Mercurial > repos > jay > pdaug_sequence_similarity_network

annotate PDAUG_Word_Vector_Descriptor/PDAUG_Word_Vector_Descriptor.py @ 0:e650de82bcc7 draft

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"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"
author jay
date Wed, 28 Oct 2020 01:50:00 +0000
parents
children 5ae3966929db
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e650de82bcc7 "planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"
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1 import numpy as np
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2 import os
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3 import pandas as pd
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4 from Bio import SeqIO
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5 from nltk import bigrams
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6 from nltk import trigrams
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7 import gensim
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8 import argparse
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10 parser = argparse.ArgumentParser()
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12 parser.add_argument("-M", "--ModelInput", required=True, default=None, help="Path to target tsv file")
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13 parser.add_argument("-R", "--row", required=True, default=None, help="Path to target tsv file")
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14 parser.add_argument("-I", "--InputFasta", required=True, default=6, help="Path to target tsv file")
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15 parser.add_argument("-O", "--OutFile", required=False, default='model.txt', help="Path to target tsv file")
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16 parser.add_argument("-P", "--positive", required=True, help="Path to target tsv file")
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17 parser.add_argument("-N", "--negative", required=True, help="Path to target tsv file")
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19 args = parser.parse_args()
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21 seed = 42
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22 np.random.seed(seed)
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24 new_model = gensim.models.KeyedVectors.load_word2vec_format(args.ModelInput, binary=False)
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26 import time
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27 t0 = time.time()
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29 temp_word = np.zeros(shape=(int(args.row), 200))
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31 for index, seqs in enumerate(SeqIO.parse(args.InputFasta, 'fasta')):
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32 seq_sum = 0
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33 tri_seq = trigrams(seqs.seq)
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34 for item in ((tri_seq)):
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35 tri_str = item[0] + item[1] + item[2]
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36 if tri_str not in list(new_model.wv.vocab):
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37 continue
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38 seq_sum = seq_sum + new_model[tri_str]
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40 temp_word[index] = seq_sum
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42 t1 = time.time()
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45 temp_word = temp_word
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48 clm = [x for x in range(0,temp_word.shape[1])]
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49 y_temp_word = np.vstack((np.ones((int(args.positive), 1)), np.zeros((int(args.negative),1))))
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51 c, r = y_temp_word.shape
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52 y_temp_word = y_temp_word.reshape(c,)
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54 class_label = pd.DataFrame(y_temp_word, columns=["Class_label"])
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56 df = pd.DataFrame(temp_word, columns=clm)
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57 df = pd.concat([df,class_label], axis=1)
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59 df.to_csv(args.OutFile, index=None, sep="\t")