def test_transform(self):
for elements, stack in self.stacks:
np.random.seed(42)
Xt_stack, _, _ = stack.fit(self.X, self.y).transform(self.X)
np.random.seed(42)
Xt_elements = None
for i, element in enumerate(elements):
Xt_element, _, _ = element.fit(self.X,
self.y).transform(self.X)
Xt_elements = PhotonDataHelper.stack_data_horizontally(
Xt_elements, Xt_element)
np.testing.assert_array_equal(Xt_stack, Xt_elements)
def inverse_transform(self, X, y=None, **kwargs):
new_X = None
for i in range(X.shape[1]):
feature = X[:, i]
transformer = self.encoder_list[i]
if transformer is not None:
feature = np.reshape(feature, (-1, 1))
trans_X = transformer.inverse_transform(feature)
else:
trans_X = feature
new_X = PhotonDataHelper.stack_data_horizontally(new_X, trans_X)
return new_X
def test_predict(self):
for elements, stack in [
([self.svc, self.tree], self.estimator_stack),
([self.estimator_branch_1,
self.estimator_branch_2], self.estimator_branch_stack)
]:
np.random.seed(42)
stack = stack.fit(self.X, self.y)
yt_stack = stack.predict(self.X)
np.random.seed(42)
Xt_elements = None
for i, element in enumerate(elements):
Xt_element = element.fit(self.X, self.y).predict(self.X)
Xt_elements = PhotonDataHelper.stack_data_horizontally(
Xt_elements, Xt_element)
np.testing.assert_array_equal(yt_stack, Xt_elements)
