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Custom PyMC3 models built on top of the scikit-learn API. Check out thedocs.

• Reusable PyMC3 models including LinearRegression and HierarchicalLogisticRegression
• A base class, BayesianModel, for building your own PyMC3 models

The latest release of PyMC3 Models can be installed from PyPI usingpip:

pip install pymc3_models

The current development branch of PyMC3 Models can be installed from GitHub, also usingpip:

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.gitcd pymc3_modelsvirtualenv venvsource venv/bin/activatepip install -r requirements.txt

Since PyMC3 Models is built on top of scikit-learn, you can use the same methods as with a scikit-learn model.

frompymc3_modelsimportLinearRegressionLR=LinearRegression()LR.fit(X,Y)LR.predict(X)LR.score(X,Y)

For more info, seeCONTRIBUTING.

Please note that this project is released with aContributor Code of Conduct. By participating in this project you agree to abide by its terms. SeeCODE_OF_CONDUCT.

This library is built on top ofPyMC3 andscikit-learn.

Apache License, Version 2.0