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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	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	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
3	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
4	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
5	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
6	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
7	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
8	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
9	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
10	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
11	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
12	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
13	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
14	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
15	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
16	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
17	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
18	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
19	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
20	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
21	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
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white
26	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white
29	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
30	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
31	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
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white
34	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white
35	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
36	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
37	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white
38	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white
42	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white
43	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red
44	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
45	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
46	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
47	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
48	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white
70	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
71	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
72	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
73	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
81	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
82	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
83	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
84	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
92	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
95	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	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	0.327634969460444
max_error	2.8
median_absolute_error	0.4
mean_absolute_error	0.536566589684373
mean_squared_error	0.490685142417244
root_mean_squared_error	0.700225411853709
r2	0.324226399436746
r2_adj	0.32108813194187
aic	-910.663781877801
bic	-874.640996362191

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	343.768160123172	57.294693353861994	115.02384208650274	1.6396704849276214e-116
Residual	1292	643.56	0.4981114551083591
Total	1298	942.505003849115

Rows: 1-3 | Columns: 6

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

model.score()
Out[4]: 0.317181344001621

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	7.2	0.21	0.37	1.6	0.049	0.9924	23.0	94.0	3.16	0.48	10.9	7	1	white	5.6
2	5.9	0.37	0.14	6.3	0.036	0.9944	34.0	185.0	3.17	0.63	9.8	5	0	white	5.2
3	5.0	0.3	0.33	3.7	0.03	0.9887	54.0	173.0	3.36	0.3	13.0	7	1	white	7.0
4	5.5	0.12	0.33	1.0	0.038	0.99164	23.0	131.0	3.25	0.45	9.8	5	0	white	5.2
5	5.6	0.41	0.22	7.1	0.05	0.9931	44.0	154.0	3.3	0.4	10.5	5	0	white	5.6
6	7.2	0.3	0.26	1.5	0.041	0.99154	46.0	178.0	3.19	0.56	11.3	6	0	white	5.6
7	5.9	0.2	0.23	1.5	0.037	0.99021	38.0	93.0	3.36	0.49	12.0	6	0	white	6.0
8	6.1	0.3	0.56	2.8	0.044	0.9924	47.0	179.0	3.3	0.57	10.9	7	1	white	6.2
9	6.9	0.28	0.37	9.1	0.037	0.9948	16.0	76.0	3.05	0.54	11.1	5	0	white	5.4
10	6.0	0.1	0.24	1.1	0.041	0.9927	15.0	65.0	3.61	0.61	10.3	7	1	white	6.2
11	6.2	0.25	0.54	7.0	0.046	0.99454	58.0	176.0	3.19	0.7	10.4	5	0	white	5.4
12	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	5.8
13	6.5	0.33	0.38	2.5	0.047	0.98964	30.0	148.0	3.17	0.43	12.7	6	0	white	6.0
14	6.0	0.41	0.05	1.5	0.063	0.9932	17.0	120.0	3.21	0.56	9.2	6	0	white	5.2
15	6.0	0.29	0.41	10.8	0.048	0.9937	55.0	149.0	3.09	0.59	10.9666666666667	7	1	white	6.4
16	7.1	0.38	0.29	13.6	0.041	0.99461	30.0	137.0	3.02	0.96	12.1	6	0	white	5.8
17	8.4	0.62	0.09	2.2	0.084	0.9964	11.0	108.0	3.15	0.66	9.8	5	0	red	5.2
18	6.7	0.24	0.36	8.4	0.042	0.99473	42.0	123.0	3.34	0.52	10.9	6	0	white	5.8
19	6.2	0.27	0.18	1.5	0.028	0.99228	20.0	111.0	3.41	0.5	10.0	5	0	white	6.8
20	6.2	0.17	0.3	1.1	0.037	0.993	14.0	79.0	3.5	0.54	10.3	6	0	white	6.4
21	6.4	0.46	0.08	4.9	0.046	0.99445	34.0	144.0	3.1	0.56	10.0	5	0	white	5.8
22	6.0	0.24	0.34	1.0	0.036	0.99097	52.0	184.0	3.44	0.44	11.45	6	0	white	6.0
23	5.9	0.62	0.28	3.5	0.039	0.9907	55.0	152.0	3.44	0.44	12.0	6	0	white	6.2
24	8.2	0.37	0.36	1.0	0.034	0.9906	17.0	93.0	3.04	0.32	11.7	8	1	white	6.6
25	9.9	0.49	0.58	3.5	0.094	1.0004	9.0	43.0	3.29	0.58	9.0	5	0	red	6.2
26	5.8	0.22	0.3	1.1	0.047	0.992	36.0	131.0	3.26	0.45	10.4	5	0	white	5.8
27	8.9	0.43	0.45	1.9	0.052	0.9948	6.0	16.0	3.35	0.7	12.5	6	0	red	5.8
28	7.7	0.31	0.36	4.3	0.026	0.99152	15.0	87.0	3.11	0.48	12.0	5	0	white	5.6
29	7.1	0.24	0.41	17.8	0.046	0.9998	39.0	145.0	3.32	0.39	8.7	5	0	white	5.8
30	6.6	0.41	0.27	10.7	0.11	0.99672	20.0	103.0	3.08	0.41	9.0	6	0	white	5.4
31	6.8	0.18	0.24	9.8	0.058	0.9952	64.0	188.0	3.13	0.51	10.6	6	0	white	6.4
32	5.7	0.18	0.36	1.2	0.046	0.99199	9.0	71.0	3.7	0.68	10.9	7	1	white	6.2
33	6.4	0.16	0.44	1.2	0.051	0.99058	39.0	122.0	3.11	0.75	11.3	7	1	white	6.8
34	7.0	0.15	0.28	14.7	0.051	0.99792	29.0	149.0	2.96	0.39	9.0	7	1	white	7.0
35	6.8	0.915	0.29	4.8	0.07	0.99577	15.0	39.0	3.53	0.54	11.1	5	0	red	5.8
36	7.9	0.53	0.24	2.0	0.072	0.996	15.0	105.0	3.27	0.54	9.4	6	0	red	5.2
37	6.7	0.34	0.43	1.6	0.041	0.99014	29.0	114.0	3.23	0.44	12.6	6	0	white	6.0
38	6.4	0.23	0.26	8.1	0.054	0.9954	47.0	181.0	3.12	0.49	9.4	5	0	white	5.0
39	6.2	0.35	0.2	18.1	0.069	0.99908	33.0	158.0	3.15	0.5	8.8	6	0	white	5.4
40	7.2	0.4	0.62	10.8	0.041	0.9976	70.0	189.0	3.08	0.49	8.6	4	0	white	4.8
41	8.3	0.27	0.39	2.4	0.058	0.9955	16.0	107.0	3.28	0.59	10.3	5	0	white	6.4
42	5.9	0.19	0.21	1.7	0.045	0.99341	57.0	135.0	3.32	0.44	9.5	5	0	red	5.8
43	6.6	0.56	0.22	8.9	0.034	0.99675	27.0	133.0	3.2	0.51	9.1	5	0	white	5.0
44	6.9	0.38	0.38	13.1	0.112	0.99792	14.0	94.0	3.02	0.48	9.2	5	0	white	5.6
45	7.7	0.835	0.0	2.6	0.081	0.9975	6.0	14.0	3.3	0.52	9.3	5	0	red	5.4
46	7.1	0.44	0.62	11.8	0.044	0.9975	52.0	152.0	3.12	0.46	8.7	6	0	white	5.6
47	7.7	0.27	0.68	3.5	0.358	0.9972	5.0	10.0	3.25	1.08	9.9	7	1	red	6.6
48	6.4	0.36	0.53	2.2	0.23	0.9934	19.0	35.0	3.37	0.93	12.4	6	0	red	5.8
49	7.0	0.2	0.35	8.8	0.037	0.99388	31.0	103.0	3.13	0.49	11.0	6	0	white	6.0
50	8.0	0.3	0.28	5.7	0.044	0.9948	31.0	124.0	3.16	0.51	10.2	6	0	white	6.0
51	7.5	0.42	0.31	1.6	0.08	0.9978	15.0	42.0	3.31	0.64	9.0	5	0	red	5.6
