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https://github.com/EpistasisLab/tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
https://github.com/EpistasisLab/tpot

adsp ag066833 aiml alzheimer alzheimers automated-machine-learning automation automl data-science feature-engineering gradient-boosting hyperparameter-optimization machine-learning model-selection nia parameter-tuning python random-forest scikit-learn u01ag066833

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A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

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---

To try the ![NEW!](https://raw.githubusercontent.com/EpistasisLab/tpot/master/images/NEW-small.gif "NEW!") TPOT2 (*alpha*) please go [here](https://github.com/EpistasisLab/tpot2)!



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**TPOT** stands for **T**ree-based **P**ipeline **O**ptimization **T**ool. Consider TPOT your **Data Science Assistant**. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.



![TPOT Demo](https://github.com/EpistasisLab/tpot/blob/master/images/tpot-demo.gif "TPOT Demo")



TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.



![An example Machine Learning pipeline](https://github.com/EpistasisLab/tpot/blob/master/images/tpot-ml-pipeline.png "An example Machine Learning pipeline")



An example Machine Learning pipeline



Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.



![An example TPOT pipeline](https://github.com/EpistasisLab/tpot/blob/master/images/tpot-pipeline-example.png "An example TPOT pipeline")



TPOT is built on top of scikit-learn, so all of the code it generates should look familiar... if you're familiar with scikit-learn, anyway.



**TPOT is still under active development** and we encourage you to check back on this repository regularly for updates.



For further information about TPOT, please see the [project documentation](http://epistasislab.github.io/tpot/).



## License



Please see the [repository license](https://github.com/EpistasisLab/tpot/blob/master/LICENSE) for the licensing and usage information for TPOT.



Generally, we have licensed TPOT to make it as widely usable as possible.



## Installation



We maintain the [TPOT installation instructions](http://epistasislab.github.io/tpot/installing/) in the documentation. TPOT requires a working installation of Python.



## Usage



TPOT can be used [on the command line](http://epistasislab.github.io/tpot/using/#tpot-on-the-command-line) or [with Python code](http://epistasislab.github.io/tpot/using/#tpot-with-code).



Click on the corresponding links to find more information on TPOT usage in the documentation.



## Examples



### Classification



Below is a minimal working example with the optical recognition of handwritten digits dataset.



```python

from tpot import TPOTClassifier

from sklearn.datasets import load_digits

from sklearn.model_selection import train_test_split



digits = load_digits()

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,

                                                    train_size=0.75, test_size=0.25, random_state=42)



tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2, random_state=42)

tpot.fit(X_train, y_train)

print(tpot.score(X_test, y_test))

tpot.export('tpot_digits_pipeline.py')

```



Running this code should discover a pipeline that achieves about 98% testing accuracy, and the corresponding Python code should be exported to the `tpot_digits_pipeline.py` file and look similar to the following:



```python

import numpy as np

import pandas as pd

from sklearn.ensemble import RandomForestClassifier

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.pipeline import make_pipeline, make_union

from sklearn.preprocessing import PolynomialFeatures

from tpot.builtins import StackingEstimator

from tpot.export_utils import set_param_recursive



# NOTE: Make sure that the outcome column is labeled 'target' in the data file

tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)

features = tpot_data.drop('target', axis=1)

training_features, testing_features, training_target, testing_target = \

            train_test_split(features, tpot_data['target'], random_state=42)



# Average CV score on the training set was: 0.9799428471757372

exported_pipeline = make_pipeline(

    PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),

    StackingEstimator(estimator=LogisticRegression(C=0.1, dual=False, penalty="l1")),

    RandomForestClassifier(bootstrap=True, criterion="entropy", max_features=0.35000000000000003, min_samples_leaf=20, min_samples_split=19, n_estimators=100)

)

# Fix random state for all the steps in exported pipeline

set_param_recursive(exported_pipeline.steps, 'random_state', 42)



exported_pipeline.fit(training_features, training_target)

results = exported_pipeline.predict(testing_features)

```



### Regression



Similarly, TPOT can optimize pipelines for regression problems. Below is a minimal working example with the practice Boston housing prices data set.



```python

from tpot import TPOTRegressor

from sklearn.datasets import load_boston

from sklearn.model_selection import train_test_split



housing = load_boston()

X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target,

                                                    train_size=0.75, test_size=0.25, random_state=42)



tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2, random_state=42)

tpot.fit(X_train, y_train)

print(tpot.score(X_test, y_test))

tpot.export('tpot_boston_pipeline.py')

```



which should result in a pipeline that achieves about 12.77 mean squared error (MSE), and the Python code in `tpot_boston_pipeline.py` should look similar to:



```python

import numpy as np

import pandas as pd

from sklearn.ensemble import ExtraTreesRegressor

from sklearn.model_selection import train_test_split

from sklearn.pipeline import make_pipeline

from sklearn.preprocessing import PolynomialFeatures

from tpot.export_utils import set_param_recursive



# NOTE: Make sure that the outcome column is labeled 'target' in the data file

tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)

features = tpot_data.drop('target', axis=1)

training_features, testing_features, training_target, testing_target = \

            train_test_split(features, tpot_data['target'], random_state=42)



# Average CV score on the training set was: -10.812040755234403

exported_pipeline = make_pipeline(

    PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),

    ExtraTreesRegressor(bootstrap=False, max_features=0.5, min_samples_leaf=2, min_samples_split=3, n_estimators=100)

)

# Fix random state for all the steps in exported pipeline

set_param_recursive(exported_pipeline.steps, 'random_state', 42)



exported_pipeline.fit(training_features, training_target)

results = exported_pipeline.predict(testing_features)

```



Check the documentation for [more examples and tutorials](http://epistasislab.github.io/tpot/examples/).



## Contributing to TPOT



We welcome you to [check the existing issues](https://github.com/EpistasisLab/tpot/issues/) for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please [file a new issue](https://github.com/EpistasisLab/tpot/issues/new) so we can discuss it.



Before submitting any contributions, please review our [contribution guidelines](http://epistasislab.github.io/tpot/contributing/).



## Having problems or have questions about TPOT?



Please [check the existing open and closed issues](https://github.com/EpistasisLab/tpot/issues?utf8=%E2%9C%93&q=is%3Aissue) to see if your issue has already been attended to. If it hasn't, [file a new issue](https://github.com/EpistasisLab/tpot/issues/new) on this repository so we can review your issue.



## Citing TPOT



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](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},

  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](http://link.springer.com/chapter/10.1007/978-3-319-31204-0_9). *Applications of Evolutionary Computation*, pages 123-137.



BibTeX entry:



```bibtex

@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](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},

    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},

}

```



Alternatively, you can cite the repository directly with the following DOI:



[![DOI](https://zenodo.org/badge/20747/rhiever/tpot.svg)](https://zenodo.org/badge/latestdoi/20747/rhiever/tpot)



## Support for TPOT



TPOT was developed in the [Computational Genetics Lab](http://epistasis.org/) at the [University of Pennsylvania](https://www.upenn.edu/) with funding from the [NIH](http://www.nih.gov/) under grant R01 AI117694. We are incredibly grateful for the support of the NIH and the University of Pennsylvania during the development of this project.



The TPOT logo was designed by Todd Newmuis, who generously donated his time to the project.