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verticapy.machine_learning.vertica.decomposition.SVD

class verticapy.machine_learning.vertica.decomposition.SVD(name: str = None, overwrite_model: bool = False, n_components: int = 0, method: Literal['lapack'] = 'lapack')

Creates an SVD (Singular Value Decomposition) object using the Vertica SVD algorithm.

Parameters

name: str, optional

Name of the model. The model is stored in the database.

overwrite_model: bool, optional

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

n_components: int, optional

The number of components to keep in the model. If this value is not provided, all components are kept. The maximum number of components is the number of non-zero singular values returned by the internal call to SVD. This number is less than or equal to SVD (number of columns, number of rows).

method: str, optional

The method used to calculate SVD.

• lapack:

  Lapack definition.

Attributes

Many attributes are created during the fitting phase.

values_: numpy.array

Matrix of the right singular vectors.

values_: numpy.array

Array of the singular values for each input feature.

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 winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white
6	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white
7	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white
8	4.5	0.19	0.21	0.95	0.033	89.0	159.0	0.99332	3.34	0.42	8.0	5	0	white
9	4.6	0.445	0.0	1.4	0.053	11.0	178.0	0.99426	3.79	0.55	10.2	5	0	white
10	4.6	0.52	0.15	2.1	0.054	8.0	65.0	0.9934	3.9	0.56	13.1	4	0	red
11	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6	0	white
12	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
13	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
14	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red
15	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white
16	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
17	4.8	0.13	0.32	1.2	0.042	40.0	98.0	0.9898	3.42	0.64	11.8	7	1	white
18	4.8	0.17	0.28	2.9	0.03	22.0	111.0	0.9902	3.38	0.34	11.3	7	1	white
19	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7	1	white
20	4.8	0.225	0.38	1.2	0.074	47.0	130.0	0.99132	3.31	0.4	10.3	6	0	white
21	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7	1	white
22	4.8	0.29	0.23	1.1	0.044	38.0	180.0	0.98924	3.28	0.34	11.9	6	0	white
23	4.8	0.33	0.0	6.5	0.028	34.0	163.0	0.9937	3.35	0.61	9.9	5	0	white
24	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white
25	4.8	0.65	0.12	1.1	0.013	4.0	10.0	0.99246	3.32	0.36	13.5	4	0	white
26	4.9	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
27	4.9	0.33	0.31	1.2	0.016	39.0	150.0	0.98713	3.33	0.59	14.0	8	1	white
28	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
29	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
30	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
31	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
32	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
33	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white
34	5.0	0.17	0.56	1.5	0.026	24.0	115.0	0.9906	3.48	0.39	10.8	7	1	white
35	5.0	0.2	0.4	1.9	0.015	20.0	98.0	0.9897	3.37	0.55	12.05	6	0	white
36	5.0	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
37	5.0	0.24	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5	0	white
38	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6	0	white
39	5.0	0.24	0.34	1.1	0.034	49.0	158.0	0.98774	3.32	0.32	13.1	7	1	white
40	5.0	0.255	0.22	2.7	0.043	46.0	153.0	0.99238	3.75	0.76	11.3	6	0	white
41	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
42	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
43	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
44	5.0	0.29	0.54	5.7	0.035	54.0	155.0	0.98976	3.27	0.34	12.9	8	1	white
45	5.0	0.3	0.33	3.7	0.03	54.0	173.0	0.9887	3.36	0.3	13.0	7	1	white
46	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
47	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
48	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
49	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
50	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white
51	5.0	0.33	0.23	11.8	0.03	23.0	158.0	0.99322	3.41	0.64	11.8	6	0	white
52	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
53	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
54	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red
55	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red
56	5.0	0.42	0.24	2.0	0.06	19.0	50.0	0.9917	3.72	0.74	14.0	8	1	red
57	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
58	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
59	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white
60	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white
61	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red
62	5.0	1.02	0.04	1.4	0.045	41.0	85.0	0.9938	3.75	0.48	10.5	4	0	red
63	5.0	1.04	0.24	1.6	0.05	32.0	96.0	0.9934	3.74	0.62	11.5	5	0	red
64	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
65	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
66	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
67	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white
68	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5	0	white
69	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7	1	white
70	5.1	0.26	0.33	1.1	0.027	46.0	113.0	0.98946	3.35	0.43	11.4	7	1	white
71	5.1	0.26	0.34	6.4	0.034	26.0	99.0	0.99449	3.23	0.41	9.2	6	0	white
72	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
73	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
74	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6	0	white
75	5.1	0.305	0.13	1.75	0.036	17.0	73.0	0.99	3.4	0.51	12.3333333333333	5	0	white
76	5.1	0.31	0.3	0.9	0.037	28.0	152.0	0.992	3.54	0.56	10.1	6	0	white
77	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
78	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
79	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
80	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7	1	white
81	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
82	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
83	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
84	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
85	5.1	0.42	0.0	1.8	0.044	18.0	88.0	0.99157	3.68	0.73	13.6	7	1	red
86	5.1	0.42	0.01	1.5	0.017	25.0	102.0	0.9894	3.38	0.36	12.3	7	1	white
87	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red
88	5.1	0.51	0.18	2.1	0.042	16.0	101.0	0.9924	3.46	0.87	12.9	7	1	red
89	5.1	0.52	0.06	2.7	0.052	30.0	79.0	0.9932	3.32	0.43	9.3	5	0	white
90	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red
91	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
92	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
93	5.2	0.16	0.34	0.8	0.029	26.0	77.0	0.99155	3.25	0.51	10.1	6	0	white
94	5.2	0.17	0.27	0.7	0.03	11.0	68.0	0.99218	3.3	0.41	9.8	5	0	white
95	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white
96	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
97	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
98	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
99	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6	0	white
100	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white

