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verticapy.machine_learning.vertica.decomposition.PCA

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

Creates a PCA (Principal Component Analysis) object using the Vertica PCA 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).

scale: bool, optional

A Boolean value that specifies whether to standardize the columns during the preparation step.

method: str, optional

The method used to calculate PCA.

• lapack:

  Lapack definition.

Attributes

Many attributes are created during the fitting phase.

principal_components_: numpy.array

Matrix of the principal components.

mean_: numpy.array

List of the averages of each input feature.

cos2_: numpy.array

Quality of representation of each observation in the principal component space. A high cos2 value indicates that the observation is well-represented in the reduced-dimensional space defined by the principal components, while a low value suggests poor representation.

explained_variance_: numpy.array

Represents the proportion of the total variance in the original dataset that is captured by a specific principal component or a combination of principal components.

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

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.

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

data.drop("color")

Model Initialization

First we import the PCA model:

from verticapy.machine_learning.vertica import PCA

Then we can create the model:

model = PCA(
    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               3.26018188659356  
volatile_acidity           0.255704870531137  
citric_acid                0.232055113862187  
residual_sugar             0.784335096491506  
chlorides                 0.0412739282041459  
free_sulfur_dioxide        0.743079726641283  
total_sulfur_dioxide        1.03787465340886  
density                   0.0561515090418321  
pH                         0.801959298244123  
sulphates                  0.303876275255451  
alcohol                     4.02272456571807  
quality                     2.54477536599627  
good                       0.348243267430713  
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([0.95351037, 0.04062083, 0.00482552])

