vastorbit.machine_learning.vast.linear_model.PoissonRegressor¶

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

Creates an PoissonRegressor 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 PoissonRegressor model:

from vastorbit.machine_learning.vast import PoissonRegressor

Then we can create the model:

model = PoissonRegressor(
    tol = 1e-6,
    alpha = 1,
    max_iter = 100,
    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.

Metrics¶

We can get the entire report using:

model.report()

	value
explained_variance	0.005729900173940372
max_error	7.235282431564016
median_absolute_error	4.230854290146871
mean_absolute_error	4.051821006209925
mean_squared_error	16.193842579956193
root_mean_squared_error	4.1394627321506485
r2	-22.73173236265499
r2_adj	-22.84109518460271
aic	3659.2435010662607
bic	3695.321218130935

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	21762.455908300675	3627.075984716779	229.35219288344015	3.422967368677542e-200
Residual	1302	20590.39799327866	15.814437782856114
Total	1308	966.3720397249851

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	9.2	0.16	0.49	2.0	0.044	18.0	107.0	0.99514	3.1	0.53	10.2	4	0	white	0.14	1.7567522931465274
2	9.8	0.16	0.46	1.8	0.046	23.0	130.0	0.99587	3.04	0.67	9.6	5	0	white	0.1	1.7517624933301672
3	7.1	0.21	0.28	2.7	0.034	23.0	111.0	0.99405	3.35	0.64	10.2	4	0	white	0.18	1.7690521922332318
4	7.0	0.16	0.26	6.85	0.047	30.0	220.0	0.99622	3.38	0.58	10.1	6	0	white	0.09	1.7657545959884795
5	6.8	0.26	0.43	11.75	0.045	53.0	198.0	0.9969	3.26	0.55	9.5	5	0	white	0.16	1.75975630639991
6	6.4	0.27	0.17	8.4	0.044	60.0	198.0	0.99578	3.21	0.47	9.4	5	0	white	0.15	1.7641163219728915
7	6.9	0.43	0.28	9.4	0.056	29.0	183.0	0.99594	3.17	0.43	9.4	5	0	white	0.14	1.754816596931804
8	7.6	0.285	0.32	14.6	0.063	32.0	201.0	0.998	3.0	0.45	9.2	5	0	white	0.05	1.7473643103089163
9	6.6	0.26	0.27	1.5	0.04	19.0	114.0	0.99295	3.36	0.62	10.5	6	0	white	0.11	1.7729582403012532
10	6.6	0.56	0.15	10.0	0.037	38.0	157.0	0.99642	3.28	0.52	9.4	5	0	white	0.2	1.7509599960376983
11	7.8	0.32	0.33	10.4	0.031	47.0	194.0	0.99692	3.07	0.58	9.6	6	0	white	0.09	1.7503765647132206
12	6.6	0.33	0.24	16.05	0.045	31.0	147.0	0.99822	3.08	0.52	9.2	5	0	white	0.2	1.7513882869893362
13	7.5	0.27	0.79	11.95	0.04	51.0	159.0	0.99839	2.98	0.44	8.7	5	0	white	0.14	1.757585831506643
14	5.2	0.16	0.34	0.8	0.029	26.0	77.0	0.99155	3.25	0.51	10.1	6	0	white	0.09	1.7893445144521123
15	6.1	0.27	0.31	1.5	0.035	17.0	83.0	0.99076	3.32	0.44	11.1	7	1	white	0.2	1.7772043314758499
16	7.4	0.56	0.09	1.5	0.071	19.0	117.0	0.99496	3.22	0.53	9.8	5	0	white	0.14	1.754645032462573
17	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	0.03	1.7518243203856414
18	7.6	0.31	0.23	12.7	0.054	20.0	139.0	0.99836	3.16	0.5	9.7	4	0	white	0.19	1.748067537465013
19	8.6	0.23	0.25	11.3	0.031	13.0	96.0	0.99645	3.11	0.4	10.8	5	0	white	0.15	1.7446325593074334
20	6.8	0.21	0.36	18.1	0.046	32.0	133.0	1.0	3.27	0.48	8.8	5	0	white	0.05	1.7522693768235056
21	6.4	0.31	0.4	6.4	0.039	39.0	191.0	0.99513	3.14	0.52	9.8	5	0	white	0.03	1.7680928379198104
22	7.1	0.34	0.31	5.2	0.032	36.0	140.0	0.99166	3.35	0.47	12.3	7	1	white	0.04	1.762001501398871
23	7.3	0.51	0.29	11.3	0.034	61.0	224.0	0.99683	3.14	0.56	9.5	6	0	white	0.0	1.7467548909325896
24	7.5	0.41	0.23	14.8	0.054	28.0	174.0	0.99898	3.18	0.49	9.7	5	0	white	0.02	1.7429572460333667
25	6.5	0.18	0.33	1.4	0.029	35.0	138.0	0.99114	3.36	0.6	11.5	7	1	white	0.19	1.7771989627134435
