vastorbit.machine_learning.vast.svm.LinearSVR

class vastorbit.machine_learning.vast.svm.LinearSVR(name: str = None, overwrite_model: bool = False, **kwargs)

Creates an LinearSVR object using scikit-learn for training and the scalability of VAST DataBase for the inferences.

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.

• **kwargs (scikit-learn model parameters.)

Variables:

• created (Many attributes are)

• phase. (during the fitting)

• 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.

• feature_importances_ (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.

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 vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit 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 vastorbit are used as intended without interfering with functions from other libraries.

For this example, we will use the winequality dataset.

import vastorbit.datasets as vod

data = vod.load_winequality()

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	6.3	0.67	0.48	12.6	0.052	57.0	222.0	0.9979	3.17	0.52	9.3	6	0	white
2	7.4	0.4	0.29	5.4	0.044	31.0	122.0	0.994	3.3	0.5	11.1	8	1	white
3	7.1	0.26	0.31	2.2	0.044	29.0	128.0	0.9937	3.34	0.64	10.9	8	1	white
4	9.0	0.31	0.48	6.6	0.043	11.0	73.0	0.9938	2.9	0.38	11.6	5	0	white
5	6.3	0.39	0.24	6.9	0.069	9.0	117.0	0.9942	3.15	0.35	10.2	4	0	white
6	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white
7	7.1	0.19	0.28	3.6	0.033	16.0	78.0	0.993	2.91	0.78	11.4	6	0	white
8	7.3	0.25	0.36	13.1	0.05	35.0	200.0	0.9986	3.04	0.46	8.9	7	1	white
9	7.9	0.2	0.34	1.2	0.04	29.0	118.0	0.9932	3.14	0.41	10.6	6	0	white
10	7.1	0.26	0.32	5.9	0.037	39.0	97.0	0.9934	3.31	0.4	11.6	6	0	white
11	7.0	0.2	0.34	5.7	0.035	32.0	83.0	0.9928	3.19	0.46	11.5	6	0	white
12	6.9	0.3	0.33	4.1	0.035	26.0	155.0	0.9925	3.25	0.79	12.3	8	1	white
13	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
14	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
15	5.9	0.415	0.02	0.8	0.038	22.0	63.0	0.9932	3.36	0.36	9.3	5	0	white
16	6.6	0.23	0.26	1.3	0.045	16.0	128.0	0.9934	3.36	0.6	10.0	6	0	white
17	8.6	0.55	0.35	15.55	0.057	35.5	366.5	1.0001	3.04	0.63	11.0	3	0	white
18	6.9	0.35	0.74	1.0	0.044	18.0	132.0	0.992	3.13	0.55	10.2	5	0	white
19	7.6	0.14	0.74	1.6	0.04	27.0	103.0	0.9916	3.07	0.4	10.8	7	1	white
20	9.2	0.28	0.49	11.8	0.042	29.0	137.0	0.998	3.1	0.34	10.1	4	0	white
21	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white
22	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
23	9.8	0.25	0.74	10.0	0.056	36.0	225.0	0.9977	3.06	0.43	10.0	4	0	white
24	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
25	6.8	0.22	0.49	0.9	0.052	26.0	128.0	0.991	3.25	0.35	11.4	6	0	white
26	7.2	0.22	0.49	1.0	0.045	34.0	140.0	0.99	3.05	0.34	12.7	6	0	white
27	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
28	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
29	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
30	7.0	0.39	0.24	1.0	0.048	8.0	119.0	0.9923	3.0	0.31	10.1	4	0	white
31	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
32	6.5	0.36	0.49	2.9	0.03	16.0	94.0	0.9902	3.1	0.49	12.1	7	1	white
33	7.1	0.29	0.49	1.2	0.031	32.0	99.0	0.9893	3.07	0.33	12.2	6	0	white
34	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
35	6.9	0.23	0.24	14.2	0.053	19.0	94.0	0.9982	3.17	0.5	9.6	5	0	white
36	8.5	0.56	0.74	17.85	0.051	51.0	243.0	1.0005	2.99	0.7	9.2	5	0	white
37	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
38	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white
39	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white
40	7.2	0.26	0.74	13.6	0.05	56.0	162.0	0.998	3.03	0.44	8.8	5	0	white
41	7.2	0.31	0.24	1.4	0.057	17.0	117.0	0.9928	3.16	0.35	10.5	5	0	white
42	8.0	0.25	0.49	1.2	0.061	27.0	117.0	0.9938	3.08	0.34	9.4	5	0	white
