In [2]:

from pycaret.datasets import get_data
get_data('index')

	Dataset	Data Types	Default Task	Target Variable	# Instances	# Attributes	Missing Values
0	cancer	Multivariate	Classification	Class	683	10	N
1	credit	Multivariate	Classification	default	24000	24	N
2	employee	Multivariate	Classification	left	14999	10	N
3	heart	Multivariate	Classification	DEATH	200	17	N
4	hepatitis	Multivariate	Classification	Class	154	32	Y
5	income	Multivariate	Classification	income >50K	32561	14	Y
6	iris	Multivariate	Classification	Class	100	5	N
7	juice	Multivariate	Classification	Purchase	1070	15	N
8	heart_disease	Multivariate	Classification	Disease	270	14	N
9	glass	Multivariate	Multiclass Classification	Type	214	10	N
10	satellite	Multivariate	Multiclass Classification	Class	6435	37	N
11	poker	Multivariate	Multiclass Classification	CLASS	100000	11	N
12	automobile	Multivariate	Regression	price	202	26	Y
13	bike	Multivariate	Regression	cnt	17379	15	N
14	boston	Multivariate	Regression	medv	506	14	N
15	diamond	Multivariate	Regression	Price	6000	8	N
16	gold	Multivariate	Regression	Gold_T+22	2558	121	N
17	tweets	Text	NLP	tweet	8594	2	N
18	amazon	Text	NLP / Classification	reviewText	20000	2	N
19	kiva	Text	NLP / Classification	en	6818	7	N
20	spx	Text	NLP / Regression	text	874	4	N
21	germany	Multivariate	Association Rule Mining	InvoiceNo, Description	9495	8	N
22	france	Multivariate	Association Rule Mining	InvoiceNo, Description	8557	8	N

In [3]:

data = get_data('france')

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
0	536370	22728	ALARM CLOCK BAKELIKE PINK	24	12/1/2010 8:45	3.75	12583.0	France
1	536370	22727	ALARM CLOCK BAKELIKE RED	24	12/1/2010 8:45	3.75	12583.0	France
2	536370	22726	ALARM CLOCK BAKELIKE GREEN	12	12/1/2010 8:45	3.75	12583.0	France
3	536370	21724	PANDA AND BUNNIES STICKER SHEET	12	12/1/2010 8:45	0.85	12583.0	France
4	536370	21883	STARS GIFT TAPE	24	12/1/2010 8:45	0.65	12583.0	France

In [4]:

from pycaret.arules import *

In [5]:

s = setup(data, 'InvoiceNo', item_id='Description')

In [6]:

a1 = create_model()

In [7]:

a1

Out[7]:

	antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction
0	(JUMBO BAG WOODLAND ANIMALS)	(POSTAGE)	0.0651	0.6746	0.0651	1.0000	1.4823	0.0212	inf
1	(SET/6 RED SPOTTY PAPER PLATES, SET/20 RED RET...	(SET/6 RED SPOTTY PAPER CUPS)	0.0868	0.1171	0.0846	0.9750	8.3236	0.0744	35.3145
2	(SET/6 RED SPOTTY PAPER CUPS, SET/20 RED RETRO...	(SET/6 RED SPOTTY PAPER PLATES)	0.0868	0.1085	0.0846	0.9750	8.9895	0.0752	35.6616
3	(SET/6 RED SPOTTY PAPER PLATES, POSTAGE, SET/2...	(SET/6 RED SPOTTY PAPER CUPS)	0.0716	0.1171	0.0694	0.9697	8.2783	0.0610	29.1345
4	(POSTAGE, SET/6 RED SPOTTY PAPER CUPS, SET/20 ...	(SET/6 RED SPOTTY PAPER PLATES)	0.0716	0.1085	0.0694	0.9697	8.9406	0.0617	29.4208
...	...	...	...	...	...	...	...	...	...
136	(STRAWBERRY LUNCH BOX WITH CUTLERY)	(LUNCH BOX WITH CUTLERY RETROSPOT )	0.1041	0.1236	0.0542	0.5208	4.2124	0.0414	1.8289
137	(LUNCH BAG APPLE DESIGN)	(LUNCH BAG SPACEBOY DESIGN )	0.1085	0.1041	0.0564	0.5200	4.9942	0.0451	1.8664
138	(LUNCH BAG APPLE DESIGN)	(LUNCH BAG RED RETROSPOT)	0.1085	0.1323	0.0564	0.5200	3.9298	0.0420	1.8077
139	(PLASTERS IN TIN CIRCUS PARADE , POSTAGE)	(PLASTERS IN TIN SPACEBOY)	0.1258	0.1193	0.0629	0.5000	4.1909	0.0479	1.7614
140	(LUNCH BAG RED RETROSPOT, POSTAGE)	(LUNCH BAG APPLE DESIGN)	0.1041	0.1085	0.0521	0.5000	4.6100	0.0408	1.7831

141 rows × 9 columns

In [8]:

plot_model(a1)

In [9]:

plot_model(a1, plot='3d')

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