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_binary_blocks_one_slack_graph(): #testing cutting plane ssvm on easy binary dataset # generate graphs explicitly for each example print("testing %s" % inference_method) X, Y = generate_blocks(n_samples=3) crf = GraphCRF(inference_method=inference_method) clf = OneSlackSSVM(model=crf, max_iter=100, C=1, check_constraints=True, break_on_bad=True, n_jobs=1, tol=.1) x1, x2, x3 = X y1, y2, y3 = Y n_states = len(np.unique(Y)) # delete some rows to make it more fun x1, y1 = x1[:, :-1], y1[:, :-1] x2, y2 = x2[:-1], y2[:-1] # generate graphs X_ = [x1, x2, x3] G = [make_grid_edges(x) for x in X_] # reshape / flatten x and y X_ = [x.reshape(-1, n_states) for x in X_] Y = [y.ravel() for y in [y1, y2, y3]] X = zip(X_, G) clf.fit(X, Y) Y_pred = clf.predict(X) for y, y_pred in zip(Y, Y_pred): assert_array_equal(y, y_pred)
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, 0, 1, 0, -4, 0]) # unary # pairwise crf = GridCRF(inference_method=inference_method) crf.initialize(X, Y) 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) 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) # returned y_hat is one-hot version of other if isinstance(const_cont[0], tuple): 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_binary_blocks_cutting_plane(): #testing cutting plane ssvm on easy binary dataset # generate graphs explicitly for each example for inference_method in get_installed(["dai", "lp", "qpbo", "ad3", 'ogm']): print("testing %s" % inference_method) X, Y = generate_blocks(n_samples=3) crf = GraphCRF(inference_method=inference_method) clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True, break_on_bad=False, n_jobs=1) x1, x2, x3 = X y1, y2, y3 = Y n_states = len(np.unique(Y)) # delete some rows to make it more fun x1, y1 = x1[:, :-1], y1[:, :-1] x2, y2 = x2[:-1], y2[:-1] # generate graphs X_ = [x1, x2, x3] G = [make_grid_edges(x) for x in X_] # reshape / flatten x and y X_ = [x.reshape(-1, n_states) for x in X_] Y = [y.ravel() for y in [y1, y2, y3]] X = zip(X_, G) clf.fit(X, Y) Y_pred = clf.predict(X) for y, y_pred in zip(Y, Y_pred): assert_array_equal(y, y_pred)
def test_binary_blocks(): X, Y = generate_blocks(n_samples=10) crf = GridCRF() clf = StructuredPerceptron(model=crf, max_iter=40) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_binary_blocks_one_slack_graph(): #testing cutting plane ssvm on easy binary dataset # generate graphs explicitly for each example X, Y = generate_blocks(n_samples=3) crf = GraphCRF(inference_method=inference_method) clf = OneSlackSSVM(model=crf, max_iter=100, C=1, check_constraints=True, break_on_bad=True, n_jobs=1, tol=.1) x1, x2, x3 = X y1, y2, y3 = Y n_states = len(np.unique(Y)) # delete some rows to make it more fun x1, y1 = x1[:, :-1], y1[:, :-1] x2, y2 = x2[:-1], y2[:-1] # generate graphs X_ = [x1, x2, x3] G = [make_grid_edges(x) for x in X_] # reshape / flatten x and y X_ = [x.reshape(-1, n_states) for x in X_] Y = [y.ravel() for y in [y1, y2, y3]] X = list(zip(X_, G)) clf.fit(X, Y) Y_pred = clf.predict(X) for y, y_pred in zip(Y, Y_pred): assert_array_equal(y, y_pred)
def test_max_product_binary_blocks(): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] w = np.array([1, 0, 0, 1, 0, -4, 0]) # unary # pairwise crf = GridCRF(inference_method="max-product") crf.initialize(X, Y) y_hat = crf.inference(x, w) assert_array_equal(y, y_hat)
def test_binary_blocks_batches_n_slack(): #testing cutting plane ssvm on easy binary dataset X, Y = generate_blocks(n_samples=5) crf = GridCRF(inference_method=inference_method) clf = NSlackSSVM(model=crf, max_iter=20, batch_size=1, C=100) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_binary_blocks_crf_n8_lp(): X, Y = generate_blocks(n_samples=1, noise=1) x, y = X[0], Y[0] w = np.array([1, 0, 0, 1, 1, -1.4, 1]) # unary # pairwise crf = GridCRF(neighborhood=8) crf.initialize(X, Y) y_hat = crf.inference(x, w) assert_array_equal(y, y_hat)
