import numpy as np

import pandas as pd

方法一：

def generate_C1(data_set):

c1 = set()

for items in data_set:

for item in items:

item_set = frozenset([item])

c1.add(item_set)

return c1

#计算Ck在数据集D中的支持度，并返回支持度大于minSupport的数据集

def get_supports(data_set, ck, min_support,supports):

freq_set = set()

item_count = {}

supports = {}

for data in data_set:

for item in ck:

if item.issubset(data):

if item not in item_count:

item_count[item] = 1

else:

item_count[item] += 1

n= float(len(data_set))

for item in item_count:

if (item_count[item] / n) >= min_support:

freq_set.add(item)

supports[item] = item_count[item] / n

return freq_set

#剪枝

def get_new_set(d, k):

new_set = set()

n = len(d)

freqset_list = list(d)

for i in range(n):

for j in range(i + 1, n):

L1= list(freqset_list[i])

L2 = list(freqset_list[j])

L1.sort()

L2.sort()

若两个集合的前k-2个项相同时,则将两个集合合并

if L1[0:k - 2] == L2[0:k - 2]:

freq_item = freqset_list[i] | freqset_list[j]

new_set.add(freq_item)

return new_set

def apriori(data_set, min_support, k):

freq_sets = []

supports = {}

c1=generate_C1(data_set)

L1= get_supports(data_set, c1, min_support,supports)

Lksub1 = L1.copy()

freq_sets.append(Lksub1)

for i in range(2,k+1):

ci=get_new_set(Lksub1,i)

Li=get_supports(data_set, ci, min_support,supports)

Lksub1 = Li.copy()

freq_sets.append(Lksub1)

return freq_sets,supports

def find_rules(freq_sets, supports, min_conf):

rules = []

n = len(freq_sets)

for i in range(n - 1):

for freq_set in freq_sets[i]:#前项

for sub_set in freq_sets[i + 1]:#后项

if freq_set.issubset(sub_set):#是否频繁

support = supports[sub_set]

conf = supports[sub_set] / supports[freq_set]#定义置信度

rule = (freq_set, sub_set - freq_set, support, conf)

if conf >= min_conf:

print([i for i in freq_set], "-->", [i for i in sub_set - freq_set], 'support:', support, 'conf:', conf)

rules.append(rule)

return rules

if name == 'main':

inputfile = 'G:/untitled/venv/tmp/apriori_rules2.csv'

data = pd.read_csv(inputfile)

data = pd.DataFrame(data)

data = data.drop_duplicates()

data_set = []

groups = data.groupby(by='日期')

for group in groups:

if len(group[1]) >= 2:

data_set.append(group[1]['股票代码'].tolist())

L, support = apriori(data_set, k=3, min_support=0.4)

find_rules = find_rules(L, support, min_conf=0.6)

print('关联规则：\n', find_rules)

outputfile = 'G:/untitled/venv/tmp/apriori_rules3.csv'

df=data = pd.DataFrame( find_rules)

df.to_csv(outputfile, index=False,header=None)