def test_augmented_L_construction(self):
# 5 LFs
n = 3
m = 5
k = 2
L = np.array([[0, 0, 0, 1, 0], [0, 1, 1, 0, -1], [0, 0, 0, 0, -1]])
L_shift = L + 1
lm = LabelModel(cardinality=k, verbose=False)
lm._set_constants(L_shift)
lm._create_tree()
L_aug = lm._get_augmented_label_matrix(L_shift, higher_order=True)
# Should have 10 columns:
# - 5 * 2 = 10 for the sources
self.assertEqual(L_aug.shape, (3, 10))
# 13 total nonzero entries
self.assertEqual(L_aug.sum(), 13)
# Next, check the singleton entries
for i in range(n):
for j in range(m):
if L_shift[i, j] > 0:
self.assertEqual(L_aug[i, j * k + L_shift[i, j] - 1], 1)
# Finally, check the clique entries
# Singleton clique 1
self.assertEqual(len(lm.c_tree.node[1]["members"]), 1)
j = lm.c_tree.node[1]["start_index"]
self.assertEqual(L_aug[0, j], 1)
# Singleton clique 2
self.assertEqual(len(lm.c_tree.node[2]["members"]), 1)
j = lm.c_tree.node[2]["start_index"]
self.assertEqual(L_aug[0, j + 1], 0)
def _set_up_model(self, L: np.ndarray, class_balance: List[float] = [0.5, 0.5]):
label_model = LabelModel(cardinality=2, verbose=False)
label_model.train_config = TrainConfig() # type: ignore
L_aug = L + 1
label_model._set_constants(L_aug)
label_model._create_tree()
label_model._generate_O(L_aug)
label_model._build_mask()
label_model._get_augmented_label_matrix(L_aug)
label_model._set_class_balance(class_balance=class_balance, Y_dev=None)
label_model._init_params()
return label_model
def setUpClass(cls) -> None:
known_dimensions = KnownDimensions(num_classes=7,
num_examples=1000,
num_functions=10)
possible_function_values = np.eye(known_dimensions.num_classes)
choice_set = np.random.choice(
known_dimensions.num_classes,
size=[
known_dimensions.num_functions, known_dimensions.num_examples
],
)
cooccurence = np.hstack(possible_function_values[choice_set])
cls.known_dimensions = known_dimensions
cls.L_ind = cooccurence
cls.O_counts = cls.L_ind.T @ cls.L_ind
cls.O = cls.O_counts / cls.known_dimensions.num_examples
model = LabelModel()
model._set_constants(known_dimensions=cls.known_dimensions)
model._create_tree()
model._generate_O_from_L_aug(cls.L_ind)
cls.model_O = model.O.detach().numpy()
