training_validation_v14.0.1.py
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# -*- coding: UTF-8 -*-
import os # Access operative sistem
#from itertools import chain # No se ocupa
from optparse import OptionParser # Number of transitions
from time import time # Return the time in seconds since the epoch as a float
from collections import Counter # Dict subclass for counting hashable objects
#import re # No se ocupa
import nltk # Natural Language Toolkit platform to work with human language data
import sklearn # Free software machine learning
import scipy.stats # library of statistical functions
import sys # to exit from Python.
from sklearn.externals import joblib # provide lightweight pipelining
from sklearn.metrics import make_scorer # Make a scorer from a performance metric or loss function
from sklearn.cross_validation import cross_val_score # Evaluate a score by cross-validation
from sklearn.grid_search import RandomizedSearchCV # Randomized search on hyper parameters
import sklearn_crfsuite # Thin CRFsuite
from sklearn_crfsuite import scorers # Added scorers.sequence_accuracy
from sklearn_crfsuite import metrics # Add flat recall score to metrics
from pandas import DataFrame as DF # Contruct dataframe object
from nltk.corpus import stopwords # To exclude top words
#-------------------------------------------------------------------------------
# Objective
# Training and evaluation of CRFs with sklearn-crfsuite.
#
# Input parameters
# (1) --inputPath Path of training and test data set
# (2) --outputPath Output path to place output files
# (3) --trainingFile File with training data set
# (4) --testFile File with test data set
# (5) --reportName Number of run
# (6) --variant Part of S2 variant
# (7) --nrules Number of crf transitions
# (8) --S1 Inner word features set
# (9) --S2 Complete word features
# (10) --S3 Extended context features
# (11) --S4 Semantic features
# (12) --excludeStopWords
# (13) --excludeSymbols
# Output
# 1) Best model
# 2) Report
# Examples
# python3 training_validation_v14.0.1.py
# --inputPath /home/egaytan/automatic-extraction-growth-conditions/CRF/input/
# --trainingFile training-data-set-70-NER.txt
# --testFile test-data-set-30-NER.txt
# --outputPath /home/egaytan/automatic-extraction-growth-conditions/CRF/
# --nrules 500
# --reportName Run1
# --variant 11
# --S1
# --S2
# --S3
# --S4
# python3 /home/egaytan/automatic-extraction-growth-conditions/CRF/bin/training/training_validation_v14.0.1.py --inputPath /home/egaytan/automatic-extraction-growth-conditions/CRF/data-sets --trainingFile training-data-set-70-NER.txt --testFile test-data-set-30-NER.txt --outputPath /home/egaytan/automatic-extraction-growth-conditions/CRF/ --nrules 500 --reportName Run1 --variant 10 > ../../outputs/enero/Run1_v10.txt
##################################################################
# FEATURES #
##################################################################
#================== COMPLETE WORD FEATURES ======================#
def isGreek(word):
## Complete word are greek letters
alphabet = ['Α','Β','Γ','Δ','Ε','Ζ','Η','Θ','Ι','Κ','Λ','Μ','Ν','Ξ','Ο','Π','Ρ','Σ','Τ','Υ','Φ','Χ','Ψ','Ω',
'α','β','γ','δ','ε','ζ','η','θ','ι','κ','λ','μ','ν','ξ','ο','π','ρ','ς','σ','τ','υ','φ','χ','ψ','ω']
if word in alphabet:
return True
else:
return False
#================ INNER OF THE WORD FEATURES ====================#
def hGreek(word):
## Search for at least has one greek letter
alphabet = ['Α','Β','Γ','Δ','Ε','Ζ','Η','Θ','Ι','Κ','Λ','Μ','Ν','Ξ','Ο','Π','Ρ','Σ','Τ','Υ','Φ','Χ','Ψ','Ω','α','β','γ','δ','ε','ζ','η','θ','ι','κ','λ','μ','ν','ξ','ο','π','ρ','ς','σ','τ','υ','φ','χ','ψ','ω']
# hexadicimal code
matches = [letter for letter in word if letter in alphabet]
if (len(matches) > 0):
return(True)
else: return(False)
## At least a greek letter
def hNumber(word):
## Al leats has one greek letter
for l in word:
if l.isdigit():
return True
return False
def hUpper(word):
## At least an upper letter
for l in word:
if l.isupper(): return True
return False
def hLower(word):
## At least a lower letter
for l in word:
if l.islower(): return True
return False
#============================FEATURES===========================#
def word2features(sent, i, S1, S2, S3, S4, v): #SA, v
