tagging.py
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# -*- coding: UTF-8 -*-
import os
import re
from pandas import DataFrame as DF
from optparse import OptionParser
from time import time
from collections import Counter
import nltk
import sklearn
import scipy.stats
import sys
import joblib
from sklearn.metrics import make_scorer
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RandomizedSearchCV
import sklearn_crfsuite
from sklearn_crfsuite import scorers
from sklearn_crfsuite import metrics
from nltk.corpus import stopwords
import training_validation_v14 as training
#-------------------------------------------------------------------------------
# Objective
# Tagging transformed file with CRF model with sklearn-crfsuite.
#
# Input parameters
# --inputPath=PATH Path of transformed files x|y|z
# --outputPath Output path to place output files
# --outputFileI Output tagged file I
# --outputFileII Output tagged file II
# --modelPath Path to CRF model
# --modelName Model name
# --infoPath Path of GSE-GSM index file
# --infoFile GSE-GSM index file",
# --variant Part of S2 variant
# --S1 Inner word features set
# --S2 Complete word features
# --S3 Extended context features
# --S4 Semantic features
# --filteringStopWords Filtering stop words
# --filterSymbols Filtering punctuation marks
# Output
# 1) Tagged files in transformed format
# Examples
# --inputPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/input/
# --outputPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/output/
# --outputFileI annot-input_bg_outputI.txt
# --outputFileII annot-input_bg_outputII.txt
# --modelPath /home/egaytan/automatic-extraction-growth-conditions/CRF/models
# --modelName model_Run3_v10_S1_False_S2_True_S3_False_S4_False_Run3_v10
# --infoPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/mapping
# --infoFile bg_sentences_midx.txt
# --variant 13
#python3 tagging.py --inputPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/input/ --outputPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/output/ --outputFileI annot-input_bg_outputI.txt --outputFileII annot-input_bg_outputII.txt --modelPath /home/egaytan/automatic-extraction-growth-conditions/CRF/models --modelName model_Run3_v10_S1_False_S2_True_S3_False_S4_False_Run3_v10 --infoPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/mapping --infoFile bg_sentences_midx.txt --variant 13 --S4 --S1 > ../../reports/output_tagging_report.txt
#python3 predict-annot/bin/tagging/tagging.py --inputPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/input/ --outputPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/output/ --outputFileI annot-input_bg_outputI_v4.txt --outputFileII annot-input_bg_outputII_v4 --modelPath /home/egaytan/automatic-extraction-growth-conditions/CRF/models --modelName model_Run3_v10_S1_False_S2_True_S3_False_S4_False_Run3_v10 --infoPath /home/egaytan/automatic-extraction-growth-conditions/predict-annot/mapping --infoFile bg_sentences_midx_v4.txt --variant 13 --S4 --S1 > predict-annot/reports/output_tagging_report_v4.txt
__author__ = 'egaytan'
##########################################
# MAIN PROGRAM #
##########################################
if __name__ == "__main__":
# Defining parameters
parser = OptionParser()
parser.add_option("--inputPath", dest="inputPath", help="Path of training data set", metavar="PATH")
parser.add_option("--outputPath", dest="outputPath", help="Output path to place output files", metavar="PATH")
parser.add_option("--outputFileI", dest="outFileI", help="Output tagged file I", metavar="FILE")
parser.add_option("--outputFileII", dest="outFileII", help="Output tagged file II", metavar="FILE")
parser.add_option("--modelPath", dest="modelPath", help="Path to read CRF model", metavar="PATH")
parser.add_option("--modelName", dest="modelName", help="Model name", metavar="TEXT")
parser.add_option("--infoPath", dest="infoPath", help="Path of GSE-GSM index file", metavar="PATH")
parser.add_option("--infoFile", dest="idx", help="GSE-GSM index file", metavar="FILE")
parser.add_option("--variant", dest="variant", help="Run variant", metavar="FILE")
parser.add_option("--S1", dest="S1", help="General features", action="store_true", default=False)
parser.add_option("--S2", dest="S2", help="Inner/Complete word features", action="store_true", default=False)
parser.add_option("--S3", dest="S3", help="Extended context features", action="store_true", default=False)
parser.add_option("--S4", dest="S4", help="Semantic features", action="store_true", default=False)
parser.add_option("--filterStopWords", dest="filterStopWords", help="Filtering stop words", action="store_true", default=False)
parser.add_option("--filterSymbols", dest="filterSymbols", help="Filtering punctuation marks", action="store_true", default=False)
(options, args) = parser.parse_args()
if len(args) > 0:
parser.error("Any parameter given.")
sys.exit(1)
print('-------------------------------- PARAMETERS --------------------------------')
print("--inputPath Path of training data set : " + str(options.inputPath ))
print("--outputPath Output path to place output files: " + str(options.outputPath ))
print("--outputFileI Output tagged file I : " + str(options.outFileI ))
print("--outputFileII Output tagged file II : " + str(options.outFileII ))
print("--modelPath Path to read CRF model : " + str(options.modelPath ))
print("--modelName Model name : " + str(options.modelName ))
print("--infoPath Path of GSE-GSM index file : " + str(options.infoPath ))
print("--infoFile GSE-GSM index file : " + str(options.idx ))
print("--variant Run variant : " + str(options.variant ))
print("--S1 General features : " + str(options.S1 ))
print("--S2 Inner/Complete word features : " + str(options.S2 ))
print("--S3 Extended context features : " + str(options.S3 ))
print("--S4 Semantic features : " + str(options.S4 ))
print("--filteringStopWords Filtering stop words : " + str(options.filterStopWords ))
print("--filterSymbols Filtering punctuation marks : " + str(options.filterSymbols ))
symbols = ['.', ',', ':', ';', '?', '!', '\'', '"', '<', '>', '(', ')', '-', '_', '/', '\\', '¿', '¡', '+', '{',
'}', '[', ']', '*', '%', '$', '#', '&', '°', '`', '...']
