import os, sys import glob, itertools import pandas as pd WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') sys.exit(-1) def load_langs(path): with open(path) as fr: langs = [l.strip() for l in fr] return langs def load_sentences(raw_data, split, direction): src, tgt = direction.split('-') src_path = f"{raw_data}/{split}.{direction}.{src}" tgt_path = f"{raw_data}/{split}.{direction}.{tgt}" if os.path.exists(src_path) and os.path.exists(tgt_path): return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())] else: return [] def swap_direction(d): src, tgt = d.split('-') return f'{tgt}-{src}' def get_all_test_data(raw_data, directions, split='test'): test_data = [ x for dd in directions for d in [dd, swap_direction(dd)] for x in load_sentences(raw_data, split, d) ] # all_test_data = {s for _, d in test_data for s in d} all_test_data = {} for lang, d in test_data: for s in d: s = s.strip() lgs = all_test_data.get(s, set()) lgs.add(lang) all_test_data[s] = lgs return all_test_data, test_data def check_train_sentences(raw_data, direction, all_test_data, mess_up_train={}): src, tgt = direction.split('-') tgt_path = f"{raw_data}/train.{direction}.{tgt}" src_path = f"{raw_data}/train.{direction}.{src}" print(f'check training data in {raw_data}/train.{direction}') size = 0 if not os.path.exists(tgt_path) or not os.path.exists(src_path): return mess_up_train, size with open(src_path) as f, open(tgt_path) as g: for src_line, tgt_line in zip(f, g): s = src_line.strip() t = tgt_line.strip() size += 1 if s in all_test_data: langs = mess_up_train.get(s, set()) langs.add(direction) mess_up_train[s] = langs if t in all_test_data: langs = mess_up_train.get(t, set()) langs.add(direction) mess_up_train[t] = langs return mess_up_train, size def check_train_all(raw_data, directions, all_test_data): mess_up_train = {} data_sizes = {} for direction in directions: _, size = check_train_sentences(raw_data, direction, all_test_data, mess_up_train) data_sizes[direction] = size return mess_up_train, data_sizes def count_train_in_other_set(mess_up_train): train_in_others = [(direction, s) for s, directions in mess_up_train.items() for direction in directions] counts = {} for direction, s in train_in_others: counts[direction] = counts.get(direction, 0) + 1 return counts def train_size_if_remove_in_otherset(data_sizes, mess_up_train): counts_in_other = count_train_in_other_set(mess_up_train) remain_sizes = [] for direction, count in counts_in_other.items(): remain_sizes.append((direction, data_sizes[direction] - count, data_sizes[direction], count, 100 * count / data_sizes[direction] )) return remain_sizes def remove_messed_up_sentences(raw_data, direction, mess_up_train, mess_up_train_pairs, corrected_langs): split = 'train' src_lang, tgt_lang = direction.split('-') tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" src = f"{raw_data}/{split}.{direction}.{src_lang}" print(f'working on {direction}: ', src, tgt) if not os.path.exists(tgt) or not os.path.exists(src) : return corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" line_num = 0 keep_num = 0 with open(src, encoding='utf8',) as fsrc, \ open(tgt, encoding='utf8',) as ftgt, \ open(corrected_src, 'w', encoding='utf8') as fsrc_corrected, \ open(corrected_tgt, 'w', encoding='utf8') as ftgt_corrected: for s, t in zip(fsrc, ftgt): s = s.strip() t = t.strip() if t not in mess_up_train \ and s not in mess_up_train \ and (s, t) not in mess_up_train_pairs \ and (t, s) not in mess_up_train_pairs: corrected_langs.add(direction) print(s, file=fsrc_corrected) print(t, file=ftgt_corrected) keep_num += 1 line_num += 1 if line_num % 1000 == 0: print(f'completed {line_num} lines', end='\r') return line_num, keep_num ########## def merge_valid_test_messup(mess_up_train_valid, mess_up_train_test): merged_mess = [] for s in set(list(mess_up_train_valid.keys()) + list(mess_up_train_test.keys())): if not s: continue valid = mess_up_train_valid.get(s, set()) test = mess_up_train_test.get(s, set()) merged_mess.append((s, valid | test)) return dict(merged_mess) ######### def check_train_pairs(raw_data, direction, all_test_data, mess_up_train={}): src, tgt = direction.split('-') #a hack; TODO: check the reversed directions path1 = f"{raw_data}/train.{src}-{tgt}.{src}" path2 = f"{raw_data}/train.{src}-{tgt}.