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import json
from collections import defaultdict, Counter
from copy import deepcopy
import random
random.seed(1234)
def generate_padded_dataset(des_size):
input_files = ['../../Documents/character-mining/json/friends_season_01.json',
'../../Documents/character-mining/json/friends_season_02.json',
'../../Documents/character-mining/json/friends_season_03.json',
'../../Documents/character-mining/json/friends_season_04.json',
'../../Documents/character-mining/json/friends_season_05.json',
'../../Documents/character-mining/json/friends_season_06.json',
'../../Documents/character-mining/json/friends_season_07.json',
'../../Documents/character-mining/json/friends_season_08.json',
'../../Documents/character-mining/json/friends_season_09.json',
'../../Documents/character-mining/json/friends_season_10.json'
]
season_samples = defaultdict(list)
for file in input_files:
data = json.load(open(file))
for episode_dict in data['episodes']:
for idx, scene_dict in enumerate(episode_dict['scenes']):
if scene_dict['plots'] is not None:
entities = Counter()
entities.update(scene_dict['rc_entities'].keys())
cur = idx
dialog_len = len(scene_dict['utterances'])
while (dialog_len < des_size and cur < len(episode_dict['scenes'])-1):
cur += 1
entities.update(episode_dict['scenes'][cur]['rc_entities'].keys())
dialog_len += len(episode_dict['scenes'][cur]['utterances'])
if dialog_len < des_size:
cur = idx
while (cur > 0 and dialog_len < des_size):
cur -= 1
entities.update(episode_dict['scenes'][cur]['rc_entities'].keys())
dialog_len += len(episode_dict['scenes'][cur]['utterances'])
masking_map = {}
for vi, ki in enumerate(entities.keys()):
masking_map[ki] = '@ent%02d' % vi
masked_passages = []
for i, passage in enumerate(scene_dict['plots']):
masked_sentence = []
ent_list = {}
for ent, index_list in scene_dict['rc_entities'].iteritems():
for index in index_list['p_ent']:
if i == index[0]:
ent_list[index[1]] = (index[1], index[2], ent)
jump = 0
for j, token in enumerate(passage.split(' ')):
if jump > 0:
jump -= 1
continue
if j in ent_list:
masked_sentence.append(masking_map[ent_list[j][2]])
jump = ent_list[j][1] - ent_list[j][0] - 1
else:
masked_sentence.append(token)
masked_passages.append(masked_sentence)
cur = idx
dialog_len = len(scene_dict['utterances'])
next_dialog = []
while (dialog_len < des_size and cur < len(episode_dict['scenes'])-1):
cur += 1
for i, utterance in enumerate(episode_dict['scenes'][cur]['utterances']):
if utterance['tokens_with_note'] is not None:
tokens = [w for sent in utterance['tokens_with_note'] for w in sent]
else:
tokens = [w for sent in utterance['tokens'] for w in sent]
masked_utter = {}
masked_utter['speakers'] = utterance['speakers']
masked_utter['tokens'] = []
ent_list = {}
for ent, index_list in episode_dict['scenes'][cur]['rc_entities'].iteritems():
for index in index_list['u_ent']:
if i == index[0]:
ent_list[index[1]] = (index[1], index[2], ent)
for index in index_list['s_ent']:
if i == index[0]:
masked_utter['speakers'][index[1]] = masking_map[ent]
jump = 0
for j, token in enumerate(tokens):
if jump > 0:
jump -= 1
continue
if j in ent_list:
masked_utter['tokens'].append(masking_map[ent_list[j][2]])
jump = ent_list[j][1] - ent_list[j][0] - 1
else:
masked_utter['tokens'].append(token)
next_dialog.append(masked_utter)
dialog_len += 1
if dialog_len == des_size:
break
prev_dialog = []
if dialog_len < des_size:
cur = idx
while (dialog_len < des_size and cur >0):
cur -= 1
for i, utterance in enumerate(reversed(episode_dict['scenes'][cur]['utterances'])):
if utterance['tokens_with_note'] is not None:
tokens = [w for sent in utterance['tokens_with_note'] for w in sent]
else:
tokens = [w for sent in utterance['tokens'] for w in sent]
