vastorbit.machine_learning.vast.linear_model.Ridge

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

Creates a Ridge 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 Ridge model:

from vastorbit.machine_learning.vast import Ridge

Then we can create the model:

model = Ridge(
    tol = 1e-6,
    alpha = 0.5,
    max_iter = 100,
    solver = 'cholesky',
)

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	0.09853742657372833
max_error	2.787660357774259
median_absolute_error	0.5982438991489742
mean_absolute_error	0.6427562812466034
mean_squared_error	73.28304611353323
root_mean_squared_error	0.8292957156719737
r2	0.09253555929188284
r2_adj	0.08828514036584945
aic	5545.260184986937
bic	5581.222043833587

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	78.50255328080179	13.083758880133631	0.17754701171463996	0.9829655225407988
Residual	1281	94399.19581631111	73.69180001273311
Total	1287	1074.583850931679

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	0.27	0.25	5.0	0.024	52.0	99.0	0.9925	2.87	0.49	11.4	5	0	white	0.16	5.969065989519746
2	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	0.18	5.869497132644156
3	6.2	0.23	0.35	0.7	0.051	24.0	111.0	0.9916	3.37	0.43	11.0	3	0	white	0.1	6.0546598056992345
4	6.8	0.24	0.29	9.5	0.042	56.0	157.0	0.99586	3.11	0.51	10.1	6	0	white	0.11	5.889013809440309
5	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	0.17	5.869709495051355
6	6.4	0.32	0.27	4.9	0.034	18.0	122.0	0.9916	3.36	0.71	12.5	6	0	white	0.07	5.910452804536682
7	8.2	0.34	0.29	5.2	0.076	19.0	92.0	0.99138	2.95	0.39	12.5	6	0	white	0.19	5.756630016284401
8	6.9	0.24	0.23	7.1	0.041	20.0	97.0	0.99246	3.1	0.85	11.4	6	0	white	0.06	5.937257349367567
9	6.0	0.16	0.22	1.6	0.042	36.0	106.0	0.9905	3.24	0.32	11.4	6	0	white	0.0	6.137378096606
10	5.8	0.32	0.2	2.6	0.027	17.0	123.0	0.98936	3.36	0.78	13.9	7	1	white	0.06	5.973684685542979
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	0.16	5.855663573907519
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	0.13	6.192519115360003
13	6.8	0.19	0.4	9.85	0.055	41.0	103.0	0.99532	2.98	0.56	10.5	6	0	white	0.07	5.922305634976469
14	5.9	0.32	0.28	4.7	0.039	34.0	94.0	0.98964	3.22	0.57	13.1	7	1	white	0.19	5.912968398449511
15	8.0	0.2	0.44	1.0	0.057	24.0	111.0	0.99158	3.09	0.32	11.2	6	0	white	0.08	6.064277434568909
16	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	0.17	5.920848487282326
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	0.16	5.940308443690418
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	0.12	5.920848487282326
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	0.05	5.902033899769618
20	7.1	0.36	0.28	2.4	0.036	35.0	115.0	0.98936	3.19	0.44	13.5	7	1	white	0.04	5.903542091648358
21	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	0.16	6.144506874497989
22	5.9	0.23	0.24	3.8	0.038	61.0	152.0	0.99139	3.31	0.5	11.3	7	1	white	0.16	6.0271557718630735
23	6.4	0.28	0.44	7.1	0.048	49.0	179.0	0.99528	3.15	0.48	9.2	5	0	white	0.09	5.889182518360972
24	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	0.19	6.0487597383182035
25	8.1	0.26	0.27	4.3	0.03	43.0	123.0	0.99212	3.16	0.33	11.2	6	0	white	0.12	5.986476156456159
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	0.01	5.945409711375209
27	8.1	0.2	0.28	0.9	0.023	49.0	87.0	0.99062	2.92	0.36	11.1	6	0	white	0.12	6.141352813227952