## Getting word features
## Saving CoreNLP annotations
listElem = sent[i].split('|')
## Split CoreNLP output by columns
word = listElem[0]
lemma = listElem[1]
postag = listElem[2]
ner = listElem[3]
#=========================== G =============================#
## NAME LEVEL G
## FUTURE TYPE General features
## Adding to features dictionary
features = {
## basal features
'lemma': lemma,
'postag': postag
}
## Anterior lemma and postag
## need more tha one word in sentence
if i > 0:
## Split CoreNLP output by columns
listElem = sent[i - 1].split('|')
## Saving CoreNLP annotations
lemma0 = listElem[1]
postag0 = listElem[2]
## Adding features to dictionary
features.update({
#LemaG anterior
'-1:lemma': lemma0,
#Postag anterior
'-1:postag': postag0,
})
## Posterior lemma and postag
## is not the last word
if i < len(sent) - 1:
## Posterior word
listElem = sent[i + 1].split('|')
## Saving CoreNLP annotations
lemma2 = listElem[1]
postag2 = listElem[2]
## Adding to features dictionary
features.update({
#LemaG posterior
'+1:lemma': lemma2,
#Postag posterior
'+1:postag': postag2,
})
#=========================== S1 =============================#
## NAME LEVEL S1
## FEATURE TYPE Inner word features
if S1:
## Adding features to dictionary
features.update({
'hUpper' : hUpper(word),
'hLower' : hLower(word),
'hGreek' : hGreek(word),
'symb' : word.isalnum()
})
#========== Variants of inner words features ============#
if v == 10:
#word first character
features['word[:1]']= word[:1]
#word second character
if len(word)>1:
features['word[:2]']= word[:2]
if v == 11:
#lemma and postag first dharacter
features['lemma[:1]']= lemma[:1]
features['postag[:1]']= postag[:1]
#lemma and postag secondChar
if len(lemma)>1:
features['lemma[:2]']= lemma[:2]
if len(postag)>1:
features['postag[:2]']= postag[:2]
if v == 12:
#word first character
features['word[:1]']= word[:1]
#word second character
if len(word)>1:
features['word[:2]']= word[:2]
#postag first character
features['postag[:1]']= postag[:1]
#postag second character
if len(postag)>1:
features['postag[:2]']= postag[:2]
if v == 13:
#lemma first character
features['lemma[:1]']= lemma[:1]
#lemma second character
if len(lemma)>1:
features['lemma[:2]']= lemma[:2]
#=========================== S2 =============================#
## NAME LEVEL S2
## FEATURE TYPE Complete word features
if S2:
#Add features to dictionary
features.update({
'word' : word,
'isUpper' : word.isupper(),
'isLower' : word.islower(),
'isGreek' : isGreek(word),
'isNumber' : word.isdigit()
})
## Anterior word
## sentence needs more tha one word
if i > 0:
## Split CoreNLP output by columns
listElem = sent[i - 1].split('|')
## Saving CoreNLP annotations
word0 = listElem[0]
features['-1:word']= word0
## Posterior word
## is not the last word
if i < len(sent)-1:
## Split CoreNLP output by columns
listElem = sent[i + 1].split('|')
## Saving CoreNLP annotations
word2 = listElem[0]
features['+1:word']= word2
#=========================== S3 =============================#
## NAME LEVEL S3
## FEATURE TYPE Extended context features
if S3:
## more than two words in sentence
if i > 1:
## Split CoreNLP output by columns
listElem = sent[i - 2].split('|')
## Saving CoreNLP annotations
## two anterior lemma and postag
lemma01 = listElem[1]
postag01 = listElem[2]
features['-2:lemma']= lemma01
features['-2:postag']= postag01
## is not the penultimate word
if i < len(sent) - 2:
## Split CoreNLP output by columns
listElem = sent[i + 2].split('|')
## Saving CoreNLP annotations
lemma02 = listElem[1]
postag02 = listElem[2]
## two posterior lemma and postag
features['+2:lemma']= lemma02
features['+2:postag']= postag02
#=========================== S4 =============================#
## NAME LEVEL S4if S4:
## FEATURE TYPE NER
if S4:
## more than one word in sentence
if i > 0:
## Split CoreNLP output by columns
listElem = sent[i - 1].split('|')
## =============== Anterior ner ====================##
## Saving CoreNLP annotations according column position
ner0 = listElem[3]
## Adding to features dictionary
features['-1:ner'] = ner
## is not the last word
if i < len(sent) - 1:
## Split CoreNLP output by columns
listElem = sent[i + 1].split('|')
## ============= Posterior ner ====================##
## Saving CoreNLP annotations according column position
ner2 = listElem[3]