43	7.0	0.18	0.49	5.3	0.04	34.0	125.0	0.9914	3.24	0.4	12.2	6	0	white
44	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
45	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
46	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
47	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
48	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
49	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
50	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
51	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
52	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
53	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
54	8.3	0.21	0.49	19.8	0.054	50.0	231.0	1.0012	2.99	0.54	9.2	5	0	white
55	6.9	0.34	0.74	11.2	0.069	44.0	150.0	0.9968	3.0	0.81	9.2	5	0	white
56	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
57	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
58	7.1	0.22	0.74	2.7	0.044	42.0	144.0	0.991	3.31	0.41	12.2	6	0	white
59	7.9	0.11	0.49	4.5	0.048	27.0	133.0	0.9946	3.24	0.42	10.6	6	0	white
60	8.5	0.17	0.74	3.6	0.05	29.0	128.0	0.9928	3.28	0.4	12.4	6	0	white
61	6.4	0.145	0.49	5.4	0.048	54.0	164.0	0.9946	3.56	0.44	10.8	6	0	white
62	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
63	8.3	0.19	0.49	1.2	0.051	11.0	137.0	0.9918	3.06	0.46	11.0	6	0	white
64	8.0	0.44	0.49	9.1	0.031	46.0	151.0	0.9926	3.16	0.27	12.7	8	1	white
65	7.0	0.2	0.74	0.8	0.044	19.0	163.0	0.9931	3.46	0.53	10.2	5	0	white
66	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
67	7.1	0.25	0.49	3.0	0.03	30.0	96.0	0.9903	3.13	0.39	12.3	7	1	white
68	6.5	0.24	0.24	1.6	0.046	15.0	60.0	0.9928	3.19	0.39	9.8	5	0	white
69	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
70	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
71	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
72	7.8	0.21	0.49	1.35	0.052	6.0	48.0	0.9911	3.15	0.28	11.4	5	0	white
73	7.0	0.2	0.49	5.9	0.038	39.0	128.0	0.9938	3.21	0.48	10.8	6	0	white
74	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
75	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
76	7.1	0.85	0.49	8.7	0.028	40.0	184.0	0.9962	3.22	0.36	10.7	5	0	white
77	7.6	0.51	0.24	1.2	0.04	10.0	104.0	0.992	3.05	0.29	10.8	6	0	white
78	7.9	0.22	0.24	4.6	0.044	39.0	159.0	0.9927	2.99	0.28	11.5	6	0	white
79	7.7	0.16	0.49	2.0	0.056	20.0	124.0	0.9948	3.32	0.49	10.7	6	0	white
80	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
81	6.6	0.25	0.24	1.7	0.048	26.0	124.0	0.9942	3.37	0.6	10.1	6	0	white
82	6.7	0.16	0.49	2.4	0.046	57.0	187.0	0.9952	3.62	0.81	10.4	6	0	white
83	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
84	7.5	0.32	0.24	4.6	0.053	8.0	134.0	0.9958	3.14	0.5	9.1	3	0	white
85	7.4	0.28	0.49	1.5	0.034	20.0	126.0	0.9918	2.98	0.39	10.6	6	0	white
86	6.2	0.15	0.49	0.9	0.033	17.0	51.0	0.9932	3.3	0.7	9.4	6	0	white
87	6.7	0.25	0.74	19.4	0.054	44.0	169.0	1.0004	3.51	0.45	9.8	6	0	white
88	6.5	0.26	0.74	13.3	0.044	68.0	224.0	0.9972	3.18	0.54	9.5	6	0	white
89	7.9	0.16	0.74	17.85	0.037	52.0	187.0	0.9998	2.99	0.41	9.3	5	0	white
90	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white
91	7.5	0.2	0.49	1.3	0.031	8.0	97.0	0.9918	3.06	0.62	11.1	5	0	white
92	8.0	0.3	0.49	9.4	0.046	47.0	188.0	0.9964	3.14	0.48	10.0	5	0	white
93	8.0	0.34	0.49	9.0	0.033	39.0	180.0	0.9936	3.13	0.38	12.3	8	1	white
94	7.7	0.35	0.49	8.65	0.033	42.0	186.0	0.9931	3.14	0.38	12.4	8	1	white
95	7.6	0.29	0.49	9.6	0.03	45.0	197.0	0.9938	3.13	0.38	12.3	7	1	white
96	6.7	0.62	0.24	1.1	0.039	6.0	62.0	0.9934	3.41	0.32	10.4	5	0	white
97	6.8	0.27	0.49	1.2	0.044	35.0	126.0	0.99	3.13	0.48	12.1	7	1	white
98	7.7	0.27	0.49	1.8	0.041	23.0	86.0	0.9914	3.16	0.42	12.5	6	0	white
99	6.7	0.51	0.24	2.1	0.043	14.0	155.0	0.9904	3.22	0.6	13.0	6	0	white
100	7.4	0.19	0.49	9.3	0.03	26.0	132.0	0.994	2.99	0.32	11.0	7	1	white

