You need to install the lightgbm package directly from conda using the following command before installing TPOT. This is to ensure that you get the version that is compatible with your system.

TPOT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

TPOT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with TPOT.

Documentation for TPOT, a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

An sklearn baseestimator that uses genetic programming to optimize a classification pipeline. For more parameters, see the TPOTEstimator class. Parameters:

Typical TPOT runs will take hours to days to finish (unless it's a small dataset), but you can always interrupt the run partway through and see the best results so far. TPOT also provides a warm_start and a periodic_checkpoint_folder parameter that lets …

Using TPOT See the Tutorials Folder for more instructions and examples. Best Practices 1 TPOT uses dask for parallel processing. When Python is parallelized, each module is imported within each processes. Therefore it is important to protect all code within a if __name__ == "__main__" when running TPOT from a script.

The latest stable release of TPOT is on the main branch, whereas the latest version of TPOT in development is on the development branch. Make sure you are looking at and working on the correct branch if you're looking to contribute code.

TPOT now has more built-in configurations, including TPOT MDR and TPOT light, for both classification and regression problems. TPOTClassifier and TPOTRegressor now expose three useful internal attributes, fitted_pipeline_, pareto_front_fitted_pipelines_, and evaluated_individuals_.

This class contains the function definition for encoding the input features as a Heterozygote Advantage genetic model. The encoding used is AA (0)->0, Aa (1)->1, aa (2)->0. Source code in tpot/builtin_modules/genetic_encoders.py