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        "author": "Wenjie Zheng",
        "author_email": "work@zhengwenjie.net",
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            "License :: OSI Approved",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
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            "Topic :: Software Development"
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        "description": "![](train-gap-test.svg)\n\n# TSCV: Time Series Cross-Validation\n\nThis repository is a [scikit-learn](https://scikit-learn.org) extension for time series cross-validation.\nIt introduces **gaps** between the training set and the test set, which mitigates the temporal dependence of time series and prevents information leak.\n\n## Installation\n\n```bash\npip install tscv\n```\n\n## Update\n\n```bash\npip install tscv --upgrade\n```\n\nI recommend you to update it often.\n\n## Usage\n\nThis extension defines 3 cross-validator classes and 1 function:\n- `GapLeavePOut`\n- `GapKFold`\n- `GapWalkForward`\n- `gap_train_test_split`\n\nThe three classes can all be passed, as the `cv` argument, to the `cross_val_score` function in `scikit-learn`, just like the native cross-validator classes in `scikit-learn`.\n\nThe one function is an alternative to the `train_test_split` function in `scikit-learn`.\n\n## Examples\n\nThe following example uses `GapKFold` instead of `KFold` as the cross-validator.\n```python\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn import svm\nfrom sklearn.model_selection import cross_val_score\nfrom tscv import GapKFold\n\niris = datasets.load_iris()\nclf = svm.SVC(kernel='linear', C=1)\n\n# use GapKFold as the cross-validator\ncv = GapKFold(n_splits=5, gap_before=5, gap_after=5)\nscores = cross_val_score(clf, iris.data, iris.target, cv=cv)\n```\n\nThe following example uses `gap_train_test_split` to split the data set into the training set and the test set.\n```python\nimport numpy as np\nfrom tscv import gap_train_test_split\n\nX, y = np.arange(20).reshape((10, 2)), np.arange(10)\nX_train, X_test, y_train, y_test = gap_train_test_split(X, y, test_size=2, gap_size=2)\n```\n\n## Support\nSee the documentation [here](http://www.zhengwenjie.net/tscv/).\n\nIf you need any further help, please use the issue tracker.\n\n## Contributing\n- Report bugs in the issue tracker\n- Express your use cases in the issue tracker\n\n## Authors\nThis extension is mainly developed by me, Wenjie Zheng.\n\nThe `GapWalkForward` cross-validator is adapted from the `TimeSeriesSplit` of `scikit-learn`.\n\n## Acknowledgment\n- I would like to thank Christoph Bergmeir, Prabir Burman, and Jeffrey Racine for the helpful discussion.\n- I would like to thank Jacques Joubert for encouraging me to develop this package.\n\n## License\nBSD-3-Clause\n\n\n",
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