vastorbit.machine_learning.vast.linear_model.LinearRegression

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

Creates a LinearRegression 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 LinearRegression model:

from vastorbit.machine_learning.vast import LinearRegression

Then we can create the model:

model = LinearRegression(
    fit_intercept = True,
)

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:

result = model.report()

	value
explained_variance	0.16505489958831387
max_error	3.298639757725084
median_absolute_error	0.5454115376987031
mean_absolute_error	0.6173154087221755
mean_squared_error	80008.70770108665
root_mean_squared_error	0.7909656083643224
r2	0.16452323562841564
r2_adj	0.16070245774256997
aic	14905.526087587452
bic	14941.658308590111

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:

result = model.report(metrics = "anova")

	Df	SS	MS	F	p_value
Regression	6	161.27763098619997	26.879605164366662	0.00033414982750402624	0.9999999998314684
Residual	1312	105539608.50159091	80441.77477255404
Total	1318	1019.9029567854415

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	7.1	0.27	0.31	18.2	0.046	55.0	252.0	1.0	3.07	0.56	8.7	5	0	white	0.07	5.627027081137584
2	6.8	0.27	0.12	1.3	0.04	87.0	168.0	0.992	3.18	0.41	10.0	5	0	white	0.04	6.057549067663302
3	6.9	0.49	0.24	1.2	0.049	13.0	125.0	0.9932	3.17	0.51	9.4	5	0	white	0.13	5.712338038706378
4	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	0.1	6.03172262072917
5	7.4	0.21	0.27	1.2	0.041	27.0	99.0	0.9927	3.19	0.33	9.8	6	0	white	0.17	6.0566398818722575
6	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	5.921392425670604
7	6.9	0.23	0.38	8.3	0.047	47.0	162.0	0.9954	3.34	0.52	10.5	7	1	white	0.06	5.862833192459988
8	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	0.01	5.921392425670604
9	6.7	0.22	0.37	1.6	0.028	24.0	102.0	0.9913	3.29	0.59	11.6	7	1	white	0.12	6.149577466890662
10	6.0	0.27	0.27	1.6	0.046	32.0	113.0	0.9924	3.41	0.51	10.5	7	1	white	0.17	5.872313841110241
11	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	0.06	5.952576208344482
12	6.6	0.24	0.29	2.0	0.023	19.0	86.0	0.99	3.25	0.45	12.5	6	0	white	0.13	6.339868322875077
13	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	0.19	5.805496828840035
14	7.1	0.55	0.13	1.7	0.073	21.0	165.0	0.994	2.97	0.58	9.2	6	0	white	0.02	5.626325499747651
15	5.9	0.36	0.41	1.3	0.047	45.0	104.0	0.9917	3.33	0.51	10.6	6	0	white	0.15	5.8574692473173116
16	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	0.07	5.673138403865096
17	7.4	0.29	0.29	1.6	0.045	53.0	180.0	0.9936	3.34	0.68	10.5	6	0	white	0.12	5.881049066937891
18	7.0	0.28	0.36	1.0	0.035	8.0	70.0	0.9899	3.09	0.46	12.1	6	0	white	0.1	6.331006952330057
19	6.3	0.19	0.28	1.8	0.022	28.0	158.0	0.9907	3.2	0.64	11.4	6	0	white	0.09	6.223879853853589
20	7.2	0.4	0.62	10.8	0.041	70.0	189.0	0.9976	3.08	0.49	8.6	4	0	white	0.12	5.524774714222531
21	6.8	0.23	0.32	1.6	0.026	43.0	147.0	0.9904	3.29	0.54	12.5	6	0	white	0.0	6.2961703865057075
22	5.7	0.335	0.34	1.0	0.04	13.0	174.0	0.992	3.27	0.66	10.0	5	0	white	0.08	5.800402656790936
23	7.2	0.4	0.62	10.8	0.041	70.0	189.0	0.9976	3.08	0.49	8.6	4	0	white	0.14	5.524774714222531
24	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	0.1	5.788135549870674
25	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	0.09	5.830744592684368
