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falsefriends.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import collections
import inspect
import logging
from sklearn import naive_bayes, neighbors, svm, tree
from falsefriends import bilingual_lexicon, classifier, linear_trans, similar_words, util, wiki_parser, word_vectors
if __name__ == '__main__':
# noinspection PyUnusedLocal
def command_similar_words(args_):
with open('resources/equal_words.txt', 'w') as file:
for line in similar_words.exact_matching():
file.write(line + '\n')
with open('resources/similar_words.txt', 'w') as file:
for line in similar_words.similar_matching():
file.write(line + '\n')
def command_wiki_parser(args_):
wiki_parser.pre_process_wiki(args_.input_file_name, args_.output_file_name, args_.lang)
def command_word_vectors(args_):
word_vectors.train_model(args_.input_file_name,
args_.output_file_name,
use_plain_word2vec=args_.use_plain_word2vec,
size=args_.size,
phrases_n_gram=args_.phrases_n_gram,
threads=args_.threads)
# noinspection PyUnusedLocal
def command_bilingual_lexicon(args_):
if args_.random_pair_per_synset:
lexicon = bilingual_lexicon.random_pair_per_synset_bilingual_lexicon()
else:
lexicon = bilingual_lexicon.bilingual_lexicon()
for lemma_name_spa, lemma_name_por in lexicon:
print("{} {}".format(lemma_name_spa, lemma_name_por))
# noinspection PyPep8Naming,PyUnusedLocal
def command_linear_trans(args_):
model_es = word_vectors.load_model(args_.model_es_file_name)
model_pt = word_vectors.load_model(args_.model_pt_file_name)
if args_.random_pair_per_synset:
lexicon = bilingual_lexicon.random_pair_per_synset_bilingual_lexicon()
elif args_.most_frequent:
lexicon = bilingual_lexicon.most_frequent_bilingual_lexicon_based_on_external_count(model_es.vocab,
model_pt.vocab)
else:
lexicon = bilingual_lexicon.bilingual_lexicon()
X, Y = zip(*word_vectors.bilingual_lexicon_vectors(model_es, model_pt, bilingual_lexicon=lexicon))
T = linear_trans.linear_transformation(X, Y, args_.backwards)
linear_trans.save_linear_transformation(args_.translation_matrix_file_name, T)
def command_out_of_vocabulary(args_):
friend_pairs = util.read_words(args_.friends_file_name)
model_es, model_pt = util.read_models(args_)
words_es = (friend_pair.word_es for friend_pair in friend_pairs)
words_pt = (friend_pair.word_pt for friend_pair in friend_pairs)
print("OOV es:")
for word_es in word_vectors.words_out_of_vocabulary(model_es, words_es):
print(word_es)
print('')
print("OOV pt:")
for word_pt in word_vectors.words_out_of_vocabulary(model_pt, words_pt):
print(word_pt)
CLF_OPTIONS = {
'DT': tree.DecisionTreeClassifier(),
'GNB': naive_bayes.GaussianNB(),
'kNN': neighbors.KNeighborsClassifier(),
'SVM': svm.SVC(),
}
# noinspection PyShadowingNames
def _print_metrics_matrix(measures):
np = measures['Neg. Precision']
nr = measures['Neg. Recall']
nf = measures['Neg. F1-score']
pp = measures['Precision']
pr = measures['Recall']
pf = measures['F1-score']
print(" precision recall f1-score")
print('')
print(" False {np:0.4f} {nr:0.4f} {nf:0.4f}".format(np=np, nr=nr, nf=nf))
print(" True {pp:0.4f} {pr:0.4f} {pf:0.4f}".format(pp=pp, pr=pr, pf=pf))
print('')
print("avg / total {ap:0.4f} {ar:0.4f} {af:0.4f}".format(ap=(np + pp) / 2,
ar=(nr + pr) / 2,
af=(nf + pf) / 2))
print('')
def _print_confusion_matrix(measures):
tn = measures['tn']
fp = measures['fp']
fn = measures['fn']
tp = measures['tp']
print("Confusion matrix")
print('')
print("\t\t(classified as)")
print("\t\tTrue\tFalse")
print("(are)\tTrue\t{tp:0.4f}\t{fn:0.4f}".format(tp=tp, fn=fn))
print("(are)\tFalse\t{fp:0.4f}\t{tn:0.4f}".format(fp=fp, tn=tn))
print('')
# noinspection PyPep8Naming
def command_classify(args_):
training_friend_pairs = util.read_words(args_.training_friends_file_name)
testing_friend_pairs = util.read_words(args_.testing_friends_file_name)
model_es, model_pt = util.read_models(args_)
T = linear_trans.load_linear_transformation(args_.translation_matrix_file_name)
clf = classifier.build_classifier(CLF_OPTIONS[args_.classifier])
if args_.cross_validation:
friend_pairs = training_friend_pairs + testing_friend_pairs
X, y, = classifier.features_and_labels(friend_pairs, model_es, model_pt, T,
backwards=args_.backwards, topx=args_.top,
use_taxonomy=args_.use_taxonomy)
measures = classifier.classify_with_cross_validation(X, y, clf=clf)
print('')
print("Cross-validation measures with 95% of confidence:")
for measure_name, (mean, delta) in measures.items():
print("{measure_name}: {mean:0.4f} ± {delta:0.4f} --- [{inf:0.4f}, {sup:0.4f}]".format(
measure_name=measure_name, mean=mean, delta=delta, inf=mean - delta, sup=mean + delta))
