Metadata-Version: 2.1
Name: boostsrl
Version: 0.4.0
Summary: Python wrappers for using BoostSRL jar files.
Home-page: https://starling.utdallas.edu/software/boostsrl-python-wrappers/
Author: Alexander L. Hayes (hayesall)
Author-email: alexander@batflyer.net
License: GPL-3.0
Download-URL: https://github.com/starling-lab/boostsrl-python-package
Description: ########
        boostsrl
        ########
        
        |License|_ |Travis|_ |Codecov|_
        
        .. |License| image:: https://img.shields.io/github/license/starling-lab/boostsrl-python-package.svg
        .. _License: LICENSE
        
        .. |Travis| image:: https://travis-ci.org/starling-lab/boostsrl-python-package.svg?branch=master
        .. _Travis: https://travis-ci.org/starling-lab/boostsrl-python-package
        
        .. |Codecov| image:: https://codecov.io/gh/starling-lab/boostsrl-python-package/branch/master/graphs/badge.svg?branch=master
        .. _Codecov: https://codecov.io/github/starling-lab/boostsrl-python-package?branch=master
        
        **boostsrl** is a set of Python wrappers around
        `BoostSRL <https://starling.utdallas.edu/software/BoostSRL>`_ with a scikit-learn interface.
        
        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 boostsrl
        
        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 boostsrl.rdn import RDN
            >>> from boostsrl import Background
            >>> from boostsrl import example_data
            >>> bk = Background(
            ...     modes=example_data.train.modes,
            ... )
            >>> clf = RDN(
            ...     background=bk,
            ...     target='cancer',
            ...     max_tree_depth=2,
            ...     node_size=1,
            ... )
            >>> 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 ``boostsrl.Database`` objects, so this hides some of
        the complexity behind the scenes.
        
        This example abstracts away some complexity in exchange for compactness.
        For more thorough examples, see the `'docs/examples/' <https://github.com/starling-lab/boostsrl-python-package/tree/master/docs/examples>`_ directory.
        
        Contributing
        ------------
        
        Please refer to `CONTRIBUTING.md <.github/CONTRIBUTING.md>`_ for information on
        submitting issues and pull requests.
        
        Versioning
        ----------
        
        We use `SemVer <https://semver.org>`_ for versioning.
        See `Releases <https://github.com/starling-lab/boostsrl-python-package/releases>`_
        for stable versions that are available, or the
        `Project Page on PyPi <https://pypi.org/project/boostsrl/>`_.
        
Keywords: machine-learning-algorithms machine-learning statistical-learning pattern-classification artificial-intelligence
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
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: docs
Provides-Extra: tests
