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verticapy.machine_learning.vertica.decomposition.MCA

class verticapy.machine_learning.vertica.decomposition.MCA(name: str = None, overwrite_model: bool = False)

Creates a MCA (multiple correspondence analysis) object using the Vertica PCA algorithm. MCA is a PCA applied to a complete disjunctive table. The input relation is transformed to a TCDT (transformed complete disjunctive table) before applying the PCA.

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. The model is stored in the database.

overwrite_model: bool, optional

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

Attributes

Many attributes are created during the fitting phase.

principal_components_: numpy.array

Matrix of the principal components.

mean_: numpy.array

List of the averages of each input feature.

cos2_: numpy.array

Quality of representation of each observation in the principal component space. A high cos2 value indicates that the observation is well-represented in the reduced-dimensional space defined by the principal components, while a low value suggests poor representation.

explained_variance_: numpy.array

Represents the proportion of the total variance in the original dataset that is captured by a specific principal component or a combination of principal components.

Note

All attributes can be accessed using the get_attributes() method.

Note

Several other attributes can be accessed by using the get_vertica_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 Titanic dataset.

import verticapy.datasets as vpd

data = vpd.load_titanic()

	123 pclass Integer	123 survived Integer	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(8)	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Numeric(12)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	Abc home.dest Varchar(100)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	30.0	1	2	113781	151.55	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	39.0	0	0	112050	0.0	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22	Montevideo, Uruguay
6	1	0		male	47.0	1	0	PC 17757	227.525	C62 C64	C	[null]	124	New York, NY
7	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]	New York, NY
8	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]	Montreal, PQ
9	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]	Winnipeg, MN
10	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148	San Francisco, CA
11	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]	Trenton, NJ
12	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]	London / Winnipeg, MB
13	1	0		male	41.0	0	0	113054	30.5	A21	S	[null]	[null]	Pomeroy, WA
14	1	0		male	48.0	0	0	PC 17591	50.4958	B10	C	[null]	208	Omaha, NE
15	1	0		male	[null]	0	0	112379	39.6	[null]	C	[null]	[null]	Philadelphia, PA
16	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]	Washington, DC
17	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]	[null]
18	1	0		male	33.0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]	New York, NY
19	1	0		male	28.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
20	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
21	1	0		male	49.0	0	0	19924	26.0	[null]	S	[null]	[null]	Ascot, Berkshire / Rochester, NY
22	1	0		male	36.0	1	0	19877	78.85	C46	S	[null]	172	Little Onn Hall, Staffs
23	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]	Amenia, ND
24	1	0		male	[null]	0	0	112051	0.0	[null]	S	[null]	[null]	Liverpool, England / Belfast
25	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]	Los Angeles, CA
26	1	0		male	[null]	0	0	110465	52.0	A14	S	[null]	[null]	Stoughton, MA
27	1	0		male	47.0	0	0	5727	25.5875	E58	S	[null]	[null]	Victoria, BC
28	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]	Lakewood, NJ
29	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]	Roachdale, IN
30	1	0		male	70.0	1	1	WE/P 5735	71.0	B22	S	[null]	269	Milwaukee, WI
31	1	0		male	39.0	1	0	PC 17599	71.2833	C85	C	[null]	[null]	New York, NY
32	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]	Montreal, PQ
33	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62	Deephaven, MN / Cedar Rapids, IA
34	1	0		male	39.0	0	0	PC 17580	29.7	A18	C	[null]	133	Philadelphia, PA
35	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]	New York, NY
36	1	0		male	[null]	0	0	PC 17483	221.7792	C95	S	[null]	[null]	[null]
37	1	0		male	30.0	0	0	113051	27.75	C111	C	[null]	[null]	New York, NY
38	1	0		male	19.0	3	2	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
39	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
40	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]	Westcliff-on-Sea, Essex
41	1	0		male	[null]	0	0	112058	0.0	B102	S	[null]	[null]	[null]
42	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]	Scituate, MA
43	1	0		male	47.0	0	0	111320	38.5	E63	S	[null]	275	St Anne's-on-Sea, Lancashire
