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# We can debug at this file
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from __future__ import division
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from torch.utils.data import Dataset
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import numpy as np
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import sys
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import random
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import copy
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import torch
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from sklearn import preprocessing
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import seaborn as sns
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import matplotlib.pyplot as plt
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# np.random.seed(1)
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class DataHelper_t(Dataset):
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def __init__(self, file_path, node_feature_path, neg_size, hist_len, directed=False, transform=None, tlp_flag=False):
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self.node2hist = dict()
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self.neg_size = neg_size # 5
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self.hist_len = hist_len # 5
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self.directed = directed
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self.transform = transform
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# self.max_d_time = -sys.maxint # Time interval [0, T]
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self.max_d_time = -sys.maxsize # 1.0
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self.NEG_SAMPLING_POWER = 0.75
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self.neg_table_size = int(1e8) # 10^8
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self.all_edge_index = []
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self.test_edge_index = []
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self.node_time_nodes = dict()
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self.node_set = set()
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self.degrees = dict()
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self.edge_list = []
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self.node_rate = {}
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self.edge_rate = {}
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self.node_sum = {}
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self.edge_sum = {}
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self.time_stamp = []
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self.time_edges_dict = {}
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self.time_nodes_dict = {}
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print('loading data...')
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infile = torch.load(file_path)
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for i in range(infile.size(0)):
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s_node = int(infile[i][0].item()) # source node
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t_node = int(infile[i][1].item()) # target node
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d_time = float(infile[i][2].item()) # time slot, delta t
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self.node_set.update([s_node, t_node])
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if s_node not in self.degrees:
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self.degrees[s_node] = 0
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if t_node not in self.degrees:
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self.degrees[t_node] = 0
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self.all_edge_index.append([s_node, t_node])
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if s_node not in self.node2hist: # node2hist {node: [(historical neighbor, time)], ……}
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self.node2hist[s_node] = list()
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if not directed: # undirected
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if t_node not in self.node2hist:
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self.node2hist[t_node] = list()
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if s_node not in self.node_time_nodes:
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self.node_time_nodes[s_node] = dict() # for the new added s_node, create a dict for it
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if d_time not in self.node_time_nodes[s_node]:
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self.node_time_nodes[s_node][d_time] = list() # for the new time,
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self.node_time_nodes[s_node][d_time].append(t_node)
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if not directed: # undirected
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if t_node not in self.node_time_nodes:
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self.node_time_nodes[t_node] = dict()
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if d_time not in self.node_time_nodes[t_node]:
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self.node_time_nodes[t_node][d_time] = list()
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self.node_time_nodes[t_node][d_time].append(s_node)
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if d_time > self.max_d_time:
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self.max_d_time = d_time # record the max time
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self.degrees[s_node] += 1 # node degree
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self.degrees[t_node] += 1
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self.time_stamp.append(d_time)
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self.edge_list.append((s_node, t_node, d_time)) # edge list
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if not directed:
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self.edge_list.append((t_node, s_node, d_time))
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if tlp_flag:
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if d_time >= 1.0:
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self.test_edge_index.append([s_node, t_node])
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continue
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self.node2hist[s_node].append((t_node, d_time))
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if not directed:
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self.node2hist[t_node].append((s_node, d_time)) # because undirected, so add the inverse version
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