Rows: 1-100 | Columns: 14

Note

vastorbit 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 vastorbit environment.

You can easily divide your dataset into training and testing subsets using the VastFrame.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 = vod.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, vastorbit 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 VastFrame.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 vastorbit.machine_learning.vast import LinearSVR

Then we can create the model:

model = LinearSVR(
    C = 1.0,
    intercept_scaling = 1.0,
    acceptable_error_margin = 0.1,
    max_iter = 1000,
)

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,
)

Important

To train a model, you can directly use the VastFrame 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 vastorbit, 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	0.07277841502832838
max_error	3.186658166861232
median_absolute_error	0.5944001825231662
mean_absolute_error	0.6233558819062552
mean_squared_error	11.759384104831529
root_mean_squared_error	0.8259908409845707
r2	0.061856763449650076
r2_adj	0.05757300437864388
aic	3269.9646618002557
bic	3306.107732861082

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	85.13991379622394	14.189985632703989	1.2141231205420655	0.296100249890431
Residual	1314	15357.290217032014	11.687435477193313
Total	1320	1003.3156699470254

Rows: 1-3 | Columns: 6

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

model.score()

Prediction

Prediction is straight-forward:

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

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 seedrand Decimal(26, 6)	123 prediction Double
1	8.8	0.19	0.3	5.0	0.028	34.0	120.0	0.99242	2.94	0.47	11.2	5	0	white	0.14	6.170163561417711
2	8.8	0.27	0.25	5.0	0.024	52.0	99.0	0.9925	2.87	0.49	11.4	5	0	white	0.14	6.082645500986424
3	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	0.02	6.116622803727634
4	6.1	0.33	0.32	7.8	0.052	52.0	183.0	0.99657	3.39	0.65	9.5	5	0	white	0.19	5.938639942867827
5	7.2	0.37	0.4	11.6	0.032	34.0	214.0	0.9963	3.1	0.51	9.8	6	0	white	0.18	5.9757675129718475
6	6.2	0.28	0.28	4.3	0.026	22.0	105.0	0.989	2.98	0.64	13.1	8	1	white	0.0	6.03096859185781
7	7.1	0.17	0.4	14.55	0.047	47.0	156.0	0.99945	3.34	0.78	9.1	6	0	white	0.13	6.1744542104865365
8	6.7	0.4	0.22	8.8	0.052	24.0	113.0	0.99576	3.22	0.45	9.4	5	0	white	0.04	5.852151078732707
