Metadata-Version: 2.1
Name: wildboar
Version: 1.0.5
Summary: Time series learning with Python
Home-page: https://github.com/isakkarlsson/wildboar
Author: Isak Samsten
Author-email: isak@samsten.se
License: GPLv3
Description: # wildboar
        ![Python version](https://img.shields.io/badge/python-3.7%20|%203.8%20|%203.9-blue)
        [![Build Status](https://travis-ci.com/isaksamsten/wildboar.svg?branch=master)](https://travis-ci.com/isaksamsten/wildboar)
        [![Docs Status](https://img.shields.io/badge/docs-passing-success)](http://isaksamsten.github.io/wildboar/index.html)
        [![PyPI version](https://badge.fury.io/py/wildboar.svg)](https://badge.fury.io/py/wildboar)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4264063.svg)](https://doi.org/10.5281/zenodo.4264063)
        
        [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 GNU General Public License Version 3.
        
        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  | ``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: 1.0.5
        - Current development release: 1.0.5dev
        
        ### 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 and 3.8.
        
        ### 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](https://black.readthedocs.io).
        
        ## Usage
        
        ```python
        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](https://isaksamsten.github.io/wildboar/master/tutorial.html) 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](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`
        
Keywords: machine learning,time series distance
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Provides: wildboar
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
