class LogisticRegressionKernelTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = LogisticRegression()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test.iloc[:20],
y_test.iloc[:20],
shap='kernel',
model_output='probability',
X_background=shap.sample(X_train, 5),
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
def test_shap_values(self):
self.assertIsInstance(self.explainer.shap_base_value(),
(np.floating, float))
self.assertTrue(self.explainer.get_shap_values_df().shape == (
len(self.explainer), len(self.explainer.merged_cols)))
self.assertIsInstance(self.explainer.get_shap_values_df(),
pd.DataFrame)
def setUp(self):
#X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)
df = pd.read_csv(Path.cwd() / "tests" / "test_assets" /
"pipeline_data.csv")
X = df[['age', 'fare', 'embarked', 'sex', 'pclass']]
y = df['survived'].astype(int)
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(
steps=[('imputer', SimpleImputer(
strategy='median')), ('scaler', StandardScaler())])
categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(
steps=[('imputer', SimpleImputer(
strategy='most_frequent')), ('ordinal', OrdinalEncoder())])
preprocessor = ColumnTransformer(
transformers=[('num', numeric_transformer, numeric_features),
('cat', categorical_transformer,
categorical_features)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(
steps=[('preprocessor',
preprocessor), ('classifier', RandomForestClassifier())])
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2)
clf.fit(X_train, y_train)
self.explainer = ClassifierExplainer(clf, X_test, y_test)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=50,
max_depth=4).fit(X_train, y_train)
X_test.reset_index(drop=True, inplace=True)
X_test.index = X_test.index.astype(str)
X_test1, y_test1 = X_test.iloc[:100], y_test.iloc[:100]
X_test2, y_test2 = X_test.iloc[100:], y_test.iloc[100:]
self.explainer = ClassifierExplainer(model,
X_test1,
y_test1,
cats=['Sex', 'Deck'])
def index_exists_func(index):
return index in X_test2.index
def index_list_func():
# only returns first 50 indexes
return list(X_test2.index[:50])
def y_func(index):
idx = X_test2.index.get_loc(index)
return y_test2.iloc[[idx]]
def X_func(index):
idx = X_test2.index.get_loc(index)
return X_test2.iloc[[idx]]
self.explainer.set_index_exists_func(index_exists_func)
self.explainer.set_index_list_func(index_list_func)
self.explainer.set_X_row_func(X_func)
self.explainer.set_y_func(y_func)
def get_multiclass_explainer(xgboost=False, include_y=True):
X_train, y_train, X_test, y_test = titanic_embarked()
train_names, test_names = titanic_names()
if xgboost:
model = XGBClassifier().fit(X_train, y_train)
else:
model = RandomForestClassifier(n_estimators=50, max_depth=10).fit(X_train, y_train)
if include_y:
if xgboost:
multi_explainer = ClassifierExplainer(model, X_test, y_test,
model_output='logodds',
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
else:
multi_explainer = ClassifierExplainer(model, X_test, y_test,
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
else:
if xgboost:
multi_explainer = ClassifierExplainer(model, X_test,
model_output='logodds',
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
else:
multi_explainer = ClassifierExplainer(model, X_test,
cats=['Sex', 'Deck'],
labels=['Queenstown', 'Southampton', 'Cherbourg'])
multi_explainer.calculate_properties()
return multi_explainer
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test, roc_auc_score, n_jobs=-1)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_train.iloc[:50], y_train.iloc[:50],
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
cv=3)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = XGBClassifier()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test,
cats=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
def setUp(self):
X_train, y_train, X_test, y_test = titanic_embarked()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck'],
idxs=test_names,
labels=['Queenstown', 'Southampton', 'Cherbourg'])
class XGBMultiClassifierExplainerTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_embarked()
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = XGBClassifier(n_estimators=5)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
model_output='raw',
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck'],
idxs=test_names,
labels=['Queenstown', 'Southampton', 'Cherbourg'])
def test_graphviz_available(self):
self.assertIsInstance(self.explainer.graphviz_available, bool)
def test_shadow_trees(self):
dt = self.explainer.shadow_trees
self.assertIsInstance(dt, list)
self.assertIsInstance(
dt[0], dtreeviz.models.shadow_decision_tree.ShadowDecTree)
def test_decisionpath_df(self):
df = self.explainer.get_decisionpath_df(tree_idx=0, index=0)
self.assertIsInstance(df, pd.DataFrame)
df = self.explainer.get_decisionpath_df(tree_idx=0,
index=self.names[0])
self.assertIsInstance(df, pd.DataFrame)
df = self.explainer.get_decisionpath_df(tree_idx=0,
index=self.names[0],
pos_label=0)
self.assertIsInstance(df, pd.DataFrame)
