Metadata-Version: 2.1
Name: stream-learn
Version: 0.8.1
Summary: Python package equipped with a procedures to process data streams using estimators with API compatible with scikit-learn.
Home-page: https://github.com/w4k2/stream-learn
Maintainer: P. Ksieniewicz
Maintainer-email: pawel.ksieniewicz@pwr.edu.pl
License: MIT
Download-URL: https://github.com/w4k2/stream-learn
Description: # stream-learn
        
        [![Travis Status](https://travis-ci.org/w4k2/stream-learn.svg?branch=master)](https://travis-ci.org/w4k2/stream-learn)
        [![Coverage Status](https://coveralls.io/repos/github/w4k2/stream-learn/badge.svg?branch=master)](https://coveralls.io/github/w4k2/stream-learn?branch=master)
        [![CircleCI Status](https://circleci.com/gh/w4k2/stream-learn.svg?style=shield&circle-token=:circle-token)](https://circleci.com/gh/w4k2/stream-learn/tree/master)
        
        stream-learn is a Python package equipped with a procedures to process data streams using estimators with API compatible with scikit-learn.
        
        ## Documentation
        
        API documentation with set of examples may be found on the [documentation page](https://w4k2.github.io/stream-learn/).
        
        ## Installation
        
        stream-learn is available on the PyPi and you may install it with pip:
        
        ```
        pip install stream-learn
        ```
        
        ## Example usage
        
        ```python
        import strlearn as sl
        from sklearn.naive_bayes import GaussianNB
        from sklearn.neural_network import MLPClassifier
        
        stream = sl.streams.StreamGenerator(n_chunks=250, n_drifts=1)
        clf = GaussianNB()
        evaluator = sl.evaluators.TestThenTrainEvaluator()
        
        evaluator.process(stream, clf)
        
        print(evaluator.scores_)
        ```
        
        <!--
        ### About
        
        If you use stream-learn in a scientific publication, we would appreciate citations to the following paper:
        
        ```
        @article{key:key,
        author  = {abc},
        title   = {def},
        journal = {ghi},
        year    = {2018},
        volume  = {1},
        number  = {1},
        pages   = {1-5},
        url     = {http://jkl}
        }
        ```
        -->
        
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