def test_multilabel_fully():
# test inference and energy with fully connected model
n_features = 5
n_labels = 4
edges = np.vstack([x for x in itertools.combinations(range(n_labels), 2)])
model = MultiLabelClf(n_labels=n_labels, n_features=n_features,
edges=edges)
rnd = np.random.RandomState(0)
x = rnd.normal(size=n_features)
w = rnd.normal(size=n_features * n_labels + 4 * len(edges))
y = model.inference(x, w)
# test psi / energy
psi = model.psi(x, y)
energy = compute_energy(model._get_unary_potentials(x, w),
model._get_pairwise_potentials(x, w), edges, y)
assert_almost_equal(energy, np.dot(psi, w))
# for continuous y
#y_cont = model.inference(x, w, relaxed=True)
y_continuous = np.zeros((n_labels, 2))
pairwise_marginals = []
for edge in edges:
# indicator of one of four possible states of the edge
pw = np.zeros((2, 2))
pw[y[edge[0]], y[edge[1]]] = 1
pairwise_marginals.append(pw)
pairwise_marginals = np.vstack(pairwise_marginals)
y_continuous[np.arange(n_labels), y] = 1
assert_array_almost_equal(
psi, model.psi(x, (y_continuous, pairwise_marginals)))
def test_multilabel_independent():
# test inference and energy with independent model
edges = np.zeros((0, 2), dtype=np.int)
n_features = 5
n_labels = 4
model = MultiLabelClf(n_labels=n_labels, n_features=n_features,
edges=edges)
rnd = np.random.RandomState(0)
x = rnd.normal(size=5)
w = rnd.normal(size=n_features * n_labels)
# test inference
y = model.inference(x, w)
y_ = np.dot(w.reshape(n_labels, n_features), x) > 0
assert_array_equal(y, y_)
# test psi / energy
psi = model.psi(x, y)
energy = compute_energy(model._get_unary_potentials(x, w),
model._get_pairwise_potentials(x, w), edges, y)
assert_almost_equal(energy, np.dot(psi, w))
# for continuous y
y_continuous = np.zeros((n_labels, 2))
y_continuous[np.arange(n_labels), y] = 1
assert_array_almost_equal(
psi, model.psi(x, (y_continuous, np.zeros((0, n_labels, n_labels)))))
