verticapy.machine_learning.memmodel.preprocessing.StandardScaler#
- class verticapy.machine_learning.memmodel.preprocessing.StandardScaler(mean: list | ndarray, std: list | ndarray)#
InMemoryModel
implementation of standardScaler
.Parameters#
- mean: ArrayLike
Model’s features averages.
- std: ArrayLike
Model’s features standard deviations.
Note
StandardScaler
are defined entirely by their attributes. For example,mean
, andstd
of feature(s) define aStandardScaler
model.Attributes#
Attributes are identical to
Scaler
.Examples#
Initalization
Import the required module.
from verticapy.machine_learning.memmodel.preprocessing import StandardScaler
A StandardScaler model is defined by
mean
and ``std``values. In this example, we will use the following:mean = [0.4, 0.1] std = [0.5, 0.2]
Let’s create a
StandardScaler
model.model_sts = StandardScaler(mean, std)
Create a dataset.
data = [[0.45, 0.17]]
Making In-Memory Transformation
Use
transform()
method to do transformation.model_sts.transform(data) Out[6]: array([[0.1 , 0.35]])
Deploy SQL Code
Let’s use the following column names:
cnames = ['col1', 'col2']
Use
transform_sql()
method to get the SQL code needed to deploy the model using its attributes.model_sts.transform_sql(cnames) Out[8]: ['(col1 - 0.4) / 0.5', '(col2 - 0.1) / 0.2']
Hint
This object can be pickled and used in any in-memory environment, just like SKLEARN models.
- __init__(mean: list | ndarray, std: list | ndarray) None #
Methods
__init__
(mean, std)Returns the model attributes.
set_attributes
(**kwargs)Sets the model attributes.
transform
(X)Transforms and applies the
Scaler
model to the input matrix.Transforms and returns the SQL needed to deploy the
Scaler
.Attributes
Must be overridden in child class