verticapy.machine_learning.vertica.linear_model.LinearRegression¶

class verticapy.machine_learning.vertica.linear_model.LinearRegression(name: str = None, overwrite_model: bool = False, tol: float = 1e-06, max_iter: int = 100, solver: Literal['newton', 'bfgs'] = 'newton', fit_intercept: bool = True)¶

Creates a LinearRegression object using the Vertica Linear 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.

tol: float, optional

Determines whether the algorithm has reached the specified accuracy result.

max_iter: int, optional

Determines the maximum number of iterations the algorithm performs before achieving the specified accuracy result.

solver: str, optional

The optimizer method used to train the model.

newton:
Newton Method.

bfgs:
Broyden Fletcher Goldfarb Shanno.

fit_intercept: bool, optional

boolean, specifies whether the model includes an intercept. If set to False, no intercept is used in training the model. Note that setting fit_intercept to False does not work well with the BFGS optimizer.

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	3.8	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.

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 LinearRegression model:

from verticapy.machine_learning.vertica import LinearRegression

Then we can create the model:

model = LinearRegression(
    tol = 1e-6,
    max_iter = 100,
    solver = 'newton',
    fit_intercept = True,
)

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|std_err | t_value |p_value 
----------------+-----------+--------+---------+--------
   Intercept    | 142.55628 | 6.83038|20.87091 | 0.00000
 fixed_acidity  |  0.14577  | 0.01236|11.79777 | 0.00000
volatile_acidity| -0.73644  | 0.08665|-8.49900 | 0.00000
  citric_acid   | -0.06035  | 0.09560|-0.63122 | 0.52793
 residual_sugar |  0.04128  | 0.00384|10.75581 | 0.00000
   chlorides    | -0.18007  | 0.36608|-0.49190 | 0.62281
    density     |-138.46779 | 6.94721|-19.93142| 0.00000


==============
regularization
==============
type| lambda 
----+--------
none| 1.00000


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_demo_1529433e55a411ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_153a749c55a411ef880f0242ac120002_"', '"quality"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"'
USING PARAMETERS optimizer='newton', epsilon=1e-06, max_iterations=100, regularization='none', lambda=1, alpha=0.5, fit_intercept=true)

===============
Additional Info
===============
       Name       |Value
------------------+-----
 iteration_count  |  1  
rejected_row_count|  0  
accepted_row_count|5196 

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.

Features Importance¶

We can conveniently get the features importance:

result = model.features_importance()

Note

For LinearModel, feature importance is computed using the coefficients. These coefficients are then normalized using the feature distribution. An activation function is applied to get the final score.

Metrics¶

We can get the entire report using:

result = model.report()

	value
explained_variance	0.180747705070455
max_error	3.14583807237076
median_absolute_error	0.541169332796727
mean_absolute_error	0.621826428418051
mean_squared_error	0.646209084591811
root_mean_squared_error	0.803871310964517
r2	0.180681251562433
r2_adj	0.176882246546494
aic	-553.8960365438
bic	-517.862230187252

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:

result = model.report(metrics = "anova")

	Df	SS	MS	F	p_value
Regression	6	154.324207181299	25.720701196883166	39.58828833741362	2.1726466324715088e-44
Residual	1294	840.718019053946	0.6497048060695101
Total	1300	1026.11837048424

