def test_joint_feature_continuous(): # FIXME # first make perfect prediction, including pairwise part X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) x, y = X[0], Y[0] n_states = x.shape[-1] pw_horz = -1 * np.eye(n_states) xx, yy = np.indices(pw_horz.shape) # linear ordering constraint horizontally pw_horz[xx > yy] = 1 # high cost for unequal labels vertically pw_vert = -1 * np.eye(n_states) pw_vert[xx != yy] = 1 pw_vert *= 10 # create crf, assemble weight, make prediction for inference_method in get_installed(["lp", "ad3"]): crf = DirectionalGridCRF(inference_method=inference_method) crf.initialize(X, Y) w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) y_pred = crf.inference(x, w, relaxed=True) # compute joint_feature for prediction joint_feature_y = crf.joint_feature(x, y_pred) assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
def test_inference(): # Test inference with different weights in different directions X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) x, y = X[0], Y[0] n_states = x.shape[-1] edge_list = make_grid_edges(x, 4, return_lists=True) edges = np.vstack(edge_list) pw_horz = -1 * np.eye(n_states) xx, yy = np.indices(pw_horz.shape) # linear ordering constraint horizontally pw_horz[xx > yy] = 1 # high cost for unequal labels vertically pw_vert = -1 * np.eye(n_states) pw_vert[xx != yy] = 1 pw_vert *= 10 # generate edge weights edge_weights_horizontal = np.repeat(pw_horz[np.newaxis, :, :], edge_list[0].shape[0], axis=0) edge_weights_vertical = np.repeat(pw_vert[np.newaxis, :, :], edge_list[1].shape[0], axis=0) edge_weights = np.vstack([edge_weights_horizontal, edge_weights_vertical]) # do inference res = lp_general_graph(-x.reshape(-1, n_states), edges, edge_weights) for inference_method in get_installed(["lp", "ad3"]): # same inference through CRF inferface crf = DirectionalGridCRF(inference_method=inference_method) crf.initialize(X, Y) w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) y_pred = crf.inference(x, w, relaxed=True) if isinstance(y_pred, tuple): # ad3 produces an integer result if it found the exact solution assert_array_almost_equal(res[0], y_pred[0].reshape(-1, n_states)) assert_array_almost_equal(res[1], y_pred[1]) assert_array_equal(y, np.argmax(y_pred[0], axis=-1)) for inference_method in get_installed(["lp", "ad3", "qpbo"]): # again, this time discrete predictions only crf = DirectionalGridCRF(inference_method=inference_method) crf.initialize(X, Y) w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) y_pred = crf.inference(x, w, relaxed=False) assert_array_equal(y, y_pred)
def test_joint_feature_discrete(): X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) x, y = X[0], Y[0] for inference_method in get_installed(["lp", "ad3", "qpbo"]): crf = DirectionalGridCRF(inference_method=inference_method) crf.initialize(X, Y) joint_feature_y = crf.joint_feature(x, y) assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) # first horizontal, then vertical # we trust the unaries ;) pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states * crf.n_features:].reshape( 2, crf.n_states, crf.n_states) xx, yy = np.indices(y.shape) assert_array_equal(pw_joint_feature_vert, np.diag([9 * 4, 9 * 4, 9 * 4])) vert_joint_feature = np.diag([10 * 3, 10 * 3, 10 * 3]) vert_joint_feature[0, 1] = 10 vert_joint_feature[1, 2] = 10 assert_array_equal(pw_joint_feature_horz, vert_joint_feature)