def test_plot_trees(self):
fig = self.explainer.plot_trees(index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_trees(index=self.names[0])
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_trees(index=self.names[0], highlight_tree=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_trees(index=self.names[0], pos_label=0)
self.assertIsInstance(fig, go.Figure)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100, learning_rate=0.1, verbose=0)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test, roc_auc_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = LGBMClassifier()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test, roc_auc_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100, learning_rate=0.1, verbose=0)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = XGBClassifier(n_estimators=5)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
cats=['Sex', 'Cabin', 'Embarked'],
idxs=test_names,
labels=['Not survived', 'Survived'])
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = LogisticRegression()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
shap='linear',
cats=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
target='Survival',
labels=['Not survived', 'Survived'],
idxs=test_names)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = LogisticRegression()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test.iloc[:20],
y_test.iloc[:20],
shap='kernel',
model_output='probability',
X_background=shap.sample(X_train, 5),
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
def setUp(self):
X_train, y_train, X_test, y_test = titanic_embarked()
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = XGBClassifier(n_estimators=5)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
model_output='raw',
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
idxs=test_names,
labels=['Queenstown', 'Southampton', 'Cherbourg'])
class ClassifierCVTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_train.iloc[:50], y_train.iloc[:50],
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
cv=3)
def test_cv_permutation_importances(self):
self.assertIsInstance(self.explainer.permutation_importances(), pd.DataFrame)
self.assertIsInstance(self.explainer.permutation_importances(pos_label=0), pd.DataFrame)
def test_cv_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
self.assertIsInstance(self.explainer.metrics(pos_label=0), dict)
class NJobsMinusOneExplainerTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model, X_test, y_test, roc_auc_score, n_jobs=-1)
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.get_permutation_importances_df(), pd.DataFrame)
class ClassifierBunchTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
_, self.names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
roc_auc_score,
shap='tree',
cats=['Sex', 'Cabin', 'Embarked'],
idxs=test_names,
labels=['Not survived', 'Survived'])
def test_graphviz_available(self):
self.assertIsInstance(self.explainer.graphviz_available, bool)
def test_decision_trees(self):
dt = self.explainer.decision_trees
self.assertIsInstance(dt, list)
self.assertIsInstance(
dt[0], dtreeviz.models.shadow_decision_tree.ShadowDecTree)
def test_decisiontree_df(self):
df = self.explainer.decisiontree_df(tree_idx=0, index=0)
self.assertIsInstance(df, pd.DataFrame)
df = self.explainer.decisiontree_df(tree_idx=0, index=self.names[0])
self.assertIsInstance(df, pd.DataFrame)
def test_plot_trees(self):
fig = self.explainer.plot_trees(index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_trees(index=self.names[0])
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_trees(index=self.names[0], highlight_tree=0)
self.assertIsInstance(fig, go.Figure)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def get_catboost_classifier():
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100, verbose=0).fit(X_train, y_train)
explainer = ClassifierExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
X_cats, y_cats = explainer.X_merged, explainer.y.astype("int")
model = CatBoostClassifier(iterations=5, verbose=0).fit(X_cats, y_cats, cat_features=[5, 6, 7])
explainer = ClassifierExplainer(model, X_cats, y_cats, idxs=X_test.index)
explainer.calculate_properties(include_interactions=False)
return explainer
def get_classification_explainer(include_y=True):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = XGBClassifier().fit(X_train, y_train)
if include_y:
explainer = ClassifierExplainer(
model, X_test, y_test,
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
else:
explainer = ClassifierExplainer(
model, X_test,
cats=['Sex', 'Cabin', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
explainer.calculate_properties()
return explainer
def get_classification_explainer(xgboost=False, include_y=True):
X_train, y_train, X_test, y_test = titanic_survive()
if xgboost:
model = XGBClassifier().fit(X_train, y_train)
else:
model = RandomForestClassifier(n_estimators=50, max_depth=10).fit(X_train, y_train)
if include_y:
explainer = ClassifierExplainer(
model, X_test, y_test,
