vastorbit.machine_learning.vast.linear_model.PLSRegression¶

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

Creates an PLSRegression 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 PLSRegression model:

from vastorbit.machine_learning.vast import PLSRegression

Then we can create the model:

model = PLSRegression()

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.

Metrics¶

We can get the entire report using:

model.report()

	value
explained_variance	0.12673087017089846
max_error	67.35812683974027
median_absolute_error	64.46593009032297
mean_absolute_error	64.47755126582939
mean_squared_error	4161.040581888313
root_mean_squared_error	64.48305132874968
r2	-5118.346865489804
r2_adj	-5141.992809441258
aic	10897.7395105446
bic	10933.800793348277

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	5432242.137198795	905373.6895331325	216.39078015354167	9.711962668459322e-192
Residual	1299	5434983.976068863	4183.9753472431585
Total	1305	1010.6998468606462

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	8.3	0.23	0.3	2.1	0.049	21.0	153.0	0.9953	3.09	0.5	9.6	6	0	white	0.1	-58.57326515443723
2	6.8	0.43	0.3	3.5	0.033	27.0	135.0	0.9906	3.0	0.37	12.0	6	0	white	0.11	-58.49361702496944
3	7.6	0.26	0.36	1.6	0.032	6.0	106.0	0.993	3.15	0.4	10.4	4	0	white	0.07	-58.41750710743568
4	8.8	0.34	0.33	9.7	0.036	46.0	172.0	0.9966	3.08	0.4	10.2	5	0	white	0.12	-58.68215096076783
5	7.1	0.49	0.22	2.0	0.047	146.5	307.5	0.9924	3.24	0.37	11.0	3	0	white	0.06	-58.73149663114173
6	6.8	0.28	0.36	1.6	0.04	25.0	87.0	0.9924	3.23	0.66	10.3	6	0	white	0.13	-58.45013516778693
7	8.8	0.34	0.33	9.7	0.036	46.0	172.0	0.9966	3.08	0.4	10.2	5	0	white	0.03	-58.68215096076783
8	9.9	1.005	0.46	1.4	0.046	34.0	185.0	0.9966	3.02	0.49	10.2	4	0	white	0.19	-59.244999099576205
9	8.1	0.31	0.36	8.2	0.028	29.0	142.0	0.9925	3.01	0.34	13.0	7	1	white	0.17	-58.38853844931297
10	8.1	0.24	0.38	4.3	0.044	49.0	172.0	0.996	3.37	0.74	10.8	6	0	white	0.03	-58.5779983422428
11	7.2	0.15	0.33	1.1	0.027	16.0	63.0	0.9937	3.37	0.4	9.9	5	0	white	0.17	-58.381571530915814
12	7.0	0.12	0.32	7.2	0.058	22.0	89.0	0.9966	3.29	0.38	9.2	6	0	white	0.01	-58.6052087737996
13	7.4	0.32	0.55	16.6	0.056	53.0	238.0	1.0017	2.96	0.58	8.7	6	0	white	0.03	-58.960365773761005
14	6.8	0.31	0.42	6.9	0.046	50.0	173.0	0.9958	3.19	0.46	9.0	5	0	white	0.12	-58.66736252331357
15	6.8	0.27	0.35	7.8	0.048	76.0	197.0	0.9959	3.24	0.43	9.5	6	0	white	0.01	-58.675915705869606
16	8.1	0.22	0.28	7.7	0.043	57.0	176.0	0.9954	3.12	0.55	10.0	5	0	white	0.17	-58.56551692177333
17	7.6	0.2	0.3	14.2	0.056	53.0	212.5	0.999	3.14	0.46	8.9	8	1	white	0.03	-58.80341292798906
