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verticapy.machine_learning.vertica.neighbors.KNeighborsRegressor

class verticapy.machine_learning.vertica.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 Vertica 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).

Attributes

Many attributes are created during the fitting phase.

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 verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy 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 verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white
6	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white
7	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white
8	4.5	0.19	0.21	0.95	0.033	89.0	159.0	0.99332	3.34	0.42	8.0	5	0	white
9	4.6	0.445	0.0	1.4	0.053	11.0	178.0	0.99426	3.79	0.55	10.2	5	0	white
10	4.6	0.52	0.15	2.1	0.054	8.0	65.0	0.9934	3.9	0.56	13.1	4	0	red
11	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6	0	white
12	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
13	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
14	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red
15	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white
16	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
17	4.8	0.13	0.32	1.2	0.042	40.0	98.0	0.9898	3.42	0.64	11.8	7	1	white
18	4.8	0.17	0.28	2.9	0.03	22.0	111.0	0.9902	3.38	0.34	11.3	7	1	white
19	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7	1	white
20	4.8	0.225	0.38	1.2	0.074	47.0	130.0	0.99132	3.31	0.4	10.3	6	0	white
21	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7	1	white
22	4.8	0.29	0.23	1.1	0.044	38.0	180.0	0.98924	3.28	0.34	11.9	6	0	white
23	4.8	0.33	0.0	6.5	0.028	34.0	163.0	0.9937	3.35	0.61	9.9	5	0	white
24	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white
25	4.8	0.65	0.12	1.1	0.013	4.0	10.0	0.99246	3.32	0.36	13.5	4	0	white
26	4.9	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
27	4.9	0.33	0.31	1.2	0.016	39.0	150.0	0.98713	3.33	0.59	14.0	8	1	white
28	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
29	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
30	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
31	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
32	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
33	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white
34	5.0	0.17	0.56	1.5	0.026	24.0	115.0	0.9906	3.48	0.39	10.8	7	1	white
35	5.0	0.2	0.4	1.9	0.015	20.0	98.0	0.9897	3.37	0.55	12.05	6	0	white
36	5.0	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
37	5.0	0.24	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5	0	white
38	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6	0	white
39	5.0	0.24	0.34	1.1	0.034	49.0	158.0	0.98774	3.32	0.32	13.1	7	1	white
40	5.0	0.255	0.22	2.7	0.043	46.0	153.0	0.99238	3.75	0.76	11.3	6	0	white
41	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
42	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
43	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
44	5.0	0.29	0.54	5.7	0.035	54.0	155.0	0.98976	3.27	0.34	12.9	8	1	white
45	5.0	0.3	0.33	3.7	0.03	54.0	173.0	0.9887	3.36	0.3	13.0	7	1	white
46	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
47	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
48	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
49	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
50	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white
51	5.0	0.33	0.23	11.8	0.03	23.0	158.0	0.99322	3.41	0.64	11.8	6	0	white
52	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
53	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
54	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red
55	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red
56	5.0	0.42	0.24	2.0	0.06	19.0	50.0	0.9917	3.72	0.74	14.0	8	1	red
57	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
58	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
59	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white
60	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white
61	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red
62	5.0	1.02	0.04	1.4	0.045	41.0	85.0	0.9938	3.75	0.48	10.5	4	0	red
63	5.0	1.04	0.24	1.6	0.05	32.0	96.0	0.9934	3.74	0.62	11.5	5	0	red
64	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
65	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
66	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
67	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white
68	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5	0	white
69	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7	1	white
70	5.1	0.26	0.33	1.1	0.027	46.0	113.0	0.98946	3.35	0.43	11.4	7	1	white
71	5.1	0.26	0.34	6.4	0.034	26.0	99.0	0.99449	3.23	0.41	9.2	6	0	white
72	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
73	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
74	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6	0	white
75	5.1	0.305	0.13	1.75	0.036	17.0	73.0	0.99	3.4	0.51	12.3333333333333	5	0	white
76	5.1	0.31	0.3	0.9	0.037	28.0	152.0	0.992	3.54	0.56	10.1	6	0	white
77	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
78	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
79	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
80	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7	1	white
81	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
82	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
83	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
84	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
85	5.1	0.42	0.0	1.8	0.044	18.0	88.0	0.99157	3.68	0.73	13.6	7	1	red
86	5.1	0.42	0.01	1.5	0.017	25.0	102.0	0.9894	3.38	0.36	12.3	7	1	white
87	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red
88	5.1	0.51	0.18	2.1	0.042	16.0	101.0	0.9924	3.46	0.87	12.9	7	1	red
89	5.1	0.52	0.06	2.7	0.052	30.0	79.0	0.9932	3.32	0.43	9.3	5	0	white
90	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red
91	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
92	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
93	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
94	5.2	0.17	0.27	0.7	0.03	11.0	68.0	0.99218	3.3	0.41	9.8	5	0	white
95	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white
96	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
97	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
98	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
99	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6	0	white
100	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white

