def __initialize_explainer(self, automl_explainer_setup_obj):
return MimicWrapper(
self.__ws,
automl_explainer_setup_obj.automl_estimator,
explainable_model=automl_explainer_setup_obj.surrogate_model,
init_dataset=automl_explainer_setup_obj.X_transform,
run=self.__run,
features=automl_explainer_setup_obj.engineered_feature_names,
feature_maps=[automl_explainer_setup_obj.feature_map],
classes=automl_explainer_setup_obj.classes,
explainer_kwargs=automl_explainer_setup_obj.surrogate_model_params)
# Drop the lablled column to get the testing set.
X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
# Setup the class for explaining the AtuoML models
automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model,
'<<task>>',
X=X_train,
X_test=X_test,
y=y_train)
# Initialize the Mimic Explainer
explainer = MimicWrapper(
ws,
automl_explainer_setup_obj.automl_estimator,
LGBMExplainableModel,
init_dataset=automl_explainer_setup_obj.X_transform,
run=automl_run,
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,