{"id":13737473,"url":"https://github.com/sb-ai-lab/LightAutoML","last_synced_at":"2025-05-08T14:31:34.482Z","repository":{"id":37091764,"uuid":"482970740","full_name":"sb-ai-lab/LightAutoML","owner":"sb-ai-lab","description":"Fast and customizable framework for automatic ML model creation (AutoML)","archived":false,"fork":false,"pushed_at":"2024-11-02T10:45:26.000Z","size":100834,"stargazers_count":1204,"open_issues_count":22,"forks_count":55,"subscribers_count":21,"default_branch":"master","last_synced_at":"2024-11-14T13:52:00.762Z","etag":null,"topics":["automated-machine-learning","automatic-machine-learning","automl","automl-algorithms","binary-classification","data-science","kaggle","lama","machine-learning","multiclass-classification","nlp","python","regression"],"latest_commit_sha":null,"homepage":"https://developers.sber.ru/portal/products/lightautoml","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sb-ai-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":".github/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-04-18T19:26:28.000Z","updated_at":"2024-11-14T06:47:49.000Z","dependencies_parsed_at":"2023-10-11T04:59:40.244Z","dependency_job_id":"6aaa88a7-b245-4e98-9de1-3cef7a62597f","html_url":"https://github.com/sb-ai-lab/LightAutoML","commit_stats":{"total_commits":179,"total_committers":17,"mean_commits":"10.529411764705882","dds":0.782122905027933,"last_synced_commit":"7c7e3cee661beacf37d5b6adb5c7cb28f8475e1a"},"previous_names":["ailab-mltools/lightautoml"],"tags_count":7,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sb-ai-lab%2FLightAutoML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sb-ai-lab%2FLightAutoML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sb-ai-lab%2FLightAutoML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sb-ai-lab%2FLightAutoML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sb-ai-lab","download_url":"https://codeload.github.com/sb-ai-lab/LightAutoML/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224737408,"owners_count":17361345,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["automated-machine-learning","automatic-machine-learning","automl","automl-algorithms","binary-classification","data-science","kaggle","lama","machine-learning","multiclass-classification","nlp","python","regression"],"created_at":"2024-08-03T03:01:49.246Z","updated_at":"2025-05-08T14:31:34.473Z","avatar_url":"https://github.com/sb-ai-lab.png","language":"Python","readme":"\u003cimg src=imgs/lightautoml_logo_color.png /\u003e\n\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/lightautoml)](https://pypi.org/project/lightautoml)\n[![PyPI - Version](https://img.shields.io/pypi/v/lightautoml)](https://pypi.org/project/lightautoml)\n![pypi - Downloads](https://img.shields.io/pypi/dm/lightautoml?color=green\u0026label=PyPI%20downloads\u0026logo=pypi\u0026logoColor=green)\n[![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/sb-ai-lab/lightautoml/CI.yml)](https://github.com/sb-ai-lab/lightautoml/actions/workflows/CI.yml?query=branch%3Amaster)\n![Read the Docs](https://img.shields.io/readthedocs/lightautoml)\n### [Documentation](https://lightautoml.readthedocs.io/)  |  [Installation](#installation) | [Examples](#resources) | [Telegram chat](https://t.me/joinchat/sp8P7sdAqaU0YmRi) | [Telegram channel](https://t.me/lightautoml)\n\nLightAutoML (LAMA) allows you create machine learning models using just a few lines of code, or build your own custom pipeline using ready blocks. It supports tabular, time series, image and text data.\n\nAuthors: [Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Anton Vakhrushev](https://kaggle.com/btbpanda), [Dmitry Simakov](https://kaggle.com/simakov), Rinchin Damdinov, Vasilii Bunakov, Alexander Kirilin, Pavel Shvets.