{tgt}" if not os.path.exists(path1) or not os.path.exists(path2) : return with open(path1) as f1, open(path2) as f2: for src_line, tgt_line in zip(f1, f2): s = src_line.strip() t = tgt_line.strip() if (s, t) in all_test_data or (t, s) in all_test_data: langs = mess_up_train.get( (s, t), set()) langs.add(src) langs.add(tgt) mess_up_train[(s, t)] = langs def load_pairs(raw_data, split, direction): src, tgt = direction.split('-') src_f = f"{raw_data}/{split}.{direction}.{src}" tgt_f = f"{raw_data}/{split}.{direction}.{tgt}" if tgt != 'en_XX': src_f, tgt_f = tgt_f, src_f if os.path.exists(src_f) and os.path.exists(tgt_f): return list(zip(open(src_f).read().splitlines(), open(tgt_f).read().splitlines(), )) else: return [] # skip_langs = ['cs_CZ', 'en_XX', 'tl_XX', 'tr_TR'] def get_messed_up_test_pairs(split, directions): test_pairs = [ (d, load_pairs(raw_data, split, d)) for d in directions ] # all_test_data = {s for _, d in test_data for s in d} all_test_pairs = {} for direction, d in test_pairs: src, tgt = direction.split('-') for s in d: langs = all_test_pairs.get(s, set()) langs.add(src) langs.add(tgt) all_test_pairs[s] = langs mess_up_train_pairs = {} for direction in directions: check_train_pairs(raw_data, direction, all_test_pairs, mess_up_train_pairs) return all_test_pairs, mess_up_train_pairs if __name__ == "__main__": ####### import argparse parser = argparse.ArgumentParser() parser.add_argument( '--from-folder', required=True, type=str) parser.add_argument( '--to-folder', required=True, type=str) parser.add_argument( '--directions', default=None, type=str) args = parser.parse_args() raw_data = args.from_folder to_folder = args.to_folder os.makedirs(to_folder, exist_ok=True) if args.directions: directions = args.directions.split(',') else: raw_files = itertools.chain( glob.glob(f'{raw_data}/train*'), glob.glob(f'{raw_data}/valid*'), glob.glob(f'{raw_data}/test*'), ) directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] print('working on directions: ', directions) ########## all_test_data, test_data = get_all_test_data(raw_data, directions, 'test') print('==loaded test data==') all_valid_data, valid_data = get_all_test_data(raw_data, directions, 'valid') print('==loaded valid data==') all_valid_test_data = merge_valid_test_messup(all_test_data, all_valid_data) mess_up_train, data_sizes = check_train_all(raw_data, directions, all_valid_test_data) print('training messing up with valid, test data:', len(mess_up_train)) data_situation = train_size_if_remove_in_otherset(data_sizes, mess_up_train) df = pd.DataFrame(data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) df.sort_values('remove_percent', ascending=False) df.to_csv(f'{raw_data}/clean_summary.tsv', sep='\t') print(f'projected data clean summary in: {raw_data}/clean_summary.tsv') # correct the dataset: all_test_pairs, mess_up_test_train_pairs = get_messed_up_test_pairs('test', directions) all_valid_pairs, mess_up_valid_train_pairs = get_messed_up_test_pairs('valid', directions) all_messed_pairs = set(mess_up_test_train_pairs.keys()).union(set(mess_up_valid_train_pairs.keys())) corrected_directions = set() real_data_situation = [] for direction in directions: org_size, new_size = remove_messed_up_sentences(raw_data, direction, mess_up_train, all_messed_pairs, corrected_directions) if org_size == 0: print(f"{direction} has size 0") continue real_data_situation.append( (direction, new_size, org_size, org_size - new_size, (org_size - new_size) / org_size * 100) ) print('corrected directions: ', corrected_directions) df = pd.DataFrame(real_data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) df.sort_values('remove_percent', ascending=False) df.to_csv(f'{raw_data}/actual_clean_summary.tsv', sep='\t') print(f'actual data clean summary (which can be different from the projected one because of duplications) in: {raw_data}/actual_clean_summary.tsv') import shutil for direction in directions: src_lang, tgt_lang = direction.split('-') for split in ['train', 'valid', 'test']: # copying valid, test and uncorrected train if direction in corrected_directions and split == 'train': continue tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" src = f"{raw_data}/{split}.{direction}.{src_lang}" if not (os.path.exists(src) and os.path.exists(tgt)): continue corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" print(f'copying {src} to {corrected_src}') shutil.copyfile(src, corrected_src) print(f'copying {tgt} to {corrected_tgt}') shutil.copyfile(tgt, corrected_tgt) print('completed')