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verticapy.machine_learning.vertica.preprocessing.balance

verticapy.machine_learning.vertica.preprocessing.balance(name: str, input_relation: str, y: str, method: Literal['hybrid', 'over', 'under'] = 'hybrid', ratio: float = 0.5) → vDataFrame

Creates a view with an equal distribution of the input data based on the response_column.

Parameters

name: str

Name of the view.

input_relation: str

Relation used to create the new relation.

y: str

Response column.

method: str, optional

Method used to do the balancing.

• hybrid:

  Performs over-sampling and under-sampling on different classes so that each class is equally represented.

• over:

  Over-samples on all classes, except the most represented class, towards the most represented class’s cardinality.

• under:

  Under-samples on all classes, except the least represented class, towards the least represented class’s cardinality.

ratio: float, optional

The desired ratio between the majority class and the minority class. This value has no effect when used with the ‘hybrid’ balance method.

Returns

vDataFrame

vDataFrame of the created view.

Examples

The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.

Load data for machine learning

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the Titanic dataset.

import verticapy.datasets as vpd

data = vpd.load_titanic()

	123 pclass Integer	123 survived Integer	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(8)	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Numeric(12)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	Abc Varchar(100)
1	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22
2	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]
3	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]
4	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]
5	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]
6	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]
7	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]
8	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62
9	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]
10	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]
11	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]
12	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]
13	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110
14	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]
15	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]
16	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38
17	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]
18	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126
19	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292
20	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175
21	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]
22	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122
23	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166
24	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]
25	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207
26	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]
27	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232
28	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]
29	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]
30	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]
31	1	0		male	57.0	1	0	PC 17569	146.5208	B78	C	[null]	[null]
32	1	0		female	63.0	1	0	PC 17483	221.7792	C55 C57	S	[null]	[null]
33	1	0		male	61.0	0	0	36963	32.3208	D50	S	[null]	46
34	1	0		male	21.0	0	1	35281	77.2875	D26	S	[null]	169
35	1	0		male	51.0	0	1	PC 17597	61.3792	[null]	C	[null]	[null]
36	1	1		female	63.0	1	0	13502	77.9583	D7	S	10	[null]
37	1	1		female	32.0	0	0	11813	76.2917	D15	C	8	[null]
38	1	1		female	58.0	0	0	113783	26.55	C103	S	8	[null]
