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This part of code is adopted from https://github.com/Hanjun-Dai/graph_adversarial_attack (Copyright (c) 2018 Dai, Hanjun and Li, Hui and Tian, Tian and Huang, Xin and Wang, Lin and Zhu, Jun and Song, Le)
but modified to be integrated into the repository.
"""
import os
import sys
import numpy as np
import torch
import networkx as nx
import random
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
from copy import deepcopy
import pickle as cp
from deeprobust.graph.utils import *
import scipy.sparse as sp
from scipy.sparse.linalg.eigen.arpack import eigsh
from deeprobust.graph import utils
from deeprobust.graph.rl.env import *
class NodeInjectionEnv(NodeAttackEnv):
"""Node attack environment. It executes an action and then change the
environment status (modify the graph).
"""
def __init__(self, features, labels, idx_train, idx_val, dict_of_lists, classifier, ratio=0.01, parallel_size=1, reward_type='binary'):
"""number of injected nodes: ratio*|V|
number of modifications: ratio*|V|*|D_avg|
"""
# super(NodeInjectionEnv, self).__init__(features, labels, all_targets, list_action_space, classifier, num_mod, reward_type)
super(NodeInjectionEnv, self).__init__(features, labels, idx_val, dict_of_lists, classifier)
self.parallel_size = parallel_size
degrees = np.array([len(d) for n, d in dict_of_lists.items()])
N = len(degrees[degrees > 0])
avg_degree = degrees.sum() / N
self.n_injected = len(degrees) - N
assert self.n_injected == int(ratio * N)
self.ori_adj_size = N
self.n_perturbations = int(self.n_injected * avg_degree)
print("number of perturbations: {}".format(self.n_perturbations))
self.all_nodes = np.arange(N)
self.injected_nodes = self.all_nodes[-self.n_injected: ]
self.previous_acc = [1] * parallel_size
self.idx_train = np.hstack((idx_train, self.injected_nodes))
self.idx_val = idx_val
self.modified_label_list = []
for i in range(self.parallel_size):
self.modified_label_list.append(labels[-self.n_injected: ].clone())
def init_overall_steps(self):
self.overall_steps = 0
self.modified_list = []
for i in range(self.parallel_size):
self.modified_list.append(ModifiedGraph())
def setup(self):
self.n_steps = 0
self.first_nodes = None
self.second_nodes = None
self.rewards = None
self.binary_rewards = None
self.list_acc_of_all = []
def step(self, actions, inference=False):
'''
run actions and get reward
'''
if self.first_nodes is None: # pick the first node of edge
assert (self.n_steps + 1) % 3 == 1
self.first_nodes = actions[:]
if (self.n_steps + 1) % 3 == 2:
self.second_nodes = actions[:]
for i in range(self.parallel_size):
# add an edge from the graph
self.modified_list[i].add_edge(self.first_nodes[i], actions[i], 1.0)
if (self.n_steps + 1) % 3 == 0:
for i in range(self.parallel_size):
# change label
self.modified_label_list[i][self.first_nodes[i] - self.ori_adj_size] = actions[i]
self.first_nodes = None
self.second_nodes = None
self.n_steps += 1
self.overall_steps += 1
if not inference:
if self.isActionFinished() :
rewards = []
for i in (range(self.parallel_size)):
device = self.labels.device
extra_adj = self.modified_list[i].get_extra_adj(device=device)
adj = self.classifier.norm_tool.norm_extra(extra_adj)
labels = torch.cat((self.labels, self.modified_label_list[i]))
# self.classifier.fit(self.features, adj, labels, self.idx_train, self.idx_val, normalize=False)
self.classifier.fit(self.features, adj, labels, self.idx_train, self.idx_val, normalize=False, patience=30)
output = self.classifier(self.features, adj)
loss, correct = loss_acc(output, self.labels, self.idx_val, avg_loss=False)
acc = correct.sum()
# r = 1 if self.previous_acc[i] - acc > 0.01 else -1
r = 1 if self.previous_acc[i] - acc > 0 else -1
self.previous_acc[i] = acc
rewards.append(r)
self.rewards = np.array(rewards).astype(np.float32)
def sample_pos_rewards(self, num_samples):
assert self.list_acc_of_all is not None
cands = []
for i in range(len(self.list_acc_of_all)):
succ = np.where( self.list_acc_of_all[i] < 0.9 )[0]
for j in range(len(succ)):
cands.append((i, self.all_targets[succ[j]]))
if num_samples > len(cands):
return cands
random.shuffle(cands)
return cands[0:num_samples]
def uniformRandActions(self):
act_list = []
for i in range(self.parallel_size):
if self.first_nodes is None:
# a1: choose a node from injected nodes
cur_action = np.random.choice(self.injected_nodes)
if self.first_nodes is not None and self.second_nodes is None:
# a2: choose a node from all nodes
cur_action = np.random.randint(len(self.list_action_space))
while (self.first_nodes[i], cur_action) in self.modified_list[i].edge_set:
cur_action = np.random.randint(len(self.list_action_space))
if self.first_nodes is not None and self.second_nodes is not None:
# a3: choose label
cur_action = np.random.randint(self.labels.cpu().max() + 1)
act_list.append(cur_action)
return act_list
def isActionFinished(self):
if (self.n_steps) % 3 == 0 and self.n_steps != 0:
return True
return False
def isTerminal(self):
if self.overall_steps == 3 * self.n_perturbations:
return True
return False
def getStateRef(self):
return list(zip(self.modified_list, self.modified_label_list))
def cloneState(self):
return list(zip(deepcopy(self.modified_list), deepcopy(self.modified_label_list)))
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