Rows: 1-100 | Columns: 14

We can drop the “color” column as it is varchar type.

data.drop("color")

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 SVD model:

from verticapy.machine_learning.vertica import SVD

Then we can create the model:

model = SVD(
    n_components = 3,
)

You can select the number of components by the n_component parameter. If it is not provided, then all are considered.

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)

Important

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

Scores

The decomposition score on the dataset for each transformed column can be calculated by:

model.score()
Out[4]: 
None                                   Score  
fixed_acidity               3.19669356683238  
volatile_acidity           0.264348755504044  
citric_acid                0.228932614877665  
residual_sugar              5.38039944191312  
chlorides                 0.0427139489873566  
free_sulfur_dioxide         2.10040482930188  
total_sulfur_dioxide        4.73074234245691  
density                    0.309070644222815  
pH                          1.14052266015885  
sulphates                  0.304887828479003  
alcohol                     3.19983025734727  
quality                     2.33115583120493  
good                       0.352765059376177  
Rows: 1-13 | Columns: 2

For more details on the function, check out score()

You can also fetch the explained variance by:

model.explained_variance_
Out[5]: array([0.9883586 , 0.00807026, 0.00248987])

Principal Components

To get the transformed dataset in the form of principal components:

model.transform(data)
Out[6]: 
None                  col1                     col2                     col3  
1       0.0108141080605074     -0.00898690374891701      0.00727314637892158  
2       0.0113609654056891       -0.001188361075854      0.00674904946769466  
3       0.0166627178211831       0.0504421228529201       -0.013344560552529  
4       0.0156076978382921       0.0224989559819784      -0.0151162620266151  
5        0.012213209822063     -0.00158312495446874      0.00561623220768627  
6       0.0107729444391737      0.00256515037026606      0.00663577398135461  
7       0.0100155674790493       0.0270441151710081      0.00376563452202662  
8       0.0163554466085546       0.0466574148608037      -0.0189083140020647  
9       0.0162665231231895      -0.0352134384874341     -0.00229993058464557  
10     0.00612561656911707     -0.00800801568019847       0.0162712298233318  
11     0.00897314841200707        0.011950961619011      0.00652009642959555  
12      0.0167155507504623       0.0247094490831147       -0.012430840269128  
13      0.0103795306807778      0.00596388304507683      0.00901423507482371  
14      0.0100017231953311     -0.00992966839614197        0.010317295285701  
15     0.00105751138767118      0.00394349706810463       0.0253800950241802  
16      0.0126503572208966      -0.0113636033145426      0.00657373809766758  
17     0.00981065936878354       0.0148445654146031      0.00584151877390348  
18      0.0105589692445995     -0.00637543518725687      0.00714797062666492  
19      0.0105691701583817      -0.0113629436261072      0.00952468977202705  
20      0.0128160451773933        0.013109827216752     -0.00253564961142822  
21      0.0106152336426165     -0.00521753382020842      0.00742653982907914  
22      0.0170823856441917      -0.0089456684893137     -0.00511840498754081  
23      0.0154778450255575     -0.00844424924439236     -0.00527451937022658  
24      0.0154583833096818     -0.00938853426778954     -0.00436702722677493  
25     0.00112031244917541      0.00264822944127992       0.0247287583690205  
26        0.01148166275695       0.0036172201927973     0.000741660518064614  
27      0.0144487952276349     0.000242787333090651      0.00295673168007065  
28      0.0167162120544499       0.0247153778951736      -0.0122822601942573  
29      0.0167162120544499       0.0247153778951736      -0.0122822601942573  
30      0.0136246739310925     -0.00520528702297353     -0.00207523067964397  
31      0.0136246739310925     -0.00520528702297353     -0.00207523067964397  
32     0.00427461786124396      0.00619558925697606        0.021193124762103  
33      0.0147347506278432       0.0212604638292909     -0.00583120404868525  
34      0.0109545127582932     -0.00551409595328058      0.00576446729975465  
35     0.00934990184656775     -0.00490655658311333       0.0096981848845159  
36      0.0114821012145568      0.00362128901795188     0.000838108278379084  
37     0.00954488009164201      -0.0087742944602826      0.00667892232556901  
38      0.0097823446972645      0.00540987186800628      0.00686511042374751  
39      0.0153847215828542      0.00788842583923433     -0.00194983681799175  