masked_utter = {}
masked_utter['speakers'] = utterance['speakers']
masked_utter['tokens'] = []
ent_list = {}
for ent, index_list in episode_dict['scenes'][cur]['rc_entities'].iteritems():
for index in index_list['u_ent']:
if i == len(episode_dict['scenes'][cur]['utterances'])-index[0]-1:
ent_list[index[1]] = (index[1], index[2], ent)
for index in index_list['s_ent']:
if i == len(episode_dict['scenes'][cur]['utterances'])-index[0]-1:
masked_utter['speakers'][index[1]] = masking_map[ent]
jump = 0
for j, token in enumerate(tokens):
if jump > 0:
jump -= 1
continue
if j in ent_list:
masked_utter['tokens'].append(masking_map[ent_list[j][2]])
jump = ent_list[j][1] - ent_list[j][0] - 1
else:
masked_utter['tokens'].append(token)
prev_dialog.append(masked_utter)
dialog_len += 1
if dialog_len == des_size:
break
masked_dialog = []
for i, utterance in enumerate(scene_dict['utterances']):
if utterance['tokens_with_note'] is not None:
tokens = [w for sent in utterance['tokens_with_note'] for w in sent]
else:
tokens = [w for sent in utterance['tokens'] for w in sent]
masked_utter = {}
masked_utter['speakers'] = utterance['speakers']
masked_utter['tokens'] = []
ent_list = {}
for ent, index_list in scene_dict['rc_entities'].iteritems():
for index in index_list['u_ent']:
if i == index[0]:
ent_list[index[1]] = (index[1], index[2], ent)
for index in index_list['s_ent']:
if i == index[0]:
masked_utter['speakers'][index[1]] = masking_map[ent]
jump = 0
for j, token in enumerate(tokens):
if jump > 0:
jump -= 1
continue
if j in ent_list:
masked_utter['tokens'].append(masking_map[ent_list[j][2]])
jump = ent_list[j][1] - ent_list[j][0] - 1
else:
masked_utter['tokens'].append(token)
masked_dialog.append(masked_utter)
dialog_entities = Counter()
for ent, ent_list in scene_dict['rc_entities'].iteritems():
if len(ent_list['u_ent']) > 0 or len(ent_list['s_ent']) > 0:
dialog_entities.update([masking_map[ent]])
full_dialog = []
for u in reversed(prev_dialog):
full_dialog.append(u)
for u in masked_dialog:
full_dialog.append(u)
for u in next_dialog:
full_dialog.append(u)
for utterance in full_dialog:
utterance['tokens'] = ' '.join(utterance['tokens'])
utterance['speakers'] = ' '.join(utterance['speakers'])
for sentence in masked_passages:
for i, token in enumerate(sentence):
if token.startswith('@ent') and token in dialog_entities:
sample = {}
query = deepcopy(sentence)
query[i] = '@placeholder'
sample['query'] = ' '.join(query)
sample['answer'] = token
sample['utterances'] = full_dialog
sample['scene_id'] = scene_dict['scene_id']
season_samples[data['season_id']].append(sample)
train_samples = []
val_samples = []
test_samples = []
for season_id, s_samples in season_samples.iteritems():
l = len(s_samples)
random.shuffle(s_samples)
train_samples.extend(s_samples[:int(0.8*l)])
val_samples.extend(s_samples[int(0.8 * l):int(0.9 * l)])
test_samples.extend(s_samples[int(0.9 * l):])
train_samples = relabel(train_samples)
val_samples = relabel(val_samples)
test_samples = relabel(test_samples)
print len(train_samples)
print len(val_samples)
print len(test_samples)
prefix = 'data_check_generated/Friends_' + str(des_size) + '_samples'
dump_json({'train': train_samples, 'dev': val_samples, 'test': test_samples}, prefix)
def dump_json(splits, prefix):
for split, samples in splits.iteritems():
with open(prefix + '.' + split + '.struct.json', 'w') as fw:
json.dump(samples, fw, indent=2)
if __name__ == '__main__':
json_dir = '/Users/jdchoi/Git/character-mining/json'
output_dir = '/Users/jdchoi/Git/reading-comprehension/json'
generate_dataset(json_dir, output_dir)
# dialog_lengths = [25, 50, 100]
# for size in dialog_lengths:
# random.seed(1234)
# generate_padded_dataset(size)
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