The purpose of this project is to encode random forest models with SAT as explained in On Explaining Random Forests with SAT paper by Yacine Izza and Joao Marques-Silva with the final aim of explain model predictions.

• data : files related to datasets on which random forest model has been trained and tested
• examples : examples of utilization of the implemented functions
• model : files related to the random forest model
• create_random_forest.py : python script to generate a random forest model on the Iris dataset as described in the reference paper
• encode_rf_utils.py : implemented function to encode a Scikit learn random forest model with SAT
• mock_model.py : contains classes to instantiate a mock random forest model to be used in tests

To generate the random forest model Scikit learn library has been used.
To encode and manipulate boolean and pseudo-boolean formulas PySAT library has been used.