print('')
mean_measures = {measure_name: mean for measure_name, (mean, delta) in measures.items()}
_print_metrics_matrix(mean_measures)
_print_confusion_matrix(mean_measures)
else:
X_train, y_train = classifier.features_and_labels(training_friend_pairs, model_es, model_pt, T,
backwards=args_.backwards, topx=args_.top,
use_taxonomy=args_.use_taxonomy)
X_test, y_test = classifier.features_and_labels(testing_friend_pairs, model_es, model_pt, T,
backwards=args_.backwards, topx=args_.top,
use_taxonomy=args_.use_taxonomy)
measures = classifier.classify(X_train, X_test, y_train, y_test, clf)
print('')
_print_metrics_matrix(measures)
_print_confusion_matrix(measures)
COMMANDS = collections.OrderedDict([
(
'similar_words',
{
'function': command_similar_words,
'help': "write in files equal and similar words between Spanish and Portuguese",
'parameters': [],
}
),
(
'wiki_parser',
{
'function': command_wiki_parser,
'help': "output the pre-processed Wikipedia passed as input",
'parameters': [
{
'name': 'input_file_name',
'args': {},
},
{
'name': 'output_file_name',
'args': {},
},
{
'name': 'lang',
'args': {},
},
],
}
),
(
'word_vectors',
{
'function': command_word_vectors,
'help': "calculate the vector space from sentences",
'parameters': [
{
'name': 'input_file_name',
'args': {},
},
{
'name': 'output_file_name',
'args': {},
},
{
'name': '--use-plain-word2vec',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
},
{
'name': '--phrases-n-gram',
'args': {
'default': 1,
'type': int,
},
},
{
'name': '--size',
'args': {
'default': inspect.signature(word_vectors.train_model).parameters['size'].default,
'type': int,
},
},
{
'name': '--threads',
'args': {
'default': inspect.signature(word_vectors.train_model).parameters['threads'].default,
'type': int,
},
}
],
}
),
(
'bilingual_lexicon',
{
'function': command_bilingual_lexicon,
'help': "print the Spanish-Portuguese bilingual lexicon",
'parameters': [
{
'name': '--random-pair-per-synset',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
},
],
}
),
(
'linear_trans',
{
'function': command_linear_trans,
'help': "save the linear transformation given the models",
'parameters': [
{
'name': 'model_es_file_name',
'args': {},
},
{
'name': 'model_pt_file_name',
'args': {},
},
{
'name': 'translation_matrix_file_name',
'args': {},
},
{
'name': '--backwards',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
},
{
'name': '--most-frequent',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
},
{
'name': '--random-pair-per-synset',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
},
],
}
),
(
'out_of_vocabulary',
{
'function': command_out_of_vocabulary,
'help': "print the words out of vocabulary of a friends list",
'parameters': [
{
'name': 'friends_file_name',
'args': {},
},
{
'name': 'model_es_file_name',
'args': {},
},
{
'name': 'model_pt_file_name',
'args': {},
},
],
}
),
(
'classify',
{
'function': command_classify,
'help': "classify word pairs of friends as false or true",
'parameters': [
{
'name': 'training_friends_file_name',
'args': {},
},
{
'name': 'testing_friends_file_name',
'args': {},
},
{
'name': 'model_es_file_name',
'args': {},
},
{
'name': 'model_pt_file_name',
'args': {},
},
{
'name': 'translation_matrix_file_name',
'args': {},
},
{
'name': '--backwards',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
},
{
'name': '--classifier',
'args': {
'choices': sorted(list(CLF_OPTIONS.keys())),
'default': 'SVM',
},
},
{
'name': '--cross-validation',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
},
{
'name': '--top',
'args': {
'default': None,
'type': float,
},
},
{
'name': '--use-taxonomy',
'args': {
'action': 'store_const',
'const': True,
'default': False,
},
}
],
}
),
])
def args():
arg_parser_ = argparse.ArgumentParser()
arg_parser_.add_argument(
'-d', '--debug',
help="Debug mode",
action='store_const', dest='log_level', const=logging.DEBUG,
default=logging.WARNING,
)
arg_parser_.add_argument(
'-v', '--verbose',
help="Verbose mde",
action='store_const', dest='log_level', const=logging.INFO,
)
subparsers = arg_parser_.add_subparsers(dest='command', title='command')
for command, command_values in COMMANDS.items():
sub_parser = subparsers.add_parser(command, help=command_values['help'])
for parameter in command_values['parameters']:
sub_parser.add_argument(parameter['name'], **parameter['args'])
return arg_parser_, arg_parser_.parse_args()
arg_parser, args = args()
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=args.log_level)
if args.command:
# noinspection PyCallingNonCallable
COMMANDS[args.command]['function'](args)
else:
arg_parser.print_help()