def test_predict_values(self, estimator, explainer_type, X_y):
X, y = X_y
X_test = X[:1, :]
regressor = ExplainableRegressor(estimator, explainer_type)
regressor_predictions = regressor.fit(X, y).predict(X_test)
cloned_estimator = clone(estimator)
estimator_predictions = cloned_estimator.fit(X, y).predict(X_test)
assert regressor_predictions.shape == estimator_predictions.shape
assert regressor_predictions.shape[0] == len(regressor.explanations_)
def test_fit_values(self, estimator, explainer_type, X_y):
X, y = X_y
regressor = ExplainableRegressor(estimator, explainer_type)
regressor.fit(X, y)
cloned_estimator = clone(estimator)
cloned_estimator.fit(X, y)
estimator_fit_attributes = self._get_fit_attributes(
regressor.estimator)
cloned_estimator_fit_attributes = self._get_fit_attributes(
cloned_estimator)
np.testing.assert_array_equal(estimator_fit_attributes,
cloned_estimator_fit_attributes)
def initialize_estimator(
estimator: RegressorMixin, explainer_type: Optional[str]
) -> RegressorMixin:
if explainer_type is None:
return estimator
else:
return ExplainableRegressor(estimator, explainer_type)
def test_error_predict_not_fitted(self, estimator, explainer_type, X):
regressor = ExplainableRegressor(estimator, explainer_type)
with pytest.raises(NotFittedError):
regressor.predict(X)
def test_constructor_bad_regressor(self, bad_estimator, explainer_type):
with pytest.raises(TypeError):
ExplainableRegressor(bad_estimator, explainer_type)
def test_constructor_bad_explainer(self, estimator):
with pytest.raises(ValueError):
ExplainableRegressor(estimator, "bad")
def test_constructor(self, estimator, explainer_type):
regressor = ExplainableRegressor(estimator, explainer_type)
if explainer_type == "lime":
assert isinstance(regressor.explainer, _LimeExplainer)
elif explainer_type == "shap":
assert isinstance(regressor.explainer, _ShapExplainer)