gemseo / mlearning / transform / scaler

standard_scaler module

Scaling a variable with a statistical linear transformation.

The StandardScaler class implements the Standard scaling method applying to some parameter \(z\):

\[\bar{z} := \text{offset} + \text{coefficient}\times z = \frac{z-\text{mean}(z)}{\text{std}(z)}\]

where \(\text{offset}=-\text{mean}(z)/\text{std}(z)\) and \(\text{coefficient}=1/\text{std}(z)\).

In this standard scaling method, the scaling operation linearly transforms the original variable math:z such that in the scaled space, the original data have zero mean and unit standard deviation.

Classes:

StandardScaler([name, offset, coefficient])

Standard scaler.

class gemseo.mlearning.transform.scaler.standard_scaler.StandardScaler(name='StandardScaler', offset=0.0, coefficient=1.0)

Bases: gemseo.mlearning.transform.scaler.scaler.Scaler

Standard 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 StandardScaler.

• 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(data)	Compute Jacobian of transformer.transform().
compute_jacobian_inverse(data)	Compute Jacobian of the transformer.inverse_transform().
duplicate()	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.
inverse_transform(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)

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