Feedback

×

• Feedback
• Was this page helpful?*
  • Yes
  • No
• Do you have any additional feedback?
  • Yes
  • No
• Additional Feedback

verticapy.machine_learning.vertica.linear_model.Lasso

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

Creates a Lasso object using the Vertica Linear Regression algorithm. Lasso is a regularized regression method that uses an L1 penalty.

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.

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.

• bfgs:

  Broyden Fletcher Goldfarb Shanno.

• cgd:

  Coordinate Gradient Descent.

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 Lasso model:

from verticapy.machine_learning.vertica import Lasso

Then we can create the model:

model = Lasso(
    tol = 1e-6,
    C = 0.5,
    max_iter = 100,
    solver = 'CGD',
)

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    |  5.82095  | 7.51270| 0.77481| 0.43848
 fixed_acidity  |  0.00000  | 0.01363| 0.00000| 1.00000
volatile_acidity|  0.00000  | 0.09536| 0.00000| 1.00000
  citric_acid   |  0.00000  | 0.10561| 0.00000| 1.00000
 residual_sugar |  0.00000  | 0.00423| 0.00000| 1.00000
   chlorides    |  0.00000  | 0.43293| 0.00000| 1.00000
    density     |  0.00000  | 7.64146| 0.00000| 1.00000


==============
regularization
==============
type| lambda 
----+--------
 l1 | 0.50000


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_lasso_v_mldb_3c76c27c979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_3cb3064c979911efa8720242ac120002_"', '"quality"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"'
USING PARAMETERS optimizer='cgd', epsilon=1e-06, max_iterations=100, regularization='l1', lambda=0.5, alpha=1, fit_intercept=true)

===============
Additional Info
===============
       Name       |Value
------------------+-----
 iteration_count  |  1  
rejected_row_count|  0  
accepted_row_count|5194 

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	3.10862446895044e-15
max_error	3.179053
median_absolute_error	0.820947
mean_absolute_error	0.64669081580967
mean_squared_error	0.683229201674695
root_mean_squared_error	0.826576797203197
r2	-0.000240307236190729
r2_adj	-0.00487104939932137
aic	-482.182975922665
bic	-446.138166044954

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	0.213881554959005	0.03564692582650083	0.051893892539285384	0.9994378891436148
Residual	1296	890.247649782127	0.6869194828565796
Total	1302	890.033768227168