26	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	0.16	5.703226966466673
27	7.4	0.36	0.33	1.4	0.025	27.0	55.0	0.9915	3.21	0.33	11.2	6	0	white	0.08	6.116016384477604
28	7.8	0.28	0.32	9.0	0.036	34.0	115.0	0.9952	3.17	0.39	10.3	7	1	white	0.08	6.022923033889072
29	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	0.14	5.856819257862355
30	7.2	0.34	0.34	12.6	0.048	7.0	41.0	0.9942	3.19	0.4	11.7	5	0	white	0.17	6.192277707466559
31	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	0.05	5.964351969004497
32	6.1	0.28	0.22	1.8	0.034	32.0	116.0	0.9898	3.36	0.44	12.6	6	0	white	0.15	6.27339126218223
33	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	0.03	5.989148797041992
34	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	0.17	5.719160807328876
35	7.4	0.25	0.36	13.2	0.067	53.0	178.0	0.9976	3.01	0.48	9.0	6	0	white	0.18	5.817746500692863
36	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	0.1	5.868530845794709
37	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	0.19	5.764279012924561
38	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	0.15	6.172991242881125
39	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	0.08	5.460574103277423
40	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	0.04	6.432560649887165
41	7.7	0.34	0.27	8.8	0.063	39.0	184.0	0.9969	3.09	0.63	9.2	6	0	white	0.14	5.722531059755198
42	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	0.16	5.808400902227788
43	6.5	0.28	0.27	5.2	0.04	44.0	179.0	0.9948	3.19	0.69	9.4	6	0	white	0.01	5.739713686292646
44	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	0.06	6.479839078442581
45	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	0.07	6.154363778052982
46	7.4	0.24	0.22	10.7	0.042	26.0	81.0	0.9954	2.86	0.36	9.7	6	0	white	0.01	6.056783144525866
47	6.6	0.25	0.3	1.6	0.046	32.0	134.0	0.993	3.42	0.51	10.1	7	1	white	0.12	5.881022852325742
48	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	0.07	5.984859167739728
49	7.3	0.2	0.37	1.2	0.037	48.0	119.0	0.992	3.32	0.49	10.9	6	0	white	0.1	6.133824353185275
50	6.4	0.475	0.06	1.0	0.03	9.0	131.0	0.9904	2.97	0.29	10.8	5	0	white	0.13	6.0774683911393765
51	5.9	0.27	0.29	11.4	0.036	31.0	115.0	0.9949	3.35	0.48	10.5	8	1	white	0.15	5.907559040053343
52	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	0.11	6.176919749561108
53	6.5	0.26	0.43	8.9	0.083	50.0	171.0	0.9965	2.85	0.5	9.0	5	0	white	0.18	5.648383428804067
54	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	0.0	5.701100747622235
55	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	5.701100747622235
56	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	0.06	5.632464720274186
57	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	5.632464720274186
58	6.5	0.21	0.37	2.5	0.048	70.0	138.0	0.9917	3.33	0.75	11.4	7	1	white	0.07	6.113171978855604
59	7.8	0.4	0.49	7.8	0.06	34.0	162.0	0.9966	3.26	0.58	11.3	6	0	white	0.14	5.655594116200035
60	6.6	0.325	0.49	7.7	0.049	53.0	217.0	0.996	3.16	0.4	9.3	5	0	white	0.13	5.617926979775575
61	7.1	0.31	0.38	1.2	0.036	10.0	124.0	0.9924	3.14	0.44	9.9	6	0	white	0.17	5.963577725009429
62	7.0	0.35	0.3	6.5	0.028	27.0	87.0	0.9936	3.4	0.42	11.4	7	1	white	0.09	5.983573337610551
63	6.3	0.31	0.3	10.0	0.046	49.0	212.0	0.9962	3.74	0.55	11.9	6	0	white	0.06	5.686685052707816
64	7.2	0.25	0.28	14.4	0.055	55.0	205.0	0.9986	3.12	0.38	9.0	7	1	white	0.0	5.704889229149558
