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Les règles d'association

Data-Science-with-Python

Association Rules#

Les rÃ¨gles d'association#

Les rÃ¨gles d'association sont un algorithme d'apprentissage non supervisÃ©. Il est utilisÃ© pour analyser les habitudes d'achats des clients.

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Itemset#

U itemset est un utiliser l'algorithme du KMeans pour former un modÃ¨le, il faut des donnÃ©es numÃ©riques.

Support#

Le support d'un itemset est la frÃ©quence d'un itemset.

MÃ©thode d'optimisation#

Condition d'arrÃªt#

Association Rules#

Packages#

# !pip install -q pandas mlxtend

import re

import pandas as pd

from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, fpmax, fpgrowth

Paths & Config#

DATA_PATH = "/kaggle/input/sales-forecasting/train.csv"

SUPPORT = 0.2

Get Data#

sales_data = pd.read_csv(DATA_PATH)
sales_data.sample(10)

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	State	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales
2624	2625	CA-2018-127180	22/10/2018	24/10/2018	First Class	TA-21385	Tom Ashbrook	Home Office	United States	New York City	New York	10024.0	East	TEC-PH-10001494	Technology	Phones	Polycom CX600 IP Phone VoIP phone	2399.600
4891	4892	CA-2017-135776	23/12/2017	30/12/2017	Standard Class	EH-13765	Edward Hooks	Corporate	United States	Seattle	Washington	98103.0	West	OFF-PA-10001295	Office Supplies	Paper	Computer Printout Paper with Letter-Trim Perfo...	37.940
3462	3463	CA-2016-152611	20/02/2016	23/02/2016	Second Class	KA-16525	Kelly Andreada	Consumer	United States	Perth Amboy	New Jersey	8861.0	East	OFF-AR-10003903	Office Supplies	Art	Sanford 52201 APSCO Electric Pencil Sharpener	286.790
2295	2296	CA-2016-113145	01/11/2016	05/11/2016	Standard Class	VD-21670	Valerie Dominguez	Consumer	United States	New York City	New York	10011.0	East	OFF-PA-10002659	Office Supplies	Paper	Avoid Verbal Orders Carbonless Minifold Book	13.520
223	224	CA-2016-169397	24/12/2016	27/12/2016	First Class	JB-15925	Joni Blumstein	Consumer	United States	Dublin	Ohio	43017.0	East	TEC-MA-10001148	Technology	Machines	Swingline SM12-08 MicroCut Jam Free Shredder	479.988
3559	3560	CA-2018-152737	07/11/2018	12/11/2018	Standard Class	TS-21505	Tony Sayre	Consumer	United States	San Francisco	California	94122.0	West	TEC-AC-10004975	Technology	Accessories	Plantronics Audio 995 Wireless Stereo Headset	439.800
9642	9643	CA-2015-104563	07/03/2015	12/03/2015	Standard Class	CM-12715	Craig Molinari	Corporate	United States	Seattle	Washington	98103.0	West	FUR-CH-10002780	Furniture	Chairs	Office Star - Task Chair with Contemporary Loo...	436.704
3761	3762	CA-2018-104577	12/05/2018	17/05/2018	Standard Class	CK-12205	Chloris Kastensmidt	Consumer	United States	Everett	Massachusetts	2149.0	East	OFF-PA-10000659	Office Supplies	Paper	TOPS Carbonless Receipt Book, Four 2-3/4 x 7-1...	87.600
3551	3552	CA-2017-152555	29/03/2017	02/04/2017	Second Class	ME-17320	Maria Etezadi	Home Office	United States	Chicago	Illinois	60653.0	Central	FUR-CH-10002965	Furniture	Chairs	Global Leather Highback Executive Chair with P...	844.116
7010	7011	US-2015-135881	23/05/2015	27/05/2015	Standard Class	GT-14710	Greg Tran	Consumer	United States	New York City	New York	10035.0	East	OFF-BI-10000829	Office Supplies	Binders	Avery Non-Stick Binders	17.960

Explore Data#

sales_data.shape

(9800, 18)

sales_data.describe()

