def run_feature_select(self, article_market_path, dictionary_path, \
feature_market_path) :
loader = PickleMarket()
articles = loader.load_market(article_market_path)
[word2id, id2word] = loader.load_market(dictionary_path)
dim = len(word2id)
featuresets = list()
length = len(articles) - 1
for idx, article in enumerate(articles) :
feature = [0] * dim
for word in article['participle_title'] :
word = word.to_string()
if word in word2id :
feature[word2id[word]] += 1
for word in article['participle_content'] :
word = word.to_string()
if word in word2id :
feature[word2id[word]] += 1
featuresets.append([article['id']] + feature + [article['label']])
if idx % 100 == 0 :
print 'finish rate is %.2f%%\r' % (100.0*idx/length),
print 'finish rate is %.2f%%\r' % (100.0*idx/length)
file_operator = TextFileOperator()
# file_operator.writing(featuresets, feature_path)
loader.dump_market(featuresets, feature_market_path)
print 'finish'
def run_convert_sentences(self, sentences_path, sentences_market_path) :
file_operator = TextFileOperator()
sentences = file_operator.reading(sentences_path)
sentences = [[word.split('<:>')[0] for word in sentence] for sentence in sentences]
loader = PickleMarket()
loader.dump_market(sentences, sentences_market_path)
print 'converting sentences finished ...'
def run_create_dictionary(self, article_market_path, dictionary_path, dict_set) :
loader = PickleMarket()
word2id, id2word = dict(), dict()
index = 0
for type in dict_set.split('#') :
path = article_market_path.replace(u'car', type)
articles = loader.load_market(path)
length = len(articles) - 1
for idx, article in enumerate(articles) :
for word in article['participle_title'] :
word = word.to_string()
if word not in word2id :
word2id[word] = index
id2word[index] = word
index += 1
for word in article['participle_content'] :
word = word.to_string()
if word not in word2id :
word2id[word] = index
id2word[index] = word
index += 1
if idx % 100 == 0 :
print 'finish rate is %.2f%%\r' % (100.0*idx/length),
print 'finish rate is %.2f%%\r' % (100.0*idx/length)
loader = PickleMarket()
loader.dump_market([word2id, id2word], dictionary_path)
def run_feature_select(
self, article_market_path, pos_path, punc_path, klword_path, feature_path, feature_market_path
):
loader = PickleMarket()
pos_selector = selector.PosExtractor(pos_path, w=15, combined=False)
token_selector = selector.TokenExtractor(punc_path)
word_selector = selector.WordExtractor(klword_path, weight=1)
articles = loader.load_market(article_market_path)
length = len(articles) - 1
for idx, article in enumerate(articles):
article["features"] = list()
article["features"].extend(pos_selector.extract_feature_windows(article["participle_content"]))
article["features"].extend(
token_selector.extract_feature(
article["title"], article["content"], article["participle_title"], article["participle_content"]
)
)
article["features"].extend(
word_selector.extract_feature(article["participle_title"], article["participle_content"])
)
print "finish rate is %.2f%%\r" % (100.0 * idx / length),
print "finish rate is %.2f%%\r" % (100.0 * idx / length)
featuresets = [pos_selector.names + token_selector.names + word_selector.names]
featuresets.extend([[article["id"]] + article["features"] + [article["label"]] for article in articles])
file_operator = TextFileOperator()
file_operator.writing(featuresets, feature_path)
loader.dump_market(featuresets, feature_market_path)
print "finish"
def run_tag_sentences(self, tag_tree_path, sentences_market_path, tags_martket_path, dict_market_path) :
file_operator = TextFileOperator()
loader = PickleMarket()
sentences = loader.load_market(sentences_market_path)
cmd_list = file_operator.reading(tag_tree_path)
tag_tree = TagTree(cmd_list)
robot = Robot()
tags, tags_show, untag_sentences = robot.tag_sentences(tag_tree, sentences[0:])
loader = JsonMarket()
loader.dump_market(tags, tags_martket_path)
loader.dump_market(tag_tree.dict_tuple, dict_market_path)
print '%.2f%% article >= 1 tags, number is, %d.' \
% (100.0 * len([tag for tag in tags_show if len(tag) >= 1]) / len(sentences)) \
% len([tag for tag in tags_show if len(tag) >= 1])
def run_test(self, tag_tree_path, sentences_market_path, tags_path, \
tags_martket_path, untag_sentence_path) :
file_operator = TextFileOperator()
loader = PickleMarket()
sentences = loader.load_market(sentences_market_path)
cmd_list = file_operator.reading(tag_tree_path)
tag_tree = TagTree(cmd_list)
robot = Robot()
tags, tags_show, untag_sentences = robot.tag_sentences(tag_tree, sentences[0:])
loader = JsonMarket()
self.write_tags(sentences, tags_show, tags_path)
loader.dump_market(tags, tags_martket_path)
file_operator.writing(untag_sentences, untag_sentence_path)
# loader.dump_market(untag_sentences, sentences_market_path)
# print '%.2f%% article >= 2 tags' % (100.0 * len([tag for tag in tags_show if len(tag) >= 1]) / len(sentences))
print '%.2f%% article >= 3 tags' % (100.0 * len([tag for tag in tags_show if len(tag) >= 1]) / len(sentences))
def run_feature_select(self, article_market_path, pos_path, punc_path, klword_path, \
feature_path, feature_market_path) :
loader = PickleMarket()
pos_selector = selector.PosExtractor(pos_path, w=5, combined=True)
token_selector = selector.TokenExtractor(punc_path)
word_selector = selector.WordExtractor(klword_path, weight=1)
articles = loader.load_market(article_market_path)
length = len(articles) - 1
for idx, article in enumerate(articles) :
article['features'] = list()
article['features'].extend(pos_selector.extract_feature(article['participle_content']))
article['features'].extend(token_selector.extract_feature(article['title'], article['content'], \
article['participle_title'], \
article['participle_content']))
article['features'].extend(word_selector.extract_feature(article['participle_title'], \
article['participle_content']))
print 'finish rate is %.2f%%\r' % (100.0*idx/length),
print 'finish rate is %.2f%%\r' % (100.0*idx/length)
featuresets = [[article['id']] + article['features'] + [article['label']] for article in articles]
file_operator = TextFileOperator()
file_operator.writing(featuresets, feature_path)
loader.dump_market(featuresets, feature_market_path)
print 'finish'
def run_convert_sentences(self, sentences_path, sentences_market_path) :
file_operator = TextFileOperator()
sentences = self.read_sentences(sentences_path)
loader = PickleMarket()
loader.dump_market(sentences, sentences_market_path)
def run_convert_article(self, article_path, article_market_path) :
articles = self.read_article(article_path)
# articles = self.read_participle(articles, participle_path)
loader = PickleMarket()
loader.dump_market(articles, article_market_path)
print 'finish.'
