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
The current version of TPOT was developed at Cedars-Sinai by: - Pedro Henrique Ribeiro (Lead developer - https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/) - Anil Saini ([email protected]) - Jose Hernandez ([email protected]) - Jay Moran ([email protected]) - Nicholas Matsumoto ([email protected]) - Hyunjun Choi ([email protected]) - Miguel E. Hernandez ([email protected]) - Jason Moore ([email protected])
The original version of TPOT was primarily developed at the University of Pennsylvania by: - Randal S. Olson ([email protected]) - Weixuan Fu ([email protected]) - Daniel Angell ([email protected]) - Jason Moore ([email protected]) - and many more generous open-source contributors
Please see the repository license for the licensing and usage information for TPOT2. Generally, we have licensed TPOT2 to make it as widely usable as possible.
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. If not, see http://www.gnu.org/licenses/.
The documentation webpage can be found here.
We also recommend looking at the Tutorials folder for jupyter notebooks with examples and guides.
TPOT2 requires a working installation of Python.
We recommend using conda environments for installing TPOT2, though it would work equally well if manually installed without it.
More information on making anaconda environments found here.
conda create --name tpot2env python=3.10
conda activate tpot2env
python version <3.12 numpy scipy scikit-learn update_checker tqdm stopit pandas joblib xgboost matplotlib traitlets lightgbm optuna jupyter networkx dask distributed dask-ml dask-jobqueue func_timeout configspace
Many of the hyperparameter ranges used in our configspaces were adapted from either the original TPOT package or the AutoSklearn package.
You need to install the lightgbm package directly from conda using the following command before installing TPOT2.
This is to ensure that you get the version that is compatible with your system.
conda install --yes -c conda-forge 'lightgbm>=3.3.3'
If you want to utilize the additional features provided by TPOT2 along with scikit-learn extensions, you can install them using pip. The command to install TPOT2 with these extra features is as follows:
pip install tpot2[sklearnex]
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.
pip install -e /path/to/tpot2repo
If you downloaded with git pull, then the repository folder will be named TPOT2. (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 tpot2-main.
See the Tutorials Folder for more instructions and examples.
TPOT2 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 TPOT2 from a script. This is not required when running TPOT2 from a notebook.
For example:
#my_analysis.py
import tpot2
if __name__ == "__main__":
X, y = load_my_data()
est = tpot2.TPOTClassifier()
est.fit(X,y)
#rest of analysis
When designing custom objective functions, avoid the use of global variables.
Don't Do:
global_X = [[1,2],[4,5]]
global_y = [0,1]
def foo(est):
return my_scorer(est, X=global_X, y=global_y)
Instead use a partial
from functools import partial
def foo_scorer(est, X, y):
return my_scorer(est, X, y)
if __name__=='__main__':
X = [[1,2],[4,5]]
y = [0,1]
final_scorer = partial(foo_scorer, X=X, y=y)
Similarly when using lambda functions.
Dont Do:
def new_objective(est, a, b)
#definition
a = 100
b = 20
bad_function = lambda est : new_objective(est=est, a=a, b=b)
Do:
def new_objective(est, a, b)
#definition
a = 100
b = 20
good_function = lambda est, a=a, b=b : new_objective(est=est, a=a, b=b)
TPOT2 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, TPOT2 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.)
Setting verbose to 5 can be helpful during debugging as it will print out the error generated by failing pipelines.
We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to TPOT2, please file a new issue so we can discuss it.
If you use TPOT in a scientific publication, please consider citing at least one of the following papers:
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. Bioinformatics.36(1): 250-256.
BibTeX entry:
@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},
journal={Bioinformatics},
volume={36},
number={1},
pages={250--256},
year={2020},
publisher={Oxford University Press}
}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. Applications of Evolutionary Computation, pages 123-137.
BibTeX entry:
@inbook{Olson2016EvoBio,
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},
year={2016},
publisher={Springer International Publishing},
pages={123--137},
isbn={978-3-319-31204-0},
doi={10.1007/978-3-319-31204-0_9},
url={http://dx.doi.org/10.1007/978-3-319-31204-0_9}
}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. Proceedings of GECCO 2016, pages 485-492.
BibTeX entry:
@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},
series = {GECCO '16},
year = {2016},
isbn = {978-1-4503-4206-3},
location = {Denver, Colorado, USA},
pages = {485--492},
numpages = {8},
url = {http://doi.acm.org/10.1145/2908812.2908918},
doi = {10.1145/2908812.2908918},
acmid = {2908918},
publisher = {ACM},
address = {New York, NY, USA},
}TPOT2 was developed in the Artificial Intelligence Innovation (A2I) Lab at Cedars-Sinai with funding from the NIH 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.
The TPOT logo was designed by Todd Newmuis, who generously donated his time to the project.