	Row ID	Postal Code	Sales
count	9800.000000	9789.000000	9800.000000
mean	4900.500000	55273.322403	230.769059
std	2829.160653	32041.223413	626.651875
min	1.000000	1040.000000	0.444000
25%	2450.750000	23223.000000	17.248000
50%	4900.500000	58103.000000	54.490000
75%	7350.250000	90008.000000	210.605000
max	9800.000000	99301.000000	22638.480000

sales_data.info()

<class 'pandas.core.frame.dataframe'="">
RangeIndex: 9800 entries, 0 to 9799
Data columns (total 18 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Row ID         9800 non-null   int64  
 1   Order ID       9800 non-null   object 
 2   Order Date     9800 non-null   object 
 3   Ship Date      9800 non-null   object 
 4   Ship Mode      9800 non-null   object 
 5   Customer ID    9800 non-null   object 
 6   Customer Name  9800 non-null   object 
 7   Segment        9800 non-null   object 
 8   Country        9800 non-null   object 
 9   City           9800 non-null   object 
 10  State          9800 non-null   object 
 11  Postal Code    9789 non-null   float64
 12  Region         9800 non-null   object 
 13  Product ID     9800 non-null   object 
 14  Category       9800 non-null   object 
 15  Sub-Category   9800 non-null   object 
 16  Product Name   9800 non-null   object 
 17  Sales          9800 non-null   float64
dtypes: float64(2), int64(1), object(15)
memory usage: 1.3+ MB

Clean Data#

new_columns = [re.sub(' |-', '_', column).strip() for column in list(sales_data.columns)]
sales_data.columns = new_columns
sales_data.head()

	Row_ID	Order_ID	Order_Date	Ship_Date	Ship_Mode	Customer_ID	Customer_Name	Segment	Country	City	State	Postal_Code	Region	Product_ID	Category	Sub_Category	Product_Name	Sales
0	1	CA-2017-152156	08/11/2017	11/11/2017	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	Kentucky	42420.0	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600
1	2	CA-2017-152156	08/11/2017	11/11/2017	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	Kentucky	42420.0	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400
2	3	CA-2017-138688	12/06/2017	16/06/2017	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	California	90036.0	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200
3	4	US-2016-108966	11/10/2016	18/10/2016	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	Florida	33311.0	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775
4	5	US-2016-108966	11/10/2016	18/10/2016	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	Florida	33311.0	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680

sales_data.columns

Index(['Row_ID', 'Order_ID', 'Order_Date', 'Ship_Date', 'Ship_Mode',
       'Customer_ID', 'Customer_Name', 'Segment', 'Country', 'City', 'State',
       'Postal_Code', 'Region', 'Product_ID', 'Category', 'Sub_Category',
       'Product_Name', 'Sales'],
      dtype='object')

Transform Data#

def create_cart(item):
    return [elt for elt in list(item)]

df = sales_data[["Customer_ID", "Sub_Category"]].groupby(by="Customer_ID").agg({"Sub_Category": create_cart}).reset_index().rename(columns={"Sub_Category": "cart"})
# df = sales_data[["Customer_ID", "Product_Name"]].groupby(by="Customer_ID").agg({"Product_Name": create_cart}).reset_index().rename(columns={"Product_Name": "cart"})
df.head()

	Customer_ID	cart
0	AA-10315	[Appliances, Binders, Storage, Binders, Applia...
1	AA-10375	[Storage, Furnishings, Accessories, Binders, A...
2	AA-10480	[Paper, Furnishings, Paper, Storage, Paper, Pa...
3	AA-10645	[Chairs, Phones, Chairs, Furnishings, Envelope...
4	AB-10015	[Chairs, Art, Storage, Storage, Phones, Bookca...

df['cart_size'] = df['cart'].apply(lambda x: len(x))
df.sample(15)