44	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]	[null]
45	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]	New York, NY
46	1	0		male	38.0	0	1	PC 17582	153.4625	C91	S	[null]	147	Winnipeg, MB
47	1	0		male	46.0	0	0	PC 17593	79.2	B82 B84	C	[null]	[null]	New York, NY
48	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]	[null]
49	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]	New York, NY
50	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110	[null]
51	1	0		male	55.0	1	1	12749	93.5	B69	S	[null]	307	Montreal, PQ
52	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]	London / Middlesex
53	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]	Brighton, MA
54	1	0		male	55.0	0	0	680	50.0	C39	S	[null]	[null]	London / Birmingham
55	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38	New York, NY
56	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]	New York, NY
57	1	0		female	50.0	0	0	PC 17595	28.7125	C49	C	[null]	[null]	Paris, France New York, NY
58	1	0		male	46.0	0	0	694	26.0	[null]	S	[null]	80	Bennington, VT
59	1	0		male	50.0	0	0	113044	26.0	E60	S	[null]	[null]	London
60	1	0		male	32.5	0	0	113503	211.5	C132	C	[null]	45	[null]
61	1	0		male	58.0	0	0	11771	29.7	B37	C	[null]	258	Buffalo, NY
62	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]	Southington / Noank, CT
63	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]	Portland, OR
64	1	0		male	[null]	0	0	PC 17612	27.7208	[null]	C	[null]	[null]	Chicago, IL
65	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126	Springfield, MA
66	1	0		male	30.0	0	0	113801	45.5	[null]	S	[null]	[null]	London / New York, NY
67	1	0		male	30.0	0	0	110469	26.0	C106	S	[null]	[null]	Brockton, MA
68	1	0		male	19.0	1	0	113773	53.1	D30	S	[null]	[null]	New York, NY
69	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292	Vancouver, BC
70	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175	Dorchester, MA
71	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]	New York, NY
72	1	0		male	65.0	0	0	13509	26.55	E38	S	[null]	249	East Bridgewater, MA
73	1	0		male	44.0	2	0	19928	90.0	C78	Q	[null]	230	Fond du Lac, WI
74	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]	Montreal, PQ
75	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]	Washington, DC
76	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]	Brooklyn, NY
77	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122	Lexington, MA
78	1	0		male	64.0	0	0	693	26.0	[null]	S	[null]	263	Isle of Wight, England
79	1	0		male	65.0	0	1	113509	61.9792	B30	C	[null]	234	Providence, RI
80	1	0		male	28.5	0	0	PC 17562	27.7208	D43	C	[null]	189	?Havana, Cuba
81	1	0		male	[null]	0	0	112052	0.0	[null]	S	[null]	[null]	Belfast
82	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166	Surbiton Hill, Surrey
83	1	0		male	23.0	0	0	12749	93.5	B24	S	[null]	[null]	Montreal, PQ
84	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]	Isleworth, England
85	1	0		male	18.0	1	0	PC 17758	108.9	C65	C	[null]	[null]	Madrid, Spain
86	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207	Worcester, MA
87	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]	Rotterdam, Netherlands
88	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232	[null]
89	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]	[null]
90	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]	Trenton, NJ
91	1	0		male	[null]	0	0	113767	50.0	A32	S	[null]	[null]	Seattle, WA
92	1	0		male	36.0	0	0	13049	40.125	A10	C	[null]	[null]	Winnipeg, MB
93	1	0		male	55.0	1	0	PC 17603	59.4	[null]	C	[null]	[null]	New York, NY
94	1	0		male	33.0	0	0	113790	26.55	[null]	S	[null]	109	London
95	1	0		male	61.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	[null]	[null]	Haverford, PA / Cooperstown, NY
96	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]	Duluth, MN
97	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]	New York, NY
98	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]	St James, Long Island, NY
99	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]	Huntington, WV
100	1	0		male	[null]	0	0	113056	26.0	A19	S	[null]	[null]	Streatham, Surrey

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 MCA model:

from verticapy.machine_learning.vertica import MCA

Then we can create the model:

model = MCA()

You can select the number of components by the n_component parameter. If it is not provided, then all are considered.

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

Before fitting the model, we need to calculate the Transformed Completely Disjontive Table before fitting the model:

tcdt = data[["survived", "pclass", "sex"]].cdt()

We can now fit the model:

model.fit(tcdt)

Important

To train a model, you can directly use the vDataFrame or the name of the relation stored in the database.

