verticapy.machine_learning.vertica.tsa.ensemble.TimeSeriesByCategory¶

class verticapy.machine_learning.vertica.tsa.ensemble.TimeSeriesByCategory(name: str = None, overwrite_model: bool = False, base_model: TimeSeriesModelBase | None = None)¶

This model is built based on multiple base models. You should look at the source models to see entire examples.

Important

This is still Beta.

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.
base_model: TimeSeriesModelBase: The user should provide a base model which will be used for each category. It could be - ARIMA - ARMA - AR - :py:class:`~verticapy.machine_learning.vertica.tsa.MA’

Attributes¶

Many attributes are created during the fitting phase.

distinct: list: This provides a sequential list of the categories used to build the different models.
ts: str: The column name for time stamp.
y: str: The column name used for building the model.
_is_already_stored: bool: This tells us whether a model is stored in the Vertica database.
_get_model_names: list: This returns the list of names of the models created.

Examples¶

The following examples provide a basic understanding of usage.

Initialization¶

For this example, we will use a subset of the amazon dataset.

import verticapy.datasets as vpd

amazon_full = vpd.load_amazon()

	📅 date Date	Abc state Varchar(32)	123 number Integer
1	1998-01-01	AMAPÁ	0
2	1998-01-01	AMAZONAS	0
3	1998-01-01	DISTRITO FEDERAL	0
4	1998-01-01	ESPÍRITO SANTO	0
5	1998-01-01	MARANHÃO	0
6	1998-01-01	PARANÁ	0
7	1998-01-01	PIAUÍ	0
8	1998-01-01	RORAIMA	0
9	1998-01-01	SERGIPE	0
10	1998-01-01	SÃO PAULO	0
11	1998-02-01	GOIÁS	0
12	1998-02-01	MATO GROSSO DO SUL	0
13	1998-02-01	MINAS GERAIS	0
14	1998-02-01	PARAÍBA	0
15	1998-02-01	SANTA CATARINA	0
16	1998-02-01	SÃO PAULO	0
17	1998-03-01	AMAPÁ	0
18	1998-03-01	BAHIA	0
19	1998-03-01	MATO GROSSO DO SUL	0
20	1998-03-01	PARÁ	0
21	1998-03-01	PERNAMBUCO	0
22	1998-03-01	RIO GRANDE DO SUL	0
23	1998-04-01	CEARÁ	0
24	1998-04-01	PARANÁ	0
25	1998-04-01	PARAÍBA	0
26	1998-04-01	PARÁ	0
27	1998-05-01	ALAGOAS	0
28	1998-05-01	AMAPÁ	0
29	1998-05-01	MARANHÃO	0
30	1998-05-01	MATO GROSSO DO SUL	0
31	1998-05-01	RIO GRANDE DO SUL	0
32	1998-05-01	TOCANTINS	0
33	1998-06-01	ESPÍRITO SANTO	6
34	1998-06-01	RIO DE JANEIRO	3
35	1998-06-01	RIO GRANDE DO NORTE	1
36	1998-06-01	SÃO PAULO	451
37	1998-07-01	ESPÍRITO SANTO	37
38	1998-07-01	MARANHÃO	274
39	1998-07-01	MATO GROSSO	360
40	1998-07-01	MATO GROSSO DO SUL	3712
41	1998-07-01	PARAÍBA	0
42	1998-07-01	PARÁ	638
43	1998-07-01	RONDÔNIA	365
44	1998-07-01	SÃO PAULO	596
45	1998-08-01	ALAGOAS	1
46	1998-08-01	BAHIA	815
47	1998-08-01	DISTRITO FEDERAL	48
48	1998-08-01	ESPÍRITO SANTO	38
49	1998-08-01	MARANHÃO	1176
50	1998-08-01	MATO GROSSO	228
51	1998-08-01	MINAS GERAIS	875
52	1998-08-01	PIAUÍ	711
53	1998-08-01	RIO GRANDE DO SUL	9
54	1998-08-01	RORAIMA	0
55	1998-08-01	SERGIPE	0
56	1998-09-01	AMAPÁ	20
57	1998-09-01	DISTRITO FEDERAL	33
58	1998-09-01	PIAUÍ	1991
59	1998-09-01	RORAIMA	2
60	1998-10-01	AMAZONAS	83
61	1998-10-01	GOIÁS	1034
62	1998-10-01	MATO GROSSO	576
63	1998-10-01	PARAÍBA	179
64	1998-10-01	PARÁ	3665
65	1998-10-01	PIAUÍ	2586
66	1998-10-01	SERGIPE	0
67	1998-11-01	ALAGOAS	19
68	1998-11-01	AMAPÁ	131
69	1998-11-01	CEARÁ	575
70	1998-11-01	DISTRITO FEDERAL	0
71	1998-11-01	MARANHÃO	2237
72	1998-11-01	RIO DE JANEIRO	6
73	1998-11-01	RIO GRANDE DO SUL	28
74	1998-11-01	SÃO PAULO	488
75	1998-12-01	BAHIA	82
76	1998-12-01	MARANHÃO	1399
77	1998-12-01	MATO GROSSO	100
78	1998-12-01	PARAÍBA	51
79	1998-12-01	PERNAMBUCO	59
80	1998-12-01	RIO DE JANEIRO	1
81	1998-12-01	RONDÔNIA	33
82	1998-12-01	TOCANTINS	9
83	1999-01-01	ALAGOAS	58
84	1999-01-01	GOIÁS	14
85	1999-01-01	MATO GROSSO	239
86	1999-01-01	MINAS GERAIS	36
87	1999-01-01	PARÁ	87
88	1999-01-01	PERNAMBUCO	102
89	1999-01-01	RONDÔNIA	1
90	1999-01-01	SÃO PAULO	7
91	1999-01-01	TOCANTINS	36
92	1999-02-01	ACRE	0
93	1999-02-01	CEARÁ	16
94	1999-02-01	MATO GROSSO	69
95	1999-02-01	MATO GROSSO DO SUL	28
96	1999-02-01	PERNAMBUCO	13
97	1999-02-01	RONDÔNIA	1
98	1999-02-01	SANTA CATARINA	2
99	1999-02-01	TOCANTINS	1
100	1999-03-01	AMAPÁ	2

