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import json
import tqdm
import numpy as np
import multiprocessing as mp
import random
from collections import Counter
random.seed(13)
def _norm(x):
return ' '.join(x.strip().split())
strategies = json.load(open('./strategy.json'))
strategies = [e[1:-1] for e in strategies]
strat2id = {strat: i for i, strat in enumerate(strategies)}
original = json.load(open('./ESConv.json'))
def process_data(d):
dial = []
for uttr in d['dialog']:
text = _norm(uttr['content'])
role = uttr['speaker']
if role == 'seeker':
dial.append({
'text': text,
'speaker': 'usr',
})
else:
dial.append({
'text': text,
'speaker': 'sys',
'strategy': uttr['annotation']['strategy'],
})
d['dialog'] = dial
return d
data = []
for e in map(process_data, tqdm.tqdm(original, total=len(original))):
data.append(e)
emotions = Counter([e['emotion_type'] for e in data])
problems = Counter([e['problem_type'] for e in data])
print('emotion', emotions)
print('problem', problems)
random.shuffle(data)
dev_size = int(0.15 * len(data))
test_size = int(0.15 * len(data))
valid = data[:dev_size]
test = data[dev_size: dev_size + test_size]
train = data[dev_size + test_size:]
print('train', len(train))
with open('./train.txt', 'w') as f:
for e in train:
f.write(json.dumps(e) + '\n')
with open('./sample.json', 'w') as f:
json.dump(train[:10], f, ensure_ascii=False, indent=2)
print('valid', len(valid))
with open('./valid.txt', 'w') as f:
for e in valid:
f.write(json.dumps(e) + '\n')
print('test', len(test))
with open('./test.txt', 'w') as f:
for e in test:
f.write(json.dumps(e) + '\n')
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