def main(args):
'''
Pipeline for Graph Attention Autoencoder.
'''
G, X, Y, idx_train, idx_val, idx_test = process.load_data(args.dataset)
# add feature dimension size to the beginning of hidden_dims
feature_dim = X.shape[1]
args.hidden_dims = [feature_dim] + args.hidden_dims
# prepare the data
G_tf, S, R = process.prepare_graph_data(G)
# Train the Model
trainer = Trainer(args)
trainer(G_tf, X, S, R)
embeddings, attentions = trainer.infer(G_tf, X, S, R)
# Evaluate the quality of embeddings
classifier = Classifier(vectors=embeddings)
f1s = classifier(idx_train, idx_test, idx_val, Y, seed=0)
print f1s
def main():
saved_graph = os.path.join('assets', 'saved_graphs', 'best_dgi.pickle')
saved_logreg = os.path.join('assets', 'saved_graphs', 'best_logreg.pickle')
dataset = 'cora'
# training params
batch_size = 1
nb_epochs = 10000
patience = 25
lr = 0.001
l2_coef = 0.0
drop_prob = 0.0
hid_units = 512
sparse = True
nonlinearity = 'prelu' # special name to separate parameters
adj, features, labels, idx_train, idx_test, idx_val = process.load_data(dataset)
features, _ = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = labels.shape[1]
adj = process.normalize_adj(adj + sp.eye(adj.shape[0]))
if sparse:
adj = process.sparse_mx_to_torch_sparse_tensor(adj)
else:
adj = (adj + sp.eye(adj.shape[0])).todense()
features = torch.FloatTensor(features[np.newaxis])
if not sparse:
adj = torch.FloatTensor(adj[np.newaxis])
labels = torch.FloatTensor(labels[np.newaxis])
idx_train = torch.LongTensor(idx_train)
idx_val = torch.LongTensor(idx_val)
idx_test = torch.LongTensor(idx_test)
print("Training Nodes: {}, Testing Nodes: {}, Validation Nodes: {}".format(len(idx_train), len(idx_test), len(idx_val)))
model = DGI(ft_size, hid_units, nonlinearity)
optimiser = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=l2_coef)
if torch.cuda.is_available():
print('Using CUDA')
model.cuda()
features = features.cuda()
if sparse:
sp_adj = sp_adj.cuda()
else:
adj = adj.cuda()
labels = labels.cuda()
idx_train = idx_train.cuda()
idx_val = idx_val.cuda()
idx_test = idx_test.cuda()
b_xent = nn.BCEWithLogitsLoss()
xent = nn.CrossEntropyLoss()
cant_wait = 0
best = 1e9
best_t = 0
if not os.path.exists(saved_graph):
pbar = trange(nb_epochs)
for epoch in pbar:
model.train()
optimiser.zero_grad()
idx = np.random.permutation(nb_nodes)
shuf_fts = features[:, idx, :]
lbl_1 = torch.ones(batch_size, nb_nodes)
lbl_2 = torch.zeros(batch_size, nb_nodes)
lbl = torch.cat((lbl_1, lbl_2), 1)
if torch.cuda.is_available():
shuf_fts = shuf_fts.cuda()
lbl = lbl.cuda()
logits = model(features, shuf_fts, adj, sparse, None, None, None)
loss = b_xent(logits, lbl)
pbar.desc = 'Loss: {:.4f}'.format(loss)
if loss < best:
best = loss
best_t = epoch
cnt_wait = 0
torch.save(model.state_dict(), saved_graph)
else:
cant_wait += 1
if cant_wait == patience:
tqdm.write('Early stopping!')
break
loss.backward()
optimiser.step()
print('Loading {}th Epoch'.format(best_t) if best_t else 'Loading Existing Graph')
model.load_state_dict(torch.load(saved_graph))
embeds, _ = model.embed(features, adj, sparse, None)
train_embs = embeds[0, idx_train]
val_embs = embeds[0, idx_val]
test_embs = embeds[0, idx_test]
train_lbls = torch.argmax(labels[0, idx_train], dim=1)
val_lbls = torch.argmax(labels[0, idx_val], dim=1)
test_lbls = torch.argmax(labels[0, idx_test], dim=1)
tot = torch.zeros(1)
if torch.cuda.is_available():
tot = tot.cuda()
accs = []
print("\nValidation:")
pbar = trange(50)
for _ in pbar:
log = LogReg(hid_units, nb_classes)
opt = torch.optim.Adam(log.parameters(), lr=0.01, weight_decay=0.0)
pat_steps = 0
best_acc = torch.zeros(1)
if torch.cuda.is_available():
log.cuda()
best_acc = best_acc.cuda()
for _ in range(100):
log.train()
opt.zero_grad()
logits = log(train_embs)
loss = xent(logits, train_lbls)
loss.backward()
opt.step()
logits = log(test_embs)
preds = torch.argmax(logits, dim=1)
acc = torch.sum(preds == test_lbls).float() / test_lbls.shape[0]
accs.append(acc * 100)
pbar.desc = "Accuracy: {:.2f}%".format(100 * acc)
tot += acc
torch.save(log.state_dict(), saved_logreg)
accs = torch.stack(accs)
print('Average Accuracy: {:.2f}%'.format(accs.mean()))
print('Standard Deviation: {:.3f}'.format(accs.std()))
print("\nTesting")
logits = log(val_embs)
preds = torch.argmax(logits, dim=1)
acc = torch.sum(preds == val_lbls).float() / val_lbls.shape[0]
print("Accuracy: {:.2f}%".format(100 * acc))
from utils import process
import numpy as np
_, _, labels, _, _, _ = process.load_data('cora')
stat = np.sum(labels, axis=0)
print('cora: ', stat)
_, _, labels, _, _, _ = process.load_data('pubmed')
stat = np.sum(labels, axis=0)
print('pubmed: ', stat)
_, _, labels, _, _, _ = process.load_data('citeseer')
stat = np.sum(labels, axis=0)
print('citeseer: ', stat)
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
validation = True
# Load data
if validation:
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data_2(
dataset, seed, unlabel_prob)
else:
adj, features, y_train, y_test, train_mask, test_mask = process.load_data(
dataset, seed, unlabel_prob)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = features[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
if sparse:
def main(args):
"""
Pipeline for Graph Attention Auto-encoder.