39	1	1		female	44.0	0	0	PC 17610	27.7208	B4	C	6	[null]
40	1	1		female	41.0	0	0	16966	134.5	E40	C	3	[null]
41	1	1		female	53.0	0	0	PC 17606	27.4458	[null]	C	6	[null]
42	1	1		male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
43	1	1		female	58.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
44	1	1		male	11.0	1	2	113760	120.0	B96 B98	S	4	[null]
45	1	1		female	76.0	1	0	19877	78.85	C46	S	6	[null]
46	1	1		female	[null]	0	1	113505	55.0	E33	S	6	[null]
47	1	1		female	39.0	1	1	PC 17756	83.1583	E49	C	14	[null]
48	1	1		female	27.0	1	2	F.C. 12750	52.0	B71	S	3	[null]
49	1	1		female	[null]	0	0	17421	110.8833	[null]	C	4	[null]
50	1	1		female	35.0	0	0	113503	211.5	C130	C	4	[null]
51	1	1		female	22.0	0	1	112378	59.4	[null]	C	7	[null]
52	1	1		female	25.0	1	0	11765	55.4417	E50	C	5	[null]
53	1	1		male	48.0	1	0	PC 17572	76.7292	D33	C	3	[null]
54	1	1		female	35.0	1	0	36973	83.475	C83	S	D	[null]
55	1	1		male	27.0	0	0	PC 17572	76.7292	D49	C	3	[null]
56	1	1		female	24.0	0	0	11767	83.1583	C54	C	7	[null]
57	1	1		female	52.0	1	1	12749	93.5	B69	S	3	[null]
58	1	1		female	44.0	0	1	111361	57.9792	B18	C	4	[null]
59	1	1		female	15.0	0	1	24160	211.3375	B5	S	2	[null]
60	1	1		male	30.0	1	0	13236	57.75	C78	C	11	[null]
61	1	1		female	31.0	1	0	35273	113.275	D36	C	6	[null]
62	1	1		female	39.0	0	0	PC 17758	108.9	C105	C	8	[null]
63	1	1		female	22.0	0	1	113509	61.9792	B36	C	5	[null]
64	1	1		male	52.0	0	0	113786	30.5	C104	S	6	[null]
65	1	1		female	43.0	0	1	24160	211.3375	B3	S	2	[null]
66	1	1		female	33.0	0	0	110152	86.5	B77	S	8	[null]
67	1	1		male	45.0	1	1	16966	134.5	E34	C	3	[null]
68	1	1		female	40.0	1	1	16966	134.5	E34	C	3	[null]
69	1	1		male	48.0	1	0	19996	52.0	C126	S	5 7	[null]
70	1	1		female	[null]	0	0	PC 17585	79.2	[null]	C	D	[null]
71	1	1		female	35.0	0	0	PC 17755	512.3292	[null]	C	3	[null]
72	1	1		female	60.0	1	0	110813	75.25	D37	C	5	[null]
73	1	1		male	21.0	0	1	PC 17597	61.3792	[null]	C	A	[null]
74	2	0		male	23.0	0	0	C.A. 31030	10.5	[null]	S	[null]	[null]
75	2	0		male	28.0	0	0	244358	26.0	[null]	S	[null]	[null]
76	2	0		male	60.0	1	1	29750	39.0	[null]	S	[null]	[null]
77	2	0		female	44.0	1	0	244252	26.0	[null]	S	[null]	[null]
78	2	0		male	29.0	1	0	2003	26.0	[null]	S	[null]	[null]
79	2	0		male	18.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
80	2	0		male	18.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
81	2	0		male	54.0	0	0	28403	26.0	[null]	S	[null]	[null]
82	2	0		male	18.0	0	0	236171	13.0	[null]	S	[null]	[null]
83	2	0		male	36.0	0	0	229236	13.0	[null]	S	[null]	236
84	2	0		male	34.0	1	0	28664	21.0	[null]	S	[null]	[null]
85	2	0		male	21.0	1	0	28133	11.5	[null]	S	[null]	[null]
86	2	0		male	21.0	1	0	28134	11.5	[null]	S	[null]	[null]
87	2	0		male	24.0	0	0	233866	13.0	[null]	S	[null]	155
88	2	0		male	34.0	0	0	12233	13.0	[null]	S	[null]	[null]
89	2	0		male	30.0	0	0	250653	13.0	[null]	S	[null]	75
90	2	0		male	44.0	0	0	248746	13.0	[null]	S	[null]	35
91	2	0		male	49.0	1	2	220845	65.0	[null]	S	[null]	[null]
92	2	0		male	21.0	2	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
93	2	0		male	21.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
94	2	0		female	60.0	1	0	24065	26.0	[null]	S	[null]	[null]
95	2	0		male	24.0	2	0	C.A. 31029	31.5	[null]	S	[null]	[null]
96	2	0		male	22.0	2	0	C.A. 31029	31.5	[null]	S	[null]	[null]
97	2	0		male	35.0	0	0	233734	12.35	[null]	Q	[null]	[null]
98	2	0		male	31.0	0	0	C.A. 18723	10.5	[null]	S	[null]	165
99	2	0		male	36.0	0	0	SC/Paris 2163	12.875	D	C	[null]	[null]
100	2	0		male	[null]	0	0	SC/A.3 2861	15.5792	[null]	C	[null]	[null]

Rows: 1-100 | Columns: 14

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

Function Application

First we import the balance function:

from verticapy.machine_learning.vertica import balance

Then we can directly apply it to the dataset:

balance(
    name = "balance_model",
    input_relation = data,
    y = "survived",
    method = "under",
)

