def _compute_explanations(self, local: bool, data: Any):
"""Compute explanations using MimicWrapper.
:param local: True if local explanations are requested
and False otherwise.
:type local: bool
:param data: The data point(s) for which the explanations
need to be generated.
:type data: Any
:return: The computed explanations.
:rtype: Any
"""
if self._classes is not None:
model_task = ModelTask.Classification
else:
model_task = ModelTask.Regression
explainer = MimicExplainer(
self._model,
self._initialization_examples,
self._surrogate_model,
features=self._features,
model_task=model_task,
classes=self._classes,
categorical_features=self._categorical_features)
return explainer.explain_global(data, include_local=local)
def test_json_serialize_mimic_no_features(self, iris, iris_svm_model):
explainer = MimicExplainer(
iris_svm_model,
iris[DatasetConstants.X_TRAIN],
LGBMExplainableModel,
max_num_of_augmentations=10,
classes=iris[DatasetConstants.CLASSES].tolist())
explanation = explainer.explain_global()
verify_serialization(explanation)
def compute(self):
"""Creates an explanation by running the explainer on the model."""
if self._is_run:
return
model_task = ModelTask.Unknown
explainer = MimicExplainer(self._model,
self._initialization_examples,
self._surrogate_model,
features=self._features,
model_task=model_task,
classes=self._classes)
self._explanation = explainer.explain_global(self._evaluation_examples)
def create_explainer(model, x_train, **kwargs):
return MimicExplainer(
model,
x_train,
explainable_model,
explainable_model_args=explainable_model_args.copy(),
**kwargs)
def create_explainer(model, x_train, **kwargs):
return MimicExplainer(
model,
x_train,
explainable_model,
max_num_of_augmentations=10,
explainable_model_args=explainable_model_args.copy(),
**kwargs)
def compute(self):
"""Creates an explanation by running the explainer on the model."""
if not self._is_added:
return
if self._is_run:
return
if self._classes is not None:
model_task = ModelTask.Classification
else:
model_task = ModelTask.Regression
explainer = MimicExplainer(
self._model,
self._initialization_examples,
self._surrogate_model,
features=self._features,
model_task=model_task,
classes=self._classes,
categorical_features=self._categorical_features)
self._explanation = explainer.explain_global(self._evaluation_examples)