9	5.6	0.34	0.3	6.9	0.038	23.0	89.0	0.99266	3.25	0.49	11.1	6	0	white	0.1	5.942764583434306
10	6.9	0.29	0.41	7.8	0.046	52.0	171.0	0.99537	3.12	0.51	9.6	5	0	white	0.04	6.019480881320048
11	6.6	0.24	0.27	10.3	0.047	54.0	219.0	0.99742	3.04	0.45	8.8	5	0	white	0.19	6.056945581592966
12	6.6	0.16	0.36	1.1	0.031	27.0	93.0	0.98884	3.23	0.34	13.2	8	1	white	0.02	6.163866473749884
13	6.5	0.22	0.19	1.1	0.064	36.0	191.0	0.99297	3.05	0.5	9.5	6	0	white	0.03	5.996313305578266
14	7.6	0.31	0.24	1.8	0.037	39.0	150.0	0.9913	3.05	0.44	11.8	7	1	white	0.03	5.980807446192026
15	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	0.03	5.913323258959411
16	7.8	0.2	0.2	1.4	0.036	25.0	83.0	0.99088	3.03	0.46	11.7	6	0	white	0.18	6.1012028193031895
17	6.1	0.33	0.3	3.0	0.036	30.0	124.0	0.98922	3.31	0.4	13.1	7	1	white	0.14	5.950041164062943
18	6.7	0.24	0.36	8.4	0.042	42.0	123.0	0.99473	3.34	0.52	10.9	6	0	white	0.19	6.075076477228808
19	6.9	0.33	0.31	4.2	0.04	21.0	93.0	0.9896	3.18	0.48	13.4	7	1	white	0.03	5.954646369262468
20	6.7	0.29	0.45	14.3	0.054	30.0	181.0	0.99869	3.14	0.57	9.1	5	0	white	0.05	6.023464414252247
21	7.9	0.35	0.28	12.9	0.032	13.0	63.0	0.9932	2.99	0.43	13.0	6	0	white	0.12	5.984188815642971
22	6.8	0.19	0.33	1.3	0.031	22.0	87.0	0.98987	3.08	0.62	12.3	7	1	white	0.15	6.130510021262477
23	6.0	0.23	0.15	9.7	0.048	101.0	207.0	0.99571	3.05	0.3	9.1	5	0	white	0.08	6.035014038496094
24	7.6	0.23	0.29	8.6	0.053	65.0	146.0	0.9963	3.11	0.32	9.8	6	0	white	0.08	6.062929220962426
25	7.2	0.27	0.31	1.2	0.031	27.0	80.0	0.98892	3.03	0.33	12.7	6	0	white	0.04	6.04033311428124
26	6.6	0.23	0.37	8.5	0.036	46.0	153.0	0.99576	3.2	0.48	9.4	6	0	white	0.12	6.10373317905885
27	6.0	0.33	0.2	1.8	0.031	49.0	159.0	0.9919	3.41	0.53	11.0	6	0	white	0.05	5.948744231439401
28	6.0	0.33	0.2	1.8	0.031	49.0	159.0	0.9919	3.41	0.53	11.0	6	0	white	0.03	5.948744231439401
29	6.6	0.64	0.28	4.4	0.032	19.0	78.0	0.99036	3.11	0.62	12.9	6	0	white	0.14	5.620228802360421
30	7.0	0.48	0.12	4.5	0.05	23.0	86.0	0.99398	2.86	0.35	9.0	5	0	white	0.06	5.743133236867507
31	7.6	0.38	0.28	4.2	0.029	7.0	112.0	0.9906	3.0	0.41	12.6	6	0	white	0.14	5.9315131025245815
32	7.8	0.15	0.34	1.1	0.035	31.0	93.0	0.99096	3.07	0.72	11.3	7	1	white	0.15	6.181095206568653
33	7.8	0.31	0.4	1.6	0.027	20.0	87.0	0.9911	3.15	0.48	11.9	6	0	white	0.01	6.031899749940083
34	7.5	0.3	0.21	6.55	0.026	33.0	143.0	0.99244	2.92	0.35	11.1	5	0	white	0.14	6.025048781057586
