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