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')
original_model = run.register_model(model_name='amlcompute_deploy_model',
model_path='original_model.pkl')
# create an explainer to validate or debug the model
tabular_explainer = TabularExplainer(model,
initialization_examples=x_train,
features=attritionXData.columns,
classes=["Not leaving", "leaving"],
transformations=transformations)
# explain overall model predictions (global explanation)
# passing in test dataset for evaluation examples - note it must be a representative sample of the original data
# more data (e.g. x_train) will likely lead to higher accuracy, but at a time cost
global_explanation = tabular_explainer.explain_global(x_test)
# uploading model explanation data for storage or visualization
comment = 'Global explanation on classification model trained on IBM employee attrition dataset'
client.upload_model_explanation(global_explanation, comment=comment)
# also create a lightweight explainer for scoring time
scoring_explainer = LinearScoringExplainer(tabular_explainer)
# pickle scoring explainer locally
save(scoring_explainer, directory=OUTPUT_DIR, exist_ok=True)
# register scoring explainer
run.upload_file('IBM_attrition_explainer.pkl',
os.path.join(OUTPUT_DIR, 'scoring_explainer.pkl'))
scoring_explainer_model = run.register_model(
model_name='IBM_attrition_explainer',
model_path='IBM_attrition_explainer.pkl')