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verticapy.machine_learning.vertica.linear_model.PoissonRegressor

class verticapy.machine_learning.vertica.linear_model.PoissonRegressor(name: str = None, overwrite_model: bool = False, tol: float = 1e-06, penalty: Literal['none', 'l2', None] = 'none', C: Annotated[int | float | Decimal, 'Python Numbers'] = 1.0, max_iter: int = 100, solver: Literal['newton'] = 'newton', fit_intercept: bool = True)

Creates an PoissonRegressor object using the Vertica Poisson 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.

penalty: str, optional

Determines the method of regularization.

• None:

  No Regularization.

• l2:

  L2 Regularization.

C: PythonNumber, optional

The regularization parameter value. The value must be zero or non-negative.

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.

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 PoissonRegressor model:

from verticapy.machine_learning.vertica import PoissonRegressor

Then we can create the model:

model = PoissonRegressor(
    tol = 1e-6,
    penalty = 'L2',
    C = 1,
    max_iter = 100,
    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 |z_value |p_value 
----------------+-----------+--------+--------+--------
   Intercept    |  3.65794  | 0.94834| 3.85719| 0.00011
 fixed_acidity  |  0.00092  | 0.00534| 0.17155| 0.86379
volatile_acidity| -0.20763  | 0.04511|-4.60246| 0.00000
  citric_acid   |  0.02345  | 0.04964| 0.47232| 0.63670
 residual_sugar | -0.00262  | 0.00133|-1.96490| 0.04943
   chlorides    | -0.55681  | 0.19276|-2.88854| 0.00387
    density     | -1.80625  | 0.96386|-1.87397| 0.06093


==============
regularization
==============
type| lambda 
----+--------
 l2 | 1.00000


===========
call_string
===========
poisson_reg('"public"."_verticapy_tmp_poissonregressor_v_demo_2d640b5a55a411ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_2d7315b455a411ef880f0242ac120002_"', '"quality"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"'
USING PARAMETERS optimizer='newton', epsilon=1e-06, max_iterations=100, regularization='l2', lambda=1, alpha=0.5, fit_intercept=true)

===============
Additional Info
===============
       Name       |Value
------------------+-----
 iteration_count  |  8  
rejected_row_count|  0  
accepted_row_count|5192 

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	0.0923429105128023
max_error	3.0186250311259
median_absolute_error	0.570532817904302
mean_absolute_error	0.631846275437284
mean_squared_error	0.668059644914875
root_mean_squared_error	0.81734915728523
r2	0.0920520986208625
r2_adj	0.0878551129442254
aic	-512.246143826135
bic	-476.190347692694

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	100.88444511974	16.814074186623333	25.03351890459452	3.146018901546638e-28
Residual	1298	871.817836613913	0.6716624319059422
Total	1304	960.206896551724