52	7.0	0.14	0.4	1.7	0.035	0.9911	16.0	85.0	3.19	0.42	11.8	6	0	white	6.2
53	6.8	0.48	0.25	2.0	0.076	0.9953	29.0	61.0	3.34	0.6	10.4	5	0	red	6.2
54	7.6	0.23	0.34	1.6	0.043	0.99305	24.0	129.0	3.12	0.7	10.4	5	0	white	4.8
55	7.9	0.49	0.32	1.9	0.082	0.9968	17.0	144.0	3.2	0.55	9.5	5	0	red	5.4
56	6.9	0.22	0.49	7.0	0.063	0.9957	50.0	168.0	3.54	0.5	10.3	6	0	white	5.8
57	6.4	0.38	0.26	8.2	0.043	0.99234	28.0	98.0	2.99	0.31	11.4	6	0	white	5.2
58	7.6	0.31	0.29	10.5	0.04	0.9966	21.0	145.0	3.04	0.35	9.4	5	0	white	5.4
59	7.0	0.31	0.52	1.7	0.029	0.9918	5.0	61.0	3.07	0.43	10.4	5	0	white	5.4
60	6.4	0.31	0.53	8.8	0.057	0.99642	36.0	221.0	3.17	0.44	9.1	5	0	white	5.0
61	6.7	0.17	0.42	10.4	0.038	0.99628	85.0	182.0	3.04	0.44	8.9	6	0	white	6.0
62	6.1	0.6	0.12	1.8	0.05	0.99268	11.0	76.0	3.42	0.48	10.4	4	0	white	5.2
63	7.4	0.31	0.28	1.6	0.05	0.9929	33.0	137.0	3.31	0.56	10.5	6	0	white	6.2
64	4.7	0.67	0.09	1.0	0.02	0.98722	5.0	9.0	3.3	0.34	13.6	5	0	white	5.2
65	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	5.0
66	6.9	0.28	0.41	1.7	0.05	0.993	10.0	136.0	3.16	0.71	11.4	6	0	white	6.8
67	6.8	0.25	0.28	5.0	0.035	0.99048	42.0	126.0	3.12	0.38	12.6	7	1	white	6.0
68	7.5	0.38	0.29	12.7	0.05	0.9986	25.0	209.0	3.25	0.59	9.3	6	0	white	5.0
69	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	5.6
70	5.6	0.12	0.33	2.9	0.044	0.98896	21.0	73.0	3.17	0.32	12.9	8	1	white	6.6
71	7.6	0.23	0.29	8.6	0.053	0.9963	65.0	146.0	3.11	0.32	9.8	6	0	white	5.2
72	5.9	0.36	0.04	5.7	0.046	0.9934	21.0	87.0	3.22	0.51	10.2	5	0	white	5.0
73	6.9	0.41	0.31	2.0	0.079	0.99668	21.0	51.0	3.47	0.55	9.5	6	0	red	5.8
74	7.2	0.5	0.18	2.1	0.071	0.99761	12.0	31.0	3.52	0.72	9.6	6	0	red	5.8
75	6.2	0.21	0.24	1.2	0.051	0.99036	31.0	95.0	3.24	0.57	11.3	6	0	white	5.6
76	8.8	0.19	0.3	5.0	0.028	0.99242	34.0	120.0	2.94	0.47	11.2	5	0	white	5.8
77	8.5	0.2	0.4	1.1	0.046	0.99194	31.0	106.0	3.0	0.35	10.5	4	0	white	5.4
78	9.0	0.8	0.12	2.4	0.083	0.99836	8.0	28.0	3.33	0.65	10.4	6	0	red	5.4
79	6.6	0.31	0.28	1.4	0.035	0.98836	28.0	107.0	3.0	0.4	13.2	6	0	white	5.6
80	10.4	0.52	0.45	2.0	0.08	0.99774	6.0	13.0	3.22	0.76	11.4	6	0	red	5.8
81	6.7	0.24	0.3	3.85	0.042	0.99189	105.0	179.0	3.04	0.59	11.3	8	1	white	7.6
82	6.3	0.23	0.3	1.8	0.033	0.9906	16.0	91.0	3.28	0.4	11.8	6	0	white	5.6
83	11.5	0.45	0.5	3.0	0.078	1.0003	19.0	47.0	3.26	1.11	11.0	6	0	red	5.8
84	6.2	0.56	0.09	1.7	0.053	0.99402	24.0	32.0	3.54	0.6	11.3	5	0	red	5.2
85	7.0	0.53	0.02	1.0	0.036	0.993	39.0	107.0	3.2	0.32	9.0	5	0	white	5.2
86	7.0	0.15	0.28	14.7	0.051	0.99792	29.0	149.0	2.96	0.39	9.0	7	1	white	7.0
87	6.1	0.27	0.25	1.8	0.041	0.9929	9.0	109.0	3.08	0.54	9.0	5	0	white	5.2
88	5.1	0.14	0.25	0.7	0.039	0.9919	15.0	89.0	3.22	0.43	9.2	6	0	white	5.6
89	7.2	1.0	0.0	3.0	0.102	0.99586	7.0	16.0	3.43	0.46	10.0	5	0	red	4.8
90	6.5	0.43	0.28	11.25	0.032	0.9922	31.0	87.0	3.02	0.38	12.4	6	0	white	6.4
91	6.6	0.25	0.3	1.6	0.046	0.993	32.0	134.0	3.42	0.51	10.1	7	1	white	6.0
92	6.9	0.38	0.29	13.65	0.048	0.99784	52.0	189.0	3.0	0.6	9.5	6	0	white	6.0
93	8.6	0.685	0.1	1.6	0.092	0.99745	3.0	12.0	3.31	0.65	9.55	6	0	red	5.4
94	7.5	0.17	0.37	1.5	0.06	0.9936	18.0	75.0	3.54	0.88	10.7	5	0	white	5.4
95	6.1	0.28	0.27	8.0	0.048	0.99498	41.0	162.0	3.21	0.51	9.9	5	0	white	5.2
96	7.3	0.51	0.29	11.3	0.034	0.99683	61.0	224.0	3.14	0.56	9.5	6	0	white	5.2
97	9.4	0.16	0.23	1.6	0.042	0.9942	14.0	67.0	3.07	0.32	9.5	5	0	white	4.8
98	8.0	0.28	0.42	7.1	0.045	0.9959	41.0	169.0	3.17	0.43	10.6	5	0	white	5.6
99	8.0	0.33	0.53	2.5	0.091	0.9976	18.0	80.0	3.37	0.8	9.6	6	0	red	6.2
100	5.3	0.32	0.12	6.6	0.043	0.9937	22.0	141.0	3.36	0.6	10.4	6	0	white	6.8

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 /notebooks/ml/model_tracking_versioning/index.ipynb 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