From 0d82139180413a0801caaa2a21afddadeedae5c8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Hurier=20=28Fmind=29?= Date: Tue, 10 Dec 2024 21:02:48 +0100 Subject: [PATCH 01/10] feat(manager): switch from poetry to uv BREAKING CHANGE: --- .github/actions/setup/action.yml | 12 +- .github/workflows/check.yml | 4 +- .github/workflows/publish.yml | 8 +- .pre-commit-config.yaml | 12 +- Dockerfile | 4 +- LICENCE.txt => LICENSE.txt | 2 - README.md | 50 +- data/inputs_test.parquet | Bin 55720 -> 54110 bytes data/inputs_train.parquet | Bin 180322 -> 178675 bytes data/targets_test.parquet | Bin 28795 -> 28592 bytes data/targets_train.parquet | Bin 102798 -> 102589 bytes docker-compose.yml | 2 +- notebooks/explain.ipynb | 418 ++-- notebooks/indicators.ipynb | 3067 ++++++++++++++++------- notebooks/processing.ipynb | 17 +- notebooks/prototype.ipynb | 591 +++-- poetry.lock | 3837 ----------------------------- poetry.toml | 5 - pyproject.toml | 118 +- python_env.yaml | 194 +- requirements.txt | 250 +- src/bikes/core/metrics.py | 8 +- src/bikes/core/models.py | 4 +- src/bikes/io/datasets.py | 6 +- src/bikes/io/registries.py | 34 +- src/bikes/io/services.py | 5 +- src/bikes/jobs/evaluations.py | 37 +- src/bikes/jobs/explanations.py | 8 +- src/bikes/jobs/inference.py | 3 +- src/bikes/jobs/training.py | 7 +- src/bikes/utils/splitters.py | 35 +- tasks/__init__.py | 2 +- tasks/checks.py | 22 +- tasks/cleans.py | 10 +- tasks/commits.py | 8 +- tasks/containers.py | 2 +- tasks/docs.py | 6 +- tasks/formats.py | 6 +- tasks/installs.py | 14 +- tasks/mlflow.py | 13 +- tasks/packages.py | 10 +- tasks/projects.py | 19 +- tests/conftest.py | 21 +- tests/core/test_metrics.py | 5 +- tests/core/test_models.py | 6 +- tests/data/inputs_sample.parquet | Bin 47719 -> 35890 bytes tests/data/outputs_sample.parquet | Bin 13827 -> 14309 bytes tests/data/targets_sample.parquet | Bin 18587 -> 17966 bytes tests/io/test_configs.py | 1 + tests/io/test_datasets.py | 7 +- tests/io/test_services.py | 6 +- tests/jobs/test_base.py | 4 +- tests/jobs/test_evaluations.py | 42 +- tests/jobs/test_explanations.py | 9 +- tests/jobs/test_inference.py | 7 +- tests/jobs/test_promotion.py | 1 + tests/jobs/test_training.py | 14 +- tests/jobs/test_tuning.py | 10 +- tests/test_scripts.py | 1 + tests/utils/test_searchers.py | 6 +- uv.lock | 2473 +++++++++++++++++++ 61 files changed, 5869 insertions(+), 5594 deletions(-) rename LICENCE.txt => LICENSE.txt (96%) delete mode 100644 poetry.lock delete mode 100644 poetry.toml create mode 100644 uv.lock diff --git a/.github/actions/setup/action.yml b/.github/actions/setup/action.yml index 97486a6..3426d72 100644 --- a/.github/actions/setup/action.yml +++ b/.github/actions/setup/action.yml @@ -3,9 +3,11 @@ description: Setup for project workflows runs: using: composite steps: - - run: pipx install invoke poetry - shell: bash - - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@v4 with: - python-version: 3.12 - cache: poetry + enable-cache: true + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version-file: .python-version diff --git a/.github/workflows/check.yml b/.github/workflows/check.yml index 7351b39..745501e 100644 --- a/.github/workflows/check.yml +++ b/.github/workflows/check.yml @@ -12,5 +12,5 @@ jobs: steps: - uses: actions/checkout@v4 - uses: ./.github/actions/setup - - run: poetry install --with checks - - run: poetry run invoke checks + - run: uv sync --group=checks + - run: uv run invoke checks diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 105bf46..3457790 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -15,8 +15,8 @@ jobs: steps: - uses: actions/checkout@v4 - uses: ./.github/actions/setup - - run: poetry install --with docs - - run: poetry run invoke docs + - run: uv sync --group=docs + - run: uv run invoke docs - uses: JamesIves/github-pages-deploy-action@v4 with: folder: docs/ @@ -28,8 +28,8 @@ jobs: steps: - uses: actions/checkout@v4 - uses: ./.github/actions/setup - - run: poetry install --with dev - - run: poetry run invoke packages + - run: uv sync --only-dev + - run: uv run invoke packages - uses: docker/login-action@v3 with: registry: ghcr.io diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ea14e6e..4d8bd70 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,7 +5,7 @@ default_language_version: python: python3.12 repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 + rev: v5.0.0 hooks: - id: check-added-large-files - id: check-case-conflict @@ -16,18 +16,14 @@ repos: - id: end-of-file-fixer - id: mixed-line-ending - id: trailing-whitespace - - repo: https://github.com/python-poetry/poetry - rev: 1.8.3 - hooks: - - id: poetry-check - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.5.0 + rev: v0.8.1 hooks: - id: ruff - id: ruff-format - repo: https://github.com/commitizen-tools/commitizen - rev: v3.27.0 + rev: v4.0.0 hooks: - id: commitizen - id: commitizen-branch - stages: [push] + stages: [pre-push] diff --git a/Dockerfile b/Dockerfile index 043fa81..2142f8a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,6 +1,6 @@ # https://docs.docker.com/engine/reference/builder/ -FROM python:3.12 +FROM ghcr.io/astral-sh/uv:python3.12-bookworm COPY dist/*.whl . -RUN pip install *.whl +RUN uv pip install --system *.whl CMD ["bikes", "--help"] diff --git a/LICENCE.txt b/LICENSE.txt similarity index 96% rename from LICENCE.txt rename to LICENSE.txt index bbab95b..0e77b64 100644 --- a/LICENCE.txt +++ b/LICENSE.txt @@ -1,5 +1,3 @@ -Copyright 2024 Médéric HURIER (Fmind) - 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. diff --git a/README.md b/README.md index 749e151..d82cdcc 100644 --- a/README.md +++ b/README.md @@ -69,7 +69,7 @@ You can use this package as part of your MLOps toolkit or platform (e.g., Model - [Package](#package) - [Evolution: Changelog](#evolution-changelog) - [Format: Wheel](#format-wheel) - - [Manager: Poetry](#manager-poetry) + - [Manager: uv](#manager-uv) - [Runtime: Docker](#runtime-docker) - [Programming](#programming) - [Language: Python](#language-python) @@ -119,7 +119,7 @@ This section details the requirements, actions, and next steps to kickstart your ## Prerequisites - [Python>=3.12](https://www.python.org/downloads/): to benefit from [the latest features and performance improvements](https://docs.python.org/3/whatsnew/3.12.html) -- [Poetry>=1.8.2](https://python-poetry.org/): to initialize the project [virtual environment](https://docs.python.org/3/library/venv.html) and its dependencies +- [uv>=0.5.5](https://docs.astral.sh/uv/): to initialize the project [virtual environment](https://docs.python.org/3/library/venv.html) and its dependencies ## Installation @@ -130,10 +130,10 @@ $ git clone git@github.com:fmind/mlops-python-package # with https $ git clone https://github.com/fmind/mlops-python-package ``` -2. [Run the project installation with poetry](https://python-poetry.org/docs/) +2. [Run the project installation with uv](https://docs.astral.sh/uv/) ```bash $ cd mlops-python-package/ -$ poetry install +$ uv sync ``` 3. Adapt the code base to your desire @@ -171,26 +171,26 @@ This config file instructs the program to start a `TrainingJob` with 2 parameter You can find all the parameters of your program in the `src/[package]/jobs/*.py` files. -You can also print the full schema supported by this package using `poetry run bikes --schema`. +You can also print the full schema supported by this package using `uv run bikes --schema`. ## Execution -The project code can be executed with poetry during your development: +The project code can be executed with uv during your development: ```bash -$ poetry run [package] confs/tuning.yaml -$ poetry run [package] confs/training.yaml -$ poetry run [package] confs/promotion.yaml -$ poetry run [package] confs/inference.yaml -$ poetry run [package] confs/evaluations.yaml -$ poetry run [package] confs/explanations.yaml +$ uv run [package] confs/tuning.yaml +$ uv run [package] confs/training.yaml +$ uv run [package] confs/promotion.yaml +$ uv run [package] confs/inference.yaml +$ uv run [package] confs/evaluations.yaml +$ uv run [package] confs/explanations.yaml ``` In production, you can build, ship, and run the project as a Python package: ```bash -poetry build -poetry publish # optional +uv build +uv publish # optional python -m pip install [package] [package] confs/inference.yaml ``` @@ -211,7 +211,7 @@ with job as runner: - You can pass several config files in the command-line to merge them from left to right - You can define common configurations shared between jobs (e.g., model params) - The right job task will be selected automatically thanks to [Pydantic Discriminated Unions](https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions) - - This is a great way to run any job supported by the application (training, tuning, .... + - This is a great way to run any job supported by the application (training, tuning, ...) ## Automation @@ -233,7 +233,6 @@ $ inv --list - **checks.code** - Check the codes with ruff. - **checks.coverage** - Check the coverage with coverage. - **checks.format** - Check the formats with ruff. -- **checks.poetry** - Check poetry config files. - **checks.security** - Check the security with bandit. - **checks.test** - Check the tests with pytest. - **checks.type** - Check the types with mypy. @@ -247,15 +246,15 @@ $ inv --list - **cleans.mlruns** - Clean the mlruns folder. - **cleans.mypy** - Clean the mypy tool. - **cleans.outputs** - Clean the outputs folder. -- **cleans.poetry** - Clean poetry lock file. -- **cleans.pytest** - Clean the pytest tool. - **cleans.projects** - Run all projects tasks. +- **cleans.pytest** - Clean the pytest tool. - **cleans.python** - Clean python caches and bytecodes. - **cleans.requirements** - Clean the project requirements file. - **cleans.reset** - Run all tools, folders, and sources tasks. - **cleans.ruff** - Clean the ruff tool. - **cleans.sources** - Run all sources tasks. - **cleans.tools** - Run all tools tasks. +- **cleans.uv** - Clean uv lock file. - **cleans.venv** - Clean the venv folder. - **commits.all (commits)** - Run all commit tasks. - **commits.bump** - Bump the version of the package. @@ -272,8 +271,8 @@ $ inv --list - **formats.imports** - Format python imports with ruff. - **formats.sources** - Format python sources with ruff. - **installs.all (installs)** - Run all install tasks. -- **installs.poetry** - Install poetry packages. - **installs.pre-commit** - Install pre-commit hooks on git. +- **installs.uv** - Install uv packages. - **mlflow.all (mlflow)** - Run all mlflow tasks. - **mlflow.doctor** - Run mlflow doctor to diagnose issues. - **mlflow.serve** - Start mlflow server with the given host, port, and backend uri. @@ -684,17 +683,18 @@ Define and build modern Python package. - [Source](https://docs.python.org/3/distutils/sourcedist.html): older format, less powerful - [Conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html): slow and hard to manage -### Manager: [Poetry](https://python-poetry.org/) +### Manager: [uv](https://docs.astral.sh/uv/) - **Motivations**: - Define and build Python package - - Most popular solution by GitHub stars + - Fast and compliant package manager - Pack every metadata in a single static file - **Limitations**: - Cannot add dependencies beyond Python (e.g., CUDA) - i.e., use Docker container for this use case - **Alternatives**: - [Setuptools](https://docs.python.org/3/distutils/setupscript.html): dynamic file is slower and more risky + - [Poetry](https://python-poetry.org/): previous solution of this package - Pdm, Hatch, PipEnv: https://xkcd.com/1987/ ### Runtime: [Docker](https://www.docker.com/resources/what-container/) @@ -937,10 +937,10 @@ Using Python package for your AI/ML project has the following benefits: - Install Python package as a library (e.g., like pandas) - Expose script entry points to run a CLI or a GUI -To build a Python package with Poetry, you simply have to type in a terminal: +To build a Python package with uv, you simply have to type in a terminal: ```bash -# for all poetry project -poetry build +# for all uv project +uv build # for this project only inv packages ``` @@ -1044,7 +1044,7 @@ Semantic Versioning (SemVer) provides a simple schema to communicate code change - *Minor* (Y): minor release with new features (i.e., provide new capabilities) - *Patch* (Z): patch release to fix bugs (i.e., correct wrong behavior) -Poetry and this package leverage Semantic Versioning to let developers control the speed of adoption for new releases. +Uv and this package leverage Semantic Versioning to let developers control the speed of adoption for new releases. ## [Testing Tricks](https://en.wikipedia.org/wiki/Software_testing) diff --git a/data/inputs_test.parquet b/data/inputs_test.parquet index 1e407873df7f29f59ea416f415ae4bd6c558d49c..4d43e971b360329df533a6e91637d5a1b27cd85f 100644 GIT binary patch delta 539 zcmZ3nnfcx_<_&%=EZ660hc&gRKKId@GK=U%c|iRUTP<~;tL zT$?+j>bN&&s?FuyoM|$Nb90K_dEw17gDy#LzLux$JvnRFXGVs}fh_(^Y7CP%?v~|z zCa5+?P)34-Wilh1=;U`S!ju2+76vN0&nn5fLs+dtc=AG49gvtZgN!JPq$vZNfTRRV zZeB@-D3jO{)T@;Y{%Nt{9;cg*0FW8EXG_D5#2;y!zz-P8BVu|JYmYmx_=_>oI^asZb&ROLQy zMb>RfYTp#0D!KR-SvyqK_Nh!Z;I{)Ar6ncJ{>NCY!vLt2ali27cqvVgu=ai#)@cc9 zzoI7l><8Jba{<5e|Te3Oupf(4qg`T0F z!Q{r1(u~HF=bo(RaCCNbbaZlb+-!2Hkc-{l*`&fJ%W?9WD=H#Yo_Pj9xk!+Fd1Sb0 pc4UO_<||hwvv7dTko+Ssx$&ml3g7y=xF3;|}Vuv7p5 delta 942 zcmb8sUr19?90%~-bMD=B{sFtUt*uR+WBwGotSKp(xw)W7@?ZL3QXs>EG?|s8g_KeB zVj;30K~ZLfh}4Skw!aen(FY4!Hl-pe3QB^a2Ma^!q1&LBHHFUMoQLy|-}!#tjnXlh z-l=6NTwaBR{yMB>yD9iqW3NAp>lfjHfJ1%JUa`|)hZ$j!LmO$BCVNcb`&$oF(7 z(pWfmfx#~!6X)xM!FJ5jOSmyC$mN!y3}tVI!Rqi@b#$14*hTCJJLOmFy&4u4e=>M0 zZ06_Yl#S;M-n2{Jh#qc|=BAPZ&AV#k*OP}*`P#+}J=N`1*bud{$ErMbxj%|*O8jNRuS_hO TwPNLLx<*x+qf*to8j5}cnnhu% diff --git a/data/inputs_train.parquet b/data/inputs_train.parquet index da347d92686f715156c9a455862e5c5e3b850dba..99e8645d57b32258f8bff916ea852d5f8cc0b751 100644 GIT binary patch delta 542 zcmaFV!2S6%*M?BR$twjrC&x%CPu?lyH#uGS%I3KuaV$WAeVgm0iWxyNo2z6*7=g-k zH!I7%=G`Px>8?H9!v6a3rfr891ym(KLvV*39D zOkWroCPxScGO004_gusz$NtY%ZI!Ex#No}@vQO=n)pq{O}~N9|HJ4);vf z^_OG2HiJ>EY0Bh4e|woYS^B+Y0%J$wUGJ%^*Sqobpf zqvQ5>8<+~Y*!`VNDtxjWr>E^^QW2^0%rgMWMuJq7M~0hbM@IN=@7v8ZnS}#vkmMhM b>5d1OB&G)(VB&-mY@7_v@(c_CjzNY14v@Vx delta 1248 zcmbV~ZAg<*6vy|R=h>?ta(mp|Y-l;hKK}y$Rca>flxaa=Q|D1DwD=RYZ z<%pppavBG6iLDEYxy6_iu2p`L#`y!}Qy0Vvd-tPfA9q8pCk87hxGT{*j#3mPV24qw zWHgxqQKUubgIZEZFa5*1u=W|;WdQ#@(HHC^7iMU!6e9zR>Hd+FTC0f>WpC_ZLA zPj=fOLFlW;!TO+ZF-9$8(CVN_k3Twj0hgQC2j!xY?jnPpaUjxeh!sEVrO@Z3xNOvu zGbW`GcD9AWq~o%mZq9OC!R-? zYGR7Qg?0w#+nAutI9Q}(uwzz4vK*ijy`2%F?=dkuz~HhzAI(m|XKy6I`H0HR4Nd0i zK+;YtsFqp*JAER2KPS?vIp-^uMB0HNUI!$(ixpH^A%k9P1d%!+DWYA9J?|v^A86@L zJ46u^e|c@#Ou2{6@Fk1EfmS7HbV9u7UYi2P?WD^I2GVy6KErR?N+2X;Z2Uj7#p=Sq ziVYVi43i}mHd17OknFH@Xlht`GhSGFGg8>(Fd1R#5Oieg|C{{{ro8z^w|6zCpyZz`MtNf^l0YU(qkrf-2l|f#07_THXEz*$cGU~ mWLolS)7){Rz%i0vi8O1GWO594keM+MNd+h31cIWRlJuXMyqS*x diff --git a/data/targets_test.parquet b/data/targets_test.parquet index 10c3c207e07254305f517ea1da25e1ed68e851ef..6151a149abd97bddad86fa11975b0f67738f6e76 100644 GIT binary patch delta 160 zcmezUfN{fp#tl8Po8QQ4drz*BM>;H0|^hOjHKM4jO6mkQ}Zu~flZdwkzrr}B8HD_3=9E| GL52W>mNLcw delta 172 zcmdmRpYitt#tl8P%xW{rCcn&x-P|Lq?aiXLZVB^bk4y!&eHWS3Zj?_J4AGf9D^qCl z_6$QNu(~*AOck4FWxi!(HPtiFGn(9!EzM{&c|~@eq@$-J5V(Q}$4DS{Dvt~|&5n%l y-K?Jz&BPX3>g#M$Ie9{!Dr=^frK98IQ+XG}z-CD5$S^Pf5yKZY28IB~AVUB&=Q`*B diff --git a/data/targets_train.parquet b/data/targets_train.parquet index de9a2b91f12fdeb81fccc6b5b2efee217206b112..4d741fe30c276c309ccd9c187c5a299bd1a2dc75 100644 GIT binary patch delta 151 zcmeBM%(izS+lJ!-&02xmwE`KxTTJinVl-l8n0}y(F#$-ab}=flb*$u6>pMK%r;E{k zduKP}9Y$6QJwrW%=~=yu(u~H_+j<%6I95^9X%a^z!gL|Mgp-@d1Sb0c4UO_cAH+tXePGM tQeS72%IS0a8C6*`y(}Far(f@9ydVZPNK!