Rows: 1-3 | Columns: 6

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

model.score()
Out[2]: 0.0920520986208624

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	5.96408524587908
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	5.93286581364805
3	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	5.52549594605966
4	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	5.89411324433011
5	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	5.84950090283437
6	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	5.89785947921917
7	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	5.85682090235143
8	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	6.13950678022681
9	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	6.02396627899044
10	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	5.91875139522335
11	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	5.76060739052605
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	6.29128725708322
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	6.19806381799953
14	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	6.10856758815882
15	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	5.92409281926805
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	6.0080967959057
17	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	5.92969979046602
18	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	5.78445509430058
19	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	5.58030787281983
20	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	6.23852932063882
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	5.9305556160846
22	5.2	0.285	0.29	5.15	0.035	64.0	138.0	0.9895	3.19	0.34	12.4	8	1	white	5.98995983668608
23	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	5.92696551786351
24	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	6.23079854962436
25	5.3	0.24	0.33	1.3	0.033	25.0	97.0	0.9906	3.59	0.38	11.0	8	1	white	6.10832562596103
26	5.3	0.32	0.12	6.6	0.043	22.0	141.0	0.9937	3.36	0.6	10.4	6	0	white	5.83042961132736
27	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	5.86295368987045
28	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	5.85622945296247
29	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	5.71920671828528
30	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	5.56131892598952
31	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	5.10336722355192
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	5.36944934113766
33	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	5.97469953107445
34	5.4	0.23	0.36	1.5	0.03	74.0	121.0	0.98976	3.24	0.99	12.1	7	1	white	6.14223476157019
35	5.4	0.24	0.18	2.3	0.05	22.0	145.0	0.99207	3.24	0.46	10.3	5	0	white	5.99839125569445
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	6.08607975001349
37	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	6.08607975001349
38	5.4	0.29	0.47	3.0	0.052	47.0	145.0	0.993	3.29	0.75	10.0	6	0	white	5.94936902425593
39	5.4	0.31	0.47	3.0	0.053	46.0	144.0	0.9931	3.29	0.76	10.0	5	0	white	5.9203473744162
40	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	5.77473862569968
41	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	5.83330331307712
42	5.5	0.24	0.32	8.7	0.06	19.0	102.0	0.994	3.27	0.31	10.4	5	0	white	5.86542061894812
43	5.5	0.28	0.21	1.6	0.032	23.0	85.0	0.99027	3.42	0.42	12.5	5	0	white	6.04411740746269
44	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	6.00824413316825
45	5.5	0.32	0.13	1.3	0.037	45.0	156.0	0.99184	3.26	0.38	10.7	5	0	white	5.95403565649581
46	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	5.92261520832905
47	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	5.92262942886831
48	5.6	0.19	0.31	2.7	0.027	11.0	100.0	0.98964	3.46	0.4	13.2	7	1	white	6.17960126470233
49	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	6.19594511681524
50	5.6	0.2	0.66	10.2	0.043	78.0	175.0	0.9945	2.98	0.43	10.4	7	1	white	5.98994723320613
51	5.6	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	5.83959865129805
52	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	6.10164011194004
53	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	5.85768114337298
54	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	5.85544893531896
55	5.6	0.28	0.4	6.1	0.034	36.0	118.0	0.99144	3.21	0.43	12.1	7	1	white	5.98129257454964
56	5.6	0.33	0.28	1.2	0.031	33.0	97.0	0.99126	3.49	0.58	10.9	6	0	white	5.99095892954565
57	5.6	0.34	0.1	1.3	0.031	20.0	68.0	0.9906	3.36	0.51	11.2	7	1	white	5.95889733839898
58	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	5.85458087587969
59	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	5.7055571513039
60	5.6	0.54	0.04	1.7	0.049	5.0	13.0	0.9942	3.72	0.58	11.4	5	0	red	5.60905068290718
61	5.6	0.615	0.0	1.6	0.089	16.0	59.0	0.9943	3.58	0.52	9.9	5	0	red	5.39611902311535
62	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	5.348522633416
63	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	6.13305346565877
64	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	6.13507796164131
65	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	5.76016006196222
66	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	5.99010568418281
67	5.7	0.245	0.33	1.1	0.049	28.0	150.0	0.9927	3.13	0.42	9.3	5	0	white	6.03033177422262
68	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	5.65901900041832
69	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	5.65901900041832
70	5.7	0.26	0.27	4.1	0.201	73.5	189.5	0.9942	3.27	0.38	9.4	6	0	white	5.45804901449927
71	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	5.94781412007608
72	5.7	0.27	0.16	9.0	0.053	32.0	111.0	0.99474	3.36	0.37	10.4	6	0	white	5.81856443239386
73	5.7	0.29	0.16	7.9	0.044	48.0	197.0	0.99512	3.21	0.36	9.4	5	0	white	5.83633204594768
74	5.7	0.31	0.29	7.3	0.05	33.0	143.0	0.99332	3.31	0.5	11.0666666666667	6	0	white	5.83852064699744
75	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	6.00058810853733
76	5.7	0.32	0.5	2.6	0.049	17.0	155.0	0.9927	3.22	0.64	10.0	6	0	white	5.93752781573745
77	5.7	0.33	0.15	1.9	0.05	20.0	93.0	0.9934	3.38	0.62	9.9	5	0	white	5.87684338121465
78	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	5.65493616230026
79	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	5.81579997560291
80	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	5.77958103329099
81	5.7	1.13	0.09	1.5	0.172	7.0	19.0	0.994	3.5	0.48	9.8	4	0	red	4.64383501812902
82	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	5.91568152182088
83	5.8	0.13	0.26	5.1	0.039	19.0	103.0	0.99478	3.36	0.47	9.3	6	0	white	6.11338297243243
84	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	6.07167527217327
85	5.8	0.17	0.3	1.4	0.037	55.0	130.0	0.9909	3.29	0.38	11.3	6	0	white	6.17748308429788
86	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	6.15547355330887
87	5.8	0.18	0.37	1.2	0.036	19.0	74.0	0.98853	3.09	0.49	12.7	7	1	white	6.20798588330179
88	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	6.12164355237213
89	5.8	0.2	0.3	1.5	0.031	21.0	57.0	0.99115	3.44	0.55	11.0	6	0	white	6.15527698127614
90	5.8	0.25	0.24	13.3	0.044	41.0	137.0	0.9972	3.34	0.42	9.5	5	0	white	5.79210711777424
91	5.8	0.25	0.28	11.1	0.056	45.0	175.0	0.99755	3.42	0.43	9.5	5	0	white	5.78850189715883
92	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	5.85402183490828
93	5.8	0.27	0.2	7.3	0.04	42.0	145.0	0.99442	3.15	0.48	9.8	5	0	white	5.89641953291704
94	5.8	0.28	0.27	2.6	0.054	30.0	156.0	0.9914	3.53	0.42	12.4	5	0	white	5.95279667074333
95	5.8	0.28	0.3	1.5	0.026	31.0	114.0	0.98952	3.32	0.6	12.5	7	1	white	6.08865699433472
96	5.8	0.28	0.34	4.0	0.031	40.0	99.0	0.9896	3.39	0.39	12.8	7	1	white	6.03695005693814
97	5.8	0.29	0.26	1.7	0.063	3.0	11.0	0.9915	3.39	0.54	13.5	6	0	red	5.92222453424129
98	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	6.00116587958363
99	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	6.03495071671737
100	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	5.95914978269256

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]: 
{'penalty': 'l2',
 'tol': 1e-06,
 'C': 1,
 '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]: '3.65793639898766 + 0.000916177178773125 * "fixed_acidity" + -0.207630569132817 * "volatile_acidity" + 0.0234463122665005 * "citric_acid" + -0.00261517074737228 * "residual_sugar" + -0.556805941021139 * "chlorides" + -1.80624981742585 * "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([1.82606644])

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, penalty: Literal['none', 'l2', None] = 'none', C: Annotated[int | float | Decimal, 'Python Numbers'] = 1.0, max_iter: int = 100, solver: Literal['newton'] = '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