cats=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
else:
explainer = ClassifierExplainer(
model, X_test,
cats=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
explainer.calculate_properties()
return explainer
class ClassifierBaseExplainerTestsPipeline(unittest.TestCase):
def setUp(self):
#X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)
df = pd.read_csv(Path.cwd() / "tests" / "test_assets" /
"pipeline_data.csv")
X = df[['age', 'fare', 'embarked', 'sex', 'pclass']]
y = df['survived'].astype(int)
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(
steps=[('imputer', SimpleImputer(
strategy='median')), ('scaler', StandardScaler())])
categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(
steps=[('imputer', SimpleImputer(
strategy='most_frequent')), ('ordinal', OrdinalEncoder())])
preprocessor = ColumnTransformer(
transformers=[('num', numeric_transformer, numeric_features),
('cat', categorical_transformer,
categorical_features)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(
steps=[('preprocessor',
preprocessor), ('classifier', RandomForestClassifier())])
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2)
clf.fit(X_train, y_train)
self.explainer = ClassifierExplainer(clf, X_test, y_test)
def test_columns_ranked_by_shap(self):
self.assertIsInstance(self.explainer.columns_ranked_by_shap(), list)
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_contrib_df(self):
self.assertIsInstance(self.explainer.get_contrib_df(0), pd.DataFrame)
self.assertIsInstance(
self.explainer.get_contrib_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_pdp_df(self):
self.assertIsInstance(self.explainer.pdp_df("age"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("sex"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("age", index=0),
pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("sex", index=0),
pd.DataFrame)
class ClassifierBunchTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
idxs=test_names,
labels=['Not survived', 'Survived'])
def test_pos_label(self):
self.explainer.pos_label = 1
self.explainer.pos_label = "Not survived"
self.assertIsInstance(self.explainer.pos_label, int)
self.assertIsInstance(self.explainer.pos_label_str, str)
self.assertEquals(self.explainer.pos_label, 0)
self.assertEquals(self.explainer.pos_label_str, "Not survived")
def test_get_prop_for_label(self):
self.explainer.pos_label = 1
tmp = self.explainer.pred_percentiles
self.explainer.pos_label = 0
self.assertTrue(
np.alltrue(
self.explainer.get_prop_for_label("pred_percentiles", 1) ==
tmp))
def test_pred_probas(self):
self.assertIsInstance(self.explainer.pred_probas, np.ndarray)
def test_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
self.assertIsInstance(self.explainer.metrics(cutoff=0.9), dict)
def test_precision_df(self):
self.assertIsInstance(self.explainer.precision_df(), pd.DataFrame)
self.assertIsInstance(self.explainer.precision_df(multiclass=True),
pd.DataFrame)
self.assertIsInstance(self.explainer.precision_df(quantiles=4),
pd.DataFrame)
def test_lift_curve_df(self):
self.assertIsInstance(self.explainer.lift_curve_df(), pd.DataFrame)
def test_prediction_result_markdown(self):
self.assertIsInstance(self.explainer.prediction_result_markdown(0),
str)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_plot_precision(self):
fig = self.explainer.plot_precision()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_precision(multiclass=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_precision(quantiles=10, cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_cumulutive_precision(self):
fig = self.explainer.plot_cumulative_precision()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_cumulative_precision(percentile=0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_cumulative_precision(percentile=0.1,
pos_label=0)
self.assertIsInstance(fig, go.Figure)
def test_plot_confusion_matrix(self):
fig = self.explainer.plot_confusion_matrix(normalized=False,
binary=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=False,
binary=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=True,
binary=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=True,
binary=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_lift_curve(self):
fig = self.explainer.plot_lift_curve()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_lift_curve(self):
fig = self.explainer.plot_lift_curve()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_classification(self):
fig = self.explainer.plot_classification()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(cutoff=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(cutoff=1)
self.assertIsInstance(fig, go.Figure)
def test_plot_roc_auc(self):
fig = self.explainer.plot_roc_auc(0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_roc_auc(0.0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_roc_auc(1.0)
self.assertIsInstance(fig, go.Figure)
def test_plot_pr_auc(self):
fig = self.explainer.plot_pr_auc(0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pr_auc(0.0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pr_auc(1.0)
