vastorbit.machine_learning.vast.linear_model.Lasso¶

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

Creates a Lasso 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 Lasso model:

from vastorbit.machine_learning.vast import Lasso

Then we can create the model:

model = Lasso(
    tol = 1e-6,
    alpha = 0.5,
    max_iter = 100,
)

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	-6.661338147750939e-16
max_error	2.8161252900232014
median_absolute_error	0.6882705413949076
mean_absolute_error	0.6552147266120845
mean_squared_error	0.772291719589362
root_mean_squared_error	0.8367908188528812
r2	-0.00034645858802329066
r2_adj	-0.004900387838044473
aic	-328.21117261145594
bic	-292.04645174547494

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	0.8913378067049595	0.14855630111749327	0.18515447668083806	0.9810020855231005
Residual	1318	1057.4802639547493	0.8023370743207506
Total	1324	1058.975094339618

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	9.1	0.29	0.33	2.05	0.063	13.0	27.0	0.99516	3.26	0.84	11.7	7	1	red	0.04	5.816125290023201
2	9.6	0.5	0.36	2.8	0.1159999999999999	26.0	55.0	0.99722	3.18	0.68	10.9	5	0	red	0.04	5.816125290023201
3	7.7	1.005	0.15	2.1	0.102	11.0	32.0	0.99604	3.23	0.48	10.0	5	0	red	0.05	5.816125290023201
4	8.0	0.18	0.37	0.9	0.049	36.0	109.0	0.99007	2.89	0.44	12.7	6	0	red	0.09	5.816125290023201
5	7.0	0.51	0.09	2.1	0.062	4.0	9.0	0.99584	3.35	0.54	10.5	5	0	red	0.01	5.816125290023201
6	8.6	0.83	0.0	2.8	0.095	17.0	43.0	0.99822	3.33	0.6	10.4	6	0	red	0.0	5.816125290023201
7	7.9	0.31	0.32	1.9	0.066	14.0	36.0	0.99364	3.41	0.56	12.6	6	0	red	0.13	5.816125290023201
8	6.4	0.795	0.0	2.2	0.065	28.0	52.0	0.99378	3.49	0.52	11.6	5	0	red	0.0	5.816125290023201
9	7.2	0.34	0.21	2.5	0.075	41.0	68.0	0.99586	3.37	0.54	10.1	6	0	red	0.09	5.816125290023201
10	7.7	0.58	0.01	1.8	0.088	12.0	18.0	0.99568	3.32	0.56	10.5	7	1	red	0.13	5.816125290023201
11	7.1	0.59	0.0	2.1	0.091	9.0	14.0	0.99488	3.42	0.55	11.5	7	1	red	0.16	5.816125290023201
12	8.1	0.82	0.0	4.1	0.095	5.0	14.0	0.99854	3.36	0.53	9.6	5	0	red	0.0	5.816125290023201
13	8.9	0.745	0.18	2.5	0.077	15.0	48.0	0.99739	3.2	0.47	9.7	6	0	red	0.11	5.816125290023201
14	6.9	0.49	0.19	1.7	0.079	13.0	26.0	0.99547	3.38	0.64	9.8	6	0	red	0.13	5.816125290023201
15	9.5	0.39	0.41	8.9	0.069	18.0	39.0	0.99859	3.29	0.81	10.9	7	1	red	0.1	5.816125290023201
16	8.3	0.33	0.42	2.3	0.07	9.0	20.0	0.99426	3.38	0.77	12.7	7	1	red	0.08	5.816125290023201
17	8.2	0.64	0.27	2.0	0.095	5.0	77.0	0.99747	3.13	0.62	9.1	6	0	red	0.1	5.816125290023201
