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.

Classes:

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

Min-max scaler.

name

The name of the transformer.

Type

str

parameters

The parameters of the transformer.

Type

str

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.

Return type

None

Attributes:

 CROSSED coefficient The scaling coefficient. offset The scaling offset.

Methods:

 Compute Jacobian of transformer.transform(). Compute Jacobian of the transformer.inverse_transform(). Duplicate the current object. fit(data, *args) Fit the transformer to the data. fit_transform(data, *args) Fit the transformer to the data and transform the data. Perform an inverse transform on the data. transform(data) Transform the data.
CROSSED = False
property coefficient

The scaling coefficient.

compute_jacobian(data)

Compute Jacobian of transformer.transform().

Parameters

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

Returns

The Jacobian matrix.

Return type

numpy.ndarray

compute_jacobian_inverse(data)

Compute Jacobian of the transformer.inverse_transform().

Parameters

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

Returns

The Jacobian matrix.

Return type

numpy.ndarray

duplicate()

Duplicate the current object.

Returns

A deepcopy of the current instance.

Return type

gemseo.mlearning.transform.transformer.Transformer

fit(data, *args)[source]

Fit the transformer to the data.

Parameters
• data (numpy.ndarray) – The data to be fitted.

• *args (Union[float, int, str]) –

Return type

None

fit_transform(data, *args)

Fit the transformer to the data and transform the data.

Parameters
• data (numpy.ndarray) – The data to be transformed.

• *args (Union[float, int, str]) –

Returns

The transformed data.

Return type

numpy.ndarray

inverse_transform(data)

Perform an inverse transform on the data.

Parameters

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

Returns

The inverse transformed data.

Return type

numpy.ndarray

property offset

The scaling offset.

transform(data)

Transform the data.

Parameters

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

Returns

The transformed data.

Return type

numpy.ndarray