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import os
import json
import csv
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import math, copy, time
from torch.autograd import Variable
from scipy import stats
import pandas as pd
from sklearn.model_selection import KFold
import pickle
from sklearn.model_selection import train_test_split
import os.path
def make_vocab():
#0: pad
#1: start
#2: end
word2idx = {}
idx2word = {}
word2idx['0'] = 0
word2idx['1'] = 1
word2idx['2'] = 2
word2idx['A'] = 3
word2idx['C'] = 4
word2idx['D'] = 5
word2idx['E'] = 6
word2idx['F'] = 7
word2idx['G'] = 8
word2idx['H'] = 9
word2idx['I'] = 10
word2idx['K'] = 11
word2idx['L'] = 12
word2idx['M'] = 13
word2idx['N'] = 14
word2idx['P'] = 15
word2idx['Q'] = 16
word2idx['R'] = 17
word2idx['S'] = 18
word2idx['T'] = 19
word2idx['V'] = 20
word2idx['W'] = 21
word2idx['Y'] = 22
for key, value in word2idx.items():
idx2word[value] = key
return word2idx, idx2word
def AAindex(path, word2idx):
with open(path) as csvfile:
reader = csv.reader(csvfile)
AAindex_dict = {}
AAindex_matrix = []
skip = 1
for row in reader:
if skip == 1:
skip = 0
header = np.array(row)[1:].tolist()
continue
tmp = []
for j in np.array(row)[1:]:
try:
tmp.append(float(j))
except:
tmp.append(0)
AAindex_matrix.append(np.array(tmp))
dim = np.shape(AAindex_matrix)[0]
AAindex_matrix = np.array(AAindex_matrix)
for i in range(len(header)):
AAindex_dict[header[i]] = AAindex_matrix[:, i]
#print (AAindex_matrix)
emb = np.zeros((len(word2idx), dim))
for key, value in word2idx.items():
if key in AAindex_dict:
emb[value] = AAindex_dict[key]
else:
pass
return emb, AAindex_dict
def onehot_encoding(seq_list_, max_len, word2idx):
#0: pad
#1: start
#2: end
seq_list = [i for i in seq_list_]
X = np.zeros((len(seq_list), max_len)).astype(int)
AA_mask = []
nonAA_mask = []
for i in range(len(seq_list)):
if len(seq_list[i]) >= max_len - 2:
a_seq = '1' + seq_list[i][:max_len-2].upper() + '2'
else:
a_seq = '1' + seq_list[i].upper() + '2'
if len(a_seq) > max_len:
iter_num = max_len
else:
iter_num = len(a_seq)
for j in range(iter_num):
if a_seq[j] not in word2idx:
continue
else:
X[i,j] = word2idx[a_seq[j]]
tmp = np.zeros(max_len)
tmp[1:iter_num+1] = 1
AA_mask.append(tmp.astype(int))
nonAA_mask.append((1-tmp).astype(int))
return np.array(X), np.array(AA_mask), np.array(nonAA_mask)
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