40      0.0148611056419884       0.0061308952609764     -0.00358340879790029  
41      0.0173895891344854       0.0115009120151289      -0.0069213084272989  
42      0.0173895891344854       0.0115009120151289      -0.0069213084272989  
43      0.0121643687586682      0.00976266966246829    -0.000797056270560854  
44      0.0152536586305299       0.0137425595116523     -0.00203872797701861  
45      0.0168497956817113      0.00888850199443394     -0.00497628224283744  
46      0.0159577446628235    -0.000308884162375855     -0.00662477199184711  
47     0.00901925111535918       -0.014608096467652       0.0100922049391821  
48     0.00901925111535918       -0.014608096467652       0.0100922049391821  
49     0.00901925111535918       -0.014608096467652       0.0100922049391821  
50      0.0121376232603184      0.00791387265659954     0.000825965463865728  
51      0.0148159331848086      -0.0176893913881306      0.00119415364309956  
52      0.0110690260632225     -0.00566710155619228      0.00568347667217994  
53      0.0110690260632225     -0.00566710155619228      0.00568347667217994  
54     0.00610760123535686       0.0112349312993762       0.0161564895686208  
55     0.00797094362520398      0.00891093373305621       0.0124018649063269  
56     0.00506201349163946      0.00707814959395135       0.0203449582810378  
57      0.0125014629881725      0.00511287008052087     7.31990933828669e-05  
58      0.0103808460535981      0.00597608952054056      0.00930357835576712  
59      0.0156267641483374     -0.00737015988358075     0.000296320931996781  
60      0.0105716460915439       0.0390215154904031      0.00145608459238926  
61     0.00461567673665519       0.0049791934244293        0.018138757313459  
62     0.00865600250002652       0.0190843469128548      0.00382400225369011  
63     0.00944116324386852      0.00739348227175051      0.00626789774520582  
64     0.00845119313041551      -0.0106681490480454        0.012723953808117  
65     0.00840739769300279     -0.00766543030535406      0.00809400514385817  
66      0.0141461253309525      0.00400108861898725     -0.00378600461398395  
67      0.0144459310487507      0.00930219157574227     -0.00528452907136737  
68     0.00926654633906355      -0.0121787695713679      0.00956171013538002  
69     0.00802816459064073       0.0201851385051224      0.00907586192594589  
70       0.011286840064499       0.0167666532148082      0.00238562301666936  
71     0.00957808963795109     0.000653930534385808      0.00470240138816252  
72      0.0103352354224899    -0.000322779278897781      0.00594078563297295  
73      0.0103352354224899    -0.000322779278897781      0.00594078563297295  
74      0.0144581674641318      0.00105048013198394    -0.000880308960696798  
75     0.00704632805117089     -0.00125311656786212       0.0133444561329968  
76      0.0143405642039511      -0.0114881981132763     -0.00142287511458727  
77     0.00850613281699714     -0.00441482870613591       0.0127431247799744  
78     0.00850613281699714     -0.00441482870613591       0.0127431247799744  
79     0.00850613281699714     -0.00441482870613591       0.0127431247799744  
80      0.0126897436110269       0.0106316956818193      0.00031262943042162  
81      0.0117011565329233      0.00511316508798839      0.00305555709434668  
82      0.0117011565329233      0.00511316508798839      0.00305555709434668  
83      0.0117011565329233      0.00511316508798839      0.00305555709434668  
84      0.0069155247298427     -0.00290726213742451       0.0148426427801898  
85     0.00842520674025287     -0.00406786111660374         0.01442894666402  
86     0.00982671259866448    -0.000974362410638909      0.00930281068428658  
87     0.00448585606275903      0.00750788014098886       0.0184363410346446  
88     0.00953612346062723     -0.00953706740255909       0.0121541620122911  
89     0.00786669266643752      0.00980318166512076      0.00588189636259572  
90     0.00814908792851708     -0.00753491705347241       0.0145490178143425  
91     0.00571174655543743     -0.00138118892270593       0.0176180582249371  
92     0.00571174655543743     -0.00138118892270593       0.0176180582249371  
93     0.00759694685748644      0.00644547936960487      0.00873790743769185  
94     0.00644068949568042     -0.00603581304579231       0.0118720332715975  
95       0.012191422156916       0.0149750163321693     -0.00135461681419584  
96     0.00880846552072471     -0.00757448402887002      0.00955177673886906  
97      0.0059799495172463      0.00199945607844215       0.0159252209807065  
98      0.0177817555288171     -0.00791052940334145     -0.00973689484084486  
99      0.0128497955493046     -0.00265075743350479    -3.69216962120902e-05  
100      0.012062677406264      -0.0118012043899065       0.0061936554761859  
Rows: 1-100 | Columns: 3