\n\n\u003ca name=\"quicktour\"\u003e\u003c/a\u003e\n# Quick tour\n\nThere are two ways to solve machine learning problems using LightAutoML:\n* Ready-to-use preset:\n    ```python\n    from lightautoml.automl.presets.tabular_presets import TabularAutoML\n    from lightautoml.tasks import Task\n\n    automl = TabularAutoML(task = Task(name = 'binary', metric = 'auc'))\n    oof_preds = automl.fit_predict(train_df, roles = {'target': 'my_target', 'drop': ['column_to_drop']}).data\n    test_preds = automl.predict(test_df).data\n    ```\n\n* As a framework:\u003c/br\u003e\n    LighAutoML framework has a lot of ready-to-use parts and extensive customization options, to learn more check out the [resources](#resources) section.\n\n\u003ca name=\"resources\"\u003e\u003c/a\u003e\n# Resources\n\n### Kaggle kernel examples of LightAutoML usage:\n\n- [Tabular Playground Series April 2021 competition solution](https://www.kaggle.com/alexryzhkov/n3-tps-april-21-lightautoml-starter)\n- [Titanic competition solution (80% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-titanic-love)\n- [Titanic **12-code-lines** competition solution (78% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-extreme-short-titanic-solution)\n- [House prices competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-houseprices-love)\n- [Natural Language Processing with Disaster Tweets solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-nlp)\n- [Tabular Playground Series March 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-for-tabulardatamarch)\n- [Tabular Playground Series February 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-tabulardata-love)\n- [Interpretable WhiteBox solution](https://www.kaggle.com/simakov/lama-whitebox-preset-example)\n- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)\n- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)\n- [Tabular Playground Series November 2022 competition solution with Neural Networks](https://www.kaggle.com/code/mikhailkuz/lightautoml-nn-happiness)\n\n### Google Colab tutorials and [other examples](examples/):\n\n- [`Tutorial_1_basics.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_1_basics.ipynb) - get started with LightAutoML on tabular data.\n- [`Tutorial_2_WhiteBox_AutoWoE.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_2_WhiteBox_AutoWoE.ipynb) - creating interpretable models.\n- [`Tutorial_3_sql_data_source.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_3_sql_data_source.ipynb) - shows how to use LightAutoML presets (both standalone and time utilized variants) for solving ML tasks on tabular data from SQL data base instead of CSV.\n- [`Tutorial_4_NLP_Interpretation.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_4_NLP_Interpretation.ipynb) - example of using TabularNLPAutoML preset, LimeTextExplainer.\n- [`Tutorial_5_uplift.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_5_uplift.ipynb) - shows how to use LightAutoML for a uplift-modeling task.\n- [`Tutorial_6_custom_pipeline.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_6_custom_pipeline.ipynb) - shows how to create your own pipeline from specified blocks: pipelines for feature generation and feature selection, ML algorithms, hyperparameter optimization etc.\n- [`Tutorial_7_ICE_and_PDP_interpretation.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_7_ICE_and_PDP_interpretation.ipynb) - shows how to obtain local and global interpretation of model results using ICE and PDP approaches.\n- [`Tutorial_8_CV_preset.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_8_CV_preset.ipynb) - example of using TabularCVAutoML preset in CV multi-class classification task.\n- [`Tutorial_9_neural_networks.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_9_neural_networks.ipynb) - example of using Tabular preset with neural networks.\n- [`Tutorial_10_relational_data_with_star_scheme.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_10_relational_data_with_star_scheme.ipynb) - example of using Tabular preset with neural networks.\n- [`Tutorial_11_time_series.ipynb`](https://colab.research.google.com/github/sb-ai-lab/LightAutoML/blob/master/examples/tutorials/Tutorial_11_time_series.ipynb) - example of using Tabular preset with timeseries data.\n\n**Note 1**: for production you have no need to use profiler (which increase work time and memory consomption), so please do not turn it on - it is in off state by default\n\n**Note 2**: to take a look at this report after the run, please comment last line of demo with report deletion command.