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data_dyn_cite.py
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from __future__ import division
from torch.utils.data import Dataset
import numpy as np
import sys
import random
import copy
import torch
from sklearn import preprocessing
class DataHelper(Dataset):
def __init__(self, file_path, node_feature_path, neg_size, hist_len, directed=False, transform=None, tlp_flag=False):
self.node2hist = dict()
self.neg_size = neg_size # 5
self.hist_len = hist_len # 5
self.directed = directed
self.transform = transform
# self.max_d_time = -sys.maxint # Time interval [0, T]
self.max_d_time = -sys.maxsize # 1.0
self.NEG_SAMPLING_POWER = 0.75
self.neg_table_size = int(1e8) # 10^8
self.all_edge_index = []
self.node_time_nodes = dict()
self.node_set = set()
self.degrees = dict()
self.edge_list = []
self.node_rate = {}
self.edge_rate = {}
self.node_sum = {}
self.edge_sum = {}
self.time_stamp = []
self.time_edges_dict = {}
self.time_nodes_dict = {}
print('loading data...')
infile = torch.load(file_path)
for i in range(infile.size(0)):
s_node = int(infile[i][0].item()) # source node
t_node = int(infile[i][1].item()) # target node
d_time = float(infile[i][2].item()) # time slot, delta t
self.node_set.update([s_node, t_node])
if s_node not in self.degrees:
self.degrees[s_node] = 0
if t_node not in self.degrees:
self.degrees[t_node] = 0
self.all_edge_index.append([s_node, t_node])
if s_node not in self.node2hist: # node2hist {node: [(historical neighbor, time)], ……}
self.node2hist[s_node] = list()
if not directed: # undirected
if t_node not in self.node2hist:
self.node2hist[t_node] = list()
if tlp_flag:
if d_time >= 1.0:
continue
# the new added node's degree is 0
self.edge_list.append((s_node, t_node, d_time)) # edge list
if not directed:
self.edge_list.append((t_node, s_node, d_time))
self.node2hist[s_node].append((t_node, d_time))
if not directed:
self.node2hist[t_node].append((s_node, d_time)) # because undirected, so add the inverse version
if s_node not in self.node_time_nodes:
self.node_time_nodes[s_node] = dict() # for the new added s_node, create a dict for it
if d_time not in self.node_time_nodes[s_node]:
self.node_time_nodes[s_node][d_time] = list() # for the new time,
self.node_time_nodes[s_node][d_time].append(t_node)
if not directed: # undirected
if t_node not in self.node_time_nodes:
self.node_time_nodes[t_node] = dict()
if d_time not in self.node_time_nodes[t_node]:
self.node_time_nodes[t_node][d_time] = list()
self.node_time_nodes[t_node][d_time].append(s_node)
if d_time > self.max_d_time:
self.max_d_time = d_time # record the max time
self.degrees[s_node] += 1 # node degree
self.degrees[t_node] += 1
self.time_stamp.append(d_time)
if not self.time_edges_dict.__contains__(d_time):
self.time_edges_dict[d_time] = []
self.time_edges_dict[d_time].append((s_node, t_node))
if not self.time_nodes_dict.__contains__(d_time):
self.time_nodes_dict[d_time] = []
self.time_nodes_dict[d_time].append(s_node)
self.time_nodes_dict[d_time].append(t_node)
# a = torch.load(node_feature_path).numpy()
self.node_features = torch.load(node_feature_path).numpy()
self.node_features = preprocessing.StandardScaler().fit_transform(self.node_features)
# print("degree_features", degree_features[0:5])
self.node_list = sorted(list(self.node_set))
self.time_stamp = sorted(list(set(self.time_stamp))) # !!! time from 0 to 1
# print('time minimum:', min(self.time_stamp))
# print('time maxmum:', max(self.time_stamp))
self.node_dim = len(self.node_set)
self.data_size = 0
for s in self.node2hist:
hist = self.node2hist[s]
hist = sorted(hist,
key=lambda x: x[1]) # from past(0) to now(1). This supports the events ranked in time order.
