import apriori
def load_termlist(termsetfile=None):
if termsetfile != None:
fr2 = open(termsetfile, 'rb')
termlist = pickle.load(fr2)
print('sub_termlist is:')
for i in range(10):
print(termlist[i])
print('the number of lines :', len(termlist))
return termsetfile
if __name__ == '__main__':
min_support = 0.3
sub_termsetfile = "/home/freestyle4568/lesson/Clothes-match-txt/sub_user_termset.pickle"
termlist = load_termlist(sub_termsetfile)
termfreq_list, support_data_term = fp_growth.fptree(
termlist[0:10000], int(min_support * 10000))
print('termfreq_list is: ')
for i in range(len(termfreq_list)):
print(termfreq_list[i])
termfreqfile = '/home/freestyle4568/lesson/Clothes-match-txt/termfreq_10000.pickle'
fr_termfreq = open(termfreqfile, 'wb')
pickle.dump(termfreq_list, fr_termfreq)
fr_termfreq.close()
import fp_growth
import apriori
def load_termlist(termsetfile=None):
if termsetfile != None:
fr2 = open(termsetfile, "rb")
termlist = pickle.load(fr2)
print("sub_termlist is:")
for i in range(10):
print(termlist[i])
print("the number of lines :", len(termlist))
return termsetfile
if __name__ == "__main__":
min_support = 0.3
sub_termsetfile = "/home/freestyle4568/lesson/Clothes-match-txt/sub_user_termset.pickle"
termlist = load_termlist(sub_termsetfile)
termfreq_list, support_data_term = fp_growth.fptree(termlist[0:10000], int(min_support * 10000))
print("termfreq_list is: ")
for i in range(len(termfreq_list)):
print(termfreq_list[i])
termfreqfile = "/home/freestyle4568/lesson/Clothes-match-txt/termfreq_10000.pickle"
fr_termfreq = open(termfreqfile, "wb")
pickle.dump(termfreq_list, fr_termfreq)
fr_termfreq.close()
sub_catlist.append(catlist[i])
#===========================================================================
# 输出sub_catlist 10行
#===========================================================================
#=======================================================================
# print('sub_catlist is: ')
# for i in range(10):
# print(sub_catlist[i])
# print('sub_catlist\'s length is : ',len(sub_catlist))
# print('-----------------------------------------')
#=======================================================================
#===========================================================================
# 输出sub_catlist中的频繁项，运用fp_growth算法
#===========================================================================
catfreq_list, support_data_cat = fp_growth.fptree(sub_catlist, int(min_support*len(sub_catlist)))
#=======================================================================
# print('catfreq_list is: ')
# for i in range(len(catfreq_list)):
# print(catfreq_list[i])
# print('-----------------------------------------')
#=======================================================================
#===========================================================================
# for i in support_data.items():
# print(i)
#===========================================================================
big_rule_list = apriori.generate_rules(catfreq_list[0:2], support_data_cat, min_confidence)
#print('rule list follows: ')
cat_rule_list = []
for rule in big_rule_list:
if test_one_category in rule[0] and len(rule[0]) == 1:
if testcategory[0] in catlist[i]:
sub_catlist.append(catlist[i])
# ===========================================================================
# 输出sub_catlist 10行
# ===========================================================================
# ===========================================================================
# for i in range(10):
# print(sub_catlist[i])
print("sub_catlist's length is : ", len(sub_catlist))
# ===========================================================================
# ===========================================================================
# 输出sub_catlist中的频繁项，运用fp_growth算法
# ===========================================================================
catfreq_list, support_data = fp_growth.fptree(sub_catlist, 100000)
print("catfreq_list is: ")
for i in range(len(catfreq_list)):
print(catfreq_list[i])
# ===========================================================================
# for i in support_data.items():
# print(i)
# ===========================================================================
big_rule_list = apriori.generate_rules(catfreq_list, support_data, 0.5)
print("rule list follows: ")
for rule in big_rule_list:
if test_one in rule[0] and len(rule[0]) == 1:
print(rule)
