def test_symmetry_breaking_multiclass(self):
mu = np.array([
# LF 0
[0.75, 0.15, 0.1],
[0.20, 0.75, 0.3],
[0.05, 0.10, 0.6],
# LF 1
[0.25, 0.55, 0.3],
[0.15, 0.45, 0.4],
[0.20, 0.00, 0.3],
# LF 2
[0.5, 0.15, 0.2],
[0.3, 0.65, 0.2],
[0.2, 0.20, 0.6],
])
mu = mu[:, [1, 2, 0]]
# First test: Two "good" LFs
label_model = LabelModel(cardinality=3, verbose=False)
label_model._set_class_balance(None, None)
label_model.m = 3
label_model.mu = nn.Parameter(torch.from_numpy(mu))
label_model._break_col_permutation_symmetry()
self.assertEqual(label_model.mu.data[0, 0], 0.75)
self.assertEqual(label_model.mu.data[1, 1], 0.75)
# Test with non-uniform class balance
# It should not consider the "correct" permutation as it does not commute
label_model = LabelModel(cardinality=3, verbose=False)
label_model._set_class_balance([0.7, 0.2, 0.1], None)
label_model.m = 3
label_model.mu = nn.Parameter(torch.from_numpy(mu))
label_model._break_col_permutation_symmetry()
self.assertEqual(label_model.mu.data[0, 0], 0.15)
self.assertEqual(label_model.mu.data[1, 1], 0.3)
def test_symmetry_breaking(self):
mu = np.array([
# LF 0
[0.75, 0.25],
[0.25, 0.75],
# LF 1
[0.25, 0.75],
[0.15, 0.25],
# LF 2
[0.75, 0.25],
[0.25, 0.75],
])
mu = mu[:, [1, 0]]
# First test: Two "good" LFs
label_model = LabelModel(verbose=False)
label_model._set_class_balance(None, None)
label_model.m = 3
label_model.mu = nn.Parameter(torch.from_numpy(mu))
label_model._break_col_permutation_symmetry()
self.assertEqual(label_model.mu.data[0, 0], 0.75)
# Test with non-uniform class balance
# It should not consider the "correct" permutation as does not commute now
label_model = LabelModel(verbose=False)
label_model._set_class_balance([0.9, 0.1], None)
label_model.m = 3
label_model.mu = nn.Parameter(torch.from_numpy(mu))
label_model._break_col_permutation_symmetry()
self.assertEqual(label_model.mu.data[0, 0], 0.25)
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 test_class_balance(self):
label_model = LabelModel(cardinality=2, verbose=False)
# Test class balance
Y_dev = np.array([0, 0, 1, 1, 0, 0, 0, 0, 1, 1])
label_model._set_class_balance(class_balance=None, Y_dev=Y_dev)
np.testing.assert_array_almost_equal(label_model.p, np.array([0.6, 0.4]))
class_balance = np.array([0.0, 1.0])
with self.assertRaisesRegex(ValueError, "Class balance prior is 0"):
label_model._set_class_balance(class_balance=class_balance, Y_dev=Y_dev)
class_balance = np.array([0.0])
with self.assertRaisesRegex(ValueError, "class_balance has 1 entries."):
label_model._set_class_balance(class_balance=class_balance, Y_dev=Y_dev)
Y_dev_one_class = np.array([0, 0, 0])
with self.assertRaisesRegex(
ValueError, "Does not match LabelModel cardinality"
):
label_model._set_class_balance(class_balance=None, Y_dev=Y_dev_one_class)