self.assertIsInstance(fig, go.Figure)
class ClassifierBaseExplainerTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
roc_auc_score,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
target='Survival',
labels=['Not survived', 'Survived'],
idxs=test_names)
def test_explainer_len(self):
self.assertEqual(len(self.explainer), len(titanic_survive()[2]))
def test_int_idx(self):
self.assertEqual(self.explainer.get_int_idx(titanic_names()[1][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_columns_ranked_by_shap(self):
self.assertIsInstance(self.explainer.columns_ranked_by_shap(), list)
self.assertIsInstance(self.explainer.columns_ranked_by_shap(cats=True),
list)
def test_equivalent_col(self):
self.assertEqual(self.explainer.equivalent_col("Sex_female"), "Gender")
self.assertEqual(self.explainer.equivalent_col("Gender"), "Sex_female")
self.assertEqual(self.explainer.equivalent_col("Deck"), "Deck_A")
self.assertEqual(self.explainer.equivalent_col("Deck_A"), "Deck")
self.assertIsNone(self.explainer.equivalent_col("random"))
def test_get_col(self):
self.assertIsInstance(self.explainer.get_col("Gender"), pd.Series)
self.assertEqual(self.explainer.get_col("Gender").dtype, "object")
self.assertIsInstance(self.explainer.get_col("Deck"), pd.Series)
self.assertEqual(self.explainer.get_col("Deck").dtype, "object")
self.assertIsInstance(self.explainer.get_col("Age"), pd.Series)
self.assertEqual(self.explainer.get_col("Age").dtype, np.float)
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.permutation_importances,
pd.DataFrame)
self.assertIsInstance(self.explainer.permutation_importances_cats,
pd.DataFrame)
def test_X_cats(self):
self.assertIsInstance(self.explainer.X_cats, pd.DataFrame)
def test_columns_cats(self):
self.assertIsInstance(self.explainer.columns_cats, list)
def test_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
self.assertIsInstance(self.explainer.metrics_markdown(), str)
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.shap_top_interactions("Age"),
list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Age", topx=4), list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Age", cats=True), list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Gender", cats=True), list)
def test_permutation_importances_df(self):
self.assertIsInstance(self.explainer.permutation_importances_df(),
pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(topx=3), pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(cats=True), pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(cutoff=0.01),
pd.DataFrame)
def test_contrib_df(self):
self.assertIsInstance(self.explainer.contrib_df(0), pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, cats=False),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, topx=3),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, sort='high-to-low'),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, sort='low-to-high'),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, sort='importance'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_df(X_row=self.explainer.X.iloc[[0]]),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_df(X_row=self.explainer.X_cats.iloc[[0]]),
pd.DataFrame)
def test_contrib_summary_df(self):
self.assertIsInstance(self.explainer.contrib_summary_df(0),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, cats=False),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, topx=3),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, round=3),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(0, sort='low-to-high'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(0, sort='high-to-low'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(0, sort='importance'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(
X_row=self.explainer.X.iloc[[0]]), pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(
X_row=self.explainer.X_cats.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.shap_values.shape == (len(self.explainer),
len(self.explainer.columns)))
def test_shap_values(self):
self.assertIsInstance(self.explainer.shap_values, np.ndarray)
self.assertIsInstance(self.explainer.shap_values_cats, np.ndarray)
def test_shap_interaction_values(self):
self.assertIsInstance(self.explainer.shap_interaction_values,
np.ndarray)
self.assertIsInstance(self.explainer.shap_interaction_values_cats,
np.ndarray)
def test_mean_abs_shap(self):
self.assertIsInstance(self.explainer.mean_abs_shap, pd.DataFrame)
self.assertIsInstance(self.explainer.mean_abs_shap_cats, pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_shap_interaction_values_by_col(self):
self.assertIsInstance(
self.explainer.shap_interaction_values_by_col("Age"), np.ndarray)
self.assertEquals(
self.explainer.shap_interaction_values_by_col("Age").shape,
self.explainer.shap_values.shape)
self.assertEquals(
self.explainer.shap_interaction_values_by_col("Age",
cats=True).shape,
self.explainer.shap_values_cats.shape)
def test_pdp_result(self):
