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verticapy.machine_learning.vertica.linear_model.PLSRegression

class verticapy.machine_learning.vertica.linear_model.PLSRegression(name: str = None, overwrite_model: bool = False, n_components: int = 2, scale: bool = True)

Creates an PLSRegression object using the Vertica PLS Regression 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

Number of components to keep. Should be in [1, n_features].

scale: bool, optional

Boolean, whether to standardize the response and predictor columns.

Attributes

Many attributes are created during the fitting phase.

coef_: numpy.array

The regression coefficients. The order of coefficients is the same as the order of columns used during the fitting phase.

intercept_: float

The expected value of the dependent variable when all independent variables are zero, serving as the baseline or constant term in the model.

features_importance_: numpy.array

The importance of features is computed through the model coefficients, which are normalized based on their range. Subsequently, an activation function calculates the final score. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.

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	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	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
3	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
4	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
5	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
6	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
7	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
8	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
9	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
10	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
11	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
12	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
13	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
14	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
15	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
16	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
17	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
18	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
19	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
20	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
21	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
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white
26	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white
29	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
30	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
31	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white
34	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white
35	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
36	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
37	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white
38	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white
42	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white
43	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red
44	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
45	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
46	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
47	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
48	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white
70	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
71	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
72	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
73	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
81	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
82	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
83	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
84	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
92	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
95	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	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.

You can easily divide your dataset into training and testing subsets using the vDataFrame.train_test_split() method. This is a crucial step when preparing your data for machine learning, as it allows you to evaluate the performance of your models accurately.

data = vpd.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, VerticaPy utilizes seeded randomization to guarantee the reproducibility of your data split. However, please be aware that this approach may lead to reduced performance. For a more efficient data split, you can use the vDataFrame.to_db() method to save your results into tables or temporary tables. This will help enhance the overall performance of the process.

Model Initialization

First we import the PLSRegression model:

from verticapy.machine_learning.vertica import PLSRegression

Then we can create the model:

model = PLSRegression()

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(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "quality",
    test,
)



=======
details
=======
   predictor    |coefficient_0
----------------+-------------
   Intercept    |   5.81781   
 fixed_acidity  |   0.05849   
volatile_acidity|  -0.16311   
  citric_acid   |   0.07641   
 residual_sugar |   0.00062   
   chlorides    |  -0.06717   
    density     |  -0.21001   


=========
responses
=========
index| name  
-----+-------
  0  |quality


===========
call_string
===========
SELECT PLS_REG('"public"."_verticapy_tmp_plsregression_v_mldb_c20273be979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_c2385a10979911efa8720242ac120002_"', 'quality', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', num_components=2, scale=true);

===============
Additional Info
===============
       Name       |Value
------------------+-----
    is_scaled     |  1  
   n_components   |  2  
rejected_row_count|  0  
accepted_row_count|5198 

Important

To train a model, you can directly use the vDataFrame or the name of the relation stored in the database. The test set is optional and is only used to compute the test metrics. In verticapy, we don’t work using X matrices and y vectors. Instead, we work directly with lists of predictors and the response name.

Metrics

We can get the entire report using:

model.report()

	value
explained_variance	0.089893569307526
max_error	3.79651994782341
median_absolute_error	0.57161336429284
mean_absolute_error	0.65111690178776
mean_squared_error	0.715557395122503
root_mean_squared_error	0.845906256698993
r2	0.08979376239148
r2_adj	0.0855667984397377
aic	-420.60414825237
bic	-384.58136273676

Rows: 1-10 | Columns: 2

Important

Most metrics are computed using a single SQL query, but some of them might require multiple SQL queries. Selecting only the necessary metrics in the report can help optimize performance. E.g. model.report(metrics = ["mse", "r2"]).

For LinearModel, we can easily get the ANOVA table using:

model.report(metrics = "anova")

	Df	SS	MS	F	p_value
Regression	6	156.134354877753	26.0223924796255	36.17063314993923	1.164958290120753e-40
Residual	1292	929.509056264132	0.7194342540744056
Total	1298	1021.20708237105

Rows: 1-3 | Columns: 6

You can also use the LinearModel.score function to compute the R-squared value:

model.score()
Out[2]: 0.0897937623914801

Prediction

Prediction is straight-forward:

model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "prediction",
)

