boxcox module¶

A Box-Cox power transformation.

Transform a variable \(x\) as:

\[\begin{split}y & = (x^\lambda - 1) / \lambda, \text{for } \lambda \neq 0 \\\\ & = \log(x), \text{for } \lambda = 0\end{split}\]

Dependence¶

This transformation algorithm relies on the PowerTransformer class of scikit-learn.

class gemseo.mlearning.transform.power.boxcox.BoxCox(name=None, standardize=True)[source]¶

A Box-Cox power transformation.

Parameters

name (str) –
A name for this transformer. If None, use the class name.

By default it is set to None.
standardize (bool) –
Whether to apply zero-mean, unit-variance normalization to the transformed output.

By default it is set to True.

Return type

None

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: NoReturn

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: NoReturn

duplicate()¶

Duplicate the current object.

fit(data, *args)¶

Fit the transformer to the data.

Parameters

Return type

NoReturn

fit_transform(data, *args)¶

Fit the transformer to the data and transform the data.

Parameters

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

transform(data)¶

Transform the data.

lambdas_: ndarray¶: The parameters of the power transformation for the selected features.