verticapy.machine_learning.vertica.svm.LinearSVR¶

class verticapy.machine_learning.vertica.svm.LinearSVR(name: str = None, overwrite_model: bool = False, tol: float = 0.0001, C: float = 1.0, intercept_scaling: float = 1.0, intercept_mode: Literal['regularized', 'unregularized'] = 'regularized', acceptable_error_margin: float = 0.1, max_iter: int = 100)¶

Creates a LinearSVR object using the Vertica SVM (Support Vector Machine) algorithm. This algorithm finds the hyperplane used to approximate distribution of the data.

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

Tolerance for stopping criteria. This is used to control accuracy.

C: float, optional

Weight for misclassification cost. The algorithm minimizes the regularization cost and the misclassification cost.

intercept_scaling: float

A float value, serves as the value of a dummy feature whose coefficient Vertica uses to calculate the model intercept. Because the dummy feature is not in the training data, its values are set to a constant, by default set to 1.

intercept_mode: str, optional

Specify how to treat the intercept.

regularized:
Fits the intercept and applies a regularization.
unregularized:
Fits the intercept but does not include it in regularization.

acceptable_error_margin: float, optional

Defines the acceptable error margin. Any data points outside this region add a penalty to the cost function.

max_iter: int, optional

The maximum number of iterations that the algorithm performs.

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 LinearSVR model:

from verticapy.machine_learning.vertica import LinearSVR

Then we can create the model:

model = LinearSVR(
    tol = 1e-4,
    C = 1.0,
    intercept_scaling = 1.0,
    intercept_mode = "regularized",
    acceptable_error_margin = 0.1,
    max_iter = 100,
)

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
----------------+-----------
   Intercept    |  3.48501  
 fixed_acidity  | -0.00736  
volatile_acidity| -1.09194  
  citric_acid   |  0.18488  
 residual_sugar | -0.02110  
   chlorides    | -2.86132  
    density     |  2.97707  


===========
call_string
===========
SELECT svm_regressor('"public"."_verticapy_tmp_linearsvr_v_mldb_29ea4766979c11efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_2a20480c979c11efa8720242ac120002_"', '"quality"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"'
USING PARAMETERS error_tolerance=0.1, C=1, max_iterations=100, intercept_mode='regularized', intercept_scaling=1, epsilon=0.0001);

===============
Additional Info
===============
       Name       |Value
------------------+-----
accepted_row_count|5195 
rejected_row_count|  0  
 iteration_count  |  6  

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:

model.report()

	value
explained_variance	0.10117255969949
max_error	2.9503627228759
median_absolute_error	0.611405659829351
mean_absolute_error	0.648086024997255
mean_squared_error	0.695778977736997
root_mean_squared_error	0.83413366898657
r2	0.0993151772748029
r2_adj	0.0951421201810955
aic	-458.103357723485
bic	-422.064047444786

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	73.592645574272	12.265440929045333	17.53358191120198	1.3296988375815259e-19
Residual	1295	905.90422901357	0.6995399451842239
Total	1301	1005.79493087558

Rows: 1-3 | Columns: 6

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

model.score()
Out[2]: 0.0993151772748029

Prediction¶

Prediction is straight-forward:

