def train_models(Gs, signals, lrn): # Create data Gx, Gy = ds.perturbated_graphs(Gs['params'], Gs['create'], Gs['destroy'], pct=Gs['pct'], seed=SEED) data = ds.LinearDS2GSLinksPert(Gx, Gy, signals['samples'], signals['L'], signals['deltas'], median=signals['median'], same_coeffs=signals['same_coeffs']) data.to_unit_norm() data.add_noise(signals['noise'], test_only=signals['test_only']) sign_dist = np.median(np.linalg.norm(data.train_X-data.train_Y, axis=1)) print('Distance signals:', sign_dist) data.to_tensor() data_state = data.state_dict() # med_err = np.zeros(N_EXPS) # epochs = np.zeros(N_EXPS) # mse = np.zeros(N_EXPS) models_states = [] for i, exp in enumerate(EXPS): model = create_model(Gx, Gy, exp, lrn) # Fit models epochs, _, _ = model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y) _, med_error, mse_error = model.test(data.test_X, data.test_Y) models_states.append(model.state_dict()) print('Original Graph {}-{} ({}): mse {} - MedianErr: {}' .format(i, exp['type'], model.count_params(), mse_error, med_error)) print() return data_state, models_states, Gx, Gy
def test_model(id, signals, nn_params, model_params): Gx, Gy = data_sets.perturbated_graphs(signals['g_params'], signals['eps1'], signals['eps2'], pct=signals['pct'], perm=signals['perm']) # Define the data model data = data_sets.LinearDS2GSLinksPert(Gx, Gy, signals['N_samples'], signals['L_filter'], signals['g_params']['k'], # k is n_delts median=signals['median']) data.to_unit_norm() data.add_noise(signals['noise'], test_only=signals['test_only']) data.to_tensor() Gx.compute_laplacian('normalized') Gy.compute_laplacian('normalized') archit = GIGOArch(Gx.L.todense(), Gy.L.todense(), nn_params['Fi'], nn_params['Fo'], nn_params['Ki'], nn_params['Ko'], nn_params['C'], nn_params['nonlin'], nn_params['last_act_fn'], nn_params['batch_norm'], nn_params['arch_info']) model_params['arch'] = archit model = Model(**model_params) t_init = time.time() epochs, _, _ = model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y) t_conv = time.time() - t_init mean_err, med_err, mse = model.test(data.test_X, data.test_Y) print("DONE {}: MSE={} - Mean Err={} - Median Err={} - Params={} - t_conv={} - epochs={}".format( id, mse, mean_err, med_err, model.count_params(), round(t_conv, 4), epochs )) return mse, mean_err, med_err, model.count_params(), t_conv, epochs
def test_other_graphs(Gs, signals, lrn, data_state, models_state): med_err = np.zeros((Gs['n_graphs'], N_EXPS)) mse = np.zeros((Gs['n_graphs'], N_EXPS)) for i in range(Gs['n_graphs']): Gx, Gy = ds.perturbated_graphs(Gs['params'], Gs['create'], Gs['destroy'], pct=Gs['pct'], seed=SEED) data = ds.LinearDS2GSLinksPert(Gx, Gy, signals['samples'], signals['L'], signals['deltas'], median=signals['median'], same_coeffs=signals['same_coeffs']) data.load_state_dict(data_state, unit_norm=True) data.add_noise(signals['noise'], test_only=signals['test_only']) sign_dist = np.median(np.linalg.norm(data.train_X-data.train_Y, axis=1)) print('Distance signals:', sign_dist) data.to_tensor() # Create models for j, exp in enumerate(EXPS): model = create_model(Gx, Gy, exp, lrn) model.load_state_dict(models_state[j]) _, med_err[i, j], mse[i, j] = model.test(data.test_X, data.test_Y) print('Graph {}: {}-{} ({}): mse {} - MedianErr: {}' .format(i, j, exp['type'], model.count_params(), mse[i, j], med_err[i, j])) return med_err, mse
def test_permutated_S(self): n_samps = [50, 20, 20] L = 6 n_delts = self.G_params['k'] Gx, Gy = ds.perturbated_graphs(self.G_params, 0, 0, perm=True, seed=SEED) data = ds.LinearDS2GSLinksPert(Gx, Gy, n_samps, L, n_delts) P = data.Gy.info['perm_matrix'] self.assertFalse(np.array_equal(data.Hx, data.Hy)) self.assertFalse(np.array_equal(data.train_Sx, data.train_Sy)) self.assertFalse(np.array_equal(data.val_Sx, data.val_Sy)) self.assertFalse(np.array_equal(data.test_Sx, data.test_Sy)) self.assertTrue(np.array_equal(data.train_Sx, data.train_Sy.dot(P))) self.assertTrue(np.array_equal(data.val_Sx, data.val_Sy.dot(P))) self.assertTrue(np.array_equal(data.test_Sx, data.test_Sy.dot(P)))