	Customer_ID	cart	cart_size
378	JK-15625	[Chairs, Chairs, Bookcases, Art, Paper, Chairs...	12
363	JF-15565	[Fasteners, Paper, Appliances, Paper, Binders,...	16
422	KD-16270	[Paper, Tables, Paper, Storage, Machines, Art,...	16
505	MG-17875	[Appliances, Storage, Tables, Paper, Furnishin...	7
306	GM-14680	[Furnishings, Art, Fasteners, Phones, Tables, ...	11
415	KB-16315	[Paper, Furnishings, Binders, Art, Labels, Env...	22
594	PJ-18835	[Paper, Accessories, Accessories, Paper, Envel...	13
542	MT-17815	[Envelopes, Binders, Storage, Paper, Tables, P...	10
769	TT-21070	[Paper, Furnishings, Paper, Fasteners, Machine...	14
435	KM-16225	[Furnishings, Furnishings, Phones, Storage, Fa...	19
696	SJ-20500	[Appliances, Accessories, Binders, Chairs, Bin...	7
317	HA-14920	[Storage, Accessories, Furnishings, Binders, A...	18
384	JL-15175	[Paper, Furnishings, Chairs, Appliances, Binde...	7
251	EB-13975	[Binders, Binders, Copiers, Supplies, Binders,...	6
493	MC-18100	[Phones, Chairs, Paper, Furnishings, Storage, ...	19

X = df['cart'].values

te = TransactionEncoder()
te_ary = te.fit(X).transform(X)
df_x = pd.DataFrame(te_ary, columns=te.columns_)
df_x.head()

	Accessories	Appliances	Art	Binders	Bookcases	Chairs	Copiers	Envelopes	Fasteners	Furnishings	Labels	Machines	Paper	Phones	Storage	Supplies	Tables
0	True	True	False	True	False	False	False	False	True	True	False	False	True	True	True	True	False
1	True	False	True	True	False	False	False	False	False	True	False	False	True	True	True	False	False
2	True	False	True	False	False	False	False	False	False	True	False	False	True	True	True	False	True
3	False	False	True	True	True	True	False	True	False	True	False	False	True	True	True	False	False
4	False	False	True	False	True	True	False	False	False	False	False	False	False	True	True	False	False

frequent_itemsets = fpgrowth(df_x, min_support=SUPPORT, use_colnames=True)
#### alternatively:
#frequent_itemsets = apriori(df, min_support=SUPPORT, use_colnames=True)
# frequent_itemsets = fpmax(df, min_support=SUPPORT, use_colnames=True)

frequent_itemsets.sort_values(by='support', ascending=False).head(10)

	support	itemsets
0	0.815889	(Binders)
1	0.757881	(Paper)
2	0.658260	(Furnishings)
3	0.641866	(Storage)
4	0.641866	(Phones)
14	0.640605	(Paper, Binders)
8	0.617907	(Art)
5	0.590164	(Accessories)
15	0.544767	(Binders, Furnishings)
18	0.537201	(Binders, Storage)

from mlxtend.frequent_patterns import association_rules

association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7).head(10)

	antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction	zhangs_metric
0	(Paper)	(Binders)	0.757881	0.815889	0.640605	0.845258	1.035996	0.022258	1.189792	0.143506
1	(Binders)	(Paper)	0.815889	0.757881	0.640605	0.785162	1.035996	0.022258	1.126983	0.188720
2	(Furnishings)	(Binders)	0.658260	0.815889	0.544767	0.827586	1.014337	0.007700	1.067844	0.041359
3	(Furnishings)	(Paper)	0.658260	0.757881	0.519546	0.789272	1.041419	0.020663	1.148963	0.116379
4	(Paper, Furnishings)	(Binders)	0.519546	0.815889	0.438840	0.844660	1.035264	0.014948	1.185214	0.070896
5	(Binders, Furnishings)	(Paper)	0.544767	0.757881	0.438840	0.805556	1.062904	0.025971	1.245181	0.130003
6	(Storage)	(Binders)	0.641866	0.815889	0.537201	0.836935	1.025795	0.013509	1.129066	0.070216
7	(Storage)	(Paper)	0.641866	0.757881	0.527112	0.821218	1.083571	0.040654	1.354267	0.215353
8	(Paper, Binders)	(Storage)	0.640605	0.641866	0.451450	0.704724	1.097930	0.040267	1.212879	0.248182
9	(Paper, Storage)	(Binders)	0.527112	0.815889	0.451450	0.856459	1.049725	0.021385	1.282640	0.100171

References#

Let get in touch#

Packages#

import mlxtend

print(mlxtend.__version__)

