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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, Anton Vakhrushev, Dmitry 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:

    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 section.

Resources

Kaggle kernel examples of LightAutoML usage:

Google Colab tutorials and other examples:

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

Installation

To install LAMA framework on your machine from PyPI:

# 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:

# 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:

Contributing to LightAutoML

If you are interested in contributing to LightAutoML, please read the Contributing Guide to get started.

Support and feature requests

License

This project is licensed under the Apache License, Version 2.0. See LICENSE file for more details.

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