vastorbit.machine_learning.vast.neighbors.KNeighborsRegressor¶

class vastorbit.machine_learning.vast.neighbors.KNeighborsRegressor(name: str = None, overwrite_model: bool = False, n_neighbors: int = 5, p: int = 2)¶

[Beta Version] Creates a KNeighborsRegressor object using the k-nearest neighbors algorithm. This object uses pure SQL to compute all the distances and final score.

Warning

This algorithm uses a CROSS JOIN during computation and is therefore computationally expensive at O(n * n), where n is the total number of elements. Since KNeighborsRegressor uses the p- distance, it is highly sensitive to unnormalized data.

Important

This algorithm is not VAST Native and relies solely on SQL for attribute computation. While this model does not take advantage of the benefits provided by a model management system, including versioning and tracking, the SQL code it generates can still be used to create a pipeline.

Parameters:

n_neighbors (int, optional) – Number of neighbors to consider when computing the score.
p (int, optional) – The p of the p-distances (distance metric used during the model computation).

Variables:

created (Many attributes are)
phase. (during the fitting)
n_neighbors_ (int) – Number of neighbors.
p_ (int) – The p of the p-distances.
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.

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 KNeighborsRegressor model:

from vastorbit.machine_learning.vast import KNeighborsRegressor

Then we can create the model:

model = KNeighborsRegressor()

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:

result = model.report()

	value
explained_variance	9.992007221626409e-16
max_error	3.8
median_absolute_error	0.7999999999999998
mean_absolute_error	0.8470948012232363
mean_squared_error	1.4249847094801225
root_mean_squared_error	1.0702824800752226
r2	-0.5191985265390417
r2_adj	-0.5262048225876461
aic	477.40423564508205
bic	513.4764789076636

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 KNeighborsRegressor, we can easily get the ANOVA table using:

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

	Df	SS	MS	F	p_value
Regression	6	54.17617737003087	9.029362895005145	11.136054457759826	3.808709808621569e-12
Residual	1301	1054.8800000000008	0.8108224442736363
Total	1307	992.9701834862386