Rows: 1-100 | Columns: 3

We can reduce the number of states for the sake of ease in this example:

amazon = amazon_full[(amazon_full["state"] == "PERNAMBUCO") | (amazon_full["state"] == "SERGIPE")]

Now we can setup a base model that will be created for each unique state inside the dataset. For this example, we use ARIMA.

from verticapy.machine_learning.vertica.tsa import ARIMA

base_model = ARIMA(order = (2, 1, 2))

Finally we can now initiate our multiple models in one go:

from verticapy.machine_learning.vertica.tsa.ensemble import TimeSeriesByCategory

model = TimeSeriesByCategory(base_model = base_model)

Model Fitting¶

We can now fit the model:

model.fit(amazon, ts = "date", y = "number", by = "state")
For category: PERNAMBUCO


============
coefficients
============
parameter| value  
---------+--------
  phi_1  | 0.45168
  phi_2  | 0.01325
 theta_1 |-0.66935
 theta_2 |-0.34679


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

===============
timeseries_name
===============
"number"

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

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

===========
call_string
===========
ARIMA('"public"."_verticapy_tmp_timeseriesbycategory_v_mldb_9f553e78979e11efa8720242ac120002__000_tsbc"', '"public"."_verticapy_tmp_view_v_mldb_9ff4e3f6979e11efa8720242ac120002_"', '"number"', 'date' USING PARAMETERS p=2, d=1, q=2, missing='linear_interpolation', init_method='Zero', epsilon=1e-06, max_iterations=100);

===============
Additional Info
===============
       Name       |   Value   
------------------+-----------
        p         |     2     
        d         |     1     
        q         |     2     
       mean       |  0.27731  
      lambda      |  1.00000  
mean_squared_error|13117.49869
rejected_row_count|     0     
accepted_row_count|    239    


For category: SERGIPE


============
coefficients
============
parameter| value  
---------+--------
  phi_1  |-0.03152
  phi_2  | 0.27608
 theta_1 |-0.25561
 theta_2 |-0.73844


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

===============
timeseries_name
===============
"number"

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

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

===========
call_string
===========
ARIMA('"public"."_verticapy_tmp_timeseriesbycategory_v_mldb_9f553e78979e11efa8720242ac120002__001_tsbc"', '"public"."_verticapy_tmp_view_v_mldb_a2d9f0a2979e11efa8720242ac120002_"', '"number"', 'date' USING PARAMETERS p=2, d=1, q=2, missing='linear_interpolation', init_method='Zero', epsilon=1e-06, max_iterations=100);

===============
Additional Info
===============
       Name       |  Value  
------------------+---------
        p         |    2    
        d         |    1    
        q         |    2    
       mean       | 0.00420 
      lambda      | 1.00000 
mean_squared_error|319.36890
rejected_row_count|    0    
accepted_row_count|   239   

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.

Plots¶

We can conveniently plot the predictions on a line plot to observe the efficacy of our model. We need to provide the idx which represents the model number.

model.plot(idx = 0, npredictions = 5)

Note

You can find out the name of the category by the distinct attribute. The sequential list of categories correspond to idx = 0, 1 .... model.distinct.

__init__(name: str = None, overwrite_model: bool = False, base_model: TimeSeriesModelBase | None = None) → None¶: Must be overridden in the child class

Methods

`__init__`([name, overwrite_model, base_model])	Must be overridden in the child class
`contour`([nbins, chart])	Draws the model's contour plot.
`deploySQL`([vdf, ts, y, start, npredictions, ...])	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.
`features_importance`([idx, show, chart])	Computes the input submodel's features importance.
`fit`(input_relation, ts, y, by[, ...])	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`([idx, vdf, ts, y, start, npredictions, ...])	Draws the input submodel.
`predict`([vdf, ts, y, start, npredictions, ...])	Predicts using the input relation.
`register`(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`regression_report`([metrics, start, ...])	Computes a regression report using multiple metrics to evaluate the model (`r2`, `mse`, `max error`...).
`report`([metrics, start, npredictions, method])	Computes a regression report using multiple metrics to evaluate the model (`r2`, `mse`, `max error`...).
`score`([metric, start, npredictions, method])	Computes the model score.
`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