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import argparse
import csv
import math
import random
import unicodedata
from re import sub
from mosestokenizer import MosesTokenizer
def load_def_from_file(def_filename):
"""
def_filename: full path of the definition file
returns: list of dictionary definitions
"""
return [
unicodedata.normalize("NFC", line.rstrip("\n"))
for line in open(def_filename, encoding="utf8")
]
def clean_corpus_suffix(corpus, language):
"""
Adds '__target-language' and '__source-language' at the end of the words
"""
clean_corpus = []
tokenize = MosesTokenizer(language)
for definition in corpus:
definition = sub(r"'", "", definition)
definition = sub(r"[^\w]", " ", definition)
clean_doc = []
words = tokenize(definition)
for word in words:
clean_doc.append(word + "__%s" % language)
clean_corpus.append(" ".join(clean_doc))
return clean_corpus
def create_pos_neg_samples(length):
indices = list(range(length))
halfsize = math.ceil(length / 2)
neg_points = random.sample(indices, halfsize)
neg_indices = list(neg_points)
random.shuffle(neg_indices)
for (index, point) in zip(neg_indices, neg_points):
indices[point] = index
labels = [1] * length
for i in neg_points:
labels[i] = 0
return indices, labels
def main(args):
source_lang = args.source_lang
target_lang = args.target_lang
source_defs_filename = args.source_defs
target_defs_filename = args.target_defs
defs_source = load_def_from_file(source_defs_filename)
defs_target = load_def_from_file(target_defs_filename)
clean_source_corpus = clean_corpus_suffix(defs_source, source_lang)
clean_target_corpus = clean_corpus_suffix(defs_target, target_lang)
assert len(clean_source_corpus) == len(clean_target_corpus)
set_aside = args.set_aside
source_predict = clean_source_corpus[-set_aside:]
target_predict = clean_target_corpus[-set_aside:]
labels_predict = [1] * set_aside
# placeholder, won't be used, we can use 1 because they're correct
clean_source_corpus = clean_source_corpus[:-set_aside]
clean_target_corpus = clean_target_corpus[:-set_aside]
size = len(clean_source_corpus)
while True:
indices, labels = create_pos_neg_samples(size)
shuffled_target = [clean_target_corpus[index] for index in indices]
check = [
clean
for clean, shuf in zip(clean_target_corpus, shuffled_target)
if clean == shuf
]
halfsize = math.ceil(size / 2)
try:
assert len(check) == halfsize
except AssertionError:
pass
else:
break
assert len(clean_source_corpus) == len(shuffled_target) == size
assert len(labels) == len(clean_source_corpus) == len(shuffled_target)
with open(
f"{source_lang}_to_{target_lang}.tsv", "w", encoding="utf8", newline=""
) as tsv_file:
tsv_writer = csv.writer(tsv_file, delimiter="\t", lineterminator="\n")
tsv_writer.writerow(
[f"{source_lang} definition", f"{target_lang} definition", "is same"]
)
for row in zip(clean_source_corpus, shuffled_target, labels):
tsv_writer.writerow(row)
for row in zip(source_predict, target_predict, labels_predict):
tsv_writer.writerow(row)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Create .tsv file from two wordnet definitions"
)
parser.add_argument("source_lang", help="source language short name")
parser.add_argument("target_lang", help="target language short name")
parser.add_argument("source_defs", help="path of the source definitions")
parser.add_argument("target_defs", help="path of the target definitions")
parser.add_argument(
"-n", "--set_aside", help="set aside to validate on", type=int, default=1000
)
args = parser.parse_args()
main(args)
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