Python机器学习&数据分析-关联规则
Python机器学习&数据分析-关联规则
机器学习课程的笔记整理
一、关联规则前置知识
关联规则
- 在美国，一些年轻的父亲下班后经常要到超市去买婴儿尿布，超市也因此发现了一个规律，在购买婴儿尿布的年轻父亲们中，有30%～40%的人同时要买一些啤酒。超市随后调整了货架的摆放，把尿布和啤酒放在一起，明显增加了销售额。
-
若两个或多个变量的取值之间存在某种规律性，就称为关联
-
关联规则是寻找在同一个事件中出现的不同项的相关性，比如在一次购买活动中所买不同商品的相关性。
-
“在购买计算机的顾客中，有30％的人也同时购买了打印机”
支持度（support）：一个项集或者规则在所有事务中出现的频率，σ(X):表示项集X的支持度计数
- 项集X的支持度：s(X)=σ(X)/N
- 规则X==>Y表示物品集X对物品集Y的支持度，也就是物品集X和物品集Y同时出现的概率
- 某天共有100个顾客到商场购买物品，其中有30个顾客同时购买了啤酒和尿布，那么上述的关联规则的支持度就是30％
置信度（confidence）：确定Y在包含X的事务中出现的频繁程度。c(X → Y) = σ(X∪Y)/σ(X)
- p（Y│X）＝p（XY）/p(X)。
- 置信度反应了关联规则的可信度—购买了项目集X中的商品的顾客同时也购买了Y中商品的可能性有多大
- 购买薯片的顾客中有50％的人购买了可乐,则置信度为50％
提升度（lift）：物品集A的出现对物品集B的出现概率发生了多大的变化
- lift（A==>B）=confidence（A==>B）/support(B)=p(B|A)/p(B)
- 现在有** 1000 ** 个消费者，有** 500** 人购买了茶叶，其中有** 450人同时** 购买了咖啡，另** 50人** 没有。由于** confidence(茶叶=>咖啡)=450/500=90%** ，由此可能会认为喜欢喝茶的人往往喜欢喝咖啡。但如果另外没有购买茶叶的** 500人** ，其中同样有** 450人** 购买了咖啡，同样是很高的** 置信度90%** ,由此，得到不爱喝茶的也爱喝咖啡。这样看来，其实是否购买咖啡，与有没有购买茶叶并没有关联，两者是相互独立的，其** 提升度90%/[(450+450)/1000]=1** 。
由此可见，lift正是弥补了confidence的这一缺陷，if lift=1,X与Y独立，X对Y出现的可能性没有提升作用，其值越大(lift>1),则表明X对Y的提升程度越大，也表明关联性越强。
二、自定义购物数据集的例子
在anaconda命令行下通过
conda install -c conda-forge mlxtend
import pandas as pd
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
自定义一份购物数据集
data = {"ID":[1,2,3,4,5,6],
"Onion":[1,0,0,1,1,1],
"Potato":[1,1,0,1,1,1],
"Burger":[1,1,0,0,1,1],
"Milk":[0,1,1,1,0,1],
"Beer":[0,0,1,0,1,0]}
df = pd.DataFrame(data)
df = df[['ID', 'Onion', 'Potato', 'Burger', 'Milk', 'Beer' ]]
df
| ID | Onion | Potato | Burger | Milk | Beer | |
|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | 1 | 0 | 0 |
| 1 | 2 | 0 | 1 | 1 | 1 | 0 |
| 2 | 3 | 0 | 0 | 0 | 1 | 1 |
| 3 | 4 | 1 | 1 | 0 | 1 | 0 |
| 4 | 5 | 1 | 1 | 1 | 0 | 1 |
| 5 | 6 | 1 | 1 | 1 | 1 | 0 |
设置支持度 (support) 来选择频繁项集.
