Lasso

In [ ]:
Lasso(name: str,
      cursor = None,
      tol: float = 1e-6, 
      C: float = 1.0,
      max_iter: int = 100, 
      solver: str = 'CGD')

Creates a Lasso object using the Vertica Linear Regression function. The Lasso is a regularized regression method and uses an L1 penalty.

Parameters

Name Type Optional Description
name
str
❌
 Name of the model to be stored in the database.
cursor
DBcursor
✓
 Vertica DB cursor.
tol
float
✓
 Determines whether the algorithm has reached the specified accuracy result.
C
float
✓
 The regularization parameter value. The value must be zero or non-negative.
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
  • CGD : Coordinate Gradient Descent

Attributes

After the object is created, all parameters become attributes. Additional attributes will be created when fitting the model:

Name Type Description
coef_
tablesample
 Coefficients and their mathematical information (pvalue, std, value...)
input_relation
str
 Training relation.
X
list
 List of the predictors.
y
str
 Response column.
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.

Methods

Name Description
contour Draws the model's contour plot.
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 Linear Regression if the number of predictors is equal to 1 or 2.
predict Predicts using the input relation.
regression_report Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
score Computes the model score.
set_cursor Sets a new database cursor.
set_params Sets the parameters of the model.
shapExplainer Creates a shapExplainer for the model.
to_memmodel Converts a specified Vertica model to a memModel model.
to_python Returns the Python code needed to deploy the model without using built-in Vertica functions.
to_sklearn Converts this Vertica model to an sklearn model.
to_sql Returns the SQL code needed to deploy the model without using Vertica built-in functions.

Example

In [50]:
from verticapy.learn.linear_model import Lasso
model = Lasso(name = "public.LR_winequality",
              tol = 1e-4, 
              max_iter = 100, 
              solver = 'CGD')
display(model)

<LinearRegression>