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verticapy.machine_learning.vertica.preprocessing.OneHotEncoder

class verticapy.machine_learning.vertica.preprocessing.OneHotEncoder(name: str = None, overwrite_model: bool = False, extra_levels: dict | None = None, drop_first: bool = True, ignore_null: bool = True, separator: str = '_', column_naming: Literal['indices', 'values', 'values_relaxed'] = 'indices', null_column_name: str = 'null')

Creates a Vertica OneHotEncoder object.

Parameters

name: str, optional

Name of the model.

overwrite_model: bool, optional

If set to True, training a model with the same name as an existing model overwrites the existing model.

extra_levels: dict, optional

Additional levels in each category that are not in the input relation.

drop_first: bool, optional

If set to True, treats the first level of the categorical variable as the reference level. Otherwise, every level of the categorical variable has a corresponding column in the output view.

ignore_null: bool, optional

If set to True, Null values set all corresponding one-hot binary columns to null. Otherwise, null values in the input columns are treated as a categorical level.

separator: str, optional

The character that separates the input variable name and the indicator variable level in the output table. To avoid using any separator, set this parameter to null value.

column_naming: str, optional

Appends categorical levels to column names according to the specified method:

• indices:

  Uses integer indices to represent categorical levels.

• values :

  Uses categorical level names. If duplicate column names occur, the function attempts to disambiguate them by appending _n, where n is a zero-based integer index (_0, _1, …, _n).

null_column_name: str, optional

The string used in naming the indicator column for null values, used only if ignore_null is set to false and column_naming is set to ‘values’.

Attributes

Many attributes are created during the fitting phase.

categories_: numpy.array

ArrayLike of the categories of the different features.

column_naming_: str

Method used to name the model’s outputs.

drop_first_: bool

If False, the first dummy of each category was dropped.

Note

All attributes can be accessed using the get_attributes() method.

Note

Several other attributes can be accessed by using the get_vertica_attributes() method.

