Metadata-Version: 2.1
Name: stream-learn
Version: 0.8.4
Summary: Python package equipped with a procedures to process data streams using estimators with API compatible with scikit-learn.
Home-page: https://w4k2.github.io/stream-learn/
Maintainer: P. Ksieniewicz
Maintainer-email: pawel.ksieniewicz@pwr.edu.pl
License: GPL-3.0
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.neural_network import MLPClassifier
        from sklearn.naive_bayes import GaussianNB
        
        # Initialize list of scikit-learn classifiers with partial_fit() function
        clf = [MLPClassifier(), GaussianNB()]
        
        # Declare data stream
        stream = sl.streams.StreamGenerator(n_chunks=10, n_drifts=1)
        
        # Select vector of metrics
        metrics = [sl.utils.metrics.bac, sl.utils.metrics.f_score]
        
        # Initialize evaluator with given metrics
        evaluator = sl.evaluators.TestThenTrain(metrics)
        
        # Run evaluator over stream with classifier
        evaluator.process(stream, clf)
        ```
        
        ```python
        >>> print(evaluator.scores)
        [[[0.29730274 0.29145729]
          [0.34494021 0.36097561]
          [0.43464118 0.44878049]
          [0.42579578 0.36666667]
          [0.45569557 0.4171123 ]
          [0.47020869 0.44791667]
          [0.4645207  0.46534653]
          [0.525      0.5177665 ]
          [0.4893617  0.46875   ]]
        
         [[0.87701288 0.88038278]
          [0.90091448 0.9047619 ]
          [0.89930938 0.9047619 ]
          [0.85376189 0.82681564]
          [0.61521152 0.60913706]
          [0.64714185 0.61538462]
          [0.64556129 0.62564103]
          [0.74       0.74      ]
          [0.80820955 0.80597015]]]
        ```
        
        <!--
        
        ### 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}
        }
        ```
        -->
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