-
选择最小支持度为50%
-
apriori(df, min_support=0.5, use_colnames=True)
frequent_itemsets = apriori(df[['Onion', 'Potato', 'Burger', 'Milk', 'Beer' ]],min_support=0.5, use_colnames=True)
frequent_itemsets
| support | itemsets | |
|---|---|---|
| 0 | 0.666667 | (Onion) |
| 1 | 0.833333 | (Potato) |
| 2 | 0.666667 | (Burger) |
| 3 | 0.666667 | (Milk) |
| 4 | 0.666667 | (Onion, Potato) |
| 5 | 0.500000 | (Onion, Burger) |
| 6 | 0.666667 | (Potato, Burger) |
| 7 | 0.500000 | (Milk, Potato) |
| 8 | 0.500000 | (Onion, Potato, Burger) |
计算规则
association_rules(df, metric='lift', min_threshold=1)- 可以指定不同的衡量标准与最小阈值
rules = association_rules(frequent_itemsets,metric="lift",min_threshold=1)
rules
| antecedents | consequents | antecedent support | consequent support | support | confidence | lift | leverage | conviction | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | (Onion) | (Potato) | 0.666667 | 0.833333 | 0.666667 | 1.00 | 1.200 | 0.111111 | inf |
| 1 | (Potato) | (Onion) | 0.833333 | 0.666667 | 0.666667 | 0.80 | 1.200 | 0.111111 | 1.666667 |
| 2 | (Onion) | (Burger) | 0.666667 | 0.666667 | 0.500000 | 0.75 | 1.125 | 0.055556 | 1.333333 |
| 3 | (Burger) | (Onion) | 0.666667 | 0.666667 | 0.500000 | 0.75 | 1.125 | 0.055556 | 1.333333 |
| 4 | (Potato) | (Burger) | 0.833333 | 0.666667 | 0.666667 | 0.80 | 1.200 | 0.111111 | 1.666667 |
| 5 | (Burger) | (Potato) | 0.666667 | 0.833333 | 0.666667 | 1.00 | 1.200 | 0.111111 | inf |
| 6 | (Onion, Potato) | (Burger) | 0.666667 | 0.666667 | 0.500000 | 0.75 | 1.125 | 0.055556 | 1.333333 |
| 7 | (Onion, Burger) | (Potato) | 0.500000 | 0.833333 | 0.500000 | 1.00 | 1.200 | 0.083333 | inf |
| 8 | (Potato, Burger) | (Onion) | 0.666667 | 0.666667 | 0.500000 | 0.75 | 1.125 | 0.055556 | 1.333333 |
| 9 | (Onion) | (Potato, Burger) | 0.666667 | 0.666667 | 0.500000 | 0.75 | 1.125 | 0.055556 | 1.333333 |
| 10 | (Potato) | (Onion, Burger) | 0.833333 | 0.500000 | 0.500000 | 0.60 | 1.200 | 0.083333 | 1.250000 |
| 11 | (Burger) | (Onion, Potato) | 0.666667 | 0.666667 | 0.500000 | 0.75 | 1.125 | 0.055556 | 1.333333 |
rules[ ( rules["lift"] > 1.125) & (rules["confidence"] > 0.8) ]
| antecedents | consequents | antecedent support | consequent support | support | confidence | lift | leverage | conviction | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | (Onion) | (Potato) | 0.666667 | 0.833333 | 0.666667 | 1.0 | 1.2 | 0.111111 | inf |
| 5 | (Burger) | (Potato) | 0.666667 | 0.833333 | 0.666667 | 1.0 | 1.2 | 0.111111 | inf |
| 7 | (Onion, Burger) | (Potato) | 0.500000 | 0.833333 | 0.500000 | 1.0 | 1.2 | 0.083333 | inf |
这几条结果就比较有价值了：
- （洋葱和马铃薯）（汉堡和马铃薯）可以搭配着来卖
- 如果洋葱和汉堡都在购物篮中, 顾客买马铃薯的可能性也比较高，如果他篮子里面没有，可以推荐一下.
