forked from anonymousCML/CML
-
Notifications
You must be signed in to change notification settings - Fork 15
/
DataHandler.py
144 lines (104 loc) · 4.26 KB
/
DataHandler.py
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
143
import torch
import torch.utils.data as data
import pickle
import numpy as np
import scipy.sparse as sp
from math import ceil
import datetime
from Params import args
import graph_utils
class RecDataset(data.Dataset):
def __init__(self, data, num_item, train_mat=None, num_ng=1, is_training=True):
super(RecDataset, self).__init__()
self.data = np.array(data)
self.num_item = num_item
self.train_mat = train_mat
self.is_training = is_training
def ng_sample(self):
assert self.is_training, 'no need to sampling when testing'
dok_trainMat = self.train_mat.todok()
length = self.data.shape[0]
self.neg_data = np.random.randint(low=0, high=self.num_item, size=length)
for i in range(length): #
uid = self.data[i][0]
iid = self.neg_data[i]
if (uid, iid) in dok_trainMat:
while (uid, iid) in dok_trainMat:
iid = np.random.randint(low=0, high=self.num_item)
self.neg_data[i] = iid
self.neg_data[i] = iid
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
user = self.data[idx][0]
item_i = self.data[idx][1]
if self.is_training:
neg_data = self.neg_data
item_j = neg_data[idx]
return user, item_i, item_j
else:
return user, item_i
def getMatrix(self):
pass
def getAdj(self):
pass
def sampleLargeGraph(self):
def makeMask():
pass
def updateBdgt():
pass
def sample():
pass
def constructData(self):
pass
class RecDataset_beh(data.Dataset):
def __init__(self, beh, data, num_item, behaviors_data=None, num_ng=1, is_training=True):
super(RecDataset_beh, self).__init__()
self.data = np.array(data)
self.num_item = num_item
self.is_training = is_training
self.beh = beh
self.behaviors_data = behaviors_data
self.length = self.data.shape[0]
self.neg_data = [None]*self.length
self.pos_data = [None]*self.length
def ng_sample(self):
assert self.is_training, 'no need to sampling when testing'
for i in range(self.length):
self.neg_data[i] = [None]*len(self.beh)
self.pos_data[i] = [None]*len(self.beh)
for index in range(len(self.beh)):
train_u, train_v = self.behaviors_data[index].nonzero()
beh_dok = self.behaviors_data[index].todok()
set_pos = np.array(list(set(train_v)))
self.pos_data_index = np.random.choice(set_pos, size=self.length, replace=True, p=None)
self.neg_data_index = np.random.randint(low=0, high=self.num_item, size=self.length)
for i in range(self.length): #
uid = self.data[i][0]
iid_neg = self.neg_data[i][index] = self.neg_data_index[i]
iid_pos = self.pos_data[i][index] = self.pos_data_index[i]
if (uid, iid_neg) in beh_dok:
while (uid, iid_neg) in beh_dok:
iid_neg = np.random.randint(low=0, high=self.num_item)
self.neg_data[i][index] = iid_neg
self.neg_data[i][index] = iid_neg
if index == (len(self.beh)-1):
self.pos_data[i][index] = train_v[i]
elif (uid, iid_pos) not in beh_dok:
if len(self.behaviors_data[index][uid].data)==0:
self.pos_data[i][index] = -1
else:
t_array = self.behaviors_data[index][uid].toarray()
pos_index = np.where(t_array!=0)[1]
iid_pos = np.random.choice(pos_index, size = 1, replace=True, p=None)[0]
self.pos_data[i][index] = iid_pos
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
user = self.data[idx][0]
item_i = self.pos_data[idx]
if self.is_training:
item_j = self.neg_data[idx]
return user, item_i, item_j
else:
return user, item_i