A package for building probabilistic models in Python

pomegranate

pomegranate is a package for building probabilistic models in Python that is implemented in Cython for speed. A primary focus of pomegranate is to merge the easy-to-use API of scikit-learn with the modularity of probabilistic modeling to allow users to specify complicated models without needing to worry about implementation details. The models implemented here are built from the ground up with big data processing in mind and so natively support features like multi-threaded parallelism and out-of-core processing. Click on the binder badge above to interactively play with the tutorials!

Installation

pomegranate is pip-installable using pip install pomegranate and conda-installable using conda install pomegranate. If neither work, more detailed installation instructions can be found here.

If you get an error involving pomegranate/base.c, try installing with pip install --no-cache-dir pomegranate.

If you get an error involving pomegranate/distributions/NeuralNetworkWrapper.c: No such file or directory, try installing Cython first and then re-installing.

Dependencies

pomegranate requires:

- Cython (only if building from source)
- NumPy
- SciPy
- NetworkX
- joblib

To run the tests, you also must have nose installed.

GitHub

https://github.com/jmschrei/pomegranate

Source: https://pythonawesome.com/a-package-for-building-probabilistic-models-in-python/