三、模拟实际购物的例子
retail_shopping_basket = {'ID':[1,2,3,4,5,6],
'Basket':[['Beer', 'Diaper', 'Pretzels', 'Chips', 'Aspirin'],
['Diaper', 'Beer', 'Chips', 'Lotion', 'Juice', 'BabyFood', 'Milk'],
['Soda', 'Chips', 'Milk'],
['Soup', 'Beer', 'Diaper', 'Milk', 'IceCream'],
['Soda', 'Coffee', 'Milk', 'Bread'],
['Beer', 'Chips']
]
}
retail = pd.DataFrame(retail_shopping_basket)
retail = retail[["ID","Basket"]]
pd.options.display.max_colwidth=100
retail
| ID | Basket | |
|---|---|---|
| 0 | 1 | [Beer, Diaper, Pretzels, Chips, Aspirin] |
| 1 | 2 | [Diaper, Beer, Chips, Lotion, Juice, BabyFood, Milk] |
| 2 | 3 | [Soda, Chips, Milk] |
| 3 | 4 | [Soup, Beer, Diaper, Milk, IceCream] |
| 4 | 5 | [Soda, Coffee, Milk, Bread] |
| 5 | 6 | [Beer, Chips] |
注意:
数据集中都是字符串组成的，需要转换成数值编码
retail_id = retail.drop("Basket",1)
retail_id
| ID | |
|---|---|
| 0 | 1 |
| 1 | 2 |
| 2 | 3 |
| 3 | 4 |
| 4 | 5 |
| 5 | 6 |
retail_Basket = retail.Basket.str.join(",")
retail_Basket
0 Beer,Diaper,Pretzels,Chips,Aspirin
1 Diaper,Beer,Chips,Lotion,Juice,BabyFood,Milk
2 Soda,Chips,Milk
3 Soup,Beer,Diaper,Milk,IceCream
4 Soda,Coffee,Milk,Bread
5 Beer,Chips
Name: Basket, dtype: object
retail_Basket = retail_Basket.str.get_dummies(",")
retail_Basket
| Aspirin | BabyFood | Beer | Bread | Chips | Coffee | Diaper | IceCream | Juice | Lotion | Milk | Pretzels | Soda | Soup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 4 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 5 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
retail = retail_id.join(retail_Basket)
retail
| ID | Aspirin | BabyFood | Beer | Bread | Chips | Coffee | Diaper | IceCream | Juice | Lotion | Milk | Pretzels | Soda | Soup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 2 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 |
| 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 3 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 4 | 5 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 5 | 6 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
frequent_items_2 = apriori(retail.drop("ID",1),use_colnames=True)
frequent_items_2
| support | itemsets | |
|---|---|---|
| 0 | 0.666667 | (Beer) |
| 1 | 0.666667 | (Chips) |
| 2 | 0.500000 | (Diaper) |
| 3 | 0.666667 | (Milk) |
| 4 | 0.500000 | (Chips, Beer) |
| 5 | 0.500000 | (Diaper, Beer) |
如果光考虑支持度support(X>Y), [Beer, Chips] 和 [Beer, Diaper] 都是很频繁的，哪一种组合更相关呢？
association_rules(frequent_items_2,metric="lift")
| antecedents | consequents | antecedent support | consequent support | support | confidence | lift | leverage | conviction | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | (Chips) | (Beer) | 0.666667 | 0.666667 | 0.5 | 0.75 | 1.125 | 0.055556 | 1.333333 |
| 1 | (Beer) | (Chips) | 0.666667 | 0.666667 | 0.5 | 0.75 | 1.125 | 0.055556 | 1.333333 |
| 2 | (Diaper) | (Beer) | 0.500000 | 0.666667 | 0.5 | 1.00 | 1.500 | 0.166667 | inf |
| 3 | (Beer) | (Diaper) | 0.666667 | 0.500000 | 0.5 | 0.75 | 1.500 | 0.166667 | 2.000000 |
显然{Diaper, Beer}更相关一些
四、电影题材关联的例子
数据集来源： MovieLens (small)
movies = pd.read_csv("ml-latest-small/movies.csv")
movies.head(10)
| movieId | title | genres | |
|---|---|---|---|
| 0 | 1 | Toy Story (1995) | Adventure|Animation|Children|Comedy|Fantasy |