Principal Components

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

model.transform(data)
Out[6]: 
None                col1                  col2                  col3  
1      -3.87539586294116     -9.70263141971311      5.73365140692485  
2        1.8007158864829     -1.98185721028904     -1.57290632355351  
3       58.2585435866957      50.2581718382341       -7.157700338295  
4       47.8458923519163      22.9883205947403     -2.56964241029969  
5       11.0115318397046     -2.12012882814391      -1.8375746016803  
6      -4.60521361896981       1.5341694499065     -2.75824074459435  
7      -13.2771414595065      25.2504890643806    -0.476520460754179  
8       55.3788310502415      46.7802985538193     -7.45917494667214  
9       55.8773719495786     -33.4658268951551     -5.76129791729379  
10     -54.6555678140085     -10.2240557382057     -1.16251198171631  
11     -24.1648594959502      10.2439927861505     -3.75116700698459  
12      59.5176275497709       25.258097509706     -7.09430396571515  
13     -9.05011526637347      4.63228831511623     -3.60981634879058  
14     -12.7149929321467     -10.9367841190281     -2.67613969311331  
15      -109.83712113806     -0.19489867094966    -0.211036337506947  
16      15.9284646994512     -11.5572095245047     -2.82109432623325  
17     -15.2310907537324      13.2721178525629     -4.02619559516123  
18     -6.66895840225276     -7.21300376171408     -2.27046351728112  
19      -6.5550265533149     -12.1686938056515      5.01741673181463  
20      17.5008031764373      12.6595659366785     -5.22319739599442  
21     -6.12385714274914     -6.09641638348886      5.34842459664887  
22      64.0218965257457      -7.6609298570904     -7.00387756953786  
23      46.8056869539979     -7.53188964107484    -0.739011856339026  
24      46.5746543609322     -8.49535015989575    -0.720852160376789  
25      -109.09098458572     -1.40699916293401    -0.100865448889536  
26      3.27549637771107      2.98554661691686      6.13681673815753  
27      35.0776851714978     0.281798297982036     -6.05124271309635  
28      59.5163276885018      25.2569270184036     -7.08677590412429  
29      59.5163276885018      25.2569270184036     -7.08677590412429  
30      26.6743114830131     -4.95190539016235     -5.45347556437632  
31      26.6743114830131     -4.95190539016235     -5.45347556437632  
32      -75.180559206192      2.90998249909939    -0.653853292464544  
33      38.0171694522185      21.1542722104449     -5.62104279390702  
34     -2.37547571780678     -6.22192340926697     -3.81553048439253  
35     -19.8139354364376     -6.17937852955219     -2.78776089879949  
36       3.2747555833307      2.98500940837631      6.13920308997688  
37     -17.4503747760159     -9.74850498290234     0.446947775612021  
38     -15.3204945867663      4.06143755060113     -2.83284045680938  
39      45.1607450949272      8.14262728404729     -6.62246702457769  
40      39.6841016220826      6.39508394279764     -4.60997393999157  
41      66.8204822700176      12.3302615531612     -4.15952189478502  
42      66.8204822700176      12.3302615531612     -4.15952189478502  
43      10.5149045897773      9.18318394872144     -4.85132644225315  
44      43.5859575590291      13.7940321018126     -2.06416136230967  
45      61.0027620426438      9.58292669483431     -4.70032804310602  
46      51.7904435247003     0.533685199469238     -1.19389604379981  
47     -23.1002361108527     -15.6854930106014      -2.7681976678508  
48     -23.1002361108527     -15.6854930106014      -2.7681976678508  
49     -23.1002361108527     -15.6854930106014      -2.7681976678508  
50      10.1884723538412      7.30376481892969     -1.48712788146904  
51      39.6146400834973     -16.9614288466675      4.85177815550988  
52     -1.14706454035176     -6.34637338860446      2.39197736130292  
53     -1.14706454035176     -6.34637338860446      2.39197736130292  
54     -55.3970836353244      8.44951985367211     -2.05075012096447  
55     -35.1489550264151      6.78834277464059    -0.126587947134508  
56     -66.7170483841066      3.98339148251936     -1.12767424697563  
57      14.1943699338795      4.69155400256368      12.4206564896857  
58     -9.05233764951458      4.63067668949458     -3.60265729333253  
59      48.0643491900927     -6.72080812877328     0.746048150327689  
60     -7.53034129056041      37.1090070862536     -4.78644698313109  
61     -71.3209829620148      1.95095263269743     -1.55326105628057  
62     -27.6243365464012      17.2212353028306     -3.24496967423472  
63      -19.002636112788      5.93811212781568     -3.28776432567215  
64     -29.4490299121844     -12.1146438784988     -2.59415770058483  
65      -29.750093053573     -8.99107737676142      -3.2848216598803  
66      32.0875872308678      4.17530649922571     -1.14257902778671  
67      35.2360399214631      9.46054341305244     -5.69064790913363  
68     -20.4892045223688     -13.2472443987493     -3.47184703179337  
69     -34.6741775069697      17.9008639524076     -3.22563541364455  
70     0.730202192986314      15.6320679596781     -4.78499660843747  
71     -17.2594071938456    -0.500180186642626      1.73125273716163  
72     -9.18358839473754     -1.33806872969595      3.15843640640076  
73     -9.18358839473754     -1.33806872969595      3.15843640640076  
74      35.3631549238248      1.24771708830033     -4.80588411457336  
75     -44.8177038153327     -3.32408322957084     -1.97193555518784  
76      34.4954873429935     -10.9184000917509     -5.77391014958169  
77     -29.0409734384488     -6.03520319370948      -2.7598636252927  
78     -29.0409734384488     -6.03520319370948      -2.7598636252927  
79     -29.0409734384488     -6.03520319370948      -2.7598636252927  
80      16.0557078794116      10.0876558355333     0.301566854171818  
81      5.46454014126889      4.38084910265817     0.914005555368653  
82      5.46454014126889      4.38084910265817     0.914005555368653  
83      5.46454014126889      4.38084910265817     0.914005555368653  
84     -46.2544093147873     -5.02925948152294     -1.95463187806248  
85     -30.0115077058734     -5.79669845533482     -2.61808240679804  
86     -14.7925554427983     -2.23755020006416     -3.49815587620536  
87     -72.8022460813843      4.36130645643567     -1.45815612704746  
88     -17.8181544771345     -10.7466772356141     -2.67193978657889  
89     -35.9331209476592      7.94028679930402     -1.34254106589017  
90     -32.8783174079216     -9.22839098854763     -2.47901890123736  
91     -59.3563097995857     -3.94947065130502     -1.50484516743191  
92     -59.3563097995857     -3.94947065130502     -1.50484516743191  
93     -38.8823966562381      4.48833970280952     -3.12535345595975  
94     -51.0910613999878     -8.03023822650388     -2.46212354452028  
95      10.6850783476395      14.2732474183979     -5.15136456874776  
96     -25.5351793082778     -8.88356110618463     -1.05583066878426  
97     -56.4856688259442      -0.5290378076123     -1.58351166931636  
98      71.7372841256507     -6.28542003701019     -2.06235268516737  
99      18.1733465755406     -2.74862458726538     0.954111381558329  
100     9.66114424275959     -12.0957510762698     -1.94944007569165  
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 - PCA

