</p>
<p align="center">
<img src="https://github.com/isaksamsten/wildboar/blob/master/.github/github-logo.png?raw=true" alt="Wildboar logo" width="100px">
</p>
<h1 align="center">wildboar</h1>
<p align="center">
<img src="https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10-blue" />
<img src="https://github.com/isaksamsten/wildboar/workflows/Build,%20test%20and%20upload%20to%20PyPI/badge.svg"/>
<a href="https://badge.fury.io/py/wildboar"><img src="https://badge.fury.io/py/wildboar.svg" /></a>
<a href="https://pepy.tech/project/wildboar"><img src="https://static.pepy.tech/personalized-badge/wildboar?period=total&units=international_system&left_color=black&right_color=orange&left_text=downloads" /></a>
<a href="https://doi.org/10.5281/zenodo.4264063"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4264063.svg" /></a>
</p>
[wildboar](https://isaksamsten.github.io/wildboar/) is a Python module for temporal machine learning and fast
distance computations built on top of
[scikit-learn](https://scikit-learn.org) and [numpy](https://numpy.org)
distributed under the BSD 3-Clause license.
It is currently maintained by Isak Samsten
## Features
| **Data** | **Classification** | **Regression** | **Explainability** | **Metric** | **Unsupervised** | **Outlier** |
|-----------------------------------------------------------------------------------|----------------------------------|---------------------------------|----------------------------------|------------|-----------------------------|-----------------------------|
| [Repositories](https://isaksamsten.github.io/wildboar/master/guide/datasets.html) | ``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`` | | WDTW | ``FeatureTransform`` | |
| | ``RocketForestClassifier`` | ``RocketForestRegressor`` | | MSM | MatrixProfile | |
| | ``IntervalTreeClassifier`` | ``IntervalTreeRegressor`` | | TWE | Segmentation | |
| | ``IntervalForestClassifier`` | ``IntervalForestRegressor`` | | LCSS | Motif discovery | |
| | ``ProximityTreeClassifier`` | | | ERP | ``SAX`` | |
| | ``ProximityForestClassifier`` | | | EDR | ``PAA`` | |
| | | | | | ``MatrixProfileTransform`` | |
See the [documentation](https://isaksamsten.github.io/wildboar/master/examples.html) for examples.
## Installation
### Binaries
`wildboar` is available through `pip` and can be installed with:
pip install wildboar
Universal binaries are compiled for GNU/Linux and Python 3.8, 3.9, 3.10
### 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](https://isaksamsten.github.io/wildboar/master/install.html#build-and-compile-from-source)
## Usage
```python
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](https://isaksamsten.github.io/wildboar/master/guide.html) includes more detailed usage instructions.
## Changelog
The [changelog](https://isaksamsten.github.io/wildboar/master/more/whatsnew.html) records a history of notable changes to ``wildboar``.
## Development
Contributions are welcome! The [developer's guide](https://isaksamsten.github.io/wildboar/master/more/contributing.html) has detailed information about contributing code and more!