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

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 prediction Float(22)
1	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white	5.94318284936379
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	5.61123551983211
3	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	5.95755764918501
4	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	5.55950575505689
5	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	5.69067679337299
6	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	6.05820143883028
7	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	6.05807896296507
8	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	5.82550420298887
9	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	5.82550420298887
10	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red	5.84648444096061
11	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	5.51739723575135
12	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	5.80438642962976
13	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	6.03472083163219
14	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	5.71998535698016
15	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	6.10159609586698
16	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	5.84559973543596
17	5.6	0.18	0.29	2.3	0.04	5.0	47.0	0.99126	3.07	0.45	10.1	4	0	white	6.0889191208854
18	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	6.11861032357601
19	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white	6.11779050622569
20	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	6.10085975465337
21	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	6.15656417578091
22	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	6.03126480305702
23	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	5.81493862456251
24	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	5.39249856775268
25	5.7	0.135	0.3	4.6	0.042	19.0	101.0	0.9946	3.31	0.42	9.3	6	0	white	6.09486840853572
26	5.7	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	5.98228900031377
27	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	5.79819600039747
28	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	5.81700911111355
29	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	6.06532187525815
30	5.8	0.2	0.24	1.4	0.033	65.0	169.0	0.99043	3.59	0.56	12.3	7	1	white	6.09290835614903
31	5.8	0.2	0.27	1.4	0.031	12.0	77.0	0.9905	3.25	0.36	10.9	7	1	white	6.10438588713943
32	5.8	0.24	0.39	1.5	0.054	37.0	158.0	0.9932	3.21	0.52	9.3	6	0	white	6.02301210861724
33	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	5.92858372272901
34	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	5.97244242221761
35	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	5.92166510367182
36	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	5.98828729695845
37	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	5.73305840117388
38	5.8	0.36	0.26	3.3	0.038	40.0	153.0	0.9911	3.34	0.55	11.3	6	0	white	5.86949998643712
39	5.8	0.36	0.5	1.0	0.127	63.0	178.0	0.99212	3.1	0.45	9.7	5	0	white	5.710772241143
40	5.9	0.27	0.27	9.0	0.051	43.0	136.0	0.9941	3.25	0.53	10.7	6	0	white	5.82037261704122
41	5.9	0.28	0.34	3.6	0.04	50.0	194.0	0.9912	3.31	0.52	11.6	6	0	white	5.95915600660948
42	5.9	0.34	0.31	2.0	0.03	38.0	142.0	0.98892	3.4	0.41	12.9	7	1	white	5.94367242884656
43	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	5.76141496648468
44	5.9	0.65	0.23	5.0	0.035	20.0	128.0	0.99016	3.46	0.48	12.8	6	0	white	5.51647511573668
45	6.0	0.17	0.29	5.0	0.028	25.0	108.0	0.99076	3.14	0.34	12.3	6	0	white	6.07278054978274
46	6.0	0.2	0.32	3.0	0.031	26.0	118.0	0.99134	3.38	0.68	11.2	7	1	white	6.08090402709507
47	6.0	0.24	0.32	6.3	0.03	34.0	129.0	0.9946	3.52	0.41	10.4	5	0	white	5.98017506647248
48	6.0	0.26	0.15	1.2	0.053	35.0	124.0	0.99347	3.08	0.46	8.8	5	0	white	5.96532264083113
49	6.0	0.26	0.18	7.0	0.055	50.0	194.0	0.99591	3.21	0.43	9.0	5	0	white	5.85005204414532
50	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	6.00414411713488
51	6.0	0.27	0.4	1.7	0.021	18.0	82.0	0.9891	3.24	0.95	13.1333333333333	6	0	white	6.06862833026041
52	6.0	0.28	0.27	2.3	0.051	23.0	147.0	0.994	3.23	0.67	10.3	6	0	white	5.94976428563753
53	6.0	0.28	0.52	6.2	0.028	37.0	104.0	0.99161	3.28	0.51	11.8	7	1	white	5.97240471977779
54	6.0	0.34	0.32	3.8	0.044	13.0	116.0	0.99108	3.39	0.44	11.8	7	1	white	5.87318354476047
55	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	5.800971426216
56	6.0	0.41	0.23	1.1	0.066	22.0	148.0	0.99266	3.3	0.47	9.63333333333333	5	0	white	5.77882262799385