Rows: 1-100 | Columns: 14

Note

VerticaPy 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 VerticaPy environment.

You can easily divide your dataset into training and testing subsets using the vDataFrame.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, VerticaPy 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 vDataFrame.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 verticapy.machine_learning.vertica import KNeighborsRegressor

Then we can create the model:

model = KNeighborsRegressor()

Hint

In verticapy 1.0.x and higher, you do not need to specify the model name, as the name is automatically assigned. If you need to re-use the model, you can fetch the model name from the model’s attributes.

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 vDataFrame 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 verticapy, 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	0.347390801934063
max_error	2.8
median_absolute_error	0.4
mean_absolute_error	0.536349693251534
mean_squared_error	0.490705521472393
root_mean_squared_error	0.700503762639711
r2	0.346750425894378
r2_adj	0.343728454078932
aic	-914.170016124979
bic	-878.11971096461

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	376.713128834356	62.78552147239267	127.26264510485292	8.843716283188727e-127
Residual	1297	639.88	0.4933538936006168
Total	1303	979.533742331288

Rows: 1-3 | Columns: 6

You can also use the KNeighborsRegressor.score function to compute the R-squared value:

model.score()
Out[4]: 0.346750425894378

Prediction

Prediction is straight-forward:

model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density"
    ],
    "prediction",
)

