def test_model_loss(self):
L = np.array([[0, -1, 0], [0, 1, 0]])
label_model = LabelModel(cardinality=2, verbose=False)
label_model.fit(L, n_epochs=1)
init_loss = label_model._loss_mu().item()
label_model.fit(L, n_epochs=10)
next_loss = label_model._loss_mu().item()
self.assertLessEqual(next_loss, init_loss)
with self.assertRaisesRegex(Exception, "Loss is NaN."):
label_model.fit(L, n_epochs=10, lr=1e8)
def test_loss(self):
L = np.array([[0, -1, 0], [0, 1, -1]])
label_model = LabelModel(cardinality=2, verbose=False)
label_model.fit(L, n_epochs=1)
label_model.mu = nn.Parameter(label_model.mu_init.clone() + 0.05)
# l2_loss = l2*M*K*||mu - mu_init||_2 = 3*2*(0.05^2) = 0.03
self.assertAlmostEqual(label_model._loss_l2(l2=1.0).item(), 0.03)
self.assertAlmostEqual(label_model._loss_l2(l2=np.ones(6)).item(), 0.03)
# mu_loss = ||O - \mu^T P \mu||_2 + ||\mu^T P - diag(O)||_2
self.assertAlmostEqual(label_model._loss_mu().item(), 0.675, 3)