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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	3.9	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 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    |  3.66874  | 0.94790| 3.87040| 0.00011
 fixed_acidity  |  0.00054  | 0.00526| 0.10290| 0.91804
volatile_acidity| -0.20969  | 0.04493|-4.66709| 0.00000
  citric_acid   |  0.01685  | 0.04883| 0.34506| 0.73005
 residual_sugar | -0.00252  | 0.00133|-1.89905| 0.05756
   chlorides    | -0.48876  | 0.19110|-2.55765| 0.01054
    density     | -1.81630  | 0.96359|-1.88492| 0.05944


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


===========
call_string
===========
poisson_reg('"public"."_verticapy_tmp_poissonregressor_v_mldb_e7fe9f34979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_e82dfd06979911efa8720242ac120002_"', '"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|5199 

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	0.0995049092790506
max_error	3.06195928502207
median_absolute_error	0.588193903332221
mean_absolute_error	0.6368840993231
mean_squared_error	0.699515392235532
root_mean_squared_error	0.836370367860754
r2	0.0985192654578602
r2_adj	0.0943295796273004
aic	-449.700198982508
bic	-413.682930399334

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	87.9171591656714	14.652859860945235	20.834192408636817	2.0467009107248934e-23
Residual	1291	907.970979121721	0.7033082719765461
Total	1297	1007.19953775039

