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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 BSD 3-Clause license.

It is currently maintained by Isak Samsten

Features

Data	Classification	Regression	Explainability	Metric	Unsupervised	Outlier
Repositories	`ShapeletForestClassifier`	`ShapeletForestRegressor`	`ShapeletForestCounterfactual`	UCR-suite	`ShapeletForestTransform`	`IsolationShapeletForest`
Classification (`wildboar/ucr`)	`ExtraShapeletTreesClassifier`	`ExtraShapeletTreesRegressor`	`KNearestCounterfactual`	MASS	`RandomShapeletEmbedding`
Regression (`wildboar/tsereg`)	`RocketTreeClassifier`	`RocketRegressor`	`PrototypeCounterfactual`	DTW	`RocketTransform`
Outlier detection (`wildboar/outlier:easy`)	`RocketClassifier`	`RandomShapeletRegressor`	`IntervalImportance`	DDTW	`IntervalTransform`
	`RandomShapeletClassifier`	`RocketTreeRegressor`	`ShapeletImportance`	WDTW	`FeatureTransform`
	`RocketForestClassifier`	`RocketForestRegressor`		MSM	`MatrixProfileTransform`
	`IntervalTreeClassifier`	`IntervalTreeRegressor`		TWE	Segmentation
	`IntervalForestClassifier`	`IntervalForestRegressor`		LCSS	Motif discovery
	`ProximityTreeClassifier`			ERP	`SAX`
	`ProximityForestClassifier`			EDR	`PAA`
	`HydraClassifier`			ADTW	`MatrixProfileTransform`
	`KNeighborsClassifier`				`HydraTransform`
	`ElasticEnsembleClassifier`				`KMeans` with (W)DTW support
	`DilatedShapeletClassifier`				`KMedoids`
					`DilatedShapeletTransform`

See the documentation for examples.

Installation

Binaries

wildboar is available through pip and can be installed with:

pip install wildboar

Universal binaries are compiled for Python 3.8, 3.9, 3.10 and 3.11 running on GNU/Linux, Windows and macOS.

Compilation

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

Usage

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.

Changelog

The changelog records a history of notable changes to wildboar.

Development

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

Source code

You can check the latest sources with the command:

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

Documentation

HTML documentation: https://wildboar.dev

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. 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

Metadata

Release files for wildboar 1.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for wildboar 1.2.1
File	Size	Uploaded
wildboar-1.2.1.tar.gz	3.7 MB	Jan 10, 2025	Details

Built distributions (wheels)

Table of built distributions (wheels) for wildboar 1.2.1
File	Interpreter	ABI	Platform	Reset
wildboar-1.2.1-cp313-cp313-win_amd64.whl 2.0 MB Jan 10, 2025	CPython 3.13	CPython 3.13	Windows x86-64	Details
wildboar-1.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 14.0 MB Jan 10, 2025	CPython 3.13	CPython 3.13	Linux glibc 2.17+ x86-64	Details
wildboar-1.2.1-cp313-cp313-macosx_11_0_arm64.whl 5.5 MB Jan 10, 2025	CPython 3.13	CPython 3.13	macOS 11.0+ ARM64	Details
wildboar-1.2.1-cp313-cp313-macosx_10_13_x86_64.whl 5.6 MB Jan 10, 2025	CPython 3.13	CPython 3.13	macOS 10.13+ x86-64	Details
wildboar-1.2.1-cp313-cp313-macosx_10_13_universal2.whl 7.4 MB Jan 10, 2025	CPython 3.13	CPython 3.13	macOS 10.13+ universal2 (ARM64, x86-64)	Details
wildboar-1.2.1-cp312-cp312-win_amd64.whl 2.0 MB Jan 10, 2025	CPython 3.12	CPython 3.12	Windows x86-64	Details
wildboar-1.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 14.3 MB Jan 10, 2025	CPython 3.12	CPython 3.12	Linux glibc 2.17+ x86-64	Details
wildboar-1.2.1-cp312-cp312-macosx_11_0_arm64.whl 5.5 MB Jan 10, 2025	CPython 3.12	CPython 3.12	macOS 11.0+ ARM64	Details
wildboar-1.2.1-cp312-cp312-macosx_10_13_x86_64.whl 5.6 MB Jan 10, 2025	CPython 3.12	CPython 3.12	macOS 10.13+ x86-64	Details
wildboar-1.2.1-cp312-cp312-macosx_10_13_universal2.whl 7.4 MB Jan 10, 2025	CPython 3.12	CPython 3.12	macOS 10.13+ universal2 (ARM64, x86-64)	Details
wildboar-1.2.1-cp311-cp311-win_amd64.whl 2.0 MB Jan 10, 2025	CPython 3.11	CPython 3.11	Windows x86-64	Details
wildboar-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 13.6 MB Jan 10, 2025	CPython 3.11	CPython 3.11	Linux glibc 2.17+ x86-64	Details
wildboar-1.2.1-cp311-cp311-macosx_11_0_arm64.whl 5.5 MB Jan 10, 2025	CPython 3.11	CPython 3.11	macOS 11.0+ ARM64	Details
wildboar-1.2.1-cp311-cp311-macosx_10_9_x86_64.whl 5.6 MB Jan 10, 2025	CPython 3.11	CPython 3.11	macOS 10.9+ x86-64	Details
wildboar-1.2.1-cp311-cp311-macosx_10_9_universal2.whl 7.4 MB Jan 10, 2025	CPython 3.11	CPython 3.11	macOS 10.9+ universal2 (ARM64, x86-64)	Details
wildboar-1.2.1-cp310-cp310-win_amd64.whl 2.0 MB Jan 10, 2025	CPython 3.10	CPython 3.10	Windows x86-64	Details
wildboar-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 12.9 MB Jan 10, 2025	CPython 3.10	CPython 3.10	Linux glibc 2.17+ x86-64	Details
wildboar-1.2.1-cp310-cp310-macosx_11_0_arm64.whl 5.5 MB Jan 10, 2025	CPython 3.10	CPython 3.10	macOS 11.0+ ARM64	Details
wildboar-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl 5.6 MB Jan 10, 2025	CPython 3.10	CPython 3.10	macOS 10.9+ x86-64	Details
wildboar-1.2.1-cp310-cp310-macosx_10_9_universal2.whl 7.4 MB Jan 10, 2025	CPython 3.10	CPython 3.10	macOS 10.9+ universal2 (ARM64, x86-64)	Details
wildboar-1.2.1-cp39-cp39-win_amd64.whl 2.0 MB Jan 10, 2025	CPython 3.9	CPython 3.9	Windows x86-64	Details
wildboar-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 13.0 MB Jan 10, 2025	CPython 3.9	CPython 3.9	Linux glibc 2.17+ x86-64	Details
wildboar-1.2.1-cp39-cp39-macosx_11_0_arm64.whl 5.5 MB Jan 10, 2025	CPython 3.9	CPython 3.9	macOS 11.0+ ARM64	Details
wildboar-1.2.1-cp39-cp39-macosx_10_9_x86_64.whl 5.6 MB Jan 10, 2025	CPython 3.9	CPython 3.9	macOS 10.9+ x86-64	Details
wildboar-1.2.1-cp39-cp39-macosx_10_9_universal2.whl 7.4 MB Jan 10, 2025	CPython 3.9	CPython 3.9	macOS 10.9+ universal2 (ARM64, x86-64)	Details