In short, pull requests should:
* be well motivated
* be fomatted using Black
* add relevant tests
* add relevant documentation
## Source code
You can check the latest sources with the command:
git clone https://github.com/isaksamsten/wildboar
## Documentation
* HTML documentation: [https://isaksamsten.github.io/wildboar](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. 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`
Raw data
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"description": "</p>\n<p align=\"center\">\n<img src=\"https://github.com/isaksamsten/wildboar/blob/master/.github/github-logo.png?raw=true\" alt=\"Wildboar logo\" width=\"100px\">\n</p>\n\n<h1 align=\"center\">wildboar</h1>\n\n<p align=\"center\">\n\t<img src=\"https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10-blue\" />\n\t<img src=\"https://github.com/isaksamsten/wildboar/workflows/Build,%20test%20and%20upload%20to%20PyPI/badge.svg\"/>\n\t<a href=\"https://badge.fury.io/py/wildboar\"><img src=\"https://badge.fury.io/py/wildboar.svg\" /></a>\n\t<a href=\"https://pepy.tech/project/wildboar\"><img src=\"https://static.pepy.tech/personalized-badge/wildboar?period=total&units=international_system&left_color=black&right_color=orange&left_text=downloads\" /></a>\n\t<a href=\"https://doi.org/10.5281/zenodo.4264063\"><img src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4264063.svg\" /></a>\n</p>\n\n[wildboar](https://isaksamsten.github.io/wildboar/) is a Python module for temporal machine learning and fast\ndistance computations built on top of\n[scikit-learn](https://scikit-learn.org) and [numpy](https://numpy.org)\ndistributed under the BSD 3-Clause license. \n\nIt is currently maintained by Isak Samsten\n\n## Features\n| **Data** | **Classification** | **Regression** | **Explainability** | **Metric** | **Unsupervised** | **Outlier** |\n|-----------------------------------------------------------------------------------|----------------------------------|---------------------------------|----------------------------------|------------|-----------------------------|-----------------------------|\n| [Repositories](https://isaksamsten.github.io/wildboar/master/guide/datasets.html) | ``ShapeletForestClassifier`` | ``ShapeletForestRegressor`` | ``ShapeletForestCounterfactual`` | UCR-suite | ``ShapeletForestTransform`` | ``IsolationShapeletForest`` |\n| Classification (``wildboar/ucr``) | ``ExtraShapeletTreesClassifier`` | ``ExtraShapeletTreesRegressor`` | ``KNearestCounterfactual`` | MASS | ``RandomShapeletEmbedding`` | |\n| Regression (``wildboar/tsereg``) | ``RocketTreeClassifier`` | ``RocketRegressor`` | ``PrototypeCounterfactual`` | DTW | ``RocketTransform`` | |\n| Outlier detection (``wildboar/outlier:easy``) | ``RocketClassifier`` | ``RandomShapeletRegressor`` | ``IntervalImportance`` | DDTW | ``IntervalTransform`` | |\n| | ``RandomShapeletClassifier`` | ``RocketTreeRegressor`` | | WDTW | ``FeatureTransform`` | |\n| | ``RocketForestClassifier`` | ``RocketForestRegressor`` | | MSM | MatrixProfile | |\n| | ``IntervalTreeClassifier`` | ``IntervalTreeRegressor`` | | TWE | Segmentation | |\n| | ``IntervalForestClassifier`` | ``IntervalForestRegressor`` | | LCSS | Motif discovery | |\n| | ``ProximityTreeClassifier`` | | | ERP | ``SAX`` | |\n| | ``ProximityForestClassifier`` | | | EDR | ``PAA`` | |\n| | | | | | ``MatrixProfileTransform`` | |\n\nSee the [documentation](https://isaksamsten.github.io/wildboar/master/examples.html) for examples.\n\n## Installation\n\n### Binaries\n\n`wildboar` is available through `pip` and can be installed with:\n\n pip install wildboar\n\nUniversal binaries are compiled for GNU/Linux and Python 3.8, 3.9, 3.10\n\n### Compilation\n\nIf you already have a working installation of numpy, scikit-learn, scipy and cython,\ncompiling and installing wildboar is as simple as:\n\n pip install .\n\t\nTo install the requirements, use:\n\n pip install -r requirements.txt\n\nFor complete instructions see the [documentation](https://isaksamsten.github.io/wildboar/master/install.html#build-and-compile-from-source)\n\n## Usage\n\n```python\nfrom wildboar.ensemble import ShapeletForestClassifier\nfrom wildboar.datasets import load_dataset\nx_train, x_test, y_train, y_test = load_dataset(\"GunPoint\", merge_train_test=False)\nc = ShapeletForestClassifier()\nc.fit(x_train, y_train)\nc.score(x_test, y_test)\n``` \n\nThe [User guide](https://isaksamsten.github.io/wildboar/master/guide.html) includes more detailed usage instructions.\n\n\n## Changelog\nThe [changelog](https://isaksamsten.github.io/wildboar/master/more/whatsnew.html) records a history of notable changes to ``wildboar``.\n\n\n## Development\n\nContributions are welcome! 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