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from zipfile import ZipFile, ZIP_DEFLATED
from shutil import rmtree
import json
import os
from tqdm import tqdm
from collections import Counter
from pprint import pprint
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.tokenize.treebank import TreebankWordDetokenizer
import re
topic_map = {
1: "Ordinary Life",
2: "School Life",
3: "Culture & Education",
4: "Attitude & Emotion",
5: "Relationship",
6: "Tourism",
7: "Health",
8: "Work",
9: "Politics",
10: "Finance"
}
act_map = {
1: "inform",
2: "question",
3: "directive",
4: "commissive"
}
emotion_map = {
0: "no emotion",
1: "anger",
2: "disgust",
3: "fear",
4: "happiness",
5: "sadness",
6: "surprise"
}
def preprocess():
original_data_dir = 'ijcnlp_dailydialog'
new_data_dir = 'data'
if not os.path.exists(original_data_dir):
original_data_zip = 'ijcnlp_dailydialog.zip'
if not os.path.exists(original_data_zip):
raise FileNotFoundError(f'cannot find original data {original_data_zip} in dailydialog/, should manually download ijcnlp_dailydialog.zip from http://yanran.li/files/ijcnlp_dailydialog.zip')
else:
archive = ZipFile(original_data_zip)
archive.extractall()
os.makedirs(new_data_dir, exist_ok=True)
dataset = 'dailydialog'
splits = ['train', 'validation', 'test']
dialogues_by_split = {split:[] for split in splits}
dial2topics = {}
with open(os.path.join(original_data_dir, 'dialogues_text.txt')) as dialog_file, \
open(os.path.join(original_data_dir, 'dialogues_topic.txt')) as topic_file:
for dialog, topic in zip(dialog_file, topic_file):
topic = int(topic.strip())
dialog = dialog.replace(' __eou__ ', ' ')
if dialog in dial2topics:
dial2topics[dialog].append(topic)
else:
dial2topics[dialog] = [topic]
global topic_map, act_map, emotion_map
ontology = {'domains': {x:{'description': '', 'slots': {}} for x in topic_map.values()},
'intents': {x:{'description': ''} for x in act_map.values()},
'state': {},
'dialogue_acts': {
"categorical": [],
"non-categorical": [],
"binary": {}
}}
detokenizer = TreebankWordDetokenizer()
for data_split in splits:
archive = ZipFile(os.path.join(original_data_dir, f'{data_split}.zip'))
with archive.open(f'{data_split}/dialogues_{data_split}.txt') as dialog_file, \
archive.open(f'{data_split}/dialogues_act_{data_split}.txt') as act_file, \
archive.open(f'{data_split}/dialogues_emotion_{data_split}.txt') as emotion_file:
for dialog_line, act_line, emotion_line in tqdm(zip(dialog_file, act_file, emotion_file)):
if not dialog_line.strip():
break
utts = dialog_line.decode().split("__eou__")[:-1]
acts = act_line.decode().split(" ")[:-1]
emotions = emotion_line.decode().split(" ")[:-1]
assert (len(utts) == len(acts) == len(emotions)), "Different turns btw dialogue & emotion & action"
topics = dial2topics[dialog_line.decode().replace(' __eou__ ', ' ')]
topic = Counter(topics).most_common(1)[0][0]
domain = topic_map[topic]
dialogue_id = f'{dataset}-{data_split}-{len(dialogues_by_split[data_split])}'
dialogue = {
'dataset': dataset,
'data_split': data_split,
'dialogue_id': dialogue_id,
'original_id': f'{data_split}-{len(dialogues_by_split[data_split])}',
'domains': [domain],
'turns': []
}
for utt, act, emotion in zip(utts, acts, emotions):
speaker = 'user' if len(dialogue['turns']) % 2 == 0 else 'system'
intent = act_map[int(act)]
emotion = emotion_map[int(emotion)]
# re-tokenize
utt = ' '.join([detokenizer.detokenize(word_tokenize(s)) for s in sent_tokenize(utt)])
# replace with common apostrophe
utt = utt.replace(' ’ ', "'")
# add space after full-stop
utt = re.sub('\.(?!com)(\w)', lambda x: '. '+x.group(1), utt)
dialogue['turns'].append({
'speaker': speaker,
'utterance': utt.strip(),
'utt_idx': len(dialogue['turns']),
'dialogue_acts': {
'binary': [{
'intent': intent,
'domain': '',
'slot': ''
}],
'categorical': [],
'non-categorical': [],
},
'emotion': emotion,
})
ontology["dialogue_acts"]['binary'].setdefault((intent, '', ''), {})
ontology["dialogue_acts"]['binary'][(intent, '', '')][speaker] = True
dialogues_by_split[data_split].append(dialogue)
ontology["dialogue_acts"]['binary'] = sorted([str({'user': speakers.get('user', False), 'system': speakers.get('system', False), 'intent':da[0],'domain':da[1], 'slot':da[2]}) for da, speakers in ontology["dialogue_acts"]['binary'].items()])
dialogues = dialogues_by_split['train']+dialogues_by_split['validation']+dialogues_by_split['test']
json.dump(dialogues[:10], open(f'dummy_data.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
json.dump(ontology, open(f'{new_data_dir}/ontology.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
json.dump(dialogues, open(f'{new_data_dir}/dialogues.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
with ZipFile('data.zip', 'w', ZIP_DEFLATED) as zf:
for filename in os.listdir(new_data_dir):
zf.write(f'{new_data_dir}/{filename}')
rmtree(original_data_dir)
rmtree(new_data_dir)
return dialogues, ontology
if __name__ == '__main__':
preprocess()
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