def verify_serialization(explanation, extra_path=None, exist_ok=False):
path = 'brand/new/path'
if extra_path is not None:
path = os.path.join(path, extra_path)
save_explanation(explanation, path, exist_ok=exist_ok)
loaded_explanation = load_explanation(path)
_assert_explanation_equivalence(explanation, loaded_explanation)
def _save(self, path):
"""Save the ExplainerManager to the given path.
:param path: The directory path to save the ExplainerManager to.
:type path: str
"""
top_dir = Path(path)
top_dir.mkdir(parents=True, exist_ok=True)
# save the explanation
if self._explanation:
save_explanation(self._explanation,
top_dir / ManagerNames.EXPLAINER)
meta = {IS_RUN: self._is_run, IS_ADDED: self._is_added}
with open(Path(path) / META_JSON, 'w') as file:
json.dump(meta, file)
def verify_serialization(explanation):
paramkeys = ['MODEL_TYPE', 'MODEL_TASK', 'METHOD', 'FEATURES', 'CLASSES']
log_items = dict()
for paramkey in paramkeys:
param = getattr(ExplainParams, paramkey)
value = getattr(explanation, param, None)
if value is not None:
if isinstance(value, np.ndarray):
log_items[param] = value.tolist()
else:
log_items[param] = value
comment = json.dumps(log_items)
test_logger.setLevel(logging.INFO)
test_logger.info("validating serialization of explanation:\n%s", comment)
expljson = save_explanation(explanation)
deserialized_explanation = load_explanation(expljson)
_assert_explanation_equivalence(deserialized_explanation, explanation)
def test_save_explanation(self, iris, tabular_explainer, iris_svm_model):
explainer = tabular_explainer(iris_svm_model,
iris[DatasetConstants.X_TRAIN],
features=iris[DatasetConstants.FEATURES])
explanation = explainer.explain_local(iris[DatasetConstants.X_TEST])
save_explanation(explanation, 'brand/new/path')