18	8.2	0.74	0.09	2.0	0.067	5.0	10.0	0.99418	3.28	0.57	11.8	6	0	red	0.16	5.816125290023201
19	7.7	0.61	0.18	2.4	0.083	6.0	20.0	0.9963	3.29	0.6	10.2	6	0	red	0.11	5.816125290023201
20	6.6	0.52	0.08	2.4	0.07	13.0	26.0	0.99358	3.4	0.72	12.5	7	1	red	0.19	5.816125290023201
21	8.0	0.62	0.35	2.8	0.086	28.0	52.0	0.997	3.31	0.62	10.8	5	0	red	0.19	5.816125290023201
22	9.1	0.25	0.34	2.0	0.071	45.0	67.0	0.99769	3.44	0.86	10.2	7	1	red	0.17	5.816125290023201
23	7.9	0.3	0.68	8.3	0.05	37.5	289.0	0.99316	3.01	0.51	12.3	7	1	red	0.08	5.816125290023201
24	6.8	0.48	0.08	1.8	0.074	40.0	64.0	0.99529	3.12	0.49	9.6	5	0	red	0.03	5.816125290023201
25	11.6	0.41	0.54	1.5	0.095	22.0	41.0	0.99735	3.02	0.76	9.9	7	1	red	0.04	5.816125290023201
26	9.2	0.31	0.36	2.2	0.079	11.0	31.0	0.99615	3.33	0.86	12.0	7	1	red	0.17	5.816125290023201
27	9.4	0.4	0.47	2.5	0.087	6.0	20.0	0.99772	3.15	0.5	10.5	5	0	red	0.18	5.816125290023201
28	5.4	0.42	0.27	2.0	0.092	23.0	55.0	0.99471	3.78	0.64	12.3	7	1	red	0.05	5.816125290023201
29	7.0	0.69	0.07	2.5	0.091	15.0	21.0	0.99572	3.38	0.6	11.3	6	0	red	0.13	5.816125290023201
30	6.3	0.47	0.0	1.4	0.055	27.0	33.0	0.9922	3.45	0.48	12.3	6	0	red	0.11	5.816125290023201
31	10.7	0.4	0.37	1.9	0.081	17.0	29.0	0.99674	3.12	0.65	11.2	6	0	red	0.15	5.816125290023201
32	6.5	0.58	0.0	2.2	0.096	3.0	13.0	0.99557	3.62	0.62	11.5	4	0	red	0.06	5.816125290023201
33	8.8	0.24	0.35	1.7	0.055	13.0	27.0	0.99394	3.14	0.59	11.3	7	1	red	0.14	5.816125290023201
34	10.0	0.43	0.33	2.7	0.095	28.0	89.0	0.9984	3.22	0.68	10.0	5	0	red	0.1	5.816125290023201
35	7.2	0.48	0.07	5.5	0.089	10.0	18.0	0.99684	3.37	0.68	11.2	7	1	red	0.17	5.816125290023201
36	8.0	0.25	0.43	1.7	0.067	22.0	50.0	0.9946	3.38	0.6	11.9	6	0	red	0.18	5.816125290023201
37	10.4	0.52	0.45	2.0	0.08	6.0	13.0	0.99774	3.22	0.76	11.4	6	0	red	0.11	5.816125290023201
38	8.2	0.38	0.32	2.5	0.08	24.0	71.0	0.99624	3.27	0.85	11.0	6	0	red	0.19	5.816125290023201
39	6.1	0.58	0.23	2.5	0.044	16.0	70.0	0.99352	3.46	0.65	12.5	6	0	red	0.09	5.816125290023201
40	6.6	0.58	0.0	2.2	0.1	50.0	63.0	0.99544	3.59	0.68	11.4	6	0	red	0.01	5.816125290023201
41	8.5	0.18	0.51	1.75	0.071	45.0	88.0	0.99524	3.33	0.76	11.8	7	1	red	0.17	5.816125290023201
42	8.5	0.32	0.42	2.3	0.075	12.0	19.0	0.99434	3.14	0.71	11.8	7	1	red	0.08	5.816125290023201
43	9.9	0.54	0.26	2.0	0.111	7.0	60.0	0.99709	2.94	0.98	10.2	5	0	red	0.0	5.816125290023201
44	7.6	0.5	0.29	2.3	0.086	5.0	14.0	0.99502	3.32	0.62	11.5	6	0	red	0.14	5.816125290023201
45	6.6	0.8	0.03	7.8	0.079	6.0	12.0	0.9963	3.52	0.5	12.2	5	0	red	0.14	5.816125290023201
