-
Notifications
You must be signed in to change notification settings - Fork 9
/
data_loader.py
314 lines (277 loc) · 12.7 KB
/
data_loader.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
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
'''
@Author: tengfei ma
@Date: 2020-05-16 17:50:18
LastEditTime: 2022-06-22 10:47:01
LastEditors: xiaomingaaa 13693963350@163.com
@Description: 加载RGCN数据以及DTI
@FilePath: /Multi-task-pytorch/data_loader.py
'''
import utils
from sklearn.model_selection import train_test_split,StratifiedKFold
import numpy as np
import torch
import os
import dgllife
CHARPROTSET = {"A": 1, "C": 2, "B": 3, "E": 4, "D": 5, "G": 6,
"F": 7, "I": 8, "H": 9, "K": 10, "M": 11, "L": 12,
"O": 13, "N": 14, "Q": 15, "P": 16, "S": 17, "R": 18,
"U": 19, "T": 20, "W": 21,
"V": 22, "Y": 23, "X": 24,
"Z": 25}
CHARPROTLEN = 25 # count of categories
CHARCANSMISET = {"#": 1, "%": 2, ")": 3, "(": 4, "+": 5, "-": 6,
".": 7, "1": 8, "0": 9, "3": 10, "2": 11, "5": 12,
"4": 13, "7": 14, "6": 15, "9": 16, "8": 17, "=": 18,
"A": 19, "C": 20, "B": 21, "E": 22, "D": 23, "G": 24,
"F": 25, "I": 26, "H": 27, "K": 28, "M": 29, "L": 30,
"O": 31, "N": 32, "P": 33, "S": 34, "R": 35, "U": 36,
"T": 37, "W": 38, "V": 39, "Y": 40, "[": 41, "Z": 42,
"]": 43, "_": 44, "a": 45, "c": 46, "b": 47, "e": 48,
"d": 49, "g": 50, "f": 51, "i": 52, "h": 53, "m": 54,
"l": 55, "o": 56, "n": 57, "s": 58, "r": 59, "u": 60,
"t": 61, "y": 62}
CHARCANSMILEN = 62
CHARISOSMISET = {"#": 29, "%": 30, ")": 31, "(": 1, "+": 32, "-": 33, "/": 34, ".": 2,
"1": 35, "0": 3, "3": 36, "2": 4, "5": 37, "4": 5, "7": 38, "6": 6,
"9": 39, "8": 7, "=": 40, "A": 41, "@": 8, "C": 42, "B": 9, "E": 43,
"D": 10, "G": 44, "F": 11, "I": 45, "H": 12, "K": 46, "M": 47, "L": 13,
"O": 48, "N": 14, "P": 15, "S": 49, "R": 16, "U": 50, "T": 17, "W": 51,
"V": 18, "Y": 52, "[": 53, "Z": 19, "]": 54, "\\": 20, "a": 55, "c": 56,
"b": 21, "e": 57, "d": 22, "g": 58, "f": 23, "i": 59, "h": 24, "m": 60,
"l": 25, "o": 61, "n": 26, "s": 62, "r": 27, "u": 63, "t": 28, "y": 64}
CHARISOSMILEN = 64 # count of labels
MAX_SMI_LEN = 200
MAX_SEQ_LEN = 1200
'''
@description: label smiles string with a fixed length
@param {type}
@return:
'''
def label_smiles(line, MAX_SMI_LEN, smi_ch_id):
X = np.zeros(MAX_SMI_LEN,dtype=int)
for i, ch in enumerate(line[:MAX_SMI_LEN]):
X[i] = smi_ch_id[ch]
return X.tolist()
'''
@description: label protein sequence with a fixed length
@param {type}
@return:
'''
def label_sequence(line, MAX_SEQ_LEN, seq_ch_id):
X = np.zeros(MAX_SEQ_LEN,dtype=int)
for i, ch in enumerate(line[: MAX_SEQ_LEN]):
X[i] = seq_ch_id[ch]
return X.tolist()
'''
@description:
@param {type}
@return:
'''
def split_grams(seq, n=3):
one = zip(*[iter(seq)]*n) # handle first seq
two = zip(*[iter(seq[1:])]*n)
three = zip(*[iter(seq[2:])]*n)
total = [one,two,three]
str_ngram = set()
for ngrams in total:
for ngram in ngrams:
str_ngram.add(''.join(ngram))
#str_ngram.append(x)
return list(str_ngram)
def label_sequence_by_words(seq,words_dict,max_lenght=1200):
ngrams_words=split_grams(seq)
X=np.zeros(max_lenght,dtype=int)
for i, word in enumerate(ngrams_words[:max_lenght]):
if word in words_dict:
X[i]=words_dict[word]
else:
X[i]=words_dict['-+-']
return X
def storeWordsIntoDict(sequences,dataset):
if os.path.isfile('data/protein_words_dict_{}_full_1_3.npy'.format(dataset)):
words=np.load('data/protein_words_dict_{}_full_1_3.npy'.format(dataset),allow_pickle=True)
return words.item()
words=dict()
words['-+-']=0
max_length=0