Scores

The decomposition score on the dataset for each transformed column can be calculated by:

model.score()
Out[6]: 
None            Score  
survived_0        0.0  
survived_1        0.0  
pclass_1          0.0  
pclass_2          0.0  
pclass_3          0.0  
sex_female        0.0  
sex_male          0.0  
Rows: 1-7 | Columns: 2

For more details on the function, check out score()

You can also fetch the explained variance by:

model.explained_variance_
Out[7]: 
array([3.71602747e-01, 3.42234698e-01, 2.01530443e-01, 8.46321118e-02,
       5.05034016e-16, 1.63467134e-16, 4.43412970e-16])

Principal Components

To get the transformed dataset in the form of principal components:

model.transform(tcdt)
Out[8]: 
None                  col1                     col2                    col3   \\
1      0.00229204335572662     0.000321740516013252    0.000797337308334691   \\
2      0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
3      0.00229204335572662     0.000321740516013252    0.000797337308334691   \\
4      0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
5      0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
6      0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
7      0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
8      0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
9      0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
10     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
11     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
12     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
13     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
14     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
15     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
16     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
17     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
18     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
19     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
20     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
21     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
22     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
23     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
24     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
25     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
26     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
27     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
28     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
29     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
30     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
31     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
32     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
33     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
34     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
35     0.00229204335572662     0.000321740516013252    0.000797337308334691   \\
36     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
37     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
38     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
39     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
40     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
41     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
42     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
43     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
44     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
45     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
46     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
47     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
48     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
49     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
50     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
51     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
52     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
53     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
54     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
55     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
56     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
57     0.00229204335572662     0.000321740516013252    0.000797337308334691   \\
58     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
59     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
60     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
61     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
62     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
63     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
64     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
65     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
66     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
67     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
68     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
69     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
70     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
71     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
72     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
73     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
74     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
75     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
76     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
77     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
78     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
79     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
80     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
81     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
82     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
83     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
84     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
85     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
86     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
87     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
88     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
89     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