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

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

model.inverse_transform(data_transformed)

The variable data_transformed includes the PCA components.

Plots - SVD

You can plot the first two dimensions conveniently using:

model.plot()

Plots - Scree

You can also plot the Scree plot:

model.plot_scree()

Loading....

Parameter Modification

In order to see the parameters:

model.get_params()
Out[7]: {'n_components': 3, 'method': 'lapack'}

And to manually change some of the parameters:

model.set_params({'n_components': 3})

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[9]: 
['"fixed_acidity" * 0.0472721802865271 / 10781.471128853 + "volatile_acidity" * 0.00206840925765328 / 10781.471128853 + "citric_acid" * 0.00224024758641517 / 10781.471128853 + "residual_sugar" * 0.044395714039541 / 10781.471128853 + "chlorides" * 0.000346326837712132 / 10781.471128853 + "free_sulfur_dioxide" * 0.249206286239206 / 10781.471128853 + "total_sulfur_dioxide" * 0.962684441178257 / 10781.471128853 + "density" * 0.00670829104944216 / 10781.471128853 + "pH" * 0.0215818136774093 / 10781.471128853 + "sulphates" * 0.00345087841775058 / 10781.471128853 + "alcohol" * 0.0697381862438041 / 10781.471128853 + "quality" * 0.0391425145196623 / 10781.471128853 + "good" * 0.00126598337328779 / 10781.471128853',
 '"fixed_acidity" * 0.0396399788533397 / 974.236477296841 + "volatile_acidity" * 0.00188557954207304 / 974.236477296841 + "citric_acid" * 0.00119732121820979 / 974.236477296841 + "residual_sugar" * 0.0197505553836915 / 974.236477296841 + "chlorides" * 0.000483104744026943 / 974.236477296841 + "free_sulfur_dioxide" * 0.961682217712392 / 974.236477296841 + "total_sulfur_dioxide" * -0.258651563180513 / 974.236477296841 + "density" * 0.00544627404807701 / 974.236477296841 + "pH" * 0.0186923356923426 / 974.236477296841 + "sulphates" * 0.00373864932226108 / 974.236477296841 + "alcohol" * 0.0645579238733936 / 974.236477296841 + "quality" * 0.0415184345466081 / 974.236477296841 + "good" * 0.00393668701479983 / 974.236477296841',
 '"fixed_acidity" * 0.521916714261151 / 541.139278464763 + "volatile_acidity" * 0.02994886567606 / 541.139278464763 + "citric_acid" * 0.0173905391926748 / 541.139278464763 + "residual_sugar" * 0.0130884723973582 / 541.139278464763 + "chlorides" * 0.00460654396890658 / 541.139278464763 + "free_sulfur_dioxide" * -0.110072060878328 / 541.139278464763 + "total_sulfur_dioxide" * -0.0705721810950697 / 541.139278464763 + "density" * 0.0623122781813198 / 541.139278464763 + "pH" * 0.205991789180222 / 541.139278464763 + "sulphates" * 0.0388553814537634 / 541.139278464763 + "alcohol" * 0.719424850273745 / 541.139278464763 + "quality" * 0.378635713837342 / 541.139278464763 + "good" * 0.0187739554796354 / 541.139278464763']

To Python

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

X = [[3.8, 0.3, 0.02, 11, 0.03, 20, 113, 0.99, 3, 0.4, 12, 6, 0]]

model.to_python()(X)
Out[11]: array([[ 0.0107203 , -0.00876453,  0.0065801 ]])

Hint

The to_python() method is used to retrieve the Principal Component values. 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, n_components: int = 0, method: Literal['lapack'] = 'lapack') → 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, n_components, cutoff, ...])	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.
plot([dimensions, chart])	Draws a decomposition scatter plot.
plot_circle([dimensions, chart])	Draws a decomposition circle.
plot_scree([chart])	Draws a decomposition scree plot.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
score([X, input_relation, metric, p])	Returns the decomposition score on a dataset for each transformed column.
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, n_components, cutoff])	Applies the model on a vDataFrame.

Attributes

object_type