def task(embeds):
train_embs = embeds[0, idx_train]
val_embs = embeds[0, idx_val]
test_embs = embeds[0, idx_test]
log = LogReg(hid_units, nb_classes)
opt = torch.optim.Adam(log.parameters(), lr=0.01, weight_decay=0.0)
if torch.cuda.is_available():
log.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]
return acc.detach().cpu().numpy()
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)
tot = tot.cuda()
accs = []
for _ in range(50):
log = LogReg(hid_units, nb_classes)
opt = torch.optim.Adam(log.parameters(), lr=0.01, weight_decay=0.0)
log.cuda()
pat_steps = 0
best_acc = torch.zeros(1)
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()
def run_GCN(args,
gpu_id=None,
exp_name=None,
number=0,
return_model=False,
return_time_series=False):
random.seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
final_acc = 0
best_acc = 0
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
running_device = "cpu" if gpu_id is None \
else torch.device('cuda:{}'.format(gpu_id) if torch.cuda.is_available() else 'cpu')
dataset_kwargs = {}
train_d, adj_list, x_list = get_dataset(args, dataset_kwargs)
lable = train_d.data.y
A_I = adj_list[0]
A_I_nomal = adj_list[1]
nb_edges = train_d.data.num_edges
nb_nodes = train_d.data.num_nodes
nb_feature = train_d.data.num_features
nb_classes = int(lable.max() - lable.min()) + 1
lable_matrix = (lable.view(nb_nodes, 1).repeat(1, nb_nodes) == lable.view(
1, nb_nodes).repeat(nb_nodes, 1)) + 0
I = (torch.eye(nb_nodes).to(lable_matrix.device) == 1)
lable_matrix[I] = 0
zero_vec = 0.0 * torch.ones_like(A_I_nomal)
if args.dataset_name in [
'Photo', 'DBLP', 'Crocodile', 'CoraFull', 'WikiCS'
]:
useA = True
else:
useA = False
model = UGRL_GCN_test(nb_nodes,
nb_feature,
args.dim,
dim_x=args.dim_x,
useact=args.usingact,
liner=args.UsingLiner,
dropout=args.dropout,
useA=useA)
optimiser = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=0.0001)
model.to(running_device)
lable = lable.to(running_device)
if args.dataset_name == 'WikiCS':
train_lbls = lable[train_d.data.train_mask[:, args.NewATop]] # capture
test_lbls = lable[train_d.data.test_mask]
elif args.dataset_name in ['Cora', 'CiteSeer', 'PubMed']:
train_lbls = lable[train_d.data.train_mask]
test_lbls = lable[train_d.data.test_mask]
elif args.dataset_name in ['Photo', 'DBLP', 'Crocodile', 'CoraFull']:
train_index = []
test_index = []
for j in range(lable.max().item() + 1):
num = ((lable == j) + 0).sum().item()
index = torch.range(0, len(lable) - 1)[(lable == j)]
x_list0 = random.sample(list(index), int(len(index) * 0.1))
for x in x_list0:
train_index.append(int(x))
for c in range(len(lable)):
if int(c) not in train_index:
test_index.append(int(c))
train_lbls = lable[train_index]
test_lbls = lable[test_index]
val_lbls = lable[train_index]
A_I_nomal_dense = A_I_nomal
I_input = torch.eye(A_I_nomal.shape[1]) # .to(A_I_nomal.device)
if args.dataset_name in ['PubMed', 'CoraFull', 'DBLP']:
pass
elif args.dataset_name in ['Crocodile', 'Photo', 'WikiCS']:
A_I_nomal_dense = A_I_nomal_dense.to(running_device)
######################sparse################
if args.dataset_name in [
'PubMed', 'Crocodile', 'CoraFull', 'DBLP', 'Photo', 'WikiCS'
]:
A_I_nomal = A_I_nomal.to_sparse()
model.sparse = True
I_input = I_input.to_sparse()
######################sparse################
A_I_nomal = A_I_nomal.to(running_device)
I_input = I_input.to(A_I_nomal.device)
mask_I = I.to(running_device)
zero_vec = zero_vec.to(running_device)
my_margin = args.margin1
my_margin_2 = my_margin + args.margin2
margin_loss = torch.nn.MarginRankingLoss(margin=my_margin, reduce=False)
num_neg = args.NN
for current_iter, epoch in enumerate(
