VAR (Beta)

In [ ]:
VAR(name: str,
    p: int = 0,
    tol: float = 1e-4,
    max_iter: int = 1000,
    solver: str = "Newton")

Creates an VAR object using the Vertica linear regression function.

Parameters

Name Type Optional Description
name
str
❌
 Name of the model to be stored in the database.
p
int
✓
 Order of the AR (Auto-Regressive) part.
tol
float
✓
 Determines whether the algorithm has reached the specified accuracy result.
max_iter
int
✓
 Determines the maximum number of iterations the algorithm performs before achieving the specified accuracy result.
solver
str
✓
 The optimizer method to use to train the model.
  • Newton : Newton Method
  • BFGS : Broyden Fletcher Goldfarb Shanno

Attributes

When this object is created, all of its parameters become attributes. When fitting the model, the model will create additional attributes.:

Name Type Description
coef_
list
 Coefficients and their mathematical information (pvalue, std, value...)
deploy_predict_
list
 SQL code used to deploy the model.
input_relation
str
 Training relation.
ts
str
 vcolumn used to order the data.
X
list
 List of the responses.
test_relation
str
 Relation to use to test the model. All model methods are abstractions that simplify the process. The testing relation will be used by the methods to evaluate the model. If empty, the training relation will be used instead. This attribute can be changed at any time.
transform_relation
str
 Relation used to deploy the model.

Methods

Name Description
deploySQL Returns the SQL code needed to deploy the model.
drop Drops the model from the Vertica DB.
features_importance Computes the model features importance using the Gini Index.
fit Trains the model.
get_attr Returns the model attribute.
get_params Returns the model Parameters.
plot Draws the SARIMAX model.
predict Predicts using the input relation.
regression_report / report Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
score Computes the model score.
set_params Sets the parameters of the model.

Example

In [4]:
from verticapy.learn.tsa import VAR
model = VAR(name = "SARIMAX_cases",
            tol = 1e-4,
            max_iter = 100, 
            solver = 'BFGS',
            p = 10)
display(model)

<VAR>