MetaTL/utils.py at main · brictobrick/MetaTL · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import sys
import copy
import torch
import random
import numpy as np
from collections import defaultdict, Counter
from multiprocessing import Process, Queue
# sampler for batch generation
def random_neq(l, r, s):
    t = np.random.randint(l, r)
    while t in s:
        t = np.random.randint(l, r)
    return t

def trans_to_cuda(variable):
    if torch.cuda.is_available():
        return variable.cuda()
    else:
        return variable


def trans_to_cpu(variable):
    if torch.cuda.is_available():
        return variable.cpu()
    else:
        return variable

# train/val/test data generation
def data_load(fname, num_sample):
    usernum = 0
    itemnum = 0
    user_train = defaultdict(list)

    # assume user/item index starting from 1
    f = open('data/%s/%s_train.csv' % (fname, fname), 'r')
    for line in f:
        u, i, t = line.rstrip().split('\t')
        u = int(u)
        i = int(i)
        usernum = max(u, usernum)
        itemnum = max(i, itemnum)
        user_train[u].append(i)
    f.close()

    # read in new users for testing
    user_input_test = {}
    user_input_valid = {}
    user_valid = {}
    user_test = {}


    User_test_new = defaultdict(list)
    f = open('data/%s/%s_test_new_user.csv' % (fname, fname), 'r')
    for line in f:
        u, i, t = line.rstrip().split('\t')
        u = int(u)
        i = int(i)
        User_test_new[u].append(i)
    f.close()

    for user in User_test_new:
        if len(User_test_new[user]) > num_sample:
            if random.random()<0.3:
                user_input_valid[user] = User_test_new[user][:num_sample]
                user_valid[user] = []
                user_valid[user].append(User_test_new[user][num_sample])
            else:
                user_input_test[user] = User_test_new[user][:num_sample]
                user_test[user] = []
                user_test[user].append(User_test_new[user][num_sample])


    return [user_train, usernum, itemnum, user_input_test, user_test, user_input_valid, user_valid]


class DataLoader(object):
    def __init__(self, user_train, user_test, itemnum, parameter):
        self.curr_rel_idx = 0

        self.bs = parameter['batch_size']
        self.maxlen = parameter['K']

        self.valid_user = []
        for u in user_train:
            if len(user_train[u]) < self.maxlen or len(user_test[u]) < 1: continue
            self.valid_user.append(u)

        self.num_tris = len(self.valid_user)

        self.train = user_train
        self.test = user_test

        self.itemnum = itemnum

    def next_one_on_eval(self):
        if self.curr_tri_idx == self.num_tris:
            return "EOT", "EOT"

        u = self.valid_user[self.curr_tri_idx]

        self.curr_tri_idx += 1

        seq = np.zeros([self.maxlen], dtype=np.int32)
        pos = np.zeros([self.maxlen - 1], dtype=np.int32)
        neg = np.zeros([self.maxlen - 1], dtype=np.int32)

        idx = self.maxlen - 1

        ts = set(self.train[u])
        for i in reversed(self.train[u]):
            seq[idx] = i
            if idx > 0:
                pos[idx - 1] = i
                if i != 0: neg[idx - 1] = random_neq(1, self.itemnum + 1, ts)
            idx -= 1
            if idx == -1: break

        curr_rel = u
        support_triples, support_negative_triples, query_triples, negative_triples = [], [], [], []
        for idx in range(self.maxlen-1):
            support_triples.append([seq[idx],curr_rel,pos[idx]])
            support_negative_triples.append([seq[idx],curr_rel,neg[idx]])

        rated = ts
        rated.add(0)
        query_triples.append([seq[-1],curr_rel,self.test[u][0]])
        for _ in range(100):
            t = np.random.randint(1, self.itemnum + 1)
            while t in rated: t = np.random.randint(1, self.itemnum + 1)
            negative_triples.append([seq[-1],curr_rel,t])

        support_triples = [support_triples]
        support_negative_triples = [support_negative_triples]
        query_triples = [query_triples]
        negative_triples = [negative_triples]

        return [support_triples, support_negative_triples, query_triples, negative_triples], curr_rel