G, X, Y, idx_train, idx_val, idx_test = process.load_data(args.dataset)
print('Graph的维度：' + str(G.shape))
print('Content的维度：' + str(X.shape))
Label = np.array([np.argmax(l) for l in Y])
print('Label的维度：' + str(Label.shape))
# add feature dimension size to the beginning of hidden_dims
feature_dim = X.shape[1]
args.hidden_dims = [feature_dim] + args.hidden_dims
print('隐层单元的维度：' + str(args.hidden_dims))
# prepare the data
"""
# data_z = sio.loadmat('database/HW/nhandwritten_2views.mat')
# data_dict = dict(data_z)
# X1 = data_dict['x1']
# X2 = data_dict['x2']
# Label = data_dict['gt'].T
# Label = np.squeeze(np.array(Label))
# data_G = sio.loadmat('database/HW/hw5.mat')
# g = dict(data_G)['hw5']
# G = sp.coo_matrix(dict(data_G)['hw5'])
# G_tf, S, R = process.prepare_graph_data(G)
#
# feature_dim1 = X1.shape[1]
# args.hidden_dims1 = [feature_dim1] + args.hidden_dims1
# feature_dim2 = X2.shape[1]
# args.hidden_dims2 = [feature_dim2] + args.hidden_dims2
#
# print('Graph的维度：' + str(G.shape))
# print('Content1的维度：' + str(X1.shape))
# print('Content2的维度：' + str(X2.shape))
# print('Label的维度：' + str(Label.shape))
# print('隐层单元1的维度：' + str(args.hidden_dims1))
# print('隐层单元2的维度：' + str(args.hidden_dims2))
#
# # PreTrain the Model
# # fin = False
# trainer = Trainer(args)
# _ = trainer.assign(G_tf, X1, S, R, G_tf, X2, S, R)
# # trainer(G_tf, X, S, R, Label, fin)
# # Fintune the Model
# fin = True
# trainer(G_tf, X1, S, R, G_tf, X2, S, R, Label, fin)
"""
Pipeline for Graph Attention Auto-encoder.
"""
G, X, Y, idx_train, idx_val, idx_test = process.load_data(args.dataset)
print('Graph的维度：' + str(G.shape))
print('Content的维度：' + str(X.shape))
Label = np.array([np.argmax(l) for l in Y])
print('Label的维度：' + str(Label.shape))
# add feature dimension size to the beginning of hidden_dims
feature_dim1 = X.shape[1]
args.hidden_dims1 = [feature_dim1] + args.hidden_dims1
X2 = fft(X)
feature_dim2 = X2.shape[1]
args.hidden_dims2 = [feature_dim2] + args.hidden_dims2
print('隐层单元1的维度：' + str(args.hidden_dims1))
print('隐层单元2的维度：' + str(args.hidden_dims2))
# prepare the data
G_tf, S, R = process.prepare_graph_data(G)
# PreTrain the Model
# fin = False
trainer = Trainer(args)
_ = trainer.assign(G_tf, X, S, R, G_tf, X2, S, R)
# trainer(G_tf, X, S, R, Label, fin)
# Fintune the Model
fin = True
trainer(G_tf, X, S, R, G_tf, X2, S, R, Label, fin)
from keras.optimizers import Adam
from keras.regularizers import l2
from keras_gat import GraphAttention
# from keras_gat.utils import load_data, preprocess_features
from utils.process import load_data, preprocess_features
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
# Read data
dataset = sys.argv[1]
if dataset not in ['cora', 'citeseer', 'pubmed', 'wiki']:
print('invalid dataset')
exit()
A, X, Y_train, Y_val, Y_test, idx_train, idx_val, idx_test = load_data(dataset)
# Parameters
N = X.shape[0] # Number of nodes in the graph
F = X.shape[1] # Original feature dimension
n_classes = Y_train.shape[1] # Number of classes
F_ = 100 # Output size of first GraphAttention layer
n_attn_heads = 8 # Number of attention heads in first GAT layer
dropout_rate = 0.5 # Dropout rate (between and inside GAT layers)
l2_reg = 5e-4/2 # Factor for l2 regularization
learning_rate = 0.0005 # Learning rate for Adam
epochs = 10000 # Number of training epochs
es_patience = 100 # Patience fot early stopping
# Preprocessing operations
X = preprocess_features(X)
parser.add_argument('--beta', type=float, default=1.0,
help='parameter for I(h_i; x_j), node j is a neighbor (default: 1.0)')
parser.add_argument('--gamma', type=float, default=1.0,
help='parameter for I(w_ij; a_ij) (default: 1.0)')
parser.add_argument('--activation', default='prelu',
help='activation function')
###############################################
# This section of code adapted from Petar Veličković/DGI #
###############################################
args = parser.parse_args()
torch.cuda.set_device(args.gpu)
print('Loading ', args.dataset)
adj_ori, features, labels, idx_train, idx_val, idx_test = process.load_data(args.dataset)
features, _ = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = labels.shape[1]
adj = process.normalize_adj(adj_ori + sp.eye(adj_ori.shape[0]))
sp_adj = process.sparse_mx_to_torch_sparse_tensor(adj)
features = torch.FloatTensor(features[np.newaxis])
labels = torch.FloatTensor(labels[np.newaxis])
idx_train = torch.LongTensor(idx_train)
idx_val = torch.LongTensor(idx_val)
idx_test = torch.LongTensor(idx_test)
model = GMI(ft_size, args.hid_units, args.activation)
def train():
sparse = True
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(dataset, train_size, validation_size)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = features[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
if sparse:
biases = process.preprocess_adj_bias(adj)
else:
adj = adj.todense()
adj = adj[np.newaxis]
biases = process.adj_to_bias(adj, [nb_nodes], nhood=1)
with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, ft_size))
if sparse:
#bias_idx = tf.placeholder(tf.int64)
#bias_val = tf.placeholder(tf.float32)