self.assertIsInstance(self.explainer.get_pdp_result("Age"),
pdpbox.pdp.PDPIsolate)
self.assertIsInstance(self.explainer.get_pdp_result("Gender"),
pdpbox.pdp.PDPIsolate)
self.assertIsInstance(self.explainer.get_pdp_result("Age", index=0),
pdpbox.pdp.PDPIsolate)
self.assertIsInstance(self.explainer.get_pdp_result("Gender", index=0),
pdpbox.pdp.PDPIsolate)
self.assertIsInstance(
self.explainer.get_pdp_result("Age",
X_row=self.explainer.X.iloc[[0]]),
pdpbox.pdp.PDPIsolate)
self.assertIsInstance(
self.explainer.get_pdp_result(
"Age", X_row=self.explainer.X_cats.iloc[[0]]),
pdpbox.pdp.PDPIsolate)
def test_get_dfs(self):
cols_df, shap_df, contribs_df = self.explainer.get_dfs()
self.assertIsInstance(cols_df, pd.DataFrame)
self.assertIsInstance(shap_df, pd.DataFrame)
self.assertIsInstance(contribs_df, 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)
fig = self.explainer.plot_importances(cats=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_interactions(self):
fig = self.explainer.plot_interactions("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions("Sex_female")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions("Gender")
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_contributions(self):
fig = self.explainer.plot_shap_contributions(0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, cats=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, cutoff=0.05)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, sort='high-to-low')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, sort='low-to-high')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, sort='importance')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(
X_row=self.explainer.X.iloc[[0]], sort='importance')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(
X_row=self.explainer.X_cats.iloc[[0]], sort='importance')
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_summary(self):
fig = self.explainer.plot_shap_summary()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_summary(topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_summary(cats=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_interaction_summary(self):
fig = self.explainer.plot_shap_interaction_summary("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Age", topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Sex_female",
topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Gender")
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_dependence(self):
fig = self.explainer.plot_shap_dependence("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", "Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Gender", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_interaction(self):
fig = self.explainer.plot_shap_dependence("Age", "Sex_female")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Gender", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", "Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age",
"Sex_female",
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)
fig = self.explainer.plot_pdp("Age",
X_row=self.explainer.X_cats.iloc[[0]])
self.assertIsInstance(fig, go.Figure)
def test_yaml(self):
yaml = self.explainer.to_yaml()
self.assertIsInstance(yaml, str)
class MultiClassClassifierBunchTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_embarked()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck'],
idxs=test_names,
labels=['Queenstown', 'Southampton', 'Cherbourg'])
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)
self.assertIsInstance(self.explainer.columns_ranked_by_shap(cats=True),
list)
def test_equivalent_col(self):
self.assertEqual(self.explainer.equivalent_col("Sex_female"), "Gender")
self.assertEqual(self.explainer.equivalent_col("Gender"), "Sex_female")
self.assertIsNone(self.explainer.equivalent_col("random"))
def test_get_col(self):
self.assertIsInstance(self.explainer.get_col("Gender"), pd.Series)
self.assertEqual(self.explainer.get_col("Gender").dtype, "object")
self.assertIsInstance(self.explainer.get_col("Age"), pd.Series)
self.assertEqual(self.explainer.get_col("Age").dtype, np.float)
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.permutation_importances,
pd.DataFrame)
self.assertIsInstance(self.explainer.permutation_importances_cats,
pd.DataFrame)
def test_X_cats(self):
self.assertIsInstance(self.explainer.X_cats, pd.DataFrame)
def test_columns_cats(self):
self.assertIsInstance(self.explainer.columns_cats, list)
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.shap_top_interactions("Age"),
list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Age", topx=4), list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Age", cats=True), list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Gender", cats=True), list)
def test_permutation_importances_df(self):
self.assertIsInstance(self.explainer.permutation_importances_df(),
pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(topx=3), pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(cats=True), pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(cutoff=0.01),
