From f16d240dabf0c16012a7a2adce5d76428a25624c 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 16:07:49 +0100 Subject: [PATCH] Release/v3.0.0 (#31) --- .github/actions/setup/action.yml | 12 +- .github/workflows/check.yml | 10 +- .github/workflows/publish.yml | 8 +- .pre-commit-config.yaml | 12 +- CHANGELOG.md | 20 + Dockerfile | 4 +- LICENCE.txt => LICENSE.txt | 2 - README.md | 51 +- 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 | 4470 +++++++++++++++-------------- notebooks/indicators.ipynb | 3147 +++++++++++++------- notebooks/processing.ipynb | 15 +- notebooks/prototype.ipynb | 1320 +++++---- poetry.lock | 3837 ------------------------- poetry.toml | 5 - pyproject.toml | 120 +- python_env.yaml | 194 +- requirements.txt | 250 +- src/bikes/core/metrics.py | 8 +- src/bikes/core/models.py | 16 +- src/bikes/io/__init__.py | 2 +- src/bikes/io/datasets.py | 9 +- src/bikes/io/registries.py | 34 +- src/bikes/io/services.py | 27 +- 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/signers.py | 2 +- src/bikes/utils/splitters.py | 39 +- 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 ++++++++++++++++ 64 files changed, 8287 insertions(+), 8109 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..5fdce59 100644 --- a/.github/workflows/check.yml +++ b/.github/workflows/check.yml @@ -2,7 +2,7 @@ name: Check on: pull_request: branches: - - main + - '*' concurrency: cancel-in-progress: true group: ${{ github.workflow }}-${{ github.ref }} @@ -12,5 +12,9 @@ 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.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 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/CHANGELOG.md b/CHANGELOG.md index 5e7309f..0ce2a27 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,23 @@ +## v3.0.0 (2024-12-14) + +### Feat + +- **mlflow**: bump to 2.19.0 +- **manager**: switch from poetry to uv (#22) +- **manager**: switch from 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/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..ea8821e 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,7 @@ The package leverages several [tools](#tools) and [tips](#tips) to make your MLO You can use this package as part of your MLOps toolkit or platform (e.g., Model Registry, Experiment Tracking, Realtime Inference, ...). **Related Resources**: +- **[LLMOps Coding Package (Example)](https://github.com/callmesora/llmops-python-package/)**: Example with best practices and tools to support your LLMOps projects. - **[MLOps Coding Course (Learning)](https://github.com/MLOps-Courses/mlops-coding-course)**: Learn how to create, develop, and maintain a state-of-the-art MLOps code base. - **[Cookiecutter MLOps Package (Template)](https://github.com/fmind/cookiecutter-mlops-package)**: Start building and deploying Python packages and Docker images for MLOps tasks. @@ -69,7 +70,7 @@ 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 +120,7 @@ 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 +131,10 @@ $ 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 +172,26 @@ 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 +212,7 @@ 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 +234,6 @@ $ 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 +247,15 @@ $ 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 +272,8 @@ $ 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 +684,18 @@ 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 +938,10 @@ 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 +1045,7 @@ 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..dfdccb2 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:v2.19.0 ports: - 5000:5000 environment: diff --git a/notebooks/explain.ipynb b/notebooks/explain.ipynb index 69a6507..2bdb13d 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\"" ] }, { @@ -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", - " \n", - " \n", - " 10\n", - " numericals__hr\n", - " 0.250726\n", + " 19\n", + " numericals__registered\n", + " 0.939335\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", - " 9\n", - " numericals__mnth\n", - " 0.012079\n", + " 16\n", + " numericals__hum\n", + " 0.000064\n", + " \n", + " \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", - "1 -0.650959 -1.001739 \n", - "2 -0.723623 -1.554552 \n", - "3 -0.612611 0.499581 \n", - "4 -0.614574 -1.558409 \n", + "0 -0.004617 0.007796 \n", + "1 -0.003549 0.009620 \n", + "2 -0.005680 0.003407 \n", + "3 -0.008386 -0.001240 \n", + "4 -0.027833 0.021164 \n", "\n", " categoricals__weathersit_2 categoricals__weathersit_3 \\\n", - "0 -1.076349 0.801956 \n", - "1 -0.523719 0.824141 \n", - "2 -1.033979 0.762942 \n", - "3 0.039798 0.778988 \n", - "4 0.111731 -3.927900 \n", + "0 -0.002922 0.001006 \n", + "1 0.034864 0.004730 \n", + "2 0.007366 0.002644 \n", + "3 -0.001726 0.001956 \n", + "4 -0.005834 -0.004372 \n", "\n", " categoricals__weathersit_4 numericals__yr numericals__mnth \\\n", - "0 0.000513 31.495134 2.905728 \n", - "1 0.000501 32.251492 2.752874 \n", - "2 0.000534 32.245430 3.195950 \n", - "3 0.000518 30.905354 2.808645 \n", - "4 0.000904 47.191067 6.631704 \n", + "0 -5.551742e-07 0.079501 -0.032553 \n", + "1 -5.551742e-07 0.080114 -0.001672 \n", + "2 -5.384581e-07 0.075888 -0.039858 \n", + "3 -5.384581e-07 0.089552 -0.000123 \n", + "4 -6.086659e-07 0.018141 0.174302 \n", "\n", " numericals__hr numericals__holiday numericals__weekday \\\n", - "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 -0.012946 0.083744 25.231182 \n", + "1 0.060713 -0.055690 11.599885 \n", + "2 0.042915 -0.029475 17.027773 \n", + "3 -0.040841 0.017910 36.772224 \n", + "4 0.074836 -0.040882 38.722881 \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", - "2 1.559438 2.603706 127.694664 \n", - "3 -0.776883 1.582501 180.403839 \n", - "4 -3.774523 0.583940 202.349609 " + " numericals__registered \n", + "0 93.615906 \n", + "1 79.890282 \n", + "2 80.330391 \n", + "3 106.574913 \n", + "4 246.853470 " ] }, "execution_count": 4, @@ -384,102 +397,122 @@ "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: ['categoricals__season_1', 'categoricals__season_2', 'categoricals__season_3', 'categoricals__season_4', 'categoricals__weathersit_1', 'categoricals__weathersit_2', 'categoricals__weathersit_3', 'categoricals__weathersit_4', 'numericals__yr', 'numericals__mnth', 'numericals__hr', 'numericals__holiday', 'numericals__weekday', 'numericals__workingday', 'numericals__temp', 'numericals__atemp', 'numericals__hum', 'numericals__windspeed', 'numericals__casual', 'numericals__registered']\n" + ] + }, { "data": { "text/plain": [ ".values =\n", " 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", - "95 0.032898 0.004389 -0.022684 \n", - "96 -0.039833 -0.035329 -0.022280 \n", - "97 -0.029910 -0.054200 -0.015400 \n", - "98 0.002795 -0.055408 -0.033671 \n", - "99 0.001024 -0.061020 -0.034896 \n", + "95 0.023137 0.013332 -0.107473 \n", + "96 0.004738 -0.013562 -0.005693 \n", + "97 -0.019759 0.010218 -0.009712 \n", + "98 0.003229 -0.000393 -0.161296 \n", + "99 -0.002694 -0.024382 -0.013047 \n", "\n", " categoricals__season_4 categoricals__weathersit_1 \\\n", - "0 -0.758982 -1.474484 \n", - "1 -0.650959 -1.001739 \n", - "2 -0.723623 -1.554552 \n", - "3 -0.612611 0.499581 \n", - "4 -0.614574 -1.558409 \n", + "0 -0.004617 0.007796 \n", + "1 -0.003549 0.009620 \n", + "2 -0.005680 0.003407 \n", + "3 -0.008386 -0.001240 \n", + "4 -0.027833 0.021164 \n", ".. ... ... \n", - "95 -0.615178 0.264237 \n", - "96 -0.627357 0.463110 \n", - "97 -0.607787 0.455338 \n", - "98 -0.623416 0.560994 \n", - "99 -0.625861 0.542756 \n", + "95 -0.000028 0.015664 \n", + "96 0.006259 0.040204 \n", + "97 -0.002168 0.050850 \n", + "98 0.001767 0.032412 \n", + "99 -0.000830 0.073416 \n", "\n", " categoricals__weathersit_2 categoricals__weathersit_3 \\\n", - "0 -1.076349 0.801956 \n", - "1 -0.523719 0.824141 \n", - "2 -1.033979 0.762942 \n", - "3 0.039798 0.778988 \n", - "4 0.111731 -3.927900 \n", + "0 -0.002922 0.001006 \n", + "1 0.034864 0.004730 \n", + "2 0.007366 0.002644 \n", + "3 -0.001726 0.001956 \n", + "4 -0.005834 -0.004372 \n", ".. ... ... \n", - "95 -0.054727 0.568837 \n", - "96 -0.015223 0.565861 \n", - "97 0.003094 0.551169 \n", - "98 0.020387 0.537736 \n", - "99 0.013132 0.532625 \n", + "95 0.042348 0.001443 \n", + "96 0.015874 0.000851 \n", + "97 0.016159 0.000913 \n", + "98 0.004988 0.001736 \n", + "99 0.008803 0.005390 \n", "\n", " categoricals__weathersit_4 numericals__yr numericals__mnth \\\n", - "0 0.000513 31.495134 2.905728 \n", - "1 0.000501 32.251492 2.752874 \n", - "2 0.000534 32.245430 3.195950 \n", - "3 0.000518 30.905354 2.808645 \n", - "4 0.000904 47.191067 6.631704 \n", + "0 -5.551742e-07 0.079501 -0.032553 \n", + "1 -5.551742e-07 0.080114 -0.001672 \n", + "2 -5.384581e-07 0.075888 -0.039858 \n", + "3 -5.384581e-07 0.089552 -0.000123 \n", + "4 -6.086659e-07 0.018141 0.174302 \n", ".. ... ... ... \n", - "95 0.000399 40.124039 6.724072 \n", - "96 0.000403 35.804981 20.436369 \n", - "97 0.000411 32.999043 26.067795 \n", - "98 0.000392 32.314198 23.293106 \n", - "99 0.000388 32.588284 24.576950 \n", + "95 -5.602442e-07 1.145752 0.013935 \n", + "96 -5.903332e-07 0.702181 -0.239828 \n", + "97 -5.234687e-07 0.233502 0.053566 \n", + "98 -5.234687e-07 1.738129 -0.242034 \n", + "99 -5.903332e-07 2.091767 -0.134973 \n", "\n", " numericals__hr numericals__holiday numericals__weekday \\\n", - "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", - "95 -3.901324 0.138933 0.979801 \n", - "96 4.877304 -0.027108 2.595324 \n", - "97 5.580533 -0.012089 2.779204 \n", - "98 1.114409 0.254242 2.357220 \n", - "99 1.200160 0.254306 2.411582 \n", + "95 0.233593 -0.001234 0.026021 \n", + "96 -0.003729 -0.001246 0.083014 \n", + "97 0.239605 -0.001948 0.132541 \n", + "98 0.097312 0.015623 0.146109 \n", + "99 0.258849 -0.001218 0.013697 \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", - "95 -21.420794 -1.613000 -4.774735 \n", - "96 -20.015820 -0.422287 -6.170654 \n", - "97 -19.195932 -3.357377 -8.327322 \n", - "98 -18.854221 -0.914422 -15.955507 \n", - "99 -19.075937 -1.693157 -12.839770 \n", + "95 0.033179 0.099047 0.023928 \n", + "96 0.006335 -0.164110 -0.198730 \n", + "97 0.047977 -0.179421 -0.228707 \n", + "98 0.029174 0.148351 0.050507 \n", + "99 0.039286 0.102756 0.067073 \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", - "2 1.559438 2.603706 127.694664 \n", - "3 -0.776883 1.582501 180.403839 \n", - "4 -3.774523 0.583940 202.349609 \n", - ".. ... ... ... \n", - "95 -2.793116 1.410505 319.295746 \n", - "96 -1.366720 -0.086119 363.113678 \n", - "97 1.297852 0.499207 374.674683 \n", - "98 1.504436 -0.023106 381.828125 \n", - "99 3.381457 -0.181553 378.957825 \n", + " numericals__hum numericals__windspeed numericals__casual \\\n", + "0 -0.012946 0.083744 25.231182 \n", + "1 0.060713 -0.055690 11.599885 \n", + "2 0.042915 -0.029475 17.027773 \n", + "3 -0.040841 0.017910 36.772224 \n", + "4 0.074836 -0.040882 38.722881 \n", + ".. ... ... ... \n", + "95 0.094390 0.061557 118.444702 \n", + "96 -0.431589 0.055392 125.518913 \n", + "97 -0.302664 -0.151566 136.479645 \n", + "98 -0.501217 0.015435 146.480164 \n", + "99 -0.028538 0.062194 143.070084 \n", "\n", - "[100 rows x 19 columns]" + " numericals__registered \n", + "0 93.615906 \n", + "1 79.890282 \n", + "2 80.330391 \n", + "3 106.574913 \n", + "4 246.853470 \n", + ".. ... \n", + "95 216.020004 \n", + "96 256.033020 \n", + "97 273.089264 \n", + "98 224.673309 \n", + "99 245.905670 \n", + "\n", + "[100 rows x 20 columns]" ] }, "execution_count": 5, @@ -488,8 +521,10 @@ } ], "source": [ - "shap_values = shap.Explanation(samples_explanations, feature_names=samples_explanations.columns.to_list())\n", - "shap_values.feature_names\n", + "shap_values = shap.Explanation(\n", + " samples_explanations, feature_names=samples_explanations.columns.to_list()\n", + ")\n", + "print(\"Features:\", shap_values.feature_names)\n", "shap_values" ] }, @@ -536,47 +571,49 @@ "textposition": "auto", "type": "bar", "x": [ + "numericals__registered", "numericals__casual", - "numericals__hr", - "numericals__workingday", "numericals__yr", - "numericals__mnth", "numericals__hum", - "numericals__atemp", + "numericals__hr", + "numericals__windspeed", "numericals__temp", + "numericals__atemp", + "numericals__mnth", "numericals__weekday", - "numericals__windspeed", - "categoricals__weathersit_3", - "categoricals__season_4", - "categoricals__season_1", - "numericals__holiday", - "categoricals__weathersit_1", "categoricals__weathersit_2", + "categoricals__weathersit_1", "categoricals__season_2", "categoricals__season_3", + "numericals__workingday", + "categoricals__season_1", + "categoricals__season_4", + "numericals__holiday", + "categoricals__weathersit_3", "categoricals__weathersit_4" ], "xaxis": "x", "y": [ - 0.5791462063789368, - 0.2507259249687195, - 0.0785420835018158, - 0.038599804043769836, - 0.0120792705565691, - 0.008070561103522778, - 0.006674219388514757, - 0.006322493776679039, - 0.00494948448613286, - 0.004703037440776825, - 0.0028446626383811235, - 0.002690249355509877, - 0.0013275929959490895, - 0.0011345595121383667, - 0.0009102495387196541, - 0.0005809680442325771, - 0.0005128193879500031, - 0.00018277487833984196, - 0.0000030518597213813337 + 0.939335286617279, + 0.06013893708586693, + 0.00016115649486891925, + 0.00006372830830514431, + 0.000059160523960599676, + 0.000047698642447358, + 0.00004084747706656344, + 0.00003856617695419118, + 0.000034207892895210534, + 0.000027639429390546866, + 0.000021623562133754604, + 0.000008339592568518128, + 0.0000059107342167408206, + 0.000005159051852388075, + 0.000004351996267359937, + 0.000003020162239408819, + 0.0000021705050130549353, + 0.000001173117652797373, + 0.0000010395727940704091, + 1.8287782399539765e-10 ], "yaxis": "y" } @@ -1433,7 +1470,7 @@ } ], "source": [ - "px.bar(models_explanations, x='feature', y='importance', title='Feature Importances')" + "px.bar(models_explanations, x=\"feature\", y=\"importance\", title=\"Feature Importances\")" ] }, { @@ -1479,7 +1516,8 @@ "numericals__atemp", "numericals__hum", "numericals__windspeed", - "numericals__casual" + "numericals__casual", + "numericals__registered" ], "xaxis": "x", "y": [ @@ -1587,2104 +1625,2204 @@ "yaxis": "y", "z": [ [ - 0.169154554605484, - -0.12465005367994308, - -0.20350304245948792, - -0.7589821815490723, - -1.4744840860366821, - -1.0763494968414307, - 0.8019556403160095, - 0.0005125635070726275, - 31.495134353637695, - 2.905728340148926, - -74.03636932373047, - -0.01994742453098297, - -7.118968486785889, - 18.64079475402832, - 2.613896131515503, - 1.898716688156128, - -1.322058081626892, - -0.21036271750926971, - 147.3380889892578 + 0.006651927717030048, + 0.027727307751774788, + 0.006340912077575922, + -0.004616885911673307, + 0.007796195801347494, + -0.0029222434386610985, + 0.0010060640051960945, + -5.551742106035817e-7, + 0.07950128614902496, + -0.03255324438214302, + 0.019729897379875183, + -0.005449031479656696, + -0.09101399779319763, + -0.04055198282003403, + 0.024861138314008713, + 0.003903420874848962, + -0.01294585969299078, + 0.08374375104904175, + 25.231182098388672, + 93.61590576171875 ], [ - 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"px.imshow(samples_explanations, height=700, color_continuous_scale='Bluered', title=\"Sample Explanations\")" + "px.imshow(\n", + " samples_explanations, height=700, color_continuous_scale=\"Bluered\", title=\"Sample Explanations\"\n", + ")" ] } ], @@ -4568,7 +4708,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/notebooks/indicators.ipynb b/notebooks/indicators.ipynb index ae0dad3..b76c8fb 100644 --- a/notebooks/indicators.ipynb +++ b/notebooks/indicators.ipynb @@ -128,9 +128,9 @@ " \n", " 0\n", " file:///home/fmind/mlops-python-package/mlruns...\n", - " 2024-07-21 15:02:02.224\n", - " 598443335336095652\n", - " 2024-07-21 15:02:02.224\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... 2024-07-21 15:02:02.224 \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 598443335336095652 2024-07-21 15:02:02.224 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": [ - "(18, 30)\n" + "(27, 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", + " 2024-12-14 14:42:28.056\n", + " 300971336194126583\n", " active\n", - " 4df1e5931159491c963f2252add508cd\n", + " 9923cb99c1824857b68c0bedb63f8446\n", " Explanations\n", - " 4df1e5931159491c963f2252add508cd\n", - " 2024-07-21 15:31:20.207\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", - " 33.622\n", + " 11.979\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\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", + " 2024-12-14 14:42:13.733\n", + " 300971336194126583\n", " active\n", - " e993a53ee04e4357bad5cec101ab4031\n", + " bfce074e34834054b84e5794450a4fa2\n", " Evaluations\n", - " e993a53ee04e4357bad5cec101ab4031\n", - " 2024-07-21 15:31:07.565\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", - " 5.796\n", + " 6.245\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\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", + " 2024-12-14 14:42:05.031\n", + " 300971336194126583\n", " active\n", - " c17b893e525b44ed89e349421e72510a\n", + " 5ebe28d67ed54300a177eb61825a6688\n", " Inference\n", - " c17b893e525b44ed89e349421e72510a\n", - " 2024-07-21 15:30:59.023\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", - " 5.735\n", + " 5.424\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\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", + " 2024-12-14 14:41:57.245\n", + " 300971336194126583\n", " active\n", - " d538cc2f3f644b6e97b611c1d96801ac\n", + " f29b930df5a54f1fa9ec5fe4b27df8b9\n", " Promotion\n", - " d538cc2f3f644b6e97b611c1d96801ac\n", - " 2024-07-21 15:30:51.594\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", - " 5.375\n", + " 4.606\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\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", + " 2024-12-14 14:41:49.910\n", + " 300971336194126583\n", " active\n", - " a75ea3e9742c48fd9c34a5b8abf9bd89\n", + " 4753bf6b106845d19eca79b7cb329343\n", " Training\n", - " a75ea3e9742c48fd9c34a5b8abf9bd89\n", - " 2024-07-21 15:29:25.495\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", - " 80.807\n", + " 25.996\n", " fmind\n", " file:///home/fmind/mlops-python-package\n", " PROJECT\n", " main\n", - " ee17d0a9de59efd2eb99d667786ac417ec8b3b63\n", + " 1a4b8a25a32f0e933a7db9ec47f763cc7bd17d1c\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\": \"4753bf6b106845d19eca79b7cb329343\"...\n", " NaN\n", " NaN\n", " \n", @@ -402,32 +396,32 @@ ], "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... 2024-12-14 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 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 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 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 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", @@ -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':... 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", @@ -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 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", @@ -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\": \"4753bf6b106845d19eca79b7cb329343\"... 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", + " 2024-12-14 14:37:45.482\n", " \n", - " 2024-07-21 15:30:56.443\n", + " 2024-12-14 14:41:56.590\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'} 2024-12-14 14:37:45.482 \n", "\n", " last_updated_timestamp name tags \n", - "0 2024-07-21 15:30:56.443 bikes {} " + "0 2024-12-14 14:41:56.590 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", + " 2024-12-14 14:41:48.936\n", " None\n", " \n", - " 2024-07-21 15:30:44.679\n", + " 2024-12-14 14:41:48.936\n", " bikes\n", - " a75ea3e9742c48fd9c34a5b8abf9bd89\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", - " 2024-07-21 15:03:06.212\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", - " 2024-07-21 15:03:06.212\n", + " 2024-12-14 14:37:45.487\n", " bikes\n", - " 47302e957d8542198a281aad07b2413b\n", + " d2aed973c12b475f849743599f7bc973\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 2024-12-14 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 2024-07-21 15:30:44.679 bikes a75ea3e9742c48fd9c34a5b8abf9bd89 \n", - "1 2024-07-21 15:03:06.212 bikes 47302e957d8542198a281aad07b2413b \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, @@ -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/300971336194126583", + "300971336194126583", + "2024-12-14T14:35:02.439000", "active", "bikes", {} @@ -788,7 +804,7 @@ "showlegend": true, "type": "box", "x": [ - "2024-07-21T15:02:02.224000" + "2024-12-14T14:35:02.439000" ], "x0": " ", "xaxis": "x", @@ -1676,10 +1692,10 @@ "customdata": [ [ { - "Champion": "2" + "Champion": "3" }, "", - "2024-07-21T15:30:56.443000", + "2024-12-14T14:41:56.590000", "bikes", {} ] @@ -1704,7 +1720,7 @@ "showlegend": false, "type": "box", "x": [ - "2024-07-21T15:03:06.208000" + "2024-12-14T14:37:45.482000" ], "x0": " ", "xaxis": "x", @@ -2588,10 +2604,10 @@ "customdata": [ [ { - "Champion": "2" + "Champion": "3" }, "", - "2024-07-21T15:30:56.443000", + "2024-12-14T14:41:56.590000", "bikes", {} ] @@ -2616,7 +2632,7 @@ "showlegend": false, "type": "box", "x": [ - "2024-07-21T15:03:06.208000" + "2024-12-14T14:37:45.482000" ], "x0": " ", "xaxis": "x", @@ -3502,11 +3518,26 @@ "Champion", "None", "", - "2024-07-21T15:30:44.679000", + "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", - "a75ea3e9742c48fd9c34a5b8abf9bd89", + "ae9f673f6b264240bb79ffd94ca49fe2", "", - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/a75ea3e9742c48fd9c34a5b8abf9bd89/artifacts/model", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/ae9f673f6b264240bb79ffd94ca49fe2/artifacts/model", "READY", "", {}, @@ -3517,11 +3548,11 @@ null, "None", "", - "2024-07-21T15:03:06.212000", + "2024-12-14T14:37:45.487000", "bikes", - "47302e957d8542198a281aad07b2413b", + "d2aed973c12b475f849743599f7bc973", "", - "file:///home/fmind/mlops-python-package/mlruns/598443335336095652/47302e957d8542198a281aad07b2413b/artifacts/model", + "file:///home/fmind/mlops-python-package/mlruns/300971336194126583/d2aed973c12b475f849743599f7bc973/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]}
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mlflow.log-model.history=%{customdata[25]}
mlflow.autologging=%{customdata[26]}
