def is_visited(question_id):
visited = True
if question_id is not None:
try:
visited = (Session.query(Question).filter_by(
question_id=question_id).count() != 0)
except:
logger.error('fail to query question_id %s', question_id,
exc_info=True)
return visited
def get_analyzed_result(question_text):
if question_text is None:
return None
md5_string = md5(question_text)
ltp_result = Session.query(LtpResult).filter_by(md5=md5_string).first()
if ltp_result is not None:
analyzed_result = AnalyzedSentence(md5_string, ltp_result.json_text)
else:
try:
result_json = analyze(question_text)
except RuntimeError:
logger.error('fail to invoke ltp api, text=%s', question_text,
exc_info=True)
raise RuntimeError()
save_analyzed_result(md5_string, result_json)
analyzed_result = AnalyzedSentence(md5_string, result_json)
return analyzed_result
def k_fold_cross_dataset(k, num):
"""Generate k-fold cross test set and train set.
Example:
In: k_fold_cross_dataset(2, 10)
Out:
[
{
'test_text': 'data/2-fold-cross-10-test-text-1.txt',
'test_label': 'data/2-fold-cross-10-test-label-1.txt',
'train_text': 'data/2-fold-cross-10-train-text-1.txt',
'train_label': 'data/2-fold-cross-10-train-label-1.txt',
},
{
'test_text': 'data/2-fold-cross-10-test-text-2.txt',
'test_label': 'data/2-fold-cross-10-test-label-2.txt',
'train_text': 'data/2-fold-cross-10-train-text-2.txt',
'train_label': 'data/2-fold-cross-10-train-label-2.txt',
}
]
:param k: int
:param num: int
:return: list :raise RuntimeError:
"""
prefix = 'data/{k}-fold-cross-{num}'.format(k=k, num=num)
file_pattern = '{prefix}-{{type}}-{{{{i}}}}.txt'.format(prefix=prefix)
test_text_file_pattern = file_pattern.format(type='test-text')
test_label_file_pattern = file_pattern.format(type='test-label')
train_text_file_pattern = file_pattern.format(type='train-text')
train_label_file_pattern = file_pattern.format(type='train-label')
file_names = []
for i in range(0, k):
test_text_file = test_text_file_pattern.format(i=i)
test_label_file = test_label_file_pattern.format(i=i)
train_text_file = train_text_file_pattern.format(i=i)
train_label_file = train_label_file_pattern.format(i=i)
file_names.append({
'test_text': test_text_file,
'test_label': test_label_file,
'train_text': train_text_file,
'train_label': train_label_file})
exist = True
for i in file_names:
for file_ in i.itervalues():
if not os.path.isfile(file_):
exist = False
break
if not exist:
filtered_paragraphs = Session.query(FilteredParagraph).limit(num).all()
if len(filtered_paragraphs) != num:
raise RuntimeError()
random.shuffle(filtered_paragraphs)
folds = [[] for i in range(0, k)]
for i in range(0, num):
folds[i % k].append(filtered_paragraphs[i].paragraph)
for i in range(0, k):
test_text_file = file_names[i]['test_text']
test_label_file = file_names[i]['test_label']
train_text_file = file_names[i]['train_text']
train_label_file = file_names[i]['train_label']
test_set = folds[i]
train_set = []
for j in range(0, k):
if j != i:
train_set.extend(folds[j])
# generate test set
generate_dataset(test_set, test_text_file, test_label_file)
# generate train set
generate_dataset(train_set, train_text_file, train_label_file)
return file_names
#!/usr/bin/env python
# coding: utf-8
__author__ = 'wilfredwang'
from data_access import Session
from data_access import Paragraph
import traceback
with open('data/baidu-zhidao-paragraph.txt', 'wb') as f:
count = 0
try:
for paragraph in Session.query(Paragraph):
lines = [paragraph.question.title, '\n']
for reply in paragraph.reply:
lines.append(reply.content)
lines.append('\n')
lines.append('\n')
f.writelines([s.encode('utf-8') for s in lines])
count += 1
print count
except:
print 'error, count %d' % count
traceback.print_exc()
