Python 实现关联规则分析Apriori算法
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# -*- coding:utf-8 -*- import sys reload(sys) sys.setdefaultencoding( " utf8 " ) def load_data_set(): data_set = [ [‘beer‘, ‘baby diapers‘, ‘shorts‘] , [‘baby diapers‘, ‘shorts‘] , [‘baby diapers‘, ‘milk‘] , [‘beer‘, ‘baby diapers‘, ‘shorts‘] , [‘beer‘, ‘milk‘] , [‘baby diapers‘, ‘milk‘] , [‘beer‘, ‘milk‘] , [‘beer‘, ‘baby diapers‘, ‘milk‘, ‘shorts‘] , [‘beer‘, ‘baby diapers‘, ‘milk‘] ] return data_set def create_C1(data_set): C1 = set() for t in data_set: for item in t: item_set = frozenset([item]) C1.add(item_set) return C1 def is_apriori(Ck_item, Lksub1): for item in Ck_item: sub_Ck = Ck_item - frozenset([item]) if sub_Ck notin Lksub1: return False return True def create_Ck(Lksub1, k): Ck = set() len_Lksub1 = len(Lksub1) list_Lksub1 = list(Lksub1) for i in range(len_Lksub1): for j in range(1, len_Lksub1): l1 = list(list_Lksub1[i]) l2 = list(list_Lksub1[j]) l1.sort() l2.sort() if l1[0:k-2] == l2[0:k-2]: Ck_item = list_Lksub1[i] | list_Lksub1[j] if is_apriori(Ck_item, Lksub1): Ck.add(Ck_item) return Ck def generate_Lk_by_Ck(data_set, Ck, min_support, support_data): Lk = set() item_count = {} for t in data_set: for item in Ck: if item.issubset(t): if item notin item_count: item_count[item] = 1 else: item_count[item] += 1 t_num = float(len(data_set)) for item in item_count: if (item_count[item] / t_num) >= min_support: Lk.add(item) support_data[item] = item_count[item] / t_num return Lk def generate_L(data_set, k, min_support): support_data = {} C1 = create_C1(data_set) L1 = generate_Lk_by_Ck(data_set, C1, min_support, support_data) Lksub1 = L1.copy() L = [] L.append(Lksub1) for i in range(2, k+1): Ci = create_Ck(Lksub1, i) Li = generate_Lk_by_Ck(data_set, Ci, min_support, support_data) Lksub1 = Li.copy() L.append(Lksub1) return L, support_data def generate_big_rules(L, support_data, min_conf): big_rule_list = [] sub_set_list = [] for i in range(0, len(L)): for freq_set in L[i]: for sub_set in sub_set_list: if sub_set.issubset(freq_set): conf = support_data[freq_set] / support_data[freq_set - sub_set] big_rule = (freq_set - sub_set, sub_set, conf) if conf >= min_conf and big_rule notin big_rule_list: big_rule_list.append(big_rule) sub_set_list.append(freq_set) return big_rule_list if__name__ == "__main__": """ Test """ data_set = load_data_set() L, support_data = generate_L(data_set, k=3, min_support=0.2) big_rules_list = generate_big_rules(L, support_data, min_conf=0.7) for Lk in L: print"="*50 print"frequent " + str(len(list(Lk)[0])) + "-itemsets\t\tsupport"print"="*50 for freq_set in Lk: print freq_set, support_data[freq_set] printprint"Big Rules"for item in big_rules_list: print item[0], "=>", item[1], "conf: ", item[2]
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原文:https://www.cnblogs.com/RHadoop-Hive/p/9438914.html
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