28	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	0.04	5.547576801641213
29	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	0.05	5.652936644865338
30	7.3	0.23	0.27	2.6	0.035	39.0	120.0	0.99138	3.04	0.59	11.3	7	1	white	0.13	6.0473036990486655
31	7.4	0.25	0.28	7.25	0.028	14.0	78.0	0.99238	2.94	0.37	11.5	7	1	white	0.13	5.959485164425461
32	7.4	0.25	0.28	7.25	0.028	14.0	78.0	0.99238	2.94	0.37	11.5	7	1	white	0.07	5.959485164425461
33	5.8	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white	0.02	5.947992407736924
34	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	0.0	5.85310149236515
35	6.6	0.19	0.35	1.5	0.037	37.0	107.0	0.99006	3.18	0.68	12.0	7	1	white	0.16	6.128314313416718
36	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	0.01	6.001622279957975
37	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	0.18	6.035969416964183
38	6.5	0.21	0.51	17.6	0.045	34.0	125.0	0.99966	3.2	0.47	8.8	6	0	white	0.17	5.792401761461086
39	6.4	0.24	0.23	2.0	0.046	30.0	133.0	0.9908	3.12	0.54	11.4	7	1	white	0.06	6.021288934790709
40	6.4	0.29	0.24	3.2	0.037	31.0	95.0	0.98942	2.9	0.66	12.6	7	1	white	0.19	5.971589261391889
41	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	0.17	5.582950863003239
42	6.8	0.52	0.26	5.7	0.038	27.0	130.0	0.99	3.11	0.27	13.0	7	1	white	0.16	5.649324260494895
43	6.1	0.35	0.07	1.4	0.069	22.0	108.0	0.9934	3.23	0.52	9.2	5	0	white	0.02	5.813607603272651
44	6.3	0.14	0.39	1.2	0.044	26.0	116.0	0.992	3.26	0.53	10.3	6	0	white	0.11	6.174296350244182
45	8.2	0.28	0.42	1.8	0.031	30.0	93.0	0.9917	3.09	0.39	11.4	5	0	white	0.03	6.020730067401803
46	7.2	0.25	0.39	18.95	0.038	42.0	155.0	0.9999	2.97	0.47	9.0	6	0	white	0.04	5.719824904389421
47	7.4	0.31	0.28	1.6	0.05	33.0	137.0	0.9929	3.31	0.56	10.5	6	0	white	0.07	5.923765305826235
48	7.0	0.21	0.28	8.7	0.045	37.0	222.0	0.9954	3.25	0.54	10.4	6	0	white	0.08	5.929142638935156
49	7.0	0.21	0.28	8.6	0.045	37.0	221.0	0.9954	3.25	0.54	10.4	6	0	white	0.15	5.930851989079272
50	7.0	0.21	0.28	8.7	0.045	37.0	222.0	0.9954	3.25	0.54	10.4	6	0	white	0.19	5.929142638935156
51	6.8	0.29	0.5	13.3	0.053	48.0	194.0	0.9974	3.09	0.45	9.4	5	0	white	0.06	5.753735786989681
52	6.6	0.52	0.44	12.2	0.048	54.0	245.0	0.9975	3.26	0.54	9.3	6	0	white	0.02	5.50670928640562
53	7.1	0.21	0.27	8.6	0.056	26.0	111.0	0.9956	2.95	0.52	9.5	5	0	white	0.14	5.898607844630894
54	7.2	0.39	0.62	11.0	0.047	66.0	178.0	0.9976	3.16	0.5	8.7	5	0	white	0.17	5.6999032063682975
55	7.0	0.14	0.32	9.0	0.039	54.0	141.0	0.9956	3.22	0.43	9.4	6	0	white	0.19	6.027294671543225
56	5.5	0.16	0.22	4.5	0.03	30.0	102.0	0.9938	3.24	0.36	9.4	6	0	white	0.11	6.1123023277718485
57	6.7	0.47	0.34	8.9	0.043	31.0	172.0	0.9964	3.22	0.6	9.2	5	0	white	0.04	5.627604727814193
58	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	0.13	4.976202418533086
59	7.4	0.26	0.31	7.6	0.047	52.0	177.0	0.9962	3.13	0.45	8.9	6	0	white	0.07	5.880252529656076
60	5.7	0.44	0.13	7.0	0.025	28.0	173.0	0.9913	3.33	0.48	12.5	6	0	white	0.11	5.7467995053809755
61	7.2	0.28	0.54	16.7	0.045	54.0	200.0	0.999	3.08	0.49	9.5	6	0	white	0.02	5.724399562064983
62	6.8	0.19	0.58	14.2	0.038	51.0	164.0	0.9975	3.12	0.48	9.6	6	0	white	0.11	5.906455978084731
63	7.8	0.18	0.31	12.2	0.053	46.0	140.0	0.998	3.06	0.53	8.9	6	0	white	0.07	5.87078197366891
64	5.9	0.62	0.28	3.5	0.039	55.0	152.0	0.9907	3.44	0.44	12.0	6	0	white	0.01	5.572701498917045