You can plot the first two components 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, 'scale': False, '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" - 7.21530706479914) * -0.00740794380368571 + ("volatile_acidity" - 0.339665999692166) * -0.00118432129646007 + ("citric_acid" - 0.318633215330152) * 0.000486867004519229 + ("residual_sugar" - 5.44323533938741) * 0.0410197294279797 + ("chlorides" - 0.0560338617823611) * -0.000168197431343702 + ("free_sulfur_dioxide" - 30.5253193781745) * 0.230481489722234 + ("total_sulfur_dioxide" - 115.744574418963) * 0.972166693841427 + ("density" - 0.994696633830999) * 1.7724964231297e-06 + ("pH" - 3.21850084654456) * -0.000655520779893176 + ("sulphates" - 0.531268277666615) * -0.000704339207760154 + ("alcohol" - 10.4918008311528) * -0.0054518247482017 + ("quality" - 5.81837771279052) * -0.000532705277259338 + ("good" - 0.196552254886871) * -0.000326386219750347',
 '("fixed_acidity" - 7.21530706479914) * -0.00537208540551764 + ("volatile_acidity" - 0.339665999692166) * -0.000787179265926509 + ("citric_acid" - 0.318633215330152) * -0.000247100931315118 + ("residual_sugar" - 5.44323533938741) * 0.0186262696200211 + ("chlorides" - 0.0560338617823611) * 6.67995503853851e-05 + ("free_sulfur_dioxide" - 30.5253193781745) * 0.972615829133942 + ("total_sulfur_dioxide" - 115.744574418963) * -0.231392918488152 + ("density" - 0.994696633830999) * 1.27197739295473e-06 + ("pH" - 3.21850084654456) * 0.00064802744147177 + ("sulphates" - 0.531268277666615) * 0.000346564126401858 + ("alcohol" - 10.4918008311528) * 0.00288222663864476 + ("quality" - 5.81837771279052) * 0.00915670157688827 + ("good" - 0.196552254886871) * 0.00256735969031283',
 '("fixed_acidity" - 7.21530706479914) * 0.0238635181935159 + ("volatile_acidity" - 0.339665999692166) * 0.000908752011459457 + ("citric_acid" - 0.318633215330152) * 0.0019208440336196 + ("residual_sugar" - 5.44323533938741) * 0.995191758737064 + ("chlorides" - 0.0560338617823611) * 0.000177543760783315 + ("free_sulfur_dioxide" - 30.5253193781745) * -0.0271071934686967 + ("total_sulfur_dioxide" - 115.744574418963) * -0.0358591349601494 + ("density" - 0.994696633830999) * 0.000460901781338666 + ("pH" - 3.21850084654456) * -0.00691133676962082 + ("sulphates" - 0.531268277666615) * -0.00193655721535319 + ("alcohol" - 10.4918008311528) * -0.0826607385648245 + ("quality" - 5.81837771279052) * -0.00888756383923461 + ("good" - 0.196552254886871) * -0.00592921565549895']

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([[-4.84895114, -9.47474643,  5.70830533]])

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, scale: bool = False, 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