verticapy.machine_learning.memmodel.preprocessing.MinMaxScaler#

class verticapy.machine_learning.memmodel.preprocessing.MinMaxScaler(min_: list | ndarray, max_: list | ndarray)#

InMemoryModel implementation of MinMax scaler.

Parameters#

min_: ArrayLike: Model’s features minimums.
max_: ArrayLike: Model’s features maximums.

Note

MinMaxScaler are defined entirely by their attributes. For example, minimum, and maximum values of the input features define a MinMaxScaler model.

Attributes#

Attributes are identical to Scaler.

Examples#

Initalization

Import the required module.

from verticapy.machine_learning.memmodel.preprocessing import MinMaxScaler

A MinMaxScaler model is defined by minimum and maximum values. In this example, we will use the following:

min = [0.4, 0.1]

max = [0.5, 0.2]

Let’s create a MinMaxScaler model.

model_mms = MinMaxScaler(min, max)

Create a dataset.

data = [[0.45, 0.17]]

Making In-Memory Transformation

Use transform() method to do transformation.

model_mms.transform(data)
Out[6]: array([[0.5, 0.7]])

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_mms.transform_sql(cnames)
Out[8]: ['(col1 - 0.4) / 0.09999999999999998', '(col2 - 0.1) / 0.1']

Hint

This object can be pickled and used in any in-memory environment, just like SKLEARN models.

__init__(min_: list | ndarray, max_: list | ndarray) → None#

Methods

`__init__`(min_, max_)
`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