class SkorchRegressorTests(unittest.TestCase):
def setUp(self):
model, X, y = get_skorch_regressor()
self.explainer = RegressionExplainer(model, X, y)
def test_preds(self):
self.assertIsInstance(self.explainer.preds, np.ndarray)
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.get_permutation_importances_df(),
pd.DataFrame)
def test_shap_base_value(self):
self.assertIsInstance(self.explainer.shap_base_value(),
(np.floating, float))
def test_shap_values_shape(self):
self.assertTrue(self.explainer.get_shap_values_df().shape == (
len(self.explainer), len(self.explainer.merged_cols)))
def test_shap_values(self):
self.assertIsInstance(self.explainer.get_shap_values_df(),
pd.DataFrame)
def test_mean_abs_shap(self):
self.assertIsInstance(self.explainer.get_mean_abs_shap_df(),
pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties(include_interactions=False)
def test_pdp_df(self):
self.assertIsInstance(self.explainer.pdp_df("col1"), pd.DataFrame)
class ExtraTreesRegressorTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = ExtraTreesRegressor()
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(
model,
X_test,
y_test,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
idxs=test_names,
units="$")
def test_preds(self):
self.assertIsInstance(self.explainer.preds, np.ndarray)
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.get_permutation_importances_df(),
pd.DataFrame)
def test_shap_base_value(self):
self.assertIsInstance(self.explainer.shap_base_value(),
(np.floating, float))
def test_shap_values_shape(self):
self.assertTrue(self.explainer.get_shap_values_df().shape == (
len(self.explainer), len(self.explainer.merged_cols)))
def test_shap_values(self):
self.assertIsInstance(self.explainer.get_shap_values_df(),
pd.DataFrame)
def test_mean_abs_shap(self):
self.assertIsInstance(self.explainer.get_mean_abs_shap_df(),
pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties(include_interactions=False)
def test_pdp_df(self):
self.assertIsInstance(self.explainer.pdp_df("Age"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Gender"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Deck"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Age", index=0),
pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Gender", index=0),
pd.DataFrame)
class LinearRegressionTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_fare()
self.test_len = len(X_test)
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = LinearRegression()
model.fit(X_train, y_train)
self.explainer = RegressionExplainer(
model,
X_test,
y_test,
shap='linear',
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
idxs=test_names,
units="$")
def test_explainer_len(self):
self.assertEqual(len(self.explainer), self.test_len)
def test_int_idx(self):
self.assertEqual(self.explainer.get_idx(self.names[0]), 0)
def test_random_index(self):
self.assertIsInstance(self.explainer.random_index(), int)
self.assertIsInstance(self.explainer.random_index(return_str=True),
str)
def test_preds(self):
self.assertIsInstance(self.explainer.preds, np.ndarray)
def test_pred_percentiles(self):
self.assertIsInstance(self.explainer.pred_percentiles(), np.ndarray)
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.get_permutation_importances_df(),
pd.DataFrame)
def test_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
self.assertIsInstance(self.explainer.metrics_descriptions(), dict)
def test_mean_abs_shap_df(self):
self.assertIsInstance(self.explainer.get_mean_abs_shap_df(),
pd.DataFrame)
def test_top_interactions(self):
self.assertIsInstance(self.explainer.top_shap_interactions("Age"),
list)
self.assertIsInstance(
self.explainer.top_shap_interactions("Age", topx=4), list)
def test_contrib_df(self):
self.assertIsInstance(self.explainer.get_contrib_df(0), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_df(0, topx=3),
pd.DataFrame)
def test_shap_base_value(self):
self.assertIsInstance(self.explainer.shap_base_value(),
(np.floating, float))
def test_shap_values_shape(self):
self.assertTrue(self.explainer.get_shap_values_df().shape == (
len(self.explainer), len(self.explainer.merged_cols)))
def test_shap_values(self):
self.assertIsInstance(self.explainer.get_shap_values_df(),
pd.DataFrame)
def test_mean_abs_shap(self):
self.assertIsInstance(self.explainer.get_mean_abs_shap_df(),
pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties(include_interactions=False)
def test_pdp_df(self):
self.assertIsInstance(self.explainer.pdp_df("Age"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Gender"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Deck"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Age", index=0),
pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Gender", index=0),
pd.DataFrame)
