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Showing content from https://github.com/parsing-science/pymc3_models below:

GitHub - parsing-science/pymc3_models

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

• 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 using pip:

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

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

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

from pymc3_models import LinearRegression

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

For more info, see CONTRIBUTING.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See CODE_OF_CONDUCT.

This library is built on top of PyMC3 and scikit-learn.

Apache License, Version 2.0

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