def test_model(id, signals, nn_params, model_params): Gx, Gy = data_sets.perturbated_graphs(signals['g_params'], signals['eps1'], signals['eps2'], pct=signals['pct'], perm=signals['perm']) # Define the data model data = data_sets.LinearDS2GSLinksPert( Gx, Gy, signals['N_samples'], signals['L_filter'], signals['g_params']['k'], # k is n_delts median=signals['median']) data.to_unit_norm() data.add_noise(signals['noise'], test_only=signals['test_only']) data.to_tensor() if nn_params['arch_type'] == "basic": Gx.compute_laplacian('normalized') archit = BasicArch(Gx.L.todense(), nn_params['F'], nn_params['K'], nn_params['M'], nn_params['nonlin'], ARCH_INFO) elif nn_params['arch_type'] == "mlp": archit = MLP(nn_params['F'], nn_params['nonlin'], ARCH_INFO) elif nn_params['arch_type'] == "conv": archit = ConvNN(N, nn_params['F'], nn_params['K'], nn_params['nonlin'], nn_params['M'], ARCH_INFO) elif nn_params['arch_type'] == "linear": archit = MLP(nn_params['F'], nn_params['nonlin'], ARCH_INFO) else: raise RuntimeError("arch_type has to be either basic, mlp or conv") model_params['arch'] = archit model = Model(**model_params) t_init = time.time() epochs, _, _ = model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y) t_conv = time.time() - t_init mean_err, med_err, mse = model.test(data.test_X, data.test_Y) print( "DONE {}: MSE={} - Mean Err={} - Median Err={} - Params={} - t_conv={} - epochs={}" .format(id, mse, mean_err, med_err, model.count_params(), round(t_conv, 4), epochs), flush=True) return mse, mean_err, med_err, model.count_params(), t_conv, epochs
def test_same_S(self): n_samps = [50, 20, 20] L = 6 n_delts = self.G_params['k'] data = ds.LinearDS2GSLinksPert(self.Gx, self.Gy, n_samps, L, n_delts) self.assertFalse(np.array_equal(data.Hx, data.Hy)) self.assertTrue(np.array_equal(data.train_Sx, data.train_Sy)) self.assertTrue(np.array_equal(data.val_Sx, data.val_Sy)) self.assertTrue(np.array_equal(data.test_Sx, data.test_Sy)) for i in range(n_samps[0]): self.assertLessEqual(np.sum(data.train_Sy[i,:][data.train_Sy[i,:]!=0]), n_delts) for i in range(n_samps[1]): self.assertLessEqual(np.sum(data.train_Sx[i,:][data.train_Sx[i,:]!=0]), n_delts) for i in range(n_samps[2]): self.assertLessEqual(np.sum(data.train_Sy[i,:][data.train_Sy[i,:]!=0]), n_delts)
def test_original_graphs(data_state, models_state, Gx, Gy, signals, lrn): data = ds.LinearDS2GSLinksPert(Gx, Gy, signals['samples'], signals['L'], signals['deltas'], median=signals['median'], same_coeffs=signals['same_coeffs']) data.load_state_dict(data_state, unit_norm=True) data.add_noise(signals['noise'], test_only=signals['test_only']) sign_dist = np.median(np.linalg.norm(data.train_X-data.train_Y, axis=1)) print('Distance signals:', sign_dist) data.to_tensor() for i, exp in enumerate(EXPS): model = create_model(Gx, Gy, exp, lrn) model.load_state_dict(models_state[i]) _, med_error_train, _ = model.test(data.train_X, data.train_Y) _, med_error_test, _ = model.test(data.test_X, data.test_Y) print('Original (debug) Graph {}-{} ({}): TrainErr {} - TestErr: {}' .format(i, exp['type'], model.count_params(), med_error_train, med_error_test)) print()
def run(id, Gs, Signals, lrn, samples): Gx, Gy = ds.perturbated_graphs(Gs['params'], Gs['pct_val'][0], Gs['pct_val'][1], pct=Gs['pct'], perm=Gs['perm'], seed=SEED) data = ds.LinearDS2GSLinksPert(Gx, Gy, samples, Signals['L'], Signals['deltas'], median=Signals['median'], same_coeffs=Signals['same_coeffs']) data.to_unit_norm() data.add_noise(Signals['noise'], test_only=Signals['test_only']) data.to_tensor() params = np.zeros(N_EXPS) epochs = np.zeros(N_EXPS) med_err = np.zeros(N_EXPS) mse = np.zeros(N_EXPS) for i, exp in enumerate(EXPS): if exp['type'] == 'Linear': model = LinearModel(exp['N']) elif exp['type'] == 'Enc_Dec': clust_x = gc.MultiResGraphClustering(Gx, exp['n_enc'], k=exp['n_enc'][-1], up_method=exp['downs']) clust_y = gc.MultiResGraphClustering(Gy, exp['n_dec'], k=exp['n_enc'][-1], up_method=exp['ups']) net = GraphEncoderDecoder(exp['f_enc'], clust_x.sizes, clust_x.Ds, exp['f_dec'], clust_y.sizes, clust_y.Us, exp['f_conv'], As_dec=clust_y.As, As_enc=clust_x.As, act_fn=lrn['af'], K_dec=exp['K_dec'], K_enc=exp['K_enc'], last_act_fn=lrn['laf'], ups=exp['ups'], downs=exp['downs']) elif exp['type'] == 'AutoConv': net = ConvAutoencoder(exp['f_enc'], exp['kernel_enc'], exp['f_dec'], exp['kernel_dec']) elif exp['type'] == 'AutoFC': net = FCAutoencoder(exp['n_enc'], exp['n_dec'], bias=exp['bias']) else: raise RuntimeError('Unknown experiment type') if exp['type'] != 'Linear': model = Model(net, learning_rate=lrn['lr'], decay_rate=lrn['dr'], batch_size=lrn['batch'], epochs=lrn['epochs'], eval_freq=EVAL_F, max_non_dec=lrn['non_dec'], verbose=VERBOSE, early_stop=exp['early_stop']) epochs[i], _, _ = model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y) _, med_err[i], mse[i] = model.test(data.test_X, data.test_Y) params[i] = model.count_params() print('G: {}, {}-{} ({}): epochs {} - mse {} - MedianErr: {}'.format( id, i, exp['type'], params[i], epochs[i], mse[i], med_err[i])) return params, med_err, mse