Metadata-Version: 2.1
Name: stream-learn
Version: 0.8.3
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
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
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn

# stream-learn

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[![Coverage Status](https://coveralls.io/repos/github/w4k2/stream-learn/badge.svg?branch=master)](https://coveralls.io/github/w4k2/stream-learn?branch=master)
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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

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}
}
```
-->