tqdm(range(args.start_epoch, args.start_epoch + args.epochs + 1))):
model.train()
optimiser.zero_grad()
idx = np.random.permutation(nb_nodes)
feature_X = x_list[0].to(running_device)
lbl_z = torch.tensor([0.]).to(running_device)
feature_a = feature_X
feature_p = feature_X
feature_n = []
idx_list = []
idx_lable = []
for i in range(num_neg):
idx_0 = np.random.permutation(nb_nodes)
idx_list.append(idx_0)
idx_lable.append(lable[idx_0])
feature_temp = feature_X[idx_0]
feature_n.append(feature_temp)
h_a, h_p, h_n_lsit, h_a_0, h_p_0, h_n_0_list = model(feature_a,
feature_p,
feature_n,
A_I_nomal,
I=I_input)
s_p = F.pairwise_distance(h_a, h_p)
cos_0_list = []
for h_n_0 in h_n_0_list:
cos_0 = F.pairwise_distance(h_a_0, h_n_0)
cos_0_list.append(cos_0)
cos_0_stack = torch.stack(cos_0_list).detach()
cos_0_min = cos_0_stack.min(dim=0)[0]
cos_0_max = cos_0_stack.max(dim=0)[0]
gap = cos_0_max - cos_0_min
weight_list = []
for i in range(cos_0_stack.size()[0]):
weight_list.append((cos_0_stack[i] - cos_0_min) / gap)
s_n_list = []
s_n_cosin_list = []
for h_n in h_n_lsit:
if args.dataset_name in ['Cora', 'CiteSeer']:
s_n_cosin_list.append(cosine_dist(h_a, h_n)[mask_I].detach())
s_n = F.pairwise_distance(h_a, h_n)
s_n_list.append(s_n)
margin_label = -1 * torch.ones_like(s_p)
loss_mar = 0
mask_margin_N = 0
i = 0
for s_n in s_n_list:
loss_mar += (margin_loss(s_p, s_n, margin_label) *
weight_list[i]).mean()
mask_margin_N += torch.max((s_n - s_p.detach() - my_margin_2),
lbl_z).sum()
i += 1
mask_margin_N = mask_margin_N / num_neg
string_1 = " loss_1: {:.3f}||loss_2: {:.3f}||".format(
loss_mar.item(), mask_margin_N.item())
loss = loss_mar * args.w_loss1 + mask_margin_N * args.w_loss2 / nb_nodes
if args.dataset_name in ['Cora']:
loss = loss_mar * args.w_loss1 + mask_margin_N * args.w_loss2
loss.backward()
optimiser.step()
model.eval()
if args.dataset_name in ['Crocodile', 'WikiCS', 'Photo']:
h_p_d = h_p.detach()
S_new = cosine_dist(h_p_d, h_p_d)
model.A = normalize_graph(torch.mul(S_new,
A_I_nomal_dense)).to_sparse()
elif args.dataset_name in ['Cora', 'CiteSeer']:
h_a, h_p = model.embed(feature_a,
feature_p,
feature_n,
A_I_nomal,
I=I_input)
s_a = cosine_dist(h_a, h_a).detach()
S = (torch.stack(s_n_cosin_list).mean(dim=0).expand_as(A_I) -
s_a).detach()
# zero_vec = -9e15 * torch.ones_like(S)
one_vec = torch.ones_like(S)
s_a = torch.where(A_I_nomal > 0, one_vec, zero_vec)
attention = torch.where(S < 0, s_a, zero_vec)
attention_N = normalize_graph(attention)
attention[I] = 0
model.A = attention_N
if epoch % 50 == 0:
model.eval()
h_a, h_p = model.embed(feature_a,
feature_p,
feature_n,
A_I_nomal,
I=I_input)
if args.useNewA:
embs = h_p #torch.cat((h_a,h_p),dim=1)
else:
embs = h_a
if args.dataset_name in ['Cora', 'CiteSeer', 'PubMed']:
embs = embs / embs.norm(dim=1)[:, None]
if args.dataset_name == 'WikiCS':
train_embs = embs[train_d.data.train_mask[:, args.NewATop]]
test_embs = embs[train_d.data.test_mask]
elif args.dataset_name in ['Cora', 'CiteSeer', 'PubMed']:
train_embs = embs[train_d.data.train_mask]
test_embs = embs[train_d.data.test_mask]
# #val_embs = embs[train_d.data.val_mask]
elif args.dataset_name in [
'Photo', 'DBLP', 'Crocodile', 'CoraFull'
]:
train_embs = embs[train_index]
test_embs = embs[test_index]
#val_embs = embs[train_index]
accs = []
accs_small = []
xent = nn.CrossEntropyLoss()
for _ in range(2):
log = LogReg(args.dim, nb_classes)
opt = torch.optim.Adam(log.parameters(),
lr=1e-2,
weight_decay=args.wd)
log.to(running_device)
for _ in range(args.num1):
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)
ac = []
for i in range(nb_classes):
acc_small = torch.sum(
preds[test_lbls == i] == test_lbls[test_lbls == i]
