def main():
init("./jieba/stopwords_cn.txt", "./jieba/userdict.txt")
clfins = build('./output')
# list all videos
videos_path = './data/videos'
subjects = './data/output'
for vid in os.listdir(subjects):
vid_path = os.path.join(subjects, vid)
output_path = "%s/" % (vid_path)
video_file = os.path.join(videos_path, vid)
subjects_file = "%s/%s.subjects" % (vid_path, vid)
keypoints_file = "%s/%s.keypoints" % (vid_path, vid)
process(subjects_file, video_file, output_path, keypoints_file)
# valid_points = extract_point(zip(seconds_list, subjects_list))
output = open(keypoints_file, mode='w', encoding='utf-8')
# for second, keyword in output_points.items():
# text = merge_text2(second, sentence_timeline)
# summary = extract_summary(text, keyword, predict)
# output.write("%s\t%s\t%s|%s\n" % (second, '', keyword, summary))
output.close()
print(vid, " cut finished.")
print()
print()
print()
def tokenize(jack_code, xml_format=False): tokenizer.init(jack_code) tokenizer.run() if xml_format: return tokenizer.get_tokenized_list() else: return tokenizer.get_jack_code()
def main(_):
os.system('mkdir -p %s' % FLAGS.dir)
tokenizer.init(FLAGS.tokenizer_vocab)
global examples, vocab, unk_vocab, char_vocab, pos_vocab, tag_vocab, ner_vocab, ngram_vocab
examples = pd.read_csv(FLAGS.input)
#if 'train' in FLAGS.input:
# examples = shuffle(examples, random_state=1024)
vocab = Vocabulary(FLAGS.vocab)
# unk_vocab is actually a small vocab so will genearte unk for training
#unk_vocab = Vocabulary(FLAGS.vocab.replace('vocab.txt', 'unk_vocab.txt'))
char_vocab = Vocabulary(FLAGS.vocab.replace('vocab.txt', 'char_vocab.txt'))
pos_vocab = Vocabulary(FLAGS.vocab.replace('vocab.txt', 'pos_vocab.txt'))
tag_vocab = Vocabulary(FLAGS.vocab.replace('vocab.txt', 'tag_vocab.txt'))
ner_vocab = Vocabulary(FLAGS.vocab.replace('vocab.txt', 'ner_vocab.txt'))
ngram_vocab = Vocabulary(FLAGS.vocab.replace('vocab.txt', 'ngram_vocab.txt'))
global enprob_dict
enprob_dict = {}
enprob_file = '~/data/kaggle/toxic/train.enprob.csv' if 'train' in FLAGS.input else '~/data/kaggle/toxic/test.enprob.csv'
enprob_df = pd.read_csv(enprob_file)
for id, enprob in zip(enprob_df['id'].values, enprob_df['enprob'].values):
enprob_dict[id] = enprob
enprob_dict['0'] = 1.
pool = multiprocessing.Pool()
pool.map(build_features, range(FLAGS.num_records))
pool.close()
pool.join()
#build_features(0)
print('num_records:', counter.value)
mode = get_mode()
out_file = os.path.dirname(FLAGS.vocab) + '/{0}/num_records.txt'.format(mode)
gezi.write_to_txt(counter.value, out_file)
def main(_):
tokenizer.init(FLAGS.tokenizer_vocab)
if FLAGS.full_tokenizer:
gezi.segment.init_spacy_full()
os.system('mkdir -p %s' % FLAGS.out_dir)
print('name', FLAGS.name, 'out_dir', FLAGS.out_dir)
global counter
counter = WordCounter(write_unknown=FLAGS.write_unknown,
most_common=FLAGS.most_common,
min_count=FLAGS.min_count)
global char_counter
char_counter = WordCounter(write_unknown=FLAGS.write_unknown,
most_common=FLAGS.most_common,
min_count=FLAGS.min_count)
global ngram_counter
ngram_counter = WordCounter(write_unknown=True, min_count=FLAGS.min_count)
global pos_counter, tag_counter, ner_counter
pos_counter = WordCounter(write_unknown=True, min_count=1)
tag_counter = WordCounter(write_unknown=True, min_count=1)
ner_counter = WordCounter(write_unknown=True, min_count=1)
run(FLAGS.input)
if FLAGS.test_input and not FLAGS.name:
run(FLAGS.test_input, count=FLAGS.test_count)
if not FLAGS.name:
vocab_name = FLAGS.vocab_name or 'vocab'
os.system('mkdir -p %s' % FLAGS.out_dir)
out_txt = os.path.join(FLAGS.out_dir, '%s.txt' % vocab_name)
counter.save(out_txt)