#bias_shape = tf.placeholder(tf.int64)
bias_in = tf.sparse_placeholder(dtype=tf.float32)
else:
bias_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
logits = model.inference(ftr_in, nb_classes, nb_nodes, is_train,
attn_drop, ffd_drop,
bias_mat=bias_in,
hid_units=hid_units, n_heads=n_heads,
residual=residual, activation=nonlinearity)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
train_op = model.training(loss, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
curr_step = 0
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
sess.run(init_op)
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
for epoch in range(nb_epochs):
tr_step = 0
tr_size = features.shape[0]
while tr_step * batch_size < tr_size:
if sparse:
bbias = biases
else:
bbias = biases[tr_step*batch_size:(tr_step+1)*batch_size]
_, loss_value_tr, acc_tr = sess.run([train_op, loss, accuracy],
feed_dict={
ftr_in: features[tr_step*batch_size:(tr_step+1)*batch_size],
bias_in: bbias,
lbl_in: y_train[tr_step*batch_size:(tr_step+1)*batch_size],
msk_in: train_mask[tr_step*batch_size:(tr_step+1)*batch_size],
is_train: True,
attn_drop: 0.6, ffd_drop: 0.6})
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
tr_step += 1
if args.validate:
vl_step = 0
vl_size = features.shape[0]
while vl_step * batch_size < vl_size:
if sparse:
bbias = biases
else:
bbias = biases[vl_step*batch_size:(vl_step+1)*batch_size]
loss_value_vl, acc_vl = sess.run([loss, accuracy],
feed_dict={
ftr_in: features[vl_step*batch_size:(vl_step+1)*batch_size],
bias_in: bbias,
lbl_in: y_val[vl_step*batch_size:(vl_step+1)*batch_size],
msk_in: val_mask[vl_step*batch_size:(vl_step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
else:
tr_step = 0
vl_step = 0
vl_size = features.shape[0]
val_loss_avg = 0
val_acc_avg = 0
while tr_step * batch_size < tr_size:
if sparse:
bbias = biases
else:
bbias = biases[tr_step*batch_size:(tr_step+1)*batch_size]
loss_value_vl, acc_vl = sess.run([loss, accuracy],
feed_dict={
ftr_in: features[tr_step*batch_size:(tr_step+1)*batch_size],
bias_in: bbias,
lbl_in: y_train[tr_step*batch_size:(tr_step+1)*batch_size],
msk_in: train_mask[tr_step*batch_size:(tr_step+1)*batch_size],
is_train: False,
attn_drop: 0., ffd_drop: 0.})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
tr_step += 1
print('%d Training: loss = %.5f, acc = %.5f | Val: loss = %.5f, acc = %.5f' %
(epoch, train_loss_avg/tr_step, train_acc_avg/tr_step,
val_loss_avg/vl_step, val_acc_avg/vl_step))
if val_acc_avg/vl_step > vacc_mx or val_loss_avg/vl_step < vlss_mn:
if val_acc_avg/vl_step >= vacc_mx and val_loss_avg/vl_step <= vlss_mn:
vacc_early_model = val_acc_avg/vl_step
vlss_early_model = val_loss_avg/vl_step
saver.save(sess, checkpt_file)
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
while ts_step * batch_size < ts_size:
if sparse:
bbias = biases
else:
bbias = biases[ts_step * batch_size:(ts_step + 1) * batch_size]
loss_value_ts, acc_ts = sess.run([loss, accuracy],
feed_dict={
ftr_in: features[ts_step * batch_size:(ts_step + 1) * batch_size],
bias_in: bbias,
lbl_in: y_test[ts_step * batch_size:(ts_step + 1) * batch_size],
msk_in: test_mask[ts_step * batch_size:(ts_step + 1) * batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
print('Test loss:', ts_loss / ts_step, '; Test accuracy:', ts_acc / ts_step)
vacc_mx = np.max((val_acc_avg/vl_step, vacc_mx))
vlss_mn = np.min((val_loss_avg/vl_step, vlss_mn))
curr_step = 0
else:
curr_step += 1
if curr_step == patience:
print('Early stop! Min loss: ', vlss_mn, ', Max accuracy: ', vacc_mx)
print('Early stop model validation loss: ', vlss_early_model, ', accuracy: ', vacc_early_model)
break
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
saver.restore(sess, checkpt_file)
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
while ts_step * batch_size < ts_size:
if sparse:
bbias = biases
else:
bbias = biases[ts_step*batch_size:(ts_step+1)*batch_size]
loss_value_ts, acc_ts = sess.run([loss, accuracy],
feed_dict={
ftr_in: features[ts_step*batch_size:(ts_step+1)*batch_size],
bias_in: bbias,
lbl_in: y_test[ts_step*batch_size:(ts_step+1)*batch_size],
msk_in: test_mask[ts_step*batch_size:(ts_step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
print('Test loss:', ts_loss/ts_step, '; Test accuracy:', ts_acc/ts_step)
sess.close()
return ts_loss/ts_step, ts_acc/ts_step
def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
dataset = args.dataset
# training params
batch_size = 1
nb_epochs = 100000
patience = 100
lr = args.lr
l2_coef = args.l2
hid_units = [args.units]
n_heads = [1, 1] # layers
drop_out = args.drop
residual = False
nonlinearity = tf.nn.elu
model = SpHGAT
print('Dataset: ' + dataset)
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
sparse = True
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(dataset)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = features[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
if sparse:
biases = process.preprocess_adj_bias(adj)
else:
adj = adj.todense()
adj = adj[np.newaxis]
biases = process.adj_to_bias(adj, [nb_nodes], nhood=1)
