def test_get_raw_explanation_no_datasets_mixin(self, boston,
mimic_explainer):
model = create_sklearn_random_forest_regressor(
boston[DatasetConstants.X_TRAIN], boston[DatasetConstants.Y_TRAIN])
explainer = mimic_explainer(model, boston[DatasetConstants.X_TRAIN],
LGBMExplainableModel)
global_explanation = explainer.explain_global(
boston[DatasetConstants.X_TEST])
assert global_explanation.method == LIGHTGBM_METHOD
kwargs = {ExplainParams.METHOD: global_explanation.method}
kwargs[ExplainParams.FEATURES] = global_explanation.features
kwargs[ExplainParams.MODEL_TASK] = ExplainType.REGRESSION
kwargs[
ExplainParams.
LOCAL_IMPORTANCE_VALUES] = global_explanation._local_importance_values
kwargs[ExplainParams.EXPECTED_VALUES] = 0
kwargs[ExplainParams.CLASSIFICATION] = False
kwargs[ExplainParams.IS_ENG] = True
synthetic_explanation = _create_local_explanation(**kwargs)
num_engineered_feats = boston[DatasetConstants.X_TRAIN].shape[1]
feature_map = np.eye(5, num_engineered_feats)
feature_names = [str(i) for i in range(feature_map.shape[0])]
raw_names = feature_names[:feature_map.shape[0]]
assert not _DatasetsMixin._does_quack(synthetic_explanation)
global_raw_explanation = synthetic_explanation.get_raw_explanation(
[feature_map], raw_feature_names=raw_names)
self.validate_local_explanation_regression(synthetic_explanation,
global_raw_explanation,
feature_map,
has_eng_eval_data=False,
has_raw_eval_data=False,
has_dataset_data=False)
def test_does_quack_datasets_negative(self):
NoDatasets = type('InvalidDatasets', (BaseValid,), {})
assert not _DatasetsMixin._does_quack(NoDatasets())
class NoTrainData(object):
@property
def eval_data(self):
return [[.2, .4, .01], [.3, .2, 0]]
@property
def eval_y_predicted(self):
return None
@property
def eval_y_predicted_proba(self):
return None
NoTrainExp = type('InvalidDatasets', (NoTrainData, BaseValid), {})
assert not _DatasetsMixin._does_quack(NoTrainExp())
class NoTestData(object):
@property
def init_data(self):
return 'a_dataset_id'
@property
def eval_y_predicted(self):
return None
@property
def eval_y_predicted_proba(self):
return None
NoTestExp = type('InvalidDatasets', (NoTestData, BaseValid), {})
assert not _DatasetsMixin._does_quack(NoTestExp())
class NoEvalYPredicted(object):
@property
def init_data(self):
return 'a_dataset_id'
@property
def eval_data(self):
return[[.2, .4, .01], [.3, .2, 0]]
@property
def eval_y_predicted_proba(self):
return None
NoEvalYPExp = type('InvalidDatasets', (NoEvalYPredicted, BaseValid), {})
assert not _DatasetsMixin._does_quack(NoEvalYPExp())
class NoEvalYPredictedProba(object):
@property
def init_data(self):
return 'a_dataset_id'
@property
def eval_data(self):
return[[.2, .4, .01], [.3, .2, 0]]
@property
def eval_y_predicted(self):
return None
NoEvalYPPExp = type('InvalidDatasets', (NoEvalYPredictedProba, BaseValid), {})
assert not _DatasetsMixin._does_quack(NoEvalYPPExp())
def test_does_quack_datasets_mixin(self):
ValidDatasets = type('ValidDatasets', (_DatasetsValid,), {})
assert _DatasetsMixin._does_quack(ValidDatasets())