65	7.4	0.15	0.42	1.7	0.045	49.0	154.0	0.992	3.0	0.6	10.4	6	0	white	0.08	6.14527233599026
66	5.9	0.13	0.28	1.9	0.05	20.0	78.0	0.9918	3.43	0.64	10.8	6	0	white	0.11	6.1495521844248024
67	7.9	0.19	0.26	2.1	0.039	8.0	143.0	0.9942	3.05	0.74	9.8	5	0	white	0.04	6.076231235992088
68	5.6	0.19	0.47	4.5	0.03	19.0	112.0	0.9922	3.56	0.45	11.2	6	0	white	0.1	6.11043513524874
69	7.9	0.345	0.51	15.3	0.047	54.0	171.0	0.9987	3.09	0.51	9.1	5	0	white	0.15	5.65729134662698
70	6.7	0.31	0.31	9.9	0.04	10.0	175.0	0.9953	3.46	0.55	11.4	4	0	white	0.07	5.809447268347852
71	7.2	0.46	0.65	10.4	0.05	76.0	192.0	0.9976	3.16	0.42	8.7	5	0	white	0.15	5.622290272020756
72	7.7	0.22	0.42	1.9	0.052	10.0	87.0	0.9922	3.3	0.49	11.8	6	0	white	0.17	6.036508943141876
73	7.2	0.46	0.65	10.4	0.05	76.0	192.0	0.9976	3.16	0.42	8.7	5	0	white	0.06	5.622290272020756
74	7.0	0.3	0.51	13.6	0.05	40.0	168.0	0.9976	3.07	0.52	9.6	7	1	white	0.09	5.743487266810968
75	9.2	0.25	0.34	1.2	0.026	31.0	93.0	0.9916	2.93	0.37	11.3	7	1	white	0.03	6.062704688923436
76	8.4	0.35	0.71	12.2	0.046	22.0	160.0	0.9982	2.98	0.65	9.4	5	0	white	0.07	5.727675970504558
77	6.1	0.41	0.24	1.6	0.049	16.0	137.0	0.993	3.32	0.5	10.4	6	0	white	0.06	5.813549719736441
78	5.9	0.37	0.1	1.6	0.057	39.0	128.0	0.9924	3.24	0.48	10.1	5	0	white	0.11	5.827380202846137
79	6.0	0.26	0.5	2.2	0.048	59.0	153.0	0.9928	3.08	0.61	9.8	5	0	white	0.1	6.016057651947962
80	7.2	0.2	0.34	2.7	0.032	49.0	151.0	0.99	3.16	0.39	12.7	7	1	white	0.09	6.103274868734337
81	8.2	0.22	0.3	1.8	0.047	47.0	185.0	0.9933	3.13	0.5	10.2	6	0	white	0.07	6.02952973941054
82	8.2	0.23	0.29	1.8	0.047	47.0	187.0	0.9933	3.13	0.5	10.2	6	0	white	0.09	6.016476555241702
83	6.5	0.28	0.29	2.7	0.038	26.0	107.0	0.9912	3.32	0.41	11.6	7	1	white	0.13	5.987826866279626
84	5.7	0.18	0.22	4.2	0.042	25.0	111.0	0.994	3.35	0.39	9.4	5	0	white	0.11	6.059011450272631
85	7.4	0.26	0.3	7.9	0.049	38.0	157.0	0.9963	3.13	0.48	8.9	6	0	white	0.18	5.868240900252858
86	6.1	0.32	0.25	1.7	0.034	37.0	136.0	0.992	3.47	0.5	10.8	7	1	white	0.07	5.963945569690523
87	6.9	0.28	0.27	2.1	0.036	42.0	121.0	0.9926	3.42	0.49	10.8	7	1	white	0.03	5.992661822073934
88	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	0.07	5.812347599886957
89	7.0	0.29	0.37	4.9	0.034	26.0	127.0	0.9928	3.17	0.44	10.8	6	0	white	0.03	5.94824469742974
90	6.7	0.3	0.35	1.4	0.18	36.0	160.0	0.9937	3.11	0.54	9.4	6	0	white	0.12	5.6014353348413195
91	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	0.16	5.812347599886957
92	7.3	0.25	0.29	7.5	0.049	38.0	158.0	0.9965	3.43	0.38	9.6	5	0	white	0.07	5.885947489637097
93	7.3	0.25	0.29	7.5	0.049	38.0	158.0	0.9965	3.43	0.38	9.6	5	0	white	0.15	5.885947489637097
94	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	0.08	5.887073522849356
95	7.8	0.26	0.27	1.9	0.051	52.0	195.0	0.9928	3.23	0.5	10.9	6	0	white	0.15	5.971246726226131
96	7.0	0.32	0.24	6.2	0.048	31.0	228.0	0.9957	3.23	0.62	9.4	6	0	white	0.1	5.8272419933444635
97	5.8	0.31	0.33	1.2	0.036	23.0	99.0	0.9916	3.18	0.6	10.5	6	0	white	0.14	5.992268598946566
98	7.0	0.23	0.42	18.05	0.05	35.0	144.0	0.9999	3.22	0.42	8.8	5	0	white	0.0	5.731976614707593
99	7.0	0.23	0.42	18.05	0.05	35.0	144.0	0.9999	3.22	0.42	8.8	5	0	white	0.12	5.731976614707593
100	5.8	0.335	0.14	5.8	0.046	49.0	197.0	0.9937	3.3	0.71	10.3	5	0	white	0.19	5.827505497022953

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