self.node2hist[s] = hist
self.data_size += len(self.node2hist[s])
self.max_nei_len = max(map(lambda x: len(x), self.node2hist.values())) # 955
# print('#nodes: {}, #edges: {}, # train time_stamp: {}'.
# format(self.node_dim, len(self.edge_list), len(self.time_stamp)))
# print('avg. degree: {}'.format(sum(self.degrees.values()) / len(self.degrees)))
# print('max neighbors length: {}'.format(self.max_nei_len))
self.idx2source_id = np.zeros((self.data_size,), dtype=np.int32)
self.idx2target_id = np.zeros((self.data_size,), dtype=np.int32)
idx = 0
for s_node in self.node2hist:
for t_idx in range(len(self.node2hist[
s_node])): # Note the range here, which means from 0 to the number of historical neighbors
self.idx2source_id[
idx] = s_node
self.idx2target_id[
idx] = t_idx
idx += 1
print('init. neg_table...')
self.neg_table = np.zeros((self.neg_table_size,))
self.init_neg_table()
def get_node_dim(self):
return self.node_dim
def get_max_d_time(self):
return self.max_d_time
def init_neg_table(self):
tot_sum, cur_sum, por = 0., 0., 0.
n_id = 0
for k in range(self.node_dim):
tot_sum += np.power(self.degrees[k],
self.NEG_SAMPLING_POWER)
for k in range(self.neg_table_size):
if (k + 1.) / self.neg_table_size > por:
cur_sum += np.power(self.degrees[n_id], self.NEG_SAMPLING_POWER)
por = cur_sum / tot_sum
n_id += 1
self.neg_table[k] = n_id - 1 # negtive table size = 1e8, element inside range from 1~number of total nodes
def __len__(self):
return self.data_size
def __getitem__(self, idx):
# sampling via htne
s_node = self.idx2source_id[idx]
t_idx = self.idx2target_id[idx] # To ensure that s_node must
t_node = self.node2hist[s_node][t_idx][0] # get the global idx for the target node
e_time = self.node2hist[s_node][t_idx][1]
if t_idx == 0:
s_his_nodes = np.array(s_node).repeat(self.hist_len)
s_his_times = np.array(e_time).repeat(self.hist_len)
else:
if t_idx < self.hist_len:
s_nei_idx = np.random.choice(len(self.node2hist[s_node][:t_idx]), self.hist_len, replace=True)
else:
s_nei_idx = np.random.choice(len(self.node2hist[s_node][:t_idx]), self.hist_len, replace=False)
s_his = [(self.node2hist[s_node][:t_idx])[i] for i in s_nei_idx]
s_his_nodes = [h[0] for h in s_his]
s_his_times = np.array([h[1] for h in s_his])
t_his_list = self.node2hist[t_node]
s_idx = t_his_list.index((s_node, e_time))
if s_idx == 0:
t_his_nodes = np.array(t_node).repeat(self.hist_len)
t_his_times = np.array(e_time).repeat(self.hist_len)
else:
if s_idx < self.hist_len:
t_nei_idx = np.random.choice(len(t_his_list[:s_idx]), self.hist_len, replace=True)
else:
t_nei_idx = np.random.choice(len(t_his_list[:s_idx]), self.hist_len, replace=False)
t_his = [(t_his_list[:s_idx])[i] for i in t_nei_idx]
t_his_nodes = [h[0] for h in t_his]
t_his_times = np.array([h[1] for h in t_his])
s_his_tidx_list = []
for i in range(len(s_his_nodes)):
s_node_his = self.node2hist[s_his_nodes[i]]
his_tidx = np.argwhere(np.array([list(i) for i in s_node_his])[:, 1] <= s_his_times[i])[-1].item()
s_his_tidx_list.append(his_tidx+1)
s_his_his_nodes_list = []
s_his_his_times_list = []
for i in range(len(s_his_nodes)):
if s_his_tidx_list[i] < self.hist_len:
s_his_nei_idx = np.random.choice(np.arange(
len(self.node2hist[s_his_nodes[i]][:s_his_tidx_list[i]])), self.hist_len, replace=True)
else:
s_his_nei_idx = np.random.choice(np.arange(len(self.node2hist[s_his_nodes[i]][:s_his_tidx_list[i]])), self.hist_len, replace=False)