|JfdPmZezP$!1ULp60svZHPpbd` diff --git a/docker-compose.yml b/docker-compose.yml index bab711d..3b169d3 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -2,7 +2,7 @@ services: mlflow: - image: ghcr.io/mlflow/mlflow:v2.14.3 + image: ghcr.io/mlflow/mlflow:v2.18.0 ports: - 5000:5000 environment: diff --git a/notebooks/explain.ipynb b/notebooks/explain.ipynb index 69a6507..b06a698 100644 --- a/notebooks/explain.ipynb +++ b/notebooks/explain.ipynb @@ -29,9 +29,9 @@ } ], "source": [ - "import shap\n", "import pandas as pd\n", - "import plotly.express as px" + "import plotly.express as px\n", + "import shap" ] }, { @@ -48,8 +48,8 @@ "outputs": [], "source": [ "# note: you must run the explanations job first to generate the output\n", - "MODELS_EXPLANATIONS = '../outputs/models_explanations.parquet'\n", - "SAMPLES_EXPLANATIONS = '../outputs/samples_explanations.parquet'" + "MODELS_EXPLANATIONS = \"../outputs/models_explanations.parquet\"\n", + "SAMPLES_EXPLANATIONS = \"../outputs/samples_explanations.parquet\"" ] }, { @@ -488,7 +488,9 @@ } ], "source": [ - "shap_values = shap.Explanation(samples_explanations, feature_names=samples_explanations.columns.to_list())\n", + "shap_values = shap.Explanation(\n", + " samples_explanations, feature_names=samples_explanations.columns.to_list()\n", + ")\n", "shap_values.feature_names\n", "shap_values" ] @@ -561,7 +563,7 @@ 0.5791462063789368, 0.2507259249687195, 0.0785420835018158, - 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" 2024-07-21 15:02:02.224\n", - " 598443335336095652\n", - " 2024-07-21 15:02:02.224\n", + " 2024-12-09 19:31:02.967\n", + " 892046783494697990\n", + " 2024-12-09 19:31:02.967\n", " active\n", " bikes\n", " {}\n", @@ -141,10 +141,10 @@ ], "text/plain": [ " artifact_location creation_time \\\n", - "0 file:///home/fmind/mlops-python-package/mlruns... 2024-07-21 15:02:02.224 \n", + "0 file:///home/fmind/mlops-python-package/mlruns... 2024-12-09 19:31:02.967 \n", "\n", " experiment_id last_update_time lifecycle_stage name tags \n", - "0 598443335336095652 2024-07-21 15:02:02.224 active bikes {} " + "0 892046783494697990 2024-12-09 19:31:02.967 active bikes {} " ] }, "execution_count": 5, @@ -174,7 +174,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(18, 30)\n" + "(29, 29)\n" ] }, { @@ -222,7 +222,6 @@ " mlflow.runName\n", " mlflow.project.env\n", " mlflow.project.backend\n", - " mlflow.datasets\n", " estimator_name\n", " estimator_class\n", " mlflow.log-model.history\n", @@ -234,24 +233,24 @@ " \n", " 0\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-07-21 15:31:53.829\n", - " 598443335336095652\n", + " 2024-12-09 21:26:50.094\n", + " 892046783494697990\n", " active\n", - " 4df1e5931159491c963f2252add508cd\n", + " c21b823a68ba42b2b1ff98b2ae73c424\n", " Explanations\n", - " 4df1e5931159491c963f2252add508cd\n", - " 2024-07-21 15:31:20.207\n", + " c21b823a68ba42b2b1ff98b2ae73c424\n", + " 2024-12-09 21:26:19.324\n", " FINISHED\n", " fmind\n", " {}\n", " {'conf_file': 'confs/explanations.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 33.622\n", + " 30.770\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Explanations\n", @@ -262,35 +261,33 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", " \n", " \n", " 1\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-07-21 15:31:13.361\n", - " 598443335336095652\n", + " 2024-12-09 21:26:18.061\n", + " 892046783494697990\n", " active\n", - " e993a53ee04e4357bad5cec101ab4031\n", + " 9da0f31e36a9413eaab18149ace82c78\n", " Evaluations\n", - " e993a53ee04e4357bad5cec101ab4031\n", - " 2024-07-21 15:31:07.565\n", + " 9da0f31e36a9413eaab18149ace82c78\n", + " 2024-12-09 21:26:13.489\n", " FINISHED\n", " fmind\n", " {'example_count': 13903.0, 'mean_absolute_erro...\n", " {'conf_file': 'confs/evaluations.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 5.796\n", + " 4.572\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Evaluations\n", " virtualenv\n", " local\n", - " [{\"name\":\"ce0fe6e33c74e2fa3659d51482be5f27\",\"h...\n", " NaN\n", " NaN\n", " NaN\n", @@ -300,24 +297,24 @@ " \n", " 2\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-07-21 15:31:04.758\n", - " 598443335336095652\n", + " 2024-12-09 21:26:12.215\n", + " 892046783494697990\n", " active\n", - " c17b893e525b44ed89e349421e72510a\n", + " 8316d6855d17401b8358e88a48c159f0\n", " Inference\n", - " c17b893e525b44ed89e349421e72510a\n", - " 2024-07-21 15:30:59.023\n", + " 8316d6855d17401b8358e88a48c159f0\n", + " 2024-12-09 21:26:08.210\n", " FINISHED\n", " fmind\n", " {}\n", " {'conf_file': 'confs/inference.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 5.735\n", + " 4.005\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Inference\n", @@ -328,29 +325,28 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", " \n", " \n", " 3\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-07-21 15:30:56.969\n", - " 598443335336095652\n", + " 2024-12-09 21:26:06.933\n", + " 892046783494697990\n", " active\n", - " d538cc2f3f644b6e97b611c1d96801ac\n", + " b92f959ab1564eb897e2f66f7089f573\n", " Promotion\n", - " d538cc2f3f644b6e97b611c1d96801ac\n", - " 2024-07-21 15:30:51.594\n", + " b92f959ab1564eb897e2f66f7089f573\n", + " 2024-12-09 21:26:03.382\n", " FINISHED\n", " fmind\n", " {}\n", " {'conf_file': 'confs/promotion.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 5.375\n", + " 3.551\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Promotion\n", @@ -361,38 +357,36 @@ " NaN\n", " NaN\n", " NaN\n", - " NaN\n", " \n", " \n", " 4\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-07-21 15:30:46.302\n", - " 598443335336095652\n", + " 2024-12-09 21:26:01.071\n", + " 892046783494697990\n", " active\n", - " a75ea3e9742c48fd9c34a5b8abf9bd89\n", + " fc31f5b645204fdd9ef58fbbc0c105eb\n", " Training\n", - " a75ea3e9742c48fd9c34a5b8abf9bd89\n", - " 2024-07-21 15:29:25.495\n", + " fc31f5b645204fdd9ef58fbbc0c105eb\n", + " 2024-12-09 21:25:39.001\n", " FINISHED\n", " fmind\n", " {'training_mean_squared_error': 124.5105461557...\n", " {'conf_file': 'confs/training.yaml', 'memory':...\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 80.807\n", + " 22.070\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Training\n", " virtualenv\n", " local\n", - " NaN\n", " Pipeline\n", " sklearn.pipeline.Pipeline\n", - " [{\"run_id\": \"a75ea3e9742c48fd9c34a5b8abf9bd89\"...\n", + " [{\"run_id\": \"fc31f5b645204fdd9ef58fbbc0c105eb\"...\n", " NaN\n", " NaN\n", " \n", @@ -402,25 +396,25 @@ ], "text/plain": [ " artifact_uri end_time \\\n", - "0 file:///home/fmind/mlops-python-package/mlruns... 2024-07-21 15:31:53.829 \n", - "1 file:///home/fmind/mlops-python-package/mlruns... 2024-07-21 15:31:13.361 \n", - "2 file:///home/fmind/mlops-python-package/mlruns... 2024-07-21 15:31:04.758 \n", - "3 file:///home/fmind/mlops-python-package/mlruns... 2024-07-21 15:30:56.969 \n", - "4 file:///home/fmind/mlops-python-package/mlruns... 2024-07-21 15:30:46.302 \n", + "0 file:///home/fmind/mlops-python-package/mlruns... 2024-12-09 21:26:50.094 \n", + "1 file:///home/fmind/mlops-python-package/mlruns... 2024-12-09 21:26:18.061 \n", + "2 file:///home/fmind/mlops-python-package/mlruns... 2024-12-09 21:26:12.215 \n", + "3 file:///home/fmind/mlops-python-package/mlruns... 2024-12-09 21:26:06.933 \n", + "4 file:///home/fmind/mlops-python-package/mlruns... 2024-12-09 21:26:01.071 \n", "\n", " experiment_id lifecycle_stage run_id \\\n", - "0 598443335336095652 active 4df1e5931159491c963f2252add508cd \n", - "1 598443335336095652 active e993a53ee04e4357bad5cec101ab4031 \n", - "2 598443335336095652 active c17b893e525b44ed89e349421e72510a \n", - "3 598443335336095652 active d538cc2f3f644b6e97b611c1d96801ac \n", - "4 598443335336095652 active a75ea3e9742c48fd9c34a5b8abf9bd89 \n", + "0 892046783494697990 active c21b823a68ba42b2b1ff98b2ae73c424 \n", + "1 892046783494697990 active 9da0f31e36a9413eaab18149ace82c78 \n", + "2 892046783494697990 active 8316d6855d17401b8358e88a48c159f0 \n", + "3 892046783494697990 active b92f959ab1564eb897e2f66f7089f573 \n", + "4 892046783494697990 active fc31f5b645204fdd9ef58fbbc0c105eb \n", "\n", " run_name run_uuid start_time \\\n", - "0 Explanations 4df1e5931159491c963f2252add508cd 2024-07-21 15:31:20.207 \n", - "1 Evaluations e993a53ee04e4357bad5cec101ab4031 2024-07-21 15:31:07.565 \n", - "2 Inference c17b893e525b44ed89e349421e72510a 2024-07-21 15:30:59.023 \n", - "3 Promotion d538cc2f3f644b6e97b611c1d96801ac 2024-07-21 15:30:51.594 \n", - "4 Training a75ea3e9742c48fd9c34a5b8abf9bd89 2024-07-21 15:29:25.495 \n", + "0 Explanations c21b823a68ba42b2b1ff98b2ae73c424 2024-12-09 21:26:19.324 \n", + "1 Evaluations 9da0f31e36a9413eaab18149ace82c78 2024-12-09 21:26:13.489 \n", + "2 Inference 8316d6855d17401b8358e88a48c159f0 2024-12-09 21:26:08.210 \n", + "3 Promotion b92f959ab1564eb897e2f66f7089f573 2024-12-09 21:26:03.382 \n", + "4 Training fc31f5b645204fdd9ef58fbbc0c105eb 2024-12-09 21:25:39.001 \n", "\n", " status user_id metrics \\\n", "0 FINISHED fmind {} \n", @@ -437,11 +431,11 @@ "4 {'conf_file': 'confs/training.yaml', 'memory':... \n", "\n", " tags run_time_secs \\\n", - "0 {'mlflow.user': 'fmind', 'mlflow.source.name':... 33.622 \n", - "1 {'mlflow.user': 'fmind', 'mlflow.source.name':... 5.796 \n", - "2 {'mlflow.user': 'fmind', 'mlflow.source.name':... 5.735 \n", - "3 {'mlflow.user': 'fmind', 'mlflow.source.name':... 5.375 \n", - "4 {'mlflow.user': 'fmind', 'mlflow.source.name':... 80.807 \n", + "0 {'mlflow.user': 'fmind', 'mlflow.source.name':... 30.770 \n", + "1 {'mlflow.user': 'fmind', 'mlflow.source.name':... 4.572 \n", + "2 {'mlflow.user': 'fmind', 'mlflow.source.name':... 4.005 \n", + "3 {'mlflow.user': 'fmind', 'mlflow.source.name':... 3.551 \n", + "4 {'mlflow.user': 'fmind', 'mlflow.source.name':... 22.070 \n", "\n", " mlflow.user mlflow.source.name mlflow.source.type \\\n", "0 fmind file:///home/fmind/mlops-python-package PROJECT \n", @@ -451,11 +445,11 @@ "4 fmind file:///home/fmind/mlops-python-package PROJECT \n", "\n", " mlflow.project.entryPoint mlflow.source.git.commit \\\n", - "0 main ee17d0a9de59efd2eb99d667786ac417ec8b3b63 \n", - "1 main ee17d0a9de59efd2eb99d667786ac417ec8b3b63 \n", - "2 main ee17d0a9de59efd2eb99d667786ac417ec8b3b63 \n", - "3 main ee17d0a9de59efd2eb99d667786ac417ec8b3b63 \n", - "4 main ee17d0a9de59efd2eb99d667786ac417ec8b3b63 \n", + "0 main 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed \n", + "1 main 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed \n", + "2 main 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed \n", + "3 main 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed \n", + "4 main 6baa64548d67a5bcf0a7f1422dfc8d45ed229aed \n", "\n", " mlflow.source.git.repoURL \\\n", "0 git@github.com:fmind/mlops-python-package \n", @@ -471,19 +465,12 @@ "3 git@github.com:fmind/mlops-python-package Promotion \n", "4 git@github.com:fmind/mlops-python-package Training \n", "\n", - " mlflow.project.env mlflow.project.backend \\\n", - "0 virtualenv local \n", - "1 virtualenv local \n", - "2 virtualenv local \n", - "3 virtualenv local \n", - "4 virtualenv local \n", - "\n", - " mlflow.datasets estimator_name \\\n", - "0 NaN NaN \n", - "1 [{\"name\":\"ce0fe6e33c74e2fa3659d51482be5f27\",\"h... NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN Pipeline \n", + " mlflow.project.env mlflow.project.backend estimator_name \\\n", + "0 virtualenv local NaN \n", + "1 virtualenv local NaN \n", + "2 virtualenv local NaN \n", + "3 virtualenv local NaN \n", + "4 virtualenv local Pipeline \n", "\n", " estimator_class \\\n", "0 NaN \n", @@ -497,7 +484,7 @@ "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", - "4 [{\"run_id\": \"a75ea3e9742c48fd9c34a5b8abf9bd89\"... NaN \n", + "4 [{\"run_id\": \"fc31f5b645204fdd9ef58fbbc0c105eb\"... NaN \n", "\n", " mlflow.parentRunId \n", "0 NaN \n", @@ -523,9 +510,11 @@ "runs = pd.DataFrame(runs).assign(\n", " end_time=lambda data: pd.to_datetime(data[\"end_time\"], unit=\"ms\"),\n", " start_time=lambda data: pd.to_datetime(data[\"start_time\"], unit=\"ms\"),\n", - " run_time_secs=lambda data: (data['end_time'] - data['start_time']).map(lambda t: t.total_seconds()),\n", + " run_time_secs=lambda data: (data[\"end_time\"] - data[\"start_time\"]).map(\n", + " lambda t: t.total_seconds()\n", + " ),\n", ")\n", - "runs = pd.concat([runs, pd.json_normalize(runs['tags'])], axis=\"columns\")\n", + "runs = pd.concat([runs, pd.json_normalize(runs[\"tags\"])], axis=\"columns\")\n", "print(runs.shape)\n", "runs.head()" ] @@ -574,10 +563,10 @@ " \n", " \n", " 0\n", - " {'Champion': '2'}\n", - " 2024-07-21 15:03:06.208\n", + " {'Champion': '3'}\n", + " 2024-12-09 19:33:09.416\n", " \n", - " 2024-07-21 15:30:56.443\n", + " 2024-12-09 21:26:06.439\n", " bikes\n", " {}\n", " \n", @@ -587,10 +576,10 @@ ], "text/plain": [ " aliases creation_timestamp description \\\n", - "0 {'Champion': '2'} 2024-07-21 15:03:06.208 \n", + "0 {'Champion': '3'} 2024-12-09 19:33:09.416 \n", "\n", " last_updated_timestamp name tags \n", - "0 2024-07-21 15:30:56.443 bikes {} " + "0 2024-12-09 21:26:06.439 bikes {} " ] }, "execution_count": 7, @@ -603,10 +592,16 @@ " max_results=MAX_RESULTS, order_by=[\"creation_timestamp DESC\"]\n", ")\n", "models = [dict(model) for model in models]\n", - "models = pd.DataFrame(models).assign(\n", - " creation_timestamp=lambda data: pd.to_datetime(data[\"creation_timestamp\"], unit=\"ms\"),\n", - " last_updated_timestamp=lambda data: pd.to_datetime(data[\"last_updated_timestamp\"], unit=\"ms\"),\n", - ").drop(columns=['latest_versions'])\n", + "models = (\n", + " pd.DataFrame(models)\n", + " .assign(\n", + " creation_timestamp=lambda data: pd.to_datetime(data[\"creation_timestamp\"], unit=\"ms\"),\n", + " last_updated_timestamp=lambda data: pd.to_datetime(\n", + " data[\"last_updated_timestamp\"], unit=\"ms\"\n", + " ),\n", + " )\n", + " .drop(columns=[\"latest_versions\"])\n", + ")\n", "print(models.shape)\n", "models" ] @@ -620,7 +615,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(2, 14)\n" + "(3, 14)\n" ] }, { @@ -664,29 +659,46 @@ " \n", " 0\n", " Champion\n", - " 2024-07-21 15:30:44.679\n", + " 2024-12-09 21:25:59.981\n", " None\n", " \n", - " 2024-07-21 15:30:44.679\n", + " 2024-12-09 21:25:59.981\n", " bikes\n", - " a75ea3e9742c48fd9c34a5b8abf9bd89\n", + " fc31f5b645204fdd9ef58fbbc0c105eb\n", " \n", " file:///home/fmind/mlops-python-package/mlruns...\n", " READY\n", " \n", " {}\n", " \n", - " 2\n", + " 3\n", " \n", " \n", " 1\n", " None\n", - " 2024-07-21 15:03:06.212\n", + " 2024-12-09 20:15:47.252\n", + " None\n", + " \n", + " 2024-12-09 20:15:47.252\n", + " bikes\n", + " abad34d51a2a4cffa328136540b455c0\n", + " \n", + " file:///home/fmind/mlops-python-package/mlruns...\n", + " READY\n", + " \n", + " {}\n", + " \n", + " 2\n", + " \n", + " \n", + " 2\n", + " None\n", + " 2024-12-09 19:33:09.426\n", " None\n", " \n", - " 2024-07-21 15:03:06.212\n", + " 2024-12-09 19:33:09.426\n", " bikes\n", - " 47302e957d8542198a281aad07b2413b\n", + " ca4c7550ad354be9a2f10f7eade8d47e\n", " \n", " file:///home/fmind/mlops-python-package/mlruns...