{"id":15284546,"url":"https://github.com/wildboar-foundation/wildboar","last_synced_at":"2025-08-01T13:04:33.715Z","repository":{"id":37916781,"uuid":"132126266","full_name":"wildboar-foundation/wildboar","owner":"wildboar-foundation","description":"wildboar is a Python module for temporal machine 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align=\"center\"\u003e\n\u003cimg src=\"https://github.com/wildboar-foundation/wildboar/blob/master/.github/github-logo.png?raw=true\" alt=\"Wildboar logo\" width=\"100px\"\u003e\n\u003c/p\u003e\n\n\u003ch1 align=\"center\"\u003ewildboar\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n\t\u003cimg src=\"https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10-blue\" /\u003e\n\t\u003cimg src=\"https://github.com/wildboar-foundation/wildboar/workflows/Build,%20test%20and%20upload%20to%20PyPI/badge.svg\"/\u003e\n\t\u003ca href=\"https://badge.fury.io/py/wildboar\"\u003e\u003cimg src=\"https://badge.fury.io/py/wildboar.svg\" /\u003e\u003c/a\u003e\n\t\u003ca href=\"https://pepy.tech/project/wildboar\"\u003e\u003cimg src=\"https://static.pepy.tech/personalized-badge/wildboar?period=total\u0026units=international_system\u0026left_color=black\u0026right_color=orange\u0026left_text=downloads\" /\u003e\u003c/a\u003e\n\t\u003ca href=\"https://doi.org/10.5281/zenodo.4264063\"\u003e\u003cimg src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4264063.svg\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n[wildboar](https://wildboar.dev/) 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\n| **Data** | **Classification** | **Regression** | **Explainability** | **Metric** | **Unsupervised** | **Dimension selection** | **Outlier** |\n| --------------------------------------------------------------- | ------------------------------ | ----------------------------- | ------------------------------ | ---------- | ---------------------------- | ----------------------------- | ------------------------- |\n| [Repositories](https://wildboar.dev/master/guide/datasets.html) | `ShapeletForestClassifier` | `ShapeletForestRegressor` | `ShapeletForestCounterfactual` | UCR-suite | `ShapeletForestTransform` | `DimensionVarianceThreshold` | `IsolationShapeletForest` |\n| Classification (`wildboar/ucr`) | `ExtraShapeletTreesClassifier` | `ExtraShapeletTreesRegressor` | `KNearestCounterfactual` | MASS | `RandomShapeletEmbedding` | `SelectDimensionPercentile` | |\n| Regression (`wildboar/tsereg`) | `RocketTreeClassifier` | `RocketRegressor` | `PrototypeCounterfactual` | DTW | `RocketTransform` | `SelectDimensionTopK` | |\n| Outlier detection (`wildboar/outlier:easy`) | `RocketClassifier` | `RandomShapeletRegressor` | `IntervalImportance` | DDTW | `IntervalTransform` | `SelectDimensionSignificance` | |\n| | `RandomShapeletClassifier` | `RocketTreeRegressor` | `ShapeletImportance` | WDTW | `FeatureTransform` | | |\n| | `RocketForestClassifier` | `RocketForestRegressor` | | MSM | `MatrixProfileTransform` | | |\n| | `IntervalTreeClassifier` | `IntervalTreeRegressor` | | TWE | `FlossSegmenter` | | |\n| | `IntervalForestClassifier` | `IntervalForestRegressor` | | LCSS | Motif discovery | | |\n| | `ProximityTreeClassifier` | `CastorRegressor` | | ERP | `SAX` | | |\n| | `ProximityForestClassifier` | | | EDR | `PAA` | | |\n| | `HydraClassifier` | | | ADTW | `MatrixProfileTransform` | | |\n| | `KNeighborsClassifier` | | | | `HydraTransform` | | |\n| | `ElasticEnsembleClassifier` | | | | `KMeans` with (W)DTW support | | |\n| | `DilatedShapeletClassifier` | | | | `KMedoids` | | |\n| | `CastorClassifier` | | | | `DilatedShapeletTransform` | | |\n| | | | | | `CastorTransform` | | |\n| | | | | | `QuantTransform` | | |\n\nSee the [documentation](https://wildboar.dev/master/) 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 Python 3.9, 3.10, 3.11 and 3.12 running on\nGNU/Linux, Windows and macOS.\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\nTo install the requirements, use:\n\n pip install -r requirements.txt\n\nFor complete instructions see the [documentation](https://wildboar.dev/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://wildboar.dev/master/guide.html) includes more\ndetailed usage instructions.\n\n## Changelog\n\nThe [changelog](https://wildboar.dev/master/more/whatsnew.html) records a\nhistory of notable changes to `wildboar`.\n\n## Development\n\nContributions are welcome! The [developer's\nguide](https://wildboar.dev/master/more/contributing.html) has detailed\ninformation about contributing code and more!\n\nIn short, pull requests should:\n\n- be well motivated\n- be formatted using Black\n- add relevant tests\n- add relevant documentation\n\n## Source code\n\nYou can check the latest sources with the command:\n\n git clone https://github.com/wildboar-foundation/wildboar\n\n## Documentation\n\n- HTML documentation: [https://wildboar.dev](https://wildboar.dev)\n\n## Citation\n\nIf you use `wildboar` in a scientific publication, I would appreciate\ncitations to the paper:\n\n- Karlsson, I., Papapetrou, P. Boström, H., 2016.\n _Generalized Random Shapelet Forests_. In the Data Mining and\n Knowledge Discovery Journal\n\n - `ShapeletForestClassifier`\n\n- Isak Samsten, 2020. isaksamsten/wildboar: wildboar. Zenodo. doi:10.5281/zenodo.4264063\n- Karlsson, I., Rebane, J., Papapetrou, P. et al.\n Locally and globally explainable time series tweaking.\n Knowl Inf Syst 62, 1671–1700 (2020)\n\n - `ShapeletForestCounterfactual`\n - `KNearestCounterfactual`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwildboar-foundation%2Fwildboar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwildboar-foundation%2Fwildboar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwildboar-foundation%2Fwildboar/lists"}