File size: 4,179 Bytes
f74dd01 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | from deeprobust.graph.data import Dataset
import numpy as np
import random
import time
import argparse
import torch
import sys
import deeprobust.graph.utils as utils
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('--dropout', type=float, default=0.5)
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('--mlp', 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)
feat_train = data.feat_train
adj_train = data.adj_train
labels_train = data.labels_train
# Setup GCN Model
device = 'cuda'
model = GCN(nfeat=feat_train.shape[1], nhid=256, nclass=labels_train.max()+1, device=device, weight_decay=args.weight_decay)
model = model.to(device)
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()
feat_test, adj_test = data.feat_test, data.adj_test
embeds = model.predict().detach()
output = model.predict(feat_test, adj_test)
loss_test = F.nll_loss(output, labels_test)
acc_test = utils.accuracy(output, labels_test)
print("Test set results:",
"loss= {:.4f}".format(loss_test.item()),
"accuracy= {:.4f}".format(acc_test.item()))
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, inductive=True)
feat_train = feat_train[idx_selected]
adj_train = adj_train[np.ix_(idx_selected, idx_selected)]
labels_train = labels_train[idx_selected]
res = []
print('shape of feat_train:', feat_train.shape)
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, noval=True)
model.eval()
labels_test = torch.LongTensor(data.labels_test).cuda()
output = model.predict(feat_test, adj_test)
loss_test = F.nll_loss(output, labels_test)
acc_test = utils.accuracy(output, labels_test)
res.append(acc_test.item())
res = np.array(res)
print('Mean accuracy:', repr([res.mean(), res.std()]))
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