\n", " READY\n", @@ -701,20 +713,24 @@ ], "text/plain": [ " aliases creation_timestamp current_stage description \\\n", - "0 Champion 2024-07-21 15:30:44.679 None \n", - "1 None 2024-07-21 15:03:06.212 None \n", + "0 Champion 2024-12-09 21:25:59.981 None \n", + "1 None 2024-12-09 20:15:47.252 None \n", + "2 None 2024-12-09 19:33:09.426 None \n", "\n", " last_updated_timestamp name run_id run_link \\\n", - "0 2024-07-21 15:30:44.679 bikes a75ea3e9742c48fd9c34a5b8abf9bd89 \n", - "1 2024-07-21 15:03:06.212 bikes 47302e957d8542198a281aad07b2413b \n", + "0 2024-12-09 21:25:59.981 bikes fc31f5b645204fdd9ef58fbbc0c105eb \n", + "1 2024-12-09 20:15:47.252 bikes abad34d51a2a4cffa328136540b455c0 \n", + "2 2024-12-09 19:33:09.426 bikes ca4c7550ad354be9a2f10f7eade8d47e \n", "\n", " source status status_message \\\n", "0 file:///home/fmind/mlops-python-package/mlruns... READY \n", "1 file:///home/fmind/mlops-python-package/mlruns... READY \n", + "2 file:///home/fmind/mlops-python-package/mlruns... READY \n", "\n", " tags user_id version \n", - "0 {} 2 \n", - "1 {} 1 " + "0 {} 3 \n", + "1 {} 2 \n", + "2 {} 1 " ] }, "execution_count": 8, @@ -728,7 +744,7 @@ ")\n", "versions = [dict(version) for version in versions]\n", "versions = pd.DataFrame(versions).assign(\n", - " aliases=lambda data: data['aliases'].map(lambda x: x[0] if len(x) else None),\n", + " aliases=lambda data: data[\"aliases\"].map(lambda x: x[0] if len(x) else None),\n", " creation_timestamp=lambda data: pd.to_datetime(data[\"creation_timestamp\"], unit=\"ms\"),\n", " last_updated_timestamp=lambda data: pd.to_datetime(data[\"last_updated_timestamp\"], unit=\"ms\"),\n", ")\n", @@ -760,9 +776,9 @@ "boxpoints": "all", "customdata": [ [ - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652", - "598443335336095652", - "2024-07-21T15:02:02.224000", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990", + "892046783494697990", + "2024-12-09T19:31:02.967000", "active", "bikes", {} @@ -788,7 +804,7 @@ "showlegend": true, "type": "box", "x": [ - "2024-07-21T15:02:02.224000" + "2024-12-09T19:31:02.967000" ], "x0": " ", "xaxis": "x", @@ -1676,10 +1692,10 @@ "customdata": [ [ { - "Champion": "2" + "Champion": "3" }, "", - "2024-07-21T15:30:56.443000", + "2024-12-09T21:26:06.439000", "bikes", {} ] @@ -1704,7 +1720,7 @@ "showlegend": false, "type": "box", "x": [ - "2024-07-21T15:03:06.208000" + "2024-12-09T19:33:09.416000" ], "x0": " ", "xaxis": "x", @@ -2588,10 +2604,10 @@ "customdata": [ [ { - "Champion": "2" + "Champion": "3" }, "", - "2024-07-21T15:30:56.443000", + "2024-12-09T21:26:06.439000", "bikes", {} ] @@ -2616,7 +2632,7 @@ "showlegend": false, "type": "box", "x": [ - "2024-07-21T15:03:06.208000" + "2024-12-09T19:33:09.416000" ], "x0": " ", "xaxis": "x", @@ -3502,11 +3518,26 @@ "Champion", "None", "", - "2024-07-21T15:30:44.679000", + "2024-12-09T21:25:59.981000", + "bikes", + "fc31f5b645204fdd9ef58fbbc0c105eb", + "", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/fc31f5b645204fdd9ef58fbbc0c105eb/artifacts/model", + "READY", + "", + {}, + "", + "3" + ], + [ + null, + "None", + "", + "2024-12-09T20:15:47.252000", "bikes", - "a75ea3e9742c48fd9c34a5b8abf9bd89", + "abad34d51a2a4cffa328136540b455c0", "", - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/a75ea3e9742c48fd9c34a5b8abf9bd89/artifacts/model", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/abad34d51a2a4cffa328136540b455c0/artifacts/model", "READY", "", {}, @@ -3517,11 +3548,11 @@ null, "None", "", - "2024-07-21T15:03:06.212000", + "2024-12-09T19:33:09.426000", "bikes", - "47302e957d8542198a281aad07b2413b", + "ca4c7550ad354be9a2f10f7eade8d47e", "", - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/47302e957d8542198a281aad07b2413b/artifacts/model", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/ca4c7550ad354be9a2f10f7eade8d47e/artifacts/model", "READY", "", {}, @@ -3533,6 +3564,7 @@ "hoveron": "points", "hovertemplate": "%{hovertext}

name=%{customdata[4]}
creation_timestamp=%{x}
aliases=%{customdata[0]}
current_stage=%{customdata[1]}
description=%{customdata[2]}
last_updated_timestamp=%{customdata[3]}
run_id=%{customdata[5]}
run_link=%{customdata[6]}
source=%{customdata[7]}
status=%{customdata[8]}
status_message=%{customdata[9]}
tags=%{customdata[10]}
user_id=%{customdata[11]}
version=%{customdata[12]}", "hovertext": [ + "bikes", "bikes", "bikes" ], @@ -3550,8 +3582,9 @@ "showlegend": true, "type": "box", "x": [ - "2024-07-21T15:30:44.679000", - "2024-07-21T15:03:06.212000" + "2024-12-09T21:25:59.981000", + "2024-12-09T20:15:47.252000", + "2024-12-09T19:33:09.426000" ], "x0": " ", "xaxis": "x", @@ -4438,13 +4471,13 @@ "boxpoints": "all", "customdata": [ [ - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/4df1e5931159491c963f2252add508cd/artifacts", - "2024-07-21T15:31:53.829000", - "598443335336095652", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/c21b823a68ba42b2b1ff98b2ae73c424/artifacts", + "2024-12-09T21:26:50.094000", + "892046783494697990", "active", - "4df1e5931159491c963f2252add508cd", + "c21b823a68ba42b2b1ff98b2ae73c424", "Explanations", - "4df1e5931159491c963f2252add508cd", + "c21b823a68ba42b2b1ff98b2ae73c424", "FINISHED", "fmind", {}, @@ -4457,18 +4490,18 @@ "mlflow.project.entryPoint": "main", "mlflow.project.env": "virtualenv", "mlflow.runName": "Explanations", - "mlflow.source.git.commit": "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "mlflow.source.git.commit": "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "mlflow.source.git.repoURL": "git@github.com:fmind/mlops-python-package", "mlflow.source.name": "file:///home/fmind/mlops-python-package", "mlflow.source.type": "PROJECT", "mlflow.user": "fmind" }, - 33.622, + 30.77, "fmind", "file:///home/fmind/mlops-python-package", "PROJECT", "main", - "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "git@github.com:fmind/mlops-python-package", "git@github.com:fmind/mlops-python-package", "Explanations", @@ -4478,17 +4511,16 @@ null, null, null, - null, null ], [ - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/e993a53ee04e4357bad5cec101ab4031/artifacts", - "2024-07-21T15:31:13.361000", - "598443335336095652", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/9da0f31e36a9413eaab18149ace82c78/artifacts", + "2024-12-09T21:26:18.061000", + "892046783494697990", "active", - "e993a53ee04e4357bad5cec101ab4031", + "9da0f31e36a9413eaab18149ace82c78", "Evaluations", - "e993a53ee04e4357bad5cec101ab4031", + "9da0f31e36a9413eaab18149ace82c78", "FINISHED", "fmind", { @@ -4506,30 +4538,28 @@ "conf_file": "confs/evaluations.yaml" }, { - "mlflow.datasets": "[{\"name\":\"ce0fe6e33c74e2fa3659d51482be5f27\",\"hash\":\"ce0fe6e33c74e2fa3659d51482be5f27\",\"model\":\"06840f30fcdd4553a7aaf17e83232475\"}]", "mlflow.gitRepoURL": "git@github.com:fmind/mlops-python-package", "mlflow.project.backend": "local", "mlflow.project.entryPoint": "main", "mlflow.project.env": "virtualenv", "mlflow.runName": "Evaluations", - "mlflow.source.git.commit": "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "mlflow.source.git.commit": "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "mlflow.source.git.repoURL": "git@github.com:fmind/mlops-python-package", "mlflow.source.name": "file:///home/fmind/mlops-python-package", "mlflow.source.type": "PROJECT", "mlflow.user": "fmind" }, - 5.796, + 4.572, "fmind", "file:///home/fmind/mlops-python-package", "PROJECT", "main", - "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "git@github.com:fmind/mlops-python-package", "git@github.com:fmind/mlops-python-package", "Evaluations", "virtualenv", "local", - "[{\"name\":\"ce0fe6e33c74e2fa3659d51482be5f27\",\"hash\":\"ce0fe6e33c74e2fa3659d51482be5f27\",\"model\":\"06840f30fcdd4553a7aaf17e83232475\"}]", null, null, null, @@ -4537,13 +4567,13 @@ null ], [ - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/c17b893e525b44ed89e349421e72510a/artifacts", - "2024-07-21T15:31:04.758000", - "598443335336095652", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/8316d6855d17401b8358e88a48c159f0/artifacts", + "2024-12-09T21:26:12.215000", + "892046783494697990", "active", - "c17b893e525b44ed89e349421e72510a", + "8316d6855d17401b8358e88a48c159f0", "Inference", - "c17b893e525b44ed89e349421e72510a", + "8316d6855d17401b8358e88a48c159f0", "FINISHED", "fmind", {}, @@ -4556,18 +4586,18 @@ "mlflow.project.entryPoint": "main", "mlflow.project.env": "virtualenv", "mlflow.runName": "Inference", - "mlflow.source.git.commit": "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "mlflow.source.git.commit": "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "mlflow.source.git.repoURL": "git@github.com:fmind/mlops-python-package", "mlflow.source.name": "file:///home/fmind/mlops-python-package", "mlflow.source.type": "PROJECT", "mlflow.user": "fmind" }, - 5.735, + 4.005, "fmind", "file:///home/fmind/mlops-python-package", "PROJECT", "main", - "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "git@github.com:fmind/mlops-python-package", "git@github.com:fmind/mlops-python-package", "Inference", @@ -4577,17 +4607,16 @@ null, null, null, - null, null ], [ - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/d538cc2f3f644b6e97b611c1d96801ac/artifacts", - "2024-07-21T15:30:56.969000", - "598443335336095652", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/b92f959ab1564eb897e2f66f7089f573/artifacts", + "2024-12-09T21:26:06.933000", + "892046783494697990", "active", - "d538cc2f3f644b6e97b611c1d96801ac", + "b92f959ab1564eb897e2f66f7089f573", "Promotion", - "d538cc2f3f644b6e97b611c1d96801ac", + "b92f959ab1564eb897e2f66f7089f573", "FINISHED", "fmind", {}, @@ -4600,18 +4629,18 @@ "mlflow.project.entryPoint": "main", "mlflow.project.env": "virtualenv", "mlflow.runName": "Promotion", - "mlflow.source.git.commit": "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "mlflow.source.git.commit": "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "mlflow.source.git.repoURL": "git@github.com:fmind/mlops-python-package", "mlflow.source.name": "file:///home/fmind/mlops-python-package", "mlflow.source.type": "PROJECT", "mlflow.user": "fmind" }, - 5.375, + 3.551, "fmind", "file:///home/fmind/mlops-python-package", "PROJECT", "main", - "ee17d0a9de59efd2eb99d667786ac417ec8b3b63", + "6baa64548d67a5bcf0a7f1422dfc8d45ed229aed", "git@github.com:fmind/mlops-python-package", "git@github.com:fmind/mlops-python-package", "Promotion", @@ -4621,29 +4650,28 @@ null, null, null, - null, null ], [ - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/a75ea3e9742c48fd9c34a5b8abf9bd89/artifacts", - "2024-07-21T15:30:46.302000", - "598443335336095652", + "file:///home/fmind/mlops-python-package/mlruns/892046783494697990/fc31f5b645204fdd9ef58fbbc0c105eb/artifacts", + "2024-12-09T21:26:01.071000", + "892046783494697990", "active", - "a75ea3e9742c48fd9c34a5b8abf9bd89", + "fc31f5b645204fdd9ef58fbbc0c105eb", "Training", - "a75ea3e9742c48fd9c34a5b8abf9bd89", + "fc31f5b645204fdd9ef58fbbc0c105eb", "FINISHED", "fmind", { - "mean_squared_error": -1786.7527777777777, - "system/cpu_utilization_percentage": 38.1, - "system/disk_available_megabytes": 32585.5, - "system/disk_usage_megabytes": 73526.3, - "system/disk_usage_percentage": 69.3, - "system/network_receive_megabytes": 0.02063199999997778, - "system/network_transmit_megabytes": 0.00015000000007603376, - "system/system_memory_usage_megabytes": 10535.7, - "system/system_memory_usage_percentage": 69.8, + "mean_squared_error": -1786.7527777777775, + "system/cpu_utilization_percentage": 25, + "system/disk_available_megabytes": 11550.6, + "system/disk_usage_megabytes": 104367.7, + "system/disk_usage_percentage": 90, + "system/network_receive_megabytes": 0, + "system/network_transmit_megabytes": 0, + "system/system_memory_usage_megabytes": 6226.8, + "system/system_memory_usage_percentage": 41.3, "training_mean_absolute_error": 6.8051192198812736, "training_mean_squared_error": 124.51054615575788, "training_r2_score": 0.9948851411922456, @@ -4683,6 +4711,7 @@ "transformer__categoricals__max_categories": "None", "transformer__categoricals__min_frequency": "None", "transformer__categoricals__sparse_output": "False", + "transformer__force_int_remainder_cols": "True", "transformer__n_jobs": "None", "transformer__numericals": "passthrough", "transformer__remainder": "drop", @@ -4697,121 +4726,51 @@ "estimator_class": "sklearn.pipeline.Pipeline", "estimator_name": "Pipeline", "mlflow.gitRepoURL": "git@github.com:fmind/mlops-python-package", - "mlflow.log-model.history": "[{\"run_id\": \"a75ea3e9742c48fd9c34a5b8abf9bd89\", \"artifact_path\": \"model\", \"utc_time_created\": \"2024-07-21 15:30:23.365959\", \"flavors\": {\"python_function\": {\"model_path\": \"model.pkl\", \"predict_fn\": \"predict\", \"loader_module\": \"mlflow.sklearn\", \"python_version\": \"3.12.4\", \"env\": {\"conda\": \"conda.yaml\", \"virtualenv\": \"python_env.yaml\"}}, \"sklearn\": {\"pickled_model\": \"model.pkl\", \"sklearn_version\": \"1.4.2\", \"serialization_format\": \"cloudpickle\", \"code\": null}}, 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mlflow.parentRunId=%{customdata[27]}", "hovertext": [ - "e110400581f846d08f802c970ae25458" + "ed9728cf10d74fe6a053231b54e82e16" ], - "legendgroup": "capricious-bear-977", + "legendgroup": "adventurous-fowl-590", "line": { "color": "rgba(255,255,255,0)" }, "marker": { - "color": "#EF553B" + "color": "#FFA15A" }, - "name": "capricious-bear-977", - "offsetgroup": "capricious-bear-977", + "name": "adventurous-fowl-590", + "offsetgroup": "adventurous-fowl-590", "orientation": "h", "pointpos": 0, "showlegend": true, "type": "box", "x": [ - 37.246 + 101.932 ], "x0": " ", "xaxis": "x", @@ -8735,18 +10036,29 @@ null, null, "sklearn.pipeline.Pipeline", - "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel", "bikes.core.models.BaselineSklearnModel", + "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel", null, null, null, null, "sklearn.pipeline.Pipeline", + "bikes.core.models.BaselineSklearnModel", + "bikes.core.models.BaselineSklearnModel", "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel", + null, + null, + null, + null, + null, + null, + "sklearn.pipeline.Pipeline", + "bikes.core.models.BaselineSklearnModel", "bikes.core.models.BaselineSklearnModel", + "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel" ], "xaxis": "x", @@ -8768,6 +10080,17 @@ 1, 1, 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, 1 ], "yaxis": "y" @@ -9645,7 +10968,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/notebooks/processing.ipynb b/notebooks/processing.ipynb index 78f28c4..cf0ced5 100644 --- a/notebooks/processing.ipynb +++ b/notebooks/processing.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ "outputs": [], "source": [ "INDEX_COL = \"instant\"\n", - "TARGET_COL = 'cnt'" + "TARGET_COL = \"cnt\"" ] }, { @@ -81,7 +81,7 @@ "metadata": {}, "outputs": [], "source": [ - "SHUFFLE = False # time-sensitive\n", + "SHUFFLE = False # time-sensitive\n", "TEST_SIZE = 0.2" ] }, @@ -98,8 +98,7 @@ "metadata": {}, "outputs": [], "source": [ - "SAMPLE_RATIO = 0.15\n", - "SAMPLE_RANDOM_STATE = 0" + "SAMPLE_SIZE = 2000" ] }, { @@ -659,7 +658,7 @@ { "data": { "text/plain": [ - "((2085, 15), (2085, 1))" + "((2000, 15), (2000, 1))" ] }, "execution_count": 11, @@ -668,8 +667,8 @@ } ], "source": [ - "inputs_train_sample = inputs_train.sample(frac=SAMPLE_RATIO, random_state=SAMPLE_RANDOM_STATE)\n", - "targets_train_sample = targets_train.sample(frac=SAMPLE_RATIO, random_state=SAMPLE_RANDOM_STATE)\n", + "inputs_train_sample = inputs_train.tail(SAMPLE_SIZE)\n", + "targets_train_sample = targets_train.tail(SAMPLE_SIZE)\n", "inputs_train_sample.shape, targets_train_sample.shape" ] }, @@ -711,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/notebooks/prototype.ipynb b/notebooks/prototype.ipynb index 7d222f6..503a335 100644 --- a/notebooks/prototype.ipynb +++ b/notebooks/prototype.ipynb @@ -45,9 +45,9 @@ "outputs": [], "source": [ "import pandas as pd\n", - "import sklearn as sk\n", - "import plotly.io as pio\n", "import plotly.express as px\n", + "import plotly.io as pio\n", + "import sklearn as sk\n", "from sklearn import compose, ensemble, metrics, model_selection, pipeline, preprocessing" ] }, @@ -90,7 +90,7 @@ "source": [ "ROOT = Path(\"../\")\n", "DATA = str(ROOT / \"data\")\n", - "CACHE = str(ROOT / \".cache\")" + "CACHE = str(ROOT / \".cache\")" ] }, { @@ -141,7 +141,7 @@ "source": [ "SPLITS = 4\n", "SHUFFLE = False # required (time sensitive)\n", - "TEST_SIZE = 24 * 30 * 2 # use 2 months for backtesting" + "TEST_SIZE = 24 * 30 * 2 # use 2 months for backtesting" ] }, { @@ -575,7 +575,7 @@ " \n", " \n", " top\n", - " 2011-01-01\n", + " 2012-12-31\n", " NaN\n", " NaN\n", " NaN\n", @@ -752,7 +752,7 @@ " dteday season yr mnth hr \\\n", "count 17379 17379.000000 17379.000000 17379.000000 17379.000000 \n", "unique 731 NaN NaN NaN NaN \n", - "top 2011-01-01 NaN NaN NaN NaN \n", + "top 2012-12-31 NaN NaN NaN NaN \n", "freq 24 NaN NaN NaN NaN \n", "mean NaN 2.501640 0.502561 6.537775 11.546752 \n", "std NaN 1.106918 0.500008 3.438776 6.914405 \n", @@ -106052,8 +106052,11 @@ ], "source": [ "px.scatter_matrix(\n", - " hour, dimensions=[\"registered\", \"casual\", \"cnt\", \"mnth\", \"hr\"], color=TARGET,\n", - " height=800, title=\"Analysis of top features\",\n", + " hour,\n", + " dimensions=[\"registered\", \"casual\", \"cnt\", \"mnth\", \"hr\"],\n", + " color=TARGET,\n", + " height=800,\n", + " title=\"Analysis of top features\",\n", ")" ] }, @@ -106087,7 +106090,7 @@ ], "source": [ "inputs, targets = hour.drop(columns=TARGET), hour[TARGET]\n", - "print('Inputs:', inputs.shape, '; Targets:', targets.shape)" + "print(\"Inputs:\", inputs.shape, \"; Targets:\", targets.shape)" ] }, { @@ -106125,8 +106128,12 @@ "metadata": {}, "outputs": [], "source": [ - "assert inputs_train.index.max() < inputs_test.index.min(), \"Inputs train should be before inputs test\"\n", - "assert targets_train.index.max() < targets_test.index.min(), \"Targets train should be before targets test\"" + "assert (\n", + " inputs_train.index.max() < inputs_test.index.min()\n", + "), \"Inputs train should be before inputs test\"\n", + "assert (\n", + " targets_train.index.max() < targets_test.index.min()\n", + "), \"Targets train should be before targets test\"" ] }, { @@ -106154,7 +106161,9 @@ " \"season\",\n", " \"weathersit\",\n", "]\n", - "assert all(col in inputs.columns for col in categoricals), \"All categorical columns should be in inputs.\"" + "assert all(\n", + " col in inputs.columns for col in categoricals\n", + "), \"All categorical columns should be in inputs.\"" ] }, { @@ -106177,7 +106186,9 @@ " \"casual\",\n", " # \"registered\", # too correlated with target\n", "]\n", - "assert all(col in inputs.columns for col in numericals), \"All numerical columns should be in inputs.\"" + "assert all(\n", + " col in inputs.columns for col in numericals\n", + "), \"All numerical columns should be in inputs.\"" ] }, { @@ -106206,7 +106217,8 @@ "text/html": [ "
