| Name | wildboar JSON |
| Version |
1.2.1
JSON |
| download |
| home_page | None |
| Summary | Time series learning with Python. |
| upload_time | 2025-01-10 13:12:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2018-2022 The wildboar developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
machine learning
time series
counterfactual explanation
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
Cython
numpy
scikit-learn
scipy
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
</p>
<p align="center">
<img src="https://github.com/wildboar-foundation/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/wildboar-foundation/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://wildboar.dev/) 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://wildboar.dev/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` | `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](https://wildboar.dev/master/) 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](https://wildboar.dev/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://wildboar.dev/master/guide.html) includes more
detailed usage instructions.
## Changelog
The [changelog](https://wildboar.dev/master/more/whatsnew.html) records a
history of notable changes to `wildboar`.
## Development
Contributions are welcome! The [developer's
guide](https://wildboar.dev/master/more/contributing.html) 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](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`
Raw data
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"description": "</p>\n<p align=\"center\">\n<img src=\"https://github.com/wildboar-foundation/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/wildboar-foundation/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://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** | **Outlier** |\n| --------------------------------------------------------------- | ------------------------------ | ----------------------------- | ------------------------------ | ---------- | ---------------------------- | ------------------------- |\n| [Repositories](https://wildboar.dev/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` | `ShapeletImportance` | WDTW | `FeatureTransform` | |\n| | `RocketForestClassifier` | `RocketForestRegressor` | | MSM | `MatrixProfileTransform` | |\n| | `IntervalTreeClassifier` | `IntervalTreeRegressor` | | TWE | Segmentation | |\n| | `IntervalForestClassifier` | `IntervalForestRegressor` | | LCSS | Motif discovery | |\n| | `ProximityTreeClassifier` | | | ERP | `SAX` | |\n| | `ProximityForestClassifier` | | | EDR | `PAA` | |\n| | `HydraClassifier` | | | ADTW | `MatrixProfileTransform` | |\n| | `KNeighborsClassifier` | | | | `HydraTransform` | |\n| | `ElasticEnsembleClassifier` | | | | `KMeans` with (W)DTW support | |\n| | `DilatedShapeletClassifier` | | | | `KMedoids` | |\n| | | | | | `DilatedShapeletTransform` | |\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 3.8, 3.9, 3.10 and 3.11 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. Bostr\u00f6m, 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. 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