## Adding to features dictionary
features['+1:ner'] = ner2
if i > 1:
## Split CoreNLP output by columns
listElem = sent[i - 2].split('|')
## Saving CoreNLP annotations
## =============== 2 Anterior ner =================##
ner01 = listElem[3]
features['-2:ner']= ner01
## is not the penultimate word
if i < len(sent) - 2:
## Split CoreNLP output by columns
listElem = sent[i + 2].split('|')
## Saving CoreNLP annotations
ner02 = listElem[3]
## ============= 2 Posterior ner =================##
features['+2:ner']= ner02
return features
def sent2features(sent, S1, S2, S3, S4, v):
## Itering in sentence for each word and saving its features
return [word2features(sent, i, S1, S2, S3, S4, v) for i in range(len(sent))]
def sent2labels(sent):
## Save tag, last position by word tokens
return [elem.split('|')[-1] for elem in sent]
def sent2tokens(sent):
return [token for token, postag, label in sent]
def print_transitions(trans_features, f):
for (label_from, label_to), weight in trans_features:
f.write("{:6} -> {:7} {:0.6f}\n".format(label_from, label_to, weight))
def print_state_features(state_features, f):
for (attr, label), weight in state_features:
f.write("{:0.6f} {:8} {}\n".format(weight, label, attr.encode("utf-8")))
__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("--trainingFile", dest="trainingFile", help="File with training data set", metavar="FILE")
parser.add_option("--testFile", dest="testFile", help="File with test data set", metavar="FILE")
parser.add_option("--reportName", dest="reportName", help="Report number run", metavar="FILE")
parser.add_option("--variant", dest="variant", help="Report file", 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("--excludeStopWords", dest="excludeStopWords",help="Exclude stop words", action="store_true", default=False)
parser.add_option("--excludeSymbols", dest="excludeSymbols", help="Exclude punctuation marks", action="store_true", default=False)
parser.add_option("--nrules", dest="nrules", help="Number of crf rules on report", type="int")
(options, args) = parser.parse_args()
if len(args) > 0:
parser.error("Any parameter given.")
sys.exit(1)
print('-------------------------------- PARAMETERS --------------------------------')
print("Path of test and training data sets: " + options.inputPath)
print("Path of outputs: " + options.outputPath)
print("File with training data set: " + str(options.trainingFile))
print("File with test data set: " + str(options.testFile))
print("reportName: " + str(options.reportName))
print("Exclude stop words: " + str(options.excludeStopWords))
print("Levels: " + "S1: " + str(options.S1) + "S2: " + str(options.S2) + "S3: " + str(options.S3) + "S4: " + str(options.S4))
print("Run variant: " + str(options.variant))
print("Number of rules on report file: " + str(options.nrules))
symbols = ['.', ',', ':', ';', '?', '!', '\'', '"', '<', '>', '(', ')', '-', '_', '/', '\\', '¿', '¡', '+', '{',
'}', '[', ']', '*', '%', '$', '#', '&', '°', '`', '...']
print("Exclude symbols: " + str(options.excludeSymbols))
print('-------------------------------- PROCESSING --------------------------------')
print('Reading corpus...')
t0 = time()
sentencesTrainingData = []
sentencesTestData = []
stopwords = [word for word in stopwords.words('english')]
with open(os.path.join(options.inputPath, options.trainingFile), "r") as iFile:
for line in iFile.readlines():
listLine = []
line = line.strip('\n')
for token in line.split():
if options.excludeStopWords:
listToken = token.split('|')
lemma = listToken[1]
if lemma in stopwords:
continue
if options.excludeSymbols:
listToken = token.split('|')
lemma = listToken[1]
if lemma in symbols:
continue
listLine.append(token)
sentencesTrainingData.append(listLine)
print(" Sentences training data: " + str(len(sentencesTrainingData)))
with open(os.path.join(options.inputPath, options.testFile), "r") as iFile:
for line in iFile.readlines():
listLine = []
line = line.strip('\n')
for token in line.split():
if options.excludeStopWords:
listToken = token.split('|')
lemma = listToken[1]
if lemma in stopwords:
continue
if options.excludeSymbols:
listToken = token.split('|')
lemma = listToken[1]
if lemma in symbols:
continue
listLine.append(token)
sentencesTestData.append(listLine)
