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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 Varchar(100)
1	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22
2	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]
3	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]
4	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]
5	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]
6	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]
7	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]
8	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62
9	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]
10	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]
11	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]
12	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]
13	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110
14	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]
15	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]
16	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38
17	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]
18	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126
19	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292
20	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175
21	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]
22	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122
23	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166
24	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]
25	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207
26	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]
27	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232
28	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]
29	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]
30	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]
31	1	0		male	57.0	1	0	PC 17569	146.5208	B78	C	[null]	[null]
32	1	0		female	63.0	1	0	PC 17483	221.7792	C55 C57	S	[null]	[null]
33	1	0		male	61.0	0	0	36963	32.3208	D50	S	[null]	46
34	1	0		male	21.0	0	1	35281	77.2875	D26	S	[null]	169
35	1	0		male	51.0	0	1	PC 17597	61.3792	[null]	C	[null]	[null]
36	1	1		female	63.0	1	0	13502	77.9583	D7	S	10	[null]
37	1	1		female	32.0	0	0	11813	76.2917	D15	C	8	[null]
38	1	1		female	58.0	0	0	113783	26.55	C103	S	8	[null]
39	1	1		female	44.0	0	0	PC 17610	27.7208	B4	C	6	[null]
40	1	1		female	41.0	0	0	16966	134.5	E40	C	3	[null]
41	1	1		female	53.0	0	0	PC 17606	27.4458	[null]	C	6	[null]
42	1	1		male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
43	1	1		female	58.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
44	1	1		male	11.0	1	2	113760	120.0	B96 B98	S	4	[null]
45	1	1		female	76.0	1	0	19877	78.85	C46	S	6	[null]
46	1	1		female	[null]	0	1	113505	55.0	E33	S	6	[null]
47	1	1		female	39.0	1	1	PC 17756	83.1583	E49	C	14	[null]
48	1	1		female	27.0	1	2	F.C. 12750	52.0	B71	S	3	[null]
