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AprioriMyTest.py
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AprioriMyTest.py
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def load_data_set():
"""
Load a sample data set (From Data Mining: Concepts and Techniques, 3th Edition)
Returns:
A data set: A list of transactions. Each transaction contains several items.
"""
print("-" * 5, "load_data_set", "-" * 5)
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']]
print("data_set:",data_set)
return data_set
def create_C1(data_set):
"""
产生所有的频繁1-项集
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
"""
print("-" * 5, "create_C1", "-" * 5)
C1 = set() #用于存储1-项集
for t in data_set:
for item in t:
#rozenset() 返回一个冻结的集合,冻结后集合不能再添加或删除任何元素。
item_set = frozenset([item])
C1.add(item_set)
#print("C1:",C1)
return C1
def create_Ck(Lksub1, k):
"""
创建所有频繁项集
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.
"""
print("-" * 5, "create_Ck", "-" * 5)
Ck = set()
len_Lksub1 = len(Lksub1)
list_Lksub1 = list(Lksub1)
for i in range(len_Lksub1):
for j in range(1, len_Lksub1):
#print("-"*10)
l1 = list(list_Lksub1[i])
l2 = list(list_Lksub1[j])
l1.sort()
#print("l1:",l1)
#print("l1[0:",k-2,"]:",l1[0:k-2])
l2.sort()
#print("l2:", l2)
if l1[0:k-2] == l2[0:k-2]:
Ck_item = list_Lksub1[i] | list_Lksub1[j]
#print("Ck_item:",Ck_item)
# pruning
if is_apriori(Ck_item, Lksub1):
Ck.add(Ck_item)
return Ck
def is_apriori(Ck_item, Lksub1):
"""
用于判断待测项集的子集是否为频繁项集,只有子集是频繁子集,待测项集才有可能是频繁项集
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.
"""
#print("-" * 5, "is_apriori", "-" * 5)
for item in Ck_item:
#print("frozenset([item]:",frozenset([item]))
sub_Ck = Ck_item - frozenset([item]) #通过做差,判断该项集的所有子集是否都为频繁子集
#print("sub_Ck:", sub_Ck)
if sub_Ck not in Lksub1:
return False
return True
def 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.
"""
print("-" * 5, "generate_Lk_by_Ck", "-" * 5)
Lk = set() #存储所有k-频繁项集
item_count = {} #统计每一个频繁子集出现的次数
##频繁项集计数##
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
#print("item_count:",item_count)
t_num = float(len(data_set))
#print("t_num:",t_num)
##计算每个频繁项集的支持度##
for item in item_count:
if (item_count[item] / t_num) >= min_support:
Lk.add(item)
support_data[item] = item_count[item] / t_num
#print("support_data:", support_data)
#print("Lk:", Lk)
return Lk
def 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.
"""
print("-"*5,"generate_L","-"*5)
support_data = {}
##计算频繁1-项集##
C1 = create_C1(data_set)
L1 = generate_Lk_by_Ck(data_set, C1, min_support, support_data)
Lksub1 = L1.copy()
#print("Lksub1:",Lksub1)
##计算所有频繁项集##
L = [] #存储所有频繁项集
L.append(Lksub1)
#print("L:",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)
#print("-" * 10)
#print("L:",L)
#print("-" * 10)
#print("support_data:",support_data)
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.
"""
print("-" * 5, "generate_big_rules", "-" * 5)
print("L:",L)
print("support_data:",support_data)
big_rule_list = []
sub_set_list = []
for i in range(0, len(L)):
print("L[",i,"]:",L[i])
for freq_set in L[i]:
print("freq_set:",freq_set)
for sub_set in sub_set_list:
if sub_set.issubset(freq_set):
conf = support_data[freq_set] / support_data[freq_set - sub_set] #求条件概率,也就是置信度
big_rule = (freq_set - sub_set, sub_set, conf)
print("big_rule:",big_rule)
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__":
"""
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)