def test_explain_titanic_val(model):
# add multi-class
# add regression
titanic = load_titanic()
titanic_clean = clean(titanic)
X, y = titanic_clean.drop("survived", axis=1), titanic_clean.survived
X_train, X_val, y_train, y_val = train_test_split(X, y, stratify=y,
random_state=42)
pipe = make_pipeline(EasyPreprocessor(), model)
pipe.fit(X_train, y_train)
# without validation set
explain(pipe, feature_names=X.columns)
# with validation set
explain(pipe, X_val, y_val, feature_names=X.columns)
def test_explain_smoke_titanic():
titanic = load_titanic()
sc = SimpleClassifier().fit(titanic, target_col='survived')
explain(sc)
titanic_clean = clean(titanic)
sc = SimpleClassifier().fit(titanic_clean, target_col='survived')
explain(sc)
X, y = titanic_clean.drop("survived", axis=1), titanic_clean.survived
ep = EasyPreprocessor()
preprocessed = ep.fit_transform(X)
tree = DecisionTreeClassifier().fit(preprocessed, y)
explain(tree, feature_names=ep.get_feature_names())
pipe = make_pipeline(EasyPreprocessor(), LogisticRegression())
pipe.fit(X, y)
explain(pipe, feature_names=pipe.steps[0][1].get_feature_names())