49	1	1		female	[null]	0	0	17421	110.8833	[null]	C	4	[null]
50	1	1		female	35.0	0	0	113503	211.5	C130	C	4	[null]
51	1	1		female	22.0	0	1	112378	59.4	[null]	C	7	[null]
52	1	1		female	25.0	1	0	11765	55.4417	E50	C	5	[null]
53	1	1		male	48.0	1	0	PC 17572	76.7292	D33	C	3	[null]
54	1	1		female	35.0	1	0	36973	83.475	C83	S	D	[null]
55	1	1		male	27.0	0	0	PC 17572	76.7292	D49	C	3	[null]
56	1	1		female	24.0	0	0	11767	83.1583	C54	C	7	[null]
57	1	1		female	52.0	1	1	12749	93.5	B69	S	3	[null]
58	1	1		female	44.0	0	1	111361	57.9792	B18	C	4	[null]
59	1	1		female	15.0	0	1	24160	211.3375	B5	S	2	[null]
60	1	1		male	30.0	1	0	13236	57.75	C78	C	11	[null]
61	1	1		female	31.0	1	0	35273	113.275	D36	C	6	[null]
62	1	1		female	39.0	0	0	PC 17758	108.9	C105	C	8	[null]
63	1	1		female	22.0	0	1	113509	61.9792	B36	C	5	[null]
64	1	1		male	52.0	0	0	113786	30.5	C104	S	6	[null]
65	1	1		female	43.0	0	1	24160	211.3375	B3	S	2	[null]
66	1	1		female	33.0	0	0	110152	86.5	B77	S	8	[null]
67	1	1		male	45.0	1	1	16966	134.5	E34	C	3	[null]
68	1	1		female	40.0	1	1	16966	134.5	E34	C	3	[null]
69	1	1		male	48.0	1	0	19996	52.0	C126	S	5 7	[null]
70	1	1		female	[null]	0	0	PC 17585	79.2	[null]	C	D	[null]
71	1	1		female	35.0	0	0	PC 17755	512.3292	[null]	C	3	[null]
72	1	1		female	60.0	1	0	110813	75.25	D37	C	5	[null]
73	1	1		male	21.0	0	1	PC 17597	61.3792	[null]	C	A	[null]
74	2	0		male	23.0	0	0	C.A. 31030	10.5	[null]	S	[null]	[null]
75	2	0		male	28.0	0	0	244358	26.0	[null]	S	[null]	[null]
76	2	0		male	60.0	1	1	29750	39.0	[null]	S	[null]	[null]
77	2	0		female	44.0	1	0	244252	26.0	[null]	S	[null]	[null]
78	2	0		male	29.0	1	0	2003	26.0	[null]	S	[null]	[null]
79	2	0		male	18.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
80	2	0		male	18.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
81	2	0		male	54.0	0	0	28403	26.0	[null]	S	[null]	[null]
82	2	0		male	18.0	0	0	236171	13.0	[null]	S	[null]	[null]
83	2	0		male	36.0	0	0	229236	13.0	[null]	S	[null]	236
84	2	0		male	34.0	1	0	28664	21.0	[null]	S	[null]	[null]
85	2	0		male	21.0	1	0	28133	11.5	[null]	S	[null]	[null]
86	2	0		male	21.0	1	0	28134	11.5	[null]	S	[null]	[null]
87	2	0		male	24.0	0	0	233866	13.0	[null]	S	[null]	155
88	2	0		male	34.0	0	0	12233	13.0	[null]	S	[null]	[null]
89	2	0		male	30.0	0	0	250653	13.0	[null]	S	[null]	75
90	2	0		male	44.0	0	0	248746	13.0	[null]	S	[null]	35
91	2	0		male	49.0	1	2	220845	65.0	[null]	S	[null]	[null]
92	2	0		male	21.0	2	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
93	2	0		male	21.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
94	2	0		female	60.0	1	0	24065	26.0	[null]	S	[null]	[null]
95	2	0		male	24.0	2	0	C.A. 31029	31.5	[null]	S	[null]	[null]
96	2	0		male	22.0	2	0	C.A. 31029	31.5	[null]	S	[null]	[null]
97	2	0		male	35.0	0	0	233734	12.35	[null]	Q	[null]	[null]
98	2	0		male	31.0	0	0	C.A. 18723	10.5	[null]	S	[null]	165
99	2	0		male	36.0	0	0	SC/Paris 2163	12.875	D	C	[null]	[null]
100	2	0		male	[null]	0	0	SC/A.3 2861	15.5792	[null]	C	[null]	[null]