with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, ft_size))
if sparse:
bias_in = tf.sparse_placeholder(dtype=tf.float32)
else:
bias_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
logits = model.inference(ftr_in, nb_classes, nb_nodes, is_train,
attn_drop, ffd_drop,
bias_mat=bias_in,
hid_units=hid_units, n_heads=n_heads,
activation=nonlinearity)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
pred_all = tf.cast(tf.argmax(log_resh, 1), dtype=tf.int32)
real_all = tf.cast(tf.argmax(lab_resh, 1), dtype=tf.int32)
train_op = model.my_training(loss, lr, l2_coef)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
tlss_mn = 0.0
tacc_mx = 0.0
curr_step = 0
with tf.Session(config=config) as sess:
sess.run(init_op)
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
for epoch in range(nb_epochs):
tr_step = 0
tr_size = features.shape[0]
while tr_step * batch_size < tr_size:
if sparse:
bbias = biases
else:
bbias = biases[tr_step*batch_size:(tr_step+1)*batch_size]
_, loss_value_tr, acc_tr = sess.run([train_op, loss, accuracy],
feed_dict={
ftr_in: features[tr_step*batch_size:(tr_step+1)*batch_size],
bias_in: bbias,
lbl_in: y_train[tr_step*batch_size:(tr_step+1)*batch_size],
msk_in: train_mask[tr_step*batch_size:(tr_step+1)*batch_size],
is_train: True,
attn_drop: drop_out, ffd_drop: drop_out})
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
tr_step += 1
vl_step = 0
vl_size = features.shape[0]
while vl_step * batch_size < vl_size:
if sparse:
bbias = biases
else:
bbias = biases[vl_step*batch_size:(vl_step+1)*batch_size]
loss_value_vl, acc_vl = sess.run([loss, accuracy],
feed_dict={
ftr_in: features[vl_step*batch_size:(vl_step+1)*batch_size],
bias_in: bbias,
lbl_in: y_val[vl_step*batch_size:(vl_step+1)*batch_size],
msk_in: val_mask[vl_step*batch_size:(vl_step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
print(epoch, 'Training: loss = %.5f, acc = %.5f | Val: loss = %.5f, acc = %.5f' %
(train_loss_avg/tr_step, train_acc_avg/tr_step,
val_loss_avg/vl_step, val_acc_avg/vl_step))
if val_acc_avg/vl_step >= vacc_mx or val_loss_avg/vl_step <= vlss_mn:
vacc_mx = np.max((val_acc_avg/vl_step, vacc_mx))
vlss_mn = np.min((val_loss_avg/vl_step, vlss_mn))
curr_step = 0
while ts_step * batch_size < ts_size:
if sparse:
bbias = biases
else:
bbias = biases[ts_step * batch_size:(ts_step + 1) * batch_size]
loss_value_ts, acc_ts = sess.run(
[loss, accuracy],
feed_dict={
ftr_in: features[ts_step * batch_size:(ts_step + 1) * batch_size],
bias_in: bbias,
lbl_in: y_test[ts_step * batch_size:(ts_step + 1) * batch_size],
msk_in: test_mask[ts_step * batch_size:(ts_step + 1) * batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
tlss_mn = ts_loss / ts_step
tacc_mx = ts_acc / ts_step
else:
curr_step += 1
if curr_step == patience:
print('Early stop! Min validation loss: ', vlss_mn, ', Max accuracy: ', vacc_mx)
print('Test loss:', tlss_mn, ', accuracy {}'.format(tacc_mx))
break
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
sess.close()
def train(sparse, epochs, lr, patience, l2_coef, hid_units, n_heads, residual,
attention_drop, edge_attr_directory, node_features_path, label_path,
log_path, train_ratio):
# flags = tf.app.flags
# FLAGS = flags.FLAGS
nb_epochs = epochs
# flags.DEFINE_string('summaries_dir', log_path, 'Summaries directory')
if tf.gfile.Exists(log_path):
tf.gfile.DeleteRecursively(log_path)
tf.gfile.MakeDirs(log_path)
checkpt_file = 'pre_trained/mod_test.ckpt'
dataset = 'know'
# training params
batch_size = 1
nonlinearity = tf.nn.elu
if sparse:
model = SpGAT
else:
model = GAT
nhood = 1
in_drop = attention_drop
print('Dataset: ' + dataset)
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
adjs, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, edge_attr_name = process.load_data(
edge_attr_directory, node_features_path, label_path, train_ratio)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = features[np.newaxis]
# adj = adj[np.newaxis]
# adjs = [adj[np.newaxis] for adj in adjs]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
if sparse:
biases = process.preprocess_adj_bias(
adjs[0], to_unweighted=True
) # sparse (indices, values, dense_shape), the graph topologies (unweighted)
adjs = [
tf.SparseTensor(
*process.preprocess_adj_bias(adj, to_unweighted=False))
for adj in adjs
]
else:
biases = process.adj_to_bias(adjs[0], [nb_nodes], nhood=nhood)
# biases = process.get_bias_mat(adjs[0], [nb_nodes], nhood=nhood)
print(biases)
# with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes, ft_size))
if sparse:
# bias_in = tf.sparse_placeholder(dtype=tf.float32)
bias_in = tf.SparseTensor(*biases)
else:
bias_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32,
shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
logits = model.inference(inputs=ftr_in,
edge_adjs=adjs,
nb_classes=nb_classes,
nb_nodes=nb_nodes,
training=is_train,
attn_drop=attn_drop,
ffd_drop=ffd_drop,
bias_mat=bias_in,
hid_units=hid_units,
n_heads=n_heads,
residual=residual,
activation=nonlinearity,
edge_attr_name=edge_attr_name)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
