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main.py
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from __future__ import division
from __future__ import print_function
import copy
import os
from TIAs.TIAs import TIA_GAP, TIA, TIA_PGR
from baseline.Eclipse_main.dp_svd import train_with_Eclipse
from baseline.GAP_master.train import train_with_GAP
from baseline.LPGNet.LPGNet import train_with_LPGNet
from baseline.Lap_and_RR.LapEdge import graph_normal_training_perturb_LAP
from baseline.Lap_and_RR.RandEdge import graph_normal_training_perturb_RR
from baseline.PPRL.GNNPrivacy import train_with_PPRL
from baseline.PrivGraph_main.priv_graph import train_with_privGraph
from utils.get_network import get_network
from utils.train import graph_normal_training
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
import argparse
import numpy as np
import pandas as pd
import torch
import torch_geometric
from data.dataload import load_data
from graph_reconstruction.graph_regenerate import graph_regenerate_different
from utils.utils import split_dataset
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--algorithm', type=str, default='Original',choices=['PGR','Original','LapEdge','EdgeRand','LPGNet','PPRL','privGraph','GAP','Eclipse'])
parser.add_argument('--dataset', type=str, default='cora'
,choices=['cora', 'citeseer','duke','lastfm','emory'])
parser.add_argument('--device', type=str, default='cuda:3',choices=['cpu','cuda:3','cuda:0','cuda:1','cuda:2'])
parser.add_argument('--fastmode', action='store_true', default=False,
help='Validate during training pass.')
parser.add_argument('--seed', type=int, default=3407, help='Random seed.')
parser.add_argument('--epochs', type=int, default=100,
help='Number of epochs to train.')
parser.add_argument('--epochs_inner', type=int, default=1,
help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=5e-4,
help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=32,
help='Number of hidden units.')
parser.add_argument('--dropout', type=float, default=0.2,
help='units dropout')
parser.add_argument('--prune', type=float, default=0.5,
help='how many need to keep')
parser.add_argument('--ratio_of_train_set', type=float, default='0.1')
parser.add_argument('--mu', type=float, default='0.0')
parser.add_argument('--attacks', type=str, default='None',choices=['None','TIA','TIA-PGR'])
parser.add_argument('--network', type=str, default='GCN',choices=['GCN','GAT','GraphSAGE'])
parser.add_argument('--hops', type=int, default=2)
parser.add_argument('--eps', type=float, default=7)
args = parser.parse_args()
algorithm=args.algorithm
device=args.device
dataset_name=args.dataset
seed=args.seed
np.random.seed(seed)
torch.manual_seed(seed)
ratio_of_train_set=args.ratio_of_train_set
hidden=args.hidden
dropout=args.dropout
lr=args.lr
epochs=args.epochs
epochs_inner=args.epochs_inner
weight_decay=args.weight_decay
prune=args.prune
mu=args.mu
attack=args.attacks
hops=args.hops
eps=args.eps
network=args.network
dataset=load_data(dataset_name)
data = dataset[0]
dense_matrix = torch_geometric.utils.to_dense_adj(data.edge_index)[0]
features=data.x.to(device)
labels=data.y.to(device)
model=get_network(network, hops, features, labels, hidden, dropout, device)
preds=None
idx_train,idx_val,idx_test=split_dataset(labels,ratio_of_train_set,seed)
if algorithm=='PGR':
acc_test, num_priv_edges, num_rengen_edges, model, regen_adj = graph_regenerate_different(algorithm,features,
dense_matrix, dense_matrix,labels,
idx_train, idx_val,
idx_test, lr,
weight_decay, epochs,
