vastorbit.machine_learning.memmodel.preprocessing.StandardScaler¶

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

InMemoryModel implementation of standard Scaler.

Note

The StandardScaler is defined entirely by its attributes. For example, mean, and std of feature(s) define a StandardScaler model.

Attributes

Attributes are identical to Scaler.

Parameters:

mean (ArrayLike) – Model’s features averages.
std (ArrayLike) – Model’s features standard deviations.

Examples

Initalization

Import the required module.

from vastorbit.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)

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)

Hint

This object can be pickled and used in any in-memory environment, just like scikit-learn 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