35	7.2	0.26	0.24	7.0	0.023	19.0	130.0	0.99176	3.14	0.49	12.8	7	1	white	0.04	6.077813218675283
36	6.8	0.19	0.71	17.5	0.042	21.0	114.0	0.99784	2.85	0.5	9.5	6	0	white	0.09	6.2114334594495
37	5.6	0.28	0.27	3.9	0.043	52.0	158.0	0.99202	3.35	0.44	10.7	7	1	white	0.1	5.985794710684402
38	6.6	0.17	0.28	1.1	0.034	55.0	108.0	0.98939	3.0	0.52	11.9	7	1	white	0.01	6.133969294721746
39	6.4	0.38	0.24	7.2	0.047	41.0	151.0	0.99604	3.11	0.6	9.2	5	0	white	0.0	5.883745663262655
40	6.8	0.14	0.18	1.4	0.047	30.0	90.0	0.99164	3.27	0.54	11.2	6	0	white	0.12	6.127793692017492
41	7.0	0.29	0.33	0.9	0.041	20.0	117.0	0.99048	3.21	0.5	11.4	5	0	white	0.17	5.996625731523302
42	6.3	0.2	0.26	4.7	0.04	108.0	168.0	0.99278	3.07	0.75	10.7	7	1	white	0.01	6.093030789706409
43	7.7	0.32	0.61	11.8	0.041	66.0	188.0	0.99794	3.0	0.54	9.3	5	0	white	0.16	6.0522534372900445
44	7.6	0.31	0.27	8.8	0.021	57.0	156.0	0.99442	3.08	0.38	11.0	7	1	white	0.06	6.045275069775469
45	5.8	0.58	0.0	1.5	0.02	33.0	96.0	0.98918	3.29	0.38	12.4	6	0	white	0.12	5.655564009421114
46	7.2	0.33	0.22	4.5	0.031	10.0	73.0	0.99076	2.97	0.52	12.2	7	1	white	0.07	5.969432247613501
47	6.9	0.19	0.6	4.0	0.037	6.0	122.0	0.99255	2.92	0.59	10.4	4	0	white	0.16	6.169911293995595
48	6.6	0.085	0.33	1.4	0.036	17.0	109.0	0.99306	3.27	0.61	9.5	6	0	white	0.12	6.240401899398112
49	6.3	0.32	0.17	17.75	0.06	51.0	190.0	0.99916	3.13	0.48	8.8	6	0	white	0.16	5.934512426466723
50	7.9	0.32	0.51	1.8	0.341	17.0	56.0	0.9969	3.04	1.08	9.2	6	0	red	0.04	5.27912699960719
51	7.9	0.43	0.21	1.6	0.106	10.0	37.0	0.9966	3.17	0.91	9.5	5	0	red	0.06	5.686055185535197
52	6.9	0.4	0.14	2.4	0.085	21.0	40.0	0.9968	3.43	0.63	9.7	6	0	red	0.04	5.750356020107822
53	7.7	0.62	0.04	3.8	0.084	25.0	45.0	0.9978	3.34	0.53	9.5	5	0	red	0.19	5.505035649955413
54	7.5	0.63	0.12	5.1	0.111	50.0	110.0	0.9983	3.26	0.77	9.4	5	0	red	0.04	5.441056326320785
55	7.8	0.59	0.18	2.3	0.076	17.0	54.0	0.9975	3.43	0.59	10.0	5	0	red	0.11	5.577738083421833
56	7.5	0.52	0.16	1.9	0.085	12.0	35.0	0.9968	3.38	0.62	9.5	7	1	red	0.07	5.625127861908869
57	7.5	0.52	0.11	1.5	0.079	11.0	39.0	0.9968	3.42	0.58	9.6	5	0	red	0.16	5.631310959163073
58	8.0	0.705	0.05	1.9	0.074	8.0	19.0	0.9962	3.34	0.95	10.5	6	0	red	0.16	5.432356718777697
59	7.7	0.67	0.23	2.1	0.088	17.0	96.0	0.9962	3.32	0.48	9.5	5	0	red	0.0	5.461940951031568
60	7.7	0.69	0.22	1.9	0.084	18.0	94.0	0.9961	3.31	0.48	9.5	5	0	red	0.06	5.44718079582068
