def test_set_coordinates(self): G = graphs.FullConnected() coords = self._rs.uniform(size=(G.N, 2)) G.set_coordinates(coords) G.set_coordinates('ring2D') G.set_coordinates('random2D') G.set_coordinates('random3D') G.set_coordinates('spring') G.set_coordinates('spring', dim=3) G.set_coordinates('spring', dim=3, pos=G.coords) self.assertRaises(AttributeError, G.set_coordinates, 'community2D') G = graphs.Community() G.set_coordinates('community2D') self.assertRaises(ValueError, G.set_coordinates, 'invalid')
:param L: List of coarsened laplacians :param poolings: Pooling values :return: New list with only useful laplacians """ j = 0 Lnew = list() Lnew.append(L[0]) for p in poolings: j += int(np.log2(p)) if p > 1 else 0 Lnew.append(L[j]) return Lnew if __name__ == '__main__': from pygsp import graphs from synthetic_data.data_generation import generate_spectral_samples from graph_utils.laplacian import initialize_laplacian_tensor N = 100 G = graphs.Community(N) X, labels = generate_spectral_samples(5, 128, G, f_h=20, f_l=50, lambda_h=80, lambda_l=10) graphs, perm = coarsen(G.A, levels=3) X = perm_data(X, perm) num_levels = np.log2(np.prod([2, 4, 1])).astype(int) adjacencies, perm = coarsen(G.A, levels=num_levels) # Coarsens in powers of 2 L = [initialize_laplacian_tensor(A) for A in adjacencies] print(len(L)) L = keep_pooling_laplacians(L, [2, 4]) print([L.get_shape() for L in L])
def main(_): # Initialize tempdir if FLAGS.action == "eval" and FLAGS.read_dir is not None: FLAGS.log_dir = FLAGS.read_dir else: FLAGS.log_dir = os.path.join(FLAGS.log_dir, FLAGS.model_type) exp_n = _last_exp( FLAGS.log_dir) + 1 if FLAGS.action == "train" else _last_exp( FLAGS.log_dir) FLAGS.log_dir = os.path.join(FLAGS.log_dir, "exp_" + str(exp_n)) print(FLAGS.log_dir) if FLAGS.action == "train": if tf.io.gfile.exists(FLAGS.log_dir): tf.gfile.DeleteRecursively(FLAGS.log_dir) tf.io.gfile.makedirs(FLAGS.log_dir) # Initialize data G = graphs.Community(FLAGS.num_vertices, seed=FLAGS.seed) G.compute_laplacian("normalized") # Save graph np.save(os.path.join(FLAGS.log_dir, "graph_weights"), G.W.todense()) # Prepare pooling num_levels = _number_of_pooling_levels(FLAGS.vertex_poolings) error = True while error: try: adjacencies, perm = coarsen( G.A, levels=num_levels) # Coarsens in powers of 2 error = False except IndexError: error = True continue np.save(os.path.join(FLAGS.log_dir, "ordering"), perm) L = [initialize_laplacian_tensor(A) for A in adjacencies] L = keep_pooling_laplacians(L, FLAGS.vertex_poolings) elif FLAGS.action == "eval": W = np.load(os.path.join(FLAGS.log_dir, "graph_weights.npy")) G = graphs.Graph(W) G.compute_laplacian("normalized") perm = np.load(os.path.join(FLAGS.log_dir, "ordering.npy")) if FLAGS.action == "train": #G_nx = nx.from_numpy_matrix(G.W.todense()) if FLAGS.load_data: #num test can't exceed saved array size train_data, train_labels = load_from_npy('train', FLAGS.num_train) else: random.seed(FLAGS.seed) train_data, train_labels = epidemics_generator( g_nx=nx.from_numpy_matrix(G.W.todense()), batch_size=FLAGS.num_train, timesteps=FLAGS.num_frames, initial_nodes=random.sample(range(FLAGS.num_vertices), int(FLAGS.num_vertices / 10))) save_to_npy('train', train_data, train_labels) train_data = perm_data(train_data, perm) train_mb_source = MinibatchSource(train_data, train_labels, repeat=True) if FLAGS.load_data: #num test can't exceed saved array size test_data, test_labels = load_from_npy('test', FLAGS.num_test) else: test_data, test_labels = epidemics_generator( g_nx=nx.from_numpy_matrix(G.W.todense()), batch_size=FLAGS.num_test, timesteps=FLAGS.num_frames, initial_nodes=random.sample(range(FLAGS.num_vertices), int(FLAGS.num_vertices / 10))) save_to_npy('test', test_data, test_labels) test_data = perm_data(test_data, perm) test_mb_source = MinibatchSource(test_data, test_labels, repeat=False) if FLAGS.action == "train": params = vars(FLAGS) with open(os.path.join(FLAGS.log_dir, "params.json"), "w") as f: json.dump(params, f) # Run training and evaluation loop print("Training model...") run_training(L, train_mb_source, test_mb_source) elif FLAGS.action == "eval": print("Evaluating model...") run_eval(test_mb_source) else: raise ValueError("No valid action selected")
