Apriori和FPgrowth代码实例

最新推荐文章于 2024-04-27 19:53:25 发布

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最新推荐文章于 2024-04-27 19:53:25 发布 · 1.3k 阅读

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本文通过商场购物篮和电影数据集，实际演示了Apriori和FPgrowth算法的应用。利用Apriori进行购物篮分析，详细介绍了代码实现，并展示了使用Gephi进行的可视化效果，揭示了购买行为的关联规则。接着，文章使用FPgrowth对电影数据集进行分析，探讨不同电影推荐的关联性。

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本文分别使用商场购物篮数据集和电影数据集来分别针对Apriori和FPgrowth进行实际的运用和学习。
1.dataset：
https://github.com/ywchiu/python_for_data_science 中的Data文件夹下面有具体数据。按照本文的csv文件进行读取即可。
2.Apriori用于购物篮分析
2.1.代码

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#读取交易数据
df = pd.read_csv('F:/python_for_data_science-master/Data/Market_Basket.csv', header = None)
df.head()
df.count()#对各列的非空数值进行计数
#增添交易纪录
transactions = []
for i in range(0, 7501):
    transactions.append([str(df.values[i,j]) for j in range(0, 20)]) #第i次购买事物(即第i个客户)购买的商品清单
#transactions[1]
#使用Apriori 产生关联规则
from apyori import apriori
rules = apriori(transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2)#套用Apriori算法

# Visualising the results
results = list(rules)
results[0]
results[0].ordered_statistics[0]
#产生关联规则
for rec in results:
    left_hands = rec.ordered_statistics[0].items_base
    right_hands = rec.ordered_statistics[0].items_add
    l = ';'.join([item for item in left_hands])
    r = ';'.join([item for item in right_hands])
    print('{} => {}'.format(l,r))
#产生频繁交易集
# 'milk', 'spaghetti', 'avocado'
#'milk', 'spaghetti'
#'milk',  'avocado'
#'spaghetti', 'avocado'
import itertools
for ele in itertools.combinations(['milk', 'spaghetti', 'avocado'], 2):
    print(ele)
itemsets = []
for rec in results:    
    #print(rec.items)
    for ele in itertools.combinations(rec.items, 2):
        itemsets.append(ele)
#itemsets[0:10]
#通过Gephi来可视化网络,通过网络来关联。
import pandas
df2 = pandas.DataFrame(itemsets)
df2.columns = ['Source','Target']
df2['Type'] = 'undirected'
df2.to_csv('F:/python_for_data_science-master/result/transactions.csv')

light cream  ====>  chicken
mushroom cream sauce  ====>  escalope
pasta  ====>  escalope
fromage blanc  ====>  honey
herb & pepper  ====>  ground beef
tomato sauce  ====>  ground beef
light cream  ====>  olive oil
whole wheat pasta  ====>  olive oil
pasta  ====>  shrimp
avocado;spaghetti  ====>  milk
cake;milk  ====>  burgers
turkey;chocolate  ====>  burgers
turkey;milk  ====>  burgers
cake;frozen vegetables  ====>  tomatoes
ground beef;cereals  ====>  spaghetti
ground beef;chicken  ====>  milk
light cream;nan  ====>  chicken
chicken;milk  ====>  olive oil
chicken;spaghetti  ====>  olive oil
frozen vegetables;chocolate  ====>  shrimp
herb & pepper;chocolate  ====>  ground beef
soup;chocolate  ====>  milk
ground beef;cooking oil  ====>  spaghetti
eggs;ground beef  ====>  herb & pepper
eggs;red wine  ====>  spaghetti
mushroom cream sauce;nan  ====>  escalope
nan;pasta  ====>  escalope
ground beef;french fries  ====>  herb & pepper
fromage blanc;nan  ====>  honey
green tea;frozen vegetables  ====>  tomatoes
spaghetti;frozen vegetables  ====>  ground beef
frozen vegetables;milk  ====>  olive oil
soup;frozen vegetables  ====>  milk
milk;tomatoes  ====>  frozen vegetables
shrimp;miner