print('process sequence for {}'.format(dataset))
count=0
lens=len(sequences)
for seq in sequences:
count+=1
#print('{}/{}'.format(count, lens))
ngram_words=split_grams(seq)
if max_length<len(ngram_words):
max_length=len(ngram_words)
for w in ngram_words:
if w not in words:
words[w]=len(words)
np.save('data/protein_words_dict_{}_full_1_3.npy'.format(dataset),words)
print('max words length of protein is {}'.format(max_length))
return words
def split_dataset(dataset, ratio):
n = int(ratio * len(dataset))
dataset_1, dataset_2 = dataset[:n], dataset[n:]
#dataset_1,dataset_2=train_test_split(dataset,test_size=ratio)
return dataset_1, dataset_2
def shuffle_dataset(dataset, seed):
np.random.seed(seed)
np.random.shuffle(dataset)
return dataset
class load_data():
def __init__(self, kg_file, dti_path=None,cpi_path=None,cpi_dataset='human',dti_dataset='drugbank',cpi_gnn=False,test_model=False):
#
self.cpi_dataset=cpi_dataset
self.dti_dataset=dti_dataset
self.train_kg, self.num_nodes, self.num_rels = self._load_kg_data(
kg_file)
if test_model:
self.train_dti_set,self.val_dti_set,self.test_dti_set,self.test_sample_nodes=self._load_dti_data(dti_path)
if not cpi_gnn:
self.train_cpi_set, self.val_cpi_set, self.test_cpi_set, self.compound2smiles, self.protein2seq = self._load_cpi_data(
cpi_path)
for i in self.compound2smiles:
self.test_sample_nodes.add(int(i))
else:
self.train_set_gnn,self.val_set_gnn,self.test_set_gnn,self.smiles2graph,self.protein2seq,self.word_length= self._load_cpi_gnn(cpi_path)
for i in self.smiles2graph:
self.test_sample_nodes.add(int(i))
self.test_dti_global,self.test_sample_nodes=self._load_dti_global(dti_path)
self.test_cpi_global,self.test_smiles2graph,self.test_protein2seq,self.test_word_length=self._load_cpi_global_test(cpi_path)
for i in self.test_smiles2graph:
self.test_sample_nodes.add(int(i))
else:
self.train_dti_set,self.val_dti_set,self.test_dti_set,self.sample_nodes=self._load_dti_data(dti_path)
if not cpi_gnn:
self.train_cpi_set, self.val_cpi_set, self.test_cpi_set, self.compound2smiles, self.protein2seq = self._load_cpi_data(
cpi_path)
for i in self.compound2smiles:
self.sample_nodes.add(int(i))
else:
self.train_set_gnn,self.val_set_gnn,self.test_set_gnn,self.smiles2graph,self.protein2seq,self.word_length= self._load_cpi_gnn(cpi_path)
#self.cpi_examples, self.smiles2graph,self.protein2seq,self.word_length= self._load_cpi_gnn(cpi_path)
for i in self.smiles2graph:
self.sample_nodes.add(int(i))
def _load_kg_data(self, kg_path):
test_triples = list()
entity2id = dict()
relation2id = dict()
train_triples = list()
validate_triples = list()
with open('{}/entities.tsv'.format(kg_path),'r') as f:
for line in f:
e_name,e_id=line.strip().split('\t')
entity2id[e_name]=int(e_id)
with open('{}/relations.tsv'.format(kg_path),'r') as f:
for line in f:
r_name,r_id=line.strip().split('\t')
relation2id[r_name]=int(r_id)
num_entities = len(entity2id)
num_rels = len(relation2id)
with open('{}/drkg.tsv'.format(kg_path), 'r') as f:
for line in f:
head, rel, tail = line.strip().split('\t')
train_triples.append([entity2id[head], relation2id[rel], entity2id[tail]])
entity2id = None
relation2id = None
return train_triples, num_entities, num_rels
def _load_cpi_data(self, cpi_path):
drug2smiles = dict()
target2seq = dict()
examples = list()
if self.cpi_dataset=='celegans':
example_path='{}/celegan_examples_global_final_1_3.tsv'.format(cpi_path)
else:
example_path='{}/human_examples_global_final_1_3.tsv'.format(cpi_path)
