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")
