def test_directional_bars():
# this test is very fragile :-/
X, Y = generate_easy(n_samples=20, noise=2, box_size=2, total_size=6,
seed=2)
n_labels = 2
crf = LatentDirectionalGridCRF(n_labels=n_labels,
n_states_per_label=[1, 4])
clf = SubgradientLatentSSVM(model=crf, max_iter=75, C=10.,
learning_rate=1, momentum=0,
decay_exponent=0.5, decay_t0=10)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
def test_latent_node_boxes_latent_subgradient():
# same as above, now with elementary subgradients
X, Y = make_simple_2x2(seed=1)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
latent_svm = SubgradientLatentSSVM(model=latent_crf, max_iter=50, C=10)
G = [make_grid_edges(x) for x in X]
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = list(zip(X_flat, G, [4 * 4 for x in X_flat]))
latent_svm.fit(X_, Y_flat)
assert_equal(latent_svm.score(X_, Y_flat), 1)
def test_latent_node_boxes_latent_subgradient():
# same as above, now with elementary subgradients
X, Y = make_simple_2x2(seed=1)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
latent_svm = SubgradientLatentSSVM(model=latent_crf, max_iter=50, C=10)
G = [make_grid_edges(x) for x in X]
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = zip(X_flat, G, [4 * 4 for x in X_flat])
latent_svm.fit(X_, Y_flat)
assert_equal(latent_svm.score(X_, Y_flat), 1)
def test_objective():
# test that SubgradientLatentSSVM does the same as SubgradientSVM,
# in particular that it has the same loss, if there are no latent states.
X, Y = generate_blocks_multinomial(n_samples=10, noise=.3, seed=1)
inference_method = get_installed(["qpbo", "ad3", "lp"])[0]
n_labels = 3
crfl = LatentGridCRF(n_labels=n_labels, n_states_per_label=1,
inference_method=inference_method)
clfl = SubgradientLatentSSVM(model=crfl, max_iter=20, C=10.,
learning_rate=0.001, momentum=0.98)
crfl.initialize(X, Y)
clfl.w = np.zeros(crfl.size_joint_feature) # this disables random init
clfl.fit(X, Y)
crf = GridCRF(n_states=n_labels, inference_method=inference_method)
clf = SubgradientSSVM(model=crf, max_iter=20, C=10., learning_rate=0.001,
momentum=0.98)
clf.fit(X, Y)
assert_array_almost_equal(clf.w, clfl.w)
assert_almost_equal(clf.objective_curve_[-1], clfl.objective_curve_[-1])
assert_array_equal(clf.predict(X), clfl.predict(X))
assert_array_equal(clf.predict(X), Y)
