├── .gitignore ├── README.md ├── data ├── ind.citeseer.allx ├── ind.citeseer.ally ├── ind.citeseer.graph ├── ind.citeseer.test.index ├── ind.citeseer.tx ├── ind.citeseer.ty ├── ind.citeseer.x ├── ind.citeseer.y ├── ind.cora.allx ├── ind.cora.ally ├── ind.cora.graph ├── ind.cora.test.index ├── ind.cora.tx ├── ind.cora.ty ├── ind.cora.x ├── ind.cora.y ├── ind.pubmed.allx ├── ind.pubmed.ally ├── ind.pubmed.graph ├── ind.pubmed.test.index ├── ind.pubmed.tx ├── ind.pubmed.ty ├── ind.pubmed.x └── ind.pubmed.y ├── models.py ├── train.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/* 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Graph Attention Networks in JAX 2 | 3 | This repository implements Graph Attention Networks (GATs) in JAX. The code contains the model definition of a main GAT model with two graph attention layers, following the model used in the paper [Graph Attention Networks](https://arxiv.org/pdf/1710.10903.pdf). 4 | 5 | ## Usage 6 | Run 7 | 8 | ```python train.py``` 9 | 10 | to train a model on the Cora dataset. 11 | 12 | ## Good to know 13 | This repository implementents Graph Attention Networks, but it doesn't fully replicate the paper. For example, it doesn't include early stopping or model saving in the training loop. 14 | 15 | ## Cite 16 | If you use this code in your research, please cite the paper: 17 | ``` 18 | @article{ 19 | velickovic2018graph, 20 | title="{Graph Attention Networks}", 21 | author={Veli{\v{c}}kovi{\'{c}}, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Li{\`{o}}, Pietro and Bengio, Yoshua}, 22 | journal={International Conference on Learning Representations}, 23 | year={2018}, 24 | url={https://openreview.net/forum?id=rJXMpikCZ}, 25 | note={accepted as poster}, 26 | } 27 | ``` -------------------------------------------------------------------------------- /data/ind.citeseer.allx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gcucurull/jax-gat/4d48e2b4c7d65e7f4ee9ece094afc48cc78a8268/data/ind.citeseer.allx -------------------------------------------------------------------------------- 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19099 83 | 19637 84 | 19403 85 | 18720 86 | 19526 87 | 18905 88 | 19451 89 | 19408 90 | 18923 91 | 18794 92 | 19322 93 | 19431 94 | 18912 95 | 18841 96 | 19239 97 | 19125 98 | 19258 99 | 19565 100 | 18898 101 | 19482 102 | 19029 103 | 18778 104 | 19096 105 | 19684 106 | 19552 107 | 18765 108 | 19361 109 | 19171 110 | 19367 111 | 19623 112 | 19402 113 | 19327 114 | 19118 115 | 18888 116 | 18726 117 | 19510 118 | 18831 119 | 19490 120 | 19576 121 | 19050 122 | 18729 123 | 18896 124 | 19246 125 | 19012 126 | 18862 127 | 18873 128 | 19193 129 | 19693 130 | 19474 131 | 18953 132 | 19115 133 | 19182 134 | 19269 135 | 19116 136 | 18837 137 | 18872 138 | 19007 139 | 19212 140 | 18798 141 | 19102 142 | 18772 143 | 19660 144 | 19511 145 | 18914 146 | 18886 147 | 19672 148 | 19360 149 | 19213 150 | 18810 151 | 19420 152 | 19512 153 | 18719 154 | 19432 155 | 19350 156 | 19127 157 | 18782 158 | 19587 159 | 18924 160 | 19488 161 | 18781 162 | 19340 163 | 19190 164 | 19383 165 | 19094 166 | 18835 167 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18933 334 | 18935 335 | 19405 336 | 18936 337 | 18945 338 | 18943 339 | 18818 340 | 18797 341 | 19570 342 | 19464 343 | 19428 344 | 19093 345 | 19433 346 | 18986 347 | 19161 348 | 19255 349 | 19157 350 | 19046 351 | 19292 352 | 19434 353 | 19298 354 | 18724 355 | 19410 356 | 19694 357 | 19214 358 | 19640 359 | 19189 360 | 18963 361 | 19218 362 | 19585 363 | 19041 364 | 19550 365 | 19123 366 | 19620 367 | 19376 368 | 19561 369 | 18944 370 | 19706 371 | 19056 372 | 19283 373 | 18741 374 | 19319 375 | 19144 376 | 19542 377 | 18821 378 | 19404 379 | 19080 380 | 19303 381 | 18793 382 | 19306 383 | 19678 384 | 19435 385 | 19519 386 | 19566 387 | 19278 388 | 18946 389 | 19536 390 | 19020 391 | 19057 392 | 19198 393 | 19333 394 | 19649 395 | 19699 396 | 19399 397 | 19654 398 | 19136 399 | 19465 400 | 19321 401 | 19577 402 | 18907 403 | 19665 404 | 19386 405 | 19596 406 | 19247 407 | 19473 408 | 19568 409 | 19355 410 | 18925 411 | 19586 412 | 18982 413 | 19616 414 | 19495 415 | 19612 416 | 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19307 998 | 19288 999 | 19594 1000 | 19271 1001 | -------------------------------------------------------------------------------- /data/ind.pubmed.tx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gcucurull/jax-gat/4d48e2b4c7d65e7f4ee9ece094afc48cc78a8268/data/ind.pubmed.tx -------------------------------------------------------------------------------- /data/ind.pubmed.ty: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gcucurull/jax-gat/4d48e2b4c7d65e7f4ee9ece094afc48cc78a8268/data/ind.pubmed.ty -------------------------------------------------------------------------------- /data/ind.pubmed.x: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gcucurull/jax-gat/4d48e2b4c7d65e7f4ee9ece094afc48cc78a8268/data/ind.pubmed.x -------------------------------------------------------------------------------- /data/ind.pubmed.y: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gcucurull/jax-gat/4d48e2b4c7d65e7f4ee9ece094afc48cc78a8268/data/ind.pubmed.y -------------------------------------------------------------------------------- /models.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | import jax.numpy as np 4 | from jax import lax, random 5 | from jax.nn.initializers import glorot_normal, glorot_uniform 6 | import jax.nn as nn 7 | 8 | 9 | def Dropout(rate): 10 | """ 11 | Layer construction function for a dropout layer with given rate. 12 | This Dropout layer is modified from stax.experimental.Dropout, to use 13 | `is_training` as an argument to apply_fun, instead of defining it at 14 | definition time. 15 | 16 | Arguments: 17 | rate (float): Probability of keeping and element. 18 | """ 19 | def init_fun(rng, input_shape): 20 | return input_shape, () 21 | def apply_fun(params, inputs, is_training, **kwargs): 22 | rng = kwargs.get('rng', None) 23 | if rng is None: 24 | msg = ("Dropout layer requires apply_fun to be called with a PRNG key " 25 | "argument. That is, instead of `apply_fun(params, inputs)`, call " 26 | "it like `apply_fun(params, inputs, rng)` where `rng` is a " 27 | "jax.random.PRNGKey value.") 28 | raise ValueError(msg) 29 | keep = random.bernoulli(rng, rate, inputs.shape) 30 | outs = np.where(keep, inputs / rate, 0) 31 | # if not training, just return inputs and discard any computation done 32 | out = lax.cond(is_training, outs, lambda x: x, inputs, lambda x: x) 33 | return out 34 | return init_fun, apply_fun 35 | 36 | 37 | def GraphAttentionLayer(out_dim, dropout, residual=False): 38 | """ 39 | Layer constructor function for a Graph Attention layer. 