46	7.0	0.43	0.3	2.0	0.085	6.0	39.0	0.99346	3.33	0.46	11.9	6	0	red	0.18	5.816125290023201
47	8.8	0.955	0.05	1.8	0.075	5.0	19.0	0.99616	3.3	0.44	9.6	4	0	red	0.16	5.816125290023201
48	9.1	0.4	0.57	4.6	0.08	6.0	20.0	0.99652	3.28	0.57	12.5	6	0	red	0.05	5.816125290023201
49	7.9	0.58	0.23	2.3	0.076	23.0	94.0	0.99686	3.21	0.58	9.5	6	0	red	0.18	5.816125290023201
50	7.7	0.57	0.21	1.5	0.069	4.0	9.0	0.99458	3.16	0.54	9.8	6	0	red	0.06	5.816125290023201
51	8.6	0.42	0.39	1.8	0.068	6.0	12.0	0.99516	3.35	0.69	11.7	8	1	red	0.02	5.816125290023201
52	7.2	0.36	0.46	2.1	0.074	24.0	44.0	0.99534	3.4	0.85	11.0	7	1	red	0.09	5.816125290023201
53	6.6	0.44	0.15	2.1	0.076	22.0	53.0	0.9957	3.32	0.62	9.3	5	0	red	0.11	5.816125290023201
54	10.2	0.23	0.37	2.2	0.057	14.0	36.0	0.99614	3.23	0.49	9.3	4	0	red	0.1	5.816125290023201
55	7.1	0.75	0.01	2.2	0.059	11.0	18.0	0.99242	3.39	0.4	12.8	6	0	red	0.11	5.816125290023201
56	6.0	0.33	0.32	12.9	0.054	6.0	113.0	0.99572	3.3	0.56	11.5	4	0	red	0.04	5.816125290023201
57	7.8	0.55	0.0	1.7	0.07	7.0	17.0	0.99659	3.26	0.64	9.4	6	0	red	0.04	5.816125290023201
58	7.4	0.55	0.19	1.8	0.0819999999999999	15.0	34.0	0.99655	3.49	0.68	10.5	5	0	red	0.0	5.816125290023201
59	7.4	0.55	0.19	1.8	0.0819999999999999	15.0	34.0	0.99655	3.49	0.68	10.5	5	0	red	0.03	5.816125290023201
60	7.9	0.66	0.0	1.4	0.096	6.0	13.0	0.99569	3.43	0.58	9.5	5	0	red	0.17	5.816125290023201
61	8.3	0.4	0.41	8.2	0.05	15.0	122.0	0.9979	3.39	0.49	9.3	5	0	white	0.02	5.816125290023201
62	5.9	0.34	0.31	2.0	0.03	38.0	142.0	0.98892	3.4	0.41	12.9	7	1	white	0.06	5.816125290023201
63	7.2	0.32	0.24	5.6	0.033	23.0	120.0	0.99334	2.92	0.66	10.3	7	1	white	0.08	5.816125290023201
64	7.6	0.1	0.33	1.0	0.031	33.0	93.0	0.99094	3.06	0.68	11.2	6	0	white	0.02	5.816125290023201
65	6.6	0.21	0.29	1.8	0.026	35.0	128.0	0.99183	3.37	0.48	11.2	6	0	white	0.0	5.816125290023201
66	6.6	0.24	0.38	8.0	0.042	56.0	187.0	0.99577	3.21	0.46	9.2	5	0	white	0.02	5.816125290023201
67	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	0.12	5.816125290023201
68	7.8	0.25	0.34	13.7	0.044	66.0	184.0	0.99976	3.22	0.75	8.9	5	0	white	0.04	5.816125290023201
69	5.6	0.2	0.22	1.3	0.049	25.0	155.0	0.99296	3.74	0.43	10.0	5	0	white	0.16	5.816125290023201
70	7.5	0.26	0.3	4.6	0.027	29.0	92.0	0.99085	3.15	0.38	12.0	7	1	white	0.01	5.816125290023201
71	8.2	0.37	0.64	13.9	0.043	22.0	171.0	0.99873	2.99	0.8	9.3	5	0	white	0.13	5.816125290023201
72	7.5	0.18	0.31	6.5	0.029	53.0	160.0	0.99276	3.03	0.38	10.9	6	0	white	0.09	5.816125290023201
73	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	0.15	5.816125290023201
