PyMC3 Models
Custom PyMC3 models built on top of the scikit-learn API. Check out the docs.
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:
pip install pymc3_models
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
Usage
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)
Contribute
For more info, see CONTRIBUTING.
Contributor Code of Conduct
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.
Acknowledgments
This library is built on top of PyMC3 and scikit-learn.
License
1 maintainer
Metadata
1 maintainer
Release files for pymc3-models 2.1.0
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