	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)	123 prediction Float(22)
1	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	5.97768141845951
2	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	5.72175352637769
3	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	5.659407045481
4	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	6.05234979353485
5	5.3	0.43	0.11	1.1	0.029	6.0	51.0	0.99076	3.51	0.48	11.2	4	0	white	5.8594455169587
6	5.3	0.76	0.03	2.7	0.043	27.0	93.0	0.9932	3.34	0.38	9.2	5	0	white	5.29239501423124
7	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	6.27249010979399
8	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white	6.33311358765388
9	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white	6.23894707261292
10	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	5.71640153244301
11	5.6	0.39	0.24	4.7	0.034	27.0	77.0	0.9906	3.28	0.36	12.7	5	0	white	5.98342539882243
12	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	5.54817639045119
13	5.6	0.605	0.05	2.4	0.073	19.0	25.0	0.99258	3.56	0.55	12.9	5	0	red	5.45893735049404
14	5.7	0.14	0.3	5.4	0.045	26.0	105.0	0.99469	3.32	0.45	9.3	5	0	white	5.95642402431928
15	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	5.56465354274254
16	5.7	0.22	0.28	1.3	0.027	26.0	101.0	0.98948	3.35	0.38	12.5	7	1	white	6.26898723738041
17	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	6.07830676581353
18	5.7	0.37	0.3	1.1	0.029	24.0	88.0	0.98883	3.18	0.39	11.7	6	0	white	6.17349902837987
19	5.7	0.385	0.04	12.6	0.034	22.0	115.0	0.9964	3.28	0.63	9.9	6	0	white	5.47742850855244
20	5.7	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	5.80679295918056
21	5.7	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white	5.31341735175089
22	5.8	0.23	0.27	1.8	0.043	24.0	69.0	0.9933	3.38	0.31	9.4	6	0	white	5.9580283948844
23	5.8	0.25	0.26	13.1	0.051	44.0	148.0	0.9972	3.29	0.38	9.3	5	0	white	5.64301795197913
24	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	5.89406292707684
25	5.8	0.36	0.5	1.0	0.127	63.0	178.0	0.99212	3.1	0.45	9.7	5	0	white	5.87995043471821
26	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	5.81871783280848
27	5.9	0.13	0.28	1.9	0.05	20.0	78.0	0.9918	3.43	0.64	10.8	6	0	white	6.16043277275684
28	5.9	0.26	0.4	1.3	0.047	12.0	139.0	0.9945	3.45	0.53	10.4	5	0	white	5.90843821122925
29	5.9	0.29	0.32	1.4	0.022	17.0	47.0	0.9899	3.35	0.35	11.5	6	0	white	6.20908803694079
30	6.0	0.2	0.32	3.0	0.031	26.0	118.0	0.99134	3.38	0.68	11.2	7	1	white	6.1842059872553
31	6.0	0.24	0.27	1.9	0.048	40.0	170.0	0.9938	3.64	0.54	10.0	7	1	white	5.91256883002094
32	6.0	0.26	0.18	7.0	0.055	50.0	194.0	0.99591	3.21	0.43	9.0	5	0	white	5.68370796870616
33	6.0	0.27	0.4	1.7	0.021	18.0	82.0	0.9891	3.24	0.95	13.1333333333333	6	0	white	6.33398603388444
34	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	5.49491389079673
35	6.0	0.28	0.52	6.2	0.028	37.0	104.0	0.99161	3.28	0.51	11.8	7	1	white	6.19659863915676
36	6.0	0.33	0.26	5.1	0.051	16.0	119.0	0.99416	3.15	0.41	9.2	5	0	white	5.78765383006304
37	6.0	0.34	0.66	15.9	0.046	26.0	164.0	0.9979	3.14	0.5	8.8	6	0	white	5.73246678666622
38	6.0	0.42	0.19	2.0	0.075	22.0	47.0	0.99522	3.39	0.78	10.0	6	0	red	5.54237186062237
39	6.0	0.5	0.04	2.2	0.092	13.0	26.0	0.99647	3.46	0.47	10.0	5	0	red	5.26481032423091
40	6.0	0.52	0.33	7.7	0.046	24.0	119.0	0.99224	3.04	0.38	11.0	6	0	white	5.78210754105381
41	6.0	0.58	0.2	2.4	0.075	15.0	50.0	0.99467	3.58	0.67	12.5	6	0	red	5.42838663570716
42	6.1	0.24	0.26	1.7	0.033	61.0	134.0	0.9903	3.19	0.81	11.9	7	1	white	6.18770607184892
43	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	5.43188897095307
44	6.1	0.32	0.37	1.8	0.051	13.0	200.0	0.9945	3.49	0.44	10.5	4	0	white	5.83515549680856
45	6.1	0.34	0.21	5.0	0.042	17.0	133.0	0.99373	3.02	0.53	9.4	5	0	white	5.80305555156879
46	6.1	0.41	0.14	10.4	0.037	18.0	119.0	0.996	3.38	0.45	10.0	5	0	white	5.5458303382992
47	6.1	0.41	0.24	1.6	0.049	16.0	137.0	0.993	3.32	0.5	10.4	6	0	white	5.78808067952068
48	6.2	0.21	0.28	5.7	0.028	45.0	121.0	0.99168	3.21	1.08	12.15	7	1	white	6.14411493919024
49	6.2	0.23	0.35	0.7	0.051	24.0	111.0	0.9916	3.37	0.43	11.0	3	0	white	6.12396452689844
50	6.2	0.25	0.44	15.8	0.057	39.0	167.0	0.99804	3.14	0.51	9.2	5	0	white	5.68521949022642