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 density Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 prediction Float(22)
1	8.1	0.53	0.22	2.2	0.078	0.99678	33.0	89.0	3.26	0.46	9.6	6	0	red	5.2
2	6.8	0.21	0.31	2.9	0.046	0.9913	40.0	121.0	3.07	0.65	10.9	7	1	white	6.8
3	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	6.0
4	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	5.0
5	6.6	0.24	0.38	8.0	0.042	0.99577	56.0	187.0	3.21	0.46	9.2	5	0	white	5.0
6	6.7	0.35	0.48	8.8	0.056	0.99628	35.0	167.0	3.04	0.47	9.4	5	0	white	6.4
7	10.4	0.41	0.55	3.2	0.076	0.9996	22.0	54.0	3.15	0.89	9.9	6	0	red	5.8
8	6.3	0.43	0.32	8.8	0.042	0.99172	18.0	106.0	3.28	0.33	12.9	7	1	white	5.2
9	7.2	0.3	0.3	8.1	0.05	0.99652	40.0	188.0	3.15	0.49	9.1	6	0	white	5.4
10	8.9	0.84	0.34	1.4	0.05	0.99554	4.0	10.0	3.12	0.48	9.1	6	0	red	5.2
11	6.0	0.23	0.15	9.7	0.048	0.99571	101.0	207.0	3.05	0.3	9.1	5	0	white	5.8
12	6.8	0.26	0.34	13.9	0.034	0.9949	39.0	134.0	3.33	0.53	12.0	6	0	white	6.4
13	7.6	0.27	0.33	2.0	0.059	0.9944	19.0	175.0	3.22	0.56	9.9	5	0	white	5.8
14	6.7	0.29	0.45	14.3	0.054	0.99869	30.0	181.0	3.14	0.57	9.1	5	0	white	5.8
15	6.3	0.18	0.36	1.2	0.034	0.99074	26.0	111.0	3.16	0.51	11.0	6	0	white	6.2
16	8.9	0.4	0.51	2.6	0.052	0.995	13.0	27.0	3.32	0.9	13.4	7	1	red	5.6
17	7.1	0.17	0.43	1.3	0.023	0.99067	33.0	132.0	3.11	0.56	11.7	6	0	white	6.0
18	6.3	0.34	0.29	6.2	0.046	0.9952	29.0	227.0	3.29	0.53	10.1	6	0	white	6.8
19	8.1	0.575	0.22	2.1	0.077	0.9967	12.0	65.0	3.29	0.51	9.2	5	0	red	5.2
20	6.5	0.18	0.33	1.4	0.029	0.99114	35.0	138.0	3.36	0.6	11.5	7	1	white	6.8
21	6.0	0.64	0.05	1.9	0.066	0.99496	9.0	17.0	3.52	0.78	10.6	5	0	red	5.2
22	6.8	0.815	0.0	1.2	0.267	0.99471	16.0	29.0	3.32	0.51	9.8	3	0	red	4.2
23	7.3	0.18	0.29	1.0	0.036	0.99	26.0	101.0	3.09	0.37	11.7	6	0	white	6.0
24	8.0	0.23	0.35	9.2	0.044	0.997	53.0	186.0	3.09	0.56	9.5	7	1	white	6.6
25	8.2	0.52	0.34	1.2	0.042	0.99366	18.0	167.0	3.24	0.39	10.6	5	0	white	5.6
26	7.1	0.26	0.19	8.2	0.051	0.996	53.0	187.0	3.16	0.52	9.7	5	0	white	5.4
27	5.1	0.165	0.22	5.7	0.047	0.9934	42.0	146.0	3.18	0.55	9.9	6	0	white	6.0
28	8.6	0.485	0.29	4.1	0.026	0.9918	19.0	101.0	3.01	0.38	12.4	5	0	white	6.2
29	7.0	0.22	0.28	1.5	0.037	0.9927	29.0	115.0	3.11	0.55	10.5	6	0	white	5.8
30	7.4	0.38	0.27	7.5	0.041	0.99535	24.0	160.0	3.17	0.43	10.0	5	0	white	5.2
31	6.4	0.26	0.4	1.7	0.179	0.9925	5.0	60.0	3.09	0.54	10.1	5	0	white	6.0
32	9.2	0.27	0.34	10.5	0.043	0.9974	49.0	228.0	3.04	0.41	10.4	6	0	white	6.4
33	5.5	0.23	0.19	2.2	0.044	0.99209	39.0	161.0	3.19	0.43	10.4	6	0	white	6.0
34	6.15	0.21	0.37	3.2	0.021	0.99076	20.0	80.0	3.39	0.47	12.0	5	0	white	5.6
35	8.2	0.78	0.0	2.2	0.089	0.9978	13.0	26.0	3.37	0.46	9.6	4	0	red	4.8
36	6.2	0.37	0.22	8.3	0.025	0.9964	36.0	216.0	3.33	0.6	9.6	6	0	white	6.4
37	7.9	0.25	0.29	5.3	0.031	0.9918	33.0	117.0	3.06	0.32	11.8	7	1	white	6.2
38	6.7	0.34	0.31	16.4	0.051	0.99834	20.0	146.0	3.06	0.54	9.1	5	0	white	5.2
39	6.8	0.37	0.67	1.5	0.175	0.99244	16.0	98.0	3.06	0.56	10.3	6	0	white	5.8
40	7.5	0.25	0.47	4.1	0.041	0.99184	95.0	163.0	2.92	0.59	11.3	6	0	white	6.6
