Add new Path solution to ESCWA model #23
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| evaluation_metrics = {'Mean Mean Squared Error': 0.0029154554168954083, 'Mean Average Precision': 0.07515270506108203, 'Mean Brier Score': 0.0029154554168954083} |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import sys\n", | ||
| "from pathlib import Path\n", | ||
| "import pandas as pd\n", | ||
| "import pickle\n", | ||
| "\n", | ||
| "from sklearn.ensemble import RandomForestClassifier\n", | ||
| "\n", | ||
| "from stepshift.views import StepshiftedModels\n", | ||
| "from views_runs import DataPartitioner, ViewsRun\n", | ||
| "\n", | ||
| "PATH = Path.cwd() \n", | ||
| "sys.path.insert(0, str(Path(*[i for i in PATH.parts[:PATH.parts.index(\"views_pipeline\")+1]]) / \"common_utils\")) # PATH_COMMON_UTILS\n", | ||
| "from set_path import setup_project_paths, setup_artifacts_paths, setup_data_paths\n", | ||
| "setup_project_paths(PATH) #adds all necessary paths to sys.path\n", | ||
| "\n", | ||
| "from config_data_partitions import get_data_partitions #change to common_utils/set_partition.py\n", | ||
| "from config_hyperparameters import get_hp_config\n", | ||
| "from config_model import get_model_config" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def train(model_config, hp_config, data_partitions):\n", | ||
| " print(\"Training...\")\n", | ||
| "\n", | ||
| " # Define the artifacts path manually or according to your notebook structure\n", | ||
| " artifacts_path = Path(\"your_path_to_artifacts_directory\")\n", | ||
| "\n", | ||
| " calib_pickle_path = artifacts_path / \"model_calibration_partition.pkl\"\n", | ||
| " future_pickle_path = artifacts_path / \"model_future_partition.pkl\"\n", | ||
| "\n", | ||
| " if calib_pickle_path.exists() and future_pickle_path.exists():\n", | ||
| " print(\"Pickle files already exist. Loading models from pickle files...\")\n", | ||
| " with open(calib_pickle_path, 'rb') as file:\n", | ||
| " model_calibration_partition = pickle.load(file)\n", | ||
| " with open(future_pickle_path, 'rb') as file:\n", | ||
| " model_future_partition = pickle.load(file)\n", | ||
| "\n", | ||
| " else:\n", | ||
| " # Assuming you have loaded the dataset before calling this function\n", | ||
| " dataset = \"models/electric_relaxation/data/raw/raw.parquet\" # Load your dataset here\n", | ||
| "\n", | ||
| " calib_partition = DataPartitioner({'calib': data_partitions[\"calib_partitioner_dict\"]})\n", | ||
| " future_partition = DataPartitioner({'future': data_partitions[\"future_partitioner_dict\"]})\n", | ||
| "\n", | ||
| " base_model = RandomForestClassifier(n_estimators=hp_config[\"n_estimators\"], n_jobs=hp_config[\"n_jobs\"])\n", | ||
| " stepshifter_def = StepshiftedModels(base_model, model_config[\"steps\"], model_config[\"depvar\"])\n", | ||
| "\n", | ||
| " model_calibration_partition = ViewsRun(calib_partition, stepshifter_def)\n", | ||
| " model_calibration_partition.fit('calib', 'train', dataset)\n", | ||
| "\n", | ||
| " model_future_partition = ViewsRun(future_partition, stepshifter_def)\n", | ||
| " model_future_partition.fit('future', 'train', dataset)\n", | ||
| "\n", | ||
| " assert model_calibration_partition is not None and model_future_partition is not None, \"Model training failed.\"\n", | ||
| "\n", | ||
| " with open(calib_pickle_path, 'wb') as file:\n", | ||
| " pickle.dump(model_calibration_partition, file)\n", | ||
| " with open(future_pickle_path, 'wb') as file:\n", | ||
| " pickle.dump(model_future_partition, file)\n", | ||
| "\n", | ||
| " print(\"Models trained and saved in artifacts folder!\")\n", | ||
| "\n", | ||
| " return model_calibration_partition, model_future_partition\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "viewser", | ||
| "language": "python", | ||
| "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": "3.9.18" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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fixed a typo FYI @Polichinel