Python 實現關聯規則分析Apriori演算法

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# -*- coding:utf-8 -*-import sysreload(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_setdef create_C1(data_set):    C1 = set()    for t in data_set:        for item in t:            item_set = frozenset([item])            C1.add(item_set)    return C1def is_apriori(Ck_item, Lksub1):    for item in Ck_item:        sub_Ck = Ck_item - frozenset([item])        if sub_Ck not in Lksub1:            return False    return Truedef 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 Ckdef 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 not in 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 Lkdef 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_datadef 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 not in big_rule_list:                        big_rule_list.append(big_rule)            sub_set_list.append(freq_set)    return big_rule_listif __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]    print    print "Big Rules"    for item in big_rules_list:        print item[0], "=>", item[1], "conf: ", item[2]

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Python 實現關聯規則分析Apriori演算法