41	7.0	0.38	0.49	2.5	0.097	0.9962	33.0	85.0	3.39	0.77	11.4	6	0	red	6.2
42	6.8	0.25	0.24	1.6	0.045	0.99402	39.0	164.0	3.53	0.58	10.8	5	0	white	5.2
43	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	5.6
44	6.4	0.4	0.23	1.6	0.066	0.9958	5.0	12.0	3.34	0.56	9.2	5	0	red	6.2
45	9.8	0.51	0.19	3.2	0.081	0.9984	8.0	30.0	3.23	0.58	10.5	6	0	red	5.6
46	7.3	0.23	0.27	2.6	0.035	0.99138	39.0	120.0	3.04	0.59	11.3	7	1	white	6.6
47	10.7	0.22	0.56	8.2	0.044	0.998	37.0	181.0	2.87	0.68	9.5	6	0	white	5.2
48	5.9	0.395	0.13	2.4	0.056	0.99362	14.0	28.0	3.62	0.67	12.4	6	0	red	6.4
49	6.7	0.21	0.32	5.4	0.047	0.995	29.0	140.0	3.39	0.46	9.7	6	0	white	6.6
50	7.2	0.29	0.18	8.2	0.042	0.99644	41.0	180.0	3.16	0.49	9.1	5	0	white	5.6
51	7.5	0.51	0.02	1.7	0.084	0.99538	13.0	31.0	3.36	0.54	10.5	6	0	red	5.8
52	6.1	0.28	0.23	4.2	0.038	0.98898	13.0	95.0	2.97	0.7	13.1	6	0	white	6.6
53	7.0	0.51	0.09	2.1	0.062	0.99584	4.0	9.0	3.35	0.54	10.5	5	0	red	5.6
54	8.0	0.77	0.32	2.1	0.079	0.99656	16.0	74.0	3.27	0.5	9.8	6	0	red	5.2
55	8.3	0.39	0.7	10.6	0.045	0.9976	33.0	169.0	3.09	0.57	9.4	5	0	white	5.6
56	12.5	0.56	0.49	2.4	0.064	0.9999	5.0	27.0	3.08	0.87	10.9	5	0	red	5.6
57	5.9	0.26	0.4	1.3	0.047	0.9945	12.0	139.0	3.45	0.53	10.4	5	0	white	5.8
58	6.5	0.19	0.28	1.4	0.046	0.99038	22.0	90.0	3.18	0.51	11.7	7	1	white	6.4
59	4.9	0.235	0.27	11.75	0.03	0.9954	34.0	118.0	3.07	0.5	9.4	6	0	white	6.0
60	5.7	0.28	0.3	3.9	0.026	0.98963	36.0	105.0	3.26	0.58	12.75	6	0	white	6.8
61	6.9	0.28	0.33	1.2	0.039	0.9904	16.0	98.0	3.07	0.39	11.7	6	0	white	6.0
62	6.8	0.19	0.58	14.2	0.038	0.9975	51.0	164.0	3.12	0.48	9.6	6	0	white	5.6
63	7.0	0.23	0.42	18.05	0.05	0.9999	35.0	144.0	3.22	0.42	8.8	5	0	white	5.0
64	6.1	0.26	0.28	1.7	0.043	0.98918	24.0	98.0	3.14	0.44	12.5	6	0	white	6.2
65	6.7	0.47	0.34	8.9	0.043	0.9964	31.0	172.0	3.22	0.6	9.2	5	0	white	5.6
66	6.8	0.18	0.37	1.6	0.055	0.9934	47.0	154.0	3.08	0.45	9.1	5	0	white	5.8
67	7.3	0.74	0.08	1.7	0.094	0.99576	10.0	45.0	3.24	0.5	9.8	5	0	red	5.2
68	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	5.4
69	7.1	0.32	0.32	11.0	0.038	0.9937	16.0	66.0	3.24	0.4	11.5	3	0	white	5.6
70	7.3	0.33	0.22	1.4	0.041	0.99287	40.0	177.0	3.14	0.48	9.9	5	0	white	5.2
71	9.1	0.5	0.3	1.9	0.065	0.99774	8.0	17.0	3.32	0.71	10.5	6	0	red	5.6
72	6.6	0.7	0.08	2.6	0.106	0.99665	14.0	27.0	3.44	0.58	10.2	5	0	red	5.6
73	10.0	0.41	0.45	6.2	0.071	0.99702	6.0	14.0	3.21	0.49	11.8	7	1	red	7.0
74	7.7	0.32	0.48	2.3	0.04	0.9911	28.0	114.0	3.2	0.52	12.8	7	1	white	5.8
75	7.6	0.46	0.11	2.6	0.079	0.9968	12.0	49.0	3.21	0.57	10.0	5	0	red	5.8
76	6.7	0.26	0.29	5.8	0.025	0.9929	26.0	74.0	3.28	0.53	11.0	6	0	white	5.8
77	6.7	0.24	0.41	8.7	0.036	0.9952	29.0	148.0	3.22	0.62	9.9	6	0	white	5.6
78	5.8	0.24	0.28	1.4	0.038	0.98711	40.0	76.0	3.1	0.29	13.9	7	1	white	6.6
79	7.0	0.24	0.36	2.8	0.034	0.99	22.0	112.0	3.19	0.38	12.6	8	1	white	6.6
80	7.1	0.27	0.27	10.4	0.041	0.99335	26.0	114.0	3.04	0.52	11.5	7	1	white	6.0
81	8.0	0.42	0.32	2.5	0.08	0.99801	26.0	122.0	3.22	1.07	9.7	5	0	red	6.0
82	6.8	0.36	0.32	1.6	0.039	0.9948	10.0	124.0	3.3	0.67	9.6	5	0	white	5.6
