def gae_for(args):
print("Using {} dataset".format(args.dataset_str))
adj, features, y_test, tx, ty, test_maks, true_labels = load_data(
args.dataset_str)
n_nodes, feat_dim = features.shape
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sp.dia_matrix(
(adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(
adj)
adj = adj_train
# Before proceeding further, make the structure for doing deepWalk
if args.dw == 1:
print('Using deepWalk regularization...')
G = load_edgelist_from_csr_matrix(adj_orig, undirected=True)
print("Number of nodes: {}".format(len(G.nodes())))
num_walks = len(G.nodes()) * args.number_walks
print("Number of walks: {}".format(num_walks))
data_size = num_walks * args.walk_length
print("Data size (walks*length): {}".format(data_size))
# Some preprocessing
adj_norm = preprocess_graph(adj)
adj_label = adj_train + sp.eye(adj_train.shape[0])
# adj_label = sparse_to_tuple(adj_label)
adj_label = torch.FloatTensor(adj_label.toarray())
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float(
(adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
if args.model == 'gcn_vae':
model = GCNModelVAE(feat_dim, args.hidden1, args.hidden2, args.dropout)
else:
model = GCNModelAE(feat_dim, args.hidden1, args.hidden2, args.dropout)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
if args.dw == 1:
sg = SkipGram(args.hidden2, adj.shape[0])
optimizer_dw = optim.Adam(sg.parameters(), lr=args.lr_dw)
# Construct the nodes for doing random walk. Doing it before since the seed is fixed
nodes_in_G = list(G.nodes())
chunks = len(nodes_in_G) // args.number_walks
random.Random().shuffle(nodes_in_G)
hidden_emb = None
for epoch in tqdm(range(args.epochs)):
t = time.time()
model.train()
optimizer.zero_grad()
z, mu, logvar = model(features, adj_norm)
# After back-propagating gae loss, now do the deepWalk regularization
if args.dw == 1:
sg.train()
if args.full_number_walks > 0:
walks = build_deepwalk_corpus(G,
num_paths=args.full_number_walks,
path_length=args.walk_length,
alpha=0,
rand=random.Random(SEED))
else:
walks = build_deepwalk_corpus_iter(
G,
num_paths=args.number_walks,
path_length=args.walk_length,
alpha=0,
rand=random.Random(SEED),
chunk=epoch % chunks,
nodes=nodes_in_G)
for walk in walks:
if args.context == 1:
# Construct the pairs for predicting context node
# for each node, treated as center word
curr_pair = (int(walk[center_node_pos]), [])
for center_node_pos in range(len(walk)):
# for each window position
for w in range(-args.window_size,
args.window_size + 1):
context_node_pos = center_node_pos + w
# make soure not jump out sentence
if context_node_pos < 0 or context_node_pos >= len(
walk
) or center_node_pos == context_node_pos:
continue
context_node_idx = walk[context_node_pos]
curr_pair[1].append(int(context_node_idx))
else:
# first item in the walk is the starting node
curr_pair = (int(walk[0]), [
int(context_node_idx) for context_node_idx in walk[1:]
])
if args.ns == 1:
neg_nodes = []
pos_nodes = set(walk)
while len(neg_nodes) < args.walk_length - 1:
rand_node = random.randint(0, n_nodes - 1)
if rand_node not in pos_nodes:
neg_nodes.append(rand_node)
neg_nodes = torch.from_numpy(np.array(neg_nodes)).long()
# Do actual prediction
src_node = torch.from_numpy(np.array([curr_pair[0]])).long()
tgt_nodes = torch.from_numpy(np.array(curr_pair[1])).long()
optimizer_dw.zero_grad()
log_pos = sg(src_node, tgt_nodes, neg_sample=False)
if args.ns == 1:
loss_neg = sg(src_node, neg_nodes, neg_sample=True)
loss_dw = log_pos + loss_neg
else:
loss_dw = log_pos
loss_dw.backward(retain_graph=True)
cur_dw_loss = loss_dw.item()
optimizer_dw.step()
loss = loss_function(preds=model.dc(z),
labels=adj_label,
mu=mu,
logvar=logvar,
n_nodes=n_nodes,
norm=norm,
pos_weight=pos_weight)
loss.backward()
cur_loss = loss.item()
optimizer.step()
hidden_emb = mu.data.numpy()
roc_curr, ap_curr = get_roc_score(hidden_emb, adj_orig, val_edges,
val_edges_false)
if args.dw == 1:
tqdm.write(
"Epoch: {}, train_loss_gae={:.5f}, train_loss_dw={:.5f}, val_ap={:.5f}, time={:.5f}"
.format(epoch + 1, cur_loss, cur_dw_loss, ap_curr,
time.time() - t))
else:
tqdm.write(
"Epoch: {}, train_loss_gae={:.5f}, val_ap={:.5f}, time={:.5f}".
