def build_lda_by_keywords(keywords, num_article_for_search, num_topics=0):
if num_topics == 0:
num_topics = len(keywords)
dir_name = '../data'
if not os.path.exists(dir_name):
os.mkdir(dir_name)
filename = dir_name + '/' + str("_".join(keywords) + "_" + str(num_article_for_search))
with open(filename, 'w', encoding="utf-8") as file:
articles = []
for keyword in keywords:
articles_keyword = fetch_articles(keyword, number=num_article_for_search, days=-1)
articles.extend(articles_keyword)
log(file, "%s : %d" % (keyword, len(articles_keyword)))
texts = []
for article in articles:
tokens = cut(
article.title + article.content, using_stopwords=True, simplified_convert=True)
texts.append(tokens)
start = time.time()
model = lda.build_lda_model(texts, num_topics)
for topic_key, tokens in lda.get_topic(model, num_topics=num_topics, num_words=15).items():
log(file, tokens)
end = time.time()
log(file, "model train time : " + str(end - start))
print("\n\n\n\n", file=file)
for article in articles:
print(article.title, end="\n", file=file)
def _compute_vector(self, input_data):
weights = None
if isinstance(input_data, list):
if len(input_data) == 0:
tokens = []
elif isinstance(input_data[0], tuple):
tokens = [data_tuple[0] for data_tuple in input_data]
weights = [data_tuple[1] for data_tuple in input_data]
else:
tokens = input_data
else:
tokens = cut(input_data,
using_stopwords=True,
simplified_convert=True)
if len(tokens) > 0 and (tokens[-1] in ['八卦', '卦']):
del tokens[-1]
v1 = []
for word in tokens:
if word in self._model:
word_vector = self._model[word]
if weights:
weight = weights[tokens.index(word)]
word_vector = word_vector * weight
v1.append(word_vector)
if len(v1) is 0:
print('invalid article:', input_data)
return None
return sum(v1)
def get_sentence(keyword, number, page=1):
articles = fetch_articles(keyword,
number,
page=page,
fl='title, content',
desc=False)
result_sentences = []
for article in articles:
tokens = cut(article.title,
using_stopwords=False,
simplified_convert=True)
result_sentences.append(tokens)
if hasattr(article, 'content_sentence'):
for sen in article.content_sentence:
result_sentences.append(
cut(sen, using_stopwords=False, simplified_convert=True))
# if hasattr(article, 'content'):
# result_sentences.append(cut(article.content, using_stopwords=False, simplified_convert=True))
return result_sentences
def n_similarity_test():
sentence_list = [
'馬總統走光照 蔡正元:經專家鑑定為光影', '馬走光照瘋傳總統府譴責', '2016 全球 軍事力量排名',
'舉債也最低... 「六都還款王」第2名令人', '【北捷殺人案】鄭捷判死定讞5大理由曝光', '地震',
'日本紅十字會：捐款不用手續費 善款100%', '日本熊本強震 屏縣府擬捐香蕉賑災', '有沒有日本重新定義島的八卦',
'日本只在利益不衝突時才是朋友'
]
tokens_list = [cut(sentence) for sentence in sentence_list]
for i in range(1, len(tokens_list)):
print(tokens_list[i - 1])
print(tokens_list[i])
for model in models:
print(model.n_similarity(tokens_list[i], tokens_list[i - 1]))
def compute_vector(model, input_data, need_log=False):
if isinstance(input_data, str):
tokens = cut(input_data, using_stopwords=True, simplified_convert=True, log=need_log)
else:
tokens = input_data
if len(tokens) > 0 and (tokens[-1] in ['八卦', '卦']):
del tokens[-1]
if need_log is True:
print(tokens)
tokens_not_found = [word for word in tokens if word not in model]
if len(tokens_not_found) is not 0:
log('token not in model :' + " ".join(tokens_not_found))
v1 = [model[word] for word in tokens if word in model]
if len(v1) is 0:
print('invalid article: \'' + input_data + '\'')
return None
vector = matutils.unitvec(array(v1, float).mean(axis=0))
return vector
def build_lda_model(input_data, num_topics=1):
if len(input_data) == 0:
print('data is empty')
return
if isinstance(input_data, str):
input_data = [input_data]
texts = []
for data in input_data:
tokens = cut(data, using_stopwords=True, simplified_convert=True)
texts.append(tokens)
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
lda_model = models.ldamodel.LdaModel(
corpus, num_topics=num_topics, id2word=dictionary, passes=1)
return lda_model
def _compute_vector(self, input_data, tfidf_vectorizer=None):
weights = None
if isinstance(input_data, list):
if isinstance(input_data[0], tuple):
tokens = [data_tuple[0] for data_tuple in input_data]
weights = [data_tuple[1] for data_tuple in input_data]
else:
tokens = input_data
else:
tokens = cut(input_data,
using_stopwords=True,
simplified_convert=True)
if len(tokens) > 0 and (tokens[-1] in ['八卦', '卦']):
del tokens[-1]
v1 = []
if tfidf_vectorizer is not None:
idf_table = self.build_idf_table(tfidf_vectorizer)
for word in tokens:
if word in self.model:
word_vector = self.model[word]
if weights:
weight = weights[tokens.index(word)]
word_vector = word_vector * weight
if tfidf_vectorizer is not None and word in idf_table:
word_vector = word_vector * idf_table[word]
v1.append(word_vector)
if len(v1) is 0:
print('invalid article:', input_data)
return None
# v1 = [self.model[word] for word in tokens if word in self.model]
if tfidf_vectorizer is None:
return array(v1, float).mean(axis=0)
else:
return sum(v1)
def _split_string(article, split_content=True):
tokens = cut(article.title)
if split_content:
tokens.extend(keywords_extraction([article], 1))
return ' '.join(tokens)
from python_code.model import ptt_article_fetcher
from python_code.model.my_tokenize.tokenizer import cut
articles = ptt_article_fetcher.fetch_articles('', number=10, page=6)
using_stopwords = False
equals_tokens = []
for article in articles:
token1 = cut(article.title, using_stopwords, True)
token2 = cut(article.title, using_stopwords, False)
if token1 == token2:
equals_tokens.append(token1)
else:
print('經轉換' + str(token1))
print('未轉換' + str(token2))
for i in equals_tokens:
print(i)