out_txt = os.path.join(FLAGS.out_dir, 'char_%s.txt' % vocab_name)
char_counter.save(out_txt)
out_txt = os.path.join(FLAGS.out_dir, 'pos_vocab.txt')
pos_counter.save(out_txt)
out_txt = os.path.join(FLAGS.out_dir, 'tag_vocab.txt')
tag_counter.save(out_txt)
out_txt = os.path.join(FLAGS.out_dir, 'ner_vocab.txt')
ner_counter.save(out_txt)
out_txt = os.path.join(FLAGS.out_dir, 'ngram_vocab.txt')
if not FLAGS.max_ngrams:
ngram_counter.save(out_txt)
else:
# if later need most 2w ngram head -200000 ngram_vocab.full.txt > ngram_vocab.txt
out_full_txt = os.path.join(FLAGS.out_dir, 'ngram_vocab.full.txt')
ngram_counter.save(out_full_txt)
os.system('head -n %d %s > %s' %
(FLAGS.max_ngrams, out_full_txt, out_txt))
def main(_):
tokenizer.init(FLAGS.tokenizer_vocab)
os.system('mkdir -p %s' % FLAGS.out_dir)
print('name', FLAGS.name, 'out_dir', FLAGS.out_dir)
run(FLAGS.input)
global name
name = 'test'
if FLAGS.test_input and not FLAGS.name:
run(FLAGS.test_input)
def main():
init("./jieba/stopwords_cn.txt", "./jieba/userdict.txt")
clfins = build(config.naive_bayes_model_path)
# list all videos
input_path = './data/output'
output_path = './data/output'
for vid in os.listdir(input_path):
caption_file = os.path.join(input_path, vid, vid + '.captions')
subject_file = "%s/%s/%s.subjects" % (output_path, vid, vid)
preprocess(caption_file, subject_file, clfins)
print(vid, " classify finished.")
print()
print()
print()
def main(_):
tokenizer.init(FLAGS.tokenizer_vocab)
global examples, vocab, char_vocab
examples = pd.read_csv(FLAGS.input)
#if 'train' in FLAGS.input:
# examples = shuffle(examples, random_state=1024)
vocab = Vocabulary(FLAGS.vocab)
char_vocab = Vocabulary(FLAGS.vocab.replace('vocab.txt', 'char_vocab.txt'))
pool = multiprocessing.Pool()
pool.map(build_features, range(FLAGS.num_records))
pool.close()
pool.join()
# build_features(0)
print('num_records:', counter.value)
mode = 'train' if 'train' in FLAGS.input else 'test'
out_file = os.path.dirname(
FLAGS.vocab) + '/{0}/num_records.txt'.format(mode)
gezi.write_to_txt(counter.value, out_file)
def main(_):
tokenizer.init(FLAGS.tokenizer_vocab)
global counter
counter = WordCounter(write_unknown=FLAGS.write_unknown,
most_common=FLAGS.most_common,
min_count=FLAGS.min_count)
global char_counter
char_counter = WordCounter(write_unknown=FLAGS.write_unknown,
most_common=FLAGS.most_common,
min_count=FLAGS.min_count)
run(FLAGS.input)
if FLAGS.test_input:
run(FLAGS.test_input, count=FLAGS.test_count)
vocab_name = FLAGS.vocab_name or 'vocab'
os.system('mkdir -p %s' % FLAGS.out_dir)
out_txt = os.path.join(FLAGS.out_dir, '%s.txt' % vocab_name)
counter.save(out_txt)
out_txt = os.path.join(FLAGS.out_dir, 'char_%s.txt' % vocab_name)
char_counter.save(out_txt)
def process(caption_file, output_file, clfins=None):
if os.path.exists(output_file):
return output_file
if clfins is None:
init(config.jieba_stopwords_path, config.jieba_userdict_path)
clfins = build(config.naive_bayes_model_path)
sentence_list = preprocess(caption_file)
seconds_list, docs_list = zip(*sentence_list)
predicted_list = clfins.predict_proba(docs_list, 0.5)
target_list = []
output = open(output_file, mode='w', encoding='utf-8')
for second, content, predicted in zip(seconds_list, docs_list,
predicted_list):
name = clfins.target_name(predicted)
if name == 'Unpredict':
name = predict_internal(content)
target_list.append(name)
output.write("%s\t%s\t%s\n" % (second, content, name))
output.close()
return output_file
test_predicted = clfins.predict_proba(test_docs, 0.6)
total = 0
right = 0
for filename, doc, category, predict in zip(test_data.filenames, test_docs,
test_data.target,