	123 pclass Integer	123 survived Integer	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(8)	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Numeric(12)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	Abc Varchar(100)
1	1	0		male	47.0	1	0	PC 17757	227.525	C62 C64	C	[null]	124
2	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]
3	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]
4	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148
5	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]
6	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]
7	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]
8	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]
9	1	0		male	39.0	0	0	PC 17580	29.7	A18	C	[null]	133
10	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]
11	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]
12	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]
13	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]
14	1	0		male	32.5	0	0	113503	211.5	C132	C	[null]	45
15	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]
16	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]
17	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]
18	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]
19	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]
20	1	0		male	64.0	0	0	693	26.0	[null]	S	[null]	263
21	1	0		male	28.5	0	0	PC 17562	27.7208	D43	C	[null]	189
22	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]
23	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]
24	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]
25	1	0		male	49.0	1	1	17421	110.8833	C68	C	[null]	[null]
26	1	0		male	47.0	0	0	36967	34.0208	D46	S	[null]	[null]
27	1	0		male	64.0	1	0	110813	75.25	D37	C	[null]	[null]
28	1	1		male	0.92	1	2	113781	151.55	C22 C26	S	11	[null]
29	1	1		female	53.0	2	0	11769	51.4792	C101	S	D	[null]
30	1	1		female	18.0	1	0	PC 17757	227.525	C62 C64	C	4	[null]
31	1	1		male	80.0	0	0	27042	30.0	A23	S	B	[null]
32	1	1		male	37.0	1	1	11751	52.5542	D35	S	5	[null]
33	1	1		male	26.0	0	0	111369	30.0	C148	C	5	[null]
34	1	1		female	42.0	0	0	PC 17757	227.525	[null]	C	4	[null]
35	1	1		male	25.0	1	0	11967	91.0792	B49	C	7	[null]
36	1	1		female	35.0	0	0	PC 17760	135.6333	C99	S	8	[null]
37	1	1		female	45.0	0	0	PC 17608	262.375	[null]	C	4	[null]
38	1	1		female	22.0	0	1	113505	55.0	E33	S	6	[null]
39	1	1		female	60.0	0	0	11813	76.2917	D15	C	8	[null]
40	1	1		female	14.0	1	2	113760	120.0	B96 B98	S	4	[null]
41	1	1		female	[null]	0	0	17770	27.7208	[null]	C	5	[null]
42	1	1		male	45.0	0	0	PC 17594	29.7	A9	C	7	[null]
43	1	1		female	22.0	0	0	113781	151.55	[null]	S	11	[null]
44	1	1		female	64.0	0	2	PC 17756	83.1583	E45	C	14	[null]
45	1	1		female	36.0	0	2	WE/P 5735	71.0	B22	S	7	[null]
46	1	1		female	27.0	1	1	PC 17558	247.5208	B58 B60	C	6	[null]
47	1	1		female	54.0	1	0	36947	78.2667	D20	C	4	[null]
48	1	1		male	43.0	1	0	17765	27.7208	D40	C	5	[null]
49	1	1		female	22.0	0	2	13568	49.5	B39	C	5	[null]