18	7.0	0.28	0.33	14.6	0.043	47.0	168.0	0.9994	3.34	0.67	8.8	6	0	white	0.09	-58.88978796519038
19	6.8	0.31	0.09	1.4	0.04	56.0	145.0	0.9922	3.19	0.46	10.0	5	0	white	0.18	-58.61623926350483
20	6.7	0.31	0.08	1.3	0.038	58.0	147.0	0.9922	3.18	0.46	10.0	5	0	white	0.12	-58.622381186283306
21	7.0	0.28	0.33	14.6	0.043	47.0	168.0	0.9994	3.34	0.67	8.8	6	0	white	0.04	-58.88978796519038
22	8.4	0.2	0.38	11.8	0.055	51.0	170.0	1.0004	3.34	0.82	8.9	6	0	white	0.2	-58.81740307122048
23	7.7	0.26	0.34	6.4	0.05	36.0	163.0	0.9937	3.19	0.7	11.5	6	0	white	0.12	-58.495830879121826
24	6.3	0.21	0.28	1.5	0.051	46.0	142.0	0.9928	3.23	0.42	10.1	6	0	white	0.12	-58.4975624783967
25	9.5	0.42	0.41	2.3	0.034	22.0	145.0	0.9951	3.06	0.52	11.0	6	0	white	0.03	-58.598942536946346
26	7.6	0.29	0.26	6.5	0.042	32.0	160.0	0.9944	3.14	0.47	10.7	5	0	white	0.11	-58.60258270905935
27	7.2	0.23	0.33	12.7	0.049	50.0	183.0	0.9987	3.41	0.4	9.8	5	0	white	0.17	-58.803314967974664
28	8.3	0.33	0.43	9.2	0.046	22.0	126.0	0.9982	3.38	0.47	9.3	5	0	white	0.05	-58.76484829126127
29	6.3	0.28	0.24	8.45	0.031	32.0	172.0	0.9958	3.39	0.57	9.7	7	1	white	0.05	-58.72622741141045
30	7.8	0.27	0.28	1.8	0.05	21.0	127.0	0.9934	3.15	0.44	9.9	5	0	white	0.13	-58.524910832318604
31	6.6	0.23	0.27	5.6	0.043	43.0	164.0	0.9953	3.27	0.76	9.5	5	0	white	0.07	-58.646003996095594
32	7.5	0.33	0.32	11.1	0.036	25.0	119.0	0.9962	3.15	0.34	10.5	6	0	white	0.1	-58.707123699643624
33	8.2	0.17	0.32	1.5	0.05	17.0	101.0	0.994	3.14	0.58	9.5	5	0	white	0.1	-58.4274093679794
34	7.2	0.24	0.19	7.7	0.045	53.0	176.0	0.9958	3.17	0.38	9.5	5	0	white	0.02	-58.70510265358381
35	6.6	0.22	0.53	15.1	0.052	22.0	136.0	0.9986	2.94	0.35	9.4	5	0	white	0.1	-58.70407495119238
36	6.6	0.22	0.53	15.1	0.052	22.0	136.0	0.9986	2.94	0.35	9.4	5	0	white	0.08	-58.70407495119238
37	6.0	0.16	0.3	6.7	0.043	43.0	153.0	0.9951	3.63	0.46	10.6	5	0	white	0.02	-58.57290676946854
38	7.7	0.23	0.31	10.7	0.038	59.0	186.0	0.9969	3.12	0.55	9.5	6	0	white	0.2	-58.65706483648152
39	6.8	0.2	0.27	1.2	0.034	19.0	68.0	0.9902	3.14	0.37	11.7	4	0	white	0.14	-58.26813706699014
40	6.8	0.25	0.27	10.7	0.076	47.0	154.0	0.9967	3.05	0.38	9.0	5	0	white	0.03	-58.803647581640426
41	6.5	0.43	0.28	12.0	0.056	23.0	174.0	0.9986	3.31	0.55	9.3	5	0	white	0.09	-59.06580097068087
42	7.1	0.22	0.32	16.9	0.056	49.0	158.0	0.9998	3.37	0.38	9.6	6	0	white	0.09	-58.88012698731106
43	8.5	0.25	0.27	4.7	0.031	31.0	92.0	0.9922	3.01	0.33	12.0	6	0	white	0.14	-58.356592907871416
44	6.8	0.37	0.28	4.0	0.03	29.0	79.0	0.99	3.23	0.46	12.4	7	1	white	0.1	-58.400281389514284
45	8.5	0.25	0.27	4.7	0.031	31.0	92.0	0.9922	3.01	0.33	12.0	6	0	white	0.09	-58.356592907871416
46	7.0	0.35	0.31	1.8	0.069	15.0	162.0	0.9944	3.18	0.47	9.4	5	0	white	0.15	-58.72551363091594