| 1 | 2 | Jumanji (1995) | Adventure|Children|Fantasy |
| 2 | 3 | Grumpier Old Men (1995) | Comedy|Romance |
| 3 | 4 | Waiting to Exhale (1995) | Comedy|Drama|Romance |
| 4 | 5 | Father of the Bride Part II (1995) | Comedy |
| 5 | 6 | Heat (1995) | Action|Crime|Thriller |
| 6 | 7 | Sabrina (1995) | Comedy|Romance |
| 7 | 8 | Tom and Huck (1995) | Adventure|Children |
| 8 | 9 | Sudden Death (1995) | Action |
| 9 | 10 | GoldenEye (1995) | Action|Adventure|Thriller |
数据中包括电影名字与电影类型的标签，第一步还是先转换成one-hot格式
movies_one = movies.drop("genres",1).join(movies.genres.str.get_dummies())
pd.options.display.max_columns=100
movies_one.head()
| movieId | title | (no genres listed) | Action | Adventure | Animation | Children | Comedy | Crime | Documentary | Drama | Fantasy | Film-Noir | Horror | IMAX | Musical | Mystery | Romance | Sci-Fi | Thriller | War | Western | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Toy Story (1995) | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 2 | Jumanji (1995) | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 3 | Grumpier Old Men (1995) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | Waiting to Exhale (1995) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | Father of the Bride Part II (1995) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
movies_one.shape
(9125, 22)
数据集包括9125部电影，一共有20种不同类型。
movies_one.set_index(["movieId","title"],inplace=True)
movies_one.head()
| (no genres listed) | Action | Adventure | Animation | Children | Comedy | Crime | Documentary | Drama | Fantasy | Film-Noir | Horror | IMAX | Musical | Mystery | Romance | Sci-Fi | Thriller | War | Western | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| movieId | title | ||||||||||||||||||||
| 1 | Toy Story (1995) | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | Jumanji (1995) | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | Grumpier Old Men (1995) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4 | Waiting to Exhale (1995) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 5 | Father of the Bride Part II (1995) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
frequent_itemsets_movies = apriori(movies_one,use_colnames=True,min_support=0.025)
frequent_itemsets_movies
| support | itemsets | |
|---|---|---|
| 0 | 0.169315 | (Action) |
| 1 | 0.122411 | (Adventure) |
| 2 | 0.048986 | (Animation) |
| 3 | 0.063890 | (Children) |
| 4 | 0.363288 | (Comedy) |
| 5 | 0.120548 | (Crime) |
| 6 | 0.054247 | (Documentary) |
| 7 | 0.478356 | (Drama) |
| 8 | 0.071671 | (Fantasy) |
| 9 | 0.096110 | (Horror) |
| 10 | 0.043178 | (Musical) |
| 11 | 0.059507 | (Mystery) |
| 12 | 0.169315 | (Romance) |
| 13 | 0.086795 | (Sci-Fi) |
| 14 | 0.189479 | (Thriller) |
| 15 | 0.040219 | (War) |
| 16 | 0.058301 | (Action, Adventure) |
| 17 | 0.037589 | (Comedy, Action) |
| 18 | 0.038247 | (Action, Crime) |
| 19 | 0.051178 | (Action, Drama) |
| 20 | 0.040986 | (Action, Sci-Fi) |
| 21 | 0.062904 | (Thriller, Action) |
| 22 | 0.029260 | (Children, Adventure) |
| 23 | 0.036712 | (Comedy, Adventure) |
| 24 | 0.032438 | (Drama, Adventure) |
| 25 | 0.030685 | (Fantasy, Adventure) |
| 26 | 0.027726 | (Sci-Fi, Adventure) |
| 27 | 0.027068 | (Children, Animation) |
| 28 | 0.032877 | (Children, Comedy) |