test_predicted):
if predict is None:
continue
total = total + 1
if test_data.target_names[category] == clfins.target_name(predict):
right = right + 1
continue
print('%r => %s| right:%s, predict:%s, %s' %
(filename, doc, test_data.target_names[category],
clfins.target_name(predict), (predict)))
print(total, ",", right, ",", right / total)
def build(model_path):
clfins = Classifier(model_path)
return clfins
if __name__ == '__main__':
init("../jieba/stopwords_cn.txt", "../jieba/userdict.txt")
clfins = build("../models")
test(clfins)
test_performence(clfins)
def main():
clfins = build('./output')
init("./jieba/stopwords_cn.txt", "./jieba/userdict.txt")
# list all videos
videos_folder_path = './data/captions'
for folder in os.listdir(videos_folder_path):
if folder != '24CFC579E390B590':
# if folder != '11AFCABB49FADB3D':
pass
# continue
one_video_folder_path = os.path.join(videos_folder_path, folder)
caption_file = "./%s/%s_baidu_ocr_result.txt" % (one_video_folder_path,
folder)
file_name_output = "./%s/%s_category_result.txt" % (
one_video_folder_path, folder)
classify_result_file = "./%s/classify_result.txt" % (
one_video_folder_path)
keypoint_result_file = "./%s/keypoint_result.txt" % (
one_video_folder_path)
sentence_list = preprocess(caption_file)
seconds_list, docs_list = zip(*sentence_list)
predicted_list = clfins.predict_proba(docs_list, 0.5)
target_list = []
output = open(classify_result_file, mode='w', encoding='utf-8')
for second, content, predicted in zip(seconds_list, docs_list,
predicted_list):
target_list.append(clfins.target_name(predicted))
output.write("%s\t%s\t%s\n" %
(second, content, clfins.target_name(predicted)))
output.close()
valid_points = extract_point(zip(seconds_list, target_list))
print(valid_points)
def merge_text2(k, dict): # 以k点为中轴，获取30s内容
texts = []
for x in range(k - 2, k + 30):
if x not in dict.keys():
continue
sss = dict[x].replace("汽车之家", "").replace("之家", "").replace(
"汽车之", "").replace("看车买车用车",
"").replace("家看车",
"").replace("家买车用车",
"").strip()
sss = sss.split(" ")[0]
if x == k - 2:
texts.append(sss)
continue
if x - 1 not in dict.keys():
continue
sss0 = dict[x - 1].replace("汽车之家", "").replace(
"之家", "").replace("汽车之", "").replace("看车买车用车", "").replace(
"家看车", "").replace("家买车用车", "").strip()
sss0 = sss0.split(" ")[0]
if tf_similarity(sss, sss0) > 0.8:
# print(sss0, sss)
continue
texts.append(sss)
return '，'.join(texts)
# 重新预测一遍
sentence_timeline = dict(sentence_list)
for k in valid_points:
new_text = merge_text2(k, sentence_timeline)
# 重新计算分类
predicted_list = clfins.predict_proba([new_text], 0.5)
# predicted_list = clfins.predict([new_text])
valid_points[k] = clfins.target_name(predicted_list[0])
# print(new_text, clfins.target_name(predicted_list[0]))
# print(valid_points)
def merge_points(points):
reverse_points = {}
for k, v in points.items():
if v in reverse_points.keys():
reverse_points[v].append(k)
else:
reverse_points[v] = [k]
if 'Unpredict' in reverse_points.keys():
reverse_points.pop('Unpredict')
new_points = {}
for ks, v in reverse_points.items():
sortedv = list(v).sort()
new_points[int(v[0])] = ks
return new_points
def predict(docs_list):
predicted_list = clfins.predict(docs_list)
return clfins.target_name(predicted_list[0])
output_points = merge_points(valid_points)
print(output_points)
output = open(keypoint_result_file, mode='w', encoding='utf-8')
for second, keyword in output_points.items():
text = merge_text2(second, sentence_timeline)
summary = extract_summary(text, keyword, predict)
output.write("%s\t%s\t%s|%s\n" % (second, '', keyword, summary))
output.close()
print(folder, " finished.")