83	4.8	0.13	0.32	1.2	0.042	0.9898	40.0	98.0	3.42	0.64	11.8	7	1	white	6.8
84	7.1	0.22	0.32	16.9	0.056	0.9998	49.0	158.0	3.37	0.38	9.6	6	0	white	5.8
85	8.9	0.3	0.35	4.6	0.032	0.99458	32.0	148.0	3.15	0.45	11.5	7	1	white	6.6
86	6.4	0.26	0.26	1.1	0.052	0.99304	22.0	176.0	3.09	0.54	9.3	5	0	white	6.0
87	10.3	0.43	0.44	2.4	0.214	0.9994	5.0	12.0	3.19	0.63	9.5	6	0	red	6.0
88	5.6	0.32	0.33	7.4	0.037	0.99268	25.0	95.0	3.25	0.49	11.1	6	0	white	5.6
89	7.0	0.16	0.32	8.3	0.045	0.9958	38.0	126.0	3.21	0.34	9.2	5	0	white	5.8
90	6.9	0.32	0.13	7.8	0.042	0.996	11.0	117.0	3.23	0.37	9.2	5	0	white	5.0
91	7.9	0.66	0.0	1.4	0.096	0.99569	6.0	13.0	3.43	0.58	9.5	5	0	red	4.8
92	7.8	0.54	0.26	2.0	0.088	0.9981	23.0	48.0	3.41	0.74	9.2	6	0	red	5.2
93	7.9	0.18	0.49	5.2	0.051	0.9953	36.0	157.0	3.18	0.48	10.6	6	0	white	6.4
94	6.6	0.22	0.3	14.7	0.045	0.99704	50.0	136.0	3.14	0.37	10.6	6	0	white	6.0
95	6.0	0.31	0.38	4.8	0.04	0.98968	41.0	101.0	3.24	0.56	13.1	6	0	white	5.8
96	6.4	0.105	0.29	1.1	0.035	0.99142	44.0	140.0	3.17	0.55	10.7	7	1	white	6.6
97	5.1	0.35	0.26	6.8	0.034	0.99188	36.0	120.0	3.38	0.4	11.5	6	0	white	6.0
98	7.9	0.2	0.49	1.6	0.053	0.993	15.0	144.0	3.16	0.47	10.5	5	0	white	5.8
99	6.4	0.29	0.18	15.0	0.04	0.99736	21.0	116.0	3.14	0.5	9.2	5	0	white	4.8
100	7.5	0.35	0.28	9.6	0.051	0.9969	26.0	157.0	3.12	0.53	9.2	6	0	white	5.8

Rows: 1-100 | Columns: 15

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 vDataFrame to the predict() function, but in this case, it’s essential that the column names of the vDataFrame match the predictors and response name in the model.

Parameter Modification

In order to see the parameters:

model.get_params()
Out[5]: {'n_neighbors': 5, 'p': 2}

And to manually change some of the parameters:

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

Model Register

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to Model Tracking and Versioning for more details on model tracking and versioning.

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

Must be overridden in the child class

Methods

__init__([name, overwrite_model, n_neighbors, p])	Must be overridden in the child class
contour([nbins, chart])	Draws the model's contour plot.
deploySQL([X, test_relation, key_columns])	Returns the SQL code needed to deploy the model.
does_model_exists(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
drop()	KNeighborsRegressor models are not stored in the Vertica DB.
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, or Highcharts) to draw a specific graphic.
get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
import_models(path[, schema, kind])	Imports machine learning models.
predict(vdf[, X, name, inplace])	Predicts using the input relation.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
regression_report([metrics])	Computes a regression report
report([metrics])	Computes a regression report
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 Vertica Binary format.
to_pmml(path)	Exports the model to PMML.
to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes

object_type