| 29 | 0.032438 | (Comedy, Crime) |
| 30 | 0.104000 | (Comedy, Drama) |
| 31 | 0.026959 | (Fantasy, Comedy) |
| 32 | 0.090082 | (Comedy, Romance) |
| 33 | 0.067616 | (Crime, Drama) |
| 34 | 0.057863 | (Thriller, Crime) |
| 35 | 0.031671 | (Mystery, Drama) |
| 36 | 0.101260 | (Drama, Romance) |
| 37 | 0.087123 | (Thriller, Drama) |
| 38 | 0.031014 | (War, Drama) |
| 39 | 0.043397 | (Horror, Thriller) |
| 40 | 0.036055 | (Thriller, Mystery) |
| 41 | 0.028932 | (Thriller, Sci-Fi) |
| 42 | 0.035068 | (Comedy, Drama, Romance) |
| 43 | 0.032000 | (Crime, Thriller, Drama) |
rules_movies = association_rules(frequent_itemsets_movies,metric="lift",min_threshold=1.25)
rules_movies
| antecedents | consequents | antecedent support | consequent support | support | confidence | lift | leverage | conviction | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | (Action) | (Adventure) | 0.169315 | 0.122411 | 0.058301 | 0.344337 | 2.812955 | 0.037575 | 1.338475 |
| 1 | (Adventure) | (Action) | 0.122411 | 0.169315 | 0.058301 | 0.476276 | 2.812955 | 0.037575 | 1.586111 |
| 2 | (Action) | (Crime) | 0.169315 | 0.120548 | 0.038247 | 0.225890 | 1.873860 | 0.017836 | 1.136081 |
| 3 | (Crime) | (Action) | 0.120548 | 0.169315 | 0.038247 | 0.317273 | 1.873860 | 0.017836 | 1.216716 |
| 4 | (Action) | (Sci-Fi) | 0.169315 | 0.086795 | 0.040986 | 0.242071 | 2.789015 | 0.026291 | 1.204870 |
| 5 | (Sci-Fi) | (Action) | 0.086795 | 0.169315 | 0.040986 | 0.472222 | 2.789015 | 0.026291 | 1.573929 |
| 6 | (Thriller) | (Action) | 0.189479 | 0.169315 | 0.062904 | 0.331984 | 1.960746 | 0.030822 | 1.243510 |
| 7 | (Action) | (Thriller) | 0.169315 | 0.189479 | 0.062904 | 0.371521 | 1.960746 | 0.030822 | 1.289654 |
| 8 | (Children) | (Adventure) | 0.063890 | 0.122411 | 0.029260 | 0.457976 | 3.741299 | 0.021439 | 1.619096 |
| 9 | (Adventure) | (Children) | 0.122411 | 0.063890 | 0.029260 | 0.239033 | 3.741299 | 0.021439 | 1.230158 |
| 10 | (Fantasy) | (Adventure) | 0.071671 | 0.122411 | 0.030685 | 0.428135 | 3.497518 | 0.021912 | 1.534608 |
| 11 | (Adventure) | (Fantasy) | 0.122411 | 0.071671 | 0.030685 | 0.250671 | 3.497518 | 0.021912 | 1.238881 |
| 12 | (Sci-Fi) | (Adventure) | 0.086795 | 0.122411 | 0.027726 | 0.319444 | 2.609607 | 0.017101 | 1.289519 |
| 13 | (Adventure) | (Sci-Fi) | 0.122411 | 0.086795 | 0.027726 | 0.226500 | 2.609607 | 0.017101 | 1.180614 |
| 14 | (Children) | (Animation) | 0.063890 | 0.048986 | 0.027068 | 0.423671 | 8.648758 | 0.023939 | 1.650122 |
| 15 | (Animation) | (Children) | 0.048986 | 0.063890 | 0.027068 | 0.552573 | 8.648758 | 0.023939 | 2.092205 |
| 16 | (Children) | (Comedy) | 0.063890 | 0.363288 | 0.032877 | 0.514580 | 1.416453 | 0.009666 | 1.311672 |
| 17 | (Comedy) | (Children) | 0.363288 | 0.063890 | 0.032877 | 0.090498 | 1.416453 | 0.009666 | 1.029255 |
| 18 | (Comedy) | (Romance) | 0.363288 | 0.169315 | 0.090082 | 0.247964 | 1.464511 | 0.028572 | 1.104581 |
| 19 | (Romance) | (Comedy) | 0.169315 | 0.363288 | 0.090082 | 0.532039 | 1.464511 | 0.028572 | 1.360609 |
| 20 | (Thriller) | (Crime) | 0.189479 | 0.120548 | 0.057863 | 0.305379 | 2.533256 | 0.035022 | 1.266089 |
| 21 | (Crime) | (Thriller) | 0.120548 | 0.189479 | 0.057863 | 0.480000 | 2.533256 | 0.035022 | 1.558693 |
