Infer.NET in python

This repository contains a jupyter notebook that demonstrates how the Infer.NET framework for graph Bayesian inference can be invoked in python using pythonnet on Ubuntu 18.04.

• Install Docker
• Download or clone this repository
• Move into the repository and build the Docker image: type sudo docker build --rm -t infer_pythonnet .
• Run the image by typing sudo docker run -it -p 8888:8888 -v ${PWD}:/root/dev infer_pythonnet
• The container will start a Jupyter notebook server. Go to http://localhost:8888 and login with the token that is printed out in the terminal

Python.net cannot handle overloads of generic methods properly (see issue). Calling Variable.Observed(Array[Double]([...])) returns Variable[Double[]] instead of VariableArray[Double]. To enforce a correct return type, we could do Variable.Observed[Double](Array[Double]([...]))), but python.net throws an exception.

Possible soultions:

• Do the reflections manually, but this doesn't seem to work because Variable is an abstract class in Infer.Net and Observed is a static method. So the following does not work:

lib = clr.AddReference("Microsoft.ML.Probabilistic")
var_type = lib.GetType("Microsoft.ML.Probabilistic.Models.Variable")
method = var_type.GetMethod("Observed")
...

GetType returns a None-type, since Variable is abstract.

• Write C# library that wraps the function in a non-generic function. This seems to work, but in the end it is not needed (yet) because usage of Variable.Observed can be avoided in the simple examples.

Try more complicated examples with visualizations of the network and inference results in python.