def _assert_numpy_explanation_types(actual, expected):
# assert "_" variables equivalence
if hasattr(actual, ExplainParams.get_private(ExplainParams.LOCAL_IMPORTANCE_VALUES)):
assert(isinstance(actual._local_importance_values, np.ndarray))
assert(isinstance(expected._local_importance_values, np.ndarray))
np.testing.assert_array_equal(actual._local_importance_values, expected._local_importance_values)
if hasattr(actual, ExplainParams.get_private(ExplainParams.EVAL_DATA)):
assert(isinstance(actual._eval_data, np.ndarray))
assert(isinstance(expected._eval_data, np.ndarray))
np.testing.assert_array_equal(actual._eval_data, expected._eval_data)
def _assert_explanation_equivalence(actual, expected):
# get the non-null properties in the expected explanation
paramkeys = filter(lambda x, expected=expected: hasattr(
expected, getattr(ExplainParams, x)),
list(ExplainParams.get_serializable()))
for paramkey in paramkeys:
param = getattr(ExplainParams, paramkey)
actual_value = getattr(actual, param, None)
expected_value = getattr(expected, param, None)
if isinstance(actual_value, DatasetWrapper) or isinstance(
actual_value, DenseData):
if isinstance(actual_value.original_dataset, np.ndarray):
actual_dataset = actual_value.original_dataset.tolist()
else:
actual_dataset = actual_value.original_dataset
if isinstance(expected_value.original_dataset, np.ndarray):
expected_dataset = expected_value.original_dataset.tolist()
else:
expected_dataset = expected_value.original_dataset
np.testing.assert_array_equal(actual_dataset, expected_dataset)
elif isinstance(actual_value, (np.ndarray, collections.abc.Sequence)):
np.testing.assert_array_equal(actual_value, expected_value)
elif isinstance(actual_value, pd.DataFrame) and isinstance(
expected_value, pd.DataFrame):
np.testing.assert_array_equal(actual_value.values,
expected_value.values)
else:
assert actual_value == expected_value