print()
print()
print()
def main():
init("./jieba/stopwords_cn.txt", "./jieba/userdict.txt")
clfins = build('./output')
# list all videos
subjects = './data/output'
for vid in os.listdir(subjects):
vid_path = os.path.join(subjects, vid)
subjects_file = "%s/%s.subjects" % (vid_path, vid)
keypoints_file = "%s/%s.keypoints" % (vid_path, vid)
seconds_list = []
sentences_list = []
subjects_list = []
subjects_file = open(subjects_file, mode='r', encoding='utf-8')
for line in subjects_file.readlines():
arr = line.split("\t")
seconds_list.append(int(arr[0]))
sentences_list.append((arr[1]))
subjects_list.append((arr[2]))
subjects_file.close()
print(subjects_list)
valid_points = extract_point(zip(seconds_list, subjects_list))
print(valid_points)
def merge_text2(k, dict): # 以k点为中轴，获取30s内容
texts = []
for x in range(k - 2, k + 30):
if x not in dict.keys():
continue
sss = dict[x].replace("汽车之家", "").replace("之家", "").replace("汽车之", "").replace(
"看车买车用车", "").replace("家看车", "").replace("家买车用车", "").strip()
sss = sss.split(" ")[0]
if x == k - 2:
texts.append(sss)
continue
if x - 1 not in dict.keys():
continue
sss0 = dict[x - 1].replace("汽车之家", "").replace("之家", "").replace(
"汽车之", "").replace("看车买车用车", "").replace("家看车", "").replace("家买车用车", "").strip()
sss0 = sss0.split(" ")[0]
if tf_similarity(sss, sss0) > 0.8:
# print(sss0, sss)
continue
texts.append(sss)
return '，'.join(texts)
# 重新预测一遍
sentence_timeline = dict(zip(seconds_list, sentences_list))
for k in valid_points:
new_text = merge_text2(k, sentence_timeline)
# 重新计算分类
predicted_list = clfins.predict_proba([new_text], 0.5)
# predicted_list = clfins.predict([new_text])
valid_points[k] = clfins.target_name(predicted_list[0])
# print(new_text, clfins.target_name(predicted_list[0]))
# print(valid_points)
def merge_points(points):
reverse_points = {}
for k, v in points.items():
if v in reverse_points.keys():
reverse_points[v].append(k)
else:
reverse_points[v] = [k]
if 'Unpredict' in reverse_points.keys():
reverse_points.pop('Unpredict')
new_points = {}
for ks, v in reverse_points.items():
sortedv = list(v).sort()
new_points[int(v[0])] = ks
return new_points
def predict(docs_list):
predicted_list = clfins.predict(docs_list)
return clfins.target_name(predicted_list[0])
output_points = merge_points(valid_points)
print(output_points)
output = open(keypoints_file, mode='w', encoding='utf-8')
for second, keyword in output_points.items():
text = merge_text2(second, sentence_timeline)
summary = extract_summary(text, keyword, predict)
output.write("%s\t%s\t%s|%s\n" % (second, '', keyword, summary))
output.close()
print(vid, " cut finished.")
print()
print()
print()