Pipeline(memory='../.cache',\n",
+       "
Pipeline(memory='../.cache',\n",
        "         steps=[('transformer',\n",
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        "                                                  OneHotEncoder(handle_unknown='ignore',\n",
@@ -106642,7 +106644,7 @@
        "                                                   'workingday', 'temp',\n",
        "                                                   'atemp', 'hum', 'windspeed',\n",
        "                                                   'casual'])])),\n",
-       "                ('regressor', RandomForestRegressor(random_state=42))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
['season', 'weathersit']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual']
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RandomForestRegressor(random_state=42)
" ], "text/plain": [ "Pipeline(memory='../.cache',\n", @@ -106679,7 +106681,7 @@ " ('regressor', RandomForestRegressor(random_state=42))])" ] }, - "execution_count": 22, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -106724,7 +106726,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -106755,7 +106757,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -106775,29 +106777,24 @@ "\n", "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 0.51s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", - "\n", - "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", - "\n", - "Persisting input arguments took 0.57s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "Persisting input arguments took 0.56s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", "\n", "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 0.74s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "Persisting input arguments took 0.63s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", "\n", "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 0.70s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "Persisting input arguments took 0.76s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", "\n" ] }, { "data": { "text/html": [ - "
GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=4, test_size=1440),\n",
+       "
GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=4, test_size=1440),\n",
        "             estimator=Pipeline(memory='../.cache',\n",
        "                                steps=[('transformer',\n",
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@@ -107234,7 +107221,7 @@
        "                                        RandomForestRegressor(random_state=42))]),\n",
        "             param_grid={'regressor__max_depth': [15, 20, 25],\n",
        "                         'regressor__n_estimators': [150, 200, 250]},\n",
-       "             scoring='neg_mean_squared_error', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
['season', 'weathersit']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual']
passthrough
RandomForestRegressor(max_depth=20, n_estimators=200, random_state=42)
" ], "text/plain": [ "GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=4, test_size=1440),\n", @@ -107311,7 +107298,7 @@ " scoring='neg_mean_squared_error', verbose=1)" ] }, - "execution_count": 24, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -107332,7 +107319,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -107375,10 +107362,10 @@ " \n", " \n", " 4\n", - " 8.268552\n", - " 1.109317\n", - " 0.052984\n", - " 0.006613\n", + " 8.197777\n", + " 1.114588\n", + " 0.049695\n", + " 0.005310\n", " 20\n", " 200\n", " {'regressor__max_depth': 20, 'regressor__n_est...\n", @@ -107392,10 +107379,10 @@ " \n", " \n", " 1\n", - " 6.722793\n", - " 0.772110\n", - " 0.045184\n", - " 0.005341\n", + " 7.157419\n", + " 1.018962\n", + " 0.045709\n", + " 0.005739\n", " 15\n", " 200\n", " {'regressor__max_depth': 15, 'regressor__n_est...\n", @@ -107409,10 +107396,10 @@ " \n", " \n", " 5\n", - " 9.990789\n", - " 1.255146\n", - " 0.062621\n", - " 0.007046\n", + " 10.689916\n", + " 1.444524\n", + " 0.064643\n", + " 0.012777\n", " 20\n", " 250\n", " {'regressor__max_depth': 20, 'regressor__n_est...\n", @@ -107426,10 +107413,10 @@ " \n", " \n", " 7\n", - " 8.600052\n", - " 1.220360\n", - " 0.054089\n", - " 0.007172\n", + " 8.259196\n", + " 0.936581\n", + " 0.048372\n", + " 0.004113\n", " 25\n", " 200\n", " {'regressor__max_depth': 25, 'regressor__n_est...\n", @@ -107443,10 +107430,10 @@ " \n", " \n", " 2\n", - " 8.170008\n", - " 1.052943\n", - " 0.054579\n", - " 0.006842\n", + " 9.029868\n", + " 1.144401\n", + " 0.055069\n", + " 0.004822\n", " 15\n", " 250\n", " {'regressor__max_depth': 15, 'regressor__n_est...\n", @@ -107464,11 +107451,11 @@ ], "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", - "4 8.268552 1.109317 0.052984 0.006613 \n", - "1 6.722793 0.772110 0.045184 0.005341 \n", - "5 9.990789 1.255146 0.062621 0.007046 \n", - "7 8.600052 1.220360 0.054089 0.007172 \n", - "2 8.170008 1.052943 0.054579 0.006842 \n", + "4 8.197777 1.114588 0.049695 0.005310 \n", + "1 7.157419 1.018962 0.045709 0.005739 \n", + "5 10.689916 1.444524 0.064643 0.012777 \n", + "7 8.259196 0.936581 0.048372 0.004113 \n", + "2 9.029868 1.144401 0.055069 0.004822 \n", "\n", " param_regressor__max_depth param_regressor__n_estimators \\\n", "4 20 200 \n", @@ -107499,7 +107486,7 @@ "2 2504.611918 5 " ] }, - "execution_count": 25, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -107527,16 +107514,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(memory='../.cache',\n",
+       "
Pipeline(memory='../.cache',\n",
        "         steps=[('transformer',\n",
        "                 ColumnTransformer(transformers=[('categoricals',\n",
        "                                                  OneHotEncoder(handle_unknown='ignore',\n",
@@ -107962,7 +107938,7 @@
        "                                                   'casual'])])),\n",
        "                ('regressor',\n",
        "                 RandomForestRegressor(max_depth=20, n_estimators=200,\n",
-       "                                       random_state=42))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
['season', 'weathersit']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual']
passthrough
RandomForestRegressor(max_depth=20, n_estimators=200, random_state=42)
" ], "text/plain": [ "Pipeline(memory='../.cache',\n", @@ -108003,7 +107979,7 @@ " random_state=42))])" ] }, - "execution_count": 26, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -108022,7 +107998,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -108036,7 +108012,7 @@ " ('numericals', 'passthrough',\n", " ['yr', 'mnth', 'hr', 'holiday', 'weekday',\n", " 'workingday', 'temp', 'atemp', 'hum',\n", - " 'windspeed', 'casual'])])), ('regressor', RandomForestRegressor(max_depth=20, n_estimators=200, random_state=42))], 'transform_input': None, 'verbose': False, 'transformer': ColumnTransformer(transformers=[('categoricals',\n", + " 'windspeed', 'casual'])])), ('regressor', RandomForestRegressor(max_depth=20, n_estimators=200, random_state=42))], 'verbose': False, 'transformer': ColumnTransformer(transformers=[('categoricals',\n", " OneHotEncoder(handle_unknown='ignore',\n", " sparse_output=False),\n", " ['season', 'weathersit']),\n", @@ -108067,7 +108043,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -108089,7 +108065,7 @@ "dtype: float64" ] }, - "execution_count": 28, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -108109,7 +108085,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -108126,7 +108102,7 @@ "dtype: float64" ] }, - "execution_count": 29, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -108152,7 +108128,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -108190,7 +108166,7 @@ ], "xaxis": "x", "y": [ - -4088.298264818843, + -4088.2982648188436, -4091.734465025292, -4098.164381497614, -4105.714045881979, @@ -108212,14 +108188,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -108235,7 +108211,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -108250,18 +108226,18 @@ "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -108579,10 +108555,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -108629,8 +108605,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -108735,18 +108712,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -108755,7 +108732,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -108911,123 +108888,113 @@ "#FECB52" ], "font": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "rgb(17,17,17)", - "plot_bgcolor": "rgb(17,17,17)", + "paper_bgcolor": "white", + "plot_bgcolor": "white", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } @@ -109075,7 +109042,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -109128,7 +109095,7 @@ }, "line": { "color": [ - -4088.298264818843, + -4088.2982648188436, -4091.734465025292, -4098.164381497614, -4105.714045881979, @@ -109202,14 +109169,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -109225,7 +109192,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -109240,18 +109207,18 @@ "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -109569,10 +109536,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -109619,8 +109586,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -109725,18 +109693,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -109745,7 +109713,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -109901,123 +109869,113 @@ "#FECB52" ], "font": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "rgb(17,17,17)", - "plot_bgcolor": "rgb(17,17,17)", + "paper_bgcolor": "white", + "plot_bgcolor": "white", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } @@ -110048,16 +110006,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4706.147416021958" + "np.float64(4706.147416021958)" ] }, - "execution_count": 32, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -110076,7 +110034,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -110156,7 +110114,7 @@ "15944 50 63.895000 -13.895000" ] }, - "execution_count": 33, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -110169,7 +110127,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -110199,14 +110157,14 @@ 59.126666666666665, -14.488333333333344, -6.511500000000012, - 9.549999999999995, + 9.549999999999997, -13.895000000000003, -4.106111111111112, -5.241333333333335, -3.129369047619048, - 0.21326461988304143, + 0.21326461988304146, -6.270000000000003, - -14.069999999999991, + -14.069999999999993, -43.82499999999999, -88.88, 0.8000000000000114, @@ -110214,22 +110172,22 @@ -22.67500000000001, 10.915500000000009, -9.437708333333376, - -23.793571428571457, + -23.793571428571454, 37.28125, -14.08499999999998, - -59.41499999999997, + -59.414999999999964, -31.94749999999999, -121.14249999999998, 16.965000000000003, -57.50999999999999, -8.370000000000005, - 1.4599999999999935, + 1.4599999999999937, 9.855000000000004, 32.90375, -35.605000000000004, -2.8286904761904736, -6.449999999999999, - 1.0549999999999995, + 1.0549999999999997, -9.25, 27.32, 55.72, @@ -110251,7 +110209,7 @@ 40.015, 12.330000000000013, -12.596944444444446, - -9.743055555555555, + -9.743055555555557, -1.8949999999999996, -2.2437500000000004, -3.370000000000001, @@ -110259,14 +110217,14 @@ -38.650000000000006, -35.44499999999999, 66.42000000000002, - 13.944999999999991, + 13.944999999999993, 51.5, 76.08625, 74.42500000000001, 8.935000000000002, 17.204999999999984, 50.98686363636364, - 44.22166666666664, + 44.221666666666636, 58.81, 51.95, -42.903333333333336, @@ -110285,8 +110243,8 @@ -29.63749999999999, -23.21083333333337, 61.381497493734344, - 39.20549749373435, - 13.182807017543864, + 39.205497493734356, + 13.182807017543865, 58.02151465201467, 39.14999999999998, 27.974166666666633, @@ -110301,7 +110259,7 @@ -3.053570873570873, 0.45706435231435183, 2.039468253968254, - 11.37, + 11.370000000000001, -5.444999999999993, -67.89499999999998, -107.04999999999995, @@ -110318,13 +110276,13 @@ 71.77999999999997, 14.349999999999994, -15.625999999999976, - -1.8100000000000025, + -1.8100000000000023, 52.13499999999999, 191.245, 65.58500000000001, 56.59, 4.515, - 3.9734999999999983, + 3.9734999999999987, -1.2800000000000011, -28.319999999999993, -63.94499999999999, @@ -110354,9 +110312,9 @@ 12.25, -36.80500000000001, 24.193138528138547, - 3.741680555555575, + 3.7416805555555754, 21.2619861111111, - 13.471152777777776, + 13.471152777777775, -24.133803571428587, 25.683613095238115, 87.63999999999999, @@ -110385,7 +110343,7 @@ -2.5516666666666197, -12.41500000000002, -48.825000000000045, - -90.40999999999995, + -90.40999999999997, -119.37, -19.634583333333353, -7.490000000000009, @@ -110395,7 +110353,7 @@ 1.269999999999996, 12.14, -5.405000000000001, - -3.671130952380953, + -3.6711309523809526, 3.165, -13.145, -5.150000000000006, @@ -110430,14 +110388,14 @@ 9.595000000000027, -21.690000000000055, -39.47500000000002, - 10.225000000000025, + 10.225000000000023, -2.0249999999999773, 4.644999999999982, - 24.31, + 24.310000000000002, -35.610000000000014, -45.81593589743588, -11.995000000000005, - 20.81, + 20.810000000000002, 9.969166666666666, -8.049999999999997, 4.827763888888889, @@ -110462,7 +110420,7 @@ 93.555, 63.3, 15.515, - 22.565, + 22.564999999999998, -13.68, -4.260833333333334, -10.112261904761905, @@ -110476,7 +110434,7 @@ -21.66946428571427, -2.0200000000000102, 45.62308918128656, - 27.858176587301617, + 27.858176587301614, 16.225164160400993, 20.67360714285715, -7.545000000000016, @@ -110493,7 +110451,7 @@ 0.8040198412698398, 0.8855396825396822, 10.2, - -9.49875, + -9.498750000000001, -38.17500000000001, -108.27499999999998, 