#!/usr/bin/env python # coding: utf-8 # In[1]: KENLM_BUILD_PATH='/home/haskell/kenlm/build' # ### Preprocessing danych # In[2]: import pandas as pd import csv import regex as re # In[3]: def clean_text(text): text = text.lower().replace('-\\n', '').replace('\\n', ' ') text = re.sub(r'\p{P}', '', text) return text # In[4]: train_data = pd.read_csv('train/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) train_labels = pd.read_csv('train/expected.tsv', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) train_data = train_data[[6, 7]] train_data = pd.concat([train_data, train_labels], axis=1) train_data['text'] = train_data[6] + train_data[0] + train_data[7] train_data = train_data[['text']] with open('processed_train.txt', 'w') as file: for _, row in train_data.iterrows(): text = clean_text(str(row['text'])) file.write(text + '\n') # ### Model kenLM # In[4]: get_ipython().system('$KENLM_BUILD_PATH/bin/lmplz -o 5 --skip_symbols < processed_train.txt > model/model.arpa') # In[5]: get_ipython().system('$KENLM_BUILD_PATH/bin/build_binary model/model.arpa model/model.binary') # In[6]: get_ipython().system('rm processed_train.txt') # In[7]: get_ipython().system('rm model/model.arpa') # ### Predykcje # In[32]: import kenlm import csv import pandas as pd import regex as re from math import log10 from nltk import word_tokenize from english_words import english_words_alpha_set # In[4]: model = kenlm.Model('model/model.binary') # In[28]: def clean_text(text): text = text.lower().replace('-\\n', '').replace('\\n', ' ') text = re.sub(r'\p{P}', '', text) return text # In[29]: def predict_probs(w1, w2, w4): best_scores = [] for word in english_words_alpha_set: text = ' '.join([w1, w2, word, w4]) text_score = model.score(text, bos=False, eos=False) if len(best_scores) < 20: best_scores.append((word, text_score)) else: is_better = False worst_score = None for score in best_scores: if not worst_score: worst_score = score else: if worst_score[1] > score[1]: worst_score = score if worst_score[1] < text_score: best_scores.remove(worst_score) best_scores.append((word, text_score)) probs = sorted(best_scores, key=lambda tup: tup[1], reverse=True) pred_str = '' for word, prob in probs: pred_str += f'{word}:{prob} ' pred_str += f':{log10(0.99)}' return pred_str # In[30]: dev_data = pd.read_csv('dev-0/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) test_data = pd.read_csv('test-A/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) # In[35]: with open('dev-0/out.tsv', 'w') as file: for index, row in dev_data.iterrows(): left_text = clean_text(str(row[6])) right_text = clean_text(str(row[7])) left_words = word_tokenize(left_text) right_words = word_tokenize(right_text) if len(left_words) < 2 or len(right_words) < 2: prediction = ':1.0' else: prediction = predict_probs(left_words[len(left_words) - 2], left_words[len(left_words) - 1], right_words[0]) file.write(prediction + '\n') # In[37]: with open('test-A/out.tsv', 'w') as file: for index, row in test_data.iterrows(): left_text = clean_text(str(row[6])) right_text = clean_text(str(row[7])) left_words = word_tokenize(left_text) right_words = word_tokenize(right_text) if len(left_words) < 2 or len(right_words) < 2: prediction = ':1.0' else: prediction = predict_probs(left_words[len(left_words) - 2], left_words[len(left_words) - 1], right_words[0]) file.write(prediction + '\n')