Rows: 1-3 | Columns: 6

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

model.score()
Out[2]: 0.180681251562433

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	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white	5.16365957211161
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	5.88465295543014
3	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	5.27435447091915
4	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	5.35786734433307
5	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	5.74292482663895
6	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	6.22477650834767
7	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	6.22477650834767
8	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	5.97438698654969
9	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	5.87612885935644
10	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	5.34194182062069
11	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	5.00787325422735
12	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	6.18272867526983
13	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	5.89757470822568
14	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	6.11839232449688
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	6.11839232449688
16	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	5.92386024599961
17	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	5.48245916691221
18	5.2	0.28	0.29	1.1	0.028	18.0	69.0	0.99168	3.24	0.54	10.0	6	0	white	5.81519407526369
19	5.2	0.3	0.34	1.5	0.038	18.0	96.0	0.98942	3.56	0.48	13.0	8	1	white	6.12509627489007
20	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	5.24689986177964
21	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	6.10355604749014
22	5.2	0.38	0.26	7.7	0.053	20.0	103.0	0.9925	3.27	0.45	12.2	6	0	white	5.89776010085123
23	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	5.99876978249671
24	5.2	0.6	0.07	7.0	0.044	33.0	147.0	0.9944	3.33	0.58	9.7	5	0	white	5.45684395955746
25	5.3	0.26	0.23	5.15	0.034	48.0	160.0	0.9952	3.82	0.51	10.5	7	1	white	5.52681599465751
26	5.3	0.3	0.16	4.2	0.029	37.0	100.0	0.9905	3.3	0.36	11.8	8	1	white	6.11406571842048
27	5.3	0.47	0.1	1.3	0.036	11.0	74.0	0.99082	3.48	0.54	11.2	4	0	white	5.82720972899898
28	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	5.72312927770099
29	5.3	0.57	0.01	1.7	0.054	5.0	27.0	0.9934	3.57	0.84	12.5	7	1	red	5.41501996199736
30	5.3	0.58	0.07	6.9	0.043	34.0	149.0	0.9944	3.34	0.57	9.7	5	0	white	5.48220187342727
31	5.3	0.6	0.34	1.4	0.031	3.0	60.0	0.98854	3.27	0.38	13.0	6	0	white	6.0377240271068
32	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	5.34484257930396
33	5.4	0.17	0.27	2.7	0.049	28.0	104.0	0.99224	3.46	0.55	10.3	6	0	white	5.91128761735195
34	5.4	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white	5.79055789861229
35	5.4	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	5.82760368269609
36	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	6.21351737313779
37	5.4	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white	6.4228362564393
38	5.4	0.45	0.27	6.4	0.033	20.0	102.0	0.98944	3.22	0.27	13.4	8	1	white	6.24840874584285
39	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	6.05958171709057
40	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	6.05958171709057
41	5.4	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red	5.40024947269862
42	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	6.21089882168724
43	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	6.13341926026013
44	5.5	0.21	0.25	1.2	0.04	18.0	75.0	0.99006	3.31	0.56	11.3	6	0	white	6.13917465480097
45	5.5	0.315	0.38	2.6	0.033	10.0	69.0	0.9909	3.12	0.59	10.8	6	0	white	5.99674215919902
46	5.5	0.335	0.3	2.5	0.071	27.0	128.0	0.9924	3.14	0.51	9.6	6	0	white	5.76816846174935
47	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	5.80708713394094
48	5.6	0.18	0.31	1.5	0.038	16.0	84.0	0.9924	3.34	0.58	10.1	6	0	white	5.86095359512075
49	5.6	0.21	0.24	4.4	0.027	37.0	150.0	0.991	3.3	0.31	11.5	7	1	white	6.15863103714133
50	5.6	0.255	0.57	10.7	0.056	66.0	171.0	0.99464	3.25	0.61	10.4	7	1	white	5.85639394189801
51	5.6	0.26	0.0	10.2	0.038	13.0	111.0	0.99315	3.44	0.46	12.4	6	0	white	6.07602689344938
52	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	5.94054197279132
53	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	5.78999824345021
54	5.6	0.31	0.78	13.9	0.074	23.0	92.0	0.99677	3.39	0.48	10.5	6	0	red	5.6371341311698
55	5.6	0.32	0.33	7.4	0.037	25.0	95.0	0.99268	3.25	0.49	11.1	6	0	white	5.96160334230404
56	5.6	0.32	0.33	7.4	0.037	25.0	95.0	0.99268	3.25	0.49	11.1	6	0	white	5.96160334230404
57	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	5.48960201662808
58	5.6	0.66	0.0	2.5	0.066	7.0	15.0	0.99256	3.52	0.58	12.9	5	0	red	5.54024992090524
59	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	5.74823052521947