90     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
91     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
92     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
93     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
94     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
95     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
96     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
97     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
98     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
99     0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
100    0.00146911605526505    -0.000826178800615709     0.00235861847839741   \\
None                    col4                    col5  
1       -0.00224766909385777    1.56423268435346e-16  
2      -0.000590677084986731    1.59025353649311e-16  
3       -0.00224766909385777    1.56423268435346e-16  
4      -0.000590677084986731    1.59025353649311e-16  
5      -0.000590677084986731    1.59025353649311e-16  
6      -0.000590677084986731    1.59025353649311e-16  
7      -0.000590677084986731    1.59025353649311e-16  
8      -0.000590677084986731    1.59025353649311e-16  
9      -0.000590677084986731    1.59025353649311e-16  
10     -0.000590677084986731    1.59025353649311e-16  
11     -0.000590677084986731    1.59025353649311e-16  
12     -0.000590677084986731    1.59025353649311e-16  
13     -0.000590677084986731    1.59025353649311e-16  
14     -0.000590677084986731    1.59025353649311e-16  
15     -0.000590677084986731    1.59025353649311e-16  
16     -0.000590677084986731    1.59025353649311e-16  
17     -0.000590677084986731    1.59025353649311e-16  
18     -0.000590677084986731    1.59025353649311e-16  
19     -0.000590677084986731    1.59025353649311e-16  
20     -0.000590677084986731    1.59025353649311e-16  
21     -0.000590677084986731    1.59025353649311e-16  
22     -0.000590677084986731    1.59025353649311e-16  
23     -0.000590677084986731    1.59025353649311e-16  
24     -0.000590677084986731    1.59025353649311e-16  
25     -0.000590677084986731    1.59025353649311e-16  
26     -0.000590677084986731    1.59025353649311e-16  
27     -0.000590677084986731    1.59025353649311e-16  
28     -0.000590677084986731    1.59025353649311e-16  
29     -0.000590677084986731    1.59025353649311e-16  
30     -0.000590677084986731    1.59025353649311e-16  
31     -0.000590677084986731    1.59025353649311e-16  
32     -0.000590677084986731    1.59025353649311e-16  
33     -0.000590677084986731    1.59025353649311e-16  
34     -0.000590677084986731    1.59025353649311e-16  
35      -0.00224766909385777    1.56423268435346e-16  
36     -0.000590677084986731    1.59025353649311e-16  
37     -0.000590677084986731    1.59025353649311e-16  
38     -0.000590677084986731    1.59025353649311e-16  
39     -0.000590677084986731    1.59025353649311e-16  
40     -0.000590677084986731    1.59025353649311e-16  
41     -0.000590677084986731    1.59025353649311e-16  
42     -0.000590677084986731    1.59025353649311e-16  
43     -0.000590677084986731    1.59025353649311e-16  
44     -0.000590677084986731    1.59025353649311e-16  
45     -0.000590677084986731    1.59025353649311e-16  
46     -0.000590677084986731    1.59025353649311e-16  
47     -0.000590677084986731    1.59025353649311e-16  
48     -0.000590677084986731    1.59025353649311e-16  
49     -0.000590677084986731    1.59025353649311e-16  
50     -0.000590677084986731    1.59025353649311e-16  
51     -0.000590677084986731    1.59025353649311e-16  
52     -0.000590677084986731    1.59025353649311e-16  
53     -0.000590677084986731    1.59025353649311e-16  
54     -0.000590677084986731    1.59025353649311e-16  
55     -0.000590677084986731    1.59025353649311e-16  
56     -0.000590677084986731    1.59025353649311e-16  
57      -0.00224766909385777    1.56423268435346e-16  
58     -0.000590677084986731    1.59025353649311e-16  
59     -0.000590677084986731    1.59025353649311e-16  
60     -0.000590677084986731    1.59025353649311e-16  
61     -0.000590677084986731    1.59025353649311e-16  
62     -0.000590677084986731    1.59025353649311e-16  
63     -0.000590677084986731    1.59025353649311e-16  
64     -0.000590677084986731    1.59025353649311e-16  
65     -0.000590677084986731    1.59025353649311e-16  
66     -0.000590677084986731    1.59025353649311e-16  
67     -0.000590677084986731    1.59025353649311e-16  
68     -0.000590677084986731    1.59025353649311e-16  
69     -0.000590677084986731    1.59025353649311e-16  
70     -0.000590677084986731    1.59025353649311e-16  
71     -0.000590677084986731    1.59025353649311e-16  
72     -0.000590677084986731    1.59025353649311e-16  
73     -0.000590677084986731    1.59025353649311e-16  
74     -0.000590677084986731    1.59025353649311e-16  
75     -0.000590677084986731    1.59025353649311e-16  
76     -0.000590677084986731    1.59025353649311e-16  
77     -0.000590677084986731    1.59025353649311e-16  
78     -0.000590677084986731    1.59025353649311e-16  
79     -0.000590677084986731    1.59025353649311e-16  
80     -0.000590677084986731    1.59025353649311e-16  
81     -0.000590677084986731    1.59025353649311e-16  
82     -0.000590677084986731    1.59025353649311e-16  
83     -0.000590677084986731    1.59025353649311e-16  
84     -0.000590677084986731    1.59025353649311e-16  
85     -0.000590677084986731    1.59025353649311e-16  
86     -0.000590677084986731    1.59025353649311e-16  
87     -0.000590677084986731    1.59025353649311e-16  
88     -0.000590677084986731    1.59025353649311e-16  
89     -0.000590677084986731    1.59025353649311e-16  
90     -0.000590677084986731    1.59025353649311e-16  
91     -0.000590677084986731    1.59025353649311e-16  
92     -0.000590677084986731    1.59025353649311e-16  
93     -0.000590677084986731    1.59025353649311e-16  
94     -0.000590677084986731    1.59025353649311e-16  
95     -0.000590677084986731    1.59025353649311e-16  
96     -0.000590677084986731    1.59025353649311e-16  
97     -0.000590677084986731    1.59025353649311e-16  
98     -0.000590677084986731    1.59025353649311e-16  
99     -0.000590677084986731    1.59025353649311e-16  
100    -0.000590677084986731    1.59025353649311e-16  
Rows: 1-100 | Columns: 5