).float() / test_lbls[test_lbls == i].shape[0]
ac.append(acc_small * 100)
accs_small = ac
accs = torch.stack(accs)
string_3 = ""
for i in range(nb_classes):
string_3 = string_3 + "|{:.1f}".format(accs_small[i].item())
string_2 = Fore.GREEN + " epoch: {},accs: {:.1f},std: {:.2f} ".format(
epoch,
accs.mean().item(),
accs.std().item())
tqdm.write(string_1 + string_2 + string_3)
final_acc = accs.mean().item()
best_acc = max(best_acc, final_acc)
return final_acc, best_acc
def evaluate(embeds, idx_train, idx_val, idx_test, labels, device, isTest=True):
hid_units = embeds.shape[2]
nb_classes = labels.shape[2]
xent = nn.CrossEntropyLoss()
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)
accs = []
micro_f1s = []
macro_f1s = []
macro_f1s_val = [] ##
for _ in range(50):
log = LogReg(hid_units, nb_classes)
opt = torch.optim.Adam(log.parameters(), lr=0.01, weight_decay=0.0)
log.to(device)
val_accs = []; test_accs = []
val_micro_f1s = []; test_micro_f1s = []
val_macro_f1s = []; test_macro_f1s = []
for iter_ in range(50):
# train
log.train()
opt.zero_grad()
logits = log(train_embs)
loss = xent(logits, train_lbls)
loss.backward()
opt.step()
# val
logits = log(val_embs)
preds = torch.argmax(logits, dim=1)
val_acc = torch.sum(preds == val_lbls).float() / val_lbls.shape[0]
val_f1_macro = f1_score(val_lbls.cpu(), preds.cpu(), average='macro')
val_f1_micro = f1_score(val_lbls.cpu(), preds.cpu(), average='micro')
val_accs.append(val_acc.item())
val_macro_f1s.append(val_f1_macro)
val_micro_f1s.append(val_f1_micro)
# test
logits = log(test_embs)
preds = torch.argmax(logits, dim=1)
test_acc = torch.sum(preds == test_lbls).float() / test_lbls.shape[0]
test_f1_macro = f1_score(test_lbls.cpu(), preds.cpu(), average='macro')
test_f1_micro = f1_score(test_lbls.cpu(), preds.cpu(), average='micro')
test_accs.append(test_acc.item())
test_macro_f1s.append(test_f1_macro)
test_micro_f1s.append(test_f1_micro)
max_iter = val_accs.index(max(val_accs))
accs.append(test_accs[max_iter])
max_iter = val_macro_f1s.index(max(val_macro_f1s))
macro_f1s.append(test_macro_f1s[max_iter])
macro_f1s_val.append(val_macro_f1s[max_iter]) ###
max_iter = val_micro_f1s.index(max(val_micro_f1s))
micro_f1s.append(test_micro_f1s[max_iter])
if isTest:
print("\t[Classification] Macro-F1: {:.4f} ({:.4f}) | Micro-F1: {:.4f} ({:.4f})".format(np.mean(macro_f1s),
np.std(macro_f1s),
np.mean(micro_f1s),
np.std(micro_f1s)))
else:
return np.mean(macro_f1s_val), np.mean(macro_f1s)
test_embs = np.array(test_embs.cpu())
test_lbls = np.array(test_lbls.cpu())
run_kmeans(test_embs, test_lbls, nb_classes)
run_similarity_search(test_embs, test_lbls)
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))
#transfom to torch striuctures
dtype = torch.float
device = torch.device("cpu")
X_train = torch.tensor(X_train_df.values, device=device, dtype=dtype)
y_train = torch.tensor(y_train_df.values, device=device, dtype=dtype)
# Softmax regression model
n_features = X_train.size()[1]
n_classes = len(np.unique(y_train.round().numpy()))
model = LogReg(name='logreg', d_in=n_features, d_out=n_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
size_batch = 100
loss_hist = []
# Train
start = time.time()
for t in range(5000):
X_train_mini, y_train_mini = get_mini_batching(X_train, y_train,
size_batch)
# Berechne die Vorhersage (foward step)
outputs = model(X_train_mini)
# Berechne den Fehler (Ausgabe des Fehlers alle 50 Iterationen)
def process_inductive(dataset, gnn_type="GCNConv", K=None, random_init=False, runs=10):
hyperparameters = get_hyperparameters()
nb_epochs = hyperparameters["nb_epochs"]
patience = hyperparameters["patience"]
lr = hyperparameters["lr"]