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,
       4.58523775e-17, 3.71471553e-18, 4.48009740e-16])

Principal Components

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

model.transform(tcdt)
Out[8]: 
None                   col1                     col2                    col3                     col4                     col5                    col6   \\
1       0.00229204335572604      0.00032174051601337    0.000797337308334705     -0.00224766909385811    -1.78934016041576e-16    2.14129929065887e-16   \\
2       0.00229204335572604      0.00032174051601337    0.000797337308334705     -0.00224766909385811    -1.78934016041576e-16    2.14129929065887e-16   \\
3       0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
4       0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
5       0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
6       0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
7       0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
8       0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
9       0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
10      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
11      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
12      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
13      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
14      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
15      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
16      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
17      0.00229204335572604      0.00032174051601337    0.000797337308334705     -0.00224766909385811    -1.78934016041576e-16    2.14129929065887e-16   \\
18      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
19      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
20      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
21      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
22      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
23      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
24      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
25      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
26      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
27      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
28      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
29      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
30      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
31      0.00146911605526444    -0.000826178800615592     0.00235861847839742    -0.000590677084987079    -1.78642636707721e-16    2.13642038088269e-16   \\
32       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
33       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
34       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
35       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
36       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
37       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
38       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
39       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
40       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
41       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
42       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
43       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
44       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
45       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
46       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
47       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
48       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
49       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
50       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
51       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
52       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
53       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
54       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
55       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
56       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
57       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
58       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
59       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
60       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
61       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
62       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
63       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
64       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
65       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
66       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
67       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
68       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
69       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
70       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
71       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
72       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
73       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
74       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
75       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
76       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
77       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
78       0.0024040087227709     0.000370460404419619     0.00166740181096483      0.00135391740166905     -1.7609476160238e-16     2.1391308863139e-16   \\
79       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
80       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
81       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
82       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
83       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
84       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
85       0.0032269360232325      0.00151837972104858     0.00010612064090212    -0.000303074607201982    -1.76386140936236e-16    2.14455189717633e-16   \\
86     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
87     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
88     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
89     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
90     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
91     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
92     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
93     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
94     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
95     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
96     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
97     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
98     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
99     -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
100    -0.00288205990078061      0.00144320031180486     0.00136902297293012    -0.000247042678679404    -1.78588426599097e-16     2.1391308863139e-16   \\
None                    col7  
1      -1.31025832544873e-16  
2      -1.31025832544873e-16  
3      -1.31649248794052e-16  
4      -1.31649248794052e-16  
5      -1.31649248794052e-16  
6      -1.31649248794052e-16  
7      -1.31649248794052e-16  
8      -1.31649248794052e-16  
9      -1.31649248794052e-16  
10     -1.31649248794052e-16  
11     -1.31649248794052e-16  
12     -1.31649248794052e-16  
13     -1.31649248794052e-16  
14     -1.31649248794052e-16  
15     -1.31649248794052e-16  
16     -1.31649248794052e-16  
17     -1.31025832544873e-16  
18     -1.31649248794052e-16  
19     -1.31649248794052e-16  
20     -1.31649248794052e-16  
21     -1.31649248794052e-16  
22     -1.31649248794052e-16  
23     -1.31649248794052e-16  
24     -1.31649248794052e-16  
25     -1.31649248794052e-16  
26     -1.31649248794052e-16  
27     -1.31649248794052e-16  
28     -1.31649248794052e-16  
29     -1.31649248794052e-16  
30     -1.31649248794052e-16  
31     -1.31649248794052e-16  
32     -1.33844758193336e-16  
33     -1.34468174442515e-16  
34     -1.34468174442515e-16  
35     -1.33844758193336e-16  
36     -1.34468174442515e-16  
37     -1.33844758193336e-16  
38     -1.33844758193336e-16  
39     -1.33844758193336e-16  
40     -1.33844758193336e-16  
41     -1.33844758193336e-16  
42     -1.33844758193336e-16  
43     -1.34468174442515e-16  
44     -1.34468174442515e-16  
45     -1.34468174442515e-16  
46     -1.33844758193336e-16  
47     -1.34468174442515e-16  
48     -1.33844758193336e-16  
49     -1.33844758193336e-16  
50     -1.33844758193336e-16  
51     -1.33844758193336e-16  
52     -1.33844758193336e-16  
53     -1.33844758193336e-16  
54     -1.34468174442515e-16  
55     -1.33844758193336e-16  
56     -1.33844758193336e-16  
57     -1.33844758193336e-16  
58     -1.33844758193336e-16  
59     -1.33844758193336e-16  
60     -1.34468174442515e-16  
61     -1.33844758193336e-16  
62     -1.33844758193336e-16  
63     -1.34468174442515e-16  
64     -1.33844758193336e-16  
65     -1.33844758193336e-16  
66     -1.33844758193336e-16  
67     -1.33844758193336e-16  
68     -1.34468174442515e-16  
69     -1.34468174442515e-16  
70     -1.33844758193336e-16  
71     -1.34468174442515e-16  
72     -1.33844758193336e-16  
73     -1.34468174442515e-16  
74     -1.33844758193336e-16  
75     -1.34468174442515e-16  
76     -1.33844758193336e-16  
77     -1.34468174442515e-16  
78     -1.34468174442515e-16  
79     -1.33844758193336e-16  
80     -1.33844758193336e-16  
81     -1.33844758193336e-16  
82     -1.33844758193336e-16  
83     -1.33844758193336e-16  
84     -1.33844758193336e-16  
85     -1.33844758193336e-16  
86      -1.3403449357352e-16  
87      -1.3403449357352e-16  
88      -1.3403449357352e-16  
89      -1.3403449357352e-16  
90      -1.3403449357352e-16  
91      -1.3403449357352e-16  
92      -1.3403449357352e-16  
93      -1.3403449357352e-16  
94      -1.3403449357352e-16  
95      -1.3403449357352e-16  
96      -1.3403449357352e-16  
97      -1.3403449357352e-16  
98      -1.3403449357352e-16  
99      -1.3403449357352e-16  
100     -1.3403449357352e-16  
Rows: 1-100 | Columns: 7