74	7.9	0.51	0.36	6.2	0.051	30.0	173.0	0.9984	3.09	0.53	9.7	5	0	white	0.09	5.816125290023201
75	7.9	0.51	0.34	2.6	0.049	13.0	135.0	0.99335	3.09	0.51	10.0	5	0	white	0.16	5.816125290023201
76	7.1	0.29	0.28	9.3	0.048	50.0	141.0	0.9949	3.13	0.49	10.3	6	0	white	0.13	5.816125290023201
77	6.5	0.17	0.31	1.5	0.041	34.0	121.0	0.99092	3.06	0.46	10.5	6	0	white	0.09	5.816125290023201
78	8.0	0.23	0.28	2.7	0.048	49.0	165.0	0.9952	3.26	0.72	9.5	6	0	white	0.18	5.816125290023201
79	7.7	0.18	0.35	5.8	0.055	25.0	144.0	0.99576	3.24	0.54	10.2	6	0	white	0.05	5.816125290023201
80	6.0	0.26	0.15	1.3	0.06	51.0	154.0	0.99354	3.14	0.51	8.7	5	0	white	0.09	5.816125290023201
81	7.7	0.3	0.34	1.2	0.048	4.0	119.0	0.99084	3.18	0.34	12.1	6	0	white	0.02	5.816125290023201
82	7.9	0.16	0.3	7.4	0.05	58.0	152.0	0.99612	3.12	0.37	9.5	6	0	white	0.08	5.816125290023201
83	7.0	0.36	0.32	10.5	0.045	35.0	135.0	0.9935	3.09	0.33	11.6	8	1	white	0.0	5.816125290023201
84	6.2	0.235	0.34	1.9	0.036	4.0	117.0	0.99032	3.4	0.44	12.2	5	0	white	0.05	5.816125290023201
85	7.8	0.965	0.6	65.8	0.074	8.0	160.0	1.03898	3.39	0.69	11.7	6	0	white	0.06	5.816125290023201
86	6.4	0.24	0.25	20.2	0.083	35.0	157.0	0.99976	3.17	0.5	9.1	5	0	white	0.04	5.816125290023201
87	6.8	0.26	0.44	8.2	0.046	52.0	183.0	0.99584	3.2	0.51	9.4	5	0	white	0.06	5.816125290023201
88	7.3	0.2	0.26	1.6	0.04	36.0	123.0	0.99238	3.34	0.44	10.8	6	0	white	0.16	5.816125290023201
89	7.5	0.17	0.71	11.8	0.038	52.0	148.0	0.99801	3.03	0.46	8.9	5	0	white	0.11	5.816125290023201
90	7.2	0.26	0.4	6.3	0.047	52.0	172.0	0.99573	3.18	0.53	9.5	6	0	white	0.17	5.816125290023201
91	7.1	0.26	0.32	16.2	0.044	31.0	170.0	0.99644	3.17	0.37	11.2	5	0	white	0.12	5.816125290023201
92	7.3	0.26	0.3	9.3	0.05	35.0	154.0	0.99581	3.21	0.5	10.4	6	0	white	0.01	5.816125290023201
93	5.8	0.22	0.29	0.9	0.034	34.0	89.0	0.98936	3.14	0.36	11.1	7	1	white	0.0	5.816125290023201
94	8.0	0.22	0.31	5.6	0.049	24.0	97.0	0.993	3.1	0.42	10.9	5	0	white	0.01	5.816125290023201
95	6.5	0.22	0.29	7.4	0.028	16.0	87.0	0.99311	3.15	0.56	10.9	7	1	white	0.01	5.816125290023201
96	7.3	0.23	0.34	9.3	0.052	19.0	86.0	0.99574	3.04	0.56	10.0	5	0	white	0.09	5.816125290023201
97	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white	0.14	5.816125290023201
98	6.4	0.21	0.28	5.9	0.047	29.0	101.0	0.99278	3.15	0.4	11.0	6	0	white	0.0	5.816125290023201
99	6.7	0.15	0.38	1.7	0.037	20.0	84.0	0.99046	3.09	0.53	11.4	6	0	white	0.11	5.816125290023201
100	6.4	0.22	0.31	13.9	0.04	57.0	135.0	0.99672	3.21	0.38	10.7	5	0	white	0.09	5.816125290023201

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