verticapy.machine_learning.memmodel.preprocessing.StandardScaler¶

class verticapy.machine_learning.memmodel.preprocessing.StandardScaler(mean: Annotated[list | ndarray, 'Array Like Structure'], std: Annotated[list | ndarray, 'Array Like Structure'])¶

InMemoryModel implementation of standard Scaler.

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, and std of feature(s) define a StandardScaler 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: Annotated[list | ndarray, 'Array Like Structure'], std: Annotated[list | ndarray, 'Array Like Structure']) → None¶

Methods

`__init__`(mean, std)
`get_attributes`()	Returns the model attributes.
`set_attributes`(**kwargs)	Sets the model attributes.
`transform`(X)	Transforms and applies the `Scaler` model to the input matrix.
`transform_sql`(X)	Transforms and returns the SQL needed to deploy the `Scaler`.

Attributes

object_type

Must be overridden in child class