s_his_his = [(self.node2hist[s_his_nodes[i]][:s_his_tidx_list[i]])[a] for a in s_his_nei_idx]
s_his_his_nodes = [h[0] for h in s_his_his]
s_his_his_times = [h[1] for h in s_his_his]
s_his_his_times_list.append(s_his_his_times)
s_his_his_nodes_list.append(s_his_his_nodes)
s_his_his_times_list = np.array(s_his_his_times_list)
s_his_his_nodes_list = np.array(s_his_his_nodes_list).astype(int)
t_his_tidx_list = []
for i in range(len(t_his_nodes)):
t_node_his = self.node2hist[t_his_nodes[i]]
his_tidx = np.argwhere(np.array([list(i) for i in t_node_his])[:, 1] <= t_his_times[i])[-1].item()
t_his_tidx_list.append(his_tidx+1)
t_his_his_nodes_list = []
t_his_his_times_list = []
for i in range(len(t_his_nodes)):
if t_his_tidx_list[i] < self.hist_len:
t_his_nei_idx = np.random.choice(np.arange(
len(self.node2hist[t_his_nodes[i]][:t_his_tidx_list[i]])), self.hist_len, replace=True)
else:
t_his_nei_idx = np.random.choice(np.arange(
len(self.node2hist[t_his_nodes[i]][:t_his_tidx_list[i]])), self.hist_len, replace=False)
t_his_his = [(self.node2hist[t_his_nodes[i]][:t_his_tidx_list[i]])[a] for a in t_his_nei_idx]
t_his_his_nodes = [h[0] for h in t_his_his]
t_his_his_times = [h[1] for h in t_his_his]
t_his_his_times_list.append(t_his_his_times)
t_his_his_nodes_list.append(t_his_his_nodes)
t_his_his_times_list = np.array(t_his_his_times_list)
t_his_his_nodes_list = np.array(t_his_his_nodes_list).astype(int)
# negtive_sampling part
neg_s_nodes = self.negative_sampling().astype(int)
neg_tidx_list = []
for i in range(self.neg_size):
neg_node = self.node2hist[neg_s_nodes[i]]
if neg_node == []:
neg_tidx_list.append(0)
elif np.array([list(i) for i in neg_node])[:, 1][0] > e_time:
neg_tidx_list.append(0)
else:
his_tidx = np.argwhere(np.array([list(i) for i in neg_node])[:, 1] <= e_time)[-1].item()
neg_tidx_list.append(his_tidx+1)
neg_his_nodes_list = []
neg_his_times_list = []
# neg_his_len = []
for i in range(self.neg_size):
if len(self.node2hist[neg_s_nodes[i]][:neg_tidx_list[i]]) == 0:
neg_his_nodes = neg_s_nodes[i].repeat(self.hist_len)
neg_his_times = np.array(e_time).repeat(self.hist_len)
else:
if neg_tidx_list[i] < self.hist_len:
neg_nei_idx = np.random.choice(len(self.node2hist[neg_s_nodes[i]][:neg_tidx_list[i]]), self.hist_len, replace=True)
else:
neg_nei_idx = np.random.choice(len(self.node2hist[neg_s_nodes[i]][:neg_tidx_list[i]]), self.hist_len, replace=False)
neg_his = [(self.node2hist[neg_s_nodes[i]][:neg_tidx_list[i]])[a] for a in neg_nei_idx]
neg_his_nodes = [h[0] for h in neg_his]
neg_his_times = [h[1] for h in neg_his]
neg_his_nodes_list.append(neg_his_nodes)
neg_his_times_list.append(neg_his_times)
# neg_his_len.append(len(neg_his))
neg_his_nodes_list = np.array(neg_his_nodes_list).astype(int)
neg_his_times_list = np.array(neg_his_times_list)
neg_his_idx_list = []
for i in range(self.neg_size):
his_idx_list = []
for j in range(self.hist_len):
neg_node_his = self.node2hist[neg_his_nodes_list[i][j]]
if neg_node_his ==[]:
his_idx = 0
elif np.array([list(i) for i in neg_node_his])[:, 1][0] > e_time:
his_idx = 0
else:
his_idx = np.argwhere(np.array([list(i) for i in neg_node_his])[:, 1] <= neg_his_times_list[i][j])[-1].item()
his_idx_list.append(his_idx+1)
neg_his_idx_list.append(his_idx_list)
neg_his_idx_list = np.array(neg_his_idx_list)
neg_his_his_nodes_list = []
neg_his_his_times_list = []
for i in range(self.neg_size):
