def test_serialize_mimic_lightgbm(self):
test_logger.info("Running test_serialize_mimic_lightgbm to validate serializing explainer with lightgbm model")
x_train, x_test, y_train, _, feature_names, target_names = create_scikit_cancer_data()
model = create_sklearn_svm_classifier(x_train, y_train)
model_task = ModelTask.Unknown
kwargs = {'explainable_model_args': {'n_jobs': 1}, 'augment_data': False, 'reset_index': True}
explainer = MimicExplainer(model, x_train, LGBMExplainableModel, features=feature_names,
model_task=model_task, classes=target_names, **kwargs)
explanation = explainer.explain_global(x_test)
assert explanation.method == LIGHTGBM_METHOD
tree_explainer = shap.TreeExplainer(explainer.surrogate_model.model)
# Validate wrapped model, surrogate, and tree explainer with surrogate can be serialized
model_name = 'wrapped_model.joblib'
surrogate_name = 'surrogate_model.joblib'
tree_explainer_name = 'tree_explainer_model.joblib'
with open(model_name, 'wb') as stream:
dump(explainer.model, stream)
with open(surrogate_name, 'wb') as stream:
dump(explainer.surrogate_model.model, stream)
with open(tree_explainer_name, 'wb') as stream:
dump(tree_explainer, stream)
assert path.exists(model_name)
assert path.exists(surrogate_name)
assert path.exists(tree_explainer_name)
def test_pickle_unpickle_mimic_explainer_classification(self, surrogate_model):
x_train, x_test, y_train, _, feature_names, target_names = create_scikit_cancer_data()
model = create_sklearn_svm_classifier(x_train, y_train)
model_task = ModelTask.Unknown
surrogate_model = surrogate_model
explainer = MimicExplainer(model, x_train, surrogate_model, features=feature_names,
model_task=model_task, classes=target_names)
self._verify_explanations(explainer, x_test, get_mimic_method(surrogate_model))
recovered_explainer = self.pickle_unpickle_explainer(explainer)
self._verify_explanations(recovered_explainer, x_test, get_mimic_method(surrogate_model))
def test_serialize_kernel(self):
test_logger.info("Running test_serialize_kernel to validate inner explainer and wrapped model serialization")
x_train, _, y_train, _, feature_names, target_names = create_scikit_cancer_data()
model = create_sklearn_svm_classifier(x_train, y_train)
explainer = TabularExplainer(model,
x_train,
features=feature_names,
classes=target_names)
# Validate wrapped model and inner explainer can be serialized
model_name = 'wrapped_model.joblib'
explainer_name = 'inner_explainer.joblib'
with open(explainer_name, 'wb') as stream:
dump(explainer.explainer.explainer, stream)
with open(model_name, 'wb') as stream:
dump(explainer.model, stream)
assert path.exists(model_name)
assert path.exists(explainer_name)
