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https://github.com/sb-ai-lab/lightautoml

Fast and customizable framework for automatic ML model creation (AutoML)
https://github.com/sb-ai-lab/lightautoml

automated-machine-learning automatic-machine-learning automl automl-algorithms binary-classification data-science kaggle lama machine-learning multiclass-classification nlp python regression

Last synced: about 1 month ago
JSON representation

Fast and customizable framework for automatic ML model creation (AutoML)

Host: GitHub
URL: https://github.com/sb-ai-lab/lightautoml
Owner: sb-ai-lab
License: apache-2.0
Created: 2022-04-18T19:26:28.000Z (over 2 years ago)
Default Branch: master
Last Pushed: 2024-09-05T14:51:55.000Z (3 months ago)
Last Synced: 2024-10-15T14:41:41.692Z (about 1 month ago)
Topics: automated-machine-learning, automatic-machine-learning, automl, automl-algorithms, binary-classification, data-science, kaggle, lama, machine-learning, multiclass-classification, nlp, python, regression
Language: Python
Homepage: https://developers.sber.ru/portal/products/lightautoml
Size: 98.1 MB
Stars: 1,188
Watchers: 19
Forks: 53
Open Issues: 22
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE

Awesome Lists containing this project

README

        

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/lightautoml)](https://pypi.org/project/lightautoml)

[![PyPI - Version](https://img.shields.io/pypi/v/lightautoml)](https://pypi.org/project/lightautoml)

![pypi - Downloads](https://img.shields.io/pypi/dm/lightautoml?color=green&label=PyPI%20downloads&logo=pypi&logoColor=green)

[![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)

![Read the Docs](https://img.shields.io/readthedocs/lightautoml)

### [Documentation](https://lightautoml.readthedocs.io/)  |  [Installation](#installation) | [Examples](#resources) | [Telegram chat](https://t.me/joinchat/sp8P7sdAqaU0YmRi) | [Telegram channel](https://t.me/lightautoml)

LightAutoML (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.

Authors: [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.



# Quick tour

There are two ways to solve machine learning problems using LightAutoML:

* Ready-to-use preset:

    ```python

    from lightautoml.automl.presets.tabular_presets import TabularAutoML

    from lightautoml.tasks import Task

    automl = TabularAutoML(task = Task(name = 'binary', metric = 'auc'))

    oof_preds = automl.fit_predict(train_df, roles = {'target': 'my_target', 'drop': ['column_to_drop']}).data

    test_preds = automl.predict(test_df).data

    ```

* As a framework:

    LighAutoML framework has a lot of ready-to-use parts and extensive customization options, to learn more check out the [resources](#resources) section.



# Resources

### Kaggle kernel examples of LightAutoML usage:

- [Tabular Playground Series April 2021 competition solution](https://www.kaggle.com/alexryzhkov/n3-tps-april-21-lightautoml-starter)

- [Titanic competition solution (80% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-titanic-love)

- [Titanic **12-code-lines** competition solution (78% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-extreme-short-titanic-solution)

- [House prices competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-houseprices-love)

- [Natural Language Processing with Disaster Tweets solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-nlp)

- [Tabular Playground Series March 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-for-tabulardatamarch)

- [Tabular Playground Series February 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-tabulardata-love)

- [Interpretable WhiteBox solution](https://www.kaggle.com/simakov/lama-whitebox-preset-example)

- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)

- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)

- [Tabular Playground Series November 2022 competition solution with Neural Networks](https://www.kaggle.com/code/mikhailkuz/lightautoml-nn-happiness)

### Google Colab tutorials and [other examples](examples/):

- [`Tutorial_1_basics.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_1_basics.ipynb) - get started with LightAutoML on tabular data.

- [`Tutorial_2_WhiteBox_AutoWoE.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_2_WhiteBox_AutoWoE.ipynb) - creating interpretable models.

- [`Tutorial_3_sql_data_source.ipynb`](https://colab.research.google.com/github/AILab-MLTools/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.

- [`Tutorial_4_NLP_Interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_4_NLP_Interpretation.ipynb) - example of using TabularNLPAutoML preset, LimeTextExplainer.

- [`Tutorial_5_uplift.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_5_uplift.ipynb) - shows how to use LightAutoML for a uplift-modeling task.

- [`Tutorial_6_custom_pipeline.ipynb`](https://colab.research.google.com/github/AILab-MLTools/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.

- [`Tutorial_7_ICE_and_PDP_interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/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.

- [`Tutorial_8_CV_preset.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_8_CV_preset.ipynb) - example of using TabularCVAutoML preset in CV multi-class classification task.

- [`Tutorial_9_neural_networks.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_9_neural_networks.ipynb) - example of using Tabular preset with neural networks.

- [`Tutorial_10_relational_data_with_star_scheme.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_10_relational_data_with_star_scheme.ipynb) - example of using Tabular preset with neural networks.

- [`Tutorial_11_time_series.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_11_time_series.ipynb) - example of using Tabular preset with timeseries data.

- [`Tutorial_12_Matching.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_12_Matching.ipynb) - example of using addon for matchig.

**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

**Note 2**: to take a look at this report after the run, please comment last line of demo with report deletion command.

### Courses, videos and papers

* **LightAutoML crash courses**:

    - (Russian) [AutoML course for OpenDataScience community](https://ods.ai/tracks/automl-course-part1)

* **Video guides**:

    - (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))

    - (Russian) [LightAutoML hands-on tutorial in Kaggle Kernels](https://www.youtube.com/watch?v=TYu1UG-E9e8) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))

    - (English) [Automated Machine Learning with LightAutoML: theory and practice](https://www.youtube.com/watch?v=4pbO673B9Oo) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))

    - (English) [LightAutoML framework general overview, benchmarks and advantages for business](https://vimeo.com/485383651) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))

    - (English) [LightAutoML practical guide - ML pipeline presets overview](https://vimeo.com/487166940) ([Dmitry Simakov](https://kaggle.com/simakov))

* **Papers**:

    - 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.

* **Articles about LightAutoML**:

    - (English) [LightAutoML vs Titanic: 80% accuracy in several lines of code (Medium)](https://alexmryzhkov.medium.com/lightautoml-preset-usage-tutorial-2cce7da6f936)

    - (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)



# Installation

To install LAMA framework on your machine from PyPI:

```bash

# Base functionality:

pip install -U lightautoml

# For partial installation use corresponding option

# Extra dependencies: [nlp, cv, report] or use 'all' to install all dependencies

pip install -U lightautoml[nlp]

```

Additionally, run following commands to enable pdf report generation:

```bash

# MacOS

brew install cairo pango gdk-pixbuf libffi

# Debian / Ubuntu

sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info

# Fedora

sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2

# Windows

# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows

```



# Advanced features

### GPU and Spark pipelines

Full GPU and Spark pipelines for LightAutoML currently available for developers testing (still in progress). The code and tutorials for:

- GPU pipeline is [available here](https://github.com/Rishat-skoltech/LightAutoML_GPU)

- Spark pipeline is [available here](https://github.com/sb-ai-lab/SLAMA)



# Contributing to LightAutoML

If you are interested in contributing to LightAutoML, please read the [Contributing Guide](.github/CONTRIBUTING.md) to get started.



# Support and feature requests

- Seek prompt advice at [Telegram group](https://t.me/joinchat/sp8P7sdAqaU0YmRi).

- Open bug reports and feature requests on GitHub [issues](https://github.com/AILab-MLTools/LightAutoML/issues).



# License

This project is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/AILab-MLTools/LightAutoML/blob/master/LICENSE) file for more details.

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