mlflow.parentRunId=%{customdata[27]}", "hovertext": [ - "e110400581f846d08f802c970ae25458" + "e9980ab9c8db4f80a18aadfa47a3e83e" ], - "legendgroup": "capricious-bear-977", + "legendgroup": "smiling-hound-227", "line": { "color": "rgba(255,255,255,0)" }, "marker": { - "color": "#EF553B" + "color": "#FFA15A" }, - "name": "capricious-bear-977", - "offsetgroup": "capricious-bear-977", + "name": "smiling-hound-227", + "offsetgroup": "smiling-hound-227", "orientation": "h", "pointpos": 0, "showlegend": true, "type": "box", "x": [ - 37.246 + 132.755 ], "x0": " ", "xaxis": "x", @@ -8735,18 +9874,27 @@ 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", "sklearn.model_selection._search.GridSearchCV", "bikes.core.models.BaselineSklearnModel", "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" ], "xaxis": "x", @@ -8768,6 +9916,15 @@ 1, 1, 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, 1 ], "yaxis": "y" @@ -9645,7 +10802,7 @@ "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..c967b38 100644 --- a/notebooks/processing.ipynb +++ b/notebooks/processing.ipynb @@ -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..fc52d1e 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" ] }, { @@ -203,7 +203,7 @@ "outputs": [], "source": [ "# change the default theme\n", - "pio.templates.default = \"plotly_dark\"" + "pio.templates.default = \"plotly_white\"" ] }, { @@ -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", @@ -105220,14 +105220,14 @@ "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": { @@ -105243,7 +105243,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -105258,18 +105258,18 @@ "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" } @@ -105587,10 +105587,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -105637,8 +105637,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -105743,18 +105744,18 @@ { "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" @@ -105763,7 +105764,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -105919,123 +105920,113 @@ "#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 } } @@ -106052,8 +106043,11 @@ ], "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 +106081,7 @@ ], "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 +106119,12 @@ "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 +106152,9 @@ " \"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 +106177,9 @@ " \"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.\"" ] }, { @@ -106620,7 +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." ], "text/plain": [ "Pipeline(memory='../.cache',\n", @@ -106665,12 +106667,20 @@ "source": [ "draft = pipeline.Pipeline(\n", " steps=[\n", - " (\"transformer\", compose.ColumnTransformer([\n", - " (\"categoricals\", preprocessing.OneHotEncoder(\n", - " sparse_output=False, handle_unknown=\"ignore\"\n", - " ), categoricals),\n", - " (\"numericals\", \"passthrough\", numericals),\n", - " ], remainder=\"drop\")),\n", + " (\n", + " \"transformer\",\n", + " compose.ColumnTransformer(\n", + " [\n", + " (\n", + " \"categoricals\",\n", + " preprocessing.OneHotEncoder(sparse_output=False, handle_unknown=\"ignore\"),\n", + " categoricals,\n", + " ),\n", + " (\"numericals\", \"passthrough\", numericals),\n", + " ],\n", + " remainder=\"drop\",\n", + " ),\n", + " ),\n", " (\"regressor\", ensemble.RandomForestRegressor(random_state=RANDOM)),\n", " ],\n", " memory=CACHE,\n", @@ -106710,8 +106720,10 @@ ], "source": [ "splitter = model_selection.TimeSeriesSplit(n_splits=SPLITS, test_size=TEST_SIZE)\n", - "for train_index, test_index in splitter.split(inputs_train): # test splitter generation\n", - " print(f\"Train: {train_index.min()} - {train_index.max()}; Test: {test_index.min()} - {test_index.max()}\")" + "for train_index, test_index in splitter.split(inputs_train): # test splitter generation\n", + " print(\n", + " f\"Train: {train_index.min()} - {train_index.max()}; Test: {test_index.min()} - {test_index.max()}\"\n", + " )" ] }, { @@ -106739,7 +106751,23 @@ "text": [ "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", "\n", - "Persisting input arguments took 1.02s 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 0.79s 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 0.90s 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 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" ] }, @@ -107175,7 +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." ], "text/plain": [ "GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=4, test_size=1440),\n", @@ -107314,10 +107344,10 @@ " \n", " \n", " 4\n", - " 9.851526\n", - " 2.065956\n", - " 0.071210\n", - " 0.029418\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", @@ -107331,10 +107361,10 @@ " \n", " \n", " 1\n", - " 6.918022\n", - " 0.886340\n", - " 0.043904\n", - " 0.005060\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", @@ -107348,10 +107378,10 @@ " \n", " \n", " 5\n", - " 11.964708\n", - " 1.272631\n", - " 0.074480\n", - " 0.013998\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", @@ -107365,10 +107395,10 @@ " \n", " \n", " 7\n", - " 8.290697\n", - " 1.105596\n", - " 0.049447\n", - " 0.004403\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", @@ -107382,10 +107412,10 @@ " \n", " \n", " 2\n", - " 8.716824\n", - " 1.402426\n", - " 0.055110\n", - " 0.008181\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", @@ -107403,18 +107433,18 @@ ], "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", - "4 9.851526 2.065956 0.071210 0.029418 \n", - "1 6.918022 0.886340 0.043904 0.005060 \n", - "5 11.964708 1.272631 0.074480 0.013998 \n", - "7 8.290697 1.105596 0.049447 0.004403 \n", - "2 8.716824 1.402426 0.055110 0.008181 \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", - "1 15 200 \n", - "5 20 250 \n", - "7 25 200 \n", - "2 15 250 \n", + " param_regressor__max_depth param_regressor__n_estimators \\\n", + "4 20 200 \n", + "1 15 200 \n", + "5 20 250 \n", + "7 25 200 \n", + "2 15 250 \n", "\n", " params split0_test_score \\\n", "4 {'regressor__max_depth': 20, 'regressor__n_est... -8284.760118 \n", @@ -107890,7 +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." ], "text/plain": [ "Pipeline(memory='../.cache',\n", @@ -107971,7 +108001,7 @@ " ('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), 'transformer__n_jobs': None, 'transformer__remainder': 'drop', 'transformer__sparse_threshold': 0.3, 'transformer__transformer_weights': None, 'transformer__transformers': [('categoricals', OneHotEncoder(handle_unknown='ignore', sparse_output=False), ['season', 'weathersit']), ('numericals', 'passthrough', ['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual'])], 'transformer__verbose': False, 'transformer__verbose_feature_names_out': True, 'transformer__categoricals': OneHotEncoder(handle_unknown='ignore', sparse_output=False), 'transformer__numericals': 'passthrough', 'transformer__categoricals__categories': 'auto', 'transformer__categoricals__drop': None, 'transformer__categoricals__dtype': , 'transformer__categoricals__feature_name_combiner': 'concat', 'transformer__categoricals__handle_unknown': 'ignore', 'transformer__categoricals__max_categories': None, 'transformer__categoricals__min_frequency': None, 'transformer__categoricals__sparse_output': False, 'regressor__bootstrap': True, 'regressor__ccp_alpha': 0.0, 'regressor__criterion': 'squared_error', 'regressor__max_depth': 20, 'regressor__max_features': 1.0, 'regressor__max_leaf_nodes': None, 'regressor__max_samples': None, 'regressor__min_impurity_decrease': 0.0, 'regressor__min_samples_leaf': 1, 'regressor__min_samples_split': 2, 'regressor__min_weight_fraction_leaf': 0.0, 'regressor__monotonic_cst': None, 'regressor__n_estimators': 200, 'regressor__n_jobs': None, 'regressor__oob_score': False, 'regressor__random_state': 42, 'regressor__verbose': 0, 'regressor__warm_start': False}\n" + " 'windspeed', 'casual'])]), 'regressor': RandomForestRegressor(max_depth=20, n_estimators=200, random_state=42), 'transformer__force_int_remainder_cols': True, 'transformer__n_jobs': None, 'transformer__remainder': 'drop', 'transformer__sparse_threshold': 0.3, 'transformer__transformer_weights': None, 'transformer__transformers': [('categoricals', OneHotEncoder(handle_unknown='ignore', sparse_output=False), ['season', 'weathersit']), ('numericals', 'passthrough', ['yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday', 'temp', 'atemp', 'hum', 'windspeed', 'casual'])], 'transformer__verbose': False, 'transformer__verbose_feature_names_out': True, 'transformer__categoricals': OneHotEncoder(handle_unknown='ignore', sparse_output=False), 'transformer__numericals': 'passthrough', 'transformer__categoricals__categories': 'auto', 'transformer__categoricals__drop': None, 'transformer__categoricals__dtype': , 'transformer__categoricals__feature_name_combiner': 'concat', 'transformer__categoricals__handle_unknown': 'ignore', 'transformer__categoricals__max_categories': None, 'transformer__categoricals__min_frequency': None, 'transformer__categoricals__sparse_output': False, 'regressor__bootstrap': True, 'regressor__ccp_alpha': 0.0, 'regressor__criterion': 'squared_error', 'regressor__max_depth': 20, 'regressor__max_features': 1.0, 'regressor__max_leaf_nodes': None, 'regressor__max_samples': None, 'regressor__min_impurity_decrease': 0.0, 'regressor__min_samples_leaf': 1, 'regressor__min_samples_split': 2, 'regressor__min_weight_fraction_leaf': 0.0, 'regressor__monotonic_cst': None, 'regressor__n_estimators': 200, 'regressor__n_jobs': None, 'regressor__oob_score': False, 'regressor__random_state': 42, 'regressor__verbose': 0, 'regressor__warm_start': False}\n" ] } ], @@ -108140,14 +108170,14 @@ "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": { @@ -108163,7 +108193,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -108178,18 +108208,18 @@ "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" } @@ -108507,10 +108537,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -108557,8 +108587,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -108663,18 +108694,18 @@ { "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" @@ -108683,7 +108714,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -108839,123 +108870,113 @@ "#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 } } @@ -109130,14 +109151,14 @@ "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": { @@ -109153,7 +109174,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -109168,18 +109189,18 @@ "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" } @@ -109497,10 +109518,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -109547,8 +109568,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -109653,18 +109675,18 @@ { "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" @@ -109673,7 +109695,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -109829,123 +109851,113 @@ "#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 } } @@ -109962,7 +109974,9 @@ ], "source": [ "dimensions = [col for col in results.columns if col.startswith(\"param_\")]\n", - "px.parallel_categories(results, dimensions=dimensions, color=\"mean_test_score\", title=\"Params by test score\")" + "px.parallel_categories(\n", + " results, dimensions=dimensions, color=\"mean_test_score\", title=\"Params by test score\"\n", + ")" ] }, { @@ -109980,7 +109994,7 @@ { "data": { "text/plain": [ - "4706.147416021958" + "np.float64(4706.147416021958)" ] }, "execution_count": 32, @@ -111577,14 +111591,14 @@ "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": { @@ -111600,7 +111614,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -111615,18 +111629,18 @@ "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" } @@ -111944,10 +111958,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -111994,8 +112008,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -112100,18 +112115,18 @@ { "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" @@ -112120,7 +112135,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -112276,123 +112291,113 @@ "#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 } } @@ -112562,14 +112567,14 @@ "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": { @@ -112585,7 +112590,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -112600,18 +112605,18 @@ "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" } @@ -112929,10 +112934,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -112979,8 +112984,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -113085,18 +113091,18 @@ { "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" @@ -113105,7 +113111,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -113261,123 +113267,113 @@ "#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 } } @@ -113435,16 +113431,6 @@ "execution_count": 37, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/fmind/mlops-python-package/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning:\n", - "\n", - "Persisting input arguments took 0.54s 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": { "application/vnd.plotly.v1+json": { @@ -113529,14 +113515,14 @@ "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": { @@ -113552,7 +113538,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -113567,18 +113553,18 @@ "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" } @@ -113896,10 +113882,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -113946,8 +113932,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -114052,18 +114039,18 @@ { "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" @@ -114072,7 +114059,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -114228,123 +114215,113 @@ "#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 } } @@ -114381,7 +114358,12 @@ ], "source": [ "train_size, train_scores, test_scores = model_selection.learning_curve(\n", - " final, inputs, targets, cv=splitter, scoring=SCORING, random_state=RANDOM,\n", + " final,\n", + " inputs,\n", + " targets,\n", + " cv=splitter,\n", + " scoring=SCORING,\n", + " random_state=RANDOM,\n", ")\n", "learning = pd.DataFrame(\n", " {\n", @@ -114488,14 +114470,14 @@ "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": { @@ -114511,7 +114493,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -114526,18 +114508,18 @@ "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" } @@ -114855,10 +114837,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -114905,8 +114887,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -115011,18 +114994,18 @@ { "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" @@ -115031,7 +115014,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -115187,123 +115170,113 @@ "#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 } } @@ -115420,14 +115393,14 @@ "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": { @@ -115443,7 +115416,7 @@ { "marker": { "line": { - "color": "rgb(17,17,17)", + "color": "white", "width": 0.5 }, "pattern": { @@ -115458,18 +115431,18 @@ "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" } @@ -115787,10 +115760,10 @@ ], "scatter": [ { - "marker": { - "line": { - "color": "#283442" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -115837,8 +115810,9 @@ "scattergl": [ { "marker": { - "line": { - "color": "#283442" + "colorbar": { + "outlinewidth": 0, + "ticks": "" } }, "type": "scattergl" @@ -115943,18 +115917,18 @@ { "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" @@ -115963,7 +115937,7 @@ }, "layout": { "annotationdefaults": { - "arrowcolor": "#f2f5fa", + "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, @@ -116119,123 +116093,113 @@ "#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 } } @@ -116274,8 +116238,13 @@ "for param_name, param_range in PARAM_GRID.items():\n", " print(f\"Validation Curve for: {param_name} -> {param_range}\")\n", " train_scores, test_scores = model_selection.validation_curve(\n", - " final, inputs, targets, cv=splitter, scoring=SCORING,\n", - " param_name=param_name, param_range=param_range,\n", + " final,\n", + " inputs,\n", + " targets,\n", + " cv=splitter,\n", + " scoring=SCORING,\n", + " param_name=param_name,\n", + " param_range=param_range,\n", " )\n", " validation = pd.DataFrame(\n", " {\n", @@ -116285,7 +116254,10 @@ " }\n", " )\n", " curve = px.line(\n", - " validation, x=\"param_value\", y=[\"mean_test_score\", \"mean_train_score\"], title=f\"Validation Curve: {param_name}\"\n", + " validation,\n", + " x=\"param_value\",\n", + " y=[\"mean_test_score\", \"mean_train_score\"],\n", + " title=f\"Validation Curve: {param_name}\",\n", " )\n", " curve.show()" ] @@ -116307,7 +116279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - 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"https://fmind.github.io/mlops-python-package/" -authors = ["Médéric HURIER "] +authors = [{ name = "Médéric HURIER", email = "github@fmind.dev" }] readme = "README.md" -license = "MIT" +requires-python = ">=3.12" +dependencies = [ + "loguru>=0.7.2", + "matplotlib>=3.9.3", + "mlflow>=2.19.0", + "numba>=0.60.0", + "numpy>=2.0.2", + "omegaconf>=2.3.0", + "pandas>=2.2.3", + "pandera>=0.21.0", + "plotly>=5.24.1", + "plyer>=2.1.0", + "psutil>=6.1.0", + "pyarrow>=18.1.0", + "pydantic-settings>=2.6.1", + "pydantic>=2.10.3", + "pynvml>=12.0.0", + "scikit-learn>=1.5.2,<1.6.0", + "setuptools>=75.6.0", + "shap>=0.46.0", +] +license = { file = "LICENSE.txt" } keywords = ["mlops", "python", "package"] -packages = [{ include = "bikes", from = "src" }] + +# LINKS + +[project.urls] +Homepage = "https://github.com/fmind/mlops-python-package" +Documentation = "https://fmind.github.io/mlops-python-package/bikes.html" +Repository = "https://github.com/fmind/mlops-python-package" +"Bug Tracker" = "https://github.com/fmind/mlops-python-package/issues" +Changelog = "https://github.com/fmind/mlops-python-package/blob/main/CHANGELOG.md" # SCRIPTS -[tool.poetry.scripts] +[project.scripts] bikes = 'bikes.scripts:main' # DEPENDENCIES -[tool.poetry.dependencies] -python = "^3.12" -loguru = "^0.7.2" -matplotlib = "^3.9.0" -mlflow = "^2.14.3" -numpy = "^1.26.4" -omegaconf = "^2.3.0" -pandas = "^2.2.2" -pandera = "^0.20.1" -plotly = "^5.22.0" -plyer = "^2.1.0" -psutil = "^6.0.0" -pyarrow = "^15.0.2" -pydantic = "^2.7.4" -pydantic-settings = "^2.3.4" -pynvml = "^11.5.0" -setuptools = "^71.1.0" -scikit-learn = "1.4.2" -shap = "^0.46.0" - -[tool.poetry.group.checks.dependencies] -bandit = "^1.7.9" -coverage = "^7.5.4" -mypy = "^1.10.1" -pytest = "^8.2.2" -pytest-cov = "^5.0.0" -pytest-xdist = "^3.6.1" -pandera = { extras = ["mypy"], version = "^0.20.1" } -ruff = "^0.5.0" -pytest-mock = "^3.14.0" - -[tool.poetry.group.commits.dependencies] -commitizen = "^3.27.0" -pre-commit = "^3.7.1" - -[tool.poetry.group.dev.dependencies] -invoke = "^2.2.0" - -[tool.poetry.group.docs.dependencies] -pdoc = "^14.5.1" - -[tool.poetry.group.notebooks.dependencies] -ipykernel = "^6.29.4" -nbformat = "^5.10.4" - -# CONFIGURATIONS +[dependency-groups] +checks = [ + "bandit>=1.8.0", + "coverage>=7.6.8", + "mypy>=1.13.0", + "pandera[mypy]>=0.21.0", + "pytest-cov>=6.0.0", + "pytest-mock>=3.14.0", + "pytest-xdist>=3.6.1", + "pytest>=8.3.3", + "ruff>=0.8.1", +] +commits = ["commitizen>=4.0.0", "pre-commit>=4.0.1"] +dev = ["invoke>=2.2.0"] +docs = ["pdoc>=15.0.0"] +notebooks = ["ipykernel>=6.29.5", "nbformat>=5.10.4"] + +# TOOLS + +[tool.uv] +default-groups = ["checks", "commits", "dev", "docs", "notebooks"] [tool.bandit] targets = ["src"] @@ -75,7 +78,7 @@ targets = ["src"] name = "cz_conventional_commits" tag_format = "v$version" version_scheme = "pep440" -version_provider = "poetry" +version_provider = "pep621" changelog_start_rev = "v1.0.0" update_changelog_on_bump = true @@ -86,7 +89,6 @@ omit = ["__main__.py"] [tool.mypy] pretty = true -strict = true python_version = "3.12" check_untyped_defs = true ignore_missing_imports = true @@ -114,5 +116,5 @@ convention = "google" # SYSTEMS [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/python_env.yaml b/python_env.yaml index ee99e49..f5377db 100644 --- a/python_env.yaml +++ b/python_env.yaml @@ -1,93 +1,163 @@ { "python": "3.12", "dependencies": [ - "alembic==1.13.2", - "aniso8601==9.0.1", + "alembic==1.14.0", "annotated-types==0.7.0", "antlr4-python3-runtime==4.9.3", - "blinker==1.8.2", - "cachetools==5.4.0", - "certifi==2024.7.4", - "charset-normalizer==3.3.2", + "appnope==0.1.4", + "argcomplete==3.5.2", + "asttokens==3.0.0", + "attrs==24.2.0", + "bandit==1.8.0", + "blinker==1.9.0", + "cachetools==5.5.0", + "certifi==2024.12.14", + "cffi==1.17.1", + "cfgv==3.4.0", + "charset-normalizer==3.4.0", "click==8.1.7", - 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"pandas==2.2.2", - "pandera==0.20.3", - "pillow==10.4.0", - "plotly==5.23.0", + "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==15.0.1", + "pexpect==4.9.0", + "pillow==11.0.0", + "platformdirs==4.3.6", + "plotly==5.24.1", + "pluggy==1.5.0", "plyer==2.1.0", - "protobuf==4.25.4", - "psutil==6.0.0", - "pyarrow==15.0.2", - "pydantic-core==2.20.1", - "pydantic-settings==2.3.4", - "pydantic==2.8.2", - "pynvml==11.5.3", - "pyparsing==3.1.2", + "pre-commit==4.0.1", + "prompt-toolkit==3.0.36", + "protobuf==5.29.1", + "psutil==6.1.0", + "ptyprocess==0.7.0", + "pure-eval==0.2.3", + "pyarrow==18.1.0", + "pyasn1==0.6.1", + "pyasn1-modules==0.4.1", + "pycparser==2.22", + "pydantic==2.10.3", + "pydantic-core==2.27.1", + "pydantic-settings==2.7.0", + "pygments==2.18.0", + "pynvml==12.0.0", + "pyparsing==3.2.0", + "pytest==8.3.4", + "pytest-cov==6.0.0", + "pytest-mock==3.14.0", + "pytest-xdist==3.6.1", "python-dateutil==2.9.0.post0", "python-dotenv==1.0.1", - "pytz==2024.1", - "pyyaml==6.0.1", - "querystring-parser==1.2.4", + "pytz==2024.2", + "pyyaml==6.0.2", + "pyzmq==26.2.0", + "questionary==2.0.1", + "referencing==0.35.1", "requests==2.32.3", - "scikit-learn==1.4.2", - "scipy==1.14.0", - "setuptools==71.1.0", + "rich==13.9.4", + "rpds-py==0.22.3", + "rsa==4.9", + "ruff==0.8.3", + "scikit-learn==1.5.2", + "scipy==1.14.1", + "setuptools==75.6.0", "shap==0.46.0", - "six==1.16.0", + "six==1.17.0", "slicer==0.0.8", "smmap==5.0.1", - "sqlalchemy==2.0.31", - "sqlparse==0.5.1", - "tenacity==8.5.0", + "sqlalchemy==2.0.36", + "sqlparse==0.5.3", + "stack-data==0.6.3", + "stevedore==5.4.0", + "tenacity==9.0.0", + "termcolor==2.5.0", "threadpoolctl==3.5.0", - "tqdm==4.66.4", - "typeguard==4.3.0", + "tomlkit==0.13.2", + "tornado==6.4.2", + "tqdm==4.67.1", + "traitlets==5.14.3", + "typeguard==4.4.1", + "types-pytz==2024.2.0.20241003", "typing-extensions==4.12.2", "typing-inspect==0.9.0", - "tzdata==2024.1", - "urllib3==2.2.2", - "waitress==3.0.0", - "werkzeug==3.0.3", - "win32-setctime==1.1.0", - "wrapt==1.16.0", - "zipp==3.19.2" + "tzdata==2024.2", + "urllib3==2.2.3", + "virtualenv==20.28.0", + "waitress==3.0.2", + "wcwidth==0.2.13", + "werkzeug==3.1.3", + "win32-setctime==1.2.0", + "wrapt==1.17.0", + "zipp==3.21.0" ] } diff --git a/requirements.txt b/requirements.txt index 5221e74..4d576c8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,89 +1,161 @@ -alembic==1.13.2 ; python_version >= "3.12" and python_version < "4.0" -aniso8601==9.0.1 ; python_version >= "3.12" and python_version < "4.0" -annotated-types==0.7.0 ; python_version >= "3.12" and python_version < "4.0" -antlr4-python3-runtime==4.9.3 ; python_version >= "3.12" and python_version < "4.0" -blinker==1.8.2 ; python_version >= "3.12" and python_version < "4.0" -cachetools==5.4.0 ; python_version >= "3.12" and python_version < "4.0" -certifi==2024.7.4 ; python_version >= "3.12" and python_version < "4.0" -charset-normalizer==3.3.2 ; python_version >= "3.12" and python_version < "4.0" -click==8.1.7 ; python_version >= "3.12" and python_version < "4.0" -cloudpickle==3.0.0 ; python_version >= "3.12" and python_version < "4.0" -colorama==0.4.6 ; python_version >= "3.12" and python_version < "4.0" and (sys_platform == "win32" or platform_system == "Windows") -contourpy==1.2.1 ; python_version >= "3.12" and python_version < "4.0" -cycler==0.12.1 ; python_version >= "3.12" and python_version < "4.0" -deprecated==1.2.14 ; python_version >= "3.12" and python_version < "4.0" -docker==7.1.0 ; python_version >= "3.12" and python_version < "4.0" -entrypoints==0.4 ; python_version >= "3.12" and python_version < "4.0" -flask==3.0.3 ; python_version >= "3.12" and python_version < "4.0" -fonttools==4.53.1 ; python_version >= "3.12" and python_version < "4.0" -gitdb==4.0.11 ; python_version >= "3.12" and python_version < "4.0" -gitpython==3.1.43 ; python_version >= "3.12" and python_version < "4.0" -graphene==3.3 ; python_version >= "3.12" and python_version < "4.0" -graphql-core==3.2.3 ; python_version >= "3.12" and python_version < "4" -graphql-relay==3.2.0 ; python_version >= "3.12" and python_version < "4" -greenlet==3.0.3 ; python_version < "3.13" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32") and python_version >= "3.12" -gunicorn==22.0.0 ; python_version >= "3.12" and python_version < "4.0" and platform_system != "Windows" -idna==3.7 ; python_version >= "3.12" and python_version < "4.0" -importlib-metadata==7.2.1 ; python_version >= "3.12" and python_version < "4.0" -itsdangerous==2.2.0 ; python_version >= "3.12" and python_version < "4.0" -jinja2==3.1.4 ; python_version >= "3.12" and python_version < "4.0" -joblib==1.4.2 ; python_version >= "3.12" and python_version < "4.0" -kiwisolver==1.4.5 ; python_version >= "3.12" and python_version < "4.0" -llvmlite==0.43.0 ; python_version >= "3.12" and python_version < "4.0" -loguru==0.7.2 ; python_version >= "3.12" and python_version < "4.0" -mako==1.3.5 ; python_version >= "3.12" and python_version < "4.0" -markdown==3.6 ; python_version >= "3.12" and python_version < "4.0" -markupsafe==2.1.5 ; python_version >= "3.12" and python_version < "4.0" -matplotlib==3.9.1 ; python_version >= "3.12" and python_version < "4.0" -mlflow==2.14.3 ; python_version >= "3.12" and python_version < "4.0" -multimethod==1.10 ; python_version >= "3.12" and python_version < "4.0" -mypy-extensions==1.0.0 ; python_version >= "3.12" and python_version < "4.0" -numba==0.60.0 ; python_version >= "3.12" and python_version < "4.0" -numpy==1.26.4 ; python_version >= "3.12" and python_version < "4.0" -omegaconf==2.3.0 ; python_version >= "3.12" and python_version < "4.0" -opentelemetry-api==1.26.0 ; python_version >= "3.12" and python_version < "4.0" -opentelemetry-sdk==1.26.0 ; python_version >= "3.12" and python_version < "4.0" -opentelemetry-semantic-conventions==0.47b0 ; python_version >= "3.12" and python_version < "4.0" -packaging==24.1 ; python_version >= "3.12" and python_version < "4.0" -pandas==2.2.2 ; python_version >= "3.12" and python_version < "4.0" -pandera==0.20.3 ; python_version >= "3.12" and python_version < "4.0" -pillow==10.4.0 ; python_version >= "3.12" and python_version < "4.0" -plotly==5.23.0 ; python_version >= "3.12" and python_version < "4.0" -plyer==2.1.0 ; python_version >= "3.12" and python_version < "4.0" -protobuf==4.25.4 ; python_version >= "3.12" and python_version < "4.0" -psutil==6.0.0 ; python_version >= "3.12" and python_version < "4.0" -pyarrow==15.0.2 ; python_version >= "3.12" and python_version < "4.0" -pydantic-core==2.20.1 ; python_version >= "3.12" and python_version < "4.0" -pydantic-settings==2.3.4 ; python_version >= "3.12" and python_version < "4.0" -pydantic==2.8.2 ; python_version >= "3.12" and python_version < "4.0" -pynvml==11.5.3 ; python_version >= "3.12" and python_version < "4.0" -pyparsing==3.1.2 ; python_version >= "3.12" and python_version < "4.0" -python-dateutil==2.9.0.post0 ; python_version >= "3.12" and python_version < "4.0" -python-dotenv==1.0.1 ; python_version >= "3.12" and python_version < "4.0" -pytz==2024.1 ; python_version >= "3.12" and python_version < "4.0" -pywin32==306 ; python_version >= "3.12" and python_version < "4.0" and sys_platform == "win32" -pyyaml==6.0.1 ; python_version >= "3.12" and python_version < "4.0" -querystring-parser==1.2.4 ; python_version >= "3.12" and python_version < "4.0" -requests==2.32.3 ; python_version >= "3.12" and python_version < "4.0" -scikit-learn==1.4.2 ; python_version >= "3.12" and python_version < "4.0" -scipy==1.14.0 ; python_version >= "3.12" and python_version < "4.0" -setuptools==71.1.0 ; python_version >= "3.12" and python_version < "4.0" -shap==0.46.0 ; python_version >= "3.12" and python_version < "4.0" -six==1.16.0 ; python_version >= "3.12" and python_version < "4.0" -slicer==0.0.8 ; python_version >= "3.12" and python_version < "4.0" -smmap==5.0.1 ; python_version >= "3.12" and python_version < "4.0" -sqlalchemy==2.0.31 ; python_version >= "3.12" and python_version < "4.0" -sqlparse==0.5.1 ; python_version >= "3.12" and python_version < "4.0" -tenacity==8.5.0 ; python_version >= "3.12" and python_version < "4.0" -threadpoolctl==3.5.0 ; python_version >= "3.12" and python_version < "4.0" -tqdm==4.66.4 ; python_version >= "3.12" and python_version < "4.0" -typeguard==4.3.0 ; python_version >= "3.12" and python_version < "4.0" -typing-extensions==4.12.2 ; python_version >= "3.12" and python_version < "4.0" -typing-inspect==0.9.0 ; python_version >= "3.12" and python_version < "4.0" -tzdata==2024.1 ; python_version >= "3.12" and python_version < "4.0" -urllib3==2.2.2 ; python_version >= "3.12" and python_version < "4.0" -waitress==3.0.0 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Windows" -werkzeug==3.0.3 ; python_version >= "3.12" and python_version < "4.0" -win32-setctime==1.1.0 ; python_version >= "3.12" and python_version < "4.0" and sys_platform == "win32" -wrapt==1.16.0 ; python_version >= "3.12" and python_version < "4.0" -zipp==3.19.2 ; python_version >= "3.12" and python_version < "4.0" +# This file was autogenerated by uv via the following command: +# uv export --format=requirements-txt --no-dev --no-hashes --no-editable --no-emit-project --output-file=requirements.txt +alembic==1.14.0 +annotated-types==0.7.0 +antlr4-python3-runtime==4.9.3 +appnope==0.1.4 ; platform_system == 'Darwin' +argcomplete==3.5.2 +asttokens==3.0.0 +attrs==24.2.0 +bandit==1.8.0 +blinker==1.9.0 +cachetools==5.5.0 +certifi==2024.12.14 +cffi==1.17.1 ; implementation_name == 'pypy' +cfgv==3.4.0 +charset-normalizer==3.4.0 +click==8.1.7 +cloudpickle==3.1.0 +colorama==0.4.6 +comm==0.2.2 +commitizen==4.1.0 +contourpy==1.3.1 +coverage==7.6.9 +cycler==0.12.1 +databricks-sdk==0.39.0 +debugpy==1.8.11 +decli==0.6.2 +decorator==5.1.1 +deprecated==1.2.15 +distlib==0.3.9 +docker==7.1.0 +execnet==2.1.1 +executing==2.1.0 +fastjsonschema==2.21.1 +filelock==3.16.1 +flask==3.1.0 +fonttools==4.55.3 +gitdb==4.0.11 +gitpython==3.1.43 +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 < '3.13' 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 (python_full_version < '3.13' and platform_machine == 'amd64') or (python_full_version < '3.13' and platform_machine == 'ppc64le') or (python_full_version < '3.13' and platform_machine == 'win32') or (python_full_version < '3.13' and platform_machine == 'x86_64') +gunicorn==23.0.0 ; platform_system != 'Windows' +identify==2.6.3 +idna==3.10 +importlib-metadata==8.5.0 +iniconfig==2.0.0 +ipykernel==6.29.5 +ipython==8.18.0 +itsdangerous==2.2.0 +jedi==0.19.2 +jinja2==3.1.4 +joblib==1.4.2 +jsonschema==4.23.0 +jsonschema-specifications==2024.10.1 +jupyter-client==8.6.3 +jupyter-core==5.7.2 +kiwisolver==1.4.7 +llvmlite==0.43.0 +loguru==0.7.3 +mako==1.3.8 +markdown==3.7 +markdown-it-py==3.0.0 +markupsafe==3.0.2 +matplotlib==3.10.0 +matplotlib-inline==0.1.7 +mdurl==0.1.2 +mlflow==2.19.0 +mlflow-skinny==2.19.0 +multimethod==1.12 +mypy==1.13.0 +mypy-extensions==1.0.0 +nbformat==5.10.4 +nest-asyncio==1.6.0 +nodeenv==1.9.1 +numba==0.60.0 +numpy==2.0.2 +nvidia-ml-py==12.560.30 +omegaconf==2.3.0 +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==15.0.1 +pexpect==4.9.0 ; sys_platform != 'win32' +pillow==11.0.0 +platformdirs==4.3.6 +plotly==5.24.1 +pluggy==1.5.0 +plyer==2.1.0 +pre-commit==4.0.1 +prompt-toolkit==3.0.36 +protobuf==5.29.1 +psutil==6.1.0 +ptyprocess==0.7.0 ; sys_platform != 'win32' +pure-eval==0.2.3 +pyarrow==18.1.0 +pyasn1==0.6.1 +pyasn1-modules==0.4.1 +pycparser==2.22 ; implementation_name == 'pypy' +pydantic==2.10.3 +pydantic-core==2.27.1 +pydantic-settings==2.7.0 +pygments==2.18.0 +pynvml==12.0.0 +pyparsing==3.2.0 +pytest==8.3.4 +pytest-cov==6.0.0 +pytest-mock==3.14.0 +pytest-xdist==3.6.1 +python-dateutil==2.9.0.post0 +python-dotenv==1.0.1 +pytz==2024.2 +pywin32==308 ; sys_platform == 'win32' +pyyaml==6.0.2 +pyzmq==26.2.0 +questionary==2.0.1 +referencing==0.35.1 +requests==2.32.3 +rich==13.9.4 +rpds-py==0.22.3 +rsa==4.9 +ruff==0.8.3 +scikit-learn==1.5.2 +scipy==1.14.1 +setuptools==75.6.0 +shap==0.46.0 +six==1.17.0 +slicer==0.0.8 +smmap==5.0.1 +sqlalchemy==2.0.36 +sqlparse==0.5.3 +stack-data==0.6.3 +stevedore==5.4.0 +tenacity==9.0.0 +termcolor==2.5.0 +threadpoolctl==3.5.0 +tomlkit==0.13.2 +tornado==6.4.2 +tqdm==4.67.1 +traitlets==5.14.3 +typeguard==4.4.1 +types-pytz==2024.2.0.20241003 +typing-extensions==4.12.2 +typing-inspect==0.9.0 +tzdata==2024.2 +urllib3==2.2.3 +virtualenv==20.28.0 +waitress==3.0.2 ; platform_system == 'Windows' +wcwidth==0.2.13 +werkzeug==3.1.3 +win32-setctime==1.2.0 ; sys_platform == 'win32' +wrapt==1.17.0 +zipp==3.21.0 diff --git a/src/bikes/core/metrics.py b/src/bikes/core/metrics.py index 37b64b5..04fb788 100644 --- a/src/bikes/core/metrics.py +++ b/src/bikes/core/metrics.py @@ -10,13 +10,14 @@ import mlflow import pandas as pd import pydantic as pdt -from sklearn import metrics +from mlflow.metrics import MetricValue +from sklearn import metrics as sklearn_metrics from bikes.core import models, schemas # %% TYPINGS -MlflowMetric: T.TypeAlias = mlflow.metrics.MetricValue +MlflowMetric: T.TypeAlias = MetricValue MlflowThreshold: T.TypeAlias = mlflow.models.MetricThreshold MlflowModelValidationFailedException: T.TypeAlias = ( mlflow.models.evaluation.validation.ModelValidationFailedException @@ -117,7 +118,7 @@ class SklearnMetric(Metric): @T.override def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float: - metric = getattr(metrics, self.name) + metric = getattr(sklearn_metrics, self.name) sign = 1 if self.greater_is_better else -1 y_true = targets[schemas.TargetsSchema.cnt] y_pred = outputs[schemas.OutputsSchema.prediction] @@ -126,6 +127,7 @@ def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float: MetricKind = SklearnMetric +MetricsKind: T.TypeAlias = list[T.Annotated[MetricKind, pdt.Field(discriminator="KIND")]] # %% THRESHOLDS diff --git a/src/bikes/core/models.py b/src/bikes/core/models.py index dac1acc..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", @@ -163,7 +157,9 @@ def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSkle remainder="drop", ) regressor = ensemble.RandomForestRegressor( - max_depth=self.max_depth, n_estimators=self.n_estimators, random_state=self.random_state + max_depth=self.max_depth, + n_estimators=self.n_estimators, + random_state=self.random_state, ) # pipeline self._pipeline = pipeline.Pipeline( @@ -189,10 +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/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 623f69b..