57	6.0	0.52	0.33	7.7	0.046	24.0	119.0	0.99224	3.04	0.38	11.0	6	0	white	5.59393760737023
58	6.1	0.17	0.42	15.1	0.033	28.0	124.0	0.99684	2.87	0.47	9.5	5	0	white	5.88680042766194
59	6.1	0.21	0.4	1.4	0.066	40.5	165.0	0.9912	3.25	0.59	11.9	6	0	red	6.01722985339775
60	6.1	0.22	0.38	2.8	0.144	12.0	65.0	0.9908	2.95	0.64	11.4	6	0	white	5.74870391386795
61	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	5.52086177796249
62	6.1	0.3	0.56	2.8	0.044	47.0	179.0	0.9924	3.3	0.57	10.9	7	1	white	5.98552292470799
63	6.1	0.32	0.37	1.8	0.051	13.0	200.0	0.9945	3.49	0.44	10.5	4	0	white	5.93587515217143
64	6.1	0.34	0.25	1.8	0.084	4.0	28.0	0.99464	3.36	0.44	10.1	5	0	red	5.79784343543394
65	6.1	0.38	0.14	3.9	0.06	27.0	113.0	0.99344	3.07	0.34	9.2	4	0	white	5.75462558339173
66	6.1	0.41	0.0	1.6	0.063	36.0	87.0	0.9914	3.27	0.67	10.8	6	0	white	5.72984790959086
67	6.1	0.41	0.14	10.4	0.037	18.0	119.0	0.996	3.38	0.45	10.0	5	0	white	5.65817308615087
68	6.1	0.68	0.52	1.4	0.037	32.0	123.0	0.99022	3.24	0.45	12.0	6	0	white	5.6062628027163
69	6.1	0.715	0.1	2.6	0.053	13.0	27.0	0.99362	3.57	0.5	11.9	5	0	red	5.42941921917416
70	6.2	0.19	0.29	4.3	0.045	33.0	126.0	0.99658	3.18	0.42	9.3	6	0	white	6.03292045509511
71	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	6.08632709452218
72	6.2	0.21	0.28	5.7	0.028	45.0	121.0	0.99168	3.21	1.08	12.15	7	1	white	6.01375276500753
73	6.2	0.22	0.28	2.2	0.04	24.0	125.0	0.9917	3.19	0.48	10.5	6	0	white	6.04239401057465
74	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	5.88914104958882
75	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	5.88914104958882
76	6.2	0.27	0.49	1.4	0.05	20.0	74.0	0.9931	3.32	0.44	9.8	6	0	white	6.01905392671194
77	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	6.04228305930781
78	6.2	0.33	0.41	16.8	0.037	58.0	173.0	0.99882	3.25	0.57	9.4	6	0	white	5.66808977340845
79	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	5.80479618949575
80	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	4.9249266462402
81	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	5.69914729655292
82	6.3	0.2	0.26	1.6	0.027	36.0	141.0	0.99268	3.53	0.56	10.8	6	0	white	6.11257140275663
83	6.3	0.22	0.22	5.6	0.039	31.0	128.0	0.99296	3.12	0.46	10.4	6	0	white	5.96544972911472
84	6.3	0.23	0.31	1.5	0.022	11.0	82.0	0.9892	3.3	0.4	12.9	7	1	white	6.09511325409254
85	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	5.95795139291413
86	6.3	0.28	0.22	11.5	0.036	27.0	150.0	0.99445	3.0	0.33	10.6	6	0	white	5.78848474835925
87	6.3	0.305	0.22	16.0	0.061	26.0	141.0	0.99824	3.08	0.5	9.1	5	0	white	5.60600285118847
88	6.3	0.41	0.3	3.2	0.03	49.0	164.0	0.9927	3.53	0.79	11.7	7	1	white	5.84837991191092
89	6.3	1.02	0.0	2.0	0.083	17.0	24.0	0.99437	3.59	0.55	11.2	4	0	red	5.00546609739038
90	6.4	0.13	0.47	1.6	0.092	40.0	158.0	0.9928	3.21	0.36	9.8	6	0	white	6.04146784611245
91	6.4	0.19	0.42	2.9	0.032	32.0	83.0	0.9908	3.3	0.41	11.7	6	0	white	6.1050070923194
92	6.4	0.23	0.37	7.9	0.05	60.0	150.0	0.99488	2.86	0.49	9.3	6	0	white	5.90724634610161
93	6.4	0.27	0.29	10.8	0.028	17.0	118.0	0.99356	3.18	0.37	11.2	6	0	white	5.84661805500243
94	6.4	0.29	0.3	2.9	0.036	25.0	79.0	0.99037	3.29	0.6	12.4	7	1	white	5.96090126407699
95	6.4	0.29	0.57	1.0	0.06	15.0	120.0	0.9924	3.06	0.41	9.5	5	0	white	5.98827433260071
96	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	5.94130618586726
97	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	5.85860494159197
98	6.4	0.31	0.53	8.8	0.057	36.0	221.0	0.99642	3.17	0.44	9.1	5	0	white	5.81504084365367
99	6.4	0.37	0.37	4.85	0.041	39.5	216.5	0.99432	3.1	0.5	9.8	6	0	white	5.84280257561263
100	6.4	0.42	0.46	8.4	0.05	58.0	180.0	0.99495	3.18	0.46	9.7	6	0	white	5.7060766367367

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()

Important

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': 0.0001,
 'C': 1.0,
 'intercept_scaling': 1.0,
 'intercept_mode': 'regularized',
 'acceptable_error_margin': 0.1,
 'max_iter': 100}

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]: '3.48500968517955 + -0.0073634121360966 * "fixed_acidity" + -1.09194435585918 * "volatile_acidity" + 0.184882994335351 * "citric_acid" + -0.0210962926291445 * "residual_sugar" + -2.8613230438036 * "chlorides" + 2.97707246753078 * "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([6.16106303])

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 = 0.0001, C: float = 1.0, intercept_scaling: float = 1.0, intercept_mode: Literal['regularized', 'unregularized'] = 'regularized', acceptable_error_margin: float = 0.1, max_iter: int = 100) → 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