def test_label_model_sparse(self) -> None:
"""Test the LabelModel's estimate of P and Y on a sparse synthetic dataset.
This tests the common setting where LFs abstain most of the time, which can
cause issues for example if parameter clamping set too high (e.g. see Issue
#1422).
"""
np.random.seed(123)
P, Y, L = generate_simple_label_matrix(self.n,
self.m,
self.cardinality,
abstain_multiplier=1000.0)
# Train LabelModel
label_model = LabelModel(cardinality=self.cardinality, verbose=False)
label_model.fit(L, n_epochs=1000, lr=0.01, seed=123)
# Test estimated LF conditional probabilities
P_lm = label_model.get_conditional_probs()
np.testing.assert_array_almost_equal(P, P_lm, decimal=2)
# Test predicted labels *only on non-abstained data points*
Y_pred = label_model.predict(L, tie_break_policy="abstain")
idx, = np.where(Y_pred != -1)
acc = np.where(Y_pred[idx] == Y[idx], 1, 0).sum() / len(idx)
self.assertGreaterEqual(acc, 0.65)
# Make sure that we don't output abstain when an LF votes, per issue #1422
self.assertEqual(len(idx),
np.where((L + 1).sum(axis=1) != 0, 1, 0).sum())
def test_set_mu_eps(self):
mu_eps = 0.0123
# Construct a label matrix such that P(\lambda_1 = 0 | Y) = 0.0, so it will hit
# the mu_eps floor
L = np.array([[1, 1, 1], [1, 1, 1]])
label_model = LabelModel(verbose=False)
label_model.fit(L, mu_eps=mu_eps)
self.assertAlmostEqual(label_model.get_conditional_probs()[0, 1, 0], mu_eps)
def test_label_model_basic(self) -> None:
"""Test the LabelModel's estimate of P and Y on a simple synthetic dataset."""
np.random.seed(123)
P, Y, L = generate_simple_label_matrix(self.n, self.m, self.cardinality)
# Train LabelModel
label_model = LabelModel(cardinality=self.cardinality, verbose=False)
label_model.fit(L, n_epochs=200, lr=0.01, seed=123)
# Test estimated LF conditional probabilities
P_lm = label_model.get_conditional_probs()
np.testing.assert_array_almost_equal(P, P_lm, decimal=2)
# Test predicted labels
score = label_model.score(L, Y)
self.assertGreaterEqual(score["accuracy"], 0.9)