pd.DataFrame)
def test_contrib_df(self):
self.assertIsInstance(self.explainer.contrib_df(0), pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, cats=False),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, topx=3),
pd.DataFrame)
def test_contrib_summary_df(self):
self.assertIsInstance(self.explainer.contrib_summary_df(0),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, cats=False),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, topx=3),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, round=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.shap_values.shape == (len(self.explainer),
len(self.explainer.columns)))
def test_shap_values(self):
self.assertIsInstance(self.explainer.shap_values, np.ndarray)
self.assertIsInstance(self.explainer.shap_values_cats, np.ndarray)
def test_shap_interaction_values(self):
self.assertIsInstance(self.explainer.shap_interaction_values,
np.ndarray)
self.assertIsInstance(self.explainer.shap_interaction_values_cats,
np.ndarray)
def test_mean_abs_shap(self):
self.assertIsInstance(self.explainer.mean_abs_shap, pd.DataFrame)
self.assertIsInstance(self.explainer.mean_abs_shap_cats, pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_shap_interaction_values_by_col(self):
self.assertIsInstance(
self.explainer.shap_interaction_values_by_col("Age"), np.ndarray)
self.assertEqual(
self.explainer.shap_interaction_values_by_col("Age").shape,
self.explainer.shap_values.shape)
self.assertEqual(
self.explainer.shap_interaction_values_by_col("Age",
cats=True).shape,
self.explainer.shap_values_cats.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_get_dfs(self):
cols_df, shap_df, contribs_df = self.explainer.get_dfs()
self.assertIsInstance(cols_df, pd.DataFrame)
self.assertIsInstance(shap_df, pd.DataFrame)
self.assertIsInstance(contribs_df, 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)
fig = self.explainer.plot_importances(cats=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_interactions(self):
fig = self.explainer.plot_interactions("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions("Sex_female")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions("Gender")
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_contributions(self):
fig = self.explainer.plot_shap_contributions(0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, cats=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, cutoff=0.05)
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_summary(self):
fig = self.explainer.plot_shap_summary()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_summary(topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_summary(cats=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_interaction_summary(self):
fig = self.explainer.plot_shap_interaction_summary("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Age", topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Sex_female",
topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_interaction_summary("Gender")
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_dependence(self):
fig = self.explainer.plot_shap_dependence("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", "Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Gender", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_interaction(self):
fig = self.explainer.plot_shap_dependence("Age", "Sex_female")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Gender", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", "Gender")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age",
"Sex_female",
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)
def test_pos_label(self):
self.explainer.pos_label = 1
self.explainer.pos_label = "Southampton"
self.assertIsInstance(self.explainer.pos_label, int)
self.assertIsInstance(self.explainer.pos_label_str, str)
self.assertEqual(self.explainer.pos_label, 1)
self.assertEqual(self.explainer.pos_label_str, "Southampton")
def test_get_prop_for_label(self):
self.explainer.pos_label = 1
tmp = self.explainer.pred_percentiles
self.explainer.pos_label = 0
self.assertTrue(
np.alltrue(
self.explainer.get_prop_for_label("pred_percentiles", 1) ==
tmp))
def test_pred_probas(self):
self.assertIsInstance(self.explainer.pred_probas, np.ndarray)
def test_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
self.assertIsInstance(self.explainer.metrics(cutoff=0.9), dict)
def test_precision_df(self):
self.assertIsInstance(self.explainer.precision_df(), pd.DataFrame)
self.assertIsInstance(self.explainer.precision_df(multiclass=True),
pd.DataFrame)
self.assertIsInstance(self.explainer.precision_df(quantiles=4),
pd.DataFrame)
def test_lift_curve_df(self):
self.assertIsInstance(self.explainer.lift_curve_df(), pd.DataFrame)
def test_prediction_result_markdown(self):
self.assertIsInstance(self.explainer.prediction_result_markdown(0),
str)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_plot_precision(self):