| 22 | (Drama) | (Romance) | 0.478356 | 0.169315 | 0.101260 | 0.211684 | 1.250236 | 0.020267 | 1.053746 |
| 23 | (Romance) | (Drama) | 0.169315 | 0.478356 | 0.101260 | 0.598058 | 1.250236 | 0.020267 | 1.297810 |
| 24 | (War) | (Drama) | 0.040219 | 0.478356 | 0.031014 | 0.771117 | 1.612015 | 0.011775 | 2.279087 |
| 25 | (Drama) | (War) | 0.478356 | 0.040219 | 0.031014 | 0.064834 | 1.612015 | 0.011775 | 1.026321 |
| 26 | (Horror) | (Thriller) | 0.096110 | 0.189479 | 0.043397 | 0.451539 | 2.383052 | 0.025186 | 1.477810 |
| 27 | (Thriller) | (Horror) | 0.189479 | 0.096110 | 0.043397 | 0.229034 | 2.383052 | 0.025186 | 1.172413 |
| 28 | (Thriller) | (Mystery) | 0.189479 | 0.059507 | 0.036055 | 0.190283 | 3.197672 | 0.024779 | 1.161509 |
| 29 | (Mystery) | (Thriller) | 0.059507 | 0.189479 | 0.036055 | 0.605893 | 3.197672 | 0.024779 | 2.056601 |
| 30 | (Thriller) | (Sci-Fi) | 0.189479 | 0.086795 | 0.028932 | 0.152689 | 1.759206 | 0.012486 | 1.077769 |
| 31 | (Sci-Fi) | (Thriller) | 0.086795 | 0.189479 | 0.028932 | 0.333333 | 1.759206 | 0.012486 | 1.215781 |
| 32 | (Comedy, Drama) | (Romance) | 0.104000 | 0.169315 | 0.035068 | 0.337197 | 1.991536 | 0.017460 | 1.253291 |
| 33 | (Romance) | (Comedy, Drama) | 0.169315 | 0.104000 | 0.035068 | 0.207120 | 1.991536 | 0.017460 | 1.130057 |
| 34 | (Drama, Crime) | (Thriller) | 0.067616 | 0.189479 | 0.032000 | 0.473258 | 2.497673 | 0.019188 | 1.538742 |
| 35 | (Thriller, Drama) | (Crime) | 0.087123 | 0.120548 | 0.032000 | 0.367296 | 3.046884 | 0.021497 | 1.389989 |
| 36 | (Crime) | (Thriller, Drama) | 0.120548 | 0.087123 | 0.032000 | 0.265455 | 3.046884 | 0.021497 | 1.242778 |
| 37 | (Thriller) | (Drama, Crime) | 0.189479 | 0.067616 | 0.032000 | 0.168884 | 2.497673 | 0.019188 | 1.121845 |
rules_movies[(rules_movies.lift>4)].sort_values(by=['lift'], ascending=False)
| antecedents | consequents | antecedent support | consequent support | support | confidence | lift | leverage | conviction | |
|---|---|---|---|---|---|---|---|---|---|
| 14 | (Children) | (Animation) | 0.063890 | 0.048986 | 0.027068 | 0.423671 | 8.648758 | 0.023939 | 1.650122 |
| 15 | (Animation) | (Children) | 0.048986 | 0.063890 | 0.027068 | 0.552573 | 8.648758 | 0.023939 | 2.092205 |
Children和Animation 这俩题材是最相关的
movies[(movies.genres.str.contains('Children')) & (~movies.genres.str.contains('Animation'))]
8917
135266
Zenon: The Zequel (2001)
Adventure|Children|Comedy|Sci-Fi
8918
135268
Zenon: Z3 (2004)
Adventure|Children|Comedy
8960
139620
Everything's Gonna Be Great (1998)
Adventure|Children|Comedy|Drama
8967
140152
Dreamcatcher (2015)
Children|Crime|Documentary
8981
140747
16 Wishes (2010)
Children|Drama|Fantasy
9052
149354
Sisters (2015)
Children|Comedy
| movieId | title | genres | |
|---|---|---|---|
| 1 | 2 | Jumanji (1995) | Adventure|Children|Fantasy |
| 7 | 8 | Tom and Huck (1995) | Adventure|Children |
| 26 | 27 | Now and Then (1995) | Children|Drama |
| 32 | 34 | Babe (1995) | Children|Drama |
| 36 | 38 | It Takes Two (1995) | Children|Comedy |
| 51 | 54 | Big Green, The (1995) | Children|Comedy |
| 56 | 60 | Indian in the Cupboard, The (1995) | Adventure|Children|Fantasy |
| 74 | 80 | White Balloon, The (Badkonake sefid) (1995) | Children|Drama |