# if self.cpi_dataset=='drugbank':
# example_path='{}/final_dti_example.tsv.tsv'.format(cpi_path)
# else:
# example_path='{}/drugcentral_dti_examples.tsv'.format(cpi_path)
with open(example_path, 'r') as f:
for line in f:
l = line.strip().split('\t')
drug_id = int(l[4])
target_id = int(l[1])
seq = l[2]
smiles = l[5]
label = int(l[6])
drug2smiles[drug_id] = label_smiles(
smiles, MAX_SMI_LEN, CHARISOSMISET)
target2seq[target_id] = label_sequence(
seq, MAX_SEQ_LEN, CHARPROTSET)
examples.append([drug_id, target_id, label])
#8:1:1
train_set, test_set = train_test_split(
examples, test_size=0.2,random_state=555)
val_set, test_set = train_test_split(
test_set, test_size=0.5,random_state=555)
return train_set, val_set, test_set, drug2smiles, target2seq
#return examples, drug2smiles, target2seq
def _load_dti_data(self,dti_path):
examples=list()
sample_ndoes=set()
if self.dti_dataset=='drugbank':
example_path='{}/final_dti_example.tsv'.format(dti_path)
# example_path='dataset/redundant/dti_data.tsv'
elif self.dti_dataset=='drugcentral':
example_path='{}/drugcentral_dti_examples.tsv'.format(dti_path)
elif self.dti_dataset=='drugbank_redundant':
example_path='dataset/redundant/dti_data.tsv'
elif self.dti_dataset=='drugcentral_redundant':
example_path='dataset/redundant/drugcentral_data.tsv'
elif self.dti_dataset=='bindingdb':
example_path='dataset/bindingdb/compound_protein_interaction.tsv'
elif self.dti_dataset=='drugcentral_sparse':
example_path='dataset/dti_task/drugcentral_examples_global_final_1_9.tsv'
elif self.dti_dataset=='drugcentral_full':
example_path='dataset/dti_task/drugcentral_examples_global_final_1_29.tsv'
print(example_path)
with open(example_path,'r') as f:
for line in f:
l=line.strip().split('\t')
drug_entityid=int(l[4])
target_entityid=int(l[1])
sample_ndoes.add(drug_entityid)
sample_ndoes.add(target_entityid)
label=int(l[6])
examples.append([drug_entityid,target_entityid,label])
#### seed 3,4
train_dti_set,test_dti_set = train_test_split(examples,test_size=0.2,random_state=3)
val_dti_set,test_dti_set = train_test_split(test_dti_set,test_size=0.5,random_state=4)
return train_dti_set,val_dti_set,test_dti_set, sample_ndoes
def _load_cpi_gnn(self,cpi_path):
examples = list()
smiles_graph=dict()
protein2seq = dict()
proteins_list=set()
if self.cpi_dataset=='celegans':
example_path='{}/celegan_examples_global_final_1_3.tsv'.format(cpi_path)
elif self.cpi_dataset=='human':
example_path='{}/human_examples_global_final_1_3.tsv'.format(cpi_path)
print(example_path)
with open(example_path, 'r') as f:
for line in f:
l = line.strip().split('\t')
drug_id = int(l[4])
target_id = int(l[1])
seq = l[2]
smiles = l[5]
label = int(l[6])
proteins_list.add(seq)
protein2seq[target_id] = seq
if smiles not in smiles_graph:
c_size,features,edge_index=utils.smiles2graph(smiles)
if c_size is None and features is None and edge_index is None:
continue
smiles_graph[drug_id]=(c_size,features,edge_index)
#smiles_graph[drug_id]=dgllife.utils.smi
examples.append([drug_id, target_id, label])
words_dict=storeWordsIntoDict(list(proteins_list),self.cpi_dataset)
for p in protein2seq:
protein2seq[p]=label_sequence_by_words(protein2seq[p],words_dict)
#examples=shuffle_dataset
# train_set,test_set=train_test_split(examples,test_size=0.2,random_state=4)
# val_set,test_set=train_test_split(test_set,test_size=0.5,random_state=5)
### use shuffle
train_set, val_set, test_set=utils.StratifiedSplit(examples)
return train_set,val_set,test_set, smiles_graph,protein2seq,len(words_dict)