fig = self.explainer.plot_precision()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_precision(multiclass=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_precision(quantiles=10, cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_cumulative_precision(self):
fig = self.explainer.plot_cumulative_precision()
self.assertIsInstance(fig, go.Figure)
def test_plot_confusion_matrix(self):
fig = self.explainer.plot_confusion_matrix(normalized=False,
binary=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=False,
binary=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=True,
binary=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=True,
binary=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_lift_curve(self):
fig = self.explainer.plot_lift_curve()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_lift_curve(self):
fig = self.explainer.plot_lift_curve()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_classification(self):
fig = self.explainer.plot_classification()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(cutoff=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(cutoff=1)
self.assertIsInstance(fig, go.Figure)
def test_plot_roc_auc(self):
fig = self.explainer.plot_roc_auc(0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_roc_auc(0.0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_roc_auc(1.0)
self.assertIsInstance(fig, go.Figure)
def test_plot_pr_auc(self):
fig = self.explainer.plot_pr_auc(0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pr_auc(0.0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pr_auc(1.0)
self.assertIsInstance(fig, go.Figure)
class LogisticRegressionTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = LogisticRegression()
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
shap='linear',
cats=['Sex', 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'],
idxs=test_names)
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_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_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("Sex"), 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("Sex", index=0),
pd.DataFrame)
def test_pos_label(self):
self.explainer.pos_label = 1
self.explainer.pos_label = "Not survived"
self.assertIsInstance(self.explainer.pos_label, int)
self.assertIsInstance(self.explainer.pos_label_str, str)
self.assertEqual(self.explainer.pos_label, 0)
self.assertEqual(self.explainer.pos_label_str, "Not survived")
def test_pred_probas(self):
self.assertIsInstance(self.explainer.pred_probas(), np.ndarray)
def test_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
self.assertIsInstance(self.explainer.metrics(cutoff=0.9), dict)
def test_precision_df(self):
self.assertIsInstance(self.explainer.get_precision_df(), pd.DataFrame)
self.assertIsInstance(self.explainer.get_precision_df(multiclass=True),
pd.DataFrame)
self.assertIsInstance(self.explainer.get_precision_df(quantiles=4),
pd.DataFrame)
def test_lift_curve_df(self):
self.assertIsInstance(self.explainer.get_liftcurve_df(), pd.DataFrame)
class ClassifierBaseExplainerTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
model,
X_test,
y_test,
cats=[{
'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
target='Survival',
labels=['Not survived', 'Survived'],
idxs=test_names)
def test_explainer_len(self):
self.assertEqual(len(self.explainer), len(titanic_survive()[2]))
def test_int_idx(self):
self.assertEqual(self.explainer.get_idx(titanic_names()[1][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_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_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("Deck"), pd.Series)
self.assertTrue(is_categorical_dtype(self.explainer.get_col("Deck")))
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)
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='low-to-high'),
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='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_df(self):
self.assertIsInstance(self.explainer.mean_abs_shap_df(), pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_shap_interaction_values_by_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_prediction_result_df(self):
df = self.explainer.prediction_result_df(0)
self.assertIsInstance(df, pd.DataFrame)
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)
self.assertIsInstance(
self.explainer.pdp_df("Age", X_row=self.explainer.X.iloc[[0]]),
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_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("Gender")
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, 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)
fig = self.explainer.plot_contributions(
X_row=self.explainer.X.iloc[[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)
fig = self.explainer.plot_interactions_detailed("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_interactions_detailed("Gender")
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("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_interaction(self):
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)
