Metadata-Version: 2.1
Name: tscv
Version: 0.1.2
Summary: Time series cross-validation
Home-page: https://github.com/WenjieZ/TSCV
Author: Wenjie Zheng
Author-email: work@zhengwenjie.net
License: new BSD
Keywords: model selection,hyperparameter optimization,backtesting
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.13.3)
Requires-Dist: scikit-learn (>=0.22)

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![](train-gap-test.svg)

# TSCV: Time Series Cross-Validation

This repository is a [scikit-learn](https://scikit-learn.org) extension for time series cross-validation.
It introduces **gaps** between the training set and the test set, which mitigates the temporal dependence of time series and prevents information leakage.

## Installation

```bash
pip install tscv
```

## Usage

This extension defines 3 cross-validator classes and 1 function:
- `GapLeavePOut`
- `GapKFold`
- `GapRollForward`
- `gap_train_test_split`

The three classes can all be passed, as the `cv` argument, to
scikit-learn functions such as `cross-validate`, `cross_val_score`,
and `cross_val_predict`, just like the native cross-validator classes.

The one function is an alternative to the `train_test_split` function in `scikit-learn`.

## Examples

The following example uses `GapKFold` instead of `KFold` as the cross-validator.
```python
import numpy as np
from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import cross_val_score
from tscv import GapKFold

iris = datasets.load_iris()
clf = svm.SVC(kernel='linear', C=1)

# use GapKFold as the cross-validator
cv = GapKFold(n_splits=5, gap_before=5, gap_after=5)
scores = cross_val_score(clf, iris.data, iris.target, cv=cv)
```

The following example uses `gap_train_test_split` to split the data set into the training set and the test set.
```python
import numpy as np
from tscv import gap_train_test_split

X, y = np.arange(20).reshape((10, 2)), np.arange(10)
X_train, X_test, y_train, y_test = gap_train_test_split(X, y, test_size=2, gap_size=2)
```

## Contributing
- Report bugs in the issue tracker
- Express your use cases in the issue tracker

## Support
- [tscv.readthedocs.io](tscv.readthedocs.io)

## Acknowledgments

- I would like to thank Jeffrey Racine and Christoph Bergmeir for the helpful discussion.

## License
BSD-3-Clause

## Citation
```latex
@article{zheng2019hv,
  title={$ hv $-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)},
  author={Zheng, Wenjie},
  journal={arXiv preprint arXiv:1910.08904},
  year={2019}
}
```