0.22.0

import pandas as pd

from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

Get data#

DATASET_PATH = "Data/Sample - Superstore.xls"

data = pd.read_excel(DATASET_PATH, sheet_name="Orders")
print(data.head().T)

                                               0  ...                               4
Row ID                                         1  ...                               5
Order ID                          CA-2016-152156  ...                  US-2015-108966
Order Date                   2016-11-08 00:00:00  ...             2015-10-11 00:00:00
Ship Date                    2016-11-11 00:00:00  ...             2015-10-18 00:00:00
Ship Mode                           Second Class  ...                  Standard Class
Customer ID                             CG-12520  ...                        SO-20335
Customer Name                        Claire Gute  ...                  Sean O'Donnell
Segment                                 Consumer  ...                        Consumer
Country                            United States  ...                   United States
City                                   Henderson  ...                 Fort Lauderdale
State                                   Kentucky  ...                         Florida
Postal Code                                42420  ...                           33311
Region                                     South  ...                           South
Product ID                       FUR-BO-10001798  ...                 OFF-ST-10000760
Category                               Furniture  ...                 Office Supplies
Sub-Category                           Bookcases  ...                         Storage
Product Name   Bush Somerset Collection Bookcase  ...  Eldon Fold 'N Roll Cart System
Sales                                     261.96  ...                          22.368
Quantity                                       2  ...                               2
Discount                                     0.0  ...                             0.2
Profit                                   41.9136  ...                          2.5164

[21 rows x 5 columns]

print(data.shape)

(9994, 21)

Data Exploration#

print(data.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Row ID         9994 non-null   int64         
 1   Order ID       9994 non-null   object        
 2   Order Date     9994 non-null   datetime64[ns]
 3   Ship Date      9994 non-null   datetime64[ns]
 4   Ship Mode      9994 non-null   object        
 5   Customer ID    9994 non-null   object        
 6   Customer Name  9994 non-null   object        
 7   Segment        9994 non-null   object        
 8   Country        9994 non-null   object        
 9   City           9994 non-null   object        
 10  State          9994 non-null   object        
 11  Postal Code    9994 non-null   int64         
 12  Region         9994 non-null   object        
 13  Product ID     9994 non-null   object        
 14  Category       9994 non-null   object        
 15  Sub-Category   9994 non-null   object        
 16  Product Name   9994 non-null   object        
 17  Sales          9994 non-null   float64       
 18  Quantity       9994 non-null   int64         
 19  Discount       9994 non-null   float64       
 20  Profit         9994 non-null   float64       
dtypes: datetime64[ns](2), float64(3), int64(3), object(13)
memory usage: 1.6+ MB
None

data = data[["Customer ID", "Sub-Category"]]
print(data.sample(10))

     Customer ID Sub-Category
6496    KH-16510  Accessories
484     MT-18070       Labels
855     BK-11260        Paper
227     DS-13180      Storage
6546    DP-13000        Paper
822     TR-21325  Accessories
6610    AG-10495      Binders
2434    PJ-18835    Envelopes
6121    JR-16210    Bookcases
5197    AI-10855       Phones

print(data.describe())

       Customer ID Sub-Category
count         9994         9994
unique         793           17
top       WB-21850      Binders
freq            37         1523

Preprocessing#

flatten = lambda cart: list(cart)

carts = data.groupby(by="Customer ID").agg({"Sub-Category": flatten}).reset_index().rename(columns={"Sub-Category": "Chart"})
print(carts.head(10))

  Customer ID                                              Chart
0    AA-10315  [Appliances, Binders, Storage, Binders, Applia...
1    AA-10375  [Storage, Furnishings, Accessories, Binders, A...
2    AA-10480  [Paper, Furnishings, Paper, Storage, Paper, Pa...
3    AA-10645  [Chairs, Phones, Chairs, Furnishings, Envelope...
4    AB-10015  [Chairs, Art, Storage, Storage, Phones, Bookca...
5    AB-10060  [Accessories, Paper, Binders, Paper, Binders, ...
6    AB-10105  [Tables, Furnishings, Binders, Phones, Labels,...
7    AB-10150  [Accessories, Paper, Supplies, Art, Furnishing...
8    AB-10165  [Accessories, Paper, Art, Art, Paper, Binders,...
9    AB-10255  [Phones, Storage, Accessories, Supplies, Paper...