47	6.9	0.32	0.13	7.8	0.042	11.0	117.0	0.996	3.23	0.37	9.2	5	0	white	0.1	-58.83602635899052
48	7.6	0.32	0.58	16.75	0.05	43.0	163.0	0.9999	3.15	0.54	9.2	5	0	white	0.19	-58.80535920330166
49	6.9	0.32	0.13	7.8	0.042	11.0	117.0	0.996	3.23	0.37	9.2	5	0	white	0.2	-58.83602635899052
50	6.0	0.34	0.24	5.4	0.06	23.0	126.0	0.9951	3.25	0.44	9.0	7	1	white	0.18	-58.81845639164539
51	7.7	0.24	0.31	1.3	0.047	33.0	106.0	0.993	3.22	0.55	10.8	6	0	white	0.11	-58.45265181263055
52	6.6	0.32	0.27	10.9	0.041	37.0	146.0	0.9963	3.24	0.47	10.0	5	0	white	0.15	-58.78254238238406
53	7.1	0.18	0.32	12.2	0.048	36.0	125.0	0.9967	2.92	0.54	9.4	6	0	white	0.14	-58.634031032100175
54	6.8	0.25	0.18	1.4	0.056	13.0	137.0	0.9935	3.11	0.42	9.5	5	0	white	0.2	-58.6252745899414
55	7.0	0.22	0.26	1.1	0.037	20.0	71.0	0.9902	3.1	0.38	11.7	6	0	white	0.13	-58.290491365618465
56	7.3	0.18	0.29	1.0	0.036	26.0	101.0	0.99	3.09	0.37	11.7	6	0	white	0.03	-58.20682192648096
57	7.1	0.26	0.3	2.0	0.031	13.0	128.0	0.9917	3.19	0.49	11.4	5	0	white	0.08	-58.385804177166996
58	7.4	0.24	0.26	1.6	0.058	53.0	150.0	0.9936	3.18	0.5	9.9	7	1	white	0.09	-58.55455607338372
59	7.7	0.28	0.29	6.9	0.041	29.0	163.0	0.9952	3.44	0.6	10.5	6	0	white	0.04	-58.62079348244895
60	8.0	0.45	0.28	10.8	0.051	25.0	157.0	0.9957	3.06	0.47	11.4	7	1	white	0.06	-58.82224870873615
61	7.7	0.28	0.58	12.1	0.046	60.0	177.0	0.9983	3.08	0.46	8.9	5	0	white	0.0	-58.66022684853784
62	6.5	0.39	0.35	1.6	0.049	10.0	164.0	0.99516	3.35	0.51	9.7	5	0	white	0.18	-58.773420817428565
63	6.7	0.23	0.42	11.2	0.047	52.0	171.0	0.99758	3.54	0.74	10.4	5	0	white	0.0	-58.702346794502006
64	7.5	0.31	0.24	7.1	0.031	28.0	141.0	0.99397	3.16	0.38	10.6	7	1	white	0.09	-58.58581696060233
65	6.1	0.15	0.29	6.2	0.046	39.0	151.0	0.99471	3.6	0.44	10.6	6	0	white	0.1	-58.54618256817468
66	6.2	0.26	0.19	3.4	0.049	47.0	172.0	0.9924	3.14	0.43	10.4	6	0	white	0.07	-58.56618353231832
67	7.2	0.39	0.54	1.4	0.157	34.0	132.0	0.99449	3.11	0.53	9.0	6	0	white	0.12	-58.81472180670758
68	6.6	0.19	0.43	10.9	0.045	53.0	154.0	0.99752	3.52	0.77	10.4	6	0	white	0.1	-58.65513520888196
69	6.2	0.25	0.28	8.5	0.035	28.0	108.0	0.99486	3.4	0.42	10.4	6	0	white	0.18	-58.62648913698676
70	7.4	0.14	0.3	1.3	0.033	25.0	91.0	0.99268	3.53	0.39	10.6	6	0	white	0.05	-58.32515478621028
71	6.4	0.16	0.25	1.3	0.047	20.0	77.0	0.9933	3.61	0.54	10.2	6	0	white	0.15	-58.4858881864743
72	8.6	0.17	0.28	2.7	0.047	38.0	150.0	0.99365	3.1	0.56	10.8	6	0	white	0.18	-58.400173428611886
73	7.0	0.32	0.31	6.4	0.031	38.0	115.0	0.99235	3.38	0.58	12.2	7	1	white	0.06	-58.47551028650473
74	6.8	0.21	0.55	14.6	0.053	34.0	159.0	0.99805	2.93	0.44	9.2	5	0	white	0.19	-58.64178442300506
