Metadata-Version: 2.1
Name: pymc3-models
Version: 1.3.0
Summary: Custom PyMC3 models built on top of the scikit-learn API
Home-page: https://github.com/parsing-science/pymc3_models
Author: Nicole Carlson
Author-email: nicole@parsingscience.com
License: UNKNOWN
Description: # PyMC3 Models
        
        Custom PyMC3 models built on top of the scikit-learn API. Check out the [docs](http://pymc3-models.readthedocs.io/).
        
        ## Features
        
        - Reusable PyMC3 models including LinearRegression and HierarchicalLogisticRegression
        - A base class, BayesianModel, for building your own PyMC3 models
        
        ## Installation
        The latest release of PyMC3 Models can be installed from PyPI using `pip`:
        
        ``` bash
        pip install pymc3_models
        ```
        
        The current development branch of PyMC3 Models can be installed from GitHub, also using `pip`:
        
        ``` bash
        pip install git+https://github.com/parsing-science/pymc3_models.git
        ```
        
        To run the package locally (in a virtual environment):
        
        ```
        git clone https://github.com/parsing-science/pymc3_models.git
        cd pymc3_models
        virtualenv venv
        source venv/bin/activate
        pip install -r requirements.txt
        ```
        
        ## Usage
        Since PyMC3 Models is built on top of scikit-learn, you can use the same methods as with a scikit-learn model.
        
        ``` python
        from pymc3_models import LinearRegression
        
        LR = LinearRegression()
        LR.fit(X, Y)
        LR.predict(X)
        LR.score(X, Y)
        ```
        
        ## Contribute
        For more info, see [CONTRIBUTING](https://github.com/parsing-science/pymc3_models/blob/master/CONTRIBUTING.rst).
        
        ### Contributor Code of Conduct
        
        Please note that this project is released with a [Contributor Code of Conduct](http://contributor-covenant.org/). By participating in this project you agree to abide by its terms. See [CODE_OF_CONDUCT](https://github.com/parsing-science/pymc3_models/blob/master/CODE_OF_CONDUCT.rst).
        
        ## Acknowledgments
        This library is built on top of [PyMC3](http://docs.pymc.io/) and [scikit-learn](http://scikit-learn.org).
        
        ## License
        [Apache License, Version 2.0](https://github.com/parsing-science/pymc3_models/blob/master/LICENSE)
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Description-Content-Type: text/markdown