26	7.5	0.41	0.23	14.8	0.054	28.0	174.0	0.99898	3.18	0.49	9.7	5	0	white	0.07	1.7429572460333667
27	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	0.09	1.7763974315165252
28	5.9	0.28	0.14	8.6	0.032	30.0	142.0	0.99542	3.28	0.44	9.5	6	0	white	0.06	1.7673414588056242
29	7.2	0.22	0.24	1.4	0.041	17.0	159.0	0.99196	3.25	0.53	11.2	6	0	white	0.19	1.7691200087370336
30	6.2	0.33	0.14	4.8	0.052	27.0	128.0	0.99475	3.21	0.48	9.4	5	0	white	0.06	1.7681446086796184
31	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0.02	1.7849712757590654
32	6.9	0.28	0.22	10.0	0.052	36.0	131.0	0.99696	3.08	0.46	9.6	5	0	white	0.11	1.7581556709263917
33	6.9	0.32	0.26	2.3	0.03	11.0	103.0	0.99106	3.06	0.42	11.1	6	0	white	0.1	1.7674941403543658
34	6.6	0.41	0.16	1.4	0.037	28.0	160.0	0.99167	2.95	0.45	10.6	6	0	white	0.05	1.7670702382266674
35	7.0	0.14	0.28	1.3	0.026	10.0	56.0	0.99352	3.46	0.45	9.9	5	0	white	0.14	1.7739688642612443
36	5.8	0.3	0.27	1.7	0.014	45.0	104.0	0.98914	3.4	0.56	12.6	7	1	white	0.02	1.7780942959056159
37	7.3	0.32	0.29	1.5	0.038	32.0	144.0	0.99296	3.2	0.55	10.8	5	0	white	0.15	1.7655690962576922
38	7.8	0.28	0.31	2.1	0.046	28.0	208.0	0.99434	3.23	0.64	9.8	5	0	white	0.07	1.762164819197309
39	6.8	0.11	0.42	1.1	0.042	51.0	132.0	0.99059	3.18	0.74	11.3	7	1	white	0.12	1.7783281786330976
40	6.7	0.3	0.29	2.8	0.025	37.0	107.0	0.99159	3.31	0.63	11.3	7	1	white	0.18	1.7694807293282842
41	6.5	0.18	0.41	14.2	0.039	47.0	129.0	0.99678	3.28	0.72	10.3	7	1	white	0.02	1.7613672944085574
42	6.6	0.26	0.21	2.9	0.026	48.0	126.0	0.99089	3.22	0.38	11.3	7	1	white	0.16	1.7705267442252244
43	6.6	0.35	0.35	6.0	0.063	31.0	150.0	0.99537	3.1	0.47	9.4	6	0	white	0.1	1.7650080995730475
44	6.7	0.28	0.28	2.4	0.012	36.0	100.0	0.99064	3.26	0.39	11.7	7	1	white	0.04	1.7705964049596605
45	7.4	0.2	0.37	1.2	0.028	28.0	89.0	0.99132	3.14	0.61	11.8	6	0	white	0.07	1.7699355896431042
46	6.1	0.15	0.35	15.8	0.042	55.0	158.0	0.99642	3.24	0.37	10.6	5	0	white	0.17	1.7628047769035273
47	5.8	0.15	0.28	0.8	0.037	43.0	127.0	0.99198	3.24	0.51	9.3	5	0	white	0.09	1.7840394300756297
48	6.4	0.68	0.26	3.4	0.069	25.0	146.0	0.99347	3.18	0.4	9.3	5	0	white	0.01	1.7584513833762958
49	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	0.06	1.757085143755268
50	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	0.01	1.763724867257231
51	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white	0.19	1.7553196915540628
52	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	0.03	1.7654470604303207
53	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	0.06	1.7637260418337237
54	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	0.17	1.7828478241386476
55	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	0.05	1.7718074164876372
56	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	0.11	1.740160136970525
57	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	0.11	1.7867209349865414
58	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	0.15	1.7549494591826023
59	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	0.03	1.7697619844154096
60	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	0.04	1.7658152900276816
61	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	0.17	1.7713200388971293
62	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	0.17	1.7697619844154096
63	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	0.01	1.7715130875824596