51	6.2	0.3	0.21	1.1	0.032	31.0	111.0	0.9889	2.97	0.42	12.2	6	0	white	6.20778951792801
52	6.2	0.3	0.21	1.1	0.032	31.0	111.0	0.9889	2.97	0.42	12.2	6	0	white	6.20778951792801
53	6.2	0.3	0.31	1.2	0.048	19.0	125.0	0.98999	3.32	0.54	12.6	6	0	white	6.15357324104208
54	6.2	0.31	0.26	4.8	0.037	36.0	148.0	0.9928	3.21	0.41	10.4	6	0	white	5.93860476909483
55	6.2	0.34	0.25	12.1	0.059	33.0	171.0	0.99769	3.14	0.56	8.7	6	0	white	5.51729523908249
56	6.2	0.36	0.38	3.2	0.031	20.0	89.0	0.98956	3.06	0.33	12.0	7	1	white	6.19276465086951
57	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	6.12367119547756
58	6.2	0.46	0.25	4.4	0.066	62.0	207.0	0.9939	3.25	0.52	9.8	5	0	white	5.65376716047403
59	6.2	0.53	0.02	0.9	0.035	6.0	81.0	0.99234	3.24	0.35	9.5	4	0	white	5.63094135960377
60	6.2	0.63	0.31	1.7	0.088	15.0	64.0	0.9969	3.46	0.79	9.3	5	0	red	5.26345550904905
61	6.3	0.2	0.24	1.7	0.052	36.0	135.0	0.99374	3.8	0.66	10.8	6	0	white	5.94693168573618
62	6.3	0.22	0.3	2.0	0.05	23.0	120.0	0.99204	3.24	0.47	10.4	6	0	white	6.08295341544267
63	6.3	0.22	0.33	1.7	0.041	67.0	164.0	0.9928	3.32	0.56	10.4	6	0	white	6.0611487339436
64	6.3	0.25	0.44	11.6	0.041	48.0	195.0	0.9968	3.18	0.52	9.5	5	0	white	5.80647853950764
65	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	5.67971013349008
66	6.3	0.27	0.25	5.8	0.038	52.0	155.0	0.995	3.28	0.38	9.4	6	0	white	5.81961294817263
67	6.3	0.29	0.4	6.5	0.039	43.0	167.0	0.9953	3.15	0.44	9.3	6	0	white	5.85539883145104
68	6.4	0.125	0.36	1.4	0.044	22.0	68.0	0.99014	3.15	0.5	11.7	7	1	white	6.35869019380817
69	6.4	0.14	0.31	1.2	0.034	53.0	138.0	0.99084	3.38	0.35	11.5	7	1	white	6.28622011419552
70	6.4	0.15	0.29	1.8	0.044	21.0	115.0	0.99166	3.1	0.38	10.2	5	0	white	6.18942757590584
71	6.4	0.15	0.36	1.8	0.034	43.0	150.0	0.9922	3.42	0.69	11.0	8	1	white	6.20606957228074
72	6.4	0.2	0.28	2.5	0.032	24.0	84.0	0.99168	3.31	0.55	11.5	5	0	white	6.15533192053279
73	6.4	0.23	0.37	7.9	0.05	60.0	150.0	0.99488	2.86	0.49	9.3	6	0	white	5.91345485001143
74	6.4	0.23	0.39	1.8	0.032	23.0	118.0	0.9912	3.32	0.5	11.8	6	0	white	6.21727254464787
75	6.4	0.27	0.29	10.8	0.028	17.0	118.0	0.99356	3.18	0.37	11.2	6	0	white	5.96626646530971
76	6.4	0.29	0.24	3.2	0.037	31.0	95.0	0.98942	2.9	0.66	12.6	7	1	white	6.19668460480541
77	6.4	0.29	0.57	1.0	0.06	15.0	120.0	0.9924	3.06	0.41	9.5	5	0	white	6.11573705108845
78	6.4	0.35	0.35	5.6	0.034	9.0	148.0	0.99441	3.17	0.5	9.8	4	0	white	5.84655718927571
79	6.4	0.475	0.06	1.0	0.03	9.0	131.0	0.9904	2.97	0.29	10.8	5	0	white	5.86224900092052
80	6.4	0.56	0.15	1.8	0.078	17.0	65.0	0.99294	3.33	0.6	10.5	6	0	red	5.55732594007124
81	6.5	0.19	0.1	1.3	0.046	23.0	107.0	0.9937	3.29	0.45	10.0	5	0	white	5.90627830074964
82	6.5	0.19	0.34	1.6	0.029	39.0	116.0	0.98954	3.21	0.68	12.5	6	0	white	6.35847142985432
83	6.5	0.21	0.35	5.7	0.043	47.0	197.0	0.99392	3.24	0.5	10.1	6	0	white	6.00795002274279
84	6.5	0.22	0.19	1.1	0.064	36.0	191.0	0.99297	3.05	0.5	9.5	6	0	white	5.94262863590675
85	6.5	0.22	0.29	7.4	0.028	16.0	87.0	0.99311	3.15	0.56	10.9	7	1	white	6.05176581322169
86	6.5	0.24	0.36	2.2	0.027	36.0	134.0	0.9898	3.28	0.36	12.5	7	1	white	6.30475888410218
87	6.5	0.29	0.25	2.5	0.142	8.0	111.0	0.9927	3.0	0.44	9.9	4	0	white	5.78149987545577
88	6.5	0.36	0.38	10.2	0.028	20.0	82.0	0.99274	3.1	0.43	12.1	7	1	white	5.98703306148161
89	6.5	0.43	0.31	3.6	0.046	19.0	143.0	0.99022	3.15	0.34	12.0	8	1	white	6.02617394597122
90	6.5	0.52	0.11	1.8	0.073	13.0	38.0	0.9955	3.34	0.52	9.3	5	0	red	5.40790081885392
91	6.6	0.16	0.4	1.5	0.044	48.0	143.0	0.9912	3.54	0.52	12.4	7	1	white	6.278849435795
92	6.6	0.17	0.28	1.1	0.034	55.0	108.0	0.98939	3.0	0.52	11.9	7	1	white	6.3527845600117
93	6.6	0.17	0.35	2.6	0.03	33.0	78.0	0.99146	3.22	0.72	11.3	6	0	white	6.25004601228291
94	6.6	0.19	0.41	8.9	0.046	51.0	169.0	0.9954	3.14	0.57	9.8	6	0	white	5.95359104502416
95	6.6	0.22	0.37	1.6	0.04	31.0	101.0	0.99009	3.15	0.66	12.0	5	0	white	6.2898783957657
96	6.6	0.23	0.32	0.9	0.041	25.0	79.0	0.9926	3.39	0.54	10.2	7	1	white	6.07369239728219
97	6.6	0.23	0.32	1.7	0.024	26.0	102.0	0.99084	3.29	0.6	11.8	6	0	white	6.22981966096975
98	6.6	0.25	0.3	14.4	0.052	40.0	183.0	0.998	3.02	0.5	9.1	6	0	white	5.64176921356088
99	6.6	0.25	0.3	14.4	0.052	40.0	183.0	0.998	3.02	0.5	9.1	6	0	white	5.64176921356088
100	6.6	0.26	0.31	4.8	0.138	41.0	168.0	0.9951	3.2	0.38	9.3	5	0	white	5.6844155999808

