wildboar is a Python module for temporal machine learning and fast distance computations built on top of scikit-learn and numpy distributed under the BSD 3-Clause license.

It is currently maintained by Isak Samsten

See the documentation for examples.

wildboar is available through pip and can be installed with:

pip install wildboar

Universal binaries are compiled for Python 3.9, 3.10, 3.11 and 3.12 running on GNU/Linux, Windows and macOS.

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

pip install .

To install the requirements, use:

pip install -r requirements.txt

For complete instructions see the documentation

from wildboar.ensemble import ShapeletForestClassifier
from wildboar.datasets import load_dataset
x_train, x_test, y_train, y_test = load_dataset("GunPoint", merge_train_test=False)
c = ShapeletForestClassifier()
c.fit(x_train, y_train)
c.score(x_test, y_test)

The User guide includes more detailed usage instructions.

The changelog records a history of notable changes to wildboar.

Contributions are welcome! The developer's guide has detailed information about contributing code and more!

In short, pull requests should:

• be well motivated
• be formatted using Black
• add relevant tests
• add relevant documentation

You can check the latest sources with the command:

git clone https://github.com/wildboar-foundation/wildboar

• HTML documentation: https://wildboar.dev

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. Zenodo. doi:10.5281/zenodo.4264063

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

  • ShapeletForestCounterfactual
  • KNearestCounterfactual