class RegressionBaseExplainerTests(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 = RandomForestRegressor(n_estimators=5, max_depth=2).fit(X_train, y_train)
self.explainer = RegressionExplainer(
model, X_test, y_test, r2_score,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
idxs=test_names, target='Fare', 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_row_from_input(self):
input_row = self.explainer.get_row_from_input(
self.explainer.X.iloc[[0]].values.tolist())
self.assertIsInstance(input_row, pd.DataFrame)
input_row = self.explainer.get_row_from_input(
self.explainer.X_merged.iloc[[0]].values.tolist())
self.assertIsInstance(input_row, pd.DataFrame)
input_row = self.explainer.get_row_from_input(
self.explainer.X_merged
[self.explainer.columns_ranked_by_shap()]
.iloc[[0]].values.tolist(), ranked_by_shap=True)
self.assertIsInstance(input_row, pd.DataFrame)
def test_prediction_result_df(self):
df = self.explainer.prediction_result_df(0)
self.assertIsInstance(df, pd.DataFrame)
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_columns_ranked_by_shap(self):
self.assertIsInstance(self.explainer.columns_ranked_by_shap(), list)
def test_get_col(self):
self.assertIsInstance(self.explainer.get_col("Gender"), pd.Series)
self.assertTrue(is_categorical_dtype(self.explainer.get_col("Gender")))
self.assertIsInstance(self.explainer.get_col("Age"), pd.Series)
self.assertTrue(is_numeric_dtype(self.explainer.get_col("Age")))
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.permutation_importances(), pd.DataFrame)
def test_X_cats(self):
self.assertIsInstance(self.explainer.X_cats, 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.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_permutation_importances_df(self):
self.assertIsInstance(self.explainer.get_permutation_importances_df(), pd.DataFrame)
self.assertIsInstance(self.explainer.get_permutation_importances_df(topx=3), pd.DataFrame)
self.assertIsInstance(self.explainer.get_permutation_importances_df(cutoff=0.01), pd.DataFrame)
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)
self.assertIsInstance(self.explainer.get_contrib_df(0, sort='high-to-low'), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_df(0, sort='low-to-high'), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_df(0, sort='importance'), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_df(X_row=self.explainer.X.iloc[[0]]), pd.DataFrame)
def test_contrib_summary_df(self):
self.assertIsInstance(self.explainer.get_contrib_summary_df(0), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_summary_df(0, topx=3), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_summary_df(0, round=3), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_summary_df(0, sort='high-to-low'), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_summary_df(0, sort='low-to-high'), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_summary_df(0, sort='importance'), pd.DataFrame)
self.assertIsInstance(self.explainer.get_contrib_summary_df(X_row=self.explainer.X.iloc[[0]]), 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_shap_interaction_values(self):
self.assertIsInstance(self.explainer.shap_interaction_values(), np.ndarray)
def test_mean_abs_shap(self):
self.assertIsInstance(self.explainer.mean_abs_shap_df(), pd.DataFrame)
def test_memory_usage(self):
self.assertIsInstance(self.explainer.memory_usage(), pd.DataFrame)
self.assertIsInstance(self.explainer.memory_usage(cutoff=1000), pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_shap_interaction_values_for_col(self):
self.assertIsInstance(self.explainer.shap_interaction_values_for_col("Age"), np.ndarray)
self.assertEqual(self.explainer.shap_interaction_values_for_col("Age").shape,
self.explainer.get_shap_values_df().shape)
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)
def test_plot_importances(self):
fig = self.explainer.plot_importances()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_importances(kind='permutation')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_importances(topx=3)
self.assertIsInstance(fig, go.Figure)
def test_plot_interactions(self):
fig = self.explainer.plot_interactions_importance("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions_importance("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions_importance("Gender")
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_interactions(self):
fig = self.explainer.plot_contributions(0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, cutoff=0.05)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, sort='high-to-low')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, sort='low-to-high')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, sort='importance')
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_detailed(self):
fig = self.explainer.plot_importances_detailed()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_importances_detailed(topx=3)
self.assertIsInstance(fig, go.Figure)
def test_plot_interactions_detailed(self):
fig = self.explainer.plot_interactions_detailed("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions_detailed("Age", topx=3)
self.assertIsInstance(fig, go.Figure)
def test_plot_dependence(self):
fig = self.explainer.plot_dependence("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_dependence("Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_dependence("Age", "Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_dependence("Age", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_dependence("Gender", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
def test_plot_contributions(self):
fig = self.explainer.plot_contributions(0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, sort='high-to-low')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, sort='low-to-high')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(0, sort='importance')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_contributions(X_row=self.explainer.X.iloc[[0]])
self.assertIsInstance(fig, go.Figure)
def test_plot_interaction(self):
fig = self.explainer.plot_interaction("Age", "Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interaction("Gender", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interaction("Gender", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interaction("Age", "Gender", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
def test_plot_pdp(self):
fig = self.explainer.plot_pdp("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Gender", index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Age", index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Age", X_row=self.explainer.X.iloc[[0]])
self.assertIsInstance(fig, go.Figure)
def test_yaml(self):
yaml = self.explainer.to_yaml()
self.assertIsInstance(yaml, str)
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)