Rows: 1-3 | Columns: 6

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

model.score()
Out[2]: 0.0985192654578603

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	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white	6.20032850975741
2	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	6.22233739662392
3	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	6.17145214220889
4	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	5.99445556641177
5	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	5.93369485896087
6	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	5.83084927996444
7	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	5.986048585754
8	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	5.62545286257028
9	5.2	0.25	0.23	1.4	0.047	20.0	77.0	0.99001	3.32	0.62	11.4	5	0	white	6.03969361301039
10	5.2	0.335	0.2	1.7	0.033	17.0	74.0	0.99002	3.34	0.48	12.3	6	0	white	5.96609015206905
11	5.3	0.21	0.29	0.7	0.028	11.0	66.0	0.99215	3.3	0.4	9.8	5	0	white	6.14089946315679
12	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	5.77906963413985
13	5.4	0.33	0.31	4.0	0.03	27.0	108.0	0.99031	3.3	0.43	12.2	7	1	white	5.95504793705801
14	5.4	0.42	0.27	2.0	0.092	23.0	55.0	0.99471	3.78	0.64	12.3	7	1	red	5.64880457298853
15	5.5	0.34	0.26	2.2	0.021	31.0	119.0	0.98919	3.55	0.49	13.0	8	1	white	6.00341146083158
16	5.5	0.485	0.0	1.5	0.065	8.0	103.0	0.994	3.63	0.4	9.7	4	0	white	5.63539125146962
17	5.6	0.13	0.27	4.8	0.028	22.0	104.0	0.9948	3.34	0.45	9.2	6	0	white	6.14977747784627
18	5.6	0.25	0.19	2.4	0.049	42.0	166.0	0.992	3.25	0.43	10.4	6	0	white	5.99413400083637
19	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	5.87366327667885
20	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	5.87366327667885
21	5.7	0.12	0.26	5.5	0.034	21.0	99.0	0.99324	3.09	0.57	9.9	6	0	white	6.15050073740849
22	5.7	0.21	0.24	2.3	0.047	60.0	189.0	0.995	3.65	0.72	10.1	6	0	white	6.0245714433699
23	5.7	0.22	0.33	1.9	0.036	37.0	110.0	0.98945	3.26	0.58	12.4	6	0	white	6.12103716839245
24	5.7	0.26	0.3	1.8	0.039	30.0	105.0	0.98995	3.48	0.52	12.5	7	1	white	6.05398272151734
25	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	5.82730803434429
26	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	6.02808130423772
27	5.8	0.18	0.28	1.3	0.034	9.0	94.0	0.99092	3.21	0.52	11.2	6	0	white	6.16662720559193
28	5.8	0.27	0.2	14.95	0.044	22.0	179.0	0.9962	3.37	0.37	10.2	5	0	white	5.75472077396028
29	5.8	0.3	0.09	6.3	0.042	36.0	138.0	0.99382	3.15	0.48	9.7	5	0	white	5.86497161819535
30	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	6.00600408534323
31	5.8	0.415	0.13	1.4	0.04	11.0	64.0	0.9922	3.29	0.52	10.5	5	0	white	5.82313044692686
32	5.9	0.12	0.27	4.8	0.03	40.0	110.0	0.99226	3.55	0.68	12.1	6	0	white	6.18613859074923
33	5.9	0.14	0.2	1.6	0.04	26.0	114.0	0.99105	3.25	0.45	11.4	6	0	white	6.1861970032333
34	5.9	0.24	0.12	1.4	0.035	60.0	247.0	0.99358	3.34	0.44	9.6	6	0	white	6.03971448631876
35	5.9	0.25	0.25	11.3	0.052	30.0	165.0	0.997	3.24	0.44	9.5	6	0	white	5.80636883557108
36	5.9	0.26	0.3	1.0	0.036	38.0	114.0	0.9928	3.58	0.48	9.4	5	0	white	6.04440198763212
37	5.9	0.32	0.26	1.5	0.057	17.0	141.0	0.9917	3.24	0.36	10.7	5	0	white	5.90825168218121
38	5.9	0.36	0.41	1.3	0.047	45.0	104.0	0.9917	3.33	0.51	10.6	6	0	white	5.90548516295853
39	5.9	0.4	0.32	6.0	0.034	50.0	127.0	0.992	3.51	0.58	12.5	7	1	white	5.81203191791498
40	5.9	0.44	0.33	1.2	0.049	12.0	117.0	0.99134	3.46	0.44	11.5	5	0	white	5.79900978642892
41	6.0	0.1	0.24	1.1	0.041	15.0	65.0	0.9927	3.61	0.61	10.3	7	1	white	6.2289753672679
42	6.0	0.17	0.36	1.7	0.042	14.0	61.0	0.99144	3.22	0.54	10.8	6	0	white	6.15239517730898
43	6.0	0.23	0.34	1.3	0.025	23.0	111.0	0.98961	3.36	0.37	12.7	6	0	white	6.1506933140766
44	6.0	0.25	0.28	2.2	0.026	54.0	126.0	0.9898	3.43	0.65	12.9	8	1	white	6.09979955233949
45	6.0	0.29	0.2	12.6	0.045	45.0	187.0	0.9972	3.33	0.42	9.5	5	0	white	5.75206040548763
46	6.0	0.29	0.41	10.8	0.048	55.0	149.0	0.9937	3.09	0.59	10.9666666666667	7	1	white	5.82714794756821
47	6.0	0.33	0.27	0.8	0.185	12.0	188.0	0.9924	3.12	0.62	9.4	5	0	white	5.54229906692986
48	6.0	0.34	0.29	6.1	0.046	29.0	134.0	0.99462	3.48	0.57	10.7	6	0	white	5.81932530882925
49	6.0	0.495	0.27	5.0	0.157	17.0	129.0	0.99396	3.03	0.36	9.3	5	0	white	5.35519529166075
50	6.1	0.2	0.25	1.2	0.038	34.0	128.0	0.9921	3.24	0.44	10.1	5	0	white	6.11514947099654
51	6.1	0.22	0.5	6.6	0.045	30.0	122.0	0.99415	3.22	0.49	9.9	6	0	white	5.98957487513886
52	6.1	0.23	0.45	10.6	0.094	49.0	169.0	0.99699	3.05	0.54	8.8	5	0	white	5.74243481059561
53	6.1	0.27	0.25	1.8	0.041	9.0	109.0	0.9929	3.08	0.54	9.0	5	0	white	5.99939125662913
