def test_predict_with_no_fit(self, time_series):
base_model = LinearRegression()
gar_no_feedforward = GAR(estimator=base_model)
with pytest.raises(NotFittedError):
gar_no_feedforward.predict(time_series)
def test_fit_as_multi_output_regressor_if_target_to_feature_none(
self, estimator, X_y
):
X, y = X_y
X_train, y_train, X_test, y_test = FeatureSplitter().transform(X, y)
multi_feature_gar = MultiFeatureGAR(estimator)
multi_feature_gar.fit(X_train, y_train)
gar = GAR(estimator)
gar.fit(X_train, y_train)
pd.testing.assert_frame_equal(
multi_feature_gar.predict(X_test), gar.predict(X_test),
)
def __init__(self, p: int, horizon: int):
features = [
tuple((f"s{i}", Shift(i),
make_column_selector(dtype_include=np.number)))
for i in range(1, p + 1)
]
model = GAR(LinearRegression())
super().__init__(features=features, horizon=horizon, model=model)
def test_correct_fit_date(self, X_y):
base_model = LinearRegression()
feature_splitter = FeatureSplitter()
x, y = X_y[0], X_y[1]
x_train, y_train, x_test, y_test = feature_splitter.transform(x, y)
gar_no_feedforward = GAR(estimator=base_model)
gar_no_feedforward.fit(x_train, y_train)
predictions = gar_no_feedforward.predict(x_test)
assert len(predictions) == len(x_test)
np.testing.assert_array_equal(predictions.index, x_test.index)
gar_with_feedforward = GARFF(estimator=base_model)
gar_with_feedforward.fit(x_train, y_train)
predictions = gar_with_feedforward.predict(x_test)
assert len(predictions) == len(x_test)
np.testing.assert_array_equal(predictions.index, x_test.index)
def __init__(
self,
p: int,
horizon: Union[int, List[int]],
explainer_type: Optional[str] = None,
):
self.p = p
self.explainer_type = explainer_type
features = [
tuple((f"s{i}", Shift(i), make_column_selector(dtype_include=np.number)))
for i in range(p)
]
model = GAR(LinearRegression(), explainer_type=explainer_type)
super().__init__(features=features, horizon=horizon, model=model)
def model2():
lr = Ridge(alpha=0.1)
return GAR(lr)
def model1():
lr = LinearRegression()
return GAR(lr)