Mercurial > repos > jay > pdaug_sequence_based_peptide_generation

annotate PDAUG_Word_Vector_Model/PDAUG_Word_Vector_Model.py @ 5:4a58c72769df draft

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"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit ac4353ca5c0ac9ce60df9f4bf160ed08b99fbee3"
author jay
date Thu, 28 Jan 2021 04:13:09 +0000
parents 93f7668caa55
children
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1 import nltk
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2 from nltk import trigrams
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3 import pandas as pd
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4 from Bio import SeqIO
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5 import gensim, logging
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6 import argparse
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8 parser = argparse.ArgumentParser()
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10 parser.add_argument("-I", "--Input", required=True, default=None, help="Path to target fasta file")
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11 parser.add_argument("-M", "--min_count", required=False, default=0, help="Path to target tsv file")
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12 parser.add_argument("-W", "--window", required=False, default=5, help="Path to target tsv file")
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13 parser.add_argument("-O", "--OutFile", required=False, default='model.txt', help="Path to target tsv file")
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14 parser.add_argument("-S", "--SG", required=False, default='skip-gram', help="Training algorithm: 1 for skip-gram; otherwise CBOW")
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16 args = parser.parse_args()
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18 class ProteinSeq(object):
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19 def __init__(self):
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20 pass
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21 def __iter__(self):
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22 for index, record in enumerate(SeqIO.parse(args.Input, 'fasta')):
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23 for loop_num in range(0, 3):
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24 Ngram_list = []
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25 tri_tokens = trigrams(record.seq)
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26 for index1, item in enumerate(tri_tokens):
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27 if index1 % 3 == loop_num:
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28 tri_pep = item[0] + item[1] + item[2]
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29 Ngram_list.append(tri_pep)
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30 yield Ngram_list
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31 #min_count = 0
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32 size = 200
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33 #window = 5
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35 print (args.SG)
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36 if args.SG == 'skip-gram':
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37 SG = 1
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38 elif args.SG == 'CBOW':
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39 SG = 0
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41 sentences = ProteinSeq()
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42 model = gensim.models.Word2Vec(sentences, min_count=int(args.min_count), size=size, window=int(args.window), sg = SG, workers = 10)
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43 model.wv.save_word2vec_format(args.OutFile, binary=False)
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