def recommend_cal(text):
short_engine_wrapper = InferenceEngineWrapper(
'/root/Familia/model/webpage', 'lda.conf', 'webpage_twe_lda.model')
doc_seg_short = short_engine_wrapper.tokenize(text)
long_engine_wrapper = InferenceEngineWrapper('/root/Familia/model/webpage',
'lda.conf')
doc_seg_long = long_engine_wrapper.tokenize(text)
top_200_list = short_long_cal(short_engine_wrapper, doc_seg_short)
top_3_jobs = long_long_cal(long_engine_wrapper, doc_seg_long, top_200_list)
return top_3_jobs
def doc_distance():
category = request.form['category']
in_type = request.form['type']
if in_type == 'doc':
f1 = request.files['text1']
f2 = request.files['text2']
if save_file(f1) and save_file(f2):
f_text1 = read_file(f1)
f_text2 = read_file(f2)
else:
f_text1 = request.form['text1'].encode('utf-8').strip()
f_text2 = request.form['text2'].encode('utf-8').strip()
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_lda_conf())
doc1_seg = inference_engine_wrapper.tokenize(f_text1)
doc2_seg = inference_engine_wrapper.tokenize(f_text2)
distances = inference_engine_wrapper.cal_doc_distance(doc1_seg, doc2_seg)
return json.dumps(
{
"Jensen-Shannon Divergence": distances[0],
"Hellinger Distance": distances[1]
},
ensure_ascii=False)
def query_doc_sim():
category = request.form['category']
in_type = request.form['type']
if in_type == 'doc':
f1 = request.files['text1']
f2 = request.files['text2']
if save_file(f1) and save_file(f2):
f_text1 = read_file(f1)
f_text2 = read_file(f2)
else:
f_text1 = request.form['text1'].encode('utf-8').strip()
f_text2 = request.form['text2'].encode('utf-8').strip()
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_lda_conf(),
get_emb_file(category))
doc1_seg = inference_engine_wrapper.tokenize(f_text1)
doc2_seg = inference_engine_wrapper.tokenize(f_text2)
distances = inference_engine_wrapper.cal_query_doc_similarity(
doc1_seg, doc2_seg)
return json.dumps(
{
"LDA Similarity": distances[0],
"TWE Similarity": distances[1]
},
ensure_ascii=False)
def lda_infer():
category = request.form['category']
in_type = request.form['type']
f_text = input_doc_str(in_type)
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_lda_conf())
seg_list = inference_engine_wrapper.tokenize(f_text)
topic_dist = inference_engine_wrapper.lda_infer(seg_list)
return json_format(topic_dist)
def doc_keywords():
category = request.form['category']
word = request.form['word'].encode('utf-8').strip()
in_type = request.form['type']
f_text = input_doc_str(in_type)
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_lda_conf())
seg_list = inference_engine_wrapper.tokenize(f_text)
items = inference_engine_wrapper.cal_keywords_similarity(
word, ' '.join(seg_list))
return json_format(items)
def slda_infer():
category = request.form['category']
in_type = request.form['type']
f_text = input_doc_str(in_type)
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_slda_conf())
seg_list = inference_engine_wrapper.tokenize(f_text)
sentences = []
length = len(seg_list)
for index in range(0, length, 5):
sentences.append(seg_list[index:index + 5])
topic_dist = inference_engine_wrapper.slda_infer(sentences)
return json_format(topic_dist)
def doc_keywords_plus():
category = request.form['category']
#word = request.form['word'].encode('utf-8').strip()
in_type = request.form['type']
f_text = input_doc_str(in_type)
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_lda_conf())
seg_list = inference_engine_wrapper.tokenize(f_text)
items = {}
for x, w in jieba.analyse.extract_tags(f_text, withWeight=True):
result = inference_engine_wrapper.cal_keywords_similarity(
x.encode('utf-8').strip(), ' '.join(seg_list))
items.update(result)
return json_format(items)
def doc_topic_word_lda():
category = request.form['category']
in_type = request.form['type']
f_text = input_doc_str(in_type)
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_lda_conf())
seg_list = inference_engine_wrapper.tokenize(f_text)
topic_dist = inference_engine_wrapper.lda_infer(seg_list)
result = {}
for key, value in dict(topic_dist).items():
twe_wrapper = TopicalWordEmbeddingsWrapper(get_model_dir(category),
get_emb_file(category))
result_dict = dict(
twe_wrapper.nearest_words_around_topic(int(key), get_count()))
result[value] = result_dict
return json.dumps(result)
result.append(ent)
return result
if __name__ == '__main__':
path = '/media/iiip/数据/duanduan/data/validation.csv'
documents = read_whole_file(path)
if len(sys.argv) < 3:
sys.stderr.write("Usage:python {} {} {}\n".format(
sys.argv[0], "model_dir", "conf_file"))
exit(-1)
# 获取参数
model_dir = sys.argv[1]
conf_file = sys.argv[2]
# 创建InferenceEngineWrapper对象
inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file)
topic_result = {}
for key in documents:
print key
seg_list = inference_engine_wrapper.tokenize(documents[key])
# 进行推断
topic_dist = inference_engine_wrapper.lda_infer(seg_list)
topic_result[key] = cal_topic(topic_dist)
file = open(path.replace(".csv", "_topic.csv"), 'w')
writer = csv.writer(file)
for each in topic_result:
writer.writerow([each, topic_result[each][0], topic_result[each][1]])
file.close()
# return topic_result
import sys
from familia_wrapper import InferenceEngineWrapper
if sys.version_info < (3, 0):
input = raw_input
if __name__ == '__main__':
if len(sys.argv) < 4:
sys.stderr.write("Usage:python {} {} {} {}.\n".format(
sys.argv[0], "model_dir", "conf_file", "emb_file"))
exit(-1)
# 获取参数
model_dir = sys.argv[1]
conf_file = sys.argv[2]
emb_file = sys.argv[3]
# 创建InferenceEngineWrapper对象
inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file,
emb_file)
while True:
# 输入短文本和长文本
query = input("Enter Query: ").strip()
doc = input("Enter Document: ").strip()
query_seg = inference_engine_wrapper.tokenize(query)
doc_seg = inference_engine_wrapper.tokenize(doc)
distances = inference_engine_wrapper.cal_query_doc_similarity(
query_seg, doc_seg)
# 打印结果
print("LDA Similarity = {}".format(distances[0]))
print("TWE similarity = {}".format(distances[1]))