26.410000000000025, @@ -110503,7 +110461,7 @@ 13.19422097858282, 20.84803697691197, 70.26716396103896, - -0.9049999999999728, + -0.9049999999999727, 165.27324999999996, 94.30925000000002, -76.52226190476193, @@ -110511,27 +110469,27 @@ 81.91, 61.2405, -36.05500000000001, - 10.199166666666668, + 10.199166666666667, 5.332000000000001, - -0.12383333333333192, + -0.12383333333333191, -2.6186313131313135, -0.8143888888888897, -7.895000000000003, 18.960000000000008, 5.7450000000000045, -57.565000000000055, - 22.19, + 22.189999999999998, 33.02602922077921, -17.53450000000001, - 22.344166666666663, + 22.344166666666666, 41.958654526029534, 8.567416666666674, 26.498333333333363, - 102.835, + 102.83500000000001, 138.82999999999998, 182.0743571428572, - 92.84500000000004, - -23.769999999999985, + 92.84500000000003, + -23.769999999999982, 27.879999999999995, -3.5749999999999886, 32.459999999999994, @@ -110540,9 +110498,9 @@ -1.0399999999999991, -0.426805555555557, -2.02175, - 0.9200000000000016, + 0.9200000000000017, -6.409999999999997, - -26.31, + -26.310000000000002, 61.01999999999998, 32.954999999999984, 9.624166666666667, @@ -110554,7 +110512,7 @@ 42.44, -38.85500000000002, 94.5675, - -123.77999999999996, + -123.77999999999997, -49.72000000000003, -25.090000000000003, -4.952499999999986, @@ -110572,7 +110530,7 @@ 59.202575757575744, 85.61500000000001, 56.339999999999975, - -1.8099999999999457, + -1.8099999999999454, 46.435, 40.670000000000016, 24.220000000000027, @@ -110582,7 +110540,7 @@ -27.30000000000001, 33.33, 31.405, - 11.489999999999997, + 11.489999999999995, 25.095, 0.3228571428571314, 11.68, @@ -110590,16 +110548,16 @@ -1.1349999999999998, -0.2959999999999994, -5.155000000000001, - -13.275, + -13.274999999999999, 5.8799999999999955, - -30.099000000000014, + -30.099000000000018, 35.13333333333338, 56.35000000000002, 37.82499999999999, 24.58499999999998, 10.20999999999998, 60.065, - 120.23125, + 120.23124999999999, 22.117857142857133, 71.52000000000001, 81.55166666666665, @@ -110613,7 +110571,7 @@ -2.624545454545454, 5.169202380952381, 2.395000000000003, - -19.52500000000001, + -19.525000000000006, -3.105000000000018, 29.784999999999968, -23.125, @@ -110621,7 +110579,7 @@ 25.686140350877167, 63.81364035087722, 16.245000000000005, - -28.56, + -28.560000000000002, 18.364166666666648, -55.89499999999998, 23.781111111111045, @@ -110630,7 +110588,7 @@ 89.91, 15.078499999999991, -14.8335, - 24.615, + 24.615000000000002, -1.6224999999999987, -0.41443589743589726, 0.6064020493395494, @@ -110645,7 +110603,7 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20.071875000000006, -25.20476190476188, 9.242166666666662, -76.57, - -212.78500000000005, + -212.78500000000003, -238.2, -164.92000000000002, -89.38, @@ -111651,14 +111609,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -111674,7 +111632,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -111689,18 +111647,18 @@ "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -112018,10 +111976,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -112068,8 +112026,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -112174,18 +112133,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -112194,7 +112153,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -112350,123 +112309,113 @@ "#FECB52" ], "font": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "rgb(17,17,17)", - "plot_bgcolor": "rgb(17,17,17)", + "paper_bgcolor": "white", + "plot_bgcolor": "white", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } @@ -112514,7 +112463,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -112535,7 +112484,7 @@ "dtype: float64" ] }, - "execution_count": 35, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -112551,7 +112500,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -112615,10 +112564,10 @@ 0.0009222499647799248, 0.0009157961219602414, 0.0007241245983027822, - 0.0004666076607662229, + 0.00046660766076622284, 0.000465833261483762, 0.00029656107014607654, - 2.035477454700951e-6 + 0.000002035477454700951 ], "yaxis": "y" } @@ -112636,14 +112585,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -112659,7 +112608,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -112674,18 +112623,18 @@ "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -113003,10 +112952,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -113053,8 +113002,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -113159,18 +113109,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -113179,7 +113129,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -113335,123 +113285,113 @@ "#FECB52" ], "font": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "rgb(17,17,17)", - "plot_bgcolor": "rgb(17,17,17)", + "paper_bgcolor": "white", + "plot_bgcolor": "white", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } @@ -113506,7 +113446,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -113541,7 +113481,7 @@ "xaxis": "x", "y": [ -42275.73835281745, - -15635.5593762851, + -15635.559376285099, -13100.12372365238, -10785.358703379774, -4170.025882012518 @@ -113575,7 +113515,7 @@ -34.428947397934934, -72.32451490478466, -80.60674215971036, - -97.40430424479256, + -97.40430424479257, -115.67026325900858 ], "yaxis": "y" @@ -113593,14 +113533,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -113616,7 +113556,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -113631,18 +113571,18 @@ "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -113960,10 +113900,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -114010,8 +113950,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -114116,18 +114057,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -114136,7 +114077,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -114292,123 +114233,113 @@ "#FECB52" ], "font": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "rgb(17,17,17)", - "plot_bgcolor": "rgb(17,17,17)", + "paper_bgcolor": "white", + "plot_bgcolor": "white", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } @@ -114471,7 +114402,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -114539,7 +114470,7 @@ "xaxis": "x", "y": [ -160.74528587684654, - -123.29846931759148, + -123.29846931759147, -122.26306371153298 ], "yaxis": "y" @@ -114557,14 +114488,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -114580,7 +114511,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -114595,18 +114526,18 @@ "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -114924,10 +114855,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -114974,8 +114905,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -115080,18 +115012,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -115100,7 +115032,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -115256,123 +115188,113 @@ "#FECB52" ], "font": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "rgb(17,17,17)", - "plot_bgcolor": "rgb(17,17,17)", + "paper_bgcolor": "white", + "plot_bgcolor": "white", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } @@ -115471,7 +115393,7 @@ "xaxis": "x", "y": [ -123.54151793514892, - -123.29846931759148, + -123.29846931759147, -123.50331434659162 ], "yaxis": "y" @@ -115489,14 +115411,14 @@ "bar": [ { "error_x": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "error_y": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -115512,7 +115434,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -115527,18 +115449,18 @@ "carpet": [ { "aaxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "baxis": { - "endlinecolor": "#A2B1C6", - "gridcolor": "#506784", - "linecolor": "#506784", - "minorgridcolor": "#506784", - "startlinecolor": "#A2B1C6" + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" }, "type": "carpet" } @@ -115856,10 +115778,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -115906,8 +115828,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -116012,18 +115935,18 @@ { "cells": { "fill": { - "color": "#506784" + "color": "#EBF0F8" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "header": { "fill": { - "color": "#2a3f5f" + "color": "#C8D4E3" }, "line": { - "color": "rgb(17,17,17)" + "color": "white" } }, "type": "table" @@ -116032,7 +115955,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -116188,123 +116111,113 @@ "#FECB52" ], "font": { - "color": "#f2f5fa" + "color": "#2a3f5f" }, "geo": { - "bgcolor": "rgb(17,17,17)", - "lakecolor": "rgb(17,17,17)", - "landcolor": "rgb(17,17,17)", + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", "showlakes": true, "showland": true, - "subunitcolor": "#506784" + "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { - "style": "dark" + "style": "light" }, - "paper_bgcolor": "rgb(17,17,17)", - "plot_bgcolor": "rgb(17,17,17)", + "paper_bgcolor": "white", + "plot_bgcolor": "white", "polar": { "angularaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "radialaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "yaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" }, "zaxis": { - "backgroundcolor": "rgb(17,17,17)", - "gridcolor": "#506784", + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", "gridwidth": 2, - "linecolor": "#506784", + "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", - "zerolinecolor": "#C8D4E3" + "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { - "color": "#f2f5fa" + "color": "#2a3f5f" } }, - "sliderdefaults": { - "bgcolor": "#C8D4E3", - "bordercolor": "rgb(17,17,17)", - "borderwidth": 1, - "tickwidth": 0 - }, "ternary": { "aaxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" }, - "bgcolor": "rgb(17,17,17)", + "bgcolor": "white", "caxis": { - "gridcolor": "#506784", - "linecolor": "#506784", + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, - "updatemenudefaults": { - "bgcolor": "#506784", - "borderwidth": 0 - }, "xaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, - "gridcolor": "#283442", - "linecolor": "#506784", + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, - "zerolinecolor": "#283442", + "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } diff --git a/pyproject.toml b/pyproject.toml index f67dbd4..58f66e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ requires-python = ">=3.12" dependencies = [ "loguru>=0.7.2", "matplotlib>=3.9.3", - "mlflow>=2.18.0", + "mlflow>=2.19.0", "numba>=0.60.0", "numpy>=2.0.2", "omegaconf>=2.3.0", diff --git a/python_env.yaml b/python_env.yaml index 2834cb9..9210229 100644 --- a/python_env.yaml +++ b/python_env.yaml @@ -23,7 +23,7 @@ "contourpy==1.3.1", "coverage==7.6.9", "cycler==0.12.1", - "databricks-sdk==0.38.0", + "databricks-sdk==0.39.0", "debugpy==1.8.9", "decli==0.6.2", "decorator==5.1.1", @@ -35,10 +35,10 @@ "fastjsonschema==2.21.1", "filelock==3.16.1", "flask==3.1.0", - "fonttools==4.55.2", + "fonttools==4.55.3", "gitdb==4.0.11", "gitpython==3.1.43", - "google-auth==2.36.0", + "google-auth==2.37.0", "graphene==3.4.3", "graphql-core==3.2.5", "graphql-relay==3.2.0", @@ -68,8 +68,8 @@ "matplotlib==3.9.3", "matplotlib-inline==0.1.7", "mdurl==0.1.2", - "mlflow==2.18.0", - "mlflow-skinny==2.18.0", + "mlflow==2.19.0", + "mlflow-skinny==2.19.0", "multimethod==1.12", "mypy==1.13.0", "mypy-extensions==1.0.0", @@ -80,16 +80,16 @@ "numpy==2.0.2", "nvidia-ml-py==12.560.30", "omegaconf==2.3.0", - "opentelemetry-api==1.28.2", - "opentelemetry-sdk==1.28.2", - "opentelemetry-semantic-conventions==0.49b2", + "opentelemetry-api==1.29.0", + "opentelemetry-sdk==1.29.0", + "opentelemetry-semantic-conventions==0.50b0", "packaging==24.2", "pandas==2.2.3", "pandas-stubs==2.2.3.241126", "pandera==0.21.1", "parso==0.8.4", "pbr==6.1.0", - "pdoc==15.0.0", + "pdoc==15.0.1", "pexpect==4.9.0", "pillow==11.0.0", "platformdirs==4.3.6", @@ -127,7 +127,7 @@ "rich==13.9.4", "rpds-py==0.22.3", "rsa==4.9", - "ruff==0.8.2", + "ruff==0.8.3", "scikit-learn==1.5.2", "scipy==1.14.1", "setuptools==75.6.0", @@ -136,7 +136,7 @@ "slicer==0.0.8", "smmap==5.0.1", "sqlalchemy==2.0.36", - "sqlparse==0.5.2", + "sqlparse==0.5.3", "stack-data==0.6.3", "stevedore==5.4.0", "tenacity==9.0.0", diff --git a/requirements.txt b/requirements.txt index af15138..e86c4d6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -22,7 +22,7 @@ commitizen==4.1.0 contourpy==1.3.1 coverage==7.6.9 cycler==0.12.1 -databricks-sdk==0.38.0 +databricks-sdk==0.39.0 debugpy==1.8.9 decli==0.6.2 decorator==5.1.1 @@ -34,14 +34,14 @@ executing==2.1.0 fastjsonschema==2.21.1 filelock==3.16.1 flask==3.1.0 -fonttools==4.55.2 +fonttools==4.55.3 gitdb==4.0.11 gitpython==3.1.43 -google-auth==2.36.0 +google-auth==2.37.0 graphene==3.4.3 graphql-core==3.2.5 graphql-relay==3.2.0 -greenlet==3.1.1 ; (python_full_version == '3.12.*' and platform_machine == 'AMD64') or (python_full_version == '3.12.*' and platform_machine == 'WIN32') or (python_full_version == '3.12.*' and platform_machine == 'aarch64') or (python_full_version == '3.12.*' and platform_machine == 'amd64') or (python_full_version == '3.12.*' and platform_machine == 'ppc64le') or (python_full_version == '3.12.*' and platform_machine == 'win32') or (python_full_version == '3.12.*' and platform_machine == 'x86_64') +greenlet==3.1.1 ; (python_full_version < '3.13' and platform_machine == 'AMD64') or (python_full_version < '3.13' and platform_machine == 'WIN32') or (python_full_version < '3.13' and platform_machine == 'aarch64') or 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"https://files.pythonhosted.org/packages/7a/13/5f6654c9d915077fae255686ca6fa42095b62b7337e3e1aa9e82caa6f43a/sqlparse-0.5.2-py3-none-any.whl", hash = "sha256:e99bc85c78160918c3e1d9230834ab8d80fc06c59d03f8db2618f65f65dda55e", size = 44407 }, + { url = "https://files.pythonhosted.org/packages/a9/5c/bfd6bd0bf979426d405cc6e71eceb8701b148b16c21d2dc3c261efc61c7b/sqlparse-0.5.3-py3-none-any.whl", hash = "sha256:cf2196ed3418f3ba5de6af7e82c694a9fbdbfecccdfc72e281548517081f16ca", size = 44415 }, ] [[package]] From 2689eed1a1ca9ed7752496c5b3cb17ed59b0c9ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Hurier=20=28Fmind=29?= Date: Sat, 14 Dec 2024 10:10:22 +0100 Subject: [PATCH 06/10] refactor(code): improve abstractions (#28) --- src/bikes/io/__init__.py | 2 +- src/bikes/io/datasets.py | 3 ++- src/bikes/io/services.py | 2 +- src/bikes/utils/signers.py | 2 +- src/bikes/utils/splitters.py | 4 +++- 5 files changed, 8 insertions(+), 5 deletions(-) diff --git a/src/bikes/io/__init__.py b/src/bikes/io/__init__.py index 2a8a67d..044aa70 100644 --- a/src/bikes/io/__init__.py +++ b/src/bikes/io/__init__.py @@ -1 +1 @@ -"""Components related to external operations.""" +"""Components related to external operations (inputs and outputs).""" diff --git a/src/bikes/io/datasets.py b/src/bikes/io/datasets.py index 7cf2933..677c654 100644 --- a/src/bikes/io/datasets.py +++ b/src/bikes/io/datasets.py @@ -69,11 +69,12 @@ class ParquetReader(Reader): KIND: T.Literal["ParquetReader"] = "ParquetReader" path: str + backend: T.Literal["pyarrow", "numpy_nullable"] = "pyarrow" @T.override def read(self) -> pd.DataFrame: # can't limit rows at read time - data = pd.read_parquet(self.path) + data = pd.read_parquet(self.path, dtype_backend="pyarrow") if self.limit is not None: data = data.head(self.limit) return data diff --git a/src/bikes/io/services.py b/src/bikes/io/services.py index 8a2c572..5743bef 100644 --- a/src/bikes/io/services.py +++ b/src/bikes/io/services.py @@ -199,7 +199,7 @@ def run_context(self, run_config: RunConfig) -> T.Generator[mlflow.ActiveRun, No run (str): run parameters. Yields: - T.Generator[mlflow.ActiveRun, None, None]: active run context. Will be closed as the end of context. + T.Generator[mlflow.ActiveRun, None, None]: active run context. Will be closed at the end of context. """ with mlflow.start_run( run_name=run_config.name, diff --git a/src/bikes/utils/signers.py b/src/bikes/utils/signers.py index b976bb5..4a0a5ce 100644 --- a/src/bikes/utils/signers.py +++ b/src/bikes/utils/signers.py @@ -21,7 +21,7 @@ class Signer(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): """Base class for generating model signatures. - Allow to switch between model signing strategies. + Allow switching between model signing strategies. e.g., automatic inference, manual model signature, ... https://mlflow.org/docs/latest/models.html#model-signature-and-input-example diff --git a/src/bikes/utils/splitters.py b/src/bikes/utils/splitters.py index bee5952..0740bc0 100644 --- a/src/bikes/utils/splitters.py +++ b/src/bikes/utils/splitters.py @@ -132,7 +132,9 @@ def split( targets: schemas.Targets, groups: Index | None = None, ) -> TrainTestSplits: - splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size) + splitter = model_selection.TimeSeriesSplit( + n_splits=self.n_splits, test_size=self.test_size, gap=self.gap + ) yield from splitter.split(inputs) @T.override From bdd338a6bae777be1c1e266da479e3fbb03a6893 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Hurier=20=28Fmind=29?