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 home.dest Varchar(100)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	30.0	1	2	113781	151.55	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	39.0	0	0	112050	0.0	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22	Montevideo, Uruguay
6	1	0		male	47.0	1	0	PC 17757	227.525	C62 C64	C	[null]	124	New York, NY
7	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]	New York, NY
8	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]	Montreal, PQ
9	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]	Winnipeg, MN
10	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148	San Francisco, CA
11	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]	Trenton, NJ
12	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]	London / Winnipeg, MB
13	1	0		male	41.0	0	0	113054	30.5	A21	S	[null]	[null]	Pomeroy, WA
14	1	0		male	48.0	0	0	PC 17591	50.4958	B10	C	[null]	208	Omaha, NE
15	1	0		male	[null]	0	0	112379	39.6	[null]	C	[null]	[null]	Philadelphia, PA
16	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]	Washington, DC
17	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]	[null]
18	1	0		male	33.0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]	New York, NY
19	1	0		male	28.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
20	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
21	1	0		male	49.0	0	0	19924	26.0	[null]	S	[null]	[null]	Ascot, Berkshire / Rochester, NY
22	1	0		male	36.0	1	0	19877	78.85	C46	S	[null]	172	Little Onn Hall, Staffs
23	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]	Amenia, ND
24	1	0		male	[null]	0	0	112051	0.0	[null]	S	[null]	[null]	Liverpool, England / Belfast
25	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]	Los Angeles, CA
26	1	0		male	[null]	0	0	110465	52.0	A14	S	[null]	[null]	Stoughton, MA
27	1	0		male	47.0	0	0	5727	25.5875	E58	S	[null]	[null]	Victoria, BC
28	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]	Lakewood, NJ
29	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]	Roachdale, IN
30	1	0		male	70.0	1	1	WE/P 5735	71.0	B22	S	[null]	269	Milwaukee, WI
31	1	0		male	39.0	1	0	PC 17599	71.2833	C85	C	[null]	[null]	New York, NY
32	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]	Montreal, PQ
33	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62	Deephaven, MN / Cedar Rapids, IA
34	1	0		male	39.0	0	0	PC 17580	29.7	A18	C	[null]	133	Philadelphia, PA
35	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]	New York, NY
36	1	0		male	[null]	0	0	PC 17483	221.7792	C95	S	[null]	[null]	[null]
37	1	0		male	30.0	0	0	113051	27.75	C111	C	[null]	[null]	New York, NY
38	1	0		male	19.0	3	2	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
39	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
40	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]	Westcliff-on-Sea, Essex
41	1	0		male	[null]	0	0	112058	0.0	B102	S	[null]	[null]	[null]
42	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]	Scituate, MA
43	1	0		male	47.0	0	0	111320	38.5	E63	S	[null]	275	St Anne's-on-Sea, Lancashire
44	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]	[null]
45	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]	New York, NY
46	1	0		male	38.0	0	1	PC 17582	153.4625	C91	S	[null]	147	Winnipeg, MB
47	1	0		male	46.0	0	0	PC 17593	79.2	B82 B84	C	[null]	[null]	New York, NY
48	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]	[null]
49	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]	New York, NY
50	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110	[null]
51	1	0		male	55.0	1	1	12749	93.5	B69	S	[null]	307	Montreal, PQ
52	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]	London / Middlesex
53	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]	Brighton, MA
54	1	0		male	55.0	0	0	680	50.0	C39	S	[null]	[null]	London / Birmingham
55	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38	New York, NY
56	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]	New York, NY
57	1	0		female	50.0	0	0	PC 17595	28.7125	C49	C	[null]	[null]	Paris, France New York, NY
58	1	0		male	46.0	0	0	694	26.0	[null]	S	[null]	80	Bennington, VT
59	1	0		male	50.0	0	0	113044	26.0	E60	S	[null]	[null]	London
60	1	0		male	32.5	0	0	113503	211.5	C132	C	[null]	45	[null]
61	1	0		male	58.0	0	0	11771	29.7	B37	C	[null]	258	Buffalo, NY
62	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]	Southington / Noank, CT
63	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]	Portland, OR
64	1	0		male	[null]	0	0	PC 17612	27.7208	[null]	C	[null]	[null]	Chicago, IL
65	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126	Springfield, MA
66	1	0		male	30.0	0	0	113801	45.5	[null]	S	[null]	[null]	London / New York, NY
67	1	0		male	30.0	0	0	110469	26.0	C106	S	[null]	[null]	Brockton, MA
68	1	0		male	19.0	1	0	113773	53.1	D30	S	[null]	[null]	New York, NY
69	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292	Vancouver, BC
70	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175	Dorchester, MA
71	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]	New York, NY
72	1	0		male	65.0	0	0	13509	26.55	E38	S	[null]	249	East Bridgewater, MA
73	1	0		male	44.0	2	0	19928	90.0	C78	Q	[null]	230	Fond du Lac, WI
74	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]	Montreal, PQ
75	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]	Washington, DC
76	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]	Brooklyn, NY
77	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122	Lexington, MA
78	1	0		male	64.0	0	0	693	26.0	[null]	S	[null]	263	Isle of Wight, England
79	1	0		male	65.0	0	1	113509	61.9792	B30	C	[null]	234	Providence, RI
80	1	0		male	28.5	0	0	PC 17562	27.7208	D43	C	[null]	189	?Havana, Cuba
81	1	0		male	[null]	0	0	112052	0.0	[null]	S	[null]	[null]	Belfast
82	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166	Surbiton Hill, Surrey
83	1	0		male	23.0	0	0	12749	93.5	B24	S	[null]	[null]	Montreal, PQ
84	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]	Isleworth, England
85	1	0		male	18.0	1	0	PC 17758	108.9	C65	C	[null]	[null]	Madrid, Spain
86	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207	Worcester, MA
87	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]	Rotterdam, Netherlands
88	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232	[null]
89	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]	[null]
90	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]	Trenton, NJ
91	1	0		male	[null]	0	0	113767	50.0	A32	S	[null]	[null]	Seattle, WA
92	1	0		male	36.0	0	0	13049	40.125	A10	C	[null]	[null]	Winnipeg, MB
93	1	0		male	55.0	1	0	PC 17603	59.4	[null]	C	[null]	[null]	New York, NY
94	1	0		male	33.0	0	0	113790	26.55	[null]	S	[null]	109	London
95	1	0		male	61.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	[null]	[null]	Haverford, PA / Cooperstown, NY
96	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]	Duluth, MN
97	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]	New York, NY
98	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]	St James, Long Island, NY
99	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]	Huntington, WV
100	1	0		male	[null]	0	0	113056	26.0	A19	S	[null]	[null]	Streatham, Surrey

