小白向Apriori算法Python实现

def load_data_set():
    """
   加载一个示例集合
    Returns: 
        A data set: 一个购物列表，每个项中有不同的商品item
    """
    data_set = [[‘l1‘, ‘l2‘, ‘l5‘], [‘l2‘, ‘l4‘], [‘l2‘, ‘l3‘],
            [‘l1‘, ‘l2‘, ‘l4‘], [‘l1‘, ‘l3‘], [‘l2‘, ‘l3‘],
            [‘l1‘, ‘l3‘], [‘l1‘, ‘l2‘, ‘l3‘, ‘l5‘], [‘l1‘, ‘l2‘, ‘l3‘]]
    return data_set


def create_C1(data_set):
    """
    扫描数据集，创建元素个数为1的项集C1，作为频繁项集的候选项集C1
    """
    C1 = set()
    for t in data_set:
        for item in t:
            item_set = frozenset([item])
            """
            由于要使用字典（support_data）记录项集的支持度，需要用项集作为key，
            而可变集合无法作为字典的key，因此在合适时机应将项集转为固定集合frozenset。
            或者另一种用法：
            for item in t:
                C1.append([item])
            C1.sort()
            return map(frozenset,C1)
            """
            C1.add(item_set)
    return C1


def is_apriori(Ck_item, Lksub1):
    """
    进行剪枝，如果满足APriori，即满足支持度，返回True
    否则返回False，删除
    """
    for item in Ck_item:
        sub_Ck = Ck_item - frozenset([item])
        if sub_Ck not in Lksub1:
            return False
    return True


def create_Ck(Lksub1, k):
    """
    由Lk-1生成Ck
    具体实现方法是在Lk-1中，对所有两个项集之间只有最后一项item不同的项集的交集
    """
    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]           #求并集
                # 剪枝
                if is_apriori(Ck_item, Lksub1):
                    Ck.add(Ck_item)
    return Ck


def generate_Lk_by_Ck(data_set, Ck, min_support, support_data):
    """
    由候选频繁k项集Ck生成频繁k项集Lk
    主要内容是对Ck中的每个项集计算支持度，去掉不满足最低支持度的项集
    返回Lk，记录support_data
    """
    Lk = set()
    item_count = {}
    for t in data_set:                              #扫描所有商品，计算候选频繁项集C中项集的支持度，t为订单
        for item in Ck:                             #item为C中的项集
            if item.issubset(t):                    #如果C中的项集是t订单的子集
                if item not in item_count:          #如果item_count中还没有这个项集，计数为1
                    item_count[item] = 1
                else:                               #如果item_count中已经有了这个项集，计数加1
                    item_count[item] += 1
    t_num = float(len(data_set))                    #t_num，订单总数
    for item in item_count:                         #item_count中已经有了所有的候选项集，计算支持度
        if (item_count[item] / t_num) >= min_support:
            Lk.add(item)                            #满足最小支持度的项集add进频繁项集Lk中
            support_data[item] = item_count[item] / t_num       #记录支持度，返回Lk
    return Lk


def generate_L(data_set, k, min_support):
    """
    生成频繁集Lk，通过调用generate_Lk_by_Ck
    从C1开始共进行k轮迭代，将每次生成的Lk都append到L中，同时记录支持度support_data
    """
    support_data = {}
    C1 = create_C1(data_set)            #生成C1
    L1 = generate_Lk_by_Ck(data_set, C1, min_support, support_data)     #由C1生成L1，调用generate_Lk_by_Ck函数
    Lksub1 = L1.copy()
    L = []
    L.append(Lksub1)
    for i in range(2, k+1):                                             #由k已知进行重复迭代
        Ci = create_Ck(Lksub1, i)                                       #由Lk生成Lk+1，调用create_Ck函数
        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):
    """
    Generate big rules from frequent itemsets.
    Args:
        L: The list of Lk.
        support_data: A dictionary. The key is frequent itemset and the value is support.
        min_conf: Minimal confidence.
    Returns:
        big_rule_list: A list which contains all big rules. Each big rule is represented
                       as a 3-tuple.
    """
    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:
                        # print freq_set-sub_set, " => ", sub_set, "conf: ", conf
                        big_rule_list.append(big_rule)
            sub_set_list.append(freq_set)
    return big_rule_list


if __name__ == "__main__":                  #主程序入口
    """
    Test
    """
    data_set = load_data_set()              #加载测试数据集
    L, support_data = generate_L(data_set, k=3, min_support=0.2)            #数据集中最大商品数为3，给定默认最低支持度为0.2，调用generate_L函数
    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频繁k项集和支持度
    print
    print ("Big Rules")
    for item in big_rules_list:
        print (item[0], "=>", item[1], "conf: ", item[2])