Total release size: 173.7 MB

Release files / wildboar-1.2.1.tar.gz

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Release files / wildboar-1.2.1-cp313-cp313-macosx_10_13_universal2.whl

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Release files / wildboar-1.2.1-cp312-cp312-macosx_11_0_arm64.whl

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Release files / wildboar-1.2.1-cp310-cp310-macosx_10_9_universal2.whl

Download URL	wildboar-1.2.1-cp310-cp310-macosx_10_9_universal2.whl
Size	7.4 MB
Tags	CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)
SHA-256 checksum How to use checksums	`3244e943974dd3b687ae074b968f110143c9b82f453667f005581f529755ce48`
BLAKE2b-256 checksum How to use checksums	`3e2b389bd22866f4938bac5903e3dbd1c33e01a54150d15a19e97c4a790eca9f`
Upload date	Jan 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.12.8`

Release files / wildboar-1.2.1-cp39-cp39-win_amd64.whl

Download URL	wildboar-1.2.1-cp39-cp39-win_amd64.whl
Size	2.0 MB
Tags	CPython 3.9 Windows x86-64
SHA-256 checksum How to use checksums	`0675ae3d86402e74789705d06e6141896fcb4605fea07f53870798e3fca6d486`
BLAKE2b-256 checksum How to use checksums	`841cba2b1ae5bfaf036ca932c17e25110b0116e051c1d0f4a9f723bc3a156056`
Upload date	Jan 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.12.8`

Release files / wildboar-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	wildboar-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	13.0 MB
Tags	CPython 3.9 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`f73b85de272e1ceb83c8b15d7f6eb94fedf84694687535b9358cb8d48f9d1325`
BLAKE2b-256 checksum How to use checksums	`8fb0b40f8f537eb414564e54f05228c56ebd766580019c9c554118a8ccabdb3a`
Upload date	Jan 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.12.8`

Release files / wildboar-1.2.1-cp39-cp39-macosx_11_0_arm64.whl

Download URL	wildboar-1.2.1-cp39-cp39-macosx_11_0_arm64.whl
Size	5.5 MB
Tags	CPython 3.9 macOS 11.0+ ARM64
SHA-256 checksum How to use checksums	`31cb84be4967d7469512161ac9b67bedb74ae2396e40754e22742e5d23efaa57`
BLAKE2b-256 checksum How to use checksums	`561e40100a83430963d3e1829c494d7d08c911a75e98c42603353b5ae53a6087`
Upload date	Jan 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.12.8`

Release files / wildboar-1.2.1-cp39-cp39-macosx_10_9_x86_64.whl

Download URL	wildboar-1.2.1-cp39-cp39-macosx_10_9_x86_64.whl
Size	5.6 MB
Tags	CPython 3.9 macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`6ebe1fa2f019ed91dc60712b2c68daea52d747bd1e9ebba637694f038ea8e6bd`
BLAKE2b-256 checksum How to use checksums	`a3eea3fb856f026b03e705341a264a982bb01e04aaf9c98623307d550febaf59`
Upload date	Jan 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.12.8`

Release files / wildboar-1.2.1-cp39-cp39-macosx_10_9_universal2.whl

Download URL	wildboar-1.2.1-cp39-cp39-macosx_10_9_universal2.whl
Size	7.4 MB
Tags	CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)
SHA-256 checksum How to use checksums	`1f340683509b73fdd32741e803aba0ac7e2759b5255ca8d4aa025746ee9babf2`
BLAKE2b-256 checksum How to use checksums	`9ca452726630e28ccc6d97968a247f115a885897365c7054b164738fe88ff78f`
Upload date	Jan 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.12.8`