T = T_2 return T if __name__ == '__main__': from pygsp import graphs from synthetic_data.data_generation import generate_spectral_samples_hard N = 100 T = 128 f_h = 50 f_l = 15 lambda_h = 80 lambda_l = 15 G = graphs.Community(N, seed=15) x, _ = generate_spectral_samples_hard(1, T, G, f_h, lambda_h, f_l, lambda_l, sigma=2, sigma_n=0.001) # x = hp_hp_sample(T, G, f_h, lambda_h) plot_joint_spectrum(x[0, :, :, 0], G, "hp_hp") plot_temporal_matrix(x[0, :, :, 0], "hp_hp_t") # x = lp_lp_sample(T, G, f_l, lambda_l) plot_joint_spectrum(x[1, :, :, 0], G, "lp_lp") plot_temporal_matrix(x[1, :, :, 0], "lp_lp_t")
def test_community(self): graphs.Community() graphs.Community(comm_density=0.2) graphs.Community(k_neigh=5) graphs.Community(N=100, Nc=3, comm_sizes=[20, 50, 30])
def main(_): # Initialize tempdir if FLAGS.action == "eval" and FLAGS.read_dir is not None: FLAGS.log_dir = FLAGS.read_dir else: FLAGS.log_dir = os.path.join(FLAGS.log_dir, FLAGS.model_type) exp_n = _last_exp( FLAGS.log_dir) + 1 if FLAGS.action == "train" else _last_exp( FLAGS.log_dir) FLAGS.log_dir = os.path.join(FLAGS.log_dir, "exp_" + str(exp_n)) print(FLAGS.log_dir) if FLAGS.action == "train": if tf.gfile.Exists(FLAGS.log_dir): tf.gfile.DeleteRecursively(FLAGS.log_dir) tf.gfile.MakeDirs(FLAGS.log_dir) # Initialize data G = graphs.Community(FLAGS.num_vertices, seed=FLAGS.seed) #, world_density = 0.1) G.compute_laplacian("normalized") # Save graph np.save(os.path.join(FLAGS.log_dir, "graph_weights"), G.W.todense()) # Prepare pooling num_levels = _number_of_pooling_levels(FLAGS.vertex_poolings) error = True while error: try: adjacencies, perm = coarsen( G.A, levels=num_levels) # Coarsens in powers of 2 error = False except IndexError: error = True continue np.save(os.path.join(FLAGS.log_dir, "ordering"), perm) L = [initialize_laplacian_tensor(A) for A in adjacencies] L = keep_pooling_laplacians(L, FLAGS.vertex_poolings) #0 graph if FLAGS.zeros == True: for i in range(len(L)): L[i] *= 0 print(FLAGS.zeros) print("0 Graph \n\n") elif FLAGS.action == "eval": W = np.load(os.path.join(FLAGS.log_dir, "graph_weights.npy")) G = graphs.Graph(W) G.compute_laplacian("normalized") perm = np.load(os.path.join(FLAGS.log_dir, "ordering.npy")) if FLAGS.action == "train": train_data, train_labels = generate_spectral_samples_hard( N=FLAGS.num_train // FLAGS.num_classes, G=G, T=FLAGS.num_frames, f_h=FLAGS.f_h, f_l=FLAGS.f_h, lambda_h=FLAGS.lambda_h, lambda_l=FLAGS.lambda_l, sigma=FLAGS.sigma, sigma_n=FLAGS.sigma_n) train_data = perm_data(train_data, perm) train_mb_source = MinibatchSource(train_data, train_labels, repeat=True) test_data, test_labels = generate_spectral_samples_hard( N=FLAGS.num_test // FLAGS.num_classes, G=G, T=FLAGS.num_frames, f_h=FLAGS.f_h, f_l=FLAGS.f_h, lambda_h=FLAGS.lambda_h, lambda_l=FLAGS.lambda_l, sigma=FLAGS.sigma, sigma_n=FLAGS.sigma_n) test_data = perm_data(test_data, perm) test_mb_source = MinibatchSource(test_data, test_labels, repeat=False) if FLAGS.action == "train": params = vars(FLAGS) with open(os.path.join(FLAGS.log_dir, "params.json"), "w") as f: json.dump(params, f) # Run training and evaluation loop print("Training model...") run_training(L, train_mb_source, test_mb_source) elif FLAGS.action == "eval": print("Evaluating model...") run_eval(test_mb_source) else: raise ValueError("No valid action selected")
def test_Community(): G = graphs.Community()
def test_Community(): G = graphs.Community() needed_attributes_testing(G)