def test_deseasonalised_values(sp):
t = Deseasonalizer(sp=sp)
t.fit(y_train)
a = t.transform(y_train)
r = seasonal_decompose(y_train, period=sp)
b = y_train - r.seasonal
np.testing.assert_array_equal(a.values, b.values)
def test_deseasonalised_values(sp):
transformer = Deseasonalizer(sp=sp)
transformer.fit(y_train)
actual = transformer.transform(y_train)
r = seasonal_decompose(y_train, period=sp)
expected = y_train - r.seasonal
np.testing.assert_array_equal(actual, expected)
def test_transform_inverse_transform_equivalence(sp, model):
t = Deseasonalizer(sp=sp, model=model)
t.fit(y_train)
yit = t.inverse_transform(t.transform(y_train))
np.testing.assert_array_equal(y_train.index, yit.index)
np.testing.assert_almost_equal(y_train.values, yit.values)
def test_inverse_transform_time_index(sp, model):
t = Deseasonalizer(sp=sp, model=model)
t.fit(y_train)
yit = t.inverse_transform(y_test)
np.testing.assert_array_equal(yit.index, y_test.index)
def test_transform_time_index(sp, model):
transformer = Deseasonalizer(sp=sp, model=model)
transformer.fit(y_train)
yt = transformer.transform(y_test)
np.testing.assert_array_equal(yt.index, y_test.index)