def test_binary_blocks(): #testing subgradient ssvm on easy binary dataset X, Y = generate_blocks(n_samples=5) crf = GridCRF(inference_method=inference_method) clf = SubgradientSSVM(model=crf) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_binary_blocks_cutting_plane(): #testing cutting plane ssvm on easy binary dataset X, Y = generate_blocks(n_samples=5) crf = GridCRF(inference_method=inference_method) clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True, break_on_bad=False) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_binary_blocks_perceptron_online(): #testing subgradient ssvm on easy binary dataset X, Y = generate_blocks(n_samples=10) inference_method = get_installed(['qpbo', 'ad3', 'lp'])[0] crf = GridCRF(inference_method=inference_method) clf = StructuredPerceptron(model=crf, max_iter=20) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_binary_blocks(): #testing subgradient ssvm on easy binary dataset X, Y = generate_blocks(n_samples=5) crf = GridCRF(inference_method=inference_method) clf = SubgradientSSVM(model=crf, C=100, learning_rate=1, decay_exponent=1, momentum=0, decay_t0=10) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_blocks_crf_unaries(): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] unary_weights = np.repeat(np.eye(2), 2, axis=0) pairwise_weights = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) w = np.hstack([unary_weights.ravel(), pairwise_weights]) crf = LatentGridCRF(n_states_per_label=2, n_labels=2, n_features=2) h_hat = crf.inference(x, w) assert_array_equal(h_hat / 2, np.argmax(x, axis=-1))
def test_loss_augmentation(): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] w = np.array([1, 0, 0, 1, 0, -4, 0]) # unary # pairwise crf = GridCRF() crf.initialize(X, Y) y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True) assert_almost_equal(energy + crf.loss(y, y_hat), -np.dot(w, crf.joint_feature(x, y_hat)))
def test_binary_blocks_crf(): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] w = np.array([1, 0, 0, 1, 0, -4, 0]) # unary # pairwise for inference_method in get_installed(["dai", "qpbo", "lp", "ad3", "ogm"]): crf = GridCRF(inference_method=inference_method) crf.initialize(X, Y) y_hat = crf.inference(x, w) assert_array_equal(y, y_hat)
def test_binary_ssvm_attractive_potentials(): # test that submodular SSVM can learn the block dataset X, Y = generate_blocks(n_samples=10) crf = GridCRF(inference_method=inference_method) submodular_clf = NSlackSSVM(model=crf, max_iter=200, C=100, check_constraints=True, negativity_constraint=[5]) submodular_clf.fit(X, Y) Y_pred = submodular_clf.predict(X) assert_array_equal(Y, Y_pred) assert_true(submodular_clf.w[5] < 0)
def test_binary_blocks_crf_n8_lp(): X, Y = generate_blocks(n_samples=1, noise=1) x, y = X[0], Y[0] w = np.array([1, 0, # unary 0, 1, 1, # pairwise -1.4, 1]) crf = GridCRF(neighborhood=8) crf.initialize(X, Y) y_hat = crf.inference(x, w) assert_array_equal(y, y_hat)
def test_binary_blocks_crf(): 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]) for inference_method in get_installed(['dai', 'qpbo', 'lp', 'ad3', 'ogm']): crf = GridCRF(inference_method=inference_method) crf.initialize(X, Y) y_hat = crf.inference(x, w) assert_array_equal(y, y_hat)
def test_blocks_crf(): 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]) crf = LatentGridCRF(n_states_per_label=2, n_labels=2, n_features=2) h_hat = crf.inference(x, w) assert_array_equal(y, h_hat / 2) h = crf.latent(x, y, w) assert_equal(crf.loss(h, h_hat), 0)
def test_blocks_crf_unaries(): X, Y = generate_blocks(n_samples=1) x, _ = X[0], Y[0] unary_weights = np.repeat(np.eye(2), 2, axis=0) pairwise_weights = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) w = np.hstack([unary_weights.ravel(), pairwise_weights]) crf = LatentGridCRF(n_states_per_label=2, n_labels=2, n_features=2) h_hat = crf.inference(x, w) assert_array_equal(h_hat // 2, np.argmax(x, axis=-1))
def test_loss_augmentation(): 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 = GridCRF() crf.initialize(X, Y) y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True) assert_almost_equal(energy + crf.loss(y, y_hat), -np.dot(w, crf.psi(x, y_hat)))
