treeinterpreter.py

from treeinterpreter import treeinterpreter as ti, utils

selected_rows = [31, 85]
selected_df = X_train.iloc[selected_rows,:].values
prediction, bias, contributions = ti.predict(rf, selected_df)

for i in range(len(selected_rows)):
    print("Row", selected_rows[i])
    print("Prediction:", prediction[i][0], 'Actual Value:', y_train[selected_rows[i]])
    print("Bias (trainset mean)", bias[i])
    print("Feature contributions:")
    for c, feature in sorted(zip(contributions[i], 
                                 X_train.columns), 
                             key=lambda x: -abs(x[0])):
        print(feature, round(c, 2))
    print("-"*20)