= Date: Sat, 14 Dec 2024 10:13:27 +0100 Subject: [PATCH 07/10] refactor(actions): split check tasks into subtask for easier debugging (#29) --- .github/workflows/check.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/check.yml b/.github/workflows/check.yml index f46ad5b..5fdce59 100644 --- a/.github/workflows/check.yml +++ b/.github/workflows/check.yml @@ -13,4 +13,8 @@ jobs: - uses: actions/checkout@v4 - uses: ./.github/actions/setup - run: uv sync --group=checks - - run: uv run invoke checks + - run: uv run invoke checks.format + - run: uv run invoke checks.type + - run: uv run invoke checks.code + - run: uv run invoke checks.security + - run: uv run invoke checks.coverage From 0e1c07bb5d7696c095a6b90f8e6c588affe28662 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Hurier=20=28Fmind=29?= Date: Sat, 14 Dec 2024 10:35:19 +0100 Subject: [PATCH 08/10] fix(git): rebase commit --- src/bikes/io/services.py | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/src/bikes/io/services.py b/src/bikes/io/services.py index 5743bef..fe75b72 100644 --- a/src/bikes/io/services.py +++ b/src/bikes/io/services.py @@ -8,6 +8,7 @@ import contextlib as ctx import sys import typing as T +import warnings import loguru import mlflow @@ -113,14 +114,27 @@ def notify(self, title: str, message: str) -> None: message (str): message of the notification. """ if self.enable: - notification.notify( - title=title, - message=message, - app_name=self.app_name, - timeout=self.timeout, - ) + try: + notification.notify( + title=title, + message=message, + app_name=self.app_name, + timeout=self.timeout, + ) + except NotImplementedError: + warnings.warn("Notifications are not supported on this system.", RuntimeWarning) + self._print(title=title, message=message) else: - print(f"[{self.app_name}] {title}: {message}") + self._print(title=title, message=message) + + def _print(self, title: str, message: str) -> None: + """Print a notification to the system. + + Args: + title (str): title of the notification. + message (str): message of the notification. + """ + print(f"[{self.app_name}] {title}: {message}") class MlflowService(Service): From 8f926d13414d7519e76e9cfb2aaf0977911a1f38 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Hurier=20=28Fmind=29?= Date: Sat, 14 Dec 2024 15:51:25 +0100 Subject: [PATCH 09/10] refactor(release): prepare before release BREAKING CHANGE: --- notebooks/explain.ipynb | 4443 +++++++++++++++++++----------------- notebooks/indicators.ipynb | 3002 ++++++++++++++++-------- notebooks/prototype.ipynb | 462 ++-- python_env.yaml | 8 +- requirements.txt | 8 +- src/bikes/core/models.py | 12 +- uv.lock | 76 +- 7 files changed, 4611 insertions(+), 3400 deletions(-) diff --git a/notebooks/explain.ipynb b/notebooks/explain.ipynb index 05ea87c..2bdb13d 100644 --- a/notebooks/explain.ipynb +++ b/notebooks/explain.ipynb @@ -68,7 +68,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(19, 2)\n" + "(20, 2)\n" ] }, { @@ -98,29 +98,29 @@ " \n", " \n", " \n", - " 18\n", - " numericals__casual\n", - " 0.579146\n", + " 19\n", + " numericals__registered\n", + " 0.939335\n", " \n", " \n", - " 10\n", - " numericals__hr\n", - " 0.250726\n", - " \n", - " \n", - " 13\n", - " numericals__workingday\n", - " 0.078542\n", + " 18\n", + " numericals__casual\n", + " 0.060139\n", " \n", " \n", " 8\n", " numericals__yr\n", - " 0.038600\n", + " 0.000161\n", + " \n", + " \n", + " 16\n", + " numericals__hum\n", + " 0.000064\n", " \n", " \n", - " 9\n", - " numericals__mnth\n", - " 0.012079\n", + " 10\n", + " numericals__hr\n", + " 0.000059\n", " \n", " \n", "\n", @@ -128,11 +128,11 @@ ], "text/plain": [ " feature importance\n", - "18 numericals__casual 0.579146\n", - "10 numericals__hr 0.250726\n", - "13 numericals__workingday 0.078542\n", - "8 numericals__yr 0.038600\n", - "9 numericals__mnth 0.012079" + "19 numericals__registered 0.939335\n", + "18 numericals__casual 0.060139\n", + "8 numericals__yr 0.000161\n", + "16 numericals__hum 0.000064\n", + "10 numericals__hr 0.000059" ] }, "execution_count": 3, @@ -157,7 +157,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(100, 19)\n" + "(100, 20)\n" ] }, { @@ -200,118 +200,124 @@ " numericals__hum\n", " numericals__windspeed\n", " numericals__casual\n", + " numericals__registered\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.169155\n", - " -0.124650\n", - " -0.203503\n", - " -0.758982\n", - " -1.474484\n", - " -1.076349\n", - " 0.801956\n", - " 0.000513\n", - " 31.495134\n", - " 2.905728\n", - " -74.036369\n", - " -0.019947\n", - " -7.118968\n", - " 18.640795\n", - " 2.613896\n", - " 1.898717\n", - " -1.322058\n", - " -0.210363\n", - " 147.338089\n", + " 0.006652\n", + " 0.027727\n", + " 0.006341\n", + " -0.004617\n", + " 0.007796\n", + " -0.002922\n", + " 0.001006\n", + " -5.551742e-07\n", + " 0.079501\n", + " -0.032553\n", + " 0.019730\n", + " -0.005449\n", + " -0.091014\n", + " -0.040552\n", + " 0.024861\n", + " 0.003903\n", + " -0.012946\n", + " 0.083744\n", + " 25.231182\n", + " 93.615906\n", " \n", " \n", " 1\n", - " 0.142295\n", - " -0.242301\n", - " -0.061819\n", - " -0.650959\n", - " -1.001739\n", - " -0.523719\n", - " 0.824141\n", - " 0.000501\n", - " 32.251492\n", - " 2.752874\n", - " -68.468292\n", - " -0.037579\n", - " -4.272335\n", - " 19.723166\n", - " 1.812452\n", - " 1.462143\n", - " 0.369532\n", - " 0.374412\n", - " 117.786423\n", + " -0.000006\n", + " 0.015149\n", + " 0.013882\n", + " -0.003549\n", + " 0.009620\n", + " 0.034864\n", + " 0.004730\n", + " -5.551742e-07\n", + " 0.080114\n", + " -0.001672\n", + " 0.052958\n", + " -0.005971\n", + " 0.013171\n", + " -0.020272\n", + " 0.069661\n", + " 0.010426\n", + " 0.060713\n", + " -0.055690\n", + " 11.599885\n", + " 79.890282\n", " \n", " \n", " 2\n", - " 0.168699\n", - " -0.173419\n", - " -0.054359\n", - " -0.723623\n", - " -1.554552\n", - " -1.033979\n", - " 0.762942\n", - " 0.000534\n", - " 32.245430\n", - " 3.195950\n", - " -82.515167\n", - " 0.009938\n", - " -2.856529\n", - " 19.824303\n", - " 1.498975\n", - " 2.046780\n", - " 1.559438\n", - " 2.603706\n", - " 127.694664\n", + " 0.001510\n", + " 0.008787\n", + " -0.031662\n", + " -0.005680\n", + " 0.003407\n", + " 0.007366\n", + " 0.002644\n", + " -5.384581e-07\n", + " 0.075888\n", + " -0.039858\n", + " 0.066534\n", + " -0.008654\n", + " -0.043778\n", + " -0.015179\n", + " 0.120411\n", + " 0.004957\n", + " 0.042915\n", + " -0.029475\n", + " 17.027773\n", + " 80.330391\n", " \n", " \n", " 3\n", - " 0.253636\n", - " -0.791483\n", - " -0.218226\n", - " -0.612611\n", - " 0.499581\n", - " 0.039798\n", - " 0.778988\n", - " 0.000518\n", - " 30.905354\n", - " 2.808645\n", - " -70.180222\n", - " 0.097680\n", - " -6.699857\n", - " 19.206472\n", - " 4.468694\n", - " 7.614378\n", - " -0.776883\n", - " 1.582501\n", - " 180.403839\n", + " 0.002116\n", + " 0.000101\n", + " -0.030228\n", + " -0.008386\n", + " -0.001240\n", + " -0.001726\n", + " 0.001956\n", + " -5.384581e-07\n", + " 0.089552\n", + " -0.000123\n", + " 0.059446\n", + " -0.009938\n", + " 0.050032\n", + " -0.025889\n", + " -0.006929\n", + " -0.059649\n", + " -0.040841\n", + " 0.017910\n", + " 36.772224\n", + " 106.574913\n", " \n", " \n", " 4\n", - " 0.130902\n", - " -0.788029\n", - " -0.395371\n", - " -0.614574\n", - " -1.558409\n", - " 0.111731\n", - " -3.927900\n", - " 0.000904\n", - " 47.191067\n", - " 6.631704\n", - " -4.315243\n", - " 0.029141\n", - " -0.157620\n", - " 29.401426\n", - " 4.704289\n", - " 4.135256\n", - " -3.774523\n", - " 0.583940\n", - " 202.349609\n", + " 0.012335\n", + " 0.003437\n", + " 0.013386\n", + " -0.027833\n", + " 0.021164\n", + " -0.005834\n", + " -0.004372\n", + " -6.086659e-07\n", + " 0.018141\n", + " 0.174302\n", + " 0.120672\n", + " -0.003116\n", + " 0.040308\n", + " -0.074956\n", + " 0.069545\n", + " 0.015809\n", + " 0.074836\n", + " -0.040882\n", + " 38.722881\n", + " 246.853470\n", " \n", " \n", "\n", @@ -319,53 +325,60 @@ ], "text/plain": [ " categoricals__season_1 categoricals__season_2 categoricals__season_3 \\\n", - "0 0.169155 -0.124650 -0.203503 \n", - "1 0.142295 -0.242301 -0.061819 \n", - "2 0.168699 -0.173419 -0.054359 \n", - "3 0.253636 -0.791483 -0.218226 \n", - "4 0.130902 -0.788029 -0.395371 \n", + "0 0.006652 0.027727 0.006341 \n", + "1 -0.000006 0.015149 0.013882 \n", + "2 0.001510 0.008787 -0.031662 \n", + "3 0.002116 0.000101 -0.030228 \n", + "4 0.012335 0.003437 0.013386 \n", "\n", " categoricals__season_4 categoricals__weathersit_1 \\\n", - "0 -0.758982 -1.474484 \n", - 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"0 -74.036369 -0.019947 -7.118968 \n", - "1 -68.468292 -0.037579 -4.272335 \n", - "2 -82.515167 0.009938 -2.856529 \n", - "3 -70.180222 0.097680 -6.699857 \n", - "4 -4.315243 0.029141 -0.157620 \n", + "0 0.019730 -0.005449 -0.091014 \n", + "1 0.052958 -0.005971 0.013171 \n", + "2 0.066534 -0.008654 -0.043778 \n", + "3 0.059446 -0.009938 0.050032 \n", + "4 0.120672 -0.003116 0.040308 \n", "\n", " numericals__workingday numericals__temp numericals__atemp \\\n", - "0 18.640795 2.613896 1.898717 \n", - "1 19.723166 1.812452 1.462143 \n", - "2 19.824303 1.498975 2.046780 \n", - "3 19.206472 4.468694 7.614378 \n", - "4 29.401426 4.704289 4.135256 \n", + "0 -0.040552 0.024861 0.003903 \n", + "1 -0.020272 0.069661 0.010426 \n", + "2 -0.015179 0.120411 0.004957 \n", + "3 -0.025889 -0.006929 -0.059649 \n", + "4 -0.074956 0.069545 0.015809 \n", "\n", - " numericals__hum numericals__windspeed numericals__casual \n", - "0 -1.322058 -0.210363 147.338089 \n", - "1 0.369532 0.374412 117.786423 \n", - 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" 2024-12-12 21:12:58.800\n", - " 451770521480793486\n", - " 2024-12-12 21:12:58.800\n", + " 2024-12-14 14:35:02.439\n", + " 300971336194126583\n", + " 2024-12-14 14:35:02.439\n", " active\n", " bikes\n", " {}\n", @@ -141,10 +141,10 @@ ], "text/plain": [ " artifact_location creation_time \\\n", - "0 file:///home/fmind/mlops-python-package/mlruns... 2024-12-12 21:12:58.800 \n", + "0 file:///home/fmind/mlops-python-package/mlruns... 2024-12-14 14:35:02.439 \n", "\n", " experiment_id last_update_time lifecycle_stage name tags \n", - "0 451770521480793486 2024-12-12 21:12:58.800 active bikes {} " + "0 300971336194126583 2024-12-14 14:35:02.439 active bikes {} " ] }, "execution_count": 5, @@ -174,7 +174,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(19, 29)\n" + "(27, 29)\n" ] }, { @@ -233,24 +233,24 @@ " \n", " 0\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-12-13 21:30:22.859\n", - " 451770521480793486\n", + " 2024-12-14 14:42:28.056\n", + " 300971336194126583\n", " active\n", - " 92d0973c64dc419cacb89efe2df006a8\n", + " 9923cb99c1824857b68c0bedb63f8446\n", " Explanations\n", - " 92d0973c64dc419cacb89efe2df006a8\n", - " 2024-12-13 21:29:52.698\n", + " 9923cb99c1824857b68c0bedb63f8446\n", + " 2024-12-14 14:42:16.077\n", " FINISHED\n", " fmind\n", " {}\n", " {'conf_file': 'confs/explanations.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 30.161\n", + " 11.979\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " 5e7c0b8c7b49c29802893c9821ae8546e45952b0\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Explanations\n", @@ -265,24 +265,24 @@ " \n", " 1\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-12-13 21:29:50.751\n", - " 451770521480793486\n", + " 2024-12-14 14:42:13.733\n", + " 300971336194126583\n", " active\n", - " 855a19513f6d4282a217ef920a0d69f5\n", + " bfce074e34834054b84e5794450a4fa2\n", " Evaluations\n", - " 855a19513f6d4282a217ef920a0d69f5\n", - " 2024-12-13 21:29:45.813\n", + " bfce074e34834054b84e5794450a4fa2\n", + " 2024-12-14 14:42:07.488\n", " FINISHED\n", " fmind\n", " {'example_count': 13903.0, 'mean_absolute_erro...\n", " {'conf_file': 'confs/evaluations.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 4.938\n", + " 6.245\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " 5e7c0b8c7b49c29802893c9821ae8546e45952b0\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Evaluations\n", @@ -297,24 +297,24 @@ " \n", " 2\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-12-13 21:29:43.951\n", - " 451770521480793486\n", + " 2024-12-14 14:42:05.031\n", + " 300971336194126583\n", " active\n", - " 48f318dd0eed4c05b4bd5823f7d352f6\n", + " 5ebe28d67ed54300a177eb61825a6688\n", " Inference\n", - " 48f318dd0eed4c05b4bd5823f7d352f6\n", - " 2024-12-13 21:29:39.639\n", + " 5ebe28d67ed54300a177eb61825a6688\n", + " 2024-12-14 14:41:59.607\n", " FINISHED\n", " fmind\n", " {}\n", " {'conf_file': 'confs/inference.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 4.312\n", + " 5.424\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " 5e7c0b8c7b49c29802893c9821ae8546e45952b0\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Inference\n", @@ -329,24 +329,24 @@ " \n", " 3\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-12-13 21:29:37.770\n", - " 451770521480793486\n", + " 2024-12-14 14:41:57.245\n", + " 300971336194126583\n", " active\n", - " 05a8c27ae65a40008ae4962ad13691cf\n", + " f29b930df5a54f1fa9ec5fe4b27df8b9\n", " Promotion\n", - " 05a8c27ae65a40008ae4962ad13691cf\n", - " 2024-12-13 21:29:34.124\n", + " f29b930df5a54f1fa9ec5fe4b27df8b9\n", + " 2024-12-14 14:41:52.639\n", " FINISHED\n", " fmind\n", " {}\n", " {'conf_file': 'confs/promotion.yaml'}\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 3.646\n", + " 4.606\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " 5e7c0b8c7b49c29802893c9821ae8546e45952b0\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Promotion\n", @@ -361,24 +361,24 @@ " \n", " 4\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-12-13 21:29:31.698\n", - " 451770521480793486\n", + " 2024-12-14 14:41:49.910\n", + " 300971336194126583\n", " active\n", - " ac1ad3a71ca1438f930cf61cdaf4dda2\n", + " 4753bf6b106845d19eca79b7cb329343\n", " Training\n", - " ac1ad3a71ca1438f930cf61cdaf4dda2\n", - " 2024-12-13 21:29:08.647\n", + " 4753bf6b106845d19eca79b7cb329343\n", + " 2024-12-14 14:41:23.914\n", " FINISHED\n", " fmind\n", - " {'training_mean_squared_error': 124.5105461557...\n", + " {'training_mean_squared_error': 1.280665585452...\n", " {'conf_file': 'confs/training.yaml', 'memory':...\n", " {'mlflow.user': 'fmind', 'mlflow.source.name':...\n", - " 23.051\n", + " 25.996\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " 5e7c0b8c7b49c29802893c9821ae8546e45952b0\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\n", " git@github.com:fmind/mlops-python-package\n", " git@github.com:fmind/mlops-python-package\n", " Training\n", @@ -386,7 +386,7 @@ " local\n", " Pipeline\n", " sklearn.pipeline.Pipeline\n", - " [{\"run_id\": \"ac1ad3a71ca1438f930cf61cdaf4dda2\"...\n", + " [{\"run_id\": \"4753bf6b106845d19eca79b7cb329343\"...\n", " NaN\n", " NaN\n", " \n", @@ -396,32 +396,32 @@ ], "text/plain": [ " artifact_uri end_time \\\n", - "0 file:///home/fmind/mlops-python-package/mlruns... 