如何理解关联规则apriori算法

def local_data(file_path):    import pandas as pd

    dt = pd.read_excel(file_path)
    data = dt['con']
    locdata = []    for i in data:
        locdata.append(str(i).split(","))   # print(locdata)  # change to [[1,2,3],[1,2,3]]
    length = []    for i in locdata:
        length.append(len(i))  # 计算长度并存储
   # print(length)
    ki = length[length.index(max(length))]   # print(length[length.index(max(length))])  # length.index(max(length)读取最大值的位置，然后再定位取出最大值

    return locdata,kidef create_C1(data_set):    """
    Create frequent candidate 1-itemset C1 by scaning data set.
    Args:
        data_set: A list of transactions. Each transaction contains several items.
    Returns:
        C1: A set which contains all frequent candidate 1-itemsets    """
    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):    """
    Judge whether a frequent candidate k-itemset satisfy Apriori property.
    Args:
        Ck_item: a frequent candidate k-itemset in Ck which contains all frequent
                 candidate k-itemsets.
        Lksub1: Lk-1, a set which contains all frequent candidate (k-1)-itemsets.
    Returns:
        True: satisfying Apriori property.
        False: Not satisfying Apriori property.    """
    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):    """
    Create Ck, a set which contains all all frequent candidate k-itemsets
    by Lk-1's own connection operation.
    Args:
        Lksub1: Lk-1, a set which contains all frequent candidate (k-1)-itemsets.
        k: the item number of a frequent itemset.
    Return:
        Ck: a set which contains all all frequent candidate k-itemsets.    """
    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]                # pruning
                if is_apriori(Ck_item, Lksub1):
                    Ck.add(Ck_item)    return Ckdef generate_Lk_by_Ck(data_set, Ck, min_support, support_data):    """
    Generate Lk by executing a delete policy from Ck.
    Args:
        data_set: A list of transactions. Each transaction contains several items.
        Ck: A set which contains all all frequent candidate k-itemsets.
        min_support: The minimum support.
        support_data: A dictionary. The key is frequent itemset and the value is support.
    Returns:
        Lk: A set which contains all all frequent k-itemsets.    """
    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):    """
    Generate all frequent itemsets.
    Args:
        data_set: A list of transactions. Each transaction contains several items.
        k: Maximum number of items for all frequent itemsets.
        min_support: The minimum support.
    Returns:
        L: The list of Lk.
        support_data: A dictionary. The key is frequent itemset and the value is 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):    """
    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_listif __name__ == "__main__":    """
    Test    """
    file_path = "test_aa.xlsx"
  
    data_set,k = local_data(file_path)
    L, support_data = generate_L(data_set, k, min_support=0.2)
    big_rules_list = generate_big_rules(L, support_data, min_conf=0.4)    print(L)    for Lk in L:        if len(list(Lk)) == 0:            break
        print("="*50)        print("frequent " + str(len(list(Lk)[0])) + "-itemsetsttsupport")        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])

name    con
T1     2,3,5T2     1,2,4T3     3,5T5     2,3,4T6     2,3,5T7     1,2,4T8     3,5T9     2,3,4T10    1,2,3,4,5