print(" Sentences test data: " + str(len(sentencesTestData)))
print("Reading corpus done in: %fs" % (time() - t0))
print('-------------------------------- FEATURES --------------------------------')
Dtraning = sent2features(sentencesTrainingData[0], options.S1, options.S2, options.S3, options.S4, int(options.variant))[2]
Dtest = sent2features(sentencesTestData[0], options.S1, options.S2, options.S3, options.S4, int(options.variant))[2]
print('--------------------------Features Training ---------------------------')
print(DF(list(Dtraning.items())))
print('--------------------------- FeaturesTest -----------------------------')
print(DF(list(Dtest.items())))
t0 = time()
X_train = [sent2features(s, options.S1, options.S2, options.S3, options.S4, options.variant) for s in sentencesTrainingData]
y_train = [sent2labels(s) for s in sentencesTrainingData]
X_test = [sent2features(s, options.S1, options.S2, options.S3, options.S4, options.variant) for s in sentencesTestData]
# print X_test
y_test = [sent2labels(s) for s in sentencesTestData]
'''
Fixed parameters
crf = sklearn_crfsuite.CRF(
algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_pgossible_transitions=True
)
'''
# Hyperparameter Optimization
crf = sklearn_crfsuite.CRF(
algorithm='lbfgs',
max_iterations=100,
all_possible_transitions=True
)
params_space = {
'c1': scipy.stats.expon(scale=0.5),
'c2': scipy.stats.expon(scale=0.05),
}
# Original: labels = list(crf.classes_)
# Original: labels.remove('O')
labels = list(['Gtype', 'Gversion', 'Med', 'Phase', 'Strain', 'Substrain', 'Supp', 'Technique', 'Temp', 'OD', 'Anti', 'Agit', 'Air', 'Vess', 'pH'])
# use the same metric for evaluation
f1_scorer = make_scorer(metrics.flat_f1_score, average='weighted', labels=labels)
# search
rs = RandomizedSearchCV(crf, params_space,
cv=5,
verbose=3,
n_jobs=-1,
n_iter=100,
scoring=f1_scorer,
random_state=42)
rs.fit(X_train, y_train)
# Fixed parameters
# crf.fit(X_train, y_train)
# Best hiperparameters
# crf = rs.best_estimator_
nameReport = str(options.reportName) + '_v'+ str(options.variant) + '.txt'
with open(os.path.join(options.outputPath, "reports", "report_" + nameReport), mode="w") as oFile:
oFile.write("********** TRAINING AND TESTING REPORT **********\n")
oFile.write("Training file: " + options.trainingFile + '\n')
oFile.write('\n')
oFile.write('best params:' + str(rs.best_params_) + '\n')
oFile.write('best CV score:' + str(rs.best_score_) + '\n')
oFile.write('model size: {:0.2f}M\n'.format(rs.best_estimator_.size_ / 1000000))
print("Training done in: %fs" % (time() - t0))
t0 = time()
# Update best crf
crf = rs.best_estimator_
# Saving model
print(" Saving training model...")
t1 = time()
nameModel = 'model_' + str(options.reportName) + '_v'+ str(options.variant) + '_S1_' + str(options.S1) + '_S2_' + str(options.S2) + '_S3_' + str(options.S3) + '_S4_' + str(options.S4) + '_' + str(options.reportName) + '_v' + str(options.variant) +'.mod'
joblib.dump(crf, os.path.join(options.outputPath, "models", nameModel))
print(" Saving training model done in: %fs" % (time() - t1))
# Evaluation against test data
y_pred = crf.predict(X_test)
print("*********************************")
print("Prediction done in: %fs" % (time() - t0))
with open(os.path.join(options.outputPath, "reports", "report_" + nameReport), mode="a") as oFile:
oFile.write('\n')
oFile.write("Flat F1: " + str(metrics.flat_f1_score(y_test, y_pred, average='weighted', labels=labels)))
oFile.write('\n')
# labels = list(crf.classes_)
sorted_labels = sorted(
labels,
key=lambda name: (name[1:], name[0])
)
oFile.write(metrics.flat_classification_report( y_test, y_pred, labels=sorted_labels, digits=3))
oFile.write('\n')
oFile.write("\nTop likely transitions:\n")
print_transitions(Counter(crf.transition_features_).most_common(options.nrules), oFile)
oFile.write('\n')
oFile.write("\nTop unlikely transitions:\n")
print_transitions(Counter(crf.transition_features_).most_common()[-options.nrules:], oFile)
oFile.write('\n')
oFile.write("\nTop positive:\n")
print_state_features(Counter(crf.state_features_).most_common(options.nrules), oFile)
oFile.write('\n')
oFile.write("\nTop negative:\n")
print_state_features(Counter(crf.state_features_).most_common()[-options.nrules:], oFile)
oFile.write('\n')