2024-12-13 21:30:22.859 \n", - "1 file:///home/fmind/mlops-python-package/mlruns... 2024-12-13 21:29:50.751 \n", - "2 file:///home/fmind/mlops-python-package/mlruns... 2024-12-13 21:29:43.951 \n", - "3 file:///home/fmind/mlops-python-package/mlruns... 2024-12-13 21:29:37.770 \n", - "4 file:///home/fmind/mlops-python-package/mlruns... 2024-12-13 21:29:31.698 \n", + "0 file:///home/fmind/mlops-python-package/mlruns... 2024-12-14 14:42:28.056 \n", + "1 file:///home/fmind/mlops-python-package/mlruns... 2024-12-14 14:42:13.733 \n", + "2 file:///home/fmind/mlops-python-package/mlruns... 2024-12-14 14:42:05.031 \n", + "3 file:///home/fmind/mlops-python-package/mlruns... 2024-12-14 14:41:57.245 \n", + "4 file:///home/fmind/mlops-python-package/mlruns... 2024-12-14 14:41:49.910 \n", "\n", " experiment_id lifecycle_stage run_id \\\n", - "0 451770521480793486 active 92d0973c64dc419cacb89efe2df006a8 \n", - "1 451770521480793486 active 855a19513f6d4282a217ef920a0d69f5 \n", - "2 451770521480793486 active 48f318dd0eed4c05b4bd5823f7d352f6 \n", - "3 451770521480793486 active 05a8c27ae65a40008ae4962ad13691cf \n", - "4 451770521480793486 active ac1ad3a71ca1438f930cf61cdaf4dda2 \n", + "0 300971336194126583 active 9923cb99c1824857b68c0bedb63f8446 \n", + "1 300971336194126583 active bfce074e34834054b84e5794450a4fa2 \n", + "2 300971336194126583 active 5ebe28d67ed54300a177eb61825a6688 \n", + "3 300971336194126583 active f29b930df5a54f1fa9ec5fe4b27df8b9 \n", + "4 300971336194126583 active 4753bf6b106845d19eca79b7cb329343 \n", "\n", " run_name run_uuid start_time \\\n", - "0 Explanations 92d0973c64dc419cacb89efe2df006a8 2024-12-13 21:29:52.698 \n", - "1 Evaluations 855a19513f6d4282a217ef920a0d69f5 2024-12-13 21:29:45.813 \n", - "2 Inference 48f318dd0eed4c05b4bd5823f7d352f6 2024-12-13 21:29:39.639 \n", - "3 Promotion 05a8c27ae65a40008ae4962ad13691cf 2024-12-13 21:29:34.124 \n", - "4 Training ac1ad3a71ca1438f930cf61cdaf4dda2 2024-12-13 21:29:08.647 \n", + "0 Explanations 9923cb99c1824857b68c0bedb63f8446 2024-12-14 14:42:16.077 \n", + "1 Evaluations bfce074e34834054b84e5794450a4fa2 2024-12-14 14:42:07.488 \n", + "2 Inference 5ebe28d67ed54300a177eb61825a6688 2024-12-14 14:41:59.607 \n", + "3 Promotion f29b930df5a54f1fa9ec5fe4b27df8b9 2024-12-14 14:41:52.639 \n", + "4 Training 4753bf6b106845d19eca79b7cb329343 2024-12-14 14:41:23.914 \n", "\n", " status user_id metrics \\\n", "0 FINISHED fmind {} \n", "1 FINISHED fmind {'example_count': 13903.0, 'mean_absolute_erro... \n", "2 FINISHED fmind {} \n", "3 FINISHED fmind {} \n", - "4 FINISHED fmind {'training_mean_squared_error': 124.5105461557... \n", + "4 FINISHED fmind {'training_mean_squared_error': 1.280665585452... \n", "\n", " params \\\n", "0 {'conf_file': 'confs/explanations.yaml'} \n", @@ -431,11 +431,11 @@ "4 {'conf_file': 'confs/training.yaml', 'memory':... \n", "\n", " tags run_time_secs \\\n", - "0 {'mlflow.user': 'fmind', 'mlflow.source.name':... 30.161 \n", - "1 {'mlflow.user': 'fmind', 'mlflow.source.name':... 4.938 \n", - "2 {'mlflow.user': 'fmind', 'mlflow.source.name':... 4.312 \n", - "3 {'mlflow.user': 'fmind', 'mlflow.source.name':... 3.646 \n", - "4 {'mlflow.user': 'fmind', 'mlflow.source.name':... 23.051 \n", + "0 {'mlflow.user': 'fmind', 'mlflow.source.name':... 11.979 \n", + "1 {'mlflow.user': 'fmind', 'mlflow.source.name':... 6.245 \n", + "2 {'mlflow.user': 'fmind', 'mlflow.source.name':... 5.424 \n", + "3 {'mlflow.user': 'fmind', 'mlflow.source.name':... 4.606 \n", + "4 {'mlflow.user': 'fmind', 'mlflow.source.name':... 25.996 \n", "\n", " mlflow.user mlflow.source.name mlflow.source.type \\\n", "0 fmind file:///home/fmind/mlops-python-package PROJECT \n", @@ -445,11 +445,11 @@ "4 fmind file:///home/fmind/mlops-python-package PROJECT \n", "\n", " mlflow.project.entryPoint mlflow.source.git.commit \\\n", - "0 main 5e7c0b8c7b49c29802893c9821ae8546e45952b0 \n", - "1 main 5e7c0b8c7b49c29802893c9821ae8546e45952b0 \n", - "2 main 5e7c0b8c7b49c29802893c9821ae8546e45952b0 \n", - "3 main 5e7c0b8c7b49c29802893c9821ae8546e45952b0 \n", - "4 main 5e7c0b8c7b49c29802893c9821ae8546e45952b0 \n", + "0 main 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c \n", + "1 main 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c \n", + "2 main 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c \n", + "3 main 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c \n", + "4 main 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c \n", "\n", " mlflow.source.git.repoURL \\\n", "0 git@github.com:fmind/mlops-python-package \n", @@ -484,7 +484,7 @@ "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", - "4 [{\"run_id\": \"ac1ad3a71ca1438f930cf61cdaf4dda2\"... NaN \n", + "4 [{\"run_id\": \"4753bf6b106845d19eca79b7cb329343\"... NaN \n", "\n", " mlflow.parentRunId \n", "0 NaN \n", @@ -563,10 +563,10 @@ " \n", " \n", " 0\n", - " {'Champion': '2'}\n", - " 2024-12-12 21:15:44.102\n", + " {'Champion': '3'}\n", + " 2024-12-14 14:37:45.482\n", " \n", - " 2024-12-13 21:29:37.314\n", + " 2024-12-14 14:41:56.590\n", " bikes\n", " {}\n", " \n", @@ -576,10 +576,10 @@ ], "text/plain": [ " aliases creation_timestamp description \\\n", - "0 {'Champion': '2'} 2024-12-12 21:15:44.102 \n", + "0 {'Champion': '3'} 2024-12-14 14:37:45.482 \n", "\n", " last_updated_timestamp name tags \n", - "0 2024-12-13 21:29:37.314 bikes {} " + "0 2024-12-14 14:41:56.590 bikes {} " ] }, "execution_count": 7, @@ -615,7 +615,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(2, 14)\n" + "(3, 14)\n" ] }, { @@ -659,29 +659,46 @@ " \n", " 0\n", " Champion\n", - " 2024-12-13 21:29:30.793\n", + " 2024-12-14 14:41:48.936\n", " None\n", " \n", - " 2024-12-13 21:29:30.793\n", + " 2024-12-14 14:41:48.936\n", " bikes\n", - " ac1ad3a71ca1438f930cf61cdaf4dda2\n", + " 4753bf6b106845d19eca79b7cb329343\n", " \n", " file:///home/fmind/mlops-python-package/mlruns...\n", " READY\n", " \n", " {}\n", " \n", - " 2\n", + " 3\n", " \n", " \n", " 1\n", " None\n", - " 2024-12-12 21:15:44.106\n", + " 2024-12-14 14:39:49.417\n", + " None\n", + " \n", + " 2024-12-14 14:39:49.417\n", + " bikes\n", + " ae9f673f6b264240bb79ffd94ca49fe2\n", + " \n", + " file:///home/fmind/mlops-python-package/mlruns...\n", + " READY\n", + " \n", + " {}\n", + " \n", + " 2\n", + " \n", + " \n", + " 2\n", + " None\n", + " 2024-12-14 14:37:45.487\n", " None\n", " \n", - " 2024-12-12 21:15:44.106\n", + " 2024-12-14 14:37:45.487\n", " bikes\n", - " 797710899d04491e8b9e91394c59ccd9\n", + " d2aed973c12b475f849743599f7bc973\n", " \n", " file:///home/fmind/mlops-python-package/mlruns...\n", " READY\n", @@ -696,20 +713,24 @@ ], "text/plain": [ " aliases creation_timestamp current_stage description \\\n", - "0 Champion 2024-12-13 21:29:30.793 None \n", - "1 None 2024-12-12 21:15:44.106 None \n", + "0 Champion 2024-12-14 14:41:48.936 None \n", + "1 None 2024-12-14 14:39:49.417 None \n", + "2 None 2024-12-14 14:37:45.487 None \n", "\n", " last_updated_timestamp name run_id run_link \\\n", - "0 2024-12-13 21:29:30.793 bikes ac1ad3a71ca1438f930cf61cdaf4dda2 \n", - "1 2024-12-12 21:15:44.106 bikes 797710899d04491e8b9e91394c59ccd9 \n", + "0 2024-12-14 14:41:48.936 bikes 4753bf6b106845d19eca79b7cb329343 \n", + "1 2024-12-14 14:39:49.417 bikes ae9f673f6b264240bb79ffd94ca49fe2 \n", + "2 2024-12-14 14:37:45.487 bikes d2aed973c12b475f849743599f7bc973 \n", "\n", " source status status_message \\\n", "0 file:///home/fmind/mlops-python-package/mlruns... READY \n", "1 file:///home/fmind/mlops-python-package/mlruns... READY \n", + "2 file:///home/fmind/mlops-python-package/mlruns... READY \n", "\n", " tags user_id version \n", - "0 {} 2 \n", - "1 {} 1 " + "0 {} 3 \n", + "1 {} 2 \n", + "2 {} 1 " ] }, "execution_count": 8, @@ -755,9 +776,9 @@ "boxpoints": "all", "customdata": [ [ - "file:///home/fmind/mlops-python-package/mlruns/451770521480793486", - "451770521480793486", - "2024-12-12T21:12:58.800000", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583", + "300971336194126583", + "2024-12-14T14:35:02.439000", "active", "bikes", {} @@ -783,7 +804,7 @@ "showlegend": true, "type": "box", "x": [ - "2024-12-12T21:12:58.800000" + "2024-12-14T14:35:02.439000" ], "x0": " ", "xaxis": "x", @@ -1671,10 +1692,10 @@ "customdata": [ [ { - "Champion": "2" + "Champion": "3" }, "", - "2024-12-13T21:29:37.314000", + "2024-12-14T14:41:56.590000", "bikes", {} ] @@ -1699,7 +1720,7 @@ "showlegend": false, "type": "box", "x": [ - "2024-12-12T21:15:44.102000" + "2024-12-14T14:37:45.482000" ], "x0": " ", "xaxis": "x", @@ -2583,10 +2604,10 @@ "customdata": [ [ { - "Champion": "2" + "Champion": "3" }, "", - "2024-12-13T21:29:37.314000", + "2024-12-14T14:41:56.590000", "bikes", {} ] @@ -2611,7 +2632,7 @@ "showlegend": false, "type": "box", "x": [ - "2024-12-12T21:15:44.102000" + "2024-12-14T14:37:45.482000" ], "x0": " ", "xaxis": "x", @@ -3497,11 +3518,26 @@ "Champion", "None", "", - "2024-12-13T21:29:30.793000", + "2024-12-14T14:41:48.936000", + "bikes", + "4753bf6b106845d19eca79b7cb329343", + "", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/4753bf6b106845d19eca79b7cb329343/artifacts/model", + "READY", + "", + {}, + "", + "3" + ], + [ + null, + "None", + "", + "2024-12-14T14:39:49.417000", "bikes", - "ac1ad3a71ca1438f930cf61cdaf4dda2", + "ae9f673f6b264240bb79ffd94ca49fe2", "", - "file:///home/fmind/mlops-python-package/mlruns/451770521480793486/ac1ad3a71ca1438f930cf61cdaf4dda2/artifacts/model", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/ae9f673f6b264240bb79ffd94ca49fe2/artifacts/model", "READY", "", {}, @@ -3512,11 +3548,11 @@ null, "None", "", - "2024-12-12T21:15:44.106000", + "2024-12-14T14:37:45.487000", "bikes", - "797710899d04491e8b9e91394c59ccd9", + "d2aed973c12b475f849743599f7bc973", "", - "file:///home/fmind/mlops-python-package/mlruns/451770521480793486/797710899d04491e8b9e91394c59ccd9/artifacts/model", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/d2aed973c12b475f849743599f7bc973/artifacts/model", "READY", "", {}, @@ -3528,6 +3564,7 @@ "hoveron": "points", "hovertemplate": "%{hovertext}

name=%{customdata[4]}
creation_timestamp=%{x}
aliases=%{customdata[0]}
current_stage=%{customdata[1]}
description=%{customdata[2]}
last_updated_timestamp=%{customdata[3]}
run_id=%{customdata[5]}
run_link=%{customdata[6]}
source=%{customdata[7]}
status=%{customdata[8]}
status_message=%{customdata[9]}
tags=%{customdata[10]}
user_id=%{customdata[11]}
version=%{customdata[12]}", "hovertext": [ + "bikes", "bikes", "bikes" ], @@ -3545,8 +3582,9 @@ "showlegend": true, "type": "box", "x": [ - "2024-12-13T21:29:30.793000", - "2024-12-12T21:15:44.106000" + "2024-12-14T14:41:48.936000", + "2024-12-14T14:39:49.417000", + "2024-12-14T14:37:45.487000" ], "x0": " ", "xaxis": "x", @@ -4433,13 +4471,13 @@ "boxpoints": "all", "customdata": [ [ - "file:///home/fmind/mlops-python-package/mlruns/451770521480793486/92d0973c64dc419cacb89efe2df006a8/artifacts", - "2024-12-13T21:30:22.859000", - "451770521480793486", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/9923cb99c1824857b68c0bedb63f8446/artifacts", + "2024-12-14T14:42:28.056000", + "300971336194126583", "active", - "92d0973c64dc419cacb89efe2df006a8", + "9923cb99c1824857b68c0bedb63f8446", "Explanations", - "92d0973c64dc419cacb89efe2df006a8", + "9923cb99c1824857b68c0bedb63f8446", "FINISHED", "fmind", {}, @@ -4452,18 +4490,18 @@ "mlflow.project.entryPoint": "main", "mlflow.project.env": "virtualenv", "mlflow.runName": "Explanations", - 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4.938, + 6.245, "fmind", "file:///home/fmind/mlops-python-package", "PROJECT", "main", - "5e7c0b8c7b49c29802893c9821ae8546e45952b0", + "1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c", "git@github.com:fmind/mlops-python-package", "git@github.com:fmind/mlops-python-package", "Evaluations", @@ -4529,13 +4567,13 @@ null ], [ - "file:///home/fmind/mlops-python-package/mlruns/451770521480793486/48f318dd0eed4c05b4bd5823f7d352f6/artifacts", - "2024-12-13T21:29:43.951000", - "451770521480793486", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/5ebe28d67ed54300a177eb61825a6688/artifacts", + "2024-12-14T14:42:05.031000", + "300971336194126583", "active", - "48f318dd0eed4c05b4bd5823f7d352f6", + "5ebe28d67ed54300a177eb61825a6688", "Inference", - "48f318dd0eed4c05b4bd5823f7d352f6", + "5ebe28d67ed54300a177eb61825a6688", "FINISHED", "fmind", {}, @@ -4548,18 +4586,18 @@ "mlflow.project.entryPoint": "main", "mlflow.project.env": "virtualenv", "mlflow.runName": "Inference", - "mlflow.source.git.commit": "5e7c0b8c7b49c29802893c9821ae8546e45952b0", + "mlflow.source.git.commit": "1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c", "mlflow.source.git.repoURL": "git@github.com:fmind/mlops-python-package", "mlflow.source.name": "file:///home/fmind/mlops-python-package", "mlflow.source.type": "PROJECT", "mlflow.user": "fmind" }, - 4.312, + 5.424, "fmind", "file:///home/fmind/mlops-python-package", "PROJECT", "main", - "5e7c0b8c7b49c29802893c9821ae8546e45952b0", + "1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c", "git@github.com:fmind/mlops-python-package", "git@github.com:fmind/mlops-python-package", "Inference", @@ -4572,13 +4610,13 @@ null ], [ - "file:///home/fmind/mlops-python-package/mlruns/451770521480793486/05a8c27ae65a40008ae4962ad13691cf/artifacts", - "2024-12-13T21:29:37.770000", - "451770521480793486", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/f29b930df5a54f1fa9ec5fe4b27df8b9/artifacts", + "2024-12-14T14:41:57.245000", + "300971336194126583", "active", - "05a8c27ae65a40008ae4962ad13691cf", + "f29b930df5a54f1fa9ec5fe4b27df8b9", "Promotion", - "05a8c27ae65a40008ae4962ad13691cf", + "f29b930df5a54f1fa9ec5fe4b27df8b9", "FINISHED", "fmind", {}, @@ -4591,18 +4629,18 @@ "mlflow.project.entryPoint": "main", "mlflow.project.env": "virtualenv", "mlflow.runName": "Promotion", - "mlflow.source.git.commit": "5e7c0b8c7b49c29802893c9821ae8546e45952b0", + "mlflow.source.git.commit": "1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c", "mlflow.source.git.repoURL": "git@github.com:fmind/mlops-python-package", "mlflow.source.name": "file:///home/fmind/mlops-python-package", "mlflow.source.type": "PROJECT", "mlflow.user": "fmind" }, - 3.646, + 4.606, "fmind", "file:///home/fmind/mlops-python-package", "PROJECT", "main", - "5e7c0b8c7b49c29802893c9821ae8546e45952b0", + "1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c", "git@github.com:fmind/mlops-python-package", "git@github.com:fmind/mlops-python-package", "Promotion", @@ -4615,30 +4653,30 @@ null ], [ - "file:///home/fmind/mlops-python-package/mlruns/451770521480793486/ac1ad3a71ca1438f930cf61cdaf4dda2/artifacts", - "2024-12-13T21:29:31.698000", - "451770521480793486", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/4753bf6b106845d19eca79b7cb329343/artifacts", + "2024-12-14T14:41:49.910000", + "300971336194126583", "active", - "ac1ad3a71ca1438f930cf61cdaf4dda2", + "4753bf6b106845d19eca79b7cb329343", "Training", - "ac1ad3a71ca1438f930cf61cdaf4dda2", + "4753bf6b106845d19eca79b7cb329343", "FINISHED", "fmind", { - "mean_squared_error": -1786.7527777777777, - "system/cpu_utilization_percentage": 0, - "system/disk_available_megabytes": 14473.2, - "system/disk_usage_megabytes": 101021.7, - 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mlflow.source.git.commit=%{customdata[17]}
mlflow.source.git.repoURL=%{customdata[18]}
mlflow.gitRepoURL=%{customdata[19]}
mlflow.runName=%{customdata[20]}
mlflow.project.env=%{customdata[21]}
mlflow.project.backend=%{customdata[22]}
estimator_name=%{customdata[23]}
estimator_class=%{customdata[24]}
mlflow.log-model.history=%{customdata[25]}
mlflow.autologging=%{customdata[26]}
