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)