40 | """ 41 | _, drop_fun = Dropout(dropout) 42 | def init_fun(rng, input_shape): 43 | output_shape = input_shape[:-1] + (out_dim,) 44 | k1, k2, k3, k4 = random.split(rng, 4) 45 | W_init = glorot_uniform() 46 | # projection 47 | W = W_init(k1, (input_shape[-1], out_dim)) 48 | 49 | a_init = glorot_uniform() 50 | a1 = a_init(k2, (out_dim, 1)) 51 | a2 = a_init(k3, (out_dim, 1)) 52 | 53 | return output_shape, (W, a1, a2) 54 | 55 | def apply_fun(params, x, adj, rng, activation=nn.elu, is_training=False, 56 | **kwargs): 57 | W, a1, a2 = params 58 | k1, k2, k3 = random.split(rng, 3) 59 | x = drop_fun(None, x, is_training=is_training, rng=k1) 60 | x = np.dot(x, W) 61 | 62 | f_1 = np.dot(x, a1) 63 | f_2 = np.dot(x, a2) 64 | logits = f_1 + f_2.T 65 | coefs = nn.softmax( 66 | nn.leaky_relu(logits, negative_slope=0.2) + np.where(adj, 0., -1e9)) 67 | 68 | coefs = drop_fun(None, coefs, is_training=is_training, rng=k2) 69 | x = drop_fun(None, x, is_training=is_training, rng=k3) 70 | 71 | ret = np.matmul(coefs, x) 72 | 73 | return activation(ret) 74 | 75 | return init_fun, apply_fun 76 | 77 | 78 | def MultiHeadLayer(nheads: int, nhid: int, dropout: float, residual: bool=False, 79 | last_layer: bool=False): 80 | layer_funs, layer_inits = [], [] 81 | for head_i in range(nheads): 82 | att_init, att_fun = GraphAttentionLayer(nhid, dropout=dropout, 83 | residual=residual) 84 | layer_inits.append(att_init) 85 | layer_funs.append(att_fun) 86 | 87 | def init_fun(rng, input_shape): 88 | params = [] 89 | for att_init_fun in layer_inits: 90 | rng, layer_rng = random.split(rng) 91 | layer_shape, param = att_init_fun(layer_rng, input_shape) 92 | params.append(param) 93 | input_shape = layer_shape 94 | if not last_layer: 95 | # multiply by the number of heads 96 | input_shape = input_shape[:-1] + (input_shape[-1]*len(layer_inits),) 97 | return input_shape, params 98 | 99 | def apply_fun(params, x, adj, is_training=False, **kwargs): 100 | rng = kwargs.pop('rng', None) 101 | layer_outs = [] 102 | assert len(params) == nheads 103 | for head_i in range(nheads): 104 | layer_params = params[head_i] 105 | rng, _ = random.split(rng) 106 | layer_outs.append(layer_funs[head_i]( 107 | layer_params, x, adj, rng=rng, is_training=is_training)) 108 | if not last_layer: 109 | x = np.concatenate(layer_outs, axis=1) 110 | else: 111 | # average last layer heads 112 | x = np.mean(np.stack(layer_outs), axis=0) 113 | 114 | return x 115 | 116 | return init_fun, apply_fun 117 | 118 | 119 | def GAT(nheads: List[int], nhid: List[int], nclass: int, dropout: float, 120 | residual: bool=False): 121 | """ 122 | Graph Attention Network model definition. 123 | """ 124 | 125 | init_funs = [] 126 | attn_funs = [] 127 | 128 | nhid += [nclass] 129 | for layer_i in range(len(nhid)): 130 | last = layer_i == len(nhid) - 1 131 | layer_init, layer_fun = MultiHeadLayer(nheads[layer_i], nhid[layer_i], 132 | dropout=dropout, residual=residual, 133 | last_layer=last) 134 | attn_funs.append(layer_fun) 135 | init_funs.append(layer_init) 136 | 137 | def init_fun(rng, input_shape): 138 | params = [] 139 | for i, init_fun in enumerate(init_funs): 140 | rng, layer_rng = random.split(rng) 141 | layer_shape, param = init_fun(layer_rng, input_shape) 142 | params.append(param) 143 | input_shape = layer_shape 144 | return input_shape, params 145 | 146 | def apply_fun(params, x, adj, is_training=False, **kwargs): 147 | rng = kwargs.pop('rng', None) 148 | rngs = random.split(rng, len(attn_funs)) 149 | 150 | for i, layer_fun in enumerate(attn_funs): 151 | x = layer_fun(params[i], x, adj, rng=rngs[i], is_training=is_training) 152 | 153 | return nn.log_softmax(x) 154 | 155 | return init_fun, apply_fun 156 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import time 3 | 4 | import jax 5 | import jax.numpy as np 6 | from jax import jit, grad, random 7 | from jax.experimental import optimizers 8 | 9 | from utils import load_data 10 | from models import GAT 11 | 12 | @jit 13 | def loss(params, batch): 14 | """ 15 | The idxes of the batch indicate which nodes are used to compute the loss. 16 | """ 17 | inputs, targets, adj, is_training, rng, idx = batch 18 | preds = predict_fun(params, inputs, adj, is_training=is_training, rng=rng) 19 | ce_loss = -np.mean(np.sum(preds[idx] * targets[idx], axis=1)) 20 | l2_loss = 5e-4 * optimizers.l2_norm(params)**2 # tf doesn't use sqrt 21 | return ce_loss + l2_loss 22 | 23 | 24 | @jit 25 | def accuracy(params, batch): 26 | inputs, targets, adj, is_training, rng, idx = batch 27 | target_class = np.argmax(targets, axis=1) 28 | predicted_class = np.argmax(predict_fun(params, inputs, adj, 29 | is_training=is_training, rng=rng), axis=1) 30 | return np.mean(predicted_class[idx] == target_class[idx]) 31 | 32 | 33 | @jit 34 | def loss_accuracy(params, batch): 35 | inputs, targets, adj, is_training, rng, idx = batch 36 | preds = predict_fun(params, inputs, adj, is_training=is_training, rng=rng) 37 | target_class = np.argmax(targets, axis=1) 38 | predicted_class = np.argmax(preds, axis=1) 39 | ce_loss = -np.mean(np.sum(preds[idx] * targets[idx], axis=1)) 40 | acc = np.mean(predicted_class[idx] == target_class[idx]) 41 | return ce_loss, acc 42 | 43 | 44 | if __name__ == "__main__": 45 | parser = argparse.ArgumentParser() 46 | parser.add_argument('--seed', type=int, default=0) 47 | parser.add_argument('--hidden', type=int, default=16) 48 | parser.add_argument('--epochs', type=int, default=400) 49 | parser.add_argument('--dropout', type=float, default=0.5) 50 | parser.add_argument('--lr', type=float, default=0.005) 51 | args = parser.parse_args() 52 | 53 | # Load data 54 | adj, features, labels, idx_train, idx_val, idx_test = load_data() 55 | 56 | rng_key = random.PRNGKey(args.seed) 57 | step_size = args.lr 58 | num_epochs = args.epochs 59 | n_nodes = adj.shape[0] 60 | n_feats = features.shape[1] 61 | 62 | # GAT params 63 | nheads = [8, 1] 64 | nhid = [8] 65 | dropout = args.dropout # probability of keeping 66 | residual = False 67 | 68 | init_fun, predict_fun = GAT(nheads=nheads, 69 | nhid=nhid, 70 | nclass=labels.shape[1], 71 | dropout=dropout, 72 | residual=residual) 73 | 74 | input_shape = (-1, n_nodes, n_feats) 75 | rng_key, init_key = random.split(rng_key) 76 | _, init_params = init_fun(init_key, input_shape) 77 | 78 | opt_init, opt_update, get_params = optimizers.adam(step_size) 79 | 80 | @jit 81 | def update(i, opt_state, batch): 82 | params = get_params(opt_state) 83 | return opt_update(i, grad(loss)(params, batch), opt_state) 84 | 85 | opt_state = opt_init(init_params) 86 | 87 | print("\nStarting training...") 