format(epoch + 1, cur_loss, ap_curr,
time.time() - t))
if (epoch + 1) % 10 == 0:
tqdm.write("Evaluating intermediate results...")
kmeans = KMeans(n_clusters=args.n_clusters,
random_state=0).fit(hidden_emb)
predict_labels = kmeans.predict(hidden_emb)
cm = clustering_metrics(true_labels, predict_labels)
cm.evaluationClusterModelFromLabel(tqdm)
roc_score, ap_score = get_roc_score(hidden_emb, adj_orig,
test_edges, test_edges_false)
tqdm.write('ROC: {}, AP: {}'.format(roc_score, ap_score))
np.save('logs/emb_epoch_{}.npy'.format(epoch + 1), hidden_emb)
tqdm.write("Optimization Finished!")
roc_score, ap_score = get_roc_score(hidden_emb, adj_orig, test_edges,
test_edges_false)
tqdm.write('Test ROC score: ' + str(roc_score))
tqdm.write('Test AP score: ' + str(ap_score))
kmeans = KMeans(n_clusters=args.n_clusters, random_state=0).fit(hidden_emb)
predict_labels = kmeans.predict(hidden_emb)
cm = clustering_metrics(true_labels, predict_labels)
cm.evaluationClusterModelFromLabel(tqdm)
if args.plot == 1:
cm.plotClusters(tqdm, hidden_emb, true_labels)
def gae_for(args):
print("Using {} dataset".format(args.dataset_str))
adj, features = load_data(args.dataset_str)
n_nodes, feat_dim = features.shape
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sparse.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(adj)
adj = adj_train
# Some preprocessing
#adj_norm = preprocess_graph(adj)
# adj_label = adj_train + sparse.eye(adj_train.shape[0])
# adj_label = sparse_to_tuple(adj_label)
#adj_label = torch.FloatTensor(adj_label.toarray())
pos_weight = (adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
#G = graphs.Graph(adj)
#method = 'variation_neighborhood'
# Parameters
#r = 0.6 # the extend of dimensionality reduction (r=0 means no reduction)
#k = 5
#kmax = int(3*k)
#C, Gc, Call, Gall = coarsen(G, K=k, r=r, method=method)
#adj_coarse = Gc.W
#adj_label = torch.FloatTensor(adj_coarse.toarray())
#D = sp.sparse.diags(np.array(1/np.sum(C,0))[0])
#Pinv = C.dot(D)
#adj_temp = Pinv.dot(G.W)
#adj_norm = sparse_mx_to_torch_sparse_tensor(adj_temp)
#adj_norm = torch.FloatTensor(np.array(adj_temp.todense()))
#n_nodes = adj_coarse.shape[0]
model = GATcoarseVAE(feat_dim, args.hidden1, args.hidden2, args.dropout, args.alpha)
model2 = MLP(args.hidden2,args.num_classes)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
adj_coarse, adj_label, n_nodes = coarsening(args, adj)
pos_weight = torch.FloatTensor(np.repeat(pos_weight, n_nodes))
hidden_emb = None
for epoch in range(args.epochs):
t = time.time()
model.train()
optimizer.zero_grad()
recovered, mu, logvar = model(features, adj_coarse)
loss = loss_function(preds=recovered, labels=adj_label,
mu=mu, logvar=logvar, n_nodes=n_nodes,
norm=norm, pos_weight=pos_weight)
loss.backward()
cur_loss = loss.item()
optimizer.step()
hidden_emb = mu.data.numpy()
# roc_curr, ap_curr = get_roc_score(hidden_emb, adj_orig, val_edges, val_edges_false)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(cur_loss),
# "val_ap=", "{:.5f}".format(ap_curr),
"time=", "{:.5f}".format(time.time() - t)
)
print("Optimization Finished!")