encoder = TransactionEncoder()
encoder.fit(carts["Chart"])
chart_array = encoder.transform(carts["Chart"])
df_chart = pd.DataFrame(chart_array, columns=encoder.columns_, dtype=int)
print(df_chart.head(10))

   Accessories  Appliances  Art  Binders  Bookcases  Chairs  ...  Machines  Paper  Phones  Storage  Supplies  Tables
0            1           1    0        1          0       0  ...         0      1       1        1         1       0
1            1           0    1        1          0       0  ...         0      1       1        1         0       0
2            1           0    1        0          0       0  ...         0      1       1        1         0       1
3            0           0    1        1          1       1  ...         0      1       1        1         0       0
4            0           0    1        0          1       1  ...         0      0       1        1         0       0
5            1           1    0        1          0       1  ...         0      1       0        0         1       1
6            1           0    1        1          0       0  ...         1      0       1        1         0       1
7            1           0    1        1          0       0  ...         0      1       0        0         1       0
8            1           0    1        1          0       1  ...         0      1       0        1         0       0
9            1           0    1        1          0       0  ...         0      1       1        1         1       0

[10 rows x 17 columns]

Itemsets#

frequent_itemsets = apriori(df_chart, min_support=0.45, use_colnames=True)
print(frequent_itemsets)

     support                       itemsets
0   0.597730                  (Accessories)
1   0.622951                          (Art)
2   0.819672                      (Binders)
3   0.513241                       (Chairs)
4   0.665826                  (Furnishings)
5   0.770492                        (Paper)
6   0.644388                       (Phones)
7   0.648172                      (Storage)
8   0.508197         (Accessories, Binders)
9   0.491803           (Paper, Accessories)
10  0.523329                 (Art, Binders)
11  0.493064                   (Paper, Art)
12  0.553594         (Furnishings, Binders)
13  0.650694               (Paper, Binders)
14  0.532156              (Binders, Phones)
15  0.546028             (Storage, Binders)
16  0.535939           (Paper, Furnishings)
17  0.450189          (Furnishings, Phones)
18  0.520807                (Paper, Phones)
19  0.535939               (Paper, Storage)
20  0.451450  (Paper, Furnishings, Binders)
21  0.460277      (Paper, Storage, Binders)

Association Rules#

rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7)
rules = rules[["antecedents", "consequents", "support", "confidence", "lift", "leverage"]]
# rules
print(rules)

               antecedents       consequents   support  confidence      lift  leverage
0            (Accessories)         (Binders)  0.508197    0.850211  1.037257  0.018254
1            (Accessories)           (Paper)  0.491803    0.822785  1.067870  0.031257
2                    (Art)         (Binders)  0.523329    0.840081  1.024899  0.012714
3                    (Art)           (Paper)  0.493064    0.791498  1.027263  0.013086
4            (Furnishings)         (Binders)  0.553594    0.831439  1.014356  0.007835
5                  (Paper)         (Binders)  0.650694    0.844517  1.030311  0.019143
6                (Binders)           (Paper)  0.650694    0.793846  1.030311  0.019143
7                 (Phones)         (Binders)  0.532156    0.825832  1.007515  0.003969
8                (Storage)         (Binders)  0.546028    0.842412  1.027743  0.014740
9            (Furnishings)           (Paper)  0.535939    0.804924  1.044689  0.022926
10                (Phones)           (Paper)  0.520807    0.808219  1.048965  0.024311
11               (Storage)           (Paper)  0.535939    0.826848  1.073143  0.036529
12    (Paper, Furnishings)         (Binders)  0.451450    0.842353  1.027671  0.012156
13  (Furnishings, Binders)           (Paper)  0.451450    0.815490  1.058402  0.024911
14        (Paper, Storage)         (Binders)  0.460277    0.858824  1.047765  0.020983
15        (Paper, Binders)         (Storage)  0.460277    0.707364  1.091323  0.038516
16      (Storage, Binders)           (Paper)  0.460277    0.842956  1.094049  0.039568
17               (Storage)  (Paper, Binders)  0.460277    0.710117  1.091323  0.038516