features=automl_explainer_setup_obj.engineered_feature_names, feature_maps=[automl_explainer_setup_obj.feature_map], classes=automl_explainer_setup_obj.classes) # Compute the engineered explanations engineered_explanations = explainer.explain( ['local', 'global'], tag='engineered explanations', eval_dataset=automl_explainer_setup_obj.X_test_transform) # Compute the raw explanations raw_explanations = explainer.explain( ['local', 'global'], get_raw=True, tag='raw explanations', raw_feature_names=automl_explainer_setup_obj.raw_feature_names, eval_dataset=automl_explainer_setup_obj.X_test_transform) print("Engineered and raw explanations computed successfully") # Initialize the ScoringExplainer scoring_explainer = TreeScoringExplainer( explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map]) # Pickle scoring explainer locally save(scoring_explainer, exist_ok=True) # Upload the scoring explainer to the automl run automl_run.upload_file('outputs/scoring_explainer.pkl', 'scoring_explainer.pkl')
# Compute the engineered explanations engineered_explanations = explainer.explain( ["local", "global"], tag="engineered explanations", eval_dataset=automl_explainer_setup_obj.X_test_transform, ) # Compute the raw explanations raw_explanations = explainer.explain( ["local", "global"], get_raw=True, tag="raw explanations", raw_feature_names=automl_explainer_setup_obj.raw_feature_names, eval_dataset=automl_explainer_setup_obj.X_test_transform, raw_eval_dataset=automl_explainer_setup_obj.X_test_raw, ) print("Engineered and raw explanations computed successfully") # Initialize the ScoringExplainer scoring_explainer = TreeScoringExplainer( explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map]) # Pickle scoring explainer locally with open("scoring_explainer.pkl", "wb") as stream: joblib.dump(scoring_explainer, stream) # Upload the scoring explainer to the automl run automl_run.upload_file("outputs/scoring_explainer.pkl", "scoring_explainer.pkl")