提交 3d456595 作者: 朱学凯

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上级 2f72dddc
File added
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import numpy as np
import pandas as pd
import torch
from torch.utils import data
import json
import collections
from sklearn.preprocessing import OneHotEncoder
from subword_nmt.apply_bpe import BPE
import codecs
from tqdm import tqdm
import csv
# vocab_path = './ESPF/protein_codes_uniprot.txt'
# bpe_codes_protein = codecs.open(vocab_path)
# pbpe = BPE(bpe_codes_protein, merges=-1, separator='')
# sub_csv = pd.read_csv('./ESPF/subword_units_map_uniprot.csv')
#
# idx2word_p = sub_csv['index'].values
# words2idx_p = dict(zip(idx2word_p, range(0, len(idx2word_p))))
# vocab_path = './ESPF/drug_codes_chembl.txt'
# bpe_codes_drug = codecs.open(vocab_path)
# dbpe = BPE(bpe_codes_drug, merges=-1, separator='')
# sub_csv = pd.read_csv('./ESPF/subword_units_map_chembl.csv')
#
# idx2word_d = sub_csv['index'].values
# words2idx_d = dict(zip(idx2word_d, range(0, len(idx2word_d))))
# max_d = 205
# max_p = 545
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = index
return vocab
def protein2emb_encoder(x, words2idx_p):
max_p = 152
# t1 = pbpe.process_line(x).split() # split
t1 = x.split(',')
try:
i1 = np.asarray([words2idx_p[i] for i in t1]) # index
except:
i1 = np.array([0])
# print(x)
l = len(i1)
if l < max_p:
i = np.pad(i1, (0, max_p - l), 'constant', constant_values=0)
input_mask = ([1] * l) + ([0] * (max_p - l))
else:
i = i1[:max_p]
input_mask = [1] * max_p
return i, np.asarray(input_mask)
def drug2emb_encoder(x, dbpe, words2idx_d):
max_d = 50
# max_d = 100
t1 = dbpe.process_line(x)
t1 = t1.split() # split
try:
i1 = np.asarray([words2idx_d[i] for i in t1]) # index
except:
i1 = np.array([0])
# print(x)
l = len(i1)
print(i1)
if l < max_d:
i = np.pad(i1, (0, max_d - l), 'constant', constant_values=0)
input_mask = ([1] * l) + ([0] * (max_d - l))
else:
i = i1[:max_d]
input_mask = [1] * max_d
return i, np.asarray(input_mask)
def seq2emb_encoder(input_seq, max_len, vocab):
try:
ids = np.asarray([vocab[i] for i in input_seq])
except:
ids = np.array([0])
l = len(ids)
with open('./utils/data_analyse_train.tsv', 'w') as f:
tsv = csv.writer(f)
tsv.writerow([ids, l, '\n'])
if l < max_len:
ids = np.pad(ids, (0, max_len - l), 'constant', constant_values=0)
input_mask = np.array(([1] * l) + ([0] * (max_len - l)))
else:
ids = ids[:max_len]
input_mask = np.array([1] * max_len)
return ids, input_mask
class Data_Encoder(data.Dataset):
def __init__(self, train_file, tokenizer_config):
'Initialization'
self.begin_id = train_file["begin_id"]
self.sep_id = train_file["separate_id"]
self.max_len = train_file["max_len"]
with open(train_file["sps"], 'r') as f:
self.sps = f.readlines()
with open(train_file["smile"], 'r') as f:
self.smile = f.readlines()
with open(train_file["affinity"], 'r') as f:
self.affinity = f.readlines()
self.vocab = load_vocab(tokenizer_config["vocab_file"])
bpe_codes_drug = codecs.open(tokenizer_config["vocab_pair"])
self.dbpe = BPE(bpe_codes_drug, merges=-1, separator='')
def __len__(self):
'Denotes the total number of samples'
return len(self.sps)
def __getitem__(self, index):
'Generates one sample of data'
# Select sample
# Load data and get label
# tokenization
d = self.dbpe.process_line(self.smile[index].strip()).split()
p = self.sps[index].strip().split(',')
y = self.affinity[index].strip()
input_seq = [self.begin_id] + d + [self.sep_id] + p + [self.sep_id]
input, input_mask = seq2emb_encoder(input_seq, self.max_len, self.vocab)
return input, y
if __name__ == "__main__":
# local test
# dataFolder = './IC50/SPS/train_smile'
# with open(dataFolder, 'r') as f:
# train_smi = f.readlines()
# drug_smi = train_smi[0]
# d_v, input_mask_d = drug2emb_encoder(drug_smi)
# test load vocab
# vocab_file = './ESPF/vocab.txt'
# vocab = load_vocab(vocab_file)
# test train
df_train = {"sps": './IC50/SPS/train_sps',
"smile": './IC50/SPS/train_smile',
"affinity": './IC50/SPS/train_ic50',
"vocab_file": './ESPF/vocab.txt',
"begin_id": '[CLS]',
"separate_id": "[SEP]",
"max_len": 256
}
tokenizer_config = {"vocab_file": './ESPF/vocab.txt',
"vocab_pair": './ESPF/drug_codes_chembl.txt'
}
params = {'batch_size': 5,
'shuffle': False,
'num_workers': 0,
'drop_last': True}
trainset = Data_Encoder(df_train, tokenizer_config)
training_generator = data.DataLoader(trainset)
for i, (input, affinity) in tqdm(enumerate(training_generator)):
print('----------------')
"[ 4 209 1755 3175 6267 15433 911 535 5272 5 25 34
19 30 11 28 11 31 21 15 12 44 12 14
58 42 11 29 20 27 11 33 17 15 19 34
11 18 12 28 11 78 11 47 35 26 11 15
70 36 32 27 58 15 11 14 52 11 14 16
18 12 57 11 14 19 18 16 5]",69,"
"
import pandas as pd
sub_csv = pd.read_csv('../ESPF/subword_units_map_chembl.csv')
idx2word_d = sub_csv['index'].values
# words2idx_d = dict(zip(idx2word_d, range(0, len(idx2word_d))))
with open('../ESPF/vocab76.to', 'r') as f:
prot_vocab = f.readlines()
with open('../ESPF/vocab.txt', 'w') as f:
for prot in prot_vocab:
f.write(prot.strip() + '\n')
with open('../ESPF/vocab.txt', 'a') as f:
for drug in idx2word_d:
f.write(drug + '\n')
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