def test_smoke_partial_fit(): df = load_titanic(as_frame=True) X, y = df.drop(columns=["survived"]), df["survived"] mod = FunctionClassifier(class_based, pclass=10) assert mod.partial_fit( X, y, classes=np.unique(y)).predict(X).shape[0] == y.shape[0]
def test_works_with_gridsearch(random_xy_dataset_clf): X, y = random_xy_dataset_clf clf = FunctionClassifier(func=predict) grid = GridSearchCV(clf, cv=5, param_grid={"func": [predict, predict_variant]}) grid.fit(X, y).predict(X)
def test_smoke_with_pandas(): df = load_titanic(as_frame=True) X, y = df.drop(columns=["survived"]), df["survived"] mod = FunctionClassifier(class_based, pclass=10) params = {"pclass": [1, 2, 3], "sex": ["male", "female"]} grid = GridSearchCV(mod, cv=3, param_grid=params).fit(X, y) pd.DataFrame(grid.cv_results_)
def test_estimator_checks(test_fn): clf = FunctionClassifier(func=predict) test_fn(FunctionClassifier.__name__ + "_fallback", clf)