\n\n### Courses, videos and papers\n\n* **LightAutoML crash courses**:\n    - (Russian) [AutoML course for OpenDataScience community](https://ods.ai/tracks/automl-course-part1)\n\n* **Video guides**:\n    - (Russian) [LightAutoML webinar for Sberloga community](https://www.youtube.com/watch?v=ci8uqgWFJGg) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Dmitry Simakov](https://kaggle.com/simakov))\n    - (Russian) [LightAutoML hands-on tutorial in Kaggle Kernels](https://www.youtube.com/watch?v=TYu1UG-E9e8) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))\n    - (English) [Automated Machine Learning with LightAutoML: theory and practice](https://www.youtube.com/watch?v=4pbO673B9Oo) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))\n    - (English) [LightAutoML framework general overview, benchmarks and advantages for business](https://vimeo.com/485383651) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))\n    - (English) [LightAutoML practical guide - ML pipeline presets overview](https://vimeo.com/487166940) ([Dmitry Simakov](https://kaggle.com/simakov))\n\n* **Papers**:\n    - Anton Vakhrushev, Alexander Ryzhkov, Dmitry Simakov, Rinchin Damdinov, Maxim Savchenko, Alexander Tuzhilin [\"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem\"](https://arxiv.org/pdf/2109.01528.pdf). arXiv:2109.01528, 2021.\n\n* **Articles about LightAutoML**:\n    - (English) [LightAutoML vs Titanic: 80% accuracy in several lines of code (Medium)](https://alexmryzhkov.medium.com/lightautoml-preset-usage-tutorial-2cce7da6f936)\n    - (English) [Hands-On Python Guide to LightAutoML – An Automatic ML Model Creation Framework (Analytic Indian Mag)](https://analyticsindiamag.com/hands-on-python-guide-to-lama-an-automatic-ml-model-creation-framework/?fbclid=IwAR0f0cVgQWaLI60m1IHMD6VZfmKce0ZXxw-O8VRTdRALsKtty8a-ouJex7g)\n\n\u003ca name=\"installation\"\u003e\u003c/a\u003e\n# Installation\nTo install LAMA framework on your machine from PyPI:\n```bash\n# Base functionality:\npip install -U lightautoml\n\n# For partial installation use corresponding option\n# Extra dependencies: [nlp, cv, report] or use 'all' to install all dependencies\npip install -U lightautoml[nlp]\n# Or extra dependencies with specific version\npip install 'lightautoml[all]==0.4.0'\n```\n\nAdditionally, run following commands to enable pdf report generation:\n\n```bash\n# MacOS\nbrew install cairo pango gdk-pixbuf libffi\n\n# Debian / Ubuntu\nsudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info\n\n# Fedora\nsudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2\n\n# Windows\n# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows\n```\n\n\n\u003ca name=\"advancedfeatures\"\u003e\u003c/a\u003e\n# Advanced features\n### GPU and Spark pipelines\nFull GPU and Spark pipelines for LightAutoML currently available for developers testing (still in progress). The code and tutorials for:\n- GPU pipeline is [available here](https://github.com/Rishat-skoltech/LightAutoML_GPU)\n- Spark pipeline is [available here](https://github.com/sb-ai-lab/SLAMA)\n\n\u003ca name=\"contributing\"\u003e\u003c/a\u003e\n# Contributing to LightAutoML\nIf you are interested in contributing to LightAutoML, please read the [Contributing Guide](.github/CONTRIBUTING.md) to get started.\n\n\u003ca name=\"support\"\u003e\u003c/a\u003e\n# Support and feature requests\n- Seek prompt advice in [Telegram group](https://t.me/joinchat/sp8P7sdAqaU0YmRi).\n- Open bug reports and feature requests on GitHub [issues](https://github.com/sb-ai-lab/LightAutoML/issues).\n\n\u003ca name=\"license\"\u003e\u003c/a\u003e\n# License\nThis project is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/sb-ai-lab/LightAutoML/blob/master/LICENSE) file for more details.\n\n[Back to top](#toc)\n","funding_links":[],"categories":["参数优化","Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsb-ai-lab%2FLightAutoML","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsb-ai-lab%2FLightAutoML","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsb-ai-lab%2FLightAutoML/lists"}