shap.py

import shap

shap.initjs()

# Create object that can calculate shap values
explainer = shap.TreeExplainer(rf_classifier)

# Calculate Shap values
shap_values = explainer.shap_values(X_test, y_test)

# show feature importance for 0 class
shap.summary_plot(shap_values[0], X_test_transformed)

# show decision path for 30th example and 2nd class
shap.decision_plot(explainer.expected_value[2], shap_values[2][30, :], X_test_transformed.iloc[30, :])