def test_graph_crf_class_weights():
# no edges
crf = GraphCRF(n_states=3, n_features=3)
w = np.array([
1,
0,
0, # unary
0,
1,
0,
0,
0,
1,
0, # pairwise
0,
0,
0,
0,
0
])
x = (np.array([[1, 1.5, 1.1]]), np.empty((0, 2)))
assert_equal(crf.inference(x, w), 1)
# loss augmented inference picks last
assert_equal(crf.loss_augmented_inference(x, [1], w), 2)
# with class-weights, loss for class 1 is smaller, loss-augmented inference
# will find it
crf = GraphCRF(n_states=3, n_features=3, class_weight=[1, .1, 1])
assert_equal(crf.loss_augmented_inference(x, [1], w), 1)
def test_graph_crf_class_weights():
# no edges
crf = GraphCRF(n_states=3, n_features=3, inference_method="dai")
w = np.array([1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]) # unary # pairwise
x = (np.array([[1, 1.5, 1.1]]), np.empty((0, 2)))
assert_equal(crf.inference(x, w), 1)
# loss augmented inference picks last
assert_equal(crf.loss_augmented_inference(x, [1], w), 2)
# with class-weights, loss for class 1 is smaller, loss-augmented inference
# will find it
crf = GraphCRF(n_states=3, n_features=3, inference_method="dai", class_weight=[1, 0.1, 1])
assert_equal(crf.loss_augmented_inference(x, [1], w), 1)
def test_graph_crf_loss_augment():
x = (x_1, g_1)
y = y_1
crf = GraphCRF(n_states=2, inference_method="lp")
y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True)
# check that y_hat fulfills energy + loss condition
assert_almost_equal(np.dot(w, crf.psi(x, y_hat)) + crf.loss(y, y_hat), -energy)
def test_graph_crf_loss_augment():
x = (x_1, g_1)
y = y_1
crf = GraphCRF()
crf.initialize([x], [y])
y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True)
# check that y_hat fulfills energy + loss condition
assert_almost_equal(np.dot(w, crf.joint_feature(x, y_hat)) + crf.loss(y, y_hat),
-energy)