l2_coef = hyperparameters["l2_coef"]
drop_prob = hyperparameters["drop_prob"]
hid_units = hyperparameters["hid_units"]
nonlinearity = hyperparameters["nonlinearity"]
batch_size = hyperparameters["batch_size"]
norm_features = torch_geometric.transforms.NormalizeFeatures()
dataset_train = PPI(
"./geometric_datasets/"+dataset,
split="train",
transform=norm_features,
)
print(dataset_train)
dataset_val = PPI(
"./geometric_datasets/"+dataset,
split="val",
transform=norm_features,
)
print(dataset_val)
dataset_test = PPI(
"./geometric_datasets/"+dataset,
split="test",
transform=norm_features,
)
data = []
for d in dataset_train:
data.append(d)
for d in dataset_val:
data.append(d)
ft_size = dataset_train[0].x.shape[1]
nb_classes = dataset_train[0].y.shape[1] # multilabel
b_xent = nn.BCEWithLogitsLoss()
loader_train = DataLoader(
data,
batch_size=hyperparameters["batch_size"],
shuffle=True,
)
loader_test = DataLoader(
dataset_test,
batch_size=hyperparameters["batch_size"],
shuffle=False
)
all_accs = []
for _ in range(runs):
model = DGI(ft_size, hid_units, nonlinearity, update_rule=gnn_type, batch_size=1, K=K)
model_name = get_model_name(dataset, gnn_type, K, random_init=random_init)
print(model)
optimiser = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=l2_coef)
if torch.cuda.is_available():
print('Using CUDA')
model = model.cuda()
model.train()
torch.cuda.empty_cache()
for epoch in range(20):
if random_init:
break
total_loss = 0
batch_id = 0
model.train()
loaded = list(loader_train)
for batch in loaded:
optimiser.zero_grad()
if torch.cuda.is_available:
batch = batch.to('cuda')
nb_nodes = batch.x.shape[0]
features = batch.x
labels = batch.y
edge_index = batch.edge_index
idx = np.random.randint(0, len(data))
while idx == batch_id:
idx = np.random.randint(0, len(data))
shuf_fts = torch.nn.functional.dropout(loaded[idx].x, drop_prob)
edge_index2 = loaded[idx].edge_index
lbl_1 = torch.ones(nb_nodes)
lbl_2 = torch.zeros(shuf_fts.shape[0])
lbl = torch.cat((lbl_1, lbl_2), 0)
if torch.cuda.is_available():
shuf_fts = shuf_fts.cuda()
if edge_index2 is not None:
edge_index2 = edge_index2.cuda()
lbl = lbl.cuda()
logits = model(features, shuf_fts, edge_index, batch=batch.batch, edge_index_alt=edge_index2)
loss = b_xent(logits, lbl)
loss.backward()
optimiser.step()
batch_id += 1
total_loss += loss.item()
print(epoch, 'Train Loss:', total_loss/(len(dataset_train)))
torch.save(model.state_dict(), './trained_models/'+model_name)
torch.cuda.empty_cache()
print('Loading last epoch')
if not random_init:
model.load_state_dict(torch.load('./trained_models/'+model_name))
model.eval()
b_xent_reg = nn.BCEWithLogitsLoss(pos_weight=torch.tensor(2.25))
train_embs, whole_train_data = preprocess_embeddings(model, dataset_train)
val_embs, whole_val_data = preprocess_embeddings(model, dataset_val)
test_embs, whole_test_data = preprocess_embeddings(model, dataset_test)
for _ in range(50):
log = LogReg(hid_units, nb_classes)
opt = torch.optim.Adam(log.parameters(), lr=0.01, weight_decay=0.0)
log.cuda()
pat_steps = 0
best = 1e9
log.train()
for _ in range(250):
opt.zero_grad()
logits = log(train_embs)
loss = b_xent_reg(logits, whole_train_data.y)
loss.backward()
opt.step()
log.eval()
val_logits = log(val_embs)
loss = b_xent_reg(val_logits, whole_val_data.y)
if loss.item() < best:
best = loss.item()
pat_steps = 0
if pat_steps >= 5:
break
pat_steps += 1
log.eval()
logits = log(test_embs)
preds = torch.sigmoid(logits) > 0.5
f1 = sklearn.metrics.f1_score(whole_test_data.y.cpu(), preds.long().cpu(), average='micro')
all_accs.append(float(f1))
print()
print('Micro-averaged f1:', f1)
all_accs = torch.tensor(all_accs)
