def test_orthogonal_regression_large_to_small(self):
# tests if prediction is padded to larger feature size
n_features = (OrthogonalRegression(use_orthogonal_projector=False).fit(
self.features_large,
self.features_small).predict(self.features_large).shape[1])
self.assertTrue(
n_features == self.features_large.shape[1],
f"n_features {n_features} does not match larger feature size {self.features_large.shape[1]}",
)
def test_orthogonal_regression_use_orthogonal_projector_small_to_large(
self):
# tests if prediction is projected to prediction feature space
n_features = (OrthogonalRegression(use_orthogonal_projector=True).fit(
self.features_small,
self.features_large).predict(self.features_small).shape[1])
self.assertTrue(
n_features == self.features_large.shape[1],
f"n_features {n_features} does not match projection feature size {self.features_large.shape[1]}",
)
def test_orthogonal_regression_small_to_rotated_small(self):
# tests if OrthogonalRegression can predict rotated small features using small features with use_orthogonal_projector False
err = np.linalg.norm(
self.features_rotated_small -
OrthogonalRegression(use_orthogonal_projector=False).fit(
self.features_small, self.features_rotated_small).predict(
self.features_small))
self.assertTrue(
abs(err) < self.eps,
f"error {err} surpasses threshold for zero {self.eps}")