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.

Model Initialization

First we import the OneHotEncoder model:

from verticapy.machine_learning.vertica import OneHotEncoder

Then we can create the model:

model = OneHotEncoder(
    drop_first = False,
    column_naming = "values",
)

Hint

In verticapy 1.0.x and higher, you do not need to specify the model name, as the name is automatically assigned. If you need to re-use the model, you can fetch the model name from the model’s attributes.

Important

The model name is crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.

Model Training

We can now fit the model:

model.fit(data, ["sex", "parch"])

Important

To train a model, you can directly use the vDataFrame or the name of the relation stored in the database.

Classes

To have a look at the identified classes/categories you can use:

model.categories_
Out[5]: [['female', 'male'], ['0', '1', '2', '3', '4', '5', '6', '9']]

Conversion/Transformation

To get the transformed dataset in the form that is encoded, we can use the transform function. Let us transform the data and display the first datapoints.

model.transform(data)

	123 pclass Integer	123 survived Integer	Abc Varchar(164)	Abc sex Varchar(20)	123 sex_female Integer	123 sex_male Integer	123 age Numeric(8)	123 sibsp Integer	123 parch Integer	123 parch_0 Integer	123 parch_1 Integer	123 parch_2 Integer	123 parch_3 Integer	123 parch_4 Integer	123 parch_5 Integer	123 parch_6 Integer	123 parch_9 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 home.dest Varchar(100)
1	1	0		female	1	0	2.0	1	2	0	0	1	0	0	0	0	0	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	0	1	30.0	1	2	0	0	1	0	0	0	0	0	113781	151.55	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	1	0	25.0	1	2	0	0	1	0	0	0	0	0	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	0	1	39.0	0	0	1	0	0	0	0	0	0	0	112050	0.0	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	0	1	71.0	0	0	1	0	0	0	0	0	0	0	PC 17609	49.5042	[null]	C	[null]	22	Montevideo, Uruguay
6	1	0		male	0	1	47.0	1	0	1	0	0	0	0	0	0	0	PC 17757	227.525	C62 C64	C	[null]	124	New York, NY
7	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	PC 17318	25.925	[null]	S	[null]	[null]	New York, NY
8	1	0		male	0	1	24.0	0	1	0	1	0	0	0	0	0	0	PC 17558	247.5208	B58 B60	C	[null]	[null]	Montreal, PQ
9	1	0		male	0	1	36.0	0	0	1	0	0	0	0	0	0	0	13050	75.2417	C6	C	A	[null]	Winnipeg, MN
10	1	0		male	0	1	25.0	0	0	1	0	0	0	0	0	0	0	13905	26.0	[null]	C	[null]	148	San Francisco, CA
11	1	0		male	0	1	45.0	0	0	1	0	0	0	0	0	0	0	113784	35.5	T	S	[null]	[null]	Trenton, NJ
12	1	0		male	0	1	42.0	0	0	1	0	0	0	0	0	0	0	110489	26.55	D22	S	[null]	[null]	London / Winnipeg, MB
13	1	0		male	0	1	41.0	0	0	1	0	0	0	0	0	0	0	113054	30.5	A21	S	[null]	[null]	Pomeroy, WA