| 81 | 87 | Dunston Checks In (1996) | Children|Comedy |
| 98 | 107 | Muppet Treasure Island (1996) | Adventure|Children|Comedy|Musical |
| 114 | 126 | NeverEnding Story III, The (1994) | Adventure|Children|Fantasy |
| 125 | 146 | Amazing Panda Adventure, The (1995) | Adventure|Children |
| 137 | 158 | Casper (1995) | Adventure|Children |
| 148 | 169 | Free Willy 2: The Adventure Home (1995) | Adventure|Children|Drama |
| 160 | 181 | Mighty Morphin Power Rangers: The Movie (1995) | Action|Children |
| 210 | 238 | Far From Home: The Adventures of Yellow Dog (1995) | Adventure|Children |
| 213 | 241 | Fluke (1995) | Children|Drama |
| 215 | 243 | Gordy (1995) | Children|Comedy|Fantasy |
| 222 | 250 | Heavyweights (Heavy Weights) (1995) | Children|Comedy |
| 230 | 258 | Kid in King Arthur's Court, A (1995) | Adventure|Children|Comedy|Fantasy|Romance |
| 234 | 262 | Little Princess, A (1995) | Children|Drama |
| 280 | 314 | Secret of Roan Inish, The (1994) | Children|Drama|Fantasy|Mystery |
| 308 | 343 | Baby-Sitters Club, The (1995) | Children |
| 320 | 355 | Flintstones, The (1994) | Children|Comedy|Fantasy |
| 326 | 362 | Jungle Book, The (1994) | Adventure|Children|Romance |
| 338 | 374 | Richie Rich (1994) | Children|Comedy |
| 361 | 410 | Addams Family Values (1993) | Children|Comedy|Fantasy |
| 371 | 421 | Black Beauty (1994) | Adventure|Children|Drama |
| 404 | 455 | Free Willy (1993) | Adventure|Children|Drama |
| 431 | 484 | Lassie (1994) | Adventure|Children |
| ... | ... | ... | ... |
| 7707 | 83177 | Yogi Bear (2010) | Children|Comedy |
| 7735 | 84312 | Home Alone 4 (2002) | Children|Comedy|Crime |
| 7823 | 87383 | Curly Top (1935) | Children|Musical|Romance |
| 7900 | 89881 | Superman and the Mole-Men (1951) | Children|Mystery|Sci-Fi |
| 7929 | 90866 | Hugo (2011) | Children|Drama|Mystery |
| 7935 | 91094 | Muppets, The (2011) | Children|Comedy|Musical |
| 7942 | 91286 | Little Colonel, The (1935) | Children|Comedy|Crime|Drama |
| 7971 | 91886 | Dolphin Tale (2011) | Children|Drama |
| 8096 | 95740 | Adventures of Mary-Kate and Ashley, The: The Case of the United States Navy Adventure (1997) | Children|Musical|Mystery |
| 8199 | 98441 | Rebecca of Sunnybrook Farm (1938) | Children|Comedy|Drama|Musical |
| 8200 | 98458 | Baby Take a Bow (1934) | Children|Comedy|Drama |
| 8377 | 104074 | Percy Jackson: Sea of Monsters (2013) | Adventure|Children|Fantasy |
| 8450 | 106441 | Book Thief, The (2013) | Children|Drama|War |
| 8558 | 110461 | We Are the Best! (Vi är bäst!) (2013) | Children|Comedy|Drama |
| 8592 | 111659 | Maleficent (2014) | Action|Adventure|Children|IMAX |
| 8689 | 115139 | Challenge to Lassie (1949) | Children|Drama |
| 8761 | 118997 | Into the Woods (2014) | Children|Comedy|Fantasy|Musical |
| 8765 | 119155 | Night at the Museum: Secret of the Tomb (2014) | Adventure|Children|Comedy|Fantasy |
| 8766 | 119655 | Seventh Son (2014) | Adventure|Children|Fantasy |
| 8792 | 122932 | Elsa & Fred (2014) | Children|Comedy|Romance |
| 8845 | 130073 | Cinderella (2015) | Children|Drama|Fantasy|Romance |
| 8850 | 130450 | Pan (2015) | Adventure|Children|Fantasy |
| 8871 | 132046 | Tomorrowland (2015) | Action|Adventure|Children|Mystery|Sci-Fi |
336 rows × 3 columns