# found in the LICENSE file.
import sys
from familia_wrapper import InferenceEngineWrapper
if sys.version_info < (3, 0):
input = raw_input
if __name__ == '__main__':
if len(sys.argv) < 3:
sys.stderr.write("Usage:python {} {} {}.\n".format(
sys.argv[0], "model_dir", "conf_file"))
exit(-1)
# 获取参数
model_dir = sys.argv[1]
conf_file = sys.argv[2]
# 创建InferenceEngineWrapper对象
inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file)
while True:
# 输入两个长文本
words = input("Enter Keywords: ").strip()
doc = input("Enter Document: ").strip()
seg_list = inference_engine_wrapper.tokenize(doc)
items = inference_engine_wrapper.cal_keywords_similarity(
words, ' '.join(seg_list))
# 打印结果
print('----------------------------')
for item in items:
print(item[0] + '\t' + str(item[1]))
# Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. # # Author: [email protected] import sys from familia_wrapper import InferenceEngineWrapper if sys.version_info < (3,0): input = raw_input if __name__ == '__main__': if len(sys.argv) < 3: sys.stderr.write("Usage:python {} {} {}\n".format( sys.argv[0], "model_dir", "conf_file")) exit(-1) # 获取参数 model_dir = sys.argv[1] conf_file = sys.argv[2] # 创建InferenceEngineWrapper对象 inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file) while True: input_text = input("Enter Document: ") # 分词 seg_list = inference_engine_wrapper.tokenize(input_text) # 进行推断 topic_dist = inference_engine_wrapper.lda_infer(seg_list) # 打印结果 print("Document Topic Distribution:") print(topic_dist)
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import sys
from familia_wrapper import InferenceEngineWrapper
if sys.version_info < (3,0):
input = raw_input
if __name__ == '__main__':
if len(sys.argv) < 3:
sys.stderr.write("Usage:python {} {} {}.\n".format(
sys.argv[0], "model_dir", "conf_file"))
exit(-1)
# 获取参数
model_dir = sys.argv[1]
conf_file = sys.argv[2]
# 创建InferenceEngineWrapper对象
inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file)
while True:
# 输入两个长文本
doc1 = input("Enter Document1: ").strip()
doc2 = input("Enter Document2: ").strip()
doc1_seg = inference_engine_wrapper.tokenize(doc1)
doc2_seg = inference_engine_wrapper.tokenize(doc2)
distances = inference_engine_wrapper.cal_doc_distance(doc1_seg, doc2_seg)
# 打印结果
print("Jensen-Shannon Divergence = {}".format(distances[0]))
print("Hellinger Distance = {}".format(distances[1]))
import sys
from familia_wrapper import InferenceEngineWrapper
if sys.version_info < (3, 0):
input = raw_input
if __name__ == '__main__':
if len(sys.argv) < 3:
sys.stderr.write("Usage:python {} {} {}\n".format(
sys.argv[0], "model_dir", "conf_file"))
exit(-1)
# 获取参数
model_dir = sys.argv[1]
conf_file = sys.argv[2]
# 创建InferenceEngineWrapper对象
inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file)
while True:
input_text = input("Enter Document: ")
# 分词
seg_list = inference_engine_wrapper.tokenize(input_text.strip())
# 构建句子结构,5个词为一个句子
sentences = []
length = len(seg_list)
for index in range(0, length, 5):
sentences.append(seg_list[index:index + 5])
# 进行推断
topic_dist = inference_engine_wrapper.slda_infer(sentences)
# 打印结果
print("Document Topic Distribution:")
print(topic_dist)