def combine(category_id, category_list, category_wrapper_dict):
print 'combing'
tag_set = set([])
editor_tag_set = getCategoryEditorTag(category_id)
outfile = open('combine/' + str(category_id) + '.txt', 'wb')
outfile2 = open('final/' + str(category_id) + '.txt', 'wb')
for i in range(len(category_list)):
for j in range(len(category_list)):
if i != j:
category_i = category_list[i]
category_j = category_list[j]
if len(category_i) < len(category_j):
if text_process.isSubset(category_i, category_j) or len(
set(category_i)
& set(category_j)) == len(category_i):
if category_i in category_wrapper_dict.keys():
category_wrapper_dict[category_i].append(
category_j)
for category in category_wrapper_dict.keys():
tag_set.add(category)
for wrapper in category_wrapper_dict[category]:
tag_set.add(wrapper)
outfile.write(category + '@' +
','.join(category_wrapper_dict[category]) + '\r\n')
tag_set = tag_set | editor_tag_set
for tag in tag_set:
outfile2.write(tag + '\r\n')
def inclusionRelation(category_id, category_set):
print 'extracting inclusion relation feature'
category_feature_dict = {}
for category in category_set:
category_feature_dict.setdefault(category, 0)
outfile = open('internal/' + str(category_id) + '.csv', 'wb')
for i in range(len(category_feature_dict.keys())):
for j in range(len(category_feature_dict.keys())):
if i != j:
word_i = category_feature_dict.keys()[i]
word_j = category_feature_dict.keys()[j]
if len(word_i) < len(word_j):
if text_process.isSubset(word_i,
word_j) and len(word_i) != 1:
category_feature_dict[word_i] += 1
inclusion_max = max(category_feature_dict.values())
print 'sorting'
sorted_list = sorted(category_feature_dict.items(),
key=lambda p: p[1],
reverse=True)
print 'writing'
for val in sorted_list:
inclusion_normalize = 1.0 * val[1] / inclusion_max
outfile.write(val[0] + ',' + str(inclusion_normalize) + '\r\n')
def getMainCategoryKeywords(main_category_list,category_info_dict): main_category_keywords = [] for category in category_info_dict.keys(): for relevant_category in main_category_list: if len(category) > len(relevant_category): if text_process.isSubset(relevant_category,category): main_category_keywords.append(category) return main_category_keywords