micro_f1 = model.micro_f1(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
train_op = model.training(loss, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
vmf1_mx = 0.0
curr_step = 0
with tf.Session() as sess:
merged = tf.summary.merge_all()
train_summary_writer = tf.summary.FileWriter(log_path + '/train')
test_summary_writer = tf.summary.FileWriter(log_path + '/test')
sess.run(init_op)
train_loss_avg = 0
train_acc_avg = 0
train_microf1_avg = 0
val_loss_avg = 0
val_acc_avg = 0
val_microf1_avg = 0
for epoch in range(nb_epochs):
tr_step = 0
tr_size = features.shape[0]
while tr_step * batch_size < tr_size:
if sparse:
bbias = biases
else:
bbias = biases[tr_step * batch_size:(tr_step + 1) *
batch_size]
_, summary, loss_value_tr, acc_tr, micro_f1_tr = sess.run(
[train_op, merged, loss, accuracy, micro_f1],
feed_dict={
ftr_in:
features[tr_step * batch_size:(tr_step + 1) *
batch_size],
# bias_in: bbias,
lbl_in:
y_train[tr_step * batch_size:(tr_step + 1) *
batch_size],
msk_in:
train_mask[tr_step * batch_size:(tr_step + 1) *
batch_size],
is_train:
True,
attn_drop:
attention_drop,
ffd_drop:
in_drop
})
print(loss_value_tr)
train_microf1_avg += micro_f1_tr
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
tr_step += 1
train_summary_writer.add_summary(summary, epoch)
vl_step = 0
vl_size = features.shape[0]
while vl_step * batch_size < vl_size:
if sparse:
bbias = biases
else:
bbias = biases[vl_step * batch_size:(vl_step + 1) *
batch_size]
summary, loss_value_vl, acc_vl, micro_f1_vl = sess.run(
[merged, loss, accuracy, micro_f1],
feed_dict={
ftr_in:
features[vl_step * batch_size:(vl_step + 1) *
batch_size],
# bias_in: bbias,
lbl_in:
y_val[vl_step * batch_size:(vl_step + 1) * batch_size],
msk_in:
val_mask[vl_step * batch_size:(vl_step + 1) *
batch_size],
is_train:
False,
attn_drop:
0.0,
ffd_drop:
0.0
})
val_microf1_avg += micro_f1_vl
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
test_summary_writer.add_summary(summary, epoch)
print(
'Training: loss = %.5f, acc = %.5f, micro_f1 = %.5f | Val: loss = %.5f, acc = %.5f, micro_f1 = %.5f'
% (train_loss_avg / tr_step, train_acc_avg / tr_step,
train_microf1_avg / tr_step, val_loss_avg / vl_step,
val_acc_avg / vl_step, val_microf1_avg / vl_step))
if val_acc_avg / vl_step >= vacc_mx or val_loss_avg / vl_step <= vlss_mn:
if val_acc_avg / vl_step >= vacc_mx and val_loss_avg / vl_step <= vlss_mn:
vacc_early_model = val_acc_avg / vl_step
vlss_early_model = val_loss_avg / vl_step
saver.save(sess, checkpt_file)
vmf1_mx = np.max((val_microf1_avg / vl_step, vmf1_mx))
vacc_mx = np.max((val_acc_avg / vl_step, vacc_mx))
vlss_mn = np.min((val_loss_avg / vl_step, vlss_mn))
curr_step = 0
else:
curr_step += 1
if curr_step == patience:
print('Early stop! Min loss: ', vlss_mn,
', Max accuracy: ', vacc_mx, ', Max Micro-f1',
vmf1_mx)
print('Early stop model validation loss: ',
vlss_early_model, ', accuracy: ', vacc_early_model)
break
train_loss_avg = 0
train_acc_avg = 0
train_microf1_avg = 0
val_loss_avg = 0
val_acc_avg = 0
val_microf1_avg = 0
saver.restore(sess, checkpt_file)
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
while ts_step * batch_size < ts_size:
if sparse:
bbias = biases
else:
bbias = biases[ts_step * batch_size:(ts_step + 1) * batch_size]
loss_value_ts, acc_ts = sess.run(
[loss, accuracy],
feed_dict={
ftr_in:
features[ts_step * batch_size:(ts_step + 1) * batch_size],
# bias_in: bbias,
lbl_in:
y_test[ts_step * batch_size:(ts_step + 1) * batch_size],
msk_in:
test_mask[ts_step * batch_size:(ts_step + 1) * batch_size],
is_train:
False,
attn_drop:
0.0,
ffd_drop:
0.0
})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
print('Test loss:', ts_loss / ts_step, '; Test accuracy:',
ts_acc / ts_step)
sess.close()
def run_gat(dataset, batch_size, nb_epochs, patience, lr, l2_coef, hid_units,
n_heads, residual, nonlinearity, model, checkpt_file, nhood):
# redirect output to file
import sys
orig_stdout = sys.stdout
if os.path.isfile(os.path.dirname(checkpt_file) + 'out.txt'):
f = open(os.path.dirname(checkpt_file) + 'out.txt', 'a')
print('\n\n\n\n')
else:
f = open(os.path.dirname(checkpt_file) + 'out.txt', 'w')
sys.stdout = f
print('Dataset: ' + dataset)
print('batch_size: ' + str(batch_size))
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
print('nhood: ' + str(nhood))
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(
dataset)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
adj = adj.todense()
features = features[np.newaxis]
adj = adj[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
biases = process.adj_to_bias(adj, [nb_nodes], nhood=nhood)
with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes, ft_size))
bias_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32,
shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32,
shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
logits = model.inference(ftr_in,
nb_classes,
nb_nodes,
is_train,
attn_drop,
ffd_drop,
bias_mat=bias_in,
hid_units=hid_units,
n_heads=n_heads,
residual=residual,
