TPOT stands for Tree-based Pipeline Optimization Tool. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Consider TPOT your Data Science Assistant.
TPOT recently went through a major refactoring. The package was rewritten from scratch to improve efficiency and performance, support new features, and fix numerous bugs. New features include genetic feature selection, a significantly expanded and more flexible method of defining search spaces, multi-objective optimization, a more modular framework allowing for easier customization of the evolutionary algorithm, and more. While in development, this new version was referred to as "TPOT2" but we have now merged what was once TPOT2 into the main TPOT package. You can learn more about this new version of TPOT in our GPTP paper titled "TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning."
Ribeiro, P. et al. (2024). TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_1
If you want to utilize the additional features provided by TPOT along with `scikit-learn` extensions, you can install them using `pip`. The command to install TPOT with these extra features is as follows:
Please note that while these extensions can speed up scikit-learn packages, there are some important considerations:
These extensions may not be fully developed and tested on Arm-based CPUs, such as M1 Macs. You might encounter compatibility issues or reduced performance on such systems.
We recommend using Python 3.9 when installing these extra features, as it provides better compatibility and stability.
If you downloaded with git pull, then the repository folder will be named TPOT. (Note: this folder is the one that includes setup.py inside of it and not the folder of the same name inside it).
If you downloaded as a zip, the folder may be called tpot-main.
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. This is not required when running TPOT from a notebook.
TPOT will not check if your data is correctly formatted. It will assume that you have passed in operators that can handle the type of data that was passed in. For instance, if you pass in a pandas dataframe with categorical features and missing data, then you should also include in your configuration operators that can handle those feautures of the data. Alternatively, if you pass in `preprocessing = True`, TPOT will impute missing values, one hot encode categorical features, then standardize the data. (Note that this is currently fitted and transformed on the entire training set before splitting for CV. Later there will be an option to apply per fold, and have the parameters be learnable.)
We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please file a new issue so we can discuss it.
Hernandez, J. G., Saini, A. K., Ghosh, A., & Moore, J. H. (2025). [The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning](https://www.cell.com/patterns/fulltext/S2666-3899(25)00162-X). Patterns, 6(7).
Ribeiro, P., Saini, A., Moran, J., Matsumoto, N., Choi, H., Hernandez, M., & Moore, J. H. (2024). [TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning](https://link.springer.com/chapter/10.1007/978-981-99-8413-8_1). In Genetic programming theory and practice XX (pp. 1-17). Singapore: Springer Nature Singapore.
BitTex entry:
```bibtex
@incollection{ribeiro2024tpot2,
title={TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning},
author={Ribeiro, Pedro and Saini, Anil and Moran, Jay and Matsumoto, Nicholas and Choi, Hyunjun and Hernandez, Miguel and Moore, Jason H},
booktitle={Genetic programming theory and practice XX},
Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). [Automating biomedical data science through tree-based pipeline optimization](http://link.springer.com/chapter/10.1007/978-3-319-31204-0_9). *Applications of Evolutionary Computation*, pages 123-137.
author={Olson, Randal S. and Urbanowicz, Ryan J. and Andrews, Peter C. and Lavender, Nicole A. and Kidd, La Creis and Moore, Jason H.},
editor={Squillero, Giovanni and Burelli, Paolo},
chapter={Automating Biomedical Data Science Through Tree-Based Pipeline Optimization},
title={Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part I},
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016). [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](http://dl.acm.org/citation.cfm?id=2908918). *Proceedings of GECCO 2016*, pages 485-492.
BibTeX entry:
```bibtex
@inproceedings{OlsonGECCO2016,
author={Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan J. and Moore, Jason H.},
title={Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference 2016},
Trang T. Le, Weixuan Fu and Jason H. Moore (2020). [Scaling tree-based automated machine learning to biomedical big data with a feature set selector](https://academic.oup.com/bioinformatics/article/36/1/250/5511404). *Bioinformatics*.36(1): 250-256.
BibTeX entry:
```bibtex
@article{le2020scaling,
title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
TPOT was developed in the [Artificial Intelligence Innovation (A2I) Lab](http://epistasis.org/) at Cedars-Sinai with funding from the [NIH](http://www.nih.gov/) under grants U01 AG066833 and R01 LM010098. We are incredibly grateful for the support of the NIH and the Cedars-Sinai during the development of this project.