Rows: 1-3 | Columns: 6

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

model.score()
Out[2]: -0.000240307236190729

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	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red	5.820947
2	5.2	0.405	0.15	1.45	0.038	10.0	44.0	0.99125	3.52	0.4	11.6	4	0	white	5.820947
3	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	5.820947
4	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	5.820947
5	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	5.820947
6	5.5	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	5.820947
7	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	5.820947
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	5.820947
9	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	5.820947
10	5.6	0.235	0.29	1.2	0.047	33.0	127.0	0.991	3.34	0.5	11.0	7	1	white	5.820947
11	5.6	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	5.820947
12	5.6	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white	5.820947
13	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	5.820947
14	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	5.820947
15	5.7	0.36	0.21	6.7	0.038	51.0	166.0	0.9941	3.29	0.63	10.0	6	0	white	5.820947
16	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	5.820947
17	5.8	0.21	0.32	1.6	0.045	38.0	95.0	0.98946	3.23	0.94	12.4	8	1	white	5.820947
18	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	5.820947
19	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	5.820947
20	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	5.820947
21	5.9	0.17	0.28	0.7	0.027	5.0	28.0	0.98985	3.13	0.32	10.6	5	0	white	5.820947
22	5.9	0.2	0.23	1.5	0.037	38.0	93.0	0.99021	3.36	0.49	12.0	6	0	white	5.820947
23	5.9	0.23	0.28	8.6	0.046	37.0	142.0	0.99432	3.23	0.53	10.6	6	0	white	5.820947
24	5.9	0.24	0.3	2.0	0.033	28.0	92.0	0.99225	3.39	0.69	10.9	7	1	white	5.820947
25	5.9	0.25	0.19	12.4	0.047	50.0	162.0	0.9973	3.35	0.38	9.5	5	0	white	5.820947
26	5.9	0.26	0.27	18.2	0.048	52.0	168.0	0.9993	3.35	0.44	9.4	5	0	white	5.820947
27	5.9	0.36	0.04	5.7	0.046	21.0	87.0	0.9934	3.22	0.51	10.2	5	0	white	5.820947
28	5.9	0.415	0.13	1.4	0.04	11.0	64.0	0.9922	3.29	0.52	10.5	5	0	white	5.820947
29	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	5.820947
30	6.0	0.26	0.26	2.2	0.035	10.0	72.0	0.989465	3.11	0.48	12.15	6	0	white	5.820947
31	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	5.820947
32	6.0	0.32	0.46	1.5	0.05	56.0	189.0	0.99308	3.24	0.49	9.6	5	0	white	5.820947
33	6.0	0.36	0.32	1.1	0.053	26.0	173.0	0.99414	3.38	0.54	8.8	5	0	white	5.820947
34	6.0	0.38	0.26	6.0	0.034	42.0	134.0	0.9912	3.38	0.38	12.3	7	1	white	5.820947
35	6.0	0.42	0.41	12.4	0.032	50.0	179.0	0.99622	3.14	0.6	9.7	5	0	white	5.820947
36	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	5.820947
37	6.1	0.18	0.38	2.3	0.033	28.0	111.0	0.98962	3.16	0.49	12.4	6	0	white	5.820947
38	6.1	0.31	0.26	2.2	0.051	28.0	167.0	0.9926	3.37	0.47	10.4	6	0	white	5.820947
39	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	5.820947
40	6.2	0.16	0.33	1.1	0.057	21.0	82.0	0.991	3.32	0.46	10.9	7	1	white	5.820947
41	6.2	0.19	0.38	5.1	0.019	22.0	82.0	0.98961	3.05	0.36	12.5	6	0	white	5.820947
42	6.2	0.2	0.25	15.0	0.055	8.0	120.0	0.99767	3.19	0.53	9.6	6	0	white	5.820947
43	6.2	0.2	0.29	11.8	0.035	21.0	93.0	0.99364	3.18	0.34	11.9	6	0	white	5.820947
44	6.2	0.23	0.38	1.6	0.044	12.0	113.0	0.99176	3.3	0.73	11.4	5	0	white	5.820947
45	6.2	0.26	0.37	7.1	0.047	54.0	201.0	0.99523	3.19	0.48	9.5	6	0	white	5.820947
46	6.2	0.28	0.57	1.0	0.043	50.0	92.0	0.99004	3.17	0.36	11.5	6	0	white	5.820947
47	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	5.820947
48	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	5.820947
49	6.2	0.37	0.3	6.6	0.346	79.0	200.0	0.9954	3.29	0.58	9.6	5	0	white	5.820947
50	6.2	0.46	0.17	1.6	0.073	7.0	11.0	0.99425	3.61	0.54	11.4	5	0	red	5.820947
51	6.2	0.46	0.29	2.1	0.074	32.0	98.0	0.99578	3.33	0.62	9.8	5	0	red	5.820947
52	6.2	0.58	0.0	1.6	0.065	8.0	18.0	0.9966	3.56	0.84	9.4	5	0	red	5.820947
53	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	5.820947
54	6.3	0.21	0.4	1.7	0.031	48.0	134.0	0.9917	3.42	0.49	11.5	6	0	white	5.820947
