Metadata-Version: 2.1
Name: srlearn
Version: 0.5.3
Summary: Python wrappers for using BoostSRL jar files.
Home-page: https://hayesall.com
Author: Alexander L. Hayes (hayesall)
Author-email: alexander@batflyer.net
License: GPL-3.0
Download-URL: https://github.com/hayesall/srlearn
Description: ########
        srlearn
        ########
        
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        **srlearn** is a set of Python wrappers around
        `BoostSRL <https://starling.utdallas.edu/software/BoostSRL>`_ with a scikit-learn interface.
        
        - **Documentation**: https://srlearn.readthedocs.io/en/latest/
        - **Questions?** Contact `Alexander L. Hayes  <https://hayesall.com>`_ (`hayesall <https://github.com/hayesall>`_)
        
        Getting Started
        ---------------
        
        **Prerequisites**:
        
        - Java 1.8
        - Python (3.6, 3.7)
        
        **Installation**
        
        .. code-block:: bash
        
           pip install srlearn
        
        Basic Usage
        -----------
        
        The general setup should be similar to scikit-learn. But there are a few extra requirements in terms of setting
        background knowledge and formatting the data.
        
        A minimal working example (using the Toy-Cancer data set imported with 'example_data') is:
        
        .. code-block:: python
        
            >>> from srlearn.rdn import BoostedRDN
            >>> from srlearn import Background
            >>> from srlearn import example_data
            >>> bk = Background(
            ...     modes=example_data.train.modes,
            ...     use_std_logic_variables=True,
            ... )
            >>> clf = BoostedRDN(
            ...     background=bk,
            ...     target='cancer',
            ... )
            >>> clf.fit(example_data.train)
            >>> clf.predict_proba(example_data.test)
            array([0.88079619, 0.88079619, 0.88079619, 0.3075821 , 0.3075821 ])
            >>> clf.classes_
            array([1., 1., 1., 0., 0.])
        
        ``example_data.train`` and ``example_data.test`` are each ``srlearn.Database`` objects, so this hides some of
        the complexity behind the scenes.
        
        This example abstracts away some complexity in exchange for compactness.
        For more examples, see the `Example Gallery <https://srlearn.readthedocs.io/en/latest/auto_examples/index.html>`_.
        
        Contributing
        ------------
        
        We have adopted the `Contributor Covenant Code of Conduct <https://github.com/hayesall/srlearn/blob/master/.github/CODE_OF_CONDUCT.md>`_ version 1.4. Please read,
        follow, and report any incidents which violate this.
        
        Questions, Issues, and Pull Requests are welcome. Please refer to `CONTRIBUTING.md <https://github.com/hayesall/srlearn/blob/master/.github/CONTRIBUTING.md>`_ for
        information on submitting issues and pull requests.
        
        Versioning and Releases
        -----------------------
        
        We use `SemVer <https://semver.org>`_ for versioning.
        See `Releases <https://github.com/hayesall/srlearn/releases>`_
        for stable versions that are available, or the
        `Project Page on PyPi <https://pypi.org/project/srlearn/>`_.
        
Keywords: machine-learning-algorithms machine-learning statistical-learning pattern-classification artificial-intelligence
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Provides-Extra: tests
Provides-Extra: docs
