def test_blocks_crf_directional(): # test latent directional CRF on blocks # test that all results are the same as equivalent LatentGridCRF X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] pairwise_weights = np.array([0, 0, 0, -4, -4, 0, -4, -4, 0, 0]) unary_weights = np.repeat(np.eye(2), 2, axis=0) w = np.hstack([unary_weights.ravel(), pairwise_weights]) pw_directional = np.array([ 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0, 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0 ]) w_directional = np.hstack([unary_weights.ravel(), pw_directional]) crf = LatentGridCRF(n_states_per_label=2, inference_method='lp') crf.initialize(X, Y) directional_crf = LatentDirectionalGridCRF(n_states_per_label=2, inference_method='lp') directional_crf.initialize(X, Y) h_hat = crf.inference(x, w) h_hat_d = directional_crf.inference(x, w_directional) assert_array_equal(h_hat, h_hat_d) h = crf.latent(x, y, w) h_d = directional_crf.latent(x, y, w_directional) assert_array_equal(h, h_d) h_hat = crf.loss_augmented_inference(x, y, w) h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional) assert_array_equal(h_hat, h_hat_d) joint_feature = crf.joint_feature(x, h_hat) joint_feature_d = directional_crf.joint_feature(x, h_hat) assert_array_equal(np.dot(joint_feature, w), np.dot(joint_feature_d, w_directional))
def test_continuous_y(): for inference_method in get_installed(["lp", "ad3"]): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] w = np.array([ 1, 0, # unary 0, 1, 0, # pairwise -4, 0 ]) crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=1, inference_method=inference_method) joint_feature = crf.joint_feature(x, y) y_cont = np.zeros_like(x) gx, gy = np.indices(x.shape[:-1]) y_cont[gx, gy, y] = 1 # need to generate edge marginals vert = np.dot(y_cont[1:, :, :].reshape(-1, 2).T, y_cont[:-1, :, :].reshape(-1, 2)) # horizontal edges horz = np.dot(y_cont[:, 1:, :].reshape(-1, 2).T, y_cont[:, :-1, :].reshape(-1, 2)) pw = vert + horz joint_feature_cont = crf.joint_feature(x, (y_cont, pw)) assert_array_almost_equal(joint_feature, joint_feature_cont, 4) const = find_constraint(crf, x, y, w, relaxed=False) const_cont = find_constraint(crf, x, y, w, relaxed=True) # djoint_feature and loss are equal: assert_array_almost_equal(const[1], const_cont[1], 4) assert_almost_equal(const[2], const_cont[2], 4) if isinstance(const_cont[0], tuple): # returned y_hat is one-hot version of other assert_array_equal(const[0], np.argmax(const_cont[0][0], axis=-1)) # test loss: assert_almost_equal(crf.loss(y, const[0]), crf.continuous_loss(y, const_cont[0][0]), 4)
def test_continuous_y(): for inference_method in get_installed(["lp", "ad3"]): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] w = np.array([1, 0, # unary 0, 1, 0, # pairwise -4, 0]) crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=1, inference_method=inference_method) joint_feature = crf.joint_feature(x, y) y_cont = np.zeros_like(x) gx, gy = np.indices(x.shape[:-1]) y_cont[gx, gy, y] = 1 # need to generate edge marginals vert = np.dot(y_cont[1:, :, :].reshape(-1, 2).T, y_cont[:-1, :, :].reshape(-1, 2)) # horizontal edges horz = np.dot(y_cont[:, 1:, :].reshape(-1, 2).T, y_cont[:, :-1, :].reshape(-1, 2)) pw = vert + horz joint_feature_cont = crf.joint_feature(x, (y_cont, pw)) assert_array_almost_equal(joint_feature, joint_feature_cont, 4) const = find_constraint(crf, x, y, w, relaxed=False) const_cont = find_constraint(crf, x, y, w, relaxed=True) # djoint_feature and loss are equal: assert_array_almost_equal(const[1], const_cont[1], 4) assert_almost_equal(const[2], const_cont[2], 4) if isinstance(const_cont[0], tuple): # returned y_hat is one-hot version of other assert_array_equal(const[0], np.argmax(const_cont[0][0], axis=-1)) # test loss: assert_almost_equal(crf.loss(y, const[0]), crf.continuous_loss(y, const_cont[0][0]), 4)
def test_blocks_crf_directional(): # test latent directional CRF on blocks # test that all results are the same as equivalent LatentGridCRF X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] pairwise_weights = np.array([0, 0, 0, -4, -4, 0, -4, -4, 0, 0]) unary_weights = np.repeat(np.eye(2), 2, axis=0) w = np.hstack([unary_weights.ravel(), pairwise_weights]) pw_directional = np.array([0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0, 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0]) w_directional = np.hstack([unary_weights.ravel(), pw_directional]) crf = LatentGridCRF(n_states_per_label=2, inference_method='lp') crf.initialize(X, Y) directional_crf = LatentDirectionalGridCRF(n_states_per_label=2, inference_method='lp') directional_crf.initialize(X, Y) h_hat = crf.inference(x, w) h_hat_d = directional_crf.inference(x, w_directional) assert_array_equal(h_hat, h_hat_d) h = crf.latent(x, y, w) h_d = directional_crf.latent(x, y, w_directional) assert_array_equal(h, h_d) h_hat = crf.loss_augmented_inference(x, y, w) h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional) assert_array_equal(h_hat, h_hat_d) joint_feature = crf.joint_feature(x, h_hat) joint_feature_d = directional_crf.joint_feature(x, h_hat) assert_array_equal(np.dot(joint_feature, w), np.dot(joint_feature_d, w_directional))