75	7.0	0.22	0.26	2.8	0.036	44.0	132.0	0.99078	3.34	0.41	12.0	7	1	white	0.08	-58.322364102388605
76	9.4	0.28	0.3	1.6	0.045	36.0	139.0	0.99534	3.11	0.49	9.3	5	0	white	0.0	-58.568077571063824
77	7.5	0.24	0.62	10.6	0.045	51.0	153.0	0.99779	3.16	0.44	8.8	5	0	white	0.02	-58.57634431617525
78	6.6	0.4	0.32	1.7	0.035	39.0	84.0	0.99096	3.59	0.48	12.7	7	1	white	0.19	-58.49378348302431
79	6.1	0.41	0.04	1.3	0.036	23.0	121.0	0.99228	3.24	0.61	9.9	6	0	white	0.07	-58.76953994889065
80	6.9	0.3	0.21	7.2	0.045	54.0	190.0	0.99595	3.22	0.48	9.4	5	0	white	0.15	-58.776517343880144
81	6.9	0.35	0.55	11.95	0.038	22.0	111.0	0.99687	3.11	0.29	9.7	5	0	white	0.06	-58.671722444007266
82	7.6	0.3	0.4	2.2	0.054	29.0	175.0	0.99445	3.19	0.53	9.8	5	0	white	0.04	-58.57197923047856
83	7.5	0.23	0.35	17.8	0.058	128.0	212.0	1.00241	3.44	0.43	8.9	5	0	white	0.06	-59.026851410603314
84	7.2	0.34	0.3	8.4	0.051	40.0	167.0	0.99756	3.48	0.62	9.7	5	0	white	0.18	-58.86621577447692
85	7.7	0.29	0.29	4.8	0.06	27.0	156.0	0.99572	3.49	0.59	10.3	6	0	white	0.09	-58.7077234782687
86	7.2	0.34	0.3	8.4	0.051	40.0	167.0	0.99756	3.48	0.62	9.7	5	0	white	0.14	-58.86621577447692
87	7.7	0.4	0.27	4.5	0.034	27.0	95.0	0.99175	3.21	0.59	12.3	8	1	white	0.1	-58.51533866684326
88	7.0	0.23	0.26	7.2	0.041	21.0	90.0	0.99509	3.22	0.55	9.5	6	0	white	0.02	-58.612408693910695
89	8.3	0.26	0.31	2.0	0.029	14.0	141.0	0.99077	2.95	0.77	12.2	6	0	white	0.07	-58.2621064722196
90	7.9	0.31	0.22	13.3	0.048	46.0	212.0	0.99942	3.47	0.59	10.0	5	0	white	0.08	-58.95392610655981
91	6.5	0.18	0.29	1.7	0.035	39.0	144.0	0.9927	3.49	0.5	10.5	6	0	white	0.17	-58.41430155541003
92	7.4	0.34	0.3	14.9	0.037	70.0	169.0	0.99698	3.25	0.37	10.4	6	0	white	0.09	-58.77671825912116
93	7.0	0.32	0.29	4.9	0.036	41.0	150.0	0.99168	3.38	0.43	12.2	6	0	white	0.0	-58.456614724481
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95	6.8	0.46	0.26	2.7	0.042	28.0	83.0	0.99114	3.38	0.51	12.0	8	1	white	0.16	-58.60010199446327
96	6.5	0.19	0.27	4.9	0.037	13.0	101.0	0.9916	3.17	0.41	11.8	6	0	white	0.04	-58.36074997362885
97	9.2	0.19	0.42	2.0	0.047	16.0	104.0	0.99517	3.09	0.66	10.0	4	0	white	0.13	-58.41539961793054
98	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.997	3.26	0.65	9.8	5	0	red	0.17	-59.45187889310458
99	7.9	0.6	0.06	1.6	0.069	15.0	59.0	0.9964	3.3	0.46	9.4	5	0	red	0.06	-59.19658709433635
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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.

Parameter Modification¶

In order to see the parameters:

model.get_params()

And to manually change some of the parameters:

model.set_params({'scale': True})

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