14	1	0		male	0	1	48.0	0	0	1	0	0	0	0	0	0	0	PC 17591	50.4958	B10	C	[null]	208	Omaha, NE
15	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	112379	39.6	[null]	C	[null]	[null]	Philadelphia, PA
16	1	0		male	0	1	45.0	0	0	1	0	0	0	0	0	0	0	113050	26.55	B38	S	[null]	[null]	Washington, DC
17	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	113798	31.0	[null]	S	[null]	[null]	[null]
18	1	0		male	0	1	33.0	0	0	1	0	0	0	0	0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]	New York, NY
19	1	0		male	0	1	28.0	0	0	1	0	0	0	0	0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
20	1	0		male	0	1	17.0	0	0	1	0	0	0	0	0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
21	1	0		male	0	1	49.0	0	0	1	0	0	0	0	0	0	0	19924	26.0	[null]	S	[null]	[null]	Ascot, Berkshire / Rochester, NY
22	1	0		male	0	1	36.0	1	0	1	0	0	0	0	0	0	0	19877	78.85	C46	S	[null]	172	Little Onn Hall, Staffs
23	1	0		male	0	1	46.0	1	0	1	0	0	0	0	0	0	0	W.E.P. 5734	61.175	E31	S	[null]	[null]	Amenia, ND
24	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	112051	0.0	[null]	S	[null]	[null]	Liverpool, England / Belfast
25	1	0		male	0	1	27.0	1	0	1	0	0	0	0	0	0	0	13508	136.7792	C89	C	[null]	[null]	Los Angeles, CA
26	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	110465	52.0	A14	S	[null]	[null]	Stoughton, MA
27	1	0		male	0	1	47.0	0	0	1	0	0	0	0	0	0	0	5727	25.5875	E58	S	[null]	[null]	Victoria, BC
28	1	0		male	0	1	37.0	1	1	0	1	0	0	0	0	0	0	PC 17756	83.1583	E52	C	[null]	[null]	Lakewood, NJ
29	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	113791	26.55	[null]	S	[null]	[null]	Roachdale, IN
30	1	0		male	0	1	70.0	1	1	0	1	0	0	0	0	0	0	WE/P 5735	71.0	B22	S	[null]	269	Milwaukee, WI
31	1	0		male	0	1	39.0	1	0	1	0	0	0	0	0	0	0	PC 17599	71.2833	C85	C	[null]	[null]	New York, NY
32	1	0		male	0	1	31.0	1	0	1	0	0	0	0	0	0	0	F.C. 12750	52.0	B71	S	[null]	[null]	Montreal, PQ
33	1	0		male	0	1	50.0	1	0	1	0	0	0	0	0	0	0	PC 17761	106.425	C86	C	[null]	62	Deephaven, MN / Cedar Rapids, IA
34	1	0		male	0	1	39.0	0	0	1	0	0	0	0	0	0	0	PC 17580	29.7	A18	C	[null]	133	Philadelphia, PA
35	1	0		female	1	0	36.0	0	0	1	0	0	0	0	0	0	0	PC 17531	31.6792	A29	C	[null]	[null]	New York, NY
36	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	PC 17483	221.7792	C95	S	[null]	[null]	[null]
37	1	0		male	0	1	30.0	0	0	1	0	0	0	0	0	0	0	113051	27.75	C111	C	[null]	[null]	New York, NY
38	1	0		male	0	1	19.0	3	2	0	0	1	0	0	0	0	0	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
39	1	0		male	0	1	64.0	1	4	0	0	0	0	1	0	0	0	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
40	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	113778	26.55	D34	S	[null]	[null]	Westcliff-on-Sea, Essex
41	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	112058	0.0	B102	S	[null]	[null]	[null]
