verticapy.machine_learning.vertica.neighbors.LocalOutlierFactor.predict¶

LocalOutlierFactor.predict() → vDataFrame¶

Creates a vDataFrame of the model.

Returns¶

vDataFrame: the vDataFrame including the prediction.

Examples¶

We import verticapy:

import verticapy as vp

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

First we import the model:

from verticapy.machine_learning.vertica import LocalOutlierFactor

Then we can create the model:

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

We can now fit the model:

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

To compute the predictions:

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	0.98746	0.83	3.33466363946875
4	0.98746	0.83	3.33466363946875
5	0.98794	0.43	2.63540994699126
6	0.98794	0.43	2.63540994699126
7	0.98802	0.64	1.8182825155705
8	0.988245	0.41	1.58134352906031
9	0.9886	0.3	1.35598307014676
10	0.98862	0.38	1.82669851967438
11	0.98862	0.38	1.82669851967438
12	0.98862	0.38	1.82669851967438
13	0.98868	0.47	1.48770345546672
14	0.98878	0.58	1.52653817884389
15	0.98883	0.39	1.39532261169049
16	0.98892	0.33	1.2082734941048
17	0.98898	0.7	1.9449572852662
18	0.98912	0.42	1.25477862666665
19	0.98912	0.42	1.25477862666665
20	0.98914	0.3	1.27811025276271
21	0.98914	0.39	1.15343682988862
22	0.98915	0.38	1.09269298839458
23	0.98916	0.55	4.41599373920526
24	0.98918	0.35	1.21799298602868
25	0.98918	0.38	1.08912209627562
26	0.98924	0.34	1.39521785099859
27	0.98924	0.47	1.21215375044708
28	0.98924	0.48	1.39880211923048
29	0.98926	0.96	1.11480169203391
30	0.98934	0.5	1.18820938907835
31	0.98936	0.44	1.4196440322354
32	0.98936	0.78	2.06253552191554
33	0.98942	0.69	1.65088406101565
34	0.98944	0.46	1.19889850390061
35	0.98946	0.38	1.08161105501558
36	0.98946	0.38	1.08161105501558
37	0.9895	0.34	1.2032934968528
38	0.98952	0.48	1.15054714582466
39	0.98954	0.38	1.18028421352173
40	0.98956	0.31	1.09774498486006
41	0.98956	0.31	1.09774498486006
42	0.98956	0.33	1.05773765261828
43	0.98958	0.4	1.11244253278049
44	0.98958	0.7	1.73273772536304
45	0.98958	0.7	1.73273772536304
46	0.9896	0.39	1.1359897192294
47	0.98961	0.74	1.92230040977353
48	0.98962	0.49	1.29072485977742
49	0.98963	0.59	1.59987755169058
50	0.98964	0.35	1.10700228821976
51	0.98964	0.42	1.10442508654342
52	0.98964	0.42	1.10442508654342
53	0.98964	0.52	1.43100676998736
54	0.98964	0.57	1.33041317303047
55	0.9897	0.5	1.11660997108509
56	0.98975	0.47	1.50550766073749
57	0.98976	0.34	1.09974193722117
58	0.9898	0.38	1.33310477491582
59	0.98981	0.45	1.25677856097758
60	0.98984	0.5	1.0959033606773
61	0.98984	0.6	1.18005458042221
62	0.98984	0.6	1.18005458042221
63	0.98986	0.56	1.25250194223904
64	0.98986	0.56	1.25250194223904
65	0.98988	0.37	1.0950506531524
66	0.98988	0.53	1.23765926785239
67	0.9899	0.35	1.09458429499182
68	0.9899	0.38	1.30203983090693
69	0.9899	0.46	1.14151777210026
70	0.9899	0.68	2.57724668033739
71	0.98993	0.3	1.20657592051457
72	0.98993	0.46	1.13642171061767
73	0.98994	0.38	1.24995823767059
74	0.98998	0.37	1.08363499704423
75	0.98998	0.37	1.08363499704423
76	0.98998	0.6	1.13812190663703
77	0.98999	0.54	1.25294087324641
78	0.99	0.4	1.1502004310307
79	0.99	0.42	1.19930254309488
80	0.99	0.48	1.09132395914269
81	0.99	0.48	1.09132395914269
82	0.99	0.5	1.04987138276508
83	0.99	0.61	1.87257037193063
84	0.99002	0.98	1.11713588917019
85	0.99004	0.36	1.32364752982055
86	0.99004	0.37	1.11916401334076
87	0.99005	0.59	1.29475458774561
88	0.99008	0.66	1.20653188396538
89	0.9901	0.34	1.06832098824384
90	0.9901	0.38	1.10429409882229
91	0.99011	0.34	1.06832098824384
92	0.99012	0.37	1.14789205837579
93	0.99014	0.31	1.06796345626739
94	0.99015	0.53	1.22089327136437
95	0.99016	0.48	1.13158345559551
96	0.9902	0.34	1.06010648765885
97	0.9902	0.34	1.06010648765885
98	0.9902	0.37	1.22804412230223
99	0.9902	0.55	2.85463218401279
100	0.99021	0.49	1.18902742848595

Rows: 1-100 | Columns: 3

Note

Refer to LocalOutlierFactor for more information about the different methods and usages.