neg_h_h_nodes_list = []
neg_h_h_times_list = []
for j in range(self.hist_len):
if len(self.node2hist[neg_his_nodes_list[i][j]][0:neg_his_idx_list[i][j]]) == 0:
neg_his_his_nodes = neg_his_nodes_list[i][j].repeat(self.hist_len)
neg_his_his_times = np.array(e_time).repeat(self.hist_len)
else:
if neg_his_idx_list[i][j] < self.hist_len:
neg_his_his_idx = np.random.choice(len(self.node2hist[neg_his_nodes_list[i][j]][0:neg_his_idx_list[i][j]]), self.hist_len, replace=True)
else:
neg_his_his_idx = np.random.choice(len(self.node2hist[neg_his_nodes_list[i][j]][0:neg_his_idx_list[i][j]]), self.hist_len, replace=False)
neg_his_his = [(self.node2hist[neg_his_nodes_list[i][j]][0:neg_his_idx_list[i][j]])[a] for a in neg_his_his_idx]
neg_his_his_nodes = [h[0] for h in neg_his_his]
neg_his_his_times = [h[1] for h in neg_his_his]
neg_h_h_nodes_list.append(neg_his_his_nodes)
neg_h_h_times_list.append(neg_his_his_times)
neg_h_h_nodes_list = np.array(neg_h_h_nodes_list)
neg_h_h_times_list = np.array(neg_h_h_times_list)
neg_his_his_nodes_list.append(neg_h_h_nodes_list)
neg_his_his_times_list.append(neg_h_h_times_list)
neg_his_his_nodes_list = np.array(neg_his_his_nodes_list).astype(int)
neg_his_his_times_list = np.array(neg_his_his_times_list)
s_self_feat = self.node_features[s_node]
s_one_hop_feat = self.node_features[s_his_nodes]
s_two_hop_feat = []
for i in range(self.hist_len):
s_two_feat = self.node_features[s_his_his_nodes_list[i]]
s_two_hop_feat.append(s_two_feat)
s_two_hop_feat = np.array(s_two_hop_feat)
t_self_feat = self.node_features[t_node]
t_one_hop_feat = self.node_features[t_his_nodes]
t_two_hop_feat = []
for i in range(self.hist_len):
t_two_feat = self.node_features[t_his_his_nodes_list[i]]
t_two_hop_feat.append(t_two_feat)
t_two_hop_feat = np.array(t_two_hop_feat)
neg_self_feat = self.node_features[neg_s_nodes]
neg_one_hop_feat = []
for i in range(self.neg_size):
neg_one_feat = self.node_features[neg_his_nodes_list[i]]
neg_one_hop_feat.append(neg_one_feat)
neg_one_hop_feat = np.array(neg_one_hop_feat)
neg_two_hop_feat = []
for i in range(self.neg_size):
neg_two_h_feat = []
for j in range(self.hist_len):
neg_t_h_f = self.node_features[neg_his_his_nodes_list[i][j]]
neg_two_h_feat.append(neg_t_h_f)
neg_two_h_feat = np.array(neg_two_h_feat)
neg_two_hop_feat.append(neg_two_h_feat)
neg_two_hop_feat = np.array(neg_two_hop_feat)
s_edge_rate = len(self.node_time_nodes[s_node][e_time])
# print('s_edge_rate', s_edge_rate)
sample = {
# 's_node': s_node, # e.g., 5424
# 't_node': t_node, # e.g., 5427
'event_time': e_time,
's_history_times': s_his_times,
't_history_times': t_his_times,
's_his_his_times_list': s_his_his_times_list,
't_his_his_nodes_list': t_his_his_nodes_list,
't_his_his_times_list': t_his_his_times_list,
's_self_feat': s_self_feat,
's_one_hop_feat': s_one_hop_feat,
's_two_hop_feat': s_two_hop_feat,
't_self_feat': t_self_feat,
't_one_hop_feat': t_one_hop_feat,
't_two_hop_feat': t_two_hop_feat,
'neg_his_times_list': neg_his_times_list,
'neg_his_his_times_list': neg_his_his_times_list,
'neg_self_feat': neg_self_feat,
'neg_one_hop_feat': neg_one_hop_feat,
'neg_two_hop_feat': neg_two_hop_feat,
's_edge_rate': s_edge_rate,
}
if self.transform:
sample = self.transform(sample)
return sample
def negative_sampling(self):
rand_idx = np.random.randint(0, self.neg_table_size, (self.neg_size,))
sampled_nodes = self.neg_table[rand_idx]
return sampled_nodes