61	9.7	0.32	0.54	2.5	0.094	28.0	83.0	0.9984	3.28	0.82	9.6	5	0	red	0.05	5.917665214872522
62	6.7	0.75	0.12	2.0	0.086	12.0	80.0	0.9958	3.38	0.52	10.1	5	0	red	0.13	5.346844870268399
63	6.2	0.45	0.2	1.6	0.069	3.0	15.0	0.9958	3.41	0.56	9.2	5	0	red	0.17	5.730785478718593
64	7.7	0.49	0.26	1.9	0.062	9.0	31.0	0.9966	3.39	0.64	9.6	5	0	red	0.18	5.732917468955398
65	9.3	0.39	0.44	2.1	0.107	34.0	125.0	0.9978	3.14	1.22	9.5	5	0	red	0.07	5.784712926865215
66	7.7	0.49	0.26	1.9	0.062	9.0	31.0	0.9966	3.39	0.64	9.6	5	0	red	0.07	5.732917468955398
67	8.1	0.545	0.18	1.9	0.08	13.0	35.0	0.9972	3.3	0.59	9.0	6	0	red	0.12	5.6205999160505264
68	7.8	0.5	0.3	1.9	0.075	8.0	22.0	0.9959	3.31	0.56	10.4	6	0	red	0.01	5.695704695083162
69	8.1	0.575	0.22	2.1	0.077	12.0	65.0	0.9967	3.29	0.51	9.2	5	0	red	0.03	5.59990579099351
70	7.8	0.41	0.68	1.7	0.467	18.0	69.0	0.9973	3.08	1.31	9.3	5	0	red	0.04	4.894108590081485
71	8.0	0.33	0.53	2.5	0.091	18.0	80.0	0.9976	3.37	0.8	9.6	6	0	red	0.13	5.889986852950306
72	7.8	0.56	0.19	1.8	0.104	12.0	47.0	0.9964	3.19	0.93	9.5	5	0	red	0.17	5.5407968656975
73	8.0	0.71	0.0	2.6	0.08	11.0	34.0	0.9976	3.44	0.53	9.5	5	0	red	0.08	5.408517707153845
74	8.1	1.33	0.0	1.8	0.0819999999999999	3.0	12.0	0.9964	3.54	0.48	10.9	5	0	red	0.14	4.7051895096872824
75	8.3	0.715	0.15	1.8	0.089	10.0	52.0	0.9968	3.23	0.77	9.5	5	0	red	0.03	5.404482646397632
76	8.3	0.715	0.15	1.8	0.089	10.0	52.0	0.9968	3.23	0.77	9.5	5	0	red	0.12	5.404482646397632
77	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	0.18	5.84936008839383
78	5.8	0.68	0.02	1.8	0.087	21.0	94.0	0.9944	3.54	0.52	10.0	5	0	red	0.04	5.39313633459472
79	7.5	0.6	0.03	1.8	0.095	25.0	99.0	0.995	3.35	0.54	10.1	5	0	red	0.04	5.486629271919798
80	7.8	0.63	0.48	1.7	0.1	14.0	96.0	0.9961	3.19	0.62	9.5	5	0	red	0.09	5.516387817919156
81	7.4	0.62	0.05	1.9	0.068	24.0	42.0	0.9961	3.42	0.57	11.5	6	0	red	0.08	5.534983698338115
82	7.9	0.5	0.33	2.0	0.084	15.0	143.0	0.9968	3.2	0.55	9.5	5	0	red	0.15	5.681515616267669
83	7.9	0.49	0.32	1.9	0.0819999999999999	17.0	144.0	0.9968	3.2	0.55	9.5	5	0	red	0.02	5.6958983018328
84	8.2	0.5	0.35	2.9	0.077	21.0	127.0	0.9976	3.23	0.62	9.4	5	0	red	0.04	5.708940028049045
85	6.8	0.63	0.12	3.8	0.099	16.0	126.0	0.9969	3.28	0.61	9.5	5	0	red	0.12	5.456578645988928
86	7.6	0.55	0.21	2.2	0.071	7.0	28.0	0.9964	3.28	0.55	9.7	5	0	red	0.09	5.634665085710152
87	7.3	0.58	0.3	2.4	0.074	15.0	55.0	0.9968	3.46	0.59	10.2	5	0	red	0.19	5.605219421113908