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 @@ -87,7 +88,11 @@ def lineage( predictions: str | None = None, ) -> Lineage: return lineage.from_pandas( - df=data, name=name, source=self.path, targets=targets, predictions=predictions + df=data, + name=name, + source=self.path, + targets=targets, + predictions=predictions, ) diff --git a/src/bikes/io/registries.py b/src/bikes/io/registries.py index 82a824d..c4e33be 100644 --- a/src/bikes/io/registries.py +++ b/src/bikes/io/registries.py @@ -7,6 +7,7 @@ import mlflow import pydantic as pdt +from mlflow.pyfunc import PyFuncModel, PythonModel, PythonModelContext from bikes.core import models, schemas from bikes.utils import signers @@ -82,7 +83,10 @@ class Saver(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): @abc.abstractmethod def save( - self, model: models.Model, signature: signers.Signature, input_example: schemas.Inputs + self, + model: models.Model, + signature: signers.Signature, + input_example: schemas.Inputs, ) -> Info: """Save a model in the model registry. @@ -104,7 +108,7 @@ class CustomSaver(Saver): KIND: T.Literal["CustomSaver"] = "CustomSaver" - class Adapter(mlflow.pyfunc.PythonModel): # type: ignore[misc] + class Adapter(PythonModel): # type: ignore[misc] """Adapt a custom model to the Mlflow PyFunc flavor for saving operations. https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html?#mlflow.pyfunc.PythonModel @@ -120,14 +124,14 @@ def __init__(self, model: models.Model): def predict( self, - context: mlflow.pyfunc.PythonModelContext, + context: PythonModelContext, model_input: schemas.Inputs, params: dict[str, T.Any] | None = None, ) -> schemas.Outputs: """Generate predictions with a custom model for the given inputs. Args: - context (mlflow.pyfunc.PythonModelContext): mlflow context. + context (mlflow.PythonModelContext): mlflow context. model_input (schemas.Inputs): inputs for the mlflow model. params (dict[str, T.Any] | None): additional parameters. @@ -138,7 +142,10 @@ def predict( @T.override def save( - self, model: models.Model, signature: signers.Signature, input_example: schemas.Inputs + self, + model: models.Model, + signature: signers.Signature, + input_example: schemas.Inputs, ) -> Info: adapter = CustomSaver.Adapter(model=model) return mlflow.pyfunc.log_model( @@ -167,12 +174,15 @@ def save( self, model: models.Model, signature: signers.Signature, - input_example: schemas.Inputs | None = None, - ) -> mlflow.entities.model_registry.ModelVersion: + input_example: schemas.Inputs, + ) -> Info: builtin_model = model.get_internal_model() module = getattr(mlflow, self.flavor) return module.log_model( - builtin_model, artifact_path=self.path, signature=signature, input_example=input_example + builtin_model, + artifact_path=self.path, + signature=signature, + input_example=input_example, ) @@ -227,11 +237,11 @@ class CustomLoader(Loader): class Adapter(Loader.Adapter): """Adapt a custom model for the project inference.""" - def __init__(self, model: mlflow.pyfunc.PyFuncModel) -> None: + def __init__(self, model: PyFuncModel) -> None: """Initialize the adapter from an mlflow pyfunc model. Args: - model (mlflow.pyfunc.PyFuncModel): mlflow pyfunc model. + model (PyFuncModel): mlflow pyfunc model. """ self.model = model @@ -261,11 +271,11 @@ class BuiltinLoader(Loader): class Adapter(Loader.Adapter): """Adapt a builtin model for the project inference.""" - def __init__(self, model: mlflow.pyfunc.PyFuncModel) -> None: + def __init__(self, model: PyFuncModel) -> None: """Initialize the adapter from an mlflow pyfunc model. Args: - model (mlflow.pyfunc.PyFuncModel): mlflow pyfunc model. + model (PyFuncModel): mlflow pyfunc model. """ self.model = model diff --git a/src/bikes/io/services.py b/src/bikes/io/services.py index d821c35..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 @@ -113,11 +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): @@ -196,7 +213,7 @@ 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/jobs/evaluations.py b/src/bikes/jobs/evaluations.py index b6ecfd9..2f8b216 100644 --- a/src/bikes/jobs/evaluations.py +++ b/src/bikes/jobs/evaluations.py @@ -25,7 +25,7 @@ class EvaluationsJob(base.Job): targets (datasets.ReaderKind): reader for the targets data. model_type (str): model type (e.g. "regressor", "classifier"). alias_or_version (str | int): alias or version for the model. - metrics (metrics_.MetricKind): metrics for the reporting. + metrics (metrics_.MetricsKind): metric list to compute. evaluators (list[str]): list of evaluators to use. thresholds (dict[str, metrics_.Threshold] | None): metric thresholds. """ @@ -42,8 +42,10 @@ class EvaluationsJob(base.Job): # Model model_type: str = "regressor" alias_or_version: str | int = "Champion" + # Loader + loader: registries.LoaderKind = pdt.Field(registries.CustomLoader(), discriminator="KIND") # Metrics - metrics: list[metrics_.MetricKind] = pdt.Field([metrics_.SklearnMetric()], discriminator="KIND") + metrics: metrics_.MetricsKind = [metrics_.SklearnMetric()] # Evaluators evaluators: list[str] = ["default"] # Thresholds @@ -86,21 +88,31 @@ def run(self) -> base.Locals: ) mlflow.log_input(dataset=targets_lineage, context=self.run_config.name) logger.debug("- Targets lineage: {}", targets_lineage.to_dict()) - # dataset - logger.info("Create dataset: inputs & targets") - dataset = mlflow.data.from_pandas( - df=pd.concat([inputs, targets], axis="columns"), - name="evaluation", - source=f"{inputs_lineage.source.uri} & {targets_lineage.source.uri}", - targets=schemas.TargetsSchema.cnt, - ) - logger.debug("- Dataset: {}", dataset.to_dict()) # model logger.info("With model: {}", self.mlflow_service.registry_name) model_uri = registries.uri_for_model_alias_or_version( - name=self.mlflow_service.registry_name, alias_or_version=self.alias_or_version + name=self.mlflow_service.registry_name, + alias_or_version=self.alias_or_version, ) logger.debug("- Model URI: {}", model_uri) + # loader + logger.info("Load model: {}", self.loader) + model = self.loader.load(uri=model_uri) + logger.debug("- Model: {}", model) + # outputs + logger.info("Predict outputs: {}", len(inputs)) + outputs = model.predict(inputs=inputs) # checked + logger.debug("- Outputs shape: {}", outputs.shape) + # dataset + logger.info("Create dataset: inputs & targets & outputs") + dataset_ = pd.concat([inputs, targets, outputs], axis="columns") + dataset = mlflow.data.from_pandas( # type: ignore[attr-defined] + df=dataset_, + name="evaluation", + targets=schemas.TargetsSchema.cnt, + predictions=schemas.OutputsSchema.prediction, + ) + logger.debug("- Dataset: {}", dataset.to_dict()) # metrics logger.debug("Convert metrics: {}", self.metrics) extra_metrics = [metric.to_mlflow() for metric in self.metrics] @@ -115,7 +127,6 @@ def run(self) -> base.Locals: logger.info("Compute evaluations: {}", self.model_type) evaluations = mlflow.evaluate( data=dataset, - model=model_uri, model_type=self.model_type, evaluators=self.evaluators, extra_metrics=extra_metrics, diff --git a/src/bikes/jobs/explanations.py b/src/bikes/jobs/explanations.py index 3983a9d..163adab 100644 --- a/src/bikes/jobs/explanations.py +++ b/src/bikes/jobs/explanations.py @@ -49,7 +49,8 @@ def run(self) -> base.Locals: # model logger.info("With model: {}", self.mlflow_service.registry_name) model_uri = registries.uri_for_model_alias_or_version( - name=self.mlflow_service.registry_name, alias_or_version=self.alias_or_version + name=self.mlflow_service.registry_name, + alias_or_version=self.alias_or_version, ) logger.debug("- Model URI: {}", model_uri) # loader @@ -61,7 +62,7 @@ def run(self) -> base.Locals: logger.info("Explain model: {}", model) models_explanations = model.explain_model() logger.debug("- Models explanations shape: {}", models_explanations.shape) - # - samples + # # - samples logger.info("Explain samples: {}", len(inputs_samples)) samples_explanations = model.explain_samples(inputs=inputs_samples) logger.debug("- Samples explanations shape: {}", samples_explanations.shape) @@ -74,6 +75,7 @@ def run(self) -> base.Locals: self.samples_explanations.write(data=samples_explanations) # notify self.alerts_service.notify( - title="Explanations Job Finished", message=f"Features Count: {len(models_explanations)}" + title="Explanations Job Finished", + message=f"Features Count: {len(models_explanations)}", ) return locals() diff --git a/src/bikes/jobs/inference.py b/src/bikes/jobs/inference.py index aba3eae..878f578 100644 --- a/src/bikes/jobs/inference.py +++ b/src/bikes/jobs/inference.py @@ -47,7 +47,8 @@ def run(self) -> base.Locals: # model logger.info("With model: {}", self.mlflow_service.registry_name) model_uri = registries.uri_for_model_alias_or_version( - name=self.mlflow_service.registry_name, alias_or_version=self.alias_or_version + name=self.mlflow_service.registry_name, + alias_or_version=self.alias_or_version, ) logger.debug("- Model URI: {}", model_uri) # loader diff --git a/src/bikes/jobs/training.py b/src/bikes/jobs/training.py index 571d291..5a2f36e 100644 --- a/src/bikes/jobs/training.py +++ b/src/bikes/jobs/training.py @@ -24,7 +24,7 @@ class TrainingJob(base.Job): inputs (datasets.ReaderKind): reader for the inputs data. targets (datasets.ReaderKind): reader for the targets data. model (models.ModelKind): machine learning model to train. - metrics (metrics_.MetricKind): metrics for the reporting. + metrics (metrics_.MetricsKind): metric list to compute. splitter (splitters.SplitterKind): data sets splitter. saver (registries.SaverKind): model saver. signer (signers.SignerKind): model signer. @@ -41,7 +41,7 @@ class TrainingJob(base.Job): # Model model: models.ModelKind = pdt.Field(models.BaselineSklearnModel(), discriminator="KIND") # Metrics - metrics: list[metrics_.MetricKind] = pdt.Field([metrics_.SklearnMetric()], discriminator="KIND") + metrics: metrics_.MetricsKind = [metrics_.SklearnMetric()] # Splitter splitter: splitters.SplitterKind = pdt.Field( splitters.TrainTestSplitter(), discriminator="KIND" @@ -134,6 +134,7 @@ def run(self) -> base.Locals: logger.debug("- Model version: {}", model_version) # notify self.alerts_service.notify( - title="Training Job Finished", message=f"Model version: {model_version.version}" + title="Training Job Finished", + message=f"Model version: {model_version.version}", ) return locals() 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 bbc52ed..0740bc0 100644 --- a/src/bikes/utils/splitters.py +++ b/src/bikes/utils/splitters.py @@ -34,7 +34,10 @@ class Splitter(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid") @abc.abstractmethod def split( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + self, + inputs: schemas.Inputs, + targets: schemas.Targets, + groups: Index | None = None, ) -> TrainTestSplits: """Split a dataframe into subsets. @@ -49,7 +52,10 @@ def split( @abc.abstractmethod def get_n_splits( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + self, + inputs: schemas.Inputs, + targets: schemas.Targets, + groups: Index | None = None, ) -> int: """Get the number of splits generated. @@ -80,17 +86,26 @@ class TrainTestSplitter(Splitter): @T.override def split( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + self, + inputs: schemas.Inputs, + targets: schemas.Targets, + groups: Index | None = None, ) -> TrainTestSplits: index = np.arange(len(inputs)) # return integer position train_index, test_index = model_selection.train_test_split( - index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state + index, + shuffle=self.shuffle, + test_size=self.test_size, + random_state=self.random_state, ) yield train_index, test_index @T.override def get_n_splits( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + self, + inputs: schemas.Inputs, + targets: schemas.Targets, + groups: Index | None = None, ) -> int: return 1 @@ -112,14 +127,22 @@ class TimeSeriesSplitter(Splitter): @T.override def split( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + self, + inputs: schemas.Inputs, + 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 def get_n_splits( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + self, + inputs: schemas.Inputs, + targets: schemas.Targets, + groups: Index | None = None, ) -> int: return self.n_splits diff --git a/tasks/__init__.py b/tasks/__init__.py index 6a6751c..a7e7b04 100644 --- a/tasks/__init__.py +++ b/tasks/__init__.py @@ -1,4 +1,4 @@ -"""Task collections for the project.""" +"""Task collections of the project.""" # mypy: ignore-errors diff --git a/tasks/checks.py b/tasks/checks.py index 853d109..1046835 100644 --- a/tasks/checks.py +++ b/tasks/checks.py @@ -1,4 +1,4 @@ -"""Check tasks for pyinvoke.""" +"""Check tasks of the project.""" # %% IMPORTS @@ -8,48 +8,42 @@ # %% TASKS -@task -def poetry(ctx: Context) -> None: - """Check poetry config files.""" - ctx.run("poetry check --lock") - - @task def format(ctx: Context) -> None: """Check the formats with ruff.""" - ctx.run("poetry run ruff format --check src/ tasks/ tests/") + ctx.run("uv run ruff format --check src/ tasks/ tests/") @task def type(ctx: Context) -> None: """Check the types with mypy.""" - ctx.run("poetry run mypy src/ tasks/ tests/") + ctx.run("uv run mypy src/ tasks/ tests/") @task def code(ctx: Context) -> None: """Check the codes with ruff.""" - ctx.run("poetry run ruff check src/ tasks/ tests/") + ctx.run("uv run ruff check src/ tasks/ tests/") @task def test(ctx: Context) -> None: """Check the tests with pytest.""" - ctx.run("poetry run pytest --numprocesses='auto' tests/") + ctx.run("uv run pytest --numprocesses=auto tests/") @task def security(ctx: Context) -> None: """Check the security with bandit.""" - ctx.run("poetry run bandit --recursive --configfile=pyproject.toml src/") + ctx.run("uv run bandit --recursive --configfile=pyproject.toml src/") @task def coverage(ctx: Context) -> None: """Check the coverage with coverage.""" - ctx.run("poetry run pytest --numprocesses='auto' --cov=src/ --cov-fail-under=80 tests/") + ctx.run("uv run pytest --numprocesses=auto --cov=src/ --cov-fail-under=80 tests/") -@task(pre=[poetry, format, type, code, security, coverage], default=True) +@task(pre=[format, type, code, security, coverage], default=True) def all(_: Context) -> None: """Run all check tasks.""" diff --git a/tasks/cleans.py b/tasks/cleans.py index e29b7d0..9b13acd 100644 --- a/tasks/cleans.py +++ b/tasks/cleans.py @@ -1,4 +1,4 @@ -"""Clean tasks for pyinvoke.""" +"""Cleaning tasks of the project.""" # %% IMPORTS @@ -77,9 +77,9 @@ def venv(ctx: Context) -> None: @task -def poetry(ctx: Context) -> None: - """Clean poetry lock file.""" - ctx.run("rm -f poetry.lock") +def uv(ctx: Context) -> None: + """Clean uv lock file.""" + ctx.run("rm -f uv.lock") @task @@ -117,7 +117,7 @@ def folders(_: Context) -> None: """Run all folders tasks.""" -@task(pre=[venv, poetry, python]) +@task(pre=[venv, uv, python]) def sources(_: Context) -> None: """Run all sources tasks.""" diff --git a/tasks/commits.py b/tasks/commits.py index 88b087b..2f48bad 100644 --- a/tasks/commits.py +++ b/tasks/commits.py @@ -1,4 +1,4 @@ -"""Commits tasks for pyinvoke.""" +"""Commit tasks of the project.""" # %% IMPORTS @@ -11,19 +11,19 @@ @task def info(ctx: Context) -> None: """Print a guide for messages.""" - ctx.run("poetry run cz info") + ctx.run("uv run cz info") @task def bump(ctx: Context) -> None: """Bump the version of the package.""" - ctx.run("poetry run cz bump", pty=True) + ctx.run("uv run cz bump", pty=True) @task def commit(ctx: Context) -> None: """Commit all changes with a message.""" - ctx.run("poetry run cz commit", pty=True) + ctx.run("uv run cz commit", pty=True) @task(pre=[commit], default=True) diff --git a/tasks/containers.py b/tasks/containers.py index 87510f9..b6401fb 100644 --- a/tasks/containers.py +++ b/tasks/containers.py @@ -1,4 +1,4 @@ -"""Container tasks for pyinvoke.""" +"""Container tasks of the project.""" # %% IMPORTS diff --git a/tasks/docs.py b/tasks/docs.py index 21eea9e..3f0b34c 100644 --- a/tasks/docs.py +++ b/tasks/docs.py @@ -1,4 +1,4 @@ -"""Docs tasks for pyinvoke.""" +"""Documentation tasks of the project.""" # %% IMPORTS @@ -18,14 +18,14 @@ @task def serve(ctx: Context, format: str = DOC_FORMAT, port: int = 8088) -> None: """Serve the API docs with pdoc.""" - ctx.run(f"poetry run pdoc --docformat={format} --port={port} src/{ctx.project.package}") + ctx.run(f"uv run pdoc --docformat={format} --port={port} src/{ctx.project.package}") @task def api(ctx: Context, format: str = DOC_FORMAT, output_dir: str = OUTPUT_DIR) -> None: """Generate the API docs with pdoc.""" ctx.run( - f"poetry run pdoc --docformat={format} --output-directory={output_dir} src/{ctx.project.package}" + f"uv run pdoc --docformat={format} --output-directory={output_dir} src/{ctx.project.package}" ) diff --git a/tasks/formats.py b/tasks/formats.py index 4631bae..cd83b17 100644 --- a/tasks/formats.py +++ b/tasks/formats.py @@ -1,4 +1,4 @@ -"""Format tasks for pyinvoke.""" +"""Format tasks of the project.""" # %% IMPORTS @@ -11,13 +11,13 @@ @task def imports(ctx: Context) -> None: """Format python imports with ruff.""" - ctx.run("poetry run ruff check --select I --fix") + ctx.run("uv run ruff check --select I --fix") @task def sources(ctx: Context) -> None: """Format python sources with ruff.""" - ctx.run("poetry run ruff format src/ tasks/ tests/") + ctx.run("uv run ruff format src/ tasks/ tests/") @task(pre=[imports, sources], default=True) diff --git a/tasks/installs.py b/tasks/installs.py index 05669be..f6754a2 100644 --- a/tasks/installs.py +++ b/tasks/installs.py @@ -1,4 +1,4 @@ -"""Install tasks for pyinvoke.""" +"""Install tasks of the project.""" # %% IMPORTS @@ -9,18 +9,18 @@ @task -def poetry(ctx: Context) -> None: - """Install poetry packages.""" - ctx.run("poetry install") +def uv(ctx: Context) -> None: + """Install uv packages.""" + ctx.run("uv sync --all-groups") @task def pre_commit(ctx: Context) -> None: """Install pre-commit hooks on git.""" - ctx.run("poetry run pre-commit install --hook-type pre-push") - ctx.run("poetry run pre-commit install --hook-type commit-msg") + ctx.run("uv run pre-commit install --hook-type=pre-push") + ctx.run("uv run pre-commit install --hook-type=commit-msg") -@task(pre=[poetry, pre_commit], default=True) +@task(pre=[uv, pre_commit], default=True) def all(_: Context) -> None: """Run all install tasks.""" diff --git a/tasks/mlflow.py b/tasks/mlflow.py index 0053931..3ccf363 100644 --- a/tasks/mlflow.py +++ b/tasks/mlflow.py @@ -1,4 +1,4 @@ -"""Mlflow tasks for pyinvoke.""" +"""Mlflow tasks of the project.""" # %% IMPORTS @@ -11,16 +11,19 @@ @task def doctor(ctx: Context) -> None: """Run mlflow doctor.""" - ctx.run("poetry run mlflow doctor") + ctx.run("uv run mlflow doctor") @task def serve( - ctx: Context, host: str = "127.0.0.1", port: str = "5000", backend_uri: str = "./mlruns" + ctx: Context, + host: str = "127.0.0.1", + port: str = "5000", + backend_store_uri: str = "./mlruns", ) -> None: - """Start the mlflow server.""" + """Start an mlflow server.""" ctx.run( - f"poetry run mlflow server --host={host} --port={port} --backend-store-uri={backend_uri}" + f"uv run mlflow server --host={host} --port={port} --backend-store-uri={backend_store_uri}" ) diff --git a/tasks/packages.py b/tasks/packages.py index 38e9c3d..e476a60 100644 --- a/tasks/packages.py +++ b/tasks/packages.py @@ -1,4 +1,4 @@ -"""Package tasks for pyinvoke.""" +"""Package tasks of the project.""" # %% IMPORTS @@ -7,17 +7,13 @@ from . import cleans -# %% CONFIGS - -BUILD_FORMAT = "wheel" - # %% TASKS @task(pre=[cleans.dist]) -def build(ctx: Context, format: str = BUILD_FORMAT) -> None: +def build(ctx: Context) -> None: """Build the python package.""" - ctx.run(f"poetry build --format={format}") + ctx.run("uv build --wheel") @task(pre=[build], default=True) diff --git a/tasks/projects.py b/tasks/projects.py index 1e8333c..0e31858 100644 --- a/tasks/projects.py +++ b/tasks/projects.py @@ -1,4 +1,4 @@ -"""Project tasks for pyinvoke.""" +"""Project tasks of the project.""" # mypy: disable-error-code="arg-type" @@ -21,7 +21,11 @@ @task def requirements(ctx: Context) -> None: """Export the project requirements file.""" - ctx.run(f"poetry export --without-urls --without-hashes --output={REQUIREMENTS}") + ctx.run( + "uv export --format=requirements-txt --no-dev " + "--no-hashes --no-editable --no-emit-project " + f"--output-file={REQUIREMENTS}" + ) @task(pre=[requirements]) @@ -32,10 +36,11 @@ def environment(ctx: Context) -> None: configuration: dict[str, object] = {"python": python} with open(REQUIREMENTS, "r") as reader: dependencies: list[str] = [] - for line in reader: - dependency = line.split(" ")[0] - if "pywin32" not in dependency: - dependencies.append(dependency) + for line in reader.readlines(): + dependency = line.split(" ")[0].strip() + if "pywin32" in dependency or "#" in dependency: + continue + dependencies.append(dependency) configuration["dependencies"] = dependencies with open(ENVIRONMENT, "w") as writer: # Safe as YAML is a superset of JSON @@ -47,7 +52,7 @@ def environment(ctx: Context) -> None: def run(ctx: Context, job: str) -> None: """Run an mlflow project from the MLproject file.""" ctx.run( - f"poetry run mlflow run --experiment-name={ctx.project.repository}" + f"uv run mlflow run --experiment-name={ctx.project.repository}" f" --run-name={job.capitalize()} -P conf_file=confs/{job}.yaml ." ) diff --git a/tests/conftest.py b/tests/conftest.py index 5daa9d0..e3f145f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -8,6 +8,7 @@ import omegaconf import pytest from _pytest import logging as pl + from bikes.core import metrics, models, schemas from bikes.io import datasets, registries, services from bikes.utils import searchers, signers, splitters @@ -125,7 +126,9 @@ def targets_reader(targets_path: str) -> datasets.ParquetReader: @pytest.fixture(scope="session") def outputs_reader( - outputs_path: str, inputs_reader: datasets.ParquetReader, targets_reader: datasets.ParquetReader + outputs_path: str, + inputs_reader: datasets.ParquetReader, + targets_reader: datasets.ParquetReader, ) -> datasets.ParquetReader: """Return a reader for the outputs dataset.""" # generate outputs if it is missing @@ -146,13 +149,17 @@ def tmp_outputs_writer(tmp_outputs_path: str) -> datasets.ParquetWriter: @pytest.fixture(scope="function") -def tmp_models_explanations_writer(tmp_models_explanations_path: str) -> datasets.ParquetWriter: +def tmp_models_explanations_writer( + tmp_models_explanations_path: str, +) -> datasets.ParquetWriter: """Return a writer for the tmp model explanations dataset.""" return datasets.ParquetWriter(path=tmp_models_explanations_path) @pytest.fixture(scope="function") -def tmp_samples_explanations_writer(tmp_samples_explanations_path: str) -> datasets.ParquetWriter: +def tmp_samples_explanations_writer( + tmp_samples_explanations_path: str, +) -> datasets.ParquetWriter: """Return a writer for the tmp samples explanations dataset.""" return datasets.ParquetWriter(path=tmp_samples_explanations_path) @@ -207,7 +214,7 @@ def time_series_splitter() -> splitters.TimeSeriesSplitter: @pytest.fixture(scope="session") -def searcher() -> searchers.Searcher: +def searcher() -> searchers.GridCVSearcher: """Return the default searcher object.""" param_grid = {"max_depth": [1, 2], "n_estimators": [3]} return searchers.GridCVSearcher(param_grid=param_grid) @@ -218,7 +225,9 @@ def searcher() -> searchers.Searcher: @pytest.fixture(scope="session") def train_test_sets( - train_test_splitter: splitters.Splitter, inputs: schemas.Inputs, targets: schemas.Targets + train_test_splitter: splitters.TrainTestSplitter, + inputs: schemas.Inputs, + targets: schemas.Targets, ) -> tuple[schemas.Inputs, schemas.Targets, schemas.Inputs, schemas.Targets]: """Return the