activation=nonlinearity)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
train_op = model.training(loss, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
curr_step = 0
start = time.time()
with tf.Session() as sess:
sess.run(init_op)
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
for epoch in range(nb_epochs):
tr_step = 0
tr_size = features.shape[0]
while tr_step * batch_size < tr_size:
_, loss_value_tr, acc_tr = sess.run(
[train_op, loss, accuracy],
feed_dict={
ftr_in:
features[tr_step * batch_size:(tr_step + 1) *
batch_size],
bias_in:
biases[tr_step * batch_size:(tr_step + 1) *
batch_size],
lbl_in:
y_train[tr_step * batch_size:(tr_step + 1) *
batch_size],
msk_in:
train_mask[tr_step * batch_size:(tr_step + 1) *
batch_size],
is_train:
True,
attn_drop:
0.6,
ffd_drop:
0.6
})
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
tr_step += 1
vl_step = 0
vl_size = features.shape[0]
while vl_step * batch_size < vl_size:
loss_value_vl, acc_vl = sess.run(
[loss, accuracy],
feed_dict={
ftr_in:
features[vl_step * batch_size:(vl_step + 1) *
batch_size],
bias_in:
biases[vl_step * batch_size:(vl_step + 1) *
batch_size],
lbl_in:
y_val[vl_step * batch_size:(vl_step + 1) *
batch_size],
msk_in:
val_mask[vl_step * batch_size:(vl_step + 1) *
batch_size],
is_train:
False,
attn_drop:
0.0,
ffd_drop:
0.0
})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
print(
'Training: loss = %.5f, acc = %.5f | Val: loss = %.5f, acc = %.5f'
% (train_loss_avg / tr_step, train_acc_avg / tr_step,
val_loss_avg / vl_step, val_acc_avg / vl_step))
if val_acc_avg / vl_step >= vacc_mx or val_loss_avg / vl_step <= vlss_mn:
if val_acc_avg / vl_step >= vacc_mx and val_loss_avg / vl_step <= vlss_mn:
vacc_early_model = val_acc_avg / vl_step
vlss_early_model = val_loss_avg / vl_step
saver.save(sess, checkpt_file)
vacc_mx = np.max((val_acc_avg / vl_step, vacc_mx))
vlss_mn = np.min((val_loss_avg / vl_step, vlss_mn))
curr_step = 0
else:
curr_step += 1
if curr_step == patience:
print('Early stop! Min loss: ', vlss_mn,
', Max accuracy: ', vacc_mx)
print('Early stop model validation loss: ',
vlss_early_model, ', accuracy: ',
vacc_early_model)
break
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
saver.restore(sess, checkpt_file)
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
while ts_step * batch_size < ts_size:
loss_value_ts, acc_ts = sess.run(
[loss, accuracy],
feed_dict={
ftr_in:
features[ts_step * batch_size:(ts_step + 1) *
batch_size],
bias_in:
biases[ts_step * batch_size:(ts_step + 1) *
batch_size],
lbl_in:
y_test[ts_step * batch_size:(ts_step + 1) *
batch_size],
msk_in:
test_mask[ts_step * batch_size:(ts_step + 1) *
batch_size],
is_train:
False,
attn_drop:
0.0,
ffd_drop:
0.0
})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
print('Test loss:', ts_loss / ts_step, '; Test accuracy:',
ts_acc / ts_step, ' at epoch: ', epoch, ' elapsed time',
time.time() - start)
sess.close()
sys.stdout = orig_stdout
f.close()
from utils import process
dataset = 'cora'
# training params
batch_size = 1
nb_epochs = 10000
patience = 20
lr = 0.001
l2_coef = 0.0
drop_prob = 0.0
hid_units = 512
sparse = True
nonlinearity = 'prelu' # special name to separate parameters
adj, features, labels, idx_train, idx_val, idx_test = process.load_data(dataset, use_dgi_data=False)
features, _ = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = labels.shape[1]
adj = process.normalize_adj(adj + sp.eye(adj.shape[0]))
if sparse:
sp_adj = process.sparse_mx_to_torch_sparse_tensor(adj)
else:
adj = (adj + sp.eye(adj.shape[0])).todense()
features = torch.FloatTensor(features[np.newaxis])
if not sparse:
def run_gat(dataset,
batch_size,
nb_epochs,
patience,
lr,
l2_coef,
hid_units,
n_heads,
residual,
nonlinearity,
model,
checkpt_file,
nhood,
param_attn_drop=0.6,
param_ffd_drop=0.6,
sparse=False):
'''
Function that runs all the experiments.
:param dataset: The string name of the dataset.
:param batch_size: Number of samples per batch. Has to be one for spartial execution.
:param nb_epochs: Number of epochs that the method runs
:param patience: The number of epochs with no improvement in validation accuracy that stops the training.
:param lr: Learning rate.
:param l2_coef: The L2 regularization strength.
:param hid_units: List. Number of features the respecting layer produces from the input features.
:param n_heads: List. Number of entries is the number of layers. The elements value is the number of attention heads.
:param residual: Whether or not to use residual connections in the hidden layers.
:param nonlinearity: tensorflow function for non-linearity
:param model: Model that inherits from BasGAttn and implements the inference method
:param checkpt_file: Location where the logs, output and model checkpoints are saved
:param nhood: The neighborhood to consider. One for direct neighborhood and two for neighbors of neighbors.
:param param_attn_drop: Drops a percent of attention coefficients.
:param param_ffd_drop: Drops a percent of inputs from the previous layer.
:param sparse: If True, the model has to be SpGAT
:return: Prints and logs results.
