Normalizer¶

In [ ]:

Normalizer(name: str,
           method: str = "zscore")

Creates a Vertica normalizer object.

Name	Type	Optional	Description
name	str	❌	Name of the the model.
method	str	✓	Method to use to normalize. zscore : Normalization using the Z-Score [(x - avg) / std] robust_zscore : Normalization using the Robust Z-Score [(x - median) / (1.4826 * mad)] minmax : Normalization using the MinMax [(x - min) / (max - min)]

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

Name	Type	Description
param_	tablesample	The normalization parameters.
input_relation	str	Training relation.
X	list	List of the predictors.

Name	Description
deploySQL	Returns the SQL code needed to deploy the model.
deployInverseSQL	Returns the SQL code needed to deploy the inverse model (Normalizer ** -1).
drop	Drops the model from the Vertica DB.
fit	Trains the model.
get_attr	Returns the model attribute.
get_params	Returns the model Parameters.
inverse_transform	Applies the inverse model on a vDataFrame.
set_params	Sets the parameters of 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_sql	Returns the SQL code needed to deploy the model without using Vertica built-in functions.
transform	Applies the model on a vDataFrame.

In [33]:

from verticapy.learn.preprocessing import Normalizer
model = Normalizer(name = "public.Normalizer_iris")
display(model)

<Normalizer>