Rows: 1-3 | Columns: 6

You can also use the KNeighborsRegressor.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 density Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	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	6.0	0.26	0.5	2.2	0.048	0.9928	59.0	153.0	3.08	0.61	9.8	5	0	white	0.09	5.8
2	7.5	0.23	0.68	11.0	0.047	0.9978	37.0	133.0	2.99	0.38	8.8	5	0	white	0.11	5.8
3	7.4	0.2	1.66	2.1	0.022	0.99165	34.0	113.0	3.26	0.55	12.2	6	0	white	0.12	5.8
4	7.3	0.21	0.29	1.6	0.034	0.9917	29.0	118.0	3.3	0.5	11.0	8	1	white	0.19	5.8
5	6.7	0.64	0.23	2.1	0.08	0.99538	11.0	119.0	3.36	0.7	10.9	5	0	red	0.09	5.8
6	5.1	0.42	0.0	1.8	0.044	0.99157	18.0	88.0	3.68	0.73	13.6	7	1	red	0.01	5.8
7	7.7	0.3	0.32	1.6	0.037	0.9919	23.0	124.0	2.93	0.33	11.0	6	0	white	0.14	5.8
8	7.7	0.32	0.62	10.6	0.036	0.9978	56.0	153.0	3.13	0.44	8.9	6	0	white	0.0	5.8
9	6.7	0.105	0.32	12.4	0.051	0.998	34.0	106.0	3.54	0.45	9.2	6	0	white	0.03	5.8
10	7.6	0.21	0.44	1.9	0.036	0.9913	10.0	119.0	3.01	0.7	12.8	6	0	white	0.03	5.8
11	8.9	0.33	0.32	1.5	0.047	0.9954	11.0	200.0	3.19	0.46	9.4	5	0	white	0.01	5.8
12	6.5	0.19	0.32	1.4	0.04	0.9922	31.0	132.0	3.36	0.54	10.8	7	1	white	0.16	5.8
13	7.5	0.59	0.22	1.8	0.0819999999999999	0.99499	43.0	60.0	3.1	0.42	9.2	5	0	red	0.19	5.8
14	7.0	0.6	0.3	4.5	0.068	0.99914	20.0	110.0	3.3	1.17	10.2	5	0	red	0.0	5.8
15	7.0	0.62	0.1	1.4	0.071	0.996	27.0	63.0	3.28	0.61	9.2	5	0	red	0.1	5.8
16	5.4	0.74	0.0	1.2	0.0409999999999999	0.99258	16.0	46.0	4.01	0.59	12.5	6	0	red	0.19	5.8
17	7.5	0.27	0.79	11.95	0.04	0.99839	51.0	159.0	2.98	0.44	8.7	5	0	white	0.1	5.8
18	6.8	0.18	0.3	12.8	0.062	0.99808	19.0	171.0	3.0	0.52	9.0	7	1	white	0.18	5.8
19	5.4	0.5	0.13	5.0	0.028	0.99079	12.0	107.0	3.48	0.88	13.5	7	1	white	0.19	5.8
20	7.2	0.19	0.27	11.2	0.061	0.99772	46.0	149.0	2.99	0.59	9.3	6	0	white	0.01	5.8
21	6.8	0.3	0.22	6.2	0.06	0.99858	41.0	190.0	3.18	0.51	9.2	5	0	white	0.0	5.8
22	6.5	0.17	0.31	1.5	0.041	0.99092	34.0	121.0	3.06	0.46	10.5	6	0	white	0.07	5.8
23	6.2	0.21	0.26	13.1	0.05	0.99772	59.0	150.0	3.31	0.46	9.0	5	0	white	0.13	5.8
24	7.9	0.6	0.06	1.6	0.069	0.9964	15.0	59.0	3.3	0.46	9.4	5	0	red	0.08	5.8
25	7.3	0.65	0.0	1.2	0.065	0.9946	15.0	21.0	3.39	0.47	10.0	7	1	red	0.17	5.8
26	5.8	0.27	0.2	14.95	0.044	0.9962	22.0	179.0	3.37	0.37	10.2	5	0	white	0.17	5.8
27	6.5	0.17	0.54	8.5	0.082	0.9959	64.0	163.0	2.89	0.39	8.8	6	0	white	0.14	5.8
28	6.9	0.23	0.4	7.5	0.04	0.9927	50.0	151.0	3.11	0.27	11.4	6	0	white	0.13	5.8
29	5.8	0.25	0.26	13.1	0.051	0.9972	44.0	148.0	3.29	0.38	9.3	5	0	white	0.11	5.8
30	7.2	0.43	0.24	6.7	0.058	0.995	40.0	163.0	3.2	0.41	9.9	5	0	white	0.18	5.8
31	6.4	0.24	0.32	14.9	0.047	0.9968	54.0	162.0	3.28	0.5	10.2	6	0	white	0.13	5.8
32	6.1	0.25	0.49	7.6	0.052	0.9956	67.0	226.0	3.16	0.47	8.9	5	0	white	0.19	5.8
33	7.3	0.22	0.49	9.4	0.034	0.9939	29.0	134.0	2.99	0.32	11.0	7	1	white	0.07	5.8
34	6.4	0.18	0.74	11.9	0.046	0.9978	54.0	168.0	3.58	0.68	10.1	5	0	white	0.19	5.8
35	6.9	0.26	0.49	1.6	0.058	0.9965	39.0	166.0	3.65	0.52	9.4	4	0	white	0.11	5.8
