Custom PyMC3 models built on top of the scikit-learn API
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 install pymc3_models
The current development branch of PyMC3 Models can be installed from GitHub, also using
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.