42	1	0		male	0	1	37.0	1	0	1	0	0	0	0	0	0	0	113803	53.1	C123	S	[null]	[null]	Scituate, MA
43	1	0		male	0	1	47.0	0	0	1	0	0	0	0	0	0	0	111320	38.5	E63	S	[null]	275	St Anne's-on-Sea, Lancashire
44	1	0		male	0	1	24.0	0	0	1	0	0	0	0	0	0	0	PC 17593	79.2	B86	C	[null]	[null]	[null]
45	1	0		male	0	1	71.0	0	0	1	0	0	0	0	0	0	0	PC 17754	34.6542	A5	C	[null]	[null]	New York, NY
46	1	0		male	0	1	38.0	0	1	0	1	0	0	0	0	0	0	PC 17582	153.4625	C91	S	[null]	147	Winnipeg, MB
47	1	0		male	0	1	46.0	0	0	1	0	0	0	0	0	0	0	PC 17593	79.2	B82 B84	C	[null]	[null]	New York, NY
48	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	113796	42.4	[null]	S	[null]	[null]	[null]
49	1	0		male	0	1	45.0	1	0	1	0	0	0	0	0	0	0	36973	83.475	C83	S	[null]	[null]	New York, NY
50	1	0		male	0	1	40.0	0	0	1	0	0	0	0	0	0	0	112059	0.0	B94	S	[null]	110	[null]
51	1	0		male	0	1	55.0	1	1	0	1	0	0	0	0	0	0	12749	93.5	B69	S	[null]	307	Montreal, PQ
52	1	0		male	0	1	42.0	0	0	1	0	0	0	0	0	0	0	113038	42.5	B11	S	[null]	[null]	London / Middlesex
53	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	17463	51.8625	E46	S	[null]	[null]	Brighton, MA
54	1	0		male	0	1	55.0	0	0	1	0	0	0	0	0	0	0	680	50.0	C39	S	[null]	[null]	London / Birmingham
55	1	0		male	0	1	42.0	1	0	1	0	0	0	0	0	0	0	113789	52.0	[null]	S	[null]	38	New York, NY
56	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	PC 17600	30.6958	[null]	C	14	[null]	New York, NY
57	1	0		female	1	0	50.0	0	0	1	0	0	0	0	0	0	0	PC 17595	28.7125	C49	C	[null]	[null]	Paris, France New York, NY
58	1	0		male	0	1	46.0	0	0	1	0	0	0	0	0	0	0	694	26.0	[null]	S	[null]	80	Bennington, VT
59	1	0		male	0	1	50.0	0	0	1	0	0	0	0	0	0	0	113044	26.0	E60	S	[null]	[null]	London
60	1	0		male	0	1	32.5	0	0	1	0	0	0	0	0	0	0	113503	211.5	C132	C	[null]	45	[null]
61	1	0		male	0	1	58.0	0	0	1	0	0	0	0	0	0	0	11771	29.7	B37	C	[null]	258	Buffalo, NY
62	1	0		male	0	1	41.0	1	0	1	0	0	0	0	0	0	0	17464	51.8625	D21	S	[null]	[null]	Southington / Noank, CT
63	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	113028	26.55	C124	S	[null]	[null]	Portland, OR
64	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	PC 17612	27.7208	[null]	C	[null]	[null]	Chicago, IL
65	1	0		male	0	1	29.0	0	0	1	0	0	0	0	0	0	0	113501	30.0	D6	S	[null]	126	Springfield, MA
66	1	0		male	0	1	30.0	0	0	1	0	0	0	0	0	0	0	113801	45.5	[null]	S	[null]	[null]	London / New York, NY
67	1	0		male	0	1	30.0	0	0	1	0	0	0	0	0	0	0	110469	26.0	C106	S	[null]	[null]	Brockton, MA
68	1	0		male	0	1	19.0	1	0	1	0	0	0	0	0	0	0	113773	53.1	D30	S	[null]	[null]	New York, NY
69	1	0		male	0	1	46.0	0	0	1	0	0	0	0	0	0	0	13050	75.2417	C6	C	[null]	292	Vancouver, BC
70	1	0		male	0	1	54.0	0	0	1	0	0	0	0	0	0	0	17463	51.8625	E46	S	[null]	175	Dorchester, MA
71	1	0		male	0	1	28.0	1	0	1	0	0	0	0	0	0	0	PC 17604	82.1708	[null]	C	[null]	[null]	New York, NY
