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verticapy.machine_learning.vertica.tsa.AR.features_importance

AR.features_importance(idx: int = 0, show: bool = True, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure

Computes the model’s features importance.

Parameters

idx: int, optional

It represents the index of the predictor for which we want to compute the feature importance.

show: bool, optional

If set to True, draw the feature’s importance.

chart: PlottingObject, optional

The chart object to plot on.

**style_kwargs

Any optional parameter to pass to the Plotting functions.

Returns

obj

features importance.

Examples

We import verticapy:

import verticapy as vp

For this example, we will use the airline passengers dataset.

import verticapy.datasets as vpd

data = vpd.load_airline_passengers()

	📅 date Date	123 passengers Integer
1	1949-06-01	135
2	1950-05-01	125
3	1950-09-01	158
4	1950-11-01	114
5	1951-02-01	150
6	1951-04-01	163
7	1951-05-01	172
8	1951-07-01	199
9	1951-11-01	146
10	1952-02-01	180
11	1952-07-01	230
12	1953-02-01	196
13	1953-03-01	236
14	1953-07-01	264
15	1953-10-01	211
16	1954-10-01	229
17	1955-02-01	233
18	1955-09-01	312
19	1955-12-01	278
20	1956-01-01	284
21	1956-02-01	277
22	1956-09-01	355
23	1957-05-01	355
24	1957-09-01	404
25	1958-05-01	363
26	1958-10-01	359
27	1959-02-01	342
28	1959-04-01	396
29	1959-08-01	559
30	1959-10-01	407
31	1959-11-01	362
32	1960-05-01	472
33	1960-09-01	508
34	1960-10-01	461
35	1960-12-01	432
36	1949-03-01	132
37	1949-05-01	121
38	1949-07-01	148
39	1949-08-01	148
40	1949-10-01	119
41	1950-02-01	126
42	1950-03-01	141
43	1950-04-01	135
44	1950-08-01	170
45	1950-12-01	140
46	1951-06-01	178
47	1951-08-01	199
48	1951-10-01	162
49	1952-01-01	171
50	1952-03-01	193
51	1952-04-01	181
52	1952-08-01	242
53	1953-04-01	235
54	1953-05-01	229
55	1953-09-01	237
56	1953-11-01	180
57	1954-01-01	204
58	1954-04-01	227
59	1954-06-01	264
60	1954-07-01	302
61	1954-08-01	293
62	1954-09-01	259
63	1954-11-01	203
64	1955-03-01	267
65	1955-05-01	270
66	1955-10-01	274
67	1955-11-01	237
68	1956-05-01	318
69	1956-06-01	374
70	1956-07-01	413
71	1956-08-01	405
72	1956-11-01	271
73	1957-03-01	356
74	1957-04-01	348
75	1957-07-01	465
76	1957-11-01	305
77	1958-01-01	340
78	1958-03-01	362
79	1958-06-01	435
80	1958-07-01	491
81	1958-08-01	505
82	1959-05-01	420
83	1960-01-01	417
84	1949-02-01	118
85	1949-04-01	129
86	1949-11-01	104
87	1950-07-01	170
88	1950-10-01	133
89	1951-01-01	145
90	1951-03-01	178
91	1951-09-01	184
92	1951-12-01	166
93	1952-06-01	218
94	1952-09-01	209
95	1952-10-01	191
96	1952-12-01	194
97	1953-08-01	272
98	1953-12-01	201
99	1954-03-01	235
100	1954-05-01	234

Rows: 1-100 | Columns: 2

First we import the model:

from verticapy.machine_learning.vertica.tsa import ARIMA

Then we can create the model:

model = ARIMA(order = (12, 1, 2))

We can now fit the model:

model.fit(data, "date", "passengers")


============
coefficients
============
parameter| value  
---------+--------
  phi_1  |-0.02408
  phi_2  |-0.03398
  phi_3  |-0.02702
  phi_4  |-0.12197
  phi_5  |-0.01651
  phi_6  |-0.21558
  phi_7  |-0.00477
  phi_8  |-0.15146
  phi_9  | 0.04249
 phi_10  |-0.16296
 phi_11  | 0.04043
 phi_12  | 0.86090
 theta_1 | 0.06580
 theta_2 |-0.06794


==============
regularization
==============
none

===============
timeseries_name
===============
"passengers"

==============
timestamp_name
==============
date

==============
missing_method
==============
linear_interpolation

===========
call_string
===========
ARIMA('"public"."_verticapy_tmp_arima_v_mldb_765413e2979d11efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_76794194979d11efa8720242ac120002_"', '"passengers"', 'date' USING PARAMETERS p=12, d=1, q=2, missing='linear_interpolation', init_method='Zero', epsilon=1e-06, max_iterations=100);

===============
Additional Info
===============
       Name       |  Value  
------------------+---------
        p         |   12    
        d         |    1    
        q         |    2    
       mean       | 2.23776 
      lambda      | 1.00000 
mean_squared_error|178.86952
rejected_row_count|    0    
accepted_row_count|   144   

We can conveniently get the features importance:

model.features_importance()
Out[5]: <Axes: xlabel='Importance (%)', ylabel='Features'>

Important

For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.

Examples: ARIMA; ARMA; AR; MA;