for epoch in range(args.epochs):
model2.train()
for i, data in enumerate(train_loader):
data = data.to(args.device)
out = model2(data)
loss = F.nll_loss(out, data.y)
print("Training loss:{}".format(loss.item()))
loss.backward()
optimizer.step()
optimizer.zero_grad()
val_acc,val_loss = test(model,val_loader)
print("Validation loss:{}\taccuracy:{}".format(val_loss,val_acc))
if val_loss < min_loss:
torch.save(model.state_dict(),'latest.pth')
print("Model saved at epoch{}".format(epoch))
min_loss = val_loss
patience = 0
else:
patience += 1
if patience > args.patience:
break
test_acc,test_loss = test(model,test_loader)
print("Test accuarcy:{}".fotmat(test_acc))
# roc_score, ap_score = get_roc_score(hidden_emb, adj_orig, test_edges, test_edges_false)
# print('Test ROC score: ' + str(roc_score))
# print('Test AP score: ' + str(ap_score))
if __name__ == '__main__':
gae_for(args)
def gae_for(args):
print("Using {} dataset".format(args.dataset_str))
adj, features = load_data(args.dataset_str)
n_nodes, feat_dim = features.shape
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sp.dia_matrix(
(adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(
adj)
adj = adj_train
# Some preprocessing
adj_norm = preprocess_graph(adj)
adj_label = adj_train + sp.eye(adj_train.shape[0])
# adj_label = sparse_to_tuple(adj_label)
adj_label = torch.FloatTensor(adj_label.toarray())
pos_weight = torch.Tensor(
[float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()])
norm = adj.shape[0] * adj.shape[0] / float(
(adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
model = GCNModelVAE(feat_dim, args.hidden1, args.hidden2, args.dropout)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
hidden_emb = None
for epoch in range(args.epochs):
t = time.time()
model.train()
optimizer.zero_grad()
recovered, mu, logvar = model(features, adj_norm)
loss = loss_function(preds=recovered,
labels=adj_label,
mu=mu,
logvar=logvar,
n_nodes=n_nodes,
norm=norm,
pos_weight=pos_weight)
loss.backward()
cur_loss = loss.item()
optimizer.step()
hidden_emb = mu.data.numpy()
roc_curr, ap_curr = get_roc_score(hidden_emb, adj_orig, val_edges,
val_edges_false)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(cur_loss), "val_ap=", "{:.5f}".format(ap_curr),
"time=", "{:.5f}".format(time.time() - t))
print("Optimization Finished!")
roc_score, ap_score = get_roc_score(hidden_emb, adj_orig, test_edges,
test_edges_false)
print('Test ROC score: ' + str(roc_score))
print('Test AP score: ' + str(ap_score))
def GAEembedding(z, adj, args):
'''
GAE embedding for clustering
Param:
z,adj
Return:
Embedding from graph
'''
# true_labels = np.asarray(true_labels)
# args.model = 'gcn_vae'
# args.dw = 0
# args.epochs = 200
# args.hidden1 = 32
# args.hidden2 = 16
# args.lr = 0.01
# args.dropout = 0.
# args.dataset_sr = 'cora'
# args.walk_length = 5
# args.window_size = 3
# args.number_walks = 5
# args.full_number_walks =0
# args.lr_dw = 0.001
# args.context = 0
# args.ns = 1
# args.n_clusters = 11
# args.plot = 0
# featrues from z
# Louvain
features = z
# features = torch.DoubleTensor(features)
features = torch.FloatTensor(features)
# Old implementation
# adj, features, y_test, tx, ty, test_maks, true_labels = load_data(args.dataset_str)
n_nodes, feat_dim = features.shape
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sp.dia_matrix(
(adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(
adj)
