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)