inputs and targets train and test sets from the splitter.""" train_index, test_index = next(train_test_splitter.split(inputs=inputs, targets=targets)) @@ -259,7 +268,7 @@ def metric() -> metrics.SklearnMetric: @pytest.fixture(scope="session") -def signer() -> signers.Signer: +def signer() -> signers.InferSigner: """Return a model signer.""" return signers.InferSigner() diff --git a/tests/core/test_metrics.py b/tests/core/test_metrics.py index c8a286c..e8ec170 100644 --- a/tests/core/test_metrics.py +++ b/tests/core/test_metrics.py @@ -3,6 +3,7 @@ import mlflow import pandas as pd import pytest + from bikes.core import metrics, models, schemas # %% METRICS @@ -44,9 +45,9 @@ def test_sklearn_metric( # - scorer assert low <= scorer <= high, "Scorer should be in the expected interval!" # - mlflow metric - assert mlflow_metric.name == metric.name, "Mlflow metric name should be the same!" + assert mlflow_metric.name == metric.name, "Mlflow metric name should be the same!" # type: ignore[attr-defined] assert ( - mlflow_metric.greater_is_better == metric.greater_is_better + mlflow_metric.greater_is_better == metric.greater_is_better # type: ignore[attr-defined] ), "Mlflow metric greater is better should be the same!" # - mlflow results assert mlflow_results.metrics == { diff --git a/tests/core/test_models.py b/tests/core/test_models.py index 47ce7f0..12dea8a 100644 --- a/tests/core/test_models.py +++ b/tests/core/test_models.py @@ -3,6 +3,7 @@ import typing as T import pytest + from bikes.core import models, schemas # %% MODELS @@ -36,7 +37,10 @@ def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: with 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set(lineage.schema.input_names()) == set( data.columns ), "Lineage schema names should be the data columns!" - assert lineage.profile["num_rows"] == len( + assert lineage.profile["num_rows"] == len( # type: ignore[index] data ), "Lineage profile should contain the data row count!" diff --git a/tests/io/test_services.py b/tests/io/test_services.py index a11e28b..b631a9a 100644 --- a/tests/io/test_services.py +++ b/tests/io/test_services.py @@ -6,6 +6,7 @@ import plyer import pytest import pytest_mock as pm + from bikes.io import services # %% SERVICES @@ -36,7 +37,10 @@ def test_alerts_service( service.notify(title="test", message="hello") # then if enable: - plyer.notification.notify.assert_called_once(), "Notification method should be called!" + ( + plyer.notification.notify.assert_called_once(), + "Notification method should be called!", + ) assert capsys.readouterr().out == "", "Notification should not be printed to stdout!" else: ( diff --git a/tests/jobs/test_base.py b/tests/jobs/test_base.py index c86982a..7271462 100644 --- a/tests/jobs/test_base.py +++ b/tests/jobs/test_base.py @@ -20,7 +20,9 @@ def run(self) -> base.Locals: return locals() job = MyJob( - logger_service=logger_service, alerts_service=alerts_service, mlflow_service=mlflow_service + logger_service=logger_service, + alerts_service=alerts_service, + mlflow_service=mlflow_service, ) # when with job as runner: diff --git a/tests/jobs/test_evaluations.py b/tests/jobs/test_evaluations.py index 3f5f1bc..32c2a60 100644 --- a/tests/jobs/test_evaluations.py +++ b/tests/jobs/test_evaluations.py @@ -2,6 +2,7 @@ import _pytest.capture as pc import pytest + from bikes import jobs from bikes.core import metrics, schemas from bikes.io import datasets, registries, services @@ -43,7 +44,7 @@ def test_evaluations_job( inputs_reader: datasets.ParquetReader, targets_reader: datasets.ParquetReader, model_alias: registries.Version, - metric: metrics.Metric, + metric: metrics.SklearnMetric, capsys: pc.CaptureFixture[str], ) -> None: # given @@ -52,7 +53,9 @@ def test_evaluations_job( else: assert alias_or_version == model_alias.aliases[0], "Model alias should be the same!" run_config = mlflow_service.RunConfig( - name="EvaluationsTest", tags={"context": "evaluations"}, description="Evaluations job." + name="EvaluationsTest", + tags={"context": "evaluations"}, + description="Evaluations job.", ) # when job = jobs.EvaluationsJob( @@ -81,8 +84,11 @@ def test_evaluations_job( "targets", "targets_", "targets_lineage", - "dataset", + "outputs", + "model", "model_uri", + "dataset", + "dataset_", "extra_metrics", "validation_thresholds", "evaluations", @@ -109,23 +115,30 @@ def test_evaluations_job( assert ( out["targets_lineage"].targets == schemas.TargetsSchema.cnt ), "Targets lineage target should be cnt!" + # - outputs + assert out["outputs"].ndim == 2, "Outputs should be a dataframe!" + # - model uri + assert str(alias_or_version) in out["model_uri"], "Model URI should contain the model alias!" + assert ( + mlflow_service.registry_name in out["model_uri"] + ), "Model URI should contain the registry name!" + # - model + assert ( + out["model"].model.metadata.run_id == model_alias.run_id + ), "Model run id should be the same!" + assert out["model"].model.metadata.signature is not None, "Model should have a signature!" + assert out["model"].model.metadata.flavors.get( + "python_function" + ), "Model should have a pyfunc flavor!" # - dataset assert out["dataset"].name == "evaluation", "Dataset name should be evaluation!" - assert out["dataset"].predictions is None, "Dataset predictions should be None!" assert ( out["dataset"].targets == schemas.TargetsSchema.cnt ), "Dataset targets should be the target column!" assert ( - inputs_reader.path in out["dataset"].source.uri - ), "Dataset source should contain the inputs path!" - assert ( - targets_reader.path in out["dataset"].source.uri - ), "Dataset source should contain the targets path!" - # - model uri - assert str(alias_or_version) in out["model_uri"], "Model URI should contain the model alias!" - assert ( - mlflow_service.registry_name in out["model_uri"] - ), "Model URI should contain the registry name!" + out["dataset"].predictions == schemas.OutputsSchema.prediction + ), "Dataset predictions should be the prediction column!" + assert out["dataset"].source.to_dict().keys() == {"tags"}, "Dataset source should have tags!" # - extra metrics assert len(out["extra_metrics"]) == len( job.metrics @@ -147,6 +160,7 @@ def test_evaluations_job( assert job.metrics[0].name in out["evaluations"].metrics, "Metric should be logged in Mlflow!" # - mlflow tracking experiment = mlflow_service.client().get_experiment_by_name(name=mlflow_service.experiment_name) + assert experiment is not None, "Mlflow Experiment should exist!" assert ( experiment.name == mlflow_service.experiment_name ), "Mlflow Experiment name should be the same!" diff --git a/tests/jobs/test_explanations.py b/tests/jobs/test_explanations.py index 4285bdc..3e4ff24 100644 --- a/tests/jobs/test_explanations.py +++ b/tests/jobs/test_explanations.py @@ -2,6 +2,7 @@ import _pytest.capture as pc import pytest + from bikes import jobs from bikes.core import models from bikes.io import datasets, registries, services @@ -15,11 +16,11 @@ def test_explanations_job( mlflow_service: services.MlflowService, alerts_service: services.AlertsService, logger_service: services.LoggerService, - inputs_samples_reader: datasets.Reader, - tmp_models_explanations_writer: datasets.Writer, - tmp_samples_explanations_writer: datasets.Writer, + inputs_samples_reader: datasets.ParquetReader, + tmp_models_explanations_writer: datasets.ParquetWriter, + tmp_samples_explanations_writer: datasets.ParquetWriter, model_alias: registries.Version, - loader: registries.Loader, + loader: registries.CustomLoader, capsys: pc.CaptureFixture[str], ) -> None: # given diff --git a/tests/jobs/test_inference.py b/tests/jobs/test_inference.py index 0f8808e..d60ea4f 100644 --- a/tests/jobs/test_inference.py +++ b/tests/jobs/test_inference.py @@ -2,6 +2,7 @@ import _pytest.capture as pc import pytest + from bikes import jobs from bikes.io import datasets, registries, services @@ -14,10 +15,10 @@ def test_inference_job( mlflow_service: services.MlflowService, alerts_service: services.AlertsService, logger_service: services.LoggerService, - inputs_reader: datasets.Reader, - tmp_outputs_writer: datasets.Writer, + inputs_reader: datasets.ParquetReader, + tmp_outputs_writer: datasets.ParquetWriter, model_alias: registries.Version, - loader: registries.Loader, + loader: registries.CustomLoader, capsys: pc.CaptureFixture[str], ) -> None: # given diff --git a/tests/jobs/test_promotion.py b/tests/jobs/test_promotion.py index 0c3b81f..d38f6c0 100644 --- a/tests/jobs/test_promotion.py +++ b/tests/jobs/test_promotion.py @@ -3,6 +3,7 @@ import _pytest.capture as pc import mlflow import pytest + from bikes import jobs from bikes.io import registries, services diff --git a/tests/jobs/test_training.py b/tests/jobs/test_training.py index 902c605..0d517bc 100644 --- a/tests/jobs/test_training.py +++ b/tests/jobs/test_training.py @@ -1,6 +1,7 @@ # %% IMPORTS import _pytest.capture as pc + from bikes import jobs from bikes.core import metrics, models, schemas from bikes.io import datasets, registries, services @@ -15,12 +16,12 @@ def test_training_job( logger_service: services.LoggerService, inputs_reader: datasets.ParquetReader, targets_reader: datasets.ParquetReader, - model: models.Model, - metric: metrics.Metric, - train_test_splitter: splitters.Splitter, - saver: registries.Saver, - signer: signers.Signer, - register: registries.Register, + model: models.BaselineSklearnModel, + metric: metrics.SklearnMetric, + train_test_splitter: splitters.TrainTestSplitter, + saver: registries.CustomSaver, + signer: signers.InferSigner, + register: registries.MlflowRegister, capsys: pc.CaptureFixture[str], ) -> None: # given @@ -142,6 +143,7 @@ def test_training_job( ), "Model version run id should be the same!" # - mlflow tracking experiment = client.get_experiment_by_name(name=mlflow_service.experiment_name) + assert experiment is not None, "Mlflow Experiment should exist!" assert ( experiment.name == mlflow_service.experiment_name ), "Mlflow Experiment name should be the same!" diff --git a/tests/jobs/test_tuning.py b/tests/jobs/test_tuning.py index 7263d64..cb7e1e5 100644 --- a/tests/jobs/test_tuning.py +++ b/tests/jobs/test_tuning.py @@ -1,6 +1,7 @@ # %% IMPORTS import _pytest.capture as pc + from bikes import jobs from bikes.core import metrics, models, schemas from bikes.io import datasets, services @@ -15,10 +16,10 @@ def test_tuning_job( logger_service: services.LoggerService, inputs_reader: datasets.ParquetReader, targets_reader: datasets.ParquetReader, - model: models.Model, - metric: metrics.Metric, - time_series_splitter: splitters.Splitter, - searcher: searchers.Searcher, + model: models.BaselineSklearnModel, + metric: metrics.SklearnMetric, + time_series_splitter: splitters.TimeSeriesSplitter, + searcher: searchers.GridCVSearcher, capsys: pc.CaptureFixture[str], ) -> None: # given @@ -92,6 +93,7 @@ def test_tuning_job( ), "Best params should have the same keys!" # - mlflow tracking experiment = client.get_experiment_by_name(name=mlflow_service.experiment_name) + assert experiment is not None, "Mlflow experiment should exist!" assert ( experiment.name == mlflow_service.experiment_name ), "Mlflow experiment name should be the same!" diff --git a/tests/test_scripts.py b/tests/test_scripts.py index 60c58c7..9f00ed0 100644 --- a/tests/test_scripts.py +++ b/tests/test_scripts.py @@ -6,6 +6,7 @@ import pydantic as pdt import pytest from _pytest import capture as pc + from bikes import scripts # %% SCRIPTS diff --git a/tests/utils/test_searchers.py b/tests/utils/test_searchers.py index 7d2f1ca..30c79fb 100644 --- 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