36	7.0	0.35	0.3	6.5	0.028	0.9936	27.0	87.0	3.4	0.42	11.4	7	1	white	0.05	5.8
37	7.1	0.16	0.25	1.3	0.034	0.9915	28.0	123.0	3.27	0.55	11.4	6	0	white	0.17	5.8
38	10.0	0.91	0.42	1.6	0.056	0.9968	34.0	181.0	3.11	0.46	10.0	4	0	white	0.12	5.8
39	7.0	0.23	0.42	1.1	0.062	0.99318	35.0	100.0	3.04	0.4	9.2	5	0	white	0.05	5.8
40	6.4	0.14	0.28	7.9	0.057	0.99425	21.0	82.0	3.26	0.36	10.0	6	0	white	0.19	5.8
41	6.8	0.3	0.26	20.3	0.037	0.99727	45.0	150.0	3.04	0.38	12.3	6	0	white	0.2	5.8
42	6.8	0.29	0.32	1.8	0.032	0.99095	18.0	130.0	3.05	0.62	11.2	6	0	white	0.2	5.8
43	6.0	0.19	0.29	1.2	0.046	0.99033	29.0	92.0	3.22	0.53	11.3	6	0	white	0.1	5.8
44	6.2	0.36	0.22	5.25	0.038	0.99184	44.0	145.0	3.22	0.4	11.2	6	0	white	0.1	5.8
45	12.0	0.28	0.49	1.9	0.074	0.9976	10.0	21.0	2.98	0.66	9.9	7	1	red	0.05	5.8
46	6.4	0.57	0.02	1.8	0.067	0.997	4.0	11.0	3.46	0.68	9.5	5	0	red	0.18	5.8
47	7.0	0.45	0.34	2.7	0.0819999999999999	0.998	16.0	72.0	3.55	0.6	9.5	5	0	red	0.05	5.8
48	11.1	0.42	0.47	2.65	0.085	0.99736	9.0	34.0	3.24	0.77	12.1	7	1	red	0.14	5.8
49	8.2	0.26	0.34	2.5	0.073	0.99594	16.0	47.0	3.4	0.78	11.3	7	1	red	0.19	5.8
50	6.8	0.49	0.22	2.3	0.071	0.99438	13.0	24.0	3.41	0.83	11.3	6	0	red	0.05	5.8
51	7.2	0.6	0.04	2.5	0.076	0.99745	18.0	88.0	3.53	0.55	9.5	5	0	red	0.18	5.8
52	7.4	0.635	0.1	2.4	0.08	0.99736	16.0	33.0	3.58	0.69	10.8	7	1	red	0.02	5.8
53	7.9	0.3	0.27	8.5	0.036	0.9939	20.0	112.0	2.96	0.46	11.7	6	0	white	0.13	5.8
54	7.0	0.12	0.19	4.9	0.055	0.9953	27.0	127.0	3.29	0.41	9.4	5	0	white	0.16	5.8
55	6.8	0.27	0.28	13.3	0.076	0.9979	50.0	163.0	3.03	0.38	8.6	6	0	white	0.14	5.8
56	6.0	0.26	0.18	7.0	0.055	0.99591	50.0	194.0	3.21	0.43	9.0	5	0	white	0.19	5.8
57	6.8	0.21	0.55	14.6	0.053	0.99805	34.0	159.0	2.93	0.44	9.2	5	0	white	0.06	5.8
58	7.2	0.2	0.3	2.0	0.039	0.9911	43.0	188.0	3.3	0.41	12.0	6	0	white	0.04	5.8
59	7.8	0.735	0.08	2.4	0.092	0.9974	10.0	41.0	3.24	0.71	9.8	6	0	red	0.16	5.8
60	7.7	0.58	0.1	1.8	0.102	0.99565	28.0	109.0	3.08	0.49	9.8	6	0	red	0.17	5.8
61	10.6	0.28	0.39	15.5	0.069	1.0026	6.0	23.0	3.12	0.66	9.2	5	0	red	0.15	5.8
62	10.4	0.44	0.73	6.55	0.074	0.999	38.0	76.0	3.17	0.85	12.0	7	1	red	0.09	5.8
63	6.4	0.32	0.27	4.9	0.034	0.9916	18.0	122.0	3.36	0.71	12.5	6	0	white	0.17	5.8
64	6.7	0.3	0.44	18.75	0.057	0.99956	65.0	224.0	3.11	0.53	9.1	5	0	white	0.09	5.8
65	6.6	0.29	0.29	1.8	0.036	0.98819	38.0	102.0	3.08	0.42	13.7	7	1	white	0.1	5.8
66	6.8	0.26	0.46	8.3	0.037	0.99601	49.0	173.0	3.17	0.47	9.3	5	0	white	0.2	5.8
67	7.5	0.38	0.29	4.9	0.021	0.99026	38.0	113.0	3.08	0.48	13.0	7	1	white	0.19	5.8
68	6.2	0.16	0.32	1.1	0.036	0.99096	74.0	184.0	3.22	0.41	11.0	6	0	white	0.12	5.8
69	7.7	0.25	0.49	2.5	0.047	0.99252	31.0	169.0	3.07	0.57	10.6	6	0	white	0.0	5.8
70	6.4	0.17	0.27	9.9	0.047	0.99596	26.0	101.0	3.34	0.5	9.9	6	0	white	0.07	5.8
71	6.6	0.18	0.28	1.7	0.041	0.99207	53.0	161.0	3.13	0.45	10.2	6	0	white	0.19	5.8
72	7.1	0.2	0.27	9.6	0.037	0.99444	19.0	105.0	3.04	0.37	10.5	7	1	white	0.11	5.8
73	7.7	0.3	0.23	2.0	0.068	0.99382	28.0	138.0	3.11	0.62	9.8	5	0	white	0.03	5.8