60	5.7	0.21	0.25	1.1	0.035	26.0	81.0	0.9902	3.31	0.52	11.4	6	0	white	6.14571538930176
61	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	6.05792248510579
62	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	5.58891514130576
63	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	5.62423274337112
64	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	5.65460197786433
65	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	5.65460197786433
66	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	5.68723957392558
67	5.7	0.265	0.28	6.9	0.036	46.0	150.0	0.99299	3.36	0.44	10.8	7	1	white	5.9563172001202
68	5.7	0.28	0.35	1.2	0.052	39.0	141.0	0.99108	3.44	0.69	11.3	6	0	white	5.96734483858782
69	5.7	0.28	0.36	1.8	0.041	38.0	90.0	0.99002	3.27	0.98	11.9	7	1	white	6.14026582384901
70	5.7	0.39	0.25	4.9	0.033	49.0	113.0	0.98966	3.26	0.58	13.1	7	1	white	6.24515070428194
71	5.7	0.4	0.35	5.1	0.026	17.0	113.0	0.99052	3.18	0.67	12.4	6	0	white	6.12218588449724
72	5.7	0.41	0.21	1.9	0.048	30.0	112.0	0.99138	3.29	0.55	11.2	6	0	white	5.86813104142473
73	5.7	0.46	0.46	1.4	0.04	31.0	169.0	0.9932	3.13	0.47	8.8	5	0	white	5.54501193575709
74	5.7	0.6	0.0	1.4	0.063	11.0	18.0	0.99191	3.45	0.56	12.2	6	0	red	5.64415020215748
75	5.8	0.13	0.22	12.7	0.058	24.0	183.0	0.9956	3.32	0.42	11.7	6	0	white	5.94799412249571
76	5.8	0.13	0.22	12.7	0.058	24.0	183.0	0.9956	3.32	0.42	11.7	6	0	white	5.94799412249571
77	5.8	0.17	0.36	1.3	0.036	11.0	70.0	0.99202	3.43	0.68	10.4	7	1	white	5.93917659680355
78	5.8	0.19	0.33	4.2	0.038	49.0	133.0	0.99107	3.16	0.42	11.3	7	1	white	6.17715323314462
79	5.8	0.19	0.49	4.9	0.04	44.0	118.0	0.9935	3.34	0.38	9.5	7	1	white	5.85955687976534
80	5.8	0.2	0.16	1.4	0.042	44.0	99.0	0.98912	3.23	0.37	12.2	6	0	white	6.33375639979246
81	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	6.04946180266666
82	5.8	0.26	0.24	9.2	0.044	55.0	152.0	0.9961	3.31	0.38	9.4	5	0	white	5.63985785562051
83	5.8	0.27	0.26	3.5	0.071	26.0	69.0	0.98994	3.1	0.38	11.5	6	0	white	6.24409229959491
84	5.8	0.27	0.4	1.2	0.076	47.0	130.0	0.99185	3.13	0.45	10.3	6	0	white	5.87532700400763
85	5.8	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	6.30185418110068
86	5.8	0.28	0.34	2.2	0.037	24.0	125.0	0.98986	3.36	0.33	12.8	8	1	white	6.19543662510642
87	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	5.54116933279673
88	5.8	0.29	0.15	1.1	0.029	12.0	83.0	0.9898	3.3	0.4	11.4	6	0	white	6.16387879436914
89	5.8	0.29	0.27	1.6	0.062	17.0	140.0	0.99138	3.23	0.35	11.1	6	0	white	5.9525556959801
90	5.8	0.29	0.33	3.7	0.029	30.0	88.0	0.98994	3.25	0.42	12.3	6	0	white	6.24095822552894
91	5.8	0.29	0.38	10.7	0.038	49.0	136.0	0.99366	3.11	0.59	11.2	6	0	white	6.01017750840145
92	5.8	0.3	0.33	3.5	0.033	25.0	116.0	0.99057	3.2	0.44	11.7	6	0	white	6.1373828540834
93	5.8	0.31	0.31	7.5	0.052	55.0	230.0	0.9949	3.19	0.46	9.8	5	0	white	5.69335688529515
94	5.8	0.31	0.32	4.5	0.024	28.0	94.0	0.98906	3.25	0.52	13.7	7	1	white	6.38260850775521
95	5.8	0.315	0.27	1.55	0.026	15.0	70.0	0.98994	3.37	0.4	11.9	8	1	white	6.13795683468476
96	5.8	0.32	0.31	2.7	0.049	25.0	153.0	0.99067	3.44	0.73	12.2	7	1	white	6.07410922362973
97	5.8	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white	6.1116950924777
98	5.8	0.34	0.16	7.0	0.037	26.0	116.0	0.9949	3.46	0.45	10.0	7	1	white	5.66237657764782
99	5.8	0.345	0.15	10.8	0.033	26.0	120.0	0.99494	3.25	0.49	10.0	6	0	white	5.81134196311754
100	5.8	0.4	0.42	4.4	0.047	38.5	245.0	0.9937	3.25	0.57	9.6	6	0	white	5.65953379708074

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.

Contour plot is another useful plot that can be produced for models with two predictors.

model.contour()

Machine learning models with two predictors can usually benefit from their own contour plot. This visual representation aids in exploring predictions and gaining a deeper understanding of how these models perform in different scenarios. Please refer to Contour Plot for more examples.

Parameter Modification¶

In order to see the parameters:

model.get_params()
Out[3]: {'tol': 1e-06, 'max_iter': 100, 'solver': 'newton', 'fit_intercept': True}

And to manually change some of the parameters:

model.set_params({'tol': 0.001})

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 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]: '142.556276427116 + 0.145769130194822 * "fixed_acidity" + -0.73644455007161 * "volatile_acidity" + -0.0603450323997095 * "citric_acid" + 0.0412796262611437 * "residual_sugar" + -0.180072475028282 * "chlorides" + -138.467794908676 * "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([6.02139803])

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, tol: float = 1e-06, max_iter: int = 100, solver: Literal['newton', 'bfgs'] = 'newton', fit_intercept: bool = True) → None¶

Methods

`__init__`([name, overwrite_model, tol, ...])
`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