-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils_deepDTnet.py
executable file
·143 lines (117 loc) · 5.74 KB
/
utils_deepDTnet.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 os
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn.functional as F
from torch import nn
import random
import scipy.io
from sklearn.decomposition import non_negative_factorization
random.seed(3)
torch.manual_seed(4)
def pre_processed_DTnet_1():
dataset_dir = os.path.sep.join(['deepDTnet'])
i_m = np.genfromtxt(os.path.sep.join([dataset_dir, 'drugProtein.txt']), dtype=np.int32)
print(len(i_m), len(i_m[0]))
edge = []
for i in range(len(i_m)):
for j in range(len(i_m[0])):
if i_m[i][j] == 1:
edge.append([i, j])
print(len(edge))
# with open(os.path.sep.join([dataset_dir, "drug_target_interaction.txt"]), "w") as f0:
# for i in range(len(edge)):
# s = str(edge[i]).replace('[', ' ').replace(']', ' ')
# s = s.replace("'", ' ').replace(',', '') + '\n'
# f0.write(s)
def load_data_deepDTnet(dataset_train="DTnet_train_0.8_0", dataset_test="DTnet_test_0.8_0"):
dataset_dir = os.path.sep.join(['deepDTnet'])
# build incidence matrix
edge_train = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format(dataset_train)]), dtype=np.int32)
edge_all = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format("deepDTnet_all")]), dtype=np.int32)
# edge_train_pro = []
# for i in edge_all:
# edge_train_pro.append([i[0], i[1] + 732])
# with open(os.path.sep.join([dataset_dir, "edge_train_pro.txt"]), "w") as f0:
# for i in range(len(edge_train_pro)):
# s = str(edge_train_pro[i]).replace('[', ' ').replace(']', ' ')
# s = s.replace("'", ' ').replace(',', '') + '\n'
# f0.write(s)
edge_test = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format(dataset_test)]), dtype=np.int32)
# edge_test_pro = []
# for i in edge_test:
# edge_test_pro.append([i[0], i[1] + 732])
# with open(os.path.sep.join([dataset_dir, "edge_test_pro.txt"]), "w") as f0:
# for i in range(len(edge_test_pro)):
# s = str(edge_test_pro[i]).replace('[', ' ').replace(']', ' ')
# s = s.replace("'", ' ').replace(',', '') + '\n'
# f0.write(s)
i_m = np.genfromtxt(os.path.sep.join([dataset_dir, 'drugProtein.txt']), dtype=np.int32)
H_T = np.zeros((len(i_m), len(i_m[0])), dtype=np.int32)
H_T_all = np.zeros((len(i_m), len(i_m[0])), dtype=np.int32)
for i in edge_train:
H_T[i[0]][i[1]] = 1
for i in edge_all:
H_T_all[i[0]][i[1]] = 1
# val = np.zeros(len(edge_val))
test = np.zeros(len(edge_test))
for i in range(len(test)):
if i <= len(edge_test) // 2:
test[i] = 1
# val[i] = 1
np.set_printoptions(threshold=np.inf)
H_T = torch.Tensor(H_T)
H = H_T.t()
H_T_all = torch.Tensor(H_T_all)
H_all = H_T_all.t()
print("deepDTnet", H.size()) # 1915, 732
drug_feat = torch.eye(732)
prot_feat = torch.eye(1915)
drugDisease = torch.Tensor(np.genfromtxt(os.path.sep.join([dataset_dir, 'drugDisease.txt']), dtype=np.int32)) # 732, 440
proteinDisease = torch.Tensor(np.genfromtxt(os.path.sep.join([dataset_dir, 'proteinDisease.txt']), dtype=np.int32)) # 1915, 440
return drugDisease, proteinDisease, drug_feat, prot_feat, H, H_T, edge_test, test
def generate_data_2(dataset_str="drug_target_interaction"):
# 将数据集分为训练集，测试集
dataset_dir = os.path.sep.join(['deepDTnet'])
# edge = np.genfromtxt("edges.txt", dtype=np.int32)
edge = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format(dataset_str)]), dtype=np.int32) # dtype='U75'
# print(edge)
data = torch.utils.data.DataLoader(edge, shuffle=True)
edge_shuffled = []
for i in data:
edge_shuffled.append(i[0].tolist())
# print(edge_shuffled)
# drugs = []
# targets = []
# for i in edge:
# if i[0] not in drugs:
# drugs.append(i[0])
# if i[1] not in targets:
# targets.append(i[1])
test_ration = [0.2]
for d in test_ration:
for a in (range(1)):
edge_test = edge_shuffled[a * int(len(edge_shuffled) * d): (a + 1) * int(len(edge_shuffled) * d)]
edge_train = edge_shuffled[: a * int(len(edge_shuffled) * d)] + edge_shuffled[(a + 1) * int(len(edge_shuffled) * d):]
test_zeros = []
while len(test_zeros) < len(edge_test) * 1:
x1 = random.sample(range(0, 732), 1)[0]
y1 = random.sample(range(0, 1915), 1)[0]
if [x1, y1] not in edge.tolist() and [x1, y1] not in test_zeros:
test_zeros.append([x1, y1])
edge_test = edge_test + test_zeros
with open(os.path.sep.join([dataset_dir, "DTnet_train_{ratio}_{fold}.txt".format(ratio=d, fold=a)]), "w") as f0:
for i in range(len(edge_train)):
s = str(edge_train[i]).replace('[', ' ').replace(']', ' ')
s = s.replace("'", ' ').replace(',', '') + '\n'
f0.write(s)
with open(os.path.sep.join([dataset_dir, "DTnet_test_{ratio}_{fold}.txt".format(ratio=d, fold=a)]), "w") as f1:
for i in range(len(edge_test)):
s = str(edge_test[i]).replace('[', ' ').replace(']', ' ')
s = s.replace("'", ' ').replace(',', '') + '\n'
f1.write(s)
# with open(os.path.sep.join([dataset_dir, "DTnet_all.txt"]), "w") as f3:
# for i in range(len(edge)):
# s = str(edge[i]).replace('[', ' ').replace(']', ' ')
# s = s.replace("'", ' ').replace(',', '') + '\n'
# f3.write(s)