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attack_utils.py
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import numpy as np
import torch
import dgl
import torch.nn.functional as F
import scipy.sparse as sp
import random
from model import GCN, GAT, SGC, APPNP
class GraphEnv(object):
def __init__(self, dataset, hid_dim, device, backbone='gat'):
if dataset == 'reddit':
self.g = load_npz('reddit') # dgl.data.RedditDataset()[0]
self.discrete_feat = False
elif dataset =='ogbproducts':
self.g = load_npz('ogbproducts')
self.discrete_feat = False
elif dataset == 'cora':
self.g = dgl.data.CoraGraphDataset(verbose=False)[0]
self.discrete_feat = True
elif dataset == 'citeseer':
self.g = dgl.data.CiteseerGraphDataset(verbose=False)[0]
self.discrete_feat = True
elif dataset == 'pubmed':
self.g = dgl.data.PubmedGraphDataset(verbose=False)[0]
self.discrete_feat = False
elif dataset == 'wiki_cs':
self.g = dgl.data.WikiCSDataset(verbose=False)[0]
self.discrete_feat = False
elif dataset == 'co_computer':
self.g = dgl.data.AmazonCoBuyComputerDataset(verbose=False)[0]
self.discrete_feat = True
elif dataset == 'co_photo':
self.g = dgl.data.AmazonCoBuyPhotoDataset(verbose=False)[0]
self.discrete_feat = True
else:
raise Exception('Dataset not implemented Error.')
self.g.ndata['feat'] = F.normalize(self.g.ndata['feat'])
if 'train_mask' not in self.g.ndata:
train_mask, val_mask, test_mask = cross_validation_gen(self.g.ndata['label'])
self.g.ndata['train_mask'], self.g.ndata['val_mask'], self.g.ndata['test_mask'] = \
train_mask[:, 0], val_mask[:, 0], test_mask[:, 0]
elif dataset == 'wiki_cs':
self.g.ndata['train_mask'], self.g.ndata['val_mask'], self.g.ndata['test_mask'] = \
self.g.ndata['train_mask'][:, 0].bool(), self.g.ndata['val_mask'][:, 0].bool(), self.g.ndata['test_mask'].bool()
self.n_class = self.g.ndata['label'].max().item() + 1
self.feature_dim = self.g.ndata['feat'].shape[1]
self.degree = self.g.in_degrees().float().mean().ceil().item()
self.g = dgl.add_self_loop(self.g).to(device)
self.device = device
if backbone == 'gcn':
victim_model = GCN(self.g.ndata['feat'].shape[1], hid_dim, self.n_class).to(device)
elif backbone == 'gat':
victim_model = GAT(self.g.ndata['feat'].shape[1], hid_dim, self.n_class).to(device)
elif backbone == 'sgc':
victim_model = SGC(self.g.ndata['feat'].shape[1], hid_dim, self.n_class).to(device)
elif backbone == 'appnp':
victim_model = APPNP(self.g.ndata['feat'].shape[1], hid_dim, self.n_class).to(device)
else:
raise Exception('Model not implemented err.')
self.victim_model, self.test_idx = victim_model.train(self.g, victim_model) # victim_model, self.g.ndata['test_mask'].nonzero(as_tuple=True)[0] #
self.total_test_nodes = self.g.ndata['test_mask'].sum().item()
self.feature_budget = (self.g.ndata['feat']>0).float().sum(1).mean() if self.discrete_feat else self.g.ndata['feat'].sum(1).mean()
self.mu = self.g.ndata['feat'].mean(0)
self.sigma = self.g.ndata['feat'].std(0)
def get_loss(self, g, node_index):
with torch.no_grad():
logit = self.victim_model(g, g.ndata['feat'])[node_index]
logit = logit.reshape(1, -1)
return F.cross_entropy(logit, g.ndata['label'][node_index].reshape(1)).item(), \
logit.max(1)[1].item() != g.ndata['label'][node_index].item()
def get_reward(self, node_index, previous_g, current_g):
with torch.no_grad():
current_loss, success = self.get_loss(current_g, node_index)
previous_loss = self.get_loss(previous_g, node_index)[0]
if success:
return current_loss - previous_loss + 10, success
else:
return current_loss - previous_loss, success
def inject_node(g, feat):
nid = g.num_nodes()
g = dgl.add_nodes(g, 1, {'feat': feat.reshape(1, -1)})
g = dgl.add_edges(g, nid, nid) # add self loop
return g
def wire_edge(g, dst):
g = dgl.add_edges(g, torch.tensor([dst, g.number_of_nodes() - 1]).to(g.device),\
torch.tensor([g.number_of_nodes() - 1, dst]).to(g.device))
return g
def cross_validation_gen(y, k_fold=5):
from sklearn.model_selection import StratifiedKFold
skf = StratifiedKFold(n_splits=k_fold)
train_splits = []
val_splits = []
test_splits = []
for larger_group, smaller_group in skf.split(y, y):
train_y = y[smaller_group]
sub_skf = StratifiedKFold(n_splits=2)
train_split, val_split = next(iter(sub_skf.split(train_y, train_y)))
train = torch.zeros_like(y, dtype=torch.bool)
train[smaller_group[train_split]] = True
val = torch.zeros_like(y, dtype=torch.bool)
val[smaller_group[val_split]] = True
test = torch.zeros_like(y, dtype=torch.bool)
test[larger_group] = True
train_splits.append(train.unsqueeze(1))
val_splits.append(val.unsqueeze(1))
test_splits.append(test.unsqueeze(1))
return torch.cat(train_splits, dim=1), torch.cat(val_splits, dim=1), torch.cat(test_splits, dim=1)
def load_npz(dataset):
if dataset == 'reddit':
dataset = '12k_reddit'
elif dataset == 'ogbproducts':
dataset = '10k_ogbproducts'
else:
raise Exception('Not implemented err.')
file_name = 'datasets/{}.npz'.format(dataset)
"""Load a SparseGraph from a Numpy binary file.
Parameters
----------
file_name : str
Name of the file to load.
Returns
-------
sparse_graph : gust.SparseGraph
Graph in sparse matrix format.
"""
if not file_name.endswith('.npz'):
file_name += '.npz'
with np.load(file_name) as loader:
loader = dict(loader)
adj_matrix = sp.csr_matrix((loader['adj_data'], loader['adj_indices'],
loader['adj_indptr']), shape=loader['adj_shape'])
if 'attr_data' in loader:
attr_matrix = sp.csr_matrix((loader['attr_data'], loader['attr_indices'],
loader['attr_indptr']), shape=loader['attr_shape'])
else:
attr_matrix = None
labels = loader.get('labels')
g = dgl.DGLGraph(adj_matrix.nonzero())
g.ndata['feat'] = torch.tensor(attr_matrix.todense()).float()
split = np.load('datasets/{}_split.npy'.format(dataset), allow_pickle=True).item()
train_mask = split['train']
val_mask = split['val']
test_mask = split['test']
temp = torch.zeros(g.number_of_nodes(), dtype=torch.bool)
temp[train_mask] = True
g.ndata['train_mask'] = temp
temp = torch.zeros(g.number_of_nodes(), dtype=torch.bool)
temp[val_mask] = True
g.ndata['val_mask'] = temp
temp = torch.zeros(g.number_of_nodes(), dtype=torch.bool)
temp[test_mask] = True
g.ndata['test_mask'] = temp
g.ndata['label'] = torch.tensor(labels)
return g
def setup_seed(seed):
dgl.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True