def test_binary_ssvm_attractive_potentials_edgefeaturegraph(inference_method="qpbo"): X, Y = generate_blocks(n_samples=10) crf = GridCRF(inference_method=inference_method) ####### # convert X,Y to EdgeFeatureGraphCRF instances crf_edge = EdgeFeatureGraphCRF(inference_method=inference_method, symmetric_edge_features=[0] ) X_edge = [] Y_edge = [] for i in range(X.shape[0]): unaries = X[i].reshape((-1, 2)) edges = crf._get_edges(X[i]) edge_feats = np.ones((edges.shape[0], 1)) X_edge.append((unaries, edges, edge_feats)) Y_edge.append((Y[i].reshape((-1,)))) submodular_clf_edge = SubgradientSSVM(model=crf_edge, max_iter=100, C=1, verbose=1, zero_constraint=[4,7], negativity_constraint=[5,6], ) # fit the model with non-negativity constraint on the off-diagonal potential submodular_clf_edge.fit(X_edge, Y_edge) assert submodular_clf_edge.w[5] == submodular_clf_edge.w[6] # symmetry constraint on edge features # # # bias doesn't matter # submodular_clf_edge.w += 10*np.ones(submodular_clf_edge.w.shape) # print len(submodular_clf_edge.w), submodular_clf_edge.w Y_pred = submodular_clf_edge.predict(X_edge) assert_array_equal(Y_edge, Y_pred) # try to fit the model with non-negativity constraint on the off-diagonal potential, this time # with inverted sign on the edge features X_edge_neg = [ (x[0], x[1], -x[2]) for x in X_edge ] submodular_clf_edge = SubgradientSSVM(model=crf_edge, max_iter=100, C=1, verbose=1, zero_constraint=[4,7], negativity_constraint=[5,6], ) submodular_clf_edge.fit(X_edge_neg, Y_edge) Y_pred = submodular_clf_edge.predict(X_edge_neg) assert_array_equal(Y_edge, Y_pred)
def test_blocks_crf(): 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]) crf = LatentGridCRF(n_states_per_label=2, n_labels=2, n_features=2) h_hat = crf.inference(x, w) assert_array_equal(y, h_hat // 2) h = crf.latent(x, y, w) assert_equal(crf.loss(h, h_hat), 0)
def test_binary_blocks_cutting_plane_latent_node(): #testing cutting plane ssvm on easy binary dataset # we use the LatentNodeCRF without latent nodes and check that it does the # same as GraphCRF X, Y = generate_blocks(n_samples=3) crf = GraphCRF() clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True, break_on_bad=False, n_jobs=1) x1, x2, x3 = X y1, y2, y3 = Y n_states = len(np.unique(Y)) # delete some rows to make it more fun x1, y1 = x1[:, :-1], y1[:, :-1] x2, y2 = x2[:-1], y2[:-1] # generate graphs X_ = [x1, x2, x3] G = [make_grid_edges(x) for x in X_] # reshape / flatten x and y X_ = [x.reshape(-1, n_states) for x in X_] Y = [y.ravel() for y in [y1, y2, y3]] X = zip(X_, G) clf.fit(X, Y) Y_pred = clf.predict(X) for y, y_pred in zip(Y, Y_pred): assert_array_equal(y, y_pred) latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=0) latent_svm = LatentSSVM(NSlackSSVM(model=latent_crf, max_iter=20, C=100, check_constraints=True, break_on_bad=False, n_jobs=1), latent_iter=3) X_latent = zip(X_, G, np.zeros(len(X_))) latent_svm.fit(X_latent, Y, H_init=Y) Y_pred = latent_svm.predict(X_latent) for y, y_pred in zip(Y, Y_pred): assert_array_equal(y, y_pred) assert_array_almost_equal(latent_svm.w, clf.w)
def test_max_product_binary_blocks(): 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 = GridCRF(inference_method='max-product') crf.initialize(X, Y) y_hat = crf.inference(x, w) assert_array_equal(y, y_hat)
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) psi = crf.psi(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 psi_cont = crf.psi(x, (y_cont, pw)) assert_array_almost_equal(psi, psi_cont) const = find_constraint(crf, x, y, w, relaxed=False) const_cont = find_constraint(crf, x, y, w, relaxed=True) # dpsi and loss are equal: assert_array_almost_equal(const[1], const_cont[1]) assert_almost_equal(const[2], const_cont[2]) 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]))
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) psi = crf.psi(x, h_hat) psi_d = directional_crf.psi(x, h_hat) assert_array_equal(np.dot(psi, w), np.dot(psi_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 = GridCRF(inference_method=inference_method) crf.initialize(X, Y) psi = crf.psi(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 psi_cont = crf.psi(x, (y_cont, pw)) assert_array_almost_equal(psi, psi_cont) const = find_constraint(crf, x, y, w, relaxed=False) const_cont = find_constraint(crf, x, y, w, relaxed=True) # dpsi and loss are equal: assert_array_almost_equal(const[1], const_cont[1]) assert_almost_equal(const[2], const_cont[2]) # returned y_hat is one-hot version of other if isinstance(const_cont[0], tuple): 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]))