54	6.1	0.27	0.32	6.2	0.048	47.0	161.0	0.99281	3.22	0.6	11.0	6	0	white	5.92084951289573
55	6.1	0.27	0.44	6.7	0.041	61.0	230.0	0.99505	3.12	0.4	8.9	5	0	white	5.92151983430764
56	6.1	0.28	0.25	17.75	0.044	48.0	161.0	0.9993	3.34	0.48	9.5	5	0	white	5.67593047701386
57	6.1	0.28	0.3	7.75	0.031	33.0	139.0	0.99296	3.22	0.46	11.0	6	0	white	5.93087876468471
58	6.1	0.28	0.32	2.5	0.042	23.0	218.5	0.9935	3.27	0.6	9.8	5	0	white	5.97387666333776
59	6.1	0.32	0.33	10.7	0.036	27.0	98.0	0.99521	3.34	0.52	10.2	6	0	white	5.80266907665926
60	6.1	0.35	0.24	2.3	0.034	25.0	133.0	0.9906	3.34	0.59	12.0	7	1	white	5.93609645731085
61	6.1	0.4	0.18	9.0	0.051	28.5	259.0	0.9964	3.19	0.5	8.8	5	0	white	5.66219346658852
62	6.2	0.2	0.49	1.6	0.065	17.0	143.0	0.9937	3.22	0.52	9.2	6	0	white	6.03608303498881
63	6.2	0.235	0.34	1.9	0.036	4.0	117.0	0.99032	3.4	0.44	12.2	5	0	white	6.09485614538367
64	6.2	0.25	0.31	3.2	0.03	32.0	150.0	0.99014	3.18	0.31	12.0	6	0	white	6.07252446897251
65	6.2	0.25	0.38	7.9	0.045	54.0	208.0	0.99572	3.17	0.46	9.1	5	0	white	5.90400604220314
66	6.2	0.25	0.48	10.0	0.044	78.0	240.0	0.99655	3.25	0.47	9.5	6	0	white	5.87672473624221
67	6.2	0.28	0.45	7.5	0.045	46.0	203.0	0.99573	3.26	0.46	9.2	6	0	white	5.87972881346452
68	6.2	0.3	0.3	2.5	0.041	29.0	82.0	0.99065	3.31	0.61	11.8	7	1	white	5.98098124188257
69	6.2	0.31	0.23	3.3	0.052	34.0	113.0	0.99429	3.16	0.48	8.4	5	0	white	5.87850604688373
70	6.3	0.13	0.42	1.1	0.043	63.0	146.0	0.99066	3.13	0.72	11.2	7	1	white	6.22668370068583
71	6.3	0.19	0.29	2.0	0.022	33.0	96.0	0.98902	3.04	0.54	12.8	7	1	white	6.20307081610548
72	6.3	0.19	0.32	2.8	0.046	18.0	80.0	0.99043	2.92	0.47	11.05	6	0	white	6.10580941179001
73	6.3	0.2	0.19	12.3	0.048	54.0	145.0	0.99668	3.16	0.42	9.3	6	0	white	5.86298679000515
74	6.3	0.2	0.26	12.7	0.046	60.0	143.0	0.99526	3.26	0.35	10.8	6	0	white	5.88487858624834
75	6.3	0.2	0.4	1.5	0.037	35.0	107.0	0.9917	3.46	0.5	11.4	6	0	white	6.13409855932567
76	6.3	0.21	0.58	10.0	0.081	34.0	126.0	0.9962	2.95	0.46	8.9	5	0	white	5.83385412495296
77	6.3	0.23	0.22	3.75	0.039	37.0	116.0	0.9927	3.23	0.5	10.7	6	0	white	6.02591948441593
78	6.3	0.25	0.22	3.3	0.048	41.0	161.0	0.99256	3.16	0.5	10.5	6	0	white	5.98266990842405
79	6.3	0.25	0.22	3.3	0.048	41.0	161.0	0.99256	3.16	0.5	10.5	6	0	white	5.98266990842405
80	6.3	0.28	0.24	8.45	0.031	32.0	172.0	0.9958	3.39	0.57	9.7	7	1	white	5.88464055965873
81	6.3	0.3	0.29	2.1	0.048	33.0	142.0	0.98956	3.22	0.46	12.9	7	1	white	5.97771162735498
82	6.3	0.4	0.24	5.1	0.036	43.0	131.0	0.99186	3.24	0.44	11.3	6	0	white	5.81444932026599
83	6.3	0.47	0.0	1.4	0.055	27.0	33.0	0.9922	3.45	0.48	12.3	6	0	red	5.70335237870838
84	6.4	0.105	0.29	1.1	0.035	44.0	140.0	0.99142	3.17	0.55	10.7	7	1	white	6.26187531239827
85	6.4	0.21	0.21	5.1	0.097	21.0	105.0	0.9939	3.07	0.46	9.6	5	0	white	5.84868359298224
86	6.4	0.21	0.28	5.9	0.047	29.0	101.0	0.99278	3.15	0.4	11.0	6	0	white	6.00054244953618
87	6.4	0.22	0.49	7.5	0.054	42.0	151.0	0.9948	3.27	0.52	10.1	6	0	white	5.94270603820574
88	6.4	0.23	0.33	1.15	0.044	15.5	217.5	0.992	3.33	0.44	11.0	6	0	white	6.07003246658586
89	6.4	0.24	0.26	8.2	0.054	47.0	182.0	0.99538	3.12	0.5	9.5	5	0	white	5.87835222784195
90	6.4	0.24	0.5	11.6	0.047	60.0	211.0	0.9966	3.18	0.57	9.3	5	0	white	5.85882115247162
91	6.4	0.25	0.41	8.6	0.042	57.0	173.0	0.9965	3.0	0.44	9.1	5	0	white	5.89749993029346
92	6.4	0.26	0.21	8.2	0.05	51.0	182.0	0.99542	3.23	0.48	9.5	5	0	white	5.85984212436088
93	6.4	0.28	0.28	3.0	0.04	19.0	98.0	0.99216	3.25	0.47	11.1	6	0	white	5.98367295841877
94	6.4	0.33	0.28	1.1	0.038	30.0	110.0	0.9917	3.12	0.42	10.5	6	0	white	5.96051022485213
95	6.4	0.63	0.21	1.6	0.08	12.0	32.0	0.99689	3.58	0.66	9.8	5	0	red	5.41868965417692
96	6.5	0.13	0.37	1.0	0.036	48.0	114.0	0.9911	3.41	0.51	11.5	8	1	white	6.2400246593108
97	6.5	0.18	0.34	1.6	0.04	43.0	148.0	0.9912	3.32	0.59	11.5	8	1	white	6.14933503541038
98	6.5	0.19	0.32	1.4	0.04	31.0	132.0	0.9922	3.36	0.54	10.8	7	1	white	6.12634499562452
99	6.5	0.22	0.31	3.9	0.046	17.0	106.0	0.99098	3.15	0.31	11.5	5	0	white	6.04430130869772
100	6.5	0.22	0.32	2.2	0.028	36.0	92.0	0.99076	3.27	0.59	11.9	7	1	white	6.12739706622768

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 /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]: '3.66874013393681 + 0.000541320019358693 * "fixed_acidity" + -0.209690301463013 * "volatile_acidity" + 0.016847826241488 * "citric_acid" + -0.00252263536545883 * "residual_sugar" + -0.488755470954402 * "chlorides" + -1.81629573612736 * "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([1.82476574])

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