9ca28d2 100644 --- a/tests/utils/test_utils_array_funcs.py +++ b/tests/utils/test_utils_array_funcs.py @@ -1,17 +1,8 @@ import numpy as np import pandas as pd import pytest -from packaging import version -from probatus.utils import ( - DimensionalityError, - assure_numpy_array, - assure_pandas_df, - check_1d, - check_numeric_dtypes, - preprocess_data, - preprocess_labels, -) +from probatus.utils import assure_pandas_df, preprocess_data, preprocess_labels @pytest.fixture(scope="function") @@ -30,123 +21,6 @@ def expected_df(): return pd.DataFrame({0: [1, 2, 3]}) -def test_assure_numpy_array_list(): - """ - Test. - """ - x = [1, 2, 3] - x_array = assure_numpy_array(x) - assert isinstance(x_array, np.ndarray) - np.testing.assert_array_equal(x_array, np.array(x)) - x = [[1, 2], [3, 4]] - x_array = assure_numpy_array(x) - np.testing.assert_array_equal(x_array, np.array([[1, 2], [3, 4]])) - with pytest.raises(DimensionalityError): - assert assure_numpy_array(x, assure_1d=True) - - -def test_assure_numpy_array_array(): - """ - Test. - """ - x = np.array([1, 2, 3]) - x_array = assure_numpy_array(x) - assert isinstance(x_array, np.ndarray) - np.testing.assert_array_equal(x_array, x) - x = np.array([[1, 2], [3, 4]]) - x_array = assure_numpy_array(x) - np.testing.assert_array_equal(x_array, x) - with pytest.raises(DimensionalityError): - assert assure_numpy_array(x, assure_1d=True) - - -def test_assure_numpy_array_dataframe(): - """ - Test. - """ - x = pd.DataFrame({"x": [1, 2, 3]}) - x_array = assure_numpy_array(x) - assert isinstance(x_array, np.ndarray) - np.testing.assert_array_equal(x_array, np.array([1, 2, 3])) - x = pd.DataFrame({"x": [1, 2, 3], "y": [1, 2, 3]}) - x_array = assure_numpy_array(x) - np.testing.assert_array_equal(x_array, np.array([[1, 1], [2, 2], [3, 3]])) - with pytest.raises(DimensionalityError): - assert assure_numpy_array(x, assure_1d=True) - - -def test_assure_numpy_array_series(): - """ - Test. - """ - x = pd.Series([1, 2, 3]) - x_array = assure_numpy_array(x) - assert isinstance(x_array, np.ndarray) - np.testing.assert_array_equal(x_array, np.array([1, 2, 3])) - - -def test_check_1d_list(): - """ - Test. - """ - x = [1, 2, 3] - assert check_1d(x) - y = [[1, 2], [1, 2, 3]] - with pytest.raises(DimensionalityError): - assert check_1d(y) - y = [1, [1, 2, 3]] - with pytest.raises(DimensionalityError): - assert check_1d(y) - - -def test_check_1d_array(): - """ - Test. - """ - x = np.array([1, 2, 3]) - assert check_1d(x) - if version.parse(np.__version__) < version.parse("1.24.0"): - y = np.array([[1, 2], [1, 2, 3]]) - else: - y = np.array([[1, 2], [1, 2, 3]], dtype=object) - with pytest.raises(DimensionalityError): - assert check_1d(y) - if version.parse(np.__version__) < version.parse("1.24.0"): - y = np.array([0, [1, 2, 3]]) - else: - y = np.array([0, [1, 2, 3]], dtype=object) - with pytest.raises(DimensionalityError): - assert check_1d(y) - - -def test_check_1d_dataframe(): - """ - Test. - """ - x = pd.DataFrame({"x": [1, 2, 3]}) - assert check_1d(x) - y = pd.DataFrame({"x": [1, 2, 3], "y": [1, 2, 3]}) - with pytest.raises(DimensionalityError): - assert check_1d(y) - y = pd.DataFrame({"x": [1, 2, 3, [4, 5]]}) - with pytest.raises(DimensionalityError): - assert check_1d(y) - - -def test_check_1d_series(): - """ - Test. - """ - x = pd.Series([1, 2, 3]) - assert check_1d(x) - y = pd.Series([1, [2, 3]]) - with pytest.raises(DimensionalityError): - assert check_1d(y) - y = pd.Series([[1], [2, 3]]) - with pytest.raises(DimensionalityError): - assert check_1d(y) - - def test_assure_pandas_df_list(expected_df): """ Test. @@ -206,18 +80,6 @@ def test_assure_pandas_df_types(): assure_pandas_df(5) -def test_check_numeric_dtype_list(): - """ - Test. - """ - with pytest.raises(TypeError): - check_numeric_dtypes(["not numeric", 7, 1.0, True]) - check_numeric_dtypes([1, 2, 3]) - check_numeric_dtypes([1.0, 2.0, 3.0]) - check_numeric_dtypes([False, True, False]) - check_numeric_dtypes([1, True, 7.0]) - - def test_preprocess_labels(): """ Test.