mlflow.parentRunId=%{customdata[27]}", + "hovertext": [ + "e9980ab9c8db4f80a18aadfa47a3e83e" + ], + "legendgroup": "smiling-hound-227", + "line": { + "color": "rgba(255,255,255,0)" + }, + "marker": { + "color": "#FFA15A" }, - "name": "righteous-grub-422", - "offsetgroup": "righteous-grub-422", + "name": "smiling-hound-227", + "offsetgroup": "smiling-hound-227", "orientation": "h", "pointpos": 0, "showlegend": true, "type": "box", "x": [ - 103.824 + 132.755 ], "x0": " ", "xaxis": "x", @@ -8784,9 +9874,9 @@ null, null, "sklearn.pipeline.Pipeline", - "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel", "bikes.core.models.BaselineSklearnModel", + "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel", null, null, @@ -8797,7 +9887,15 @@ "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel", "bikes.core.models.BaselineSklearnModel", - null + null, + null, + null, + null, + "sklearn.pipeline.Pipeline", + "bikes.core.models.BaselineSklearnModel", + "bikes.core.models.BaselineSklearnModel", + "sklearn.model_selection._search.GridSearchCV", + "bikes.core.models.BaselineSklearnModel" ], "xaxis": "x", "y": [ @@ -8819,6 +9917,14 @@ 1, 1, 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, 1 ], "yaxis": "y" diff --git a/notebooks/prototype.ipynb b/notebooks/prototype.ipynb index e5e5954..fc52d1e 100644 --- a/notebooks/prototype.ipynb +++ b/notebooks/prototype.ipynb @@ -24,31 +24,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[autoreload of _virtualenv failed: Traceback (most recent call last):\n", - " File \"/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/IPython/extensions/autoreload.py\", line 276, in check\n", - " superreload(m, reload, self.old_objects)\n", - " File \"/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/IPython/extensions/autoreload.py\", line 475, in superreload\n", - " module = reload(module)\n", - " ^^^^^^^^^^^^^^\n", - " File \"/home/fmind/.local/share/uv/python/cpython-3.12.5-linux-x86_64-gnu/lib/python3.12/importlib/__init__.py\", line 128, in reload\n", - " spec = module.__spec__ = _bootstrap._find_spec(name, pkgpath, target)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"\", line 1262, in _find_spec\n", - " File \"/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/_virtualenv.py\", line 51, in find_spec\n", - " if fullname in _DISTUTILS_PATCH and self.fullname is None:\n", - " ^^^^^^^^^^^^^^^^\n", - "NameError: name '_DISTUTILS_PATCH' is not defined\n", - "]\n" - ] - } - ], + "outputs": [], "source": [ "from pathlib import Path" ] @@ -62,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -157,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -220,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -448,7 +426,7 @@ "5 1 0.24 0.2879 0.75 0.0 0 1 1 " ] }, - "execution_count": 50, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -461,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -514,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -824,7 +802,7 @@ "max 977.000000 " ] }, - "execution_count": 52, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -842,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -106090,7 +106068,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -106115,7 +106093,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -106137,7 +106115,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -106166,7 +106144,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -106181,7 +106159,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -106206,7 +106184,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -106222,13 +106200,13 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(memory='../.cache',\n",
+       "
Pipeline(memory='../.cache',\n",
        "         steps=[('transformer',\n",
        "                 ColumnTransformer(transformers=[('categoricals',\n",
        "                                                  OneHotEncoder(handle_unknown='ignore',\n",
@@ -106644,7 +106622,7 @@
        "                                                   'workingday', 'temp',\n",
        "                                                   'atemp', 'hum', 'windspeed',\n",
        "                                                   'casual'])])),\n",
-       "                ('regressor', RandomForestRegressor(random_state=42))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
['season', 'weathersit']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual']
passthrough
RandomForestRegressor(random_state=42)
" ], "text/plain": [ "Pipeline(memory='../.cache',\n", @@ -106681,7 +106659,7 @@ " ('regressor', RandomForestRegressor(random_state=42))])" ] }, - "execution_count": 60, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -106726,7 +106704,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -106757,7 +106735,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -106773,26 +106751,30 @@ "text": [ "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 0.52s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "Persisting input arguments took 0.68s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "\n", + "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", + "\n", + "Persisting input arguments took 0.69s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", "\n", "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 0.56s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "Persisting input arguments took 0.79s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", "\n", "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 0.63s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "Persisting input arguments took 0.90s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", "\n", "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 0.76s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + "Persisting input arguments took 1.00s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", "\n" ] }, { "data": { "text/html": [ - "
GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=4, test_size=1440),\n",
+       "
GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=4, test_size=1440),\n",
        "             estimator=Pipeline(memory='../.cache',\n",
        "                                steps=[('transformer',\n",
        "                                        ColumnTransformer(transformers=[('categoricals',\n",
@@ -107221,7 +107203,7 @@
        "                                        RandomForestRegressor(random_state=42))]),\n",
        "             param_grid={'regressor__max_depth': [15, 20, 25],\n",
        "                         'regressor__n_estimators': [150, 200, 250]},\n",
-       "             scoring='neg_mean_squared_error', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
['season', 'weathersit']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual']
passthrough
RandomForestRegressor(max_depth=20, n_estimators=200, random_state=42)
" ], "text/plain": [ "GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=4, test_size=1440),\n", @@ -107298,7 +107280,7 @@ " scoring='neg_mean_squared_error', verbose=1)" ] }, - "execution_count": 62, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -107319,7 +107301,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -107362,10 +107344,10 @@ " \n", " \n", " 4\n", - " 8.197777\n", - " 1.114588\n", - " 0.049695\n", - " 0.005310\n", + " 9.947961\n", + " 1.757322\n", + " 0.065129\n", + " 0.012880\n", " 20\n", " 200\n", " {'regressor__max_depth': 20, 'regressor__n_est...\n", @@ -107379,10 +107361,10 @@ " \n", " \n", " 1\n", - " 7.157419\n", - " 1.018962\n", - " 0.045709\n", - " 0.005739\n", + " 9.226854\n", + " 0.965719\n", + " 0.050904\n", + " 0.002971\n", " 15\n", " 200\n", " {'regressor__max_depth': 15, 'regressor__n_est...\n", @@ -107396,10 +107378,10 @@ " \n", " \n", " 5\n", - " 10.689916\n", - " 1.444524\n", - " 0.064643\n", - " 0.012777\n", + " 11.779174\n", + " 1.353100\n", + " 0.066897\n", + " 0.005953\n", " 20\n", " 250\n", " {'regressor__max_depth': 20, 'regressor__n_est...\n", @@ -107413,10 +107395,10 @@ " \n", " \n", " 7\n", - " 8.259196\n", - " 0.936581\n", - " 0.048372\n", - " 0.004113\n", + " 9.096495\n", + " 1.247697\n", + " 0.054601\n", + " 0.006268\n", " 25\n", " 200\n", " {'regressor__max_depth': 25, 'regressor__n_est...\n", @@ -107430,10 +107412,10 @@ " \n", " \n", " 2\n", - " 9.029868\n", - " 1.144401\n", - " 0.055069\n", - " 0.004822\n", + " 10.684988\n", + " 1.501814\n", + " 0.064013\n", + " 0.008608\n", " 15\n", " 250\n", " {'regressor__max_depth': 15, 'regressor__n_est...\n", @@ -107451,11 +107433,11 @@ ], "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", - "4 8.197777 1.114588 0.049695 0.005310 \n", - "1 7.157419 1.018962 0.045709 0.005739 \n", - "5 10.689916 1.444524 0.064643 0.012777 \n", - "7 8.259196 0.936581 0.048372 0.004113 \n", - "2 9.029868 1.144401 0.055069 0.004822 \n", + "4 9.947961 1.757322 0.065129 0.012880 \n", + "1 9.226854 0.965719 0.050904 0.002971 \n", + "5 11.779174 1.353100 0.066897 0.005953 \n", + "7 9.096495 1.247697 0.054601 0.006268 \n", + "2 10.684988 1.501814 0.064013 0.008608 \n", "\n", " param_regressor__max_depth param_regressor__n_estimators \\\n", "4 20 200 \n", @@ -107486,7 +107468,7 @@ "2 2504.611918 5 " ] }, - "execution_count": 63, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -107514,13 +107496,13 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(memory='../.cache',\n",
+       "
Pipeline(memory='../.cache',\n",
        "         steps=[('transformer',\n",
        "                 ColumnTransformer(transformers=[('categoricals',\n",
        "                                                  OneHotEncoder(handle_unknown='ignore',\n",
@@ -107938,7 +107920,7 @@
        "                                                   'casual'])])),\n",
        "                ('regressor',\n",
        "                 RandomForestRegressor(max_depth=20, n_estimators=200,\n",
-       "                                       random_state=42))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
['season', 'weathersit']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual']
passthrough
RandomForestRegressor(max_depth=20, n_estimators=200, random_state=42)
" ], "text/plain": [ "Pipeline(memory='../.cache',\n", @@ -107979,7 +107961,7 @@ " random_state=42))])" ] }, - "execution_count": 64, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -107998,7 +107980,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -108043,7 +108025,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -108065,7 +108047,7 @@ "dtype: float64" ] }, - "execution_count": 66, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -108085,7 +108067,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -108102,7 +108084,7 @@ "dtype: float64" ] }, - "execution_count": 67, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -108128,7 +108110,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -109042,7 +109024,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -110006,7 +109988,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -110015,7 +109997,7 @@ "np.float64(4706.147416021958)" ] }, - "execution_count": 70, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -110034,7 +110016,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -110114,7 +110096,7 @@ "15944 50 63.895000 -13.895000" ] }, - "execution_count": 71, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -110127,7 +110109,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -112463,7 +112445,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -112484,7 +112466,7 @@ "dtype: float64" ] }, - "execution_count": 73, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -112500,7 +112482,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -113446,7 +113428,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -114402,7 +114384,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 38, "metadata": {}, "outputs": [ { diff --git a/python_env.yaml b/python_env.yaml index 9210229..f5377db 100644 --- a/python_env.yaml +++ b/python_env.yaml @@ -11,7 +11,7 @@ "bandit==1.8.0", "blinker==1.9.0", "cachetools==5.5.0", - "certifi==2024.8.30", + "certifi==2024.12.14", "cffi==1.17.1", "cfgv==3.4.0", "charset-normalizer==3.4.0", @@ -24,7 +24,7 @@ "coverage==7.6.9", "cycler==0.12.1", "databricks-sdk==0.39.0", - "debugpy==1.8.9", + "debugpy==1.8.11", "decli==0.6.2", "decorator==5.1.1", "deprecated==1.2.15", @@ -65,7 +65,7 @@ "markdown==3.7", "markdown-it-py==3.0.0", "markupsafe==3.0.2", - "matplotlib==3.9.3", + "matplotlib==3.10.0", "matplotlib-inline==0.1.7", "mdurl==0.1.2", "mlflow==2.19.0", @@ -108,7 +108,7 @@ "pycparser==2.22", "pydantic==2.10.3", "pydantic-core==2.27.1", - "pydantic-settings==2.6.1", + "pydantic-settings==2.7.0", "pygments==2.18.0", "pynvml==12.0.0", "pyparsing==3.2.0", diff --git a/requirements.txt b/requirements.txt index e86c4d6..4d576c8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,7 +10,7 @@ attrs==24.2.0 bandit==1.8.0 blinker==1.9.0 cachetools==5.5.0 -certifi==2024.8.30 +certifi==2024.12.14 cffi==1.17.1 ; implementation_name == 'pypy' cfgv==3.4.0 charset-normalizer==3.4.0 @@ -23,7 +23,7 @@ contourpy==1.3.1 coverage==7.6.9 cycler==0.12.1 databricks-sdk==0.39.0 -debugpy==1.8.9 +debugpy==1.8.11 decli==0.6.2 decorator==5.1.1 deprecated==1.2.15 @@ -64,7 +64,7 @@ mako==1.3.8 markdown==3.7 markdown-it-py==3.0.0 markupsafe==3.0.2 -matplotlib==3.9.3 +matplotlib==3.10.0 matplotlib-inline==0.1.7 mdurl==0.1.2 mlflow==2.19.0 @@ -107,7 +107,7 @@ pyasn1-modules==0.4.1 pycparser==2.22 ; implementation_name == 'pypy' pydantic==2.10.3 pydantic-core==2.27.1 -pydantic-settings==2.6.1 +pydantic-settings==2.7.0 pygments==2.18.0 pynvml==12.0.0 pyparsing==3.2.0 diff --git a/src/bikes/core/models.py b/src/bikes/core/models.py index 07ec83c..22393c1 100644 --- a/src/bikes/core/models.py +++ b/src/bikes/core/models.py @@ -81,9 +81,6 @@ def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: def explain_model(self) -> schemas.FeatureImportances: """Explain the internal model structure. - Raises: - NotImplementedError: method not implemented. - Returns: schemas.FeatureImportances: feature importances. """ @@ -92,9 +89,6 @@ def explain_model(self) -> schemas.FeatureImportances: def explain_samples(self, inputs: schemas.Inputs) -> schemas.SHAPValues: """Explain model outputs on input samples. - Raises: - NotImplementedError: method not implemented. - Returns: schemas.SHAPValues: SHAP values. """ @@ -141,7 +135,7 @@ class BaselineSklearnModel(Model): "hum", "windspeed", "casual", - # "registered", # too correlated with target + "registered", # too correlated with target ] _categoricals: list[str] = [ "season", @@ -191,10 +185,10 @@ def explain_model(self) -> schemas.FeatureImportances: model = self.get_internal_model() regressor = model.named_steps["regressor"] transformer = model.named_steps["transformer"] - column_names = transformer.get_feature_names_out() + feature = transformer.get_feature_names_out() feature_importances = schemas.FeatureImportances( data={ - "feature": column_names, + "feature": feature, "importance": regressor.feature_importances_, } ) diff --git a/uv.lock b/uv.lock index 48e20d7..498a3a2 100644 --- a/uv.lock +++ b/uv.lock @@ -202,11 +202,11 @@ wheels = [ [[package]] name = "certifi" -version = "2024.8.30" +version = "2024.12.14" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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poetry to uv + +### Fix + +- **tasks**: merge conflict +- **tasks**: fix mlflow.serve task attribute +- **github-actions**: run check on all PR + +### Refactor + +- **release**: prepare before release +- **actions**: split check tasks into subtask for easier debugging (#29) +- **code**: improve abstractions (#28) + ## v2.0.0 (2024-07-28) ### Feat diff --git a/pyproject.toml b/pyproject.toml index 58f66e0..2ed67ee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ [project] name = "bikes" -version = "2.0.0" +version = "3.0.0" description = "Predict the number of bikes available." authors = [{ name = "Médéric HURIER", email = "github@fmind.dev" }] readme = "README.md"