def test_calibrate(reg_dataset):
x_train = reg_dataset[0]
y_train = reg_dataset[1]
x_cal = reg_dataset[2]
y_cal = reg_dataset[3]
adapt_model = Adapt_to_CP(RandomForestRegressor(n_estimators=10), True)
adapt_model.fit(x_train, y_train)
assert adapt_model.calibrate(x_cal, y_cal) == None
def test_calibrate_and_predict_upper(reg_dataset):
x_train = reg_dataset[0]
y_train = reg_dataset[1]
x_cal = reg_dataset[2]
y_cal = reg_dataset[3]
x_test = reg_dataset[4]
adapt_model = Adapt_to_CP(RandomForestRegressor(n_estimators=10), True)
adapt_model.fit(x_train, y_train)
pred = adapt_model.calibrate_and_predict(x_cal, y_cal, x_test, 0.8)
assert type(pred[2]) == np.ndarray
def test_fit(reg_dataset):
x_train = reg_dataset[0]
y_train = reg_dataset[1]
model = RandomForestRegressor(n_estimators=10)
assert Adapt_to_CP(model, True).fit(x_train, y_train) == None
def test_Adapt_to_CP_icp(mod, sk_mod, icp):
adapted_mod = Adapt_to_CP(mod, sk_mod)
assert type(adapted_mod.icp) == icp
def test_Adapt_to_CP_model(model, sk_model, adapt_model):
adapted_mod = Adapt_to_CP(model, sk_model)
assert type(adapted_mod.model) == adapt_model