print("Filtering symbols " + str(symbols) + ': ' + str(options.filterSymbols))
print('-------------------------------- PROCESSING --------------------------------')
stopwords = [word for word in stopwords.words('english')]
# Read index
idx = open(os.path.join(options.infoPath, options.idx), "r").readlines()
# Read CRF model
t0 = time()
print('Reading CRF model...')
crf = joblib.load(os.path.join(options.modelPath, options.modelName + '.mod'))
print("Reading CRF model done in: %fs" % (time() - t0))
# Reading sentences
print('Processing corpus...')
t0 = time()
labels = list(['Gtype', 'Gversion', 'Med', 'Phase', 'Strain', 'Substrain', 'Supp', 'Technique', 'Temp', 'OD', 'Anti', 'Agit', 'Air', 'Vess', 'pH'])
# Walk directory to read files
for path, dirs, files in os.walk(options.inputPath):
# For each file in dir
for file in files:
print("Preprocessing file..." + str(file))
sentencesInputData = []
sentencesOutputDataI = []
sentencesOutputDataII = []
with open(os.path.join(options.inputPath, file), "r") as iFile:
lines = iFile.readlines()
for line in lines:
listLine = []
for token in line.strip('\n').split():
if options.filterStopWords:
listToken = token.split('|')
lemma = listToken[1]
if lemma in stopwords:
continue
if options.filterSymbols:
listToken = token.split('|')
lemma = listToken[1]
if lemma in symbols:
if lemma == ',':
print("Coma , identificada")
continue
listLine.append(token)
sentencesInputData.append(listLine)
X_input = [training.sent2features(s, options.S1, options.S2, options.S3, options.S4, options.variant) for s in sentencesInputData]
print("Sentences input data: " + str(len(sentencesInputData)))
# Predicting tags
t1 = time()
print("Predicting tags with model")
y_pred = crf.predict(X_input)
print("Prediction done in: %fs" % (time() - t1))
# Tagging with CRF model
print("Tagging file")
lidx = 0
for line, tagLine in zip(lines, y_pred):
Ltags = set(labels).intersection(set(tagLine))
outputLine = ''
line = line.strip('\n')
#print("\nLine: " + str(line))
#print ("CRF tagged line: " + str(tagLine))
tb = 'O'
i = 0
if len(tagLine)==1:
if tagLine[0] in labels:
start = '<' + tagLine[0] + '> '
end = '</' + tagLine[0] + '/>'
word = line.split('|')[0] + ' '
outputLine = start + word + end
else:
outputLine = line.split(' ')[0]
#print(outputLine + '\t' + ', '.join(Ltags))
sentencesOutputDataI.append(idx[lidx].replace('\n','\t') + outputLine + ', '.join(Ltags))
sentencesOutputDataII.append(idx[lidx].replace('\n', '\t') + word.split('|')[0] + '\t' + tag)
continue
sentence = ''
sb = False
for word,tag in zip(line.split(' '), tagLine):
# start tagging
if tag in labels and tb != tag:
# start tagging
outputLine += '<' + tag + '> '
sb = True
sentence = word.split('|')[0] + ' '
tb = tag
outputLine += word.split('|')[0] + ' '
i += 1
continue
# end tagging
elif tb in labels:
if i+1==len(tagLine):
# end sentence
outputLine += word.split('|')[0] + ' '
outputLine += '</' + tag + '/> '
sentencesOutputDataII.append(idx[lidx].replace('\n', '\t') + sentence + word.split('|')[0] + '\t' +tag)
sb = False
tb = 'O'
i += 1
continue
elif tag!=tagLine[i+1]:
# start new tag
outputLine += word.split('|')[0] + ' '
outputLine += '</' + tag + '/> '
sentencesOutputDataII.append(idx[lidx].replace('\n', '\t') + sentence + word.split('|')[0] + '\t' +tag)
sb = False
tb = 'O'
i += 1
continue
# word tagged
outputLine += word.split('|')[0] + ' '
i += 1
if sb:
sentence+= word.split('|')[0] + ' '
#print(outputLine + '\t' + ', '.join(Ltags))
sentencesOutputDataI.append(idx[lidx].replace('\n', '\t') + outputLine+ ', '.join(Ltags))
lidx += 1
#print( DF(sentencesOutputDataI) )
#print( '\n'.join(sentencesOutputDataII) )
# Save tags
with open(os.path.join(options.outputPath, options.outFileII + '_' + options.modelName + '.tsv'), "w") as oFile:
for line in sentencesOutputDataII:
#print(line)
oFile.write(line + '\n')
with open(os.path.join(options.outputPath, options.outFileI + '_' + options.modelName + '.tsv'), "w") as oFileI:
for line in sentencesOutputDataI:
if re.findall('</', line):
print(line)
#oFileI.write(line + '\n')
print("Processing corpus done in: %fs" % (time() - t0))