'''
# necessary work around to run on GPU
'''
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
'''
# redirect output to file
import sys
orig_stdout = sys.stdout
if os.path.isfile(os.path.dirname(checkpt_file) + 'out.txt'):
f = open(os.path.dirname(checkpt_file) + 'out.txt', 'a')
sys.stdout = f
print('\n\n\n\n')
else:
f = open(os.path.dirname(checkpt_file) + 'out.txt', 'w')
sys.stdout = f
print('Dataset: ' + dataset)
print('batch_size: ' + str(batch_size))
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
print('nhood: ' + str(nhood))
print('attn_drop: ' + str(param_attn_drop))
print('ffd_drop: ' + str(param_ffd_drop))
# load any of the three transductive datasets
# adj has information about the connections
# features is a node node x features matrix with the features for each node
# y_... has the label for each class in a node x class matrix
# mask has 0 or 1 as value in a node vector, this is used to mask train, val and test set
# so for all nodes all information is calculated, but only nodes masked with 1 are evaluated
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(
dataset)
# preprocessing steps
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = features[np.newaxis]
# adj = adj[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
# the adjacency matrix is transformed into a bias that is added.
# when no connection between nodes exist in the specified neighborhood, the value of the attention between
# both nodes is set to a big negative value, pratically canceling out the effect.
if sparse:
biases = process.preprocess_adj_bias(adj)
else:
adj = adj.todense()
adj = adj[np.newaxis]
biases = process.adj_to_bias(adj, [nb_nodes], nhood=1)
with tf.Graph().as_default():
with tf.name_scope('input'):
# initialization
ftr_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes, ft_size))
if sparse:
bias_in = tf.sparse_placeholder(dtype=tf.float32)
else:
bias_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes,
nb_nodes))
# bias_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32,
shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32,
shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
logits = model.inference(ftr_in,
nb_classes,
nb_nodes,
is_train,
attn_drop,
ffd_drop,
bias_mat=bias_in,
hid_units=hid_units,
n_heads=n_heads,
residual=residual,
activation=nonlinearity)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
train_op = model.training(loss, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
curr_step = 0
start = time.time()
with tf.Session() as sess:
sess.run(init_op)
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
for epoch in range(nb_epochs):
tr_step = 0
tr_size = features.shape[0]
# training steps
while tr_step * batch_size < tr_size:
if sparse:
bbias = biases
else:
bbias = biases[tr_step * batch_size:(tr_step + 1) *
batch_size]
_, loss_value_tr, acc_tr = sess.run(
[train_op, loss, accuracy],
feed_dict={
ftr_in:
features[tr_step * batch_size:(tr_step + 1) *
batch_size],
bias_in:
bbias,
lbl_in:
y_train[tr_step * batch_size:(tr_step + 1) *
batch_size],
msk_in:
train_mask[tr_step * batch_size:(tr_step + 1) *
batch_size],
is_train:
True,
attn_drop:
param_attn_drop,
ffd_drop:
param_ffd_drop
})
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
tr_step += 1
vl_step = 0
vl_size = features.shape[0]
# validation steps
while vl_step * batch_size < vl_size:
if sparse:
bbias = biases
else:
bbias = biases[vl_step * batch_size:(vl_step + 1) *
batch_size]
loss_value_vl, acc_vl = sess.run(
[loss, accuracy],
feed_dict={
ftr_in:
features[vl_step * batch_size:(vl_step + 1) *
batch_size],
bias_in:
bbias,
lbl_in:
y_val[vl_step * batch_size:(vl_step + 1) *
batch_size],
msk_in:
val_mask[vl_step * batch_size:(vl_step + 1) *
batch_size],
is_train:
False,
attn_drop:
0.0,
ffd_drop:
0.0
})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
print(
'Training: loss = %.5f, acc = %.5f | Val: loss = %.5f, acc = %.5f'
% (train_loss_avg / tr_step, train_acc_avg / tr_step,
val_loss_avg / vl_step, val_acc_avg / vl_step))
# patience step
if val_acc_avg / vl_step >= vacc_mx or val_loss_avg / vl_step <= vlss_mn:
if val_acc_avg / vl_step >= vacc_mx and val_loss_avg / vl_step <= vlss_mn:
vacc_early_model = val_acc_avg / vl_step
vlss_early_model = val_loss_avg / vl_step
saver.save(sess, checkpt_file)
vacc_mx = np.max((val_acc_avg / vl_step, vacc_mx))
vlss_mn = np.min((val_loss_avg / vl_step, vlss_mn))
curr_step = 0
else:
curr_step += 1
if curr_step == patience:
print('Early stop! Min loss: ', vlss_mn,
', Max accuracy: ', vacc_mx)
print('Early stop model validation loss: ',
vlss_early_model, ', accuracy: ',
vacc_early_model)
break
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
saver.restore(sess, checkpt_file)
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
# evaluate on the training set
while ts_step * batch_size < ts_size:
if sparse:
bbias = biases
else:
bbias = biases[ts_step * batch_size:(ts_step + 1) *
batch_size]
loss_value_ts, acc_ts = sess.run(
[loss, accuracy],
feed_dict={
ftr_in:
features[ts_step * batch_size:(ts_step + 1) *
batch_size],
bias_in:
bbias,
lbl_in:
y_test[ts_step * batch_size:(ts_step + 1) *
batch_size],
msk_in:
test_mask[ts_step * batch_size:(ts_step + 1) *
batch_size],
is_train:
False,
attn_drop:
0.0,
ffd_drop:
0.0
})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
print('Test loss:', ts_loss / ts_step, '; Test accuracy:',
ts_acc / ts_step, ' at epoch: ', epoch, ' elapsed time',
time.time() - start)
# log information about the training
if os.path.isfile(os.path.dirname(checkpt_file) + 'log.csv'):
print('loading existing log')
df = pd.read_csv(os.path.dirname(checkpt_file) + 'log.csv',
index_col=['run'])
print('log: ' + str(df))
else:
print('Creating new log')
df = pd.DataFrame(columns=tracking_params + result_cols)
log = dict(
zip(tracking_params + result_cols, [
dataset, lr, l2_coef, hid_units, n_heads, residual,
str(nonlinearity).split(' ')[1], param_attn_drop,
param_ffd_drop, nhood
] + [
epoch,