65	7.3	0.26	0.33	17.85	0.049	41.5	195.0	1.0	3.06	0.44	9.1	7	1	white	0.1	5.645615584045515
66	7.0	0.24	0.3	4.2	0.04	41.0	213.0	0.9927	3.28	0.49	11.8	6	0	white	0.09	6.100208416231084
67	6.7	0.265	0.22	8.6	0.048	54.0	198.0	0.9955	3.25	0.41	10.2	5	0	white	0.19	5.833875112155567
68	8.5	0.32	0.36	14.9	0.041	47.0	190.0	0.9982	3.08	0.31	10.0	6	0	white	0.17	5.904191165055238
69	7.0	0.24	0.3	4.2	0.04	41.0	213.0	0.9927	3.28	0.49	11.8	6	0	white	0.08	6.100208416231084
70	6.4	0.24	0.22	1.5	0.038	38.0	157.0	0.9934	3.41	0.55	9.9	6	0	white	0.12	5.8083491974390995
71	9.3	0.2	0.33	1.7	0.05	28.0	178.0	0.9954	3.16	0.43	9.0	4	0	white	0.0	5.95773846427943
72	8.2	0.15	0.48	2.7	0.052	24.0	190.0	0.995	3.5	0.45	10.9	7	1	white	0.16	5.91186959143937
73	7.5	0.4	1.0	19.5	0.041	33.0	148.0	0.9977	3.24	0.38	12.0	6	0	white	0.1	5.86254384170698
74	6.5	0.18	0.34	1.6	0.04	43.0	148.0	0.9912	3.32	0.59	11.5	8	1	white	0.02	6.172770568512249
75	7.0	0.13	0.3	5.0	0.056	31.0	122.0	0.9945	3.47	0.42	10.5	6	0	white	0.04	5.956482002194207
76	7.0	0.3	0.32	6.4	0.034	28.0	97.0	0.9924	3.23	0.44	11.8	6	0	white	0.19	6.1894753088339485
77	8.7	0.34	0.46	13.8	0.055	68.0	198.0	0.9988	3.36	0.37	9.5	6	0	white	0.09	5.7699618878136505
78	6.6	0.545	0.04	2.5	0.031	48.0	111.0	0.9906	3.14	0.32	11.9	5	0	white	0.13	6.09371445121684
79	6.9	0.16	0.3	9.6	0.057	50.0	185.0	0.9978	3.39	0.38	9.6	6	0	white	0.07	5.635597061994645
80	7.5	0.22	0.29	4.8	0.05	33.0	87.0	0.994	3.14	0.42	9.9	5	0	white	0.07	6.027225346273269
81	6.7	0.16	0.28	2.5	0.046	40.0	153.0	0.9921	3.38	0.51	11.4	7	1	white	0.02	6.135243284680314
82	9.6	0.21	0.28	1.2	0.038	12.0	53.0	0.9926	2.8	0.46	10.6	5	0	white	0.17	6.386893752303621
83	8.0	0.3	0.28	5.7	0.044	31.0	124.0	0.9948	3.16	0.51	10.2	6	0	white	0.01	5.96263326666795
84	7.7	0.43	0.28	4.5	0.046	33.0	102.0	0.9918	3.16	0.56	12.2	7	1	white	0.01	6.210861284165333
85	6.5	0.18	0.26	1.4	0.041	40.0	141.0	0.9941	3.34	0.72	9.5	6	0	white	0.03	5.7543381021827145
86	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	0.0	6.03799348334482
87	8.5	0.21	0.26	9.25	0.034	73.0	142.0	0.9945	3.05	0.37	11.4	6	0	white	0.1	6.298349263966173
88	9.4	0.16	0.3	1.4	0.042	26.0	176.0	0.9954	3.15	0.46	9.1	5	0	white	0.17	5.991868977030862
89	7.8	0.22	0.36	1.4	0.056	21.0	153.0	0.993	3.2	0.53	10.4	6	0	white	0.04	6.06028624953214
90	7.4	0.35	0.31	17.95	0.062	42.0	187.0	1.0002	3.27	0.64	9.1	5	0	white	0.04	5.575535876996952
91	6.6	0.37	0.24	2.0	0.064	23.0	120.0	0.9946	3.32	0.54	9.4	5	0	white	0.0	5.59078730691175
92	7.1	0.37	0.32	1.4	0.037	27.0	126.0	0.9918	3.19	0.62	12.0	5	0	white	0.02	6.02611974919833
93	6.4	0.26	0.21	7.1	0.04	35.0	162.0	0.9956	3.39	0.58	9.9	6	0	white	0.07	5.714878646080535
94	6.4	0.26	0.21	7.1	0.04	35.0	162.0	0.9956	3.39	0.58	9.9	6	0	white	0.19	5.714878646080535
95	7.6	0.3	0.22	10.2	0.049	57.0	191.0	0.9966	3.08	0.4	9.3	6	0	white	0.19	5.846490209308541
96	8.8	0.2	0.43	15.0	0.053	60.0	184.0	1.0008	3.28	0.79	8.8	6	0	white	0.05	5.651373730232223
97	7.6	0.38	0.2	3.4	0.046	9.0	116.0	0.9944	3.15	0.41	9.4	5	0	white	0.01	5.819698152522449
98	6.8	0.3	0.35	2.8	0.038	10.0	164.0	0.9912	3.09	0.53	12.0	6	0	white	0.15	6.177179588784952
99	6.8	0.33	0.28	1.2	0.032	38.0	131.0	0.9889	3.19	0.41	13.0	6	0	white	0.09	6.4328404636031
100	7.3	0.19	0.24	6.3	0.054	34.0	231.0	0.9964	3.36	0.54	10.0	6	0	white	0.12	5.743644267808804

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