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Merge pull request #167 from EpistasisLab/doc_build
Doc build
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.github/workflows/docs.yml

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jobs:
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build_docs:
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runs-on: ubuntu-latest
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env:
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GIT_COMMITTER_NAME: "Doc Build Bot"
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GIT_COMMITTER_EMAIL: "[email protected]"
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steps:
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- name: Checkout code
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uses: actions/checkout@v2

README.md

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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
2020

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The current version of TPOT was developed at Cedars-Sinai by:
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- Pedro Henrique Ribeiro (Lead developer - https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/)
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- Anil Saini ([email protected])
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- Jose Hernandez ([email protected])
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- Jay Moran ([email protected])
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- Nicholas Matsumoto ([email protected])
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- Hyunjun Choi ([email protected])
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- Miguel E. Hernandez ([email protected])
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- Jason Moore ([email protected])
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The original version of TPOT was primarily developed at the University of Pennsylvania by:
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- Randal S. Olson ([email protected])
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- Weixuan Fu ([email protected])
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- Daniel Angell ([email protected])
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- Jason Moore ([email protected])
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- and many more generous open-source contributors
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The current version of TPOT was developed at Cedars-Sinai by:
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- Pedro Henrique Ribeiro (Lead developer - https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/)
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- Anil Saini ([email protected])
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- Jose Hernandez ([email protected])
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- Jay Moran ([email protected])
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- Nicholas Matsumoto ([email protected])
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- Hyunjun Choi ([email protected])
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- Miguel E. Hernandez ([email protected])
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- Jason Moore ([email protected])
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The original version of TPOT was primarily developed at the University of Pennsylvania by:
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- Randal S. Olson ([email protected])
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- Weixuan Fu ([email protected])
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- Daniel Angell ([email protected])
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- Jason Moore ([email protected])
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- and many more generous open-source contributors
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## License
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tpot/tests/test_estimators.py

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tpot_estimator.fit(sample_dataset[0], sample_dataset[1])
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return tpot_estimator
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# @pytest.mark.skip(reason="Errors out, skipping to build docs")
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def test_tpot_estimator_predict(tpot_estimator_with_pipeline,sample_dataset):
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#X_test = [[1, 2, 3], [4, 5, 6]]
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X_test = sample_dataset[0]

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