mt-summit-corpora/inject.py

import copy
import pandas as pd
import spacy
from spaczz.matcher import FuzzyMatcher

# spacy.require_gpu()

spacy_nlp_en = spacy.load('en_core_web_sm')
spacy_nlp_pl = spacy.load('pl_core_news_sm')

print('lemmatizing glossary')

glossary = pd.read_csv('glossary.tsv', sep='\t', header=None, names=['source', 'result'])

source_lemmatized = []
for word in glossary['source']:
    temp = []
    for token in spacy_nlp_en(word):
        temp.append(token.lemma_)
    source_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

result_lemmatized = []
for word in glossary['result']:
    temp = []
    for token in spacy_nlp_pl(word):
        temp.append(token.lemma_)
    result_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

glossary['source_lem'] = source_lemmatized
glossary['result_lem'] = result_lemmatized
glossary = glossary[['source', 'source_lem', 'result', 'result_lem']]
glossary.set_index('source_lem')

glossary.to_csv('glossary_lem.tsv', sep='\t')

dev_path = 'dev-0/'

print('lemmatizing corpus ' + dev_path)

skip_chars = ''',./!?'''

with open(dev_path + 'in.tsv', 'r') as file:
    file_lemmatized = []
    for line in file:
        temp = []
        for token in spacy_nlp_en(line):
            temp.append(token.lemma_)
        file_lemmatized.append(' '.join([x for x in temp if x not in skip_chars])
                .replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

with open(dev_path + 'expected.tsv', 'r') as file:
    file_pl_lemmatized = []
    for line in file:
        temp = []
        for token in spacy_nlp_pl(line):
            temp.append(token.lemma_)
        file_pl_lemmatized.append(' '.join([x for x in temp if x not in skip_chars])
                .replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

# glossary
glossary = pd.read_csv('glossary_lem.tsv', sep='\t', header=0, index_col=0)
train_glossary = glossary.iloc[[x for x in range(len(glossary)) if x % 6 != 0]]

# add rules to English matcher
nlp = spacy.blank("en")
matcher = FuzzyMatcher(nlp.vocab)
for word in train_glossary['source_lem']:
    matcher.add(word, [nlp(word)])

# add rules to Polish matcher
nlp_pl = spacy.blank("pl")
matcher_pl = FuzzyMatcher(nlp_pl.vocab)
for word, word_id in zip(train_glossary['result_lem'], train_glossary['source_lem']):
    matcher_pl.add(word, [nlp_pl(word)])

en = []
translation_line_counts = []
for line_id in range(len(file_lemmatized)):

    if line_id % 100 == 0:
        print('injecting glossary: ' + str(line_id) + "/" + str(len(file_lemmatized)), end='\r')

    doc = nlp(file_lemmatized[line_id])
    matches = matcher(doc)

    line_counter = 0
    for match_id, start, end, ratio in matches:
        if ratio > 90:
            doc_pl = nlp_pl(file_pl_lemmatized[line_id])
            matches_pl = matcher_pl(doc_pl)

            for match_id_pl, start_pl, end_pl, ratio_pl in matches_pl:
                if match_id_pl == glossary[glossary['source_lem'] == match_id].values[0][3]:
                    line_counter += 1
                    en.append(''.join(doc[:end].text + ' ' + train_glossary.loc[lambda df: df['source_lem'] == match_id]['result'].astype(str).values.flatten() + ' ' + doc[end:].text))

    if line_counter == 0:
        line_counter = 1
        en.append(file_lemmatized[line_id])
    translation_line_counts.append(line_counter)

print('saving files')
tlcs = copy.deepcopy(translation_line_counts)

translations = pd.read_csv(dev_path + 'expected.tsv', sep='\t', header=None, names=['text'])
translations['id'] = [x for x in range(len(translations))]

ctr = 0
sentence = ''
with open(dev_path + 'in.tsv.injected.crossvalidated', 'w') as file_en:
    with open(dev_path + 'expected.tsv.injected.crossvalidated', 'w') as file_pl:
        for i in range(len(en)):
            if i > 0:
                if en[i-1] != en[i]:
                    if ctr == 0:
                        sentence = translations.iloc[0]
                        translations.drop(sentence['id'], inplace=True)
                        sentence = sentence['text']
                        try:
                            ctr = tlcs.pop(0)
                        except:
                            pass
                    file_en.write(en[i])
                    file_pl.write(sentence + '\n')
                ctr = ctr - 1
            else:
                try:
                    ctr = tlcs.pop(0) - 1
                except:
                    pass
                sentence = translations.iloc[0]
                translations.drop(sentence['id'], inplace=True)
                sentence = sentence['text']
                file_en.write(en[i])
                file_pl.write(sentence + '\n')