library_data.py

# -*- coding: utf-8 -*

'''
This is a supporting library for the loading the data.

Paper: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. S. Kumar, X. Zhang, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019. 
'''

from __future__ import division
import numpy as np
import random
import sys
import operator
import copy
from collections import defaultdict
import os, re
import cPickle
import argparse
from sklearn.preprocessing import scale

# LOAD THE NETWORK
def load_network(args, time_scaling=True):
    '''
    This function loads the input network.

    The network should be in the following format:
    One line per interaction/edge.
    Each line should be: user, item, timestamp, state label, array of features.
    Timestamp should be in cardinal format (not in datetime).
    State label should be 1 whenever the user state changes, 0 otherwise. If there are no state labels, use 0 for all interactions.
    Feature list can be as long as desired. It should be atleast 1 dimensional. If there are no features, use 0 for all interactions. 
    '''

    network = args.network
    datapath = args.datapath

    user_sequence = []
    item_sequence = []
    label_sequence = []
    feature_sequence = []
    timestamp_sequence = []
    start_timestamp = None
    y_true_labels = []

    print "\n\n**** Loading %s network from file: %s ****" % (network, datapath)
    f = open(datapath,"r")
    f.readline()
    for cnt, l in enumerate(f):
        # FORMAT: user, item, timestamp, state label, feature list 
        ls = l.strip().split(",")
        user_sequence.append(ls[0])
        item_sequence.append(ls[1])
        if start_timestamp is None:
            start_timestamp = float(ls[2])
        timestamp_sequence.append(float(ls[2]) - start_timestamp) 
        y_true_labels.append(int(ls[3])) # label = 1 at state change, 0 otherwise
        feature_sequence.append(map(float,ls[4:]))     #list()
    f.close()

    user_sequence = np.array(user_sequence)            #user序列
    item_sequence = np.array(item_sequence)            #item序列
    timestamp_sequence = np.array(timestamp_sequence)  #item序列的时间戳序列

    print "Formating item sequence"
    nodeid = 0
    item2id = {}
    item_timedifference_sequence = []
    item_current_timestamp = defaultdict(float)
    for cnt, item in enumerate(item_sequence):
        if item not in item2id:
            item2id[item] = nodeid
            nodeid += 1
        timestamp = timestamp_sequence[cnt]
        item_timedifference_sequence.append(timestamp - item_current_timestamp[item])  #时间间隔
        item_current_timestamp[item] = timestamp         #每个item当前的时间（最新被作用的时间）
    num_items = len(item2id)
    item_sequence_id = [item2id[item] for item in item_sequence]      #item序列，新的id

    print "Formating user sequence"
    nodeid = 0
    user2id = {}
    user_timedifference_sequence = []
    user_current_timestamp = defaultdict(float)
    user_previous_itemid_sequence = []
    user_latest_itemid = defaultdict(lambda: num_items)  #当没有键时，默认返回num_items值
    for cnt, user in enumerate(user_sequence):
        if user not in user2id:       #给user编id
            user2id[user] = nodeid
            nodeid += 1
        timestamp = timestamp_sequence[cnt]
        user_timedifference_sequence.append(timestamp - user_current_timestamp[user]) #user序列的时间间隔
        user_current_timestamp[user] = timestamp                                      #user的时间戳
        user_previous_itemid_sequence.append(user_latest_itemid[user])                #user上次作用的item
        user_latest_itemid[user] = item2id[item_sequence[cnt]]                        #user最后作用的item
    num_users = len(user2id)
    user_sequence_id = [user2id[user] for user in user_sequence]

    if time_scaling:
        print "Scaling timestamps"
        user_timedifference_sequence = scale(np.array(user_timedifference_sequence) + 1)
        item_timedifference_sequence = scale(np.array(item_timedifference_sequence) + 1)

    print "*** Network loading completed ***\n\n"
    return [user2id, user_sequence_id, user_timedifference_sequence, user_previous_itemid_sequence, \
        item2id, item_sequence_id, item_timedifference_sequence, \
        timestamp_sequence, \
        feature_sequence, \
        y_true_labels]