Please refer to transform() for more details on transforming a vDataFrame.

Similarly, you can perform the inverse tranform to get the original features using:

model.inverse_transform(data_transformed)

The variable data_transformed includes the MCA components.

Plots - MCA

You can plot the first two dimensions conveniently using:

model.plot()

Plots - Scree

You can also plot the Scree plot:

model.plot_scree()

Loading....

Plots - Decomposition Circle

You can also plot the Decomposition Circles:

model.plot_circle()

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.

Model Exporting

To Memmodel

model.to_memmodel()

Note

MemModel objects serve as in-memory representations of machine learning models. They can be used for both in-database and in-memory prediction tasks. These objects can be pickled in the same way that you would pickle a scikit-learn model.

The preceding methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

SQL

To get the SQL query use below:

model.to_sql()
Out[9]: 
['("survived_0" - -0.999189627228525) * -0.181634302254853 + ("survived_1" - -0.999189627228526) * 0.316447317706109 + ("pclass_1" - -0.999189627228525) * 0.648146903282904 + ("pclass_2" - -0.999189627228525) * -0.588909547134059 + ("pclass_3" - -0.999189627228525) * -0.0749536366765789 + ("sex_female" - -0.999189627228525) * 0.272960421482605 + ("sex_male" - -0.999189627228525) * -0.140839529511909',
 '("survived_0" - -0.999189627228525) * -0.232487358829214 + ("survived_1" - -0.999189627228526) * 0.405044642937848 + ("pclass_1" - -0.999189627228525) * 0.123041881257647 + ("pclass_2" - -0.999189627228525) * 0.689909726160874 + ("pclass_3" - -0.999189627228525) * -0.327414307734689 + ("sex_female" - -0.999189627228525) * 0.380758470789969 + ("sex_male" - -0.999189627228525) * -0.196460144633645',
 '("survived_0" - -0.999189627228525) * 0.134292054542379 + ("survived_1" - -0.999189627228526) * -0.233966601691524 + ("pclass_1" - -0.999189627228525) * 0.612207252170766 + ("pclass_2" - -0.999189627228525) * 0.251905271494925 + ("pclass_3" - -0.999189627228525) * -0.386503963792653 + ("sex_female" - -0.999189627228525) * -0.517868305005975 + ("sex_male" - -0.999189627228525) * 0.267204776538708',
 '("survived_0" - -0.999189627228525) * -0.377802794939545 + ("survived_1" - -0.999189627228526) * 0.658216424961079 + ("pclass_1" - -0.999189627228525) * -0.169709763121011 + ("pclass_2" - -0.999189627228525) * -0.0518795498186791 + ("pclass_3" - -0.999189627228525) * 0.100130089738773 + ("sex_female" - -0.999189627228525) * -0.549615059411762 + ("sex_male" - -0.999189627228525) * 0.28358516578985',
 '("survived_0" - -0.999189627228525) * -0.224208471317908 + ("survived_1" - -0.999189627228526) * -0.128691086853437 + ("pclass_1" - -0.999189627228525) * 0.373337373303813 + ("pclass_2" - -0.999189627228525) * 0.309917883607947 + ("pclass_3" - -0.999189627228525) * 0.793341918270386 + ("sex_female" - -0.999189627228525) * -0.119875894561693 + ("sex_male" - -0.999189627228525) * -0.232330900412423',
 '("survived_0" - -0.999189627228525) * -0.502650716476987 + ("survived_1" - -0.999189627228526) * -0.288511253080016 + ("pclass_1" - -0.999189627228525) * 0.0269792637627993 + ("pclass_2" - -0.999189627228525) * 0.0223962478030921 + ("pclass_3" - -0.999189627228525) * 0.0573309354959348 + ("sex_female" - -0.999189627228525) * 0.372396385080935 + ("sex_male" - -0.999189627228525) * 0.721739660609242',
 '("survived_0" - -0.999189627228525) * 0.670270183632036 + ("survived_1" - -0.999189627228526) * 0.384721406421604 + ("pclass_1" - -0.999189627228525) * 0.145115433134172 + ("pclass_2" - -0.999189627228525) * 0.120464414044063 + ("pclass_3" - -0.999189627228525) * 0.308370295410028 + ("sex_female" - -0.999189627228525) * 0.239169401566029 + ("sex_male" - -0.999189627228525) * 0.463533078273215']

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [[0, 1, 0, 1, 1, 0, 1]]

model.to_python()(X)
Out[11]: 
array([[-0.23724135,  1.41279017,  0.02580683,  0.88306341,  1.51310344,
         0.92230412,  3.60684391]])

Hint

The to_python() method is used to retrieve the Principal Component values. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.

__init__(name: str = None, overwrite_model: bool = False) → None

Must be overridden in the child class

Methods

__init__([name, overwrite_model])	Must be overridden in the child class
contour([nbins, chart])	Draws the model's contour plot.
deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
deploySQL([X, n_components, cutoff, ...])	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, return_report])	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.
inverse_transform(vdf[, X])	Applies the Inverse Model on a vDataFrame.
plot([dimensions, chart])	Draws a decomposition scatter plot.
plot_circle([dimensions, chart])	Draws a decomposition circle.
plot_contrib([dimension, chart])	Draws a decomposition contribution plot of the input dimension.
plot_cos2([dimensions, chart])	Draws a MCA (multiple correspondence analysis) cos2 plot of the two input dimensions.
plot_scree([chart])	Draws a decomposition scree plot.
plot_var([dimensions, method, chart])	Draws the MCA (multiple correspondence analysis) graph.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
score([X, input_relation, metric, p])	Returns the decomposition score on a dataset for each transformed column.
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_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
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).
transform([vdf, X, n_components, cutoff])	Applies the model on a vDataFrame.

Attributes

object_type