adj = adj_train
# Before proceeding further, make the structure for doing deepWalk
# if args.dw == 1:
# print('Using deepWalk regularization...')
# G = load_edgelist_from_csr_matrix(adj_orig, undirected=True)
# print("Number of nodes: {}".format(len(G.nodes())))
# num_walks = len(G.nodes()) * args.number_walks
# print("Number of walks: {}".format(num_walks))
# data_size = num_walks * args.walk_length
# print("Data size (walks*length): {}".format(data_size))
# Some preprocessing
adj_norm = preprocess_graph(adj)
adj_label = adj_train + sp.eye(adj_train.shape[0])
# adj_label = sparse_to_tuple(adj_label)
# adj_label = torch.DoubleTensor(adj_label.toarray())
adj_label = torch.FloatTensor(adj_label.toarray())
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float(
(adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
if args.GAEmodel == 'gcn_vae':
model = GCNModelVAE(feat_dim, args.GAEhidden1, args.GAEhidden2,
args.GAEdropout)
else:
model = GCNModelAE(feat_dim, args.GAEhidden1, args.GAEhidden2,
args.GAEdropout)
if args.precisionModel == 'Double':
model = model.double()
optimizer = optim.Adam(model.parameters(), lr=args.GAElr)
# if args.dw == 1:
# sg = SkipGram(args.hidden2, adj.shape[0])
# optimizer_dw = optim.Adam(sg.parameters(), lr=args.lr_dw)
# # Construct the nodes for doing random walk. Doing it before since the seed is fixed
# nodes_in_G = list(G.nodes())
# chunks = len(nodes_in_G) // args.number_walks
# random.Random().shuffle(nodes_in_G)
hidden_emb = None
for epoch in tqdm(range(args.GAEepochs)):
t = time.time()
# mem=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
# print('Mem consumption before training: '+str(mem))
model.train()
optimizer.zero_grad()
z, mu, logvar = model(features, adj_norm)
# After back-propagating gae loss, now do the deepWalk regularization
# if args.dw == 1:
# sg.train()
# if args.full_number_walks > 0:
# walks = build_deepwalk_corpus(G, num_paths=args.full_number_walks,
# path_length=args.walk_length, alpha=0,
# rand=random.Random(SEED))
# else:
# walks = build_deepwalk_corpus_iter(G, num_paths=args.number_walks,
# path_length=args.walk_length, alpha=0,
# rand=random.Random(SEED),
# chunk=epoch % chunks,
# nodes=nodes_in_G)
# for walk in walks:
# if args.context == 1:
# # Construct the pairs for predicting context node
# # for each node, treated as center word
# curr_pair = (int(walk[center_node_pos]), [])
# for center_node_pos in range(len(walk)):
# # for each window position
# for w in range(-args.window_size, args.window_size + 1):
# context_node_pos = center_node_pos + w
# # make soure not jump out sentence
# if context_node_pos < 0 or context_node_pos >= len(walk) or center_node_pos == context_node_pos:
# continue
# context_node_idx = walk[context_node_pos]
# curr_pair[1].append(int(context_node_idx))
# else:
# # first item in the walk is the starting node
# curr_pair = (int(walk[0]), [int(context_node_idx) for context_node_idx in walk[1:]])
# if args.ns == 1:
# neg_nodes = []
# pos_nodes = set(walk)
# while len(neg_nodes) < args.walk_length - 1:
# rand_node = random.randint(0, n_nodes - 1)
# if rand_node not in pos_nodes:
# neg_nodes.append(rand_node)
# neg_nodes = torch.from_numpy(np.array(neg_nodes)).long()
# # Do actual prediction
# src_node = torch.from_numpy(np.array([curr_pair[0]])).long()
# tgt_nodes = torch.from_numpy(np.array(curr_pair[1])).long()
# optimizer_dw.zero_grad()
# log_pos = sg(src_node, tgt_nodes, neg_sample=False)
# if args.ns == 1:
# loss_neg = sg(src_node, neg_nodes, neg_sample=True)
# loss_dw = log_pos + loss_neg
# else:
# loss_dw = log_pos
# loss_dw.backward(retain_graph=True)
# cur_dw_loss = loss_dw.item()
# optimizer_dw.step()
loss = loss_function(preds=model.dc(z),