Rows: 1-100 | Columns: 15

Note

Predictions can be made automatically using the test set, in which case you don’t need to specify the predictors. Alternatively, you can pass only the vDataFrame to the predict() function, but in this case, it’s essential that the column names of the vDataFrame match the predictors and response name in the model.

Plots

If the model allows, you can also generate relevant plots. For example, regression plots can be found in the Machine Learning - Regression Plots.

model.plot()

Important

The plotting feature is typically suitable for models with fewer than three predictors.

Parameter Modification

In order to see the parameters:

model.get_params()
Out[3]: {'n_components': 2, 'scale': True}

And to manually change some of the parameters:

model.set_params({'scale': True})

Model Register

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to /notebooks/ml/model_tracking_versioning/index.ipynb 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 following methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

To SQL

You can get the SQL code by:

model.to_sql()
Out[5]: '76.36734127075228 + 0.04529218600530152 * "fixed_acidity" + -0.9894938638122842 * "volatile_acidity" + 0.524050674183808 * "citric_acid" + 0.00013145808895322417 * "residual_sugar" + -1.8289321529955385 * "chlorides" + -70.98309693952912 * "density"'

To Python

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

X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]]

model.to_python()(X)
Out[7]: array([6.25904267])

Hint

The to_python() method is used to retrieve predictions, probabilities, or cluster distances. 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 = 2, scale: bool = True) → None

Methods

__init__([name, overwrite_model, ...])
contour([nbins, chart])	Draws the model's contour plot.
deploySQL([X])	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.
features_importance([show, chart])	Computes the model's features importance.
fit(input_relation, X, y[, test_relation, ...])	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.
plot([max_nb_points, chart])	Draws the model.
predict(vdf[, X, name, inplace])	Predicts using the input relation.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
regression_report([metrics])	Computes a regression report
report([metrics])	Computes a regression report
score([metric])	Computes the model score.
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).

Attributes

object_type