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from deeprobust.graph.data import Dataset
import numpy as np
import random
import time
import argparse
import torch
import sys
from deeprobust.graph.utils import *
import torch.nn.functional as F
from configs import load_config
from utils import *
from utils_graphsaint import DataGraphSAINT
from models.gcn import GCN
from coreset import KCenter, Herding, Random, LRMC
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=int, default=0, help='gpu id')
parser.add_argument('--dataset', type=str, default='cora')
parser.add_argument('--hidden', type=int, default=256)
parser.add_argument('--normalize_features', type=bool, default=True)
parser.add_argument('--keep_ratio', type=float, default=1.0)
parser.add_argument('--lr', type=float, default=0.01)
parser.add_argument('--weight_decay', type=float, default=5e-4)
parser.add_argument('--seed', type=int, default=15, help='Random seed.')
parser.add_argument('--nlayers', type=int, default=2, help='Random seed.')
parser.add_argument('--epochs', type=int, default=400)
parser.add_argument('--inductive', type=int, default=1)
parser.add_argument('--save', type=int, default=0)
parser.add_argument('--method', type=str, choices=['kcenter', 'herding', 'random', 'lrmc'])
parser.add_argument('--lrmc_seeds_path', type=str, default=None,
help='Path to a JSON file containing L‑RMC seed nodes. Required when method=lrmc.')
parser.add_argument('--reduction_rate', type=float, required=True)
args = parser.parse_args()
torch.cuda.set_device(args.gpu_id)
args = load_config(args)
print(args)
# random seed setting
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
data_graphsaint = ['flickr', 'reddit', 'ogbn-arxiv']
if args.dataset in data_graphsaint:
data = DataGraphSAINT(args.dataset)
data_full = data.data_full
data = Transd2Ind(data_full, keep_ratio=args.keep_ratio)
else:
data_full = get_dataset(args.dataset, args.normalize_features)
data = Transd2Ind(data_full, keep_ratio=args.keep_ratio)
features = data_full.features
adj = data_full.adj
labels = data_full.labels
idx_train = data_full.idx_train
idx_val = data_full.idx_val
idx_test = data_full.idx_test
# Setup GCN Model
device = 'cuda'
model = GCN(nfeat=features.shape[1], nhid=256, nclass=labels.max()+1, device=device, weight_decay=args.weight_decay)
model = model.to(device)
model.fit(features, adj, labels, idx_train, idx_val, train_iters=600, verbose=False)
model.eval()
# You can use the inner function of model to test
model.test(idx_test)
embeds = model.predict().detach()
if args.method == 'kcenter':
agent = KCenter(data, args, device='cuda')
elif args.method == 'herding':
agent = Herding(data, args, device='cuda')
elif args.method == 'random':
agent = Random(data, args, device='cuda')
elif args.method == 'lrmc':
if args.lrmc_seeds_path is None:
raise ValueError("--lrmc_seeds_path must be specified when method='lrmc'")
agent = LRMC(data, args, device='cuda')
idx_selected = agent.select(embeds)
feat_train = features[idx_selected]
adj_train = adj[np.ix_(idx_selected, idx_selected)]
labels_train = labels[idx_selected]
if args.save:
np.save(f'saved/idx_{args.dataset}_{args.reduction_rate}_{args.method}_{args.seed}.npy', idx_selected)
res = []
runs = 10
for _ in tqdm(range(runs)):
model.initialize()
model.fit_with_val(feat_train, adj_train, labels_train, data,
train_iters=600, normalize=True, verbose=False)
model.eval()
labels_test = torch.LongTensor(data.labels_test).cuda()
# Full graph
output = model.predict(data.feat_full, data.adj_full)
loss_test = F.nll_loss(output[data.idx_test], labels_test)
acc_test = accuracy(output[data.idx_test], labels_test)
res.append(acc_test.item())
res = np.array(res)
print('Mean accuracy:', repr([res.mean(), res.std()]))
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