88 | for epoch in range(num_epochs): 89 | start_time = time.time() 90 | batch = (features, labels, adj, True, rng_key, idx_train) 91 | opt_state = update(epoch, opt_state, batch) 92 | 93 | params = get_params(opt_state) 94 | eval_batch = (features, labels, adj, False, rng_key, idx_val) 95 | train_batch = (features, labels, adj, False, rng_key, idx_train) 96 | train_loss, train_acc = loss_accuracy(params, train_batch) 97 | val_loss, val_acc = loss_accuracy(params, eval_batch) 98 | epoch_time = time.time() - start_time 99 | print((f"Iter {epoch}/{num_epochs} ({epoch_time:.4f} s) train_loss:" 100 | f"{train_loss:.4f}, train_acc: {train_acc:.4f}, val_loss:" 101 | f"{val_loss:.4f}, val_acc: {val_acc:.4f}")) 102 | 103 | # new random key at each iteration, othwerwise dropout uses always 104 | # the same mask 105 | rng_key, _ = random.split(rng_key) 106 | 107 | # now run on the test set 108 | test_batch = (features, labels, adj, False, rng_key, idx_test) 109 | test_acc = accuracy(params, test_batch) 110 | print(f'Test set acc: {test_acc}') -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import pickle as pkl 3 | from pathlib import Path 4 | 5 | import numpy as np 6 | import scipy.sparse as sp 7 | import networkx as nx 8 | 9 | 10 | def encode_onehot(labels): 11 | classes = set(labels) 12 | classes_dict = {c: np.identity(len(classes))[i, :] for i, c in 13 | enumerate(classes)} 14 | labels_onehot = np.array(list(map(classes_dict.get, labels)), 15 | dtype=np.int32) 16 | return labels_onehot 17 | 18 | 19 | def parse_index_file(filename): 20 | """Parse index file.""" 21 | index = [] 22 | for line in open(filename): 23 | index.append(int(line.strip())) 24 | return index 25 | 26 | 27 | def load_data_old(path: Path=Path('data_old/cora/'), dataset: str='cora'): 28 | """ 29 | Load citation network dataset (cora only for now). 30 | This function has been adapted from https://github.com/tkipf/pygcn 31 | """ 32 | print('Loading {} dataset...'.format(dataset)) 33 | 34 | idx_features_labels = np.genfromtxt("{}.content".format(path / dataset), 35 | dtype=np.dtype(str)) 36 | features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32) 37 | labels = encode_onehot(idx_features_labels[:, -1]) 38 | 39 | # build graph 40 | idx = np.array(idx_features_labels[:, 0], dtype=np.int32) 41 | idx_map = {j: i for i, j in enumerate(idx)} 42 | edges_unordered = np.genfromtxt("{}.cites".format(path / dataset), 43 | dtype=np.int32) 44 | edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), 45 | dtype=np.int32).reshape(edges_unordered.shape) 46 | adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), 47 | shape=(labels.shape[0], labels.shape[0]), 48 | dtype=np.float32) 49 | 50 | # build symmetric adjacency matrix 51 | adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj) 52 | 53 | features = normalize(features) 54 | adj = normalize(adj + sp.eye(adj.shape[0])) 55 | 56 | idx_train = list(range(140)) 57 | idx_val = list(range(200, 500)) 58 | idx_test = list(range(500, 1500)) 59 | 60 | features = np.array(features.todense()) 61 | 62 | # JAX doesn't support sparse matrices yet 63 | adj = np.asarray(adj.todense()) 64 | 65 | return adj, features, labels, idx_train, idx_val, idx_test 66 | 67 | 68 | def normalize(mx): 69 | """Row-normalize sparse matrix""" 70 | rowsum = np.array(mx.sum(1)) 71 | r_inv = np.power(rowsum, -1).flatten() 72 | r_inv[np.isinf(r_inv)] = 0. 73 | r_mat_inv = sp.diags(r_inv) 74 | mx = r_mat_inv.dot(mx) 75 | return mx 76 | 77 | 78 | def preprocess_features(features): 79 | """Row-normalize feature matrix.""" 80 | rowsum = np.array(features.sum(1)) 81 | r_inv = np.power(rowsum, -1).flatten() 82 | r_inv[np.isinf(r_inv)] = 0. 83 | r_mat_inv = sp.diags(r_inv) 84 | features = r_mat_inv.dot(features) 85 | return features 86 | 87 | 88 | def normalize_adj(adj): 89 | """Symmetrically normalize adjacency matrix.""" 