transfix-mt/scripts/inject.py

import os
import pandas as pd
import rapidfuzz

from rapidfuzz.fuzz import partial_ratio
from rapidfuzz.utils import default_process


def full_strip(line):
    return ' '.join(line.split())


def is_injectable(sentence_pl, sequence):
    sen = sentence_pl.split()
    window_size = len(sequence.split())
    maxx = 0
    for i in range(len(sen) - window_size + 1):
        current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)
        if current > maxx:
            maxx = current
    return maxx >= THRESHOLD


def get_injected(sentence, sentence_en, sequence, inject):
    sen = sentence.split()
    sen_en = sentence_en.split()
    window_size = len(sequence.split())
    maxx = 0
    maxx_prv = 0
    maxxi = 0
    for i in range(len(sen) - window_size + 1):
        current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)
        if current >= maxx:
            maxx_prv = maxx
            maxx = current
            maxxi = i
    if maxx_prv != maxx:
        return ' '.join(sen_en[:maxxi + window_size]) + ' $' + inject + '$ ' + ' '.join(sen_en[maxxi + window_size:])
    return sentence_en


THRESHOLD = 70

# train_in_path = '~/mt-summit-corpora/train/in.tsv'
# train_expected_path = '~/mt-summit-corpora/train/expected.tsv'

train_in_path = os.path.join(os.path.expanduser('~'), 'mt-summit-corpora/dev-0/in.tsv')
train_expected_path = os.path.join(os.path.expanduser('~'), 'mt-summit-corpora/dev-0/expected.tsv')

glossary = pd.read_csv('~/mt-summit-corpora/glossary.tsv.lemmatized', sep='\t')
glossary['source_lem'] = [str(default_process(x)) for x in glossary['source_lem']]

file_pl = pd.read_csv(train_expected_path, sep='\t', header=None, names=['text'])
file_pl['text'] = [default_process(text) for text in file_pl['text'].values.tolist()]
file_pl = file_pl['text'].values.tolist()

file_en = pd.read_csv(train_in_path, sep='\t', header=None, names=['text'])
file_en['text'] = [default_process(text) for text in file_en['text'].values.tolist()]
file_en = file_en['text'].values.tolist()

file_en_lemmatized = pd.read_csv(train_in_path + '.lemmatized', sep='\t', header=None, names=['text'])
file_en_lemmatized['text'] = [default_process(text) for text in file_en_lemmatized['text'].values.tolist()]
file_en_lemmatized = file_en_lemmatized['text'].values.tolist()

en = []
translation_line_counts = []
for line, line_en, line_pl in zip(file_en_lemmatized, file_en, file_pl):
    line = default_process(line)
    matchez = rapidfuzz.process.extract(
        query=line, choices=glossary['source_lem'], limit=5, score_cutoff=THRESHOLD, scorer=partial_ratio)
    if len(matchez) > 0:
        lines_added = 0
        for match in matchez:
            polish_translation = \
                glossary.loc[lambda df: df['source_lem'] == match[0]]['result'].astype(str).values.flatten()[0]
            if is_injectable(line_pl, polish_translation):
                en.append(get_injected(line, line_en, match[0], polish_translation))
                lines_added += 1
        if lines_added == 0:
            en.append(line_en)
            lines_added = 1
        translation_line_counts.append(lines_added)
    else:
        translation_line_counts.append(1)
        en.append(line_en)


with open(train_expected_path + '.injected', 'w') as file_pl_write:
    for line, translation_line_ct in zip(file_pl, translation_line_counts):
        for i in range(translation_line_ct):
            file_pl_write.write(full_strip(line) + '\n')


with open(train_in_path + '.injected', 'w') as file_en_write:
    for e in en:
        file_en_write.write(e + '\n')