64	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	0.13	1.757580334791013
65	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	0.05	1.7686187381489957
66	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	0.1	1.7452908564012406
67	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	0.04	1.7739170214995428
68	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	0.12	1.7756343023864551
69	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	0.19	1.7697994418734266
70	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	0.01	1.7452908564012406
71	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	0.16	1.7683849228230517
72	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	0.14	1.77263130043199
73	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	0.02	1.7766279947690888
74	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	0.04	1.7683267875710573
75	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	0.08	1.7543974279635879
76	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	0.0	1.752426119330196
77	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	0.03	1.7738820415385448
78	8.3	0.2	0.49	1.7	0.04	34.0	169.0	0.9938	3.05	0.37	10.1	5	0	white	0.01	1.7632369195650723
79	6.8	0.36	0.24	4.6	0.039	24.0	124.0	0.9909	3.27	0.34	12.6	7	1	white	0.19	1.7637609833534098
80	9.2	0.18	0.49	1.5	0.041	39.0	130.0	0.9945	3.04	0.49	9.8	7	1	white	0.02	1.756782770736878
81	7.3	0.22	0.49	9.9	0.031	48.0	161.0	0.9937	3.01	0.28	11.2	6	0	white	0.18	1.760121709996906
82	8.2	0.22	0.49	9.6	0.037	53.0	154.0	0.9951	3.02	0.33	10.6	6	0	white	0.01	1.753132905366893
83	8.2	0.24	0.49	9.3	0.038	52.0	163.0	0.9952	3.02	0.33	10.6	6	0	white	0.09	1.752880774495583
84	6.6	0.3	0.24	1.2	0.034	17.0	121.0	0.9933	3.13	0.36	9.2	5	0	white	0.14	1.7717643036021975
85	6.6	0.21	0.49	18.15	0.042	41.0	158.0	0.9997	3.28	0.39	8.7	6	0	white	0.01	1.75533808109557
86	7.5	0.23	0.49	7.7	0.049	61.0	209.0	0.9941	3.14	0.3	11.1	7	1	white	0.13	1.7609411493590006
87	7.9	0.12	0.49	5.2	0.049	33.0	152.0	0.9952	3.18	0.47	10.6	6	0	white	0.13	1.7644417632875509
88	6.6	0.19	0.99	1.2	0.122	45.0	129.0	0.9936	3.09	0.31	8.7	6	0	white	0.01	1.7833359030898501
89	7.3	0.21	0.49	1.8	0.038	44.0	152.0	0.9912	3.32	0.44	12.6	7	1	white	0.04	1.7709631178754026
90	7.9	0.42	0.49	8.2	0.056	32.0	164.0	0.9965	3.29	0.6	11.2	7	1	white	0.0	1.7509263688001988
91	6.9	0.24	0.49	1.3	0.032	35.0	148.0	0.9932	3.45	0.57	10.7	7	1	white	0.0	1.7739544943919947
92	7.8	0.3	0.74	1.8	0.033	33.0	156.0	0.991	3.29	0.52	12.8	6	0	white	0.04	1.7668839501701068
93	7.6	0.18	0.49	18.05	0.046	36.0	158.0	0.9996	3.06	0.41	9.2	5	0	white	0.17	1.7482405281093432
94	6.6	0.3	0.74	4.6	0.041	36.0	159.0	0.9946	3.21	0.45	9.9	5	0	white	0.19	1.7729847793719078
95	9.0	0.3	0.49	7.2	0.039	32.0	84.0	0.9938	2.94	0.32	11.5	6	0	white	0.04	1.7471698407916278
96	6.4	0.18	0.74	11.9	0.046	54.0	168.0	0.9978	3.58	0.68	10.1	5	0	white	0.16	1.7688994966116363
97	6.4	0.25	0.74	7.8	0.045	52.0	209.0	0.9956	3.21	0.42	9.2	6	0	white	0.05	1.7720184692313117
98	8.6	0.16	0.49	7.3	0.043	9.0	63.0	0.9953	3.13	0.59	10.5	6	0	white	0.15	1.7547467466762727
99	6.4	0.27	0.49	7.3	0.046	53.0	206.0	0.9956	3.24	0.43	9.2	6	0	white	0.02	1.7691816993925713
100	6.3	0.24	0.74	1.4	0.172	24.0	108.0	0.9932	3.27	0.39	9.9	6	0	white	0.02	1.7808163021497414

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({'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