labels=adj_label,
mu=mu,
logvar=logvar,
n_nodes=n_nodes,
norm=norm,
pos_weight=pos_weight)
loss.backward()
cur_loss = loss.item()
optimizer.step()
hidden_emb = mu.data.numpy()
# TODO, this is prediction
# roc_curr, ap_curr = get_roc_score(hidden_emb, adj_orig, val_edges, val_edges_false)
ap_curr = 0
# if args.dw == 1:
# tqdm.write("Epoch: {}, train_loss_gae={:.5f}, train_loss_dw={:.5f}, val_ap={:.5f}, time={:.5f}".format(
# epoch + 1, cur_loss, cur_dw_loss,
# ap_curr, time.time() - t))
# else:
tqdm.write(
"Epoch: {}, train_loss_gae={:.5f}, val_ap={:.5f}, time={:.5f}".
format(epoch + 1, cur_loss, ap_curr,
time.time() - t))
# if (epoch + 1) % 10 == 0:
# tqdm.write("Evaluating intermediate results...")
# kmeans = KMeans(n_clusters=args.n_clusters, random_state=0).fit(hidden_emb)
# predict_labels = kmeans.predict(hidden_emb)
# cm = clustering_metrics(true_labels, predict_labels)
# cm.evaluationClusterModelFromLabel(tqdm)
# roc_score, ap_score = get_roc_score(hidden_emb, adj_orig, test_edges, test_edges_false)
# tqdm.write('ROC: {}, AP: {}'.format(roc_score, ap_score))
# np.save('logs/emb_epoch_{}.npy'.format(epoch + 1), hidden_emb)
tqdm.write("Optimization Finished!")
roc_score, ap_score = get_roc_score(hidden_emb, adj_orig, test_edges,
test_edges_false)
tqdm.write('Test ROC score: ' + str(roc_score))
tqdm.write('Test AP score: ' + str(ap_score))
# kmeans = KMeans(n_clusters=args.n_clusters, random_state=0).fit(hidden_emb)
# predict_labels = kmeans.predict(hidden_emb)
# cm = clustering_metrics(true_labels, predict_labels)
# cm.evaluationClusterModelFromLabel(tqdm)
# if args.GAEplot == 1:
# cm.plotClusters(tqdm, hidden_emb, true_labels)
return hidden_emb
def gae_for(args):
print("Using {} dataset".format(args.dataset_str))
adj, features = load_data(args.dataset_str)
n_nodes, feat_dim = features.shape
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sp.dia_matrix(
(adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(
adj)
adj = adj_train
# Some preprocessing
adj_norm = preprocess_graph(adj)
adj_label = adj_train + sp.eye(adj_train.shape[0])
# adj_label = sparse_to_tuple(adj_label)
adj_label = torch.FloatTensor(adj_label.toarray())
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float(
(adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
lst_result = []
for i in range(10):
model = GCNModelVAE(feat_dim, args.hidden1, args.hidden2, args.dropout)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
hidden_emb = None
max_roc_ap = 0
for epoch in range(args.epochs):
t = time.time()
model.train()
optimizer.zero_grad()
recovered, mu, logvar = model(features, adj_norm)
loss = loss_function(preds=recovered,
labels=adj_label,
mu=mu,
logvar=logvar,
n_nodes=n_nodes,
norm=norm,
pos_weight=pos_weight)
loss.backward()
cur_loss = loss.item()
optimizer.step()
hidden_emb = mu.data.numpy()
roc_curr, ap_curr = get_roc_score(hidden_emb, adj_orig, val_edges,
val_edges_false)
roc_ap = roc_curr + ap_curr
if max_roc_ap < roc_ap:
max_roc_ap = roc_ap
h_emb_best_model = hidden_emb
