wildboar 1.0.7

Time series learning with Python

wildboar

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

Features

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

Installation

Dependencies

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)

Current version

• Current release: 1.0.7
• Current development release: 1.0.7dev

Binaries

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.

Compilation

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

python setup.py install

To install the requirements, use:

pip install -r requirements.txt

Development

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

Usage

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()
c.fit(x_train, 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 https://github.com/isakkarlsson/wildboar

Documentation

• HTML documentation: https://isaksamsten.github.io/wildboar

Citation

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