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) * -0.181634302254857 + ("survived_1" - -0.999189627228525) * 0.316447317706116 + ("pclass_1" - -0.999189627228525) * 0.648146903282895 + ("pclass_2" - -0.999189627228525) * -0.588909547134058 + ("pclass_3" - -0.999189627228525) * -0.0749536366765797 + ("sex_female" - -0.999189627228525) * 0.272960421482614 + ("sex_male" - -0.999189627228525) * -0.140839529511913',
 '("survived_0" - -0.999189627228525) * -0.232487358829214 + ("survived_1" - -0.999189627228525) * 0.405044642937851 + ("pclass_1" - -0.999189627228525) * 0.12304188125764 + ("pclass_2" - -0.999189627228525) * 0.689909726160875 + ("pclass_3" - -0.999189627228525) * -0.327414307734686 + ("sex_female" - -0.999189627228525) * 0.38075847078997 + ("sex_male" - -0.999189627228525) * -0.196460144633645',
 '("survived_0" - -0.999189627228525) * 0.134292054542375 + ("survived_1" - -0.999189627228525) * -0.233966601691513 + ("pclass_1" - -0.999189627228525) * 0.612207252170771 + ("pclass_2" - -0.999189627228525) * 0.25190527149492 + ("pclass_3" - -0.999189627228525) * -0.386503963792662 + ("sex_female" - -0.999189627228525) * -0.517868305005971 + ("sex_male" - -0.999189627228525) * 0.267204776538706',
 '("survived_0" - -0.999189627228525) * -0.377802794939543 + ("survived_1" - -0.999189627228525) * 0.658216424961079 + ("pclass_1" - -0.999189627228525) * -0.169709763121012 + ("pclass_2" - -0.999189627228525) * -0.0518795498186792 + ("pclass_3" - -0.999189627228525) * 0.100130089738782 + ("sex_female" - -0.999189627228525) * -0.549615059411761 + ("sex_male" - -0.999189627228525) * 0.283585165789851',
 '("survived_0" - -0.999189627228525) * 0.831334231648148 + ("survived_1" - -0.999189627228525) * 0.477168882961496 + ("pclass_1" - -0.999189627228525) * 0.105081084294435 + ("pclass_2" - -0.999189627228525) * 0.0872307718982565 + ("pclass_3" - -0.999189627228525) * 0.223297304125609 + ("sex_female" - -0.999189627228525) * 0.0516338525183842 + ("sex_male" - -0.999189627228525) * 0.100071323690393',
 '("survived_0" - -0.999189627228525) * 0.0 + ("survived_1" - -0.999189627228525) * 1.24263724796271e-16 + ("pclass_1" - -0.999189627228525) * -0.158651249622961 + ("pclass_2" - -0.999189627228525) * -0.131700877090842 + ("pclass_3" - -0.999189627228525) * -0.337133905448692 + ("sex_female" - -0.999189627228525) * 0.421207057725456 + ("sex_male" - -0.999189627228525) * 0.816339392829816',
 '("survived_0" - -0.999189627228525) * -0.247128905111581 + ("survived_1" - -0.999189627228525) * -0.141846948087061 + ("pclass_1" - -0.999189627228525) * 0.353489618841737 + ("pclass_2" - -0.999189627228525) * 0.293441702820515 + ("pclass_3" - -0.999189627228525) * 0.751165440038467 + ("sex_female" - -0.999189627228525) * 0.173694732678169 + ("sex_male" - -0.999189627228525) * 0.336636934285787']

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,  2.76206562,
         0.95707065,  2.75761838]])

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