print("Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(cur_loss), "val_ap=",
"{:.5f}".format(ap_curr), "time=",
"{:.5f}".format(time.time() - t))
roc_score, ap_score = get_roc_score(hidden_emb, adj_orig,
test_edges, test_edges_false)
print('Test ROC score: ' + str(roc_score))
print('Test AP score: ' + str(ap_score))
print("---------------------------------------")
print("Optimization Finished!: ", i)
roc_score, ap_score = get_roc_score(h_emb_best_model, adj_orig,
test_edges, test_edges_false)
# roc_score, ap_score = get_roc_score(hidden_emb, adj_orig, test_edges, test_edges_false)
lst_result.append([i, roc_score, ap_score])
print('Test ROC score: ' + str(roc_score))
print('Test AP score: ' + str(ap_score))
lst_result = np.array(lst_result)
csv_info = np.append(
lst_result,
[["mean", np.mean(lst_result[:, 1]),
np.mean(lst_result[:, 2])]],
axis=0)
csv_info = np.append(
csv_info,
[["std", np.std(lst_result[:, 1]),
np.std(lst_result[:, 2])]],
axis=0)
t = int(time.time())
folder = Path(os.path.join(os.getcwd(), "csv"))
csv_name = "{}_{}_{}_{}_{}.csv".format(args.dataset_str, args.epochs,
args.hidden1, args.hidden2, t)
df = pd.DataFrame(csv_info, columns=['run', 'ROC', "AP"])
df.to_csv(os.path.join(folder, csv_name))
def GAEembedding(z, adj, args):
'''
GAE embedding for clustering
Param:
z,adj
Return:
Embedding from graph
'''
# featrues from z
# Louvain
features = z
# features = torch.DoubleTensor(features)
features = torch.FloatTensor(features)
# Old implementation
# adj, features, y_test, tx, ty, test_maks, true_labels = load_data(args.dataset_str)
n_nodes, feat_dim = features.shape
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sp.dia_matrix(
(adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(
adj)
adj = adj_train
# Some preprocessing
adj_norm = preprocess_graph(adj)
adj_label = adj_train + sp.eye(adj_train.shape[0])
# adj_label = sparse_to_tuple(adj_label)
# adj_label = torch.DoubleTensor(adj_label.toarray())
adj_label = torch.FloatTensor(adj_label.toarray())
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float(
(adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
if args.GAEmodel == 'gcn_vae':
model = GCNModelVAE(feat_dim, args.GAEhidden1, args.GAEhidden2,
args.GAEdropout)
else:
model = GCNModelAE(feat_dim, args.GAEhidden1, args.GAEhidden2,
args.GAEdropout)
if args.precisionModel == 'Double':
model = model.double()
optimizer = optim.Adam(model.parameters(), lr=args.GAElr)
hidden_emb = None
for epoch in tqdm(range(args.GAEepochs)):
t = time.time()
# mem=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
# print('Mem consumption before training: '+str(mem))
model.train()
optimizer.zero_grad()
z, mu, logvar = model(features, adj_norm)
loss = loss_function(preds=model.dc(z),
labels=adj_label,
mu=mu,
logvar=logvar,
n_nodes=n_nodes,
norm=norm,
pos_weight=pos_weight)
loss.backward()
cur_loss = loss.item()
optimizer.step()
hidden_emb = mu.data.numpy()
# TODO, this is prediction
# roc_curr, ap_curr = get_roc_score(hidden_emb, adj_orig, val_edges, val_edges_false)
ap_curr = 0
tqdm.write(
"Epoch: {}, train_loss_gae={:.5f}, val_ap={:.5f}, time={:.5f}".
format(epoch + 1, cur_loss, ap_curr,
time.time() - t))
tqdm.write("Optimization Finished!")
roc_score, ap_score = get_roc_score(hidden_emb, adj_orig, test_edges,
test_edges_false)
tqdm.write('Test ROC score: ' + str(roc_score))
tqdm.write('Test AP score: ' + str(ap_score))
return hidden_emb