treeinterpreter_dd.py

prediction1, bias1, contributions1 = ti.predict(rf, np.array([selected_df[0]]), joint_contribution=True)
prediction2, bias2, contributions2 = ti.predict(rf, np.array([selected_df[1]]), joint_contribution=True)

aggregated_contributions1 = utils.aggregated_contribution(contributions1)
aggregated_contributions2 = utils.aggregated_contribution(contributions2)

res = []
for k in set(aggregated_contributions1.keys()).union(
              set(aggregated_contributions2.keys())):
    res.append(([X_train.columns[index] for index in k] , 
               aggregated_contributions1.get(k, 0) - aggregated_contributions2.get(k, 0)))   
         
for lst, v in (sorted(res, key=lambda x:-abs(x[1])))[:10]:
    print (lst, v)