74	6.9	0.21	0.62	6.3	0.042	0.99358	7.0	109.0	2.96	0.59	10.2	6	0	white	0.01	5.8
75	7.3	0.24	0.3	2.5	0.042	0.9911	31.0	104.0	3.05	0.56	11.3	7	1	white	0.09	5.8
76	6.6	0.18	0.26	17.3	0.051	0.9984	17.0	149.0	3.0	0.43	9.4	6	0	white	0.19	5.8
77	6.1	0.44	0.28	4.25	0.032	0.9916	43.0	132.0	3.26	0.47	11.3	7	1	white	0.02	5.8
78	6.5	0.5	0.22	4.1	0.036	0.9902	35.0	131.0	3.26	0.55	13.0	7	1	white	0.04	5.8
79	6.0	0.24	0.41	1.3	0.036	0.99018	42.0	118.0	3.04	0.64	11.7333333333333	6	0	white	0.0	5.8
80	6.0	0.29	0.41	10.8	0.048	0.9937	55.0	149.0	3.09	0.59	11.0	7	1	white	0.19	5.8
81	6.4	0.19	0.35	10.2	0.043	0.99632	40.0	106.0	3.16	0.5	9.7	6	0	white	0.19	5.8
82	7.1	0.68	0.0	2.3	0.087	0.99783	17.0	26.0	3.45	0.53	9.5	5	0	red	0.1	5.8
83	6.9	0.2	0.37	6.2	0.027	0.992	24.0	97.0	3.38	0.49	12.2	7	1	white	0.12	5.8
84	6.8	0.25	0.24	4.55	0.053	0.9955	41.0	211.0	3.37	0.67	9.5	6	0	white	0.16	5.8
85	7.4	0.3	0.22	1.4	0.046	0.9928	16.0	135.0	3.08	0.77	10.4	7	1	white	0.13	5.8
86	6.3	0.26	0.49	1.5	0.052	0.9924	34.0	134.0	2.99	0.61	9.8	6	0	white	0.07	5.8
87	6.1	0.17	0.28	4.5	0.033	0.9933	46.0	150.0	3.43	0.49	10.9	6	0	white	0.2	5.8
88	6.5	0.21	0.37	2.5	0.048	0.9917	70.0	138.0	3.33	0.75	11.4	7	1	white	0.19	5.8
89	6.4	0.31	0.26	13.2	0.046	0.9975	57.0	205.0	3.17	0.41	9.6	5	0	white	0.1	5.8
90	7.9	0.33	0.41	1.5	0.0559999999999999	0.99396	6.0	35.0	3.29	0.71	11.0	6	0	red	0.11	5.8
91	10.6	0.36	0.57	2.3	0.087	0.99676	6.0	20.0	3.14	0.72	11.1	7	1	red	0.09	5.8
92	6.7	0.15	0.29	5.0	0.058	0.9946	28.0	105.0	3.52	0.44	10.2	7	1	white	0.07	5.8
93	6.0	0.41	0.21	1.9	0.05	0.9928	29.0	122.0	3.42	0.52	10.5	6	0	white	0.16	5.8
94	8.0	0.55	0.17	8.2	0.04	0.9956	13.0	60.0	3.09	0.3	9.5	4	0	white	0.05	5.8
95	6.3	0.6	0.44	11.0	0.05	0.9972	50.0	245.0	3.19	0.57	9.3	4	0	white	0.11	5.8
96	6.4	0.3	0.51	5.5	0.048	0.9942	62.0	172.0	3.08	0.45	9.1	6	0	white	0.16	5.8
97	6.9	0.2	0.36	1.5	0.031	0.9931	38.0	147.0	3.35	0.56	11.0	6	0	white	0.04	5.8
98	9.2	0.28	0.46	3.2	0.058	0.996	39.0	133.0	3.14	0.58	9.5	5	0	white	0.0	5.8
99	7.8	0.3	0.29	16.85	0.054	0.9998	23.0	135.0	3.16	0.38	9.0	6	0	white	0.18	5.8
100	6.4	0.3	0.38	7.8	0.046	0.9955	35.0	192.0	3.1	0.37	9.0	5	0	white	0.01	5.8

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.

Parameter Modification¶

In order to see the parameters:

model.get_params()

And to manually change some of the parameters:

model.set_params({'n_neighbors': 3})

__init__(name: str = None, overwrite_model: bool = False, n_neighbors: int = 5, p: int = 2) → None¶

Methods

`__init__`([name, overwrite_model, n_neighbors, p])
`contour`([nbins, chart])	Draws the model's contour plot.
`deploySQL`([X, test_relation, key_columns])	Returns the SQL code needed to deploy the model.
`drop`()	`KNeighborsRegressor` models are not stored in the VAST DataBase.
`export_models`(name, path[, kind])	Exports machine learning models.
`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.
`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_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