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