time.time() - start, vlss_mn, vacc_mx, ts_loss / ts_step,
ts_acc / ts_step
]))
print('Adding entry: ' + str(log))
df = df.append(log, ignore_index=True)
print('saving logs')
df.to_csv(os.path.dirname(checkpt_file) + 'log.csv',
index_label='run')
print('log save succesfull')
sess.close()
# restore standard output
sys.stdout = orig_stdout
f.close()
def train(sparse, hid_units, n_heads, residual, edge_attr_directory,
node_features_path, label_path, train_ratio, model_path):
# flags = tf.app.flags
# FLAGS = flags.FLAGS
# flags.DEFINE_string('summaries_dir', log_path, 'Summaries directory')
# if tf.gfile.Exists(log_path):
# tf.gfile.DeleteRecursively(log_path)
# tf.gfile.MakeDirs(log_path)
checkpt_file = model_path
dataset = 'know'
# training params
batch_size = 1
nonlinearity = tf.nn.elu
if sparse:
model = SpGAT
else:
model = GAT
nhood = 1
# in_drop = attention_drop
print('Dataset: ' + dataset)
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
adjs, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, edge_attr_name = process.load_data(
edge_attr_directory, node_features_path, label_path, train_ratio)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = features[np.newaxis]
# adj = adj[np.newaxis]
# adjs = [adj[np.newaxis] for adj in adjs]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
if sparse:
biases = process.preprocess_adj_bias(
adjs[0], to_unweighted=True
) # sparse (indices, values, dense_shape), the graph topologies (unweighted)
adjs = [
tf.SparseTensor(
*process.preprocess_adj_bias(adj, to_unweighted=False))
for adj in adjs
]
else:
biases = process.adj_to_bias(adjs[0], [nb_nodes], nhood=nhood)
# biases = process.get_bias_mat(adjs[0], [nb_nodes], nhood=nhood)
print(biases)
# with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes, ft_size))
if sparse:
# bias_in = tf.sparse_placeholder(dtype=tf.float32)
bias_in = tf.SparseTensor(*biases)
else:
bias_in = tf.placeholder(dtype=tf.float32,
shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32,
shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
logits = model.inference(inputs=ftr_in,
edge_adjs=adjs,
nb_classes=nb_classes,
nb_nodes=nb_nodes,
training=is_train,
attn_drop=attn_drop,
ffd_drop=ffd_drop,
bias_mat=bias_in,
hid_units=hid_units,
n_heads=n_heads,
residual=residual,
activation=nonlinearity,
edge_attr_name=edge_attr_name)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
micro_f1 = model.micro_f1(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
# train_op = model.training(loss, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, checkpt_file)
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
while ts_step * batch_size < ts_size:
if sparse:
bbias = biases
else:
bbias = biases[ts_step * batch_size:(ts_step + 1) * batch_size]
loss_value_ts, acc_ts = sess.run(
[loss, accuracy],
feed_dict={
ftr_in:
features[ts_step * batch_size:(ts_step + 1) * batch_size],
# bias_in: bbias,
lbl_in:
y_test[ts_step * batch_size:(ts_step + 1) * batch_size],
msk_in:
test_mask[ts_step * batch_size:(ts_step + 1) * batch_size],
is_train:
False,
attn_drop:
0.0,
ffd_drop:
0.0
})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
print('Test loss:', ts_loss / ts_step, '; Test accuracy:',
ts_acc / ts_step)
sess.close()
nonlinearity = tf.nn.elu
model = GAT
print('Dataset: ' + dataset)
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(dataset)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
adj = adj.todense()
features = features[np.newaxis]
adj = adj[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
from utils import process from utils import metrics dataset = "citeseer" batch_size = 1 nb_epochs = 200 patience = 20 lr = 0.001 l2_coef = 0.0 drop_prob = 0.0 hid_units = 32 out_units = 16 sparse = False nonlinearity = "prelu" adj, features, labels, g = process.load_data(dataset) features, _ = process.preprocess_features(features) nb_nodes = features.shape[0] ft_size = features.shape[1] nb_classes = len(labels) adj = process.normalize_adj(adj + sp.eye(adj.shape[0])) adj = (adj + sp.eye(adj.shape[0])).todense() adj = torch.FloatTensor(adj[np.newaxis]) features = torch.FloatTensor(features[np.newaxis]) # labels = torch.FloatTensor(labels[np.newaxis]) model = DGI(ft_size, hid_units, nonlinearity) # model = GAE(ft_size, hid_units, out_units, nonlinearity)
parser.add_argument('--b', dest='beta', type=int, default=100,help='')
parser.add_argument('--c', dest='num_clusters', type=float, default=128,help='')
parser.add_argument('--a', dest='alpha', type=float, default=0.5,help='')
parser.add_argument('--test_rate', dest='test_rate', type=float, default=0.1,help='')
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parser_loader()
pruning_gcn.print_args(args)
rewind_weight = None
dataset = args.dataset
adj, features, labels, idx_train, idx_val, idx_test = process.load_data(dataset)
adj_sparse = adj
adj_train, train_edges, train_edges_false, val_edges, val_edges_false, \
test_edges, test_edges_false = process.mask_test_edges(adj, test_frac=args.test_rate, val_frac=0.05)
adj = adj_train
features, _ = process.preprocess_features(features)
features = torch.FloatTensor(features[np.newaxis]).cuda()
adj = torch.FloatTensor(adj.todense()).cuda()
labels = torch.FloatTensor(labels[np.newaxis]).cuda()
dataset_dict = {}
dataset_dict['adj'] = adj
dataset_dict['adj_sparse'] = adj_sparse
dataset_dict['features'] = features
dataset_dict['labels'] = labels
dataset_dict['val_edges'] = val_edges
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
sparse = True
for per_class in [200]:
result_path = 'result/' + dataset + str(per_class) + '/'
if not os.path.exists('result'):
os.mkdir('result')
if not os.path.exists(result_path):
os.mkdir(result_path)
metapaths, metapaths_name, adjs, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(
dataset, per_class)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
features = features[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
all_metapaths_best = (0, 0, 0, 0)
