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verticapy.machine_learning.vertica.neighbors.LocalOutlierFactor

class verticapy.machine_learning.vertica.neighbors.LocalOutlierFactor(name: str = None, overwrite_model: bool = False, n_neighbors: int = 20, p: int = 2)

[Beta Version] Creates a LocalOutlierFactor object by using the Local Outlier Factor algorithm as defined by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander. This object is using 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 LocalOutlierFactor uses the p- distance, it is highly sensitive to unnormalized data. A table is created at the end of the learning phase.

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

name: str, optional

Name of the model. This is not a built-in model, so this name is used to build the final table.

overwrite_model: bool, optional

If set to True, training a model with the same name as an existing model overwrites the existing model.

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.

n_errors_: int

Number of errors during the model fitting phase.

cnt_: int

Number of elements accepted during the model fitting phase.

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.

Model Initialization

First we import the LocalOutlierFactor model:

from verticapy.machine_learning.vertica import LocalOutlierFactor

Then we can create the model:

model = LocalOutlierFactor(
    n_neighbors = 10,
    p = 2,
)

Important

As this model is not native, it solely relies on SQL statements to compute various attributes, storing them within the object. No data is saved in the database.

Model Training

We can now fit the model:

model.fit(data, X = ["density", "sulphates"])

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.

Hint

For clustering and anomaly detection, the use of predictors is optional. In such cases, all available predictors are considered, which can include solely numerical variables or a combination of numerical and categorical variables, depending on the model’s capabilities.

Important

As this model is not native, it solely relies on SQL statements to compute various attributes, storing them within the object. No data is saved in the database.

Prediction

To find out the LOF score for each datapoint:

model.predict()

	123 density Float(22)	123 sulphates Numeric(8)	123 lof_score Float(22)
1	0.98713	0.59	4.36760240314512
2	0.9874	0.37	4.32021671814121
3	0.98758	0.39	3.7364256606699
4	0.98802	0.64	1.8182825155705
5	0.988245	0.41	1.58134352906031
6	0.98836	0.4	1.70902727034283
7	0.98862	0.38	1.82669851967438
8	0.98862	0.38	1.82669851967438
9	0.98862	0.38	1.82669851967438
10	0.98868	0.47	1.48770345546672
11	0.98871	0.34	1.79206564222151
12	0.98876	0.4	1.47345186260012
13	0.98878	0.58	1.52653817884389
14	0.9888	0.37	1.53944272970454
15	0.98883	0.39	1.39532261169049
16	0.98884	0.34	1.69190487369848
17	0.98886	0.4	1.4001457997842
18	0.98892	0.33	1.2082734941048
19	0.98898	0.7	1.9449572852662
20	0.9891	0.95	1.11415525539495
21	0.98912	0.42	1.25477862666665
22	0.98912	0.42	1.25477862666665
23	0.98914	0.39	1.15343682988862
24	0.98915	0.38	1.09269298839458
25	0.98916	0.55	4.41599373920526
26	0.98918	0.35	1.21799298602868
27	0.9892	0.37	1.18418671515733
28	0.9892	0.39	1.1009756705585
29	0.9892	0.39	1.1009756705585
30	0.9892	0.4	1.23464479079862
31	0.98924	0.34	1.39521785099859
32	0.98924	0.47	1.21215375044708
33	0.98924	0.48	1.39880211923048
34	0.98926	0.96	1.11480169203391
35	0.98928	0.79	1.72938152535089
36	0.98934	0.5	1.18820938907835
37	0.98935	0.4	1.14231527325198
38	0.98936	0.44	1.4196440322354
39	0.98936	0.78	2.06253552191554
40	0.98939	0.37	1.12412017274419
41	0.98942	0.69	1.65088406101565
42	0.98944	0.46	1.19889850390061
43	0.98946	0.38	1.08161105501558
44	0.98946	0.38	1.08161105501558
45	0.9895	0.34	1.2032934968528
46	0.98952	0.48	1.15054714582466
47	0.98956	0.33	1.05773765261828
48	0.98958	0.4	1.12126795288485
49	0.98958	0.7	1.73273772536304
50	0.98958	0.7	1.73273772536304
51	0.98961	0.74	1.92230040977353
52	0.98962	0.49	1.29072485977742
53	0.98963	0.59	1.59987755169058
54	0.98964	0.35	1.10700228821976
55	0.98964	0.57	1.33041317303047
56	0.9897	0.5	1.11660997108509
57	0.98975	0.47	1.50550766073749
58	0.98976	0.34	1.09974193722117
59	0.9898	0.37	1.10524272152466
60	0.9898	0.37	1.10524272152466
61	0.9898	0.38	1.32663668980944
62	0.9898	0.65	1.95252119221961
63	0.98981	0.45	1.25677856097758
64	0.98982	0.39	1.25515455120034
65	0.98984	0.5	1.0959033606773
66	0.98984	0.6	1.18005458042221
67	0.98984	0.6	1.18005458042221
68	0.98986	0.56	1.25250194223904
69	0.98986	0.56	1.25250194223904
70	0.98988	0.37	1.0950506531524
71	0.98988	0.53	1.23765926785239
72	0.9899	0.35	1.09458429499182
73	0.9899	0.38	1.29636607204168
74	0.9899	0.46	1.14151777210026
75	0.9899	0.68	2.57724668033739
76	0.98993	0.46	1.13642171061767
77	0.98994	0.38	1.24451142915995
78	0.98994	0.4	1.08557158090384
79	0.98998	0.37	1.08363499704423
80	0.98998	0.37	1.08363499704423
81	0.98998	0.6	1.13812190663703
82	0.98999	0.54	1.25294087324641
83	0.99	0.33	1.15492962478117
84	0.99	0.4	1.1502004310307
85	0.99	0.48	1.09132395914269
86	0.99	0.48	1.09132395914269
87	0.99	0.5	1.04987138276508
88	0.99	0.61	1.87257037193063
89	0.99	0.62	1.33148671271711
90	0.99002	0.98	1.11713588917019
91	0.99004	0.36	1.32364752982055
92	0.99004	0.37	1.11916401334076
93	0.99005	0.59	1.29475458774561
94	0.99008	0.65	1.65342345769209
95	0.99008	0.66	1.20653188396538
96	0.9901	0.34	1.06832098824384
97	0.9901	0.38	1.07298425028634
98	0.99011	0.34	1.06832098824384
99	0.99012	0.37	1.14789205837579
100	0.99014	0.31	1.06796345626739

Rows: 1-100 | Columns: 3

As shown above, a new column has been created, containing the lof score.

Plots - Outliers

Plots highlighting the outliers can be easily drawn using:

model.plot()

Important

Please refer to Machine Learning - LOF Plot for more examples.

Parameter Modification

In order to see the parameters:

model.get_params()
Out[5]: {'n_neighbors': 10, 'p': 2}

And to manually change some of the parameters:

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

Model Register

As this model is not native, it does not support model management and versioning. However, it is possible to use the SQL code it generates for deployment.

__init__(name: str = None, overwrite_model: bool = False, n_neighbors: int = 20, 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])	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()	Drops the model from the Vertica database.
export_models(name, path[, kind])	Exports machine learning models.
fit(input_relation[, X, key_columns, index, ...])	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.
plot([max_nb_points, chart])	Draws the model.
predict()	Creates a vDataFrame of the model.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
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