90 | adj = sp.coo_matrix(adj) 91 | rowsum = np.array(adj.sum(1)) 92 | d_inv_sqrt = np.power(rowsum, -0.5).flatten() 93 | d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. 94 | d_mat_inv_sqrt = sp.diags(d_inv_sqrt) 95 | return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() 96 | 97 | 98 | def preprocess_adj(adj): 99 | """Preprocessing of adjacency matrix for simple GCN model.""" 100 | adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0])) 101 | return adj_normalized 102 | 103 | 104 | def load_data(dataset_str: str = 'cora'): 105 | """ 106 | Loads input data from gcn/data directory 107 | ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object; 108 | ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object; 109 | ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training instances 110 | (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object; 111 | ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object; 112 | ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object; 113 | ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object; 114 | ind.dataset_str.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict 115 | object; 116 | ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object. 117 | All objects above must be saved using python pickle module. 118 | :param dataset_str: Dataset name 119 | :return: All data input files loaded (as well the training/test data). 120 | """ 121 | names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] 122 | objects = [] 123 | for i in range(len(names)): 124 | with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: 125 | if sys.version_info > (3, 0): 126 | objects.append(pkl.load(f, encoding='latin1')) 127 | else: 128 | objects.append(pkl.load(f)) 129 | 130 | x, y, tx, ty, allx, ally, graph = tuple(objects) 131 | test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset_str)) 132 | test_idx_range = np.sort(test_idx_reorder) 133 | 134 | if dataset_str == 'citeseer': 135 | # Fix citeseer dataset (there are some isolated nodes in the graph) 136 | # Find isolated nodes, add them as zero-vecs into the right position 137 | test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) 138 | tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) 139 | tx_extended[test_idx_range-min(test_idx_range), :] = tx 140 | tx = tx_extended 141 | ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) 142 | ty_extended[test_idx_range-min(test_idx_range), :] = ty 143 | ty = ty_extended 144 | 145 | features = sp.vstack((allx, tx)).tolil() 146 | features[test_idx_reorder, :] = features[test_idx_range, :] 147 | adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) 148 | 149 | labels = np.vstack((ally, ty)) 150 | labels[test_idx_reorder, :] = labels[test_idx_range, :] 151 | 152 | idx_test = test_idx_range.tolist() 153 | idx_train = range(len(y)) 154 | idx_val = range(len(y), len(y)+500) 155 | 156 | # features = normalize(features) 157 | # adj = normalize(adj.astype(np.float32) + sp.eye(adj.shape[0])) 158 | features = preprocess_features(features) 159 | adj = preprocess_adj(adj) 160 | 161 | # JAX doesn't support sparse matrices yet 162 | adj = np.asarray(adj.todense()) 163 | features = np.asarray(features.todense()) 164 | 165 | return adj, features, labels, list(idx_train), list(idx_val), idx_test --------------------------------------------------------------------------------