gemseo / mlearning / transform / scaler

scaler module

Scaling a variable with a linear transformation.

The Scaler class implements the default scaling method applying to some parameter \(z\):

\[\bar{z} := \text{offset} + \text{coefficient}\times z\]

where \(\bar{z}\) is the scaled version of \(z\). This scaling method is a linear transformation parameterized by an offset and a coefficient.

In this default scaling method, the offset is equal to 0 and the coefficient is equal to 1. Consequently, the scaling operation is the identity: \(\bar{z}=z\). This method has to be overloaded.

class gemseo.mlearning.transform.scaler.scaler.Scaler(name='Scaler', offset=0.0, coefficient=1.0)[source]

Bases: Transformer

Data scaler.

Parameters:
  • name (str) –

    A name for this transformer.

    By default it is set to “Scaler”.

  • offset (float | ndarray) –

    The offset of the linear transformation.

    By default it is set to 0.0.

  • coefficient (float | ndarray) –

    The coefficient of the linear transformation.

    By default it is set to 1.0.

compute_jacobian(data)[source]

Compute Jacobian of transformer.transform().

Parameters:

data (ndarray) – The data where the Jacobian is to be computed.

Returns:

The Jacobian matrix.

Return type:

ndarray

compute_jacobian_inverse(data)[source]

Compute Jacobian of the transformer.inverse_transform().

Parameters:

data (ndarray) – The data where the Jacobian is to be computed.

Returns:

The Jacobian matrix.

Return type:

ndarray

duplicate()

Duplicate the current object.

Returns:

A deepcopy of the current instance.

Return type:

Transformer

fit(data, *args)[source]

Fit the transformer to the data.

Parameters:
Return type:

None

fit_transform(data, *args)

Fit the transformer to the data and transform the data.

Parameters:
Returns:

The transformed data.

Return type:

ndarray

inverse_transform(data)[source]

Perform an inverse transform on the data.

Parameters:

data (ndarray) – The data to be inverse transformed.

Returns:

The inverse transformed data.

Return type:

ndarray

transform(data)[source]

Transform the data.

Parameters:

data (ndarray) – The data to be transformed.

Returns:

The transformed data.

Return type:

ndarray

CROSSED: ClassVar[bool] = False

Whether the fit() method requires two data arrays.

property coefficient: ndarray

The scaling coefficient.

property is_fitted: bool

Whether the transformer has been fitted from some data.

name: str

The name of the transformer.

property offset: ndarray

The scaling offset.

property parameters: dict[str, Union[bool, int, float, numpy.ndarray, str, NoneType]]

The parameters of the transformer.

Examples using Scaler

Quality measure for surrogate model comparison

Quality measure for surrogate model comparison

Quality measure for surrogate model comparison
Mixture of experts

Mixture of experts

Mixture of experts
Scaler example

Scaler example

Scaler example
Transformer pipeline example

Transformer pipeline example

Transformer pipeline example