50	1	1		female	35.0	1	0	113803	53.1	C123	S	D	[null]
51	1	1		male	53.0	0	0	113780	28.5	C51	C	B	[null]
52	1	1		female	19.0	0	0	112053	30.0	B42	S	3	[null]
53	1	1		female	58.0	0	1	PC 17582	153.4625	C125	S	3	[null]
54	1	1		male	23.0	0	1	PC 17759	63.3583	D10 D12	C	7	[null]
55	1	1		male	25.0	1	0	11765	55.4417	E50	C	5	[null]
56	1	1		male	[null]	0	0	16988	30.0	D45	S	3	[null]
57	1	1		female	35.0	1	0	113789	52.0	[null]	S	8	[null]
58	1	1		female	[null]	1	0	17464	51.8625	D21	S	8	[null]
59	1	1		male	42.0	1	0	11753	52.5542	D19	S	5	[null]
60	1	1		female	45.0	1	0	11753	52.5542	D19	S	5	[null]
61	1	1		female	39.0	0	0	24160	211.3375	[null]	S	2	[null]
62	1	1		female	16.0	0	1	PC 17592	39.4	D28	S	9	[null]
63	1	1		female	21.0	0	0	13502	77.9583	D9	S	10	[null]
64	1	1		female	18.0	1	0	113773	53.1	D30	S	10	[null]
65	1	1		male	36.0	0	0	PC 17473	26.2875	E25	S	7	[null]
66	1	1		female	37.0	1	0	19928	90.0	C78	Q	14	[null]
67	1	1		male	[null]	0	0	F.C. 12998	25.7417	[null]	C	7	[null]
68	1	1		female	22.0	1	0	113776	66.6	C2	S	8	[null]
69	1	1		female	30.0	0	0	12749	93.5	B73	S	3	[null]
70	1	1		female	33.0	0	0	PC 17613	27.7208	A11	C	11	[null]
71	1	1		female	54.0	1	0	PC 17603	59.4	[null]	C	6	[null]
72	1	1		female	18.0	2	2	PC 17608	262.375	B57 B59 B63 B66	C	4	[null]
73	1	1		female	48.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	4	[null]
74	1	1		male	35.0	0	0	PC 17475	26.2875	E24	S	5	[null]
75	1	1		female	23.0	1	0	21228	82.2667	B45	S	7	[null]
76	1	1		female	43.0	1	0	11778	55.4417	C116	C	5	[null]
77	1	1		female	39.0	1	1	110413	79.65	E67	S	8	[null]
78	1	1		female	39.0	1	1	17421	110.8833	C68	C	4	[null]
79	1	1		female	55.0	0	0	PC 17760	135.6333	C32	C	8	[null]
80	1	1		female	31.0	0	2	36928	164.8667	C7	S	8	[null]
81	2	0		male	30.0	1	0	P/PP 3381	24.0	[null]	C	[null]	[null]
82	2	0		male	30.0	0	0	248744	13.0	[null]	S	[null]	[null]
83	2	0		male	57.0	0	0	244346	13.0	[null]	S	[null]	[null]
84	2	0		male	51.0	0	0	S.O.P. 1166	12.525	[null]	S	[null]	174
85	2	0		male	[null]	0	0	239853	0.0	[null]	S	[null]	[null]
86	2	0		male	52.0	0	0	248731	13.5	[null]	S	[null]	130
87	2	0		male	37.0	1	0	SC/AH 29037	26.0	[null]	S	[null]	17
88	2	0		female	29.0	1	0	SC/AH 29037	26.0	[null]	S	[null]	[null]
89	2	0		male	29.0	0	0	W./C. 14263	10.5	[null]	S	[null]	[null]
90	2	0		female	30.0	0	0	237249	13.0	[null]	S	[null]	[null]
91	2	0		male	[null]	0	0	239853	0.0	[null]	S	[null]	[null]
92	2	0		male	17.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
93	2	0		male	18.0	0	0	C.A. 15185	10.5	[null]	S	[null]	[null]
94	2	0		male	24.0	0	0	248726	13.5	[null]	S	[null]	297
95	2	0		male	30.0	0	0	250646	13.0	[null]	S	[null]	305
96	2	0		male	52.0	0	0	250647	13.0	[null]	S	[null]	19
97	2	0		female	18.0	1	1	250650	13.0	[null]	S	[null]	[null]
98	2	0		male	23.0	2	1	29104	11.5	[null]	S	[null]	[null]
99	2	0		male	36.0	0	0	242963	13.0	[null]	S	[null]	[null]
100	2	0		male	44.0	1	0	26707	26.0	[null]	S	[null]	[null]

Rows: 1-100 | Columns: 14

See also

vDataFrame.sample() : Sampling the dataset.