gemseo / mlearning / transform / scaler

# min_max_scaler module¶

Scaling a variable with a geometrical linear transformation.

The MinMaxScaler class implements the MinMax scaling method applying to some parameter $$z$$:

$\bar{z} := \text{offset} + \text{coefficient}\times z = \frac{z-\text{min}(z)}{(\text{max}(z)-\text{min}(z))},$

where $$\text{offset}=-\text{min}(z)/(\text{max}(z)-\text{min}(z))$$ and $$\text{coefficient}=1/(\text{max}(z)-\text{min}(z))$$.

In the MinMax scaling method, the scaling operation linearly transforms the original variable $$z$$ such that the minimum of the original data corresponds to 0 and the maximum to 1.

Warning

When $$\text{min}(z)=\text{max}(z)$$, we use $$\bar{z}=\frac{z}{\text{min}(z)}-0.5$$.

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

Bases: Scaler

Min-max scaler.

Parameters:
• name (str) –

A name for this transformer.

By default it is set to “MinMaxScaler”.

• offset (float) –

The offset of the linear transformation.

By default it is set to 0.0.

• coefficient (float) –

The coefficient of the linear transformation.

By default it is set to 1.0.

compute_jacobian(data)

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)

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)

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)

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)

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 MinMaxScaler¶

Quality measure for surrogate model comparison

Quality measure for surrogate model comparison

Mixture of experts

Mixture of experts

Scaler example

Scaler example