epochs_inner, prune,
device,
mu, model,network)
elif algorithm == 'Original':
acc_test,num_priv_edges,num_rengen_edges,model,regen_adj=graph_normal_training(features, dense_matrix, labels, idx_train, idx_val, idx_test, hidden, dropout, lr,weight_decay, epochs,network,model,device)
elif algorithm == 'privGraph':
acc_test, num_priv_edges, num_rengen_edges, model, regen_adj = train_with_privGraph(eps,features, dense_matrix, labels, idx_train, idx_val, idx_test, model, network, lr,weight_decay, epochs, device)
elif algorithm == 'GAP':
acc_test,preds,data_init,model=train_with_GAP(dataset_name,eps,hops,device)
regen_adj=dense_matrix
elif algorithm == 'LPGNet':
acc_test,model,features,regen_adj=train_with_LPGNet(copy.deepcopy(data), eps, idx_train, idx_val, idx_test)
elif algorithm == 'PPRL':
acc_test,model,features,regen_adj=train_with_PPRL(copy.deepcopy(data), idx_train, idx_val, idx_test)
elif algorithm == 'Eclipse':
acc_test, num_priv_edges, num_rengen_edges, model, regen_adj = train_with_Eclipse(eps,features, dense_matrix, labels, idx_train, idx_val, idx_test, model, network, lr,weight_decay, epochs, device)
elif algorithm == 'LapEdge':
acc_test, num_priv_edges, num_rengen_edges, model, regen_adj = graph_normal_training_perturb_LAP(eps, features,
dense_matrix,
labels,
idx_train,
idx_val,
idx_test,
model, network, lr,
weight_decay,
epochs, device)
elif algorithm=='EdgeRand':
acc_test, num_priv_edges, num_rengen_edges, model, regen_adj = graph_normal_training_perturb_RR(eps, features,
dense_matrix,
labels,
idx_train,
idx_val,
idx_test,
model, network,
lr,
weight_decay,
epochs, device)
else:
raise ValueError("this algorithm is not exist")
print(f'{attack}|{network}|{algorithm}|{dataset_name}|test_acc:{acc_test}')
# pd.DataFrame([acc_test]).to_csv(
# f"TPL_result_baseline/{attack}_{network}_{algorithm}_{dataset_name}_{eps}_acc.csv",
# index=False, header=False)
# if algorithm == 'PGR':
# File_Path_Csv = os.getcwd() + f"/result_PGR/{network}/{algorithm}/{dataset_name}/{prune}/{mu}/{epochs_inner}/{hops}//"
# if not os.path.exists(File_Path_Csv):
# os.makedirs(File_Path_Csv)
# pd.DataFrame([acc_test,num_priv_edges,num_rengen_edges]).to_csv(f"{File_Path_Csv}/acc.csv",index=False, header=False)
# torch.save(regen_adj, f"{File_Path_Csv}/regen_edge.pth")
# torch.save(model.state_dict(), f'{File_Path_Csv}/model.pt')
# torch.save(labels, f'{File_Path_Csv}/labels.pth')
# torch.save(features, f'{File_Path_Csv}/features.pth')
# torch.save(device, f'{File_Path_Csv}/device.pth')
if attack=='TIA':
if algorithm == 'GAP':
TPL_M,TPL_C, TPL_I = TIA_GAP(model, data_init.x, data_init.y, data_init.adj_t, eps, hops,device, seed)
else:
TPL_M,TPL_C, TPL_I = TIA(algorithm, data, model, dense_matrix, features,regen_adj,labels, device,hops, seed)
# pd.DataFrame([TPL_M, TPL_C, TPL_I]).to_csv(
# f"TPL_result_baseline/{attack}_{network}_{algorithm}_{dataset_name}_{eps}.csv",
# index=False, header=False)
print(f'{attack}|{network}|{algorithm}|{dataset_name}|{TPL_M}|{TPL_C}|{TPL_I}')
if attack=='TIA-PGR':
TPL_M, TPL_C,TPL_I = TIA_PGR( data, model, dense_matrix, features,regen_adj,labels, device,hops, seed)
print(f'{attack}|{network}|{algorithm}|{dataset_name}|{TPL_M}|{TPL_C}|{TPL_I}')
if algorithm == 'PGR':
print(f"model acc loss (utility):{(acc_test-0.8338)/0.8338} | M-TIA:{TPL_M*100.0} | C-TIA:{TPL_C*100.0} | I-TIA :{TPL_I*100.0}")
else:
print(f"model accuracy (utility):{acc_test*100.0} | M-TIA:{TPL_M*100.0} | C-TIA:{TPL_C*100.0} | I-TIA :{TPL_I*100.0}")
if __name__ == '__main__':
main()