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main_bs.py
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main_bs.py
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"""
Our code is based on GraphENS:
https://github.com/JoonHyung-Park/GraphENS
"""
import os.path as osp
import random
import torch
import torch.nn.functional as F
from nets import *
from data_utils import *
from args import parse_args
from models import *
from losses import *
from sklearn.metrics import balanced_accuracy_score, f1_score
import statistics
import numpy as np
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
## Arg Parser ##
args = parse_args()
## Handling exception from arguments ##
assert not (args.warmup < 1 and args.tam)
# assert args.imb_ratio > 1
## Load Dataset ##
dataset = args.dataset
path = osp.join(osp.dirname(osp.realpath(__file__)), 'data', dataset)
dataset = get_dataset(dataset, path, split_type='public')
data = dataset[0]
n_cls = data.y.max().item() + 1
data = data.to(device)
def train():
global class_num_list, aggregator
global data_train_mask, data_val_mask, data_test_mask
model.train()
optimizer.zero_grad()
output = model(data.x, data.edge_index)
## Apply TAM ##
output = adjust_output(args, output, data.edge_index, data.y, \
data_train_mask, aggregator, class_num_list, epoch)
criterion(output, data.y[data_train_mask]).backward()
with torch.no_grad():
model.eval()
output = model(data.x, data.edge_index)
val_loss= F.cross_entropy(output[data_val_mask], data.y[data_val_mask])
optimizer.step()
scheduler.step(val_loss)
@torch.no_grad()
def test():
model.eval()
logits = model(data.x, data.edge_index)
accs, baccs, f1s = [], [], []
for i, mask in enumerate([data_train_mask, data_val_mask, data_test_mask]):
pred = logits[mask].max(1)[1]
y_pred = pred.cpu().numpy()
y_true = data.y[mask].cpu().numpy()
acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
bacc = balanced_accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred, average='macro')
accs.append(acc)
baccs.append(bacc)
f1s.append(f1)
return accs, baccs, f1s
## Log for Experiment Setting ##
setting_log = "Dataset: {}, ratio: {}, net: {}, n_layer: {}, feat_dim: {}, tam: {}".format(
args.dataset, str(args.imb_ratio), args.net, str(args.n_layer), str(args.feat_dim), str(args.tam))
repeatition = 10
seed = 100
avg_val_acc_f1, avg_test_acc, avg_test_bacc, avg_test_f1 = [], [], [], []
for r in range(repeatition):
## Fix seed ##
torch.cuda.empty_cache()
seed += 1
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
random.seed(seed)
np.random.seed(seed)
if args.dataset in ['squirrel', 'chameleon', 'Wisconsin']:
data_train_mask, data_val_mask, data_test_mask = data.train_mask[:,r%10].clone(), data.val_mask[:,r%10].clone(), data.test_mask[:,r%10].clone()
else:
data_train_mask, data_val_mask, data_test_mask = data.train_mask.clone(), data.val_mask.clone(), data.test_mask.clone()
## Data statistic ##
stats = data.y[data_train_mask]
n_data = []
for i in range(n_cls):
data_num = (stats == i).sum()
n_data.append(int(data_num.item()))
idx_info = get_idx_info(data.y, n_cls, data_train_mask)
class_num_list = n_data
# for artificial imbalanced setting: only the last imb_class_num classes are imbalanced
imb_class_num = n_cls // 2
new_class_num_list = []
max_num = np.max(class_num_list[:n_cls-imb_class_num])
for i in range(n_cls):
if args.imb_ratio > 1 and i > n_cls-1-imb_class_num: #only imbalance the last classes
new_class_num_list.append(min(int(max_num*(1./args.imb_ratio)), class_num_list[i]))
else:
new_class_num_list.append(class_num_list[i])
class_num_list = new_class_num_list
if args.imb_ratio > 1:
data_train_mask, idx_info = split_semi_dataset(len(data.x), n_data, n_cls, class_num_list, idx_info, data.x.device)
## Model Selection ##
if args.net == 'GCN':
model = create_gcn(nfeat=dataset.num_features, nhid=args.feat_dim,
nclass=n_cls, dropout=0.5, nlayer=args.n_layer)
elif args.net == 'GAT':
model = create_gat(nfeat=dataset.num_features, nhid=args.feat_dim,
nclass=n_cls, dropout=0.5, nlayer=args.n_layer)
elif args.net == "SAGE":
model = create_sage(nfeat=dataset.num_features, nhid=args.feat_dim,
nclass=n_cls, dropout=0.5, nlayer=args.n_layer)
else:
raise NotImplementedError("Not Implemented Architecture!")
## Criterion Selection ##
if args.loss_type == 'ce': # CE
criterion = CrossEntropy()
elif args.loss_type == 'bs':
criterion = BalancedSoftmax(class_num_list)
else:
raise NotImplementedError("Not Implemented Loss!")
model = model.to(device)
criterion = criterion.to(device)
# Set optimizer
optimizer = torch.optim.Adam([
dict(params=model.reg_params, weight_decay=5e-4),
dict(params=model.non_reg_params, weight_decay=0),], lr=0.01)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',
factor = 0.5,
patience = 100,
verbose=False)
# Train models
best_val_acc_f1 = 0
aggregator = MeanAggregation()
for epoch in range(1, 2001):
train()
accs, baccs, f1s = test()
train_acc, val_acc, tmp_test_acc = accs
train_f1, val_f1, tmp_test_f1 = f1s
val_acc_f1 = (val_acc + val_f1) / 2.
if val_acc_f1 > best_val_acc_f1:
best_val_acc_f1 = val_acc_f1
test_acc = accs[2]
test_bacc = baccs[2]
test_f1 = f1s[2]
avg_val_acc_f1.append(best_val_acc_f1)
avg_test_acc.append(test_acc)
avg_test_bacc.append(test_bacc)
avg_test_f1.append(test_f1)
## Calculate statistics ##
acc_CI = (statistics.stdev(avg_test_acc) / (repeatition ** (1/2)))
bacc_CI = (statistics.stdev(avg_test_bacc) / (repeatition ** (1/2)))
f1_CI = (statistics.stdev(avg_test_f1) / (repeatition ** (1/2)))
avg_acc = statistics.mean(avg_test_acc)
avg_bacc = statistics.mean(avg_test_bacc)
avg_f1 = statistics.mean(avg_test_f1)
avg_val_acc_f1 = statistics.mean(avg_val_acc_f1)
avg_log = 'Test Acc: {:.4f} +- {:.4f}, BAcc: {:.4f} +- {:.4f}, F1: {:.4f} +- {:.4f}, Val Acc F1: {:.4f}'
avg_log = avg_log.format(avg_acc ,acc_CI ,avg_bacc, bacc_CI, avg_f1, f1_CI, avg_val_acc_f1)
log = "{}\n{}".format(setting_log, avg_log)
print(log)