55	6.3	0.23	0.33	6.9	0.052	23.0	118.0	0.9938	3.23	0.46	10.4	6	0	white	5.820947
56	6.3	0.24	0.55	8.1	0.04	67.0	216.0	0.99596	3.24	0.5	9.2	5	0	white	5.820947
57	6.3	0.25	0.23	14.9	0.039	47.0	142.0	0.99705	3.14	0.35	9.7	6	0	white	5.820947
58	6.3	0.26	0.29	2.2	0.043	35.0	175.0	0.9918	3.38	0.43	11.6	6	0	white	5.820947
59	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	5.820947
60	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	5.820947
61	6.3	0.3	0.2	3.7	0.039	34.0	132.0	0.99158	3.0	0.38	10.7	5	0	white	5.820947
62	6.3	0.33	0.2	17.9	0.066	36.0	161.0	0.9991	3.14	0.51	8.8	5	0	white	5.820947
63	6.3	0.41	0.16	0.9	0.032	25.0	98.0	0.99274	3.16	0.42	9.5	5	0	white	5.820947
64	6.3	0.41	0.33	4.7	0.023	28.0	110.0	0.991	3.3	0.38	12.5	7	1	white	5.820947
65	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	5.820947
66	6.4	0.18	0.32	9.6	0.052	24.0	90.0	0.9963	3.35	0.49	9.4	6	0	white	5.820947
67	6.4	0.19	0.35	10.2	0.043	40.0	106.0	0.99632	3.16	0.5	9.7	6	0	white	5.820947
68	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	5.820947
69	6.4	0.2	0.32	3.1	0.041	18.0	126.0	0.9914	3.43	0.42	12.0	6	0	white	5.820947
70	6.4	0.22	0.32	7.9	0.029	34.0	124.0	0.9948	3.4	0.39	10.2	5	0	white	5.820947
71	6.4	0.25	0.32	0.9	0.034	40.0	114.0	0.99114	3.31	0.58	10.8	7	1	white	5.820947
72	6.4	0.3	0.36	2.0	0.052	18.0	141.0	0.99273	3.38	0.53	10.5	6	0	white	5.820947
73	6.4	0.31	0.09	1.4	0.066	15.0	28.0	0.99459	3.42	0.7	10.0	7	1	red	5.820947
74	6.4	0.32	0.5	10.7	0.047	57.0	206.0	0.9968	3.08	0.6	9.4	5	0	white	5.820947
75	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	5.820947
76	6.4	0.45	0.07	1.1	0.03	10.0	131.0	0.9905	2.97	0.28	10.8	5	0	white	5.820947
77	6.5	0.17	0.33	1.4	0.028	14.0	99.0	0.9928	3.23	0.55	10.1	6	0	white	5.820947
78	6.5	0.17	0.54	8.5	0.082	64.0	163.0	0.9959	2.89	0.39	8.8	6	0	white	5.820947
79	6.5	0.2	0.35	3.9	0.04	27.0	140.0	0.99102	2.98	0.53	11.8	6	0	white	5.820947
80	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	5.820947
81	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	5.820947
82	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	5.820947
83	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	5.820947
84	6.5	0.27	0.19	6.6	0.045	98.0	175.0	0.99364	3.16	0.34	10.1	6	0	white	5.820947
85	6.5	0.28	0.26	8.8	0.04	44.0	139.0	0.9956	3.32	0.37	10.2	6	0	white	5.820947
86	6.5	0.36	0.28	3.2	0.037	29.0	119.0	0.9908	3.25	0.65	12.4	8	1	white	5.820947
87	6.5	0.39	0.23	8.3	0.051	28.0	91.0	0.9952	3.44	0.55	12.1	6	0	red	5.820947
88	6.6	0.16	0.32	1.4	0.035	49.0	186.0	0.9906	3.35	0.64	12.4	8	1	white	5.820947
89	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	5.820947
90	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	5.820947
91	6.6	0.21	0.34	5.6	0.046	30.0	140.0	0.99299	3.22	0.38	11.0	5	0	white	5.820947
92	6.6	0.23	0.32	1.5	0.041	8.0	72.0	0.98949	3.22	0.39	12.7	6	0	white	5.820947
93	6.6	0.24	0.22	12.3	0.051	35.0	146.0	0.99676	3.1	0.67	9.4	5	0	white	5.820947
94	6.6	0.24	0.28	1.8	0.028	39.0	132.0	0.99182	3.34	0.46	11.4	5	0	white	5.820947
95	6.6	0.24	0.35	7.7	0.031	36.0	135.0	0.9938	3.19	0.37	10.5	5	0	white	5.820947
96	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	5.820947
97	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	5.820947
98	6.6	0.26	0.22	18.15	0.05	23.0	139.0	0.99904	3.06	0.5	9.2	5	0	white	5.820947
99	6.6	0.26	0.25	11.6	0.045	45.0	178.0	0.99691	3.33	0.43	9.8	6	0	white	5.820947
100	6.6	0.27	0.31	5.3	0.137	35.0	163.0	0.9951	3.2	0.38	9.3	5	0	white	5.820947

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,
 'C': 0.5,
 'max_iter': 100,
 'solver': 'cgd',
 '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]: '5.820947 + 0.0 * "fixed_acidity" + 0.0 * "volatile_acidity" + 0.0 * "citric_acid" + 0.0 * "residual_sugar" + 0.0 * "chlorides" + 0.0 * "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([5.820947])

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, C: Annotated[int | float | Decimal, 'Python Numbers'] = 1.0, max_iter: int = 100, solver: Literal['newton', 'bfgs', 'cgd'] = 'cgd', fit_intercept: bool = True) → None

Methods

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