72	1	0		male	0	1	65.0	0	0	1	0	0	0	0	0	0	0	13509	26.55	E38	S	[null]	249	East Bridgewater, MA
73	1	0		male	0	1	44.0	2	0	1	0	0	0	0	0	0	0	19928	90.0	C78	Q	[null]	230	Fond du Lac, WI
74	1	0		male	0	1	55.0	0	0	1	0	0	0	0	0	0	0	113787	30.5	C30	S	[null]	[null]	Montreal, PQ
75	1	0		male	0	1	47.0	0	0	1	0	0	0	0	0	0	0	113796	42.4	[null]	S	[null]	[null]	Washington, DC
76	1	0		male	0	1	37.0	0	1	0	1	0	0	0	0	0	0	PC 17596	29.7	C118	C	[null]	[null]	Brooklyn, NY
77	1	0		male	0	1	58.0	0	2	0	0	1	0	0	0	0	0	35273	113.275	D48	C	[null]	122	Lexington, MA
78	1	0		male	0	1	64.0	0	0	1	0	0	0	0	0	0	0	693	26.0	[null]	S	[null]	263	Isle of Wight, England
79	1	0		male	0	1	65.0	0	1	0	1	0	0	0	0	0	0	113509	61.9792	B30	C	[null]	234	Providence, RI
80	1	0		male	0	1	28.5	0	0	1	0	0	0	0	0	0	0	PC 17562	27.7208	D43	C	[null]	189	?Havana, Cuba
81	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	112052	0.0	[null]	S	[null]	[null]	Belfast
82	1	0		male	0	1	45.5	0	0	1	0	0	0	0	0	0	0	113043	28.5	C124	S	[null]	166	Surbiton Hill, Surrey
83	1	0		male	0	1	23.0	0	0	1	0	0	0	0	0	0	0	12749	93.5	B24	S	[null]	[null]	Montreal, PQ
84	1	0		male	0	1	29.0	1	0	1	0	0	0	0	0	0	0	113776	66.6	C2	S	[null]	[null]	Isleworth, England
85	1	0		male	0	1	18.0	1	0	1	0	0	0	0	0	0	0	PC 17758	108.9	C65	C	[null]	[null]	Madrid, Spain
86	1	0		male	0	1	47.0	0	0	1	0	0	0	0	0	0	0	110465	52.0	C110	S	[null]	207	Worcester, MA
87	1	0		male	0	1	38.0	0	0	1	0	0	0	0	0	0	0	19972	0.0	[null]	S	[null]	[null]	Rotterdam, Netherlands
88	1	0		male	0	1	22.0	0	0	1	0	0	0	0	0	0	0	PC 17760	135.6333	[null]	C	[null]	232	[null]
89	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	PC 17757	227.525	[null]	C	[null]	[null]	[null]
90	1	0		male	0	1	31.0	0	0	1	0	0	0	0	0	0	0	PC 17590	50.4958	A24	S	[null]	[null]	Trenton, NJ
91	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	113767	50.0	A32	S	[null]	[null]	Seattle, WA
92	1	0		male	0	1	36.0	0	0	1	0	0	0	0	0	0	0	13049	40.125	A10	C	[null]	[null]	Winnipeg, MB
93	1	0		male	0	1	55.0	1	0	1	0	0	0	0	0	0	0	PC 17603	59.4	[null]	C	[null]	[null]	New York, NY
94	1	0		male	0	1	33.0	0	0	1	0	0	0	0	0	0	0	113790	26.55	[null]	S	[null]	109	London
95	1	0		male	0	1	61.0	1	3	0	0	0	1	0	0	0	0	PC 17608	262.375	B57 B59 B63 B66	C	[null]	[null]	Haverford, PA / Cooperstown, NY
96	1	0		male	0	1	50.0	1	0	1	0	0	0	0	0	0	0	13507	55.9	E44	S	[null]	[null]	Duluth, MN
97	1	0		male	0	1	56.0	0	0	1	0	0	0	0	0	0	0	113792	26.55	[null]	S	[null]	[null]	New York, NY
98	1	0		male	0	1	56.0	0	0	1	0	0	0	0	0	0	0	17764	30.6958	A7	C	[null]	[null]	St James, Long Island, NY
99	1	0		male	0	1	24.0	1	0	1	0	0	0	0	0	0	0	13695	60.0	C31	S	[null]	[null]	Huntington, WV
100	1	0		male	0	1	[null]	0	0	1	0	0	0	0	0	0	0	113056	26.0	A19	S	[null]	[null]	Streatham, Surrey