with open("./results/"+model_name[:-4]+"_results.txt", "w") as f:
f.writelines([str(all_accs.mean().item())+'\n', str(all_accs.std().item())])
print(all_accs.mean())
print(all_accs.std())
def process_transductive(dataset, gnn_type='GCNConv', K=None, random_init=False, runs=10, drop_sigma=False, just_plot=False):
dataset_str = dataset
norm_features = torch_geometric.transforms.NormalizeFeatures()
dataset = Planetoid("./geometric_datasets"+'/'+dataset,
dataset,
transform=norm_features)[0]
# training params
batch_size = 1 # Transductive setting
hyperparameters = get_hyperparameters()
nb_epochs = hyperparameters["nb_epochs"]
patience = hyperparameters["patience"]
lr = hyperparameters["lr"]
xent = nn.CrossEntropyLoss()
l2_coef = hyperparameters["l2_coef"]
drop_prob = hyperparameters["drop_prob"]
hid_units = hyperparameters["hid_units"]
nonlinearity = hyperparameters["nonlinearity"]
nb_nodes = dataset.x.shape[0]
ft_size = dataset.x.shape[1]
nb_classes = torch.max(dataset.y).item()+1 # 0 based cnt
features = dataset.x
labels = dataset.y
edge_index = dataset.edge_index
edge_index, _ = torch_geometric.utils.add_remaining_self_loops(edge_index)
mask_train = dataset.train_mask
mask_val = dataset.val_mask
mask_test = dataset.test_mask
model_name = get_model_name(dataset_str, gnn_type, K, random_init=random_init, drop_sigma=drop_sigma)
with open("./results/"+model_name[:-4]+"_results.txt", "w") as f:
pass
accs = []
for i in range(runs):
model = DGI(ft_size, hid_units, nonlinearity, update_rule=gnn_type, K=K, drop_sigma=drop_sigma)
print(model, model_name, drop_sigma)
optimiser = torch.optim.Adam(model.parameters(), lr=lr)
if torch.cuda.is_available():
print('Using CUDA')
features = features.cuda()
labels = labels.cuda()
edge_index = edge_index.cuda()
mask_train = mask_train.cuda()
mask_val = mask_val.cuda()
mask_test = mask_test.cuda()
model = model.cuda()
best_t = train_transductive(dataset, dataset_str, edge_index, gnn_type, model_name, K=K, random_init=random_init, drop_sigma=drop_sigma)
xent = nn.CrossEntropyLoss()
print('Loading {}th epoch'.format(best_t))
print(model, model_name)
if not random_init:
model.load_state_dict(torch.load('./trained_models/'+model_name))
model.eval()
embeds, _ = model.embed(features, edge_index, None, standardise=False)
if just_plot:
plot_tsne(embeds, labels, model_name)
exit(0)
train_embs = embeds[mask_train, :]
val_embs = embeds[mask_val, :]
test_embs = embeds[mask_test, :]
train_lbls = labels[mask_train]
val_lbls = labels[mask_val]
test_lbls = labels[mask_test]
tot = torch.zeros(1)
tot = tot.cuda()
for _ in range(50):
log = LogReg(hid_units, nb_classes)
opt = torch.optim.Adam(log.parameters(), lr=0.01, weight_decay=0.0)
log.cuda()
pat_steps = 0
best_acc = torch.zeros(1)
best_acc = best_acc.cuda()
for _ in range(150):
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)
print(acc)
tot += acc
print('Average accuracy:', tot / 50)
all_accs = torch.stack(accs, dim=0)
with open("./results/"+model_name[:-4]+"_results.txt", "a+") as f:
f.writelines([str(all_accs.mean().item())+'\n', str(all_accs.std().item())+'\n'])
print(all_accs.mean())
print(all_accs.std())
#n_features = X.size()[1]
n_classes = 2
H_0 = 150
H_1 = 125
n_iter = 500
print('features: ', n_features)
print('classes: ', n_classes)
model = LogReg(
'logreg', n_features,
n_classes) #IMDB_NN_Model('imdb', n_features, H_0, H_1, n_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
size_batch = 256
loss_hist = []
loss_val_hist = []
acc_train = []
acc_val = []
# Train
start = time.time()
for t in range(n_iter):
X_mini, y_mini = get_mini_batching(X_train, y_train, size_batch)
# Berechne die Vorhersage (foward step)
outputs = model(X_mini)