88	7.8	0.44	0.28	2.7	0.1	18.0	95.0	0.9966	3.22	0.67	9.4	5	0	red	0.0	5.701463544185443
89	8.7	0.625	0.16	2.0	0.1009999999999999	13.0	49.0	0.9962	3.14	0.57	11.0	5	0	red	0.04	5.481492503595543
90	8.1	0.725	0.22	2.2	0.072	11.0	41.0	0.9967	3.36	0.55	9.1	5	0	red	0.04	5.444129162298411
91	7.8	0.53	0.33	2.4	0.08	24.0	144.0	0.99655	3.3	0.6	9.5	5	0	red	0.02	5.65676519599676
92	6.8	0.61	0.04	1.5	0.057	5.0	10.0	0.99525	3.42	0.6	9.5	5	0	red	0.05	5.561860634327925
93	8.0	0.38	0.06	1.8	0.078	12.0	49.0	0.99625	3.37	0.52	9.9	6	0	red	0.13	5.788572045243814
94	6.9	0.52	0.25	2.6	0.081	10.0	37.0	0.99685	3.46	0.5	11.0	5	0	red	0.1	5.643186061011196
95	7.2	0.63	0.0	1.9	0.0969999999999999	14.0	38.0	0.99675	3.37	0.58	9.0	6	0	red	0.04	5.443583850329903
96	8.2	1.0	0.09	2.3	0.065	7.0	37.0	0.99685	3.32	0.55	9.0	6	0	red	0.11	5.134349191572529
97	8.9	0.635	0.37	1.7	0.263	5.0	62.0	0.9971	3.0	1.09	9.3	5	0	red	0.03	5.108173763493909
98	7.7	0.53	0.06	1.7	0.074	9.0	39.0	0.99615	3.35	0.48	9.8	6	0	red	0.03	5.626089795482198
99	7.1	0.6	0.0	1.8	0.074	16.0	34.0	0.9972	3.47	0.7	9.9	6	0	red	0.0	5.533331738090901
100	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white	0.11	6.131507977133953

Rows: 1-100 | Columns: 16

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 VastFrame to the predict() function, but in this case, it’s essential that the column names of the VastFrame 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()

And to manually change some of the parameters:

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

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()

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)

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, **kwargs) → None

Methods

__init__([name, overwrite_model])
contour([nbins, chart])	Draws the model's contour plot.
deploySQL([X])	Returns the SQL code needed to deploy the model.
drop()	Drops the model from the VAST 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) to draw a specific graphic.
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.
regression_report([metrics])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
report([metrics])	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
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 VAST Binary format.
to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.

Attributes

object_type