Rows: 1-100 | Columns: 24

Please refer to transform() for more details on transforming a vDataFrame.

Similarly, you can perform the inverse transform to get the original features using:

model.inverse_transform(data_transformed)

The variable data_transformed includes the OneHotEncoder components.

Model Register

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to Model Tracking and Versioning for more details on model tracking and versioning.

Model Exporting

To Memmodel

model.to_memmodel()

Note

MemModel objects serve as in-memory representations of machine learning models. They can be used for both in-database and in-memory prediction tasks. These objects can be pickled in the same way that you would pickle a scikit-learn model.

The preceding methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

SQL

To get the SQL query use below:

model.to_sql()
Out[6]: 
[['(CASE WHEN "sex" = \'female\' THEN 1 ELSE 0 END) AS "sex_female"',
  '(CASE WHEN "sex" = \'male\' THEN 1 ELSE 0 END) AS "sex_male"'],
 ['(CASE WHEN "parch" = \'0\' THEN 1 ELSE 0 END) AS "parch_0"',
  '(CASE WHEN "parch" = \'1\' THEN 1 ELSE 0 END) AS "parch_1"',
  '(CASE WHEN "parch" = \'2\' THEN 1 ELSE 0 END) AS "parch_2"',
  '(CASE WHEN "parch" = \'3\' THEN 1 ELSE 0 END) AS "parch_3"',
  '(CASE WHEN "parch" = \'4\' THEN 1 ELSE 0 END) AS "parch_4"',
  '(CASE WHEN "parch" = \'5\' THEN 1 ELSE 0 END) AS "parch_5"',
  '(CASE WHEN "parch" = \'6\' THEN 1 ELSE 0 END) AS "parch_6"',
  '(CASE WHEN "parch" = \'9\' THEN 1 ELSE 0 END) AS "parch_9"']]

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [['1', '3']]

model.to_python()(X)
Out[8]: array([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]])

Hint

The to_python() method is used to transform the data and compute the different categories. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.

__init__(name: str = None, overwrite_model: bool = False, extra_levels: dict | None = None, drop_first: bool = True, ignore_null: bool = True, separator: str = '_', column_naming: Literal['indices', 'values', 'values_relaxed'] = 'indices', null_column_name: str = 'null') → None

Must be overridden in the child class

Methods

__init__([name, overwrite_model, ...])	Must be overridden in the child class
contour([nbins, chart])	Draws the model's contour plot.
deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
deploySQL([X, key_columns, exclude_columns])	Returns the SQL code needed to deploy the model.
does_model_exists(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
drop()	Drops the model from the Vertica database.
export_models(name, path[, kind])	Exports machine learning models.
fit(input_relation[, X, return_report])	Trains the model.
get_attributes([attr_name])	Returns the model attributes.
get_match_index(x, col_list[, str_check])	Returns the matching index.
get_params()	Returns the parameters of the model.
get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
import_models(path[, schema, kind])	Imports machine learning models.
inverse_transform(vdf[, X])	Applies the Inverse Model on a vDataFrame.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
set_params([parameters])	Sets the parameters of the model.
summarize()	Summarizes the model.
to_binary(path)	Exports the model to the Vertica Binary format.
to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
to_pmml(path)	Exports the model to PMML.
to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).
transform([vdf, X])	Applies the model on a vDataFrame.

Attributes

object_type