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Time series learning with Python.

Project description


Python version Build, test and upload to PyPI Docs Status PyPI version DOI

wildboar is a Python module for temporal machine learning and fast distance computations built on top of scikit-learn and numpy distributed under the GNU General Public License Version 3.

It is currently maintained by Isak Samsten


Data Classification Regression Explainability Metric Unsupervised Outlier
Repositories ShapeletForestClassifier ShapeletForestRegressor ShapeletForestCounterfactual UCR-suite ShapeletForestEmbedding IsolationShapeletForest
ExtraShapeletTreesClassifier ExtraShapeletTreesRegressor KNearestCounterfactual



wildboar requires:

  • python>=3.7
  • numpy>=1.17.4
  • scikit-learn>=0.21.3
  • scipy>=1.3.2

Some parts of wildboar is implemented using Cython. Hence, compilation requires:

  • cython (>= 0.29.14)


wildboar is available through pip and can be installed with:

pip install wildboar

Universal binaries are compiled for GNU/Linux and Python 3.7, 3.8 and 3.9.


If you already have a working installation of numpy, scikit-learn, scipy and cython, compiling and installing wildboar is as simple as:

python install

To install the requirements, use:

pip install -r requirements.txt


Contributions are welcome. Pull requests should be formatted using Black.


from wildboar.ensemble import ShapeletForestClassifier
from wildboar.datasets import load_two_lead_ecg
x_train, x_test, y_train, y_test = load_two_lead_ecg(merge_train_test=False)
c = ShapeletForestClassifier(), y_train)
c.score(x_test, y_test)

See the tutorial for more examples.

Source code

You can check the latest sources with the command:

git clone



If you use wildboar in a scientific publication, I would appreciate citations to the paper:

  • Karlsson, I., Papapetrou, P. Boström, H., 2016. Generalized Random Shapelet Forests. In the Data Mining and Knowledge Discovery Journal

    • ShapeletForestClassifier
  • Isak Samsten, 2020. isaksamsten/wildboar: wildboar (Version 1.0.3). Zenodo. doi:10.5281/zenodo.4264063

    • ShapeletForestRegressor
    • ExtraShapeletForestClassifier
    • ExtraShapeletForestRegressor
    • IsolationShapeletForest
    • ShapeletForestEmbedding
    • PrototypeCounterfactual
  • Karlsson, I., Rebane, J., Papapetrou, P. et al. Locally and globally explainable time series tweaking. Knowl Inf Syst 62, 1671–1700 (2020)

    • ShapeletForestCounterfactual
    • KNearestCounterfactual

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