gemseo / mlearning / transform / dimension_reduction

pls module

The Partial Least Square (PLS) regression to reduce the dimension of a variable.

The PLS class wraps the PCA from Scikit-learn.

Dependence

This dimension reduction algorithm relies on the PLSRegression class of the scikit-learn library.

Classes:

PLS([name, n_components])

Partial Least Square regression.

class gemseo.mlearning.transform.dimension_reduction.pls.PLS(name='PLS', n_components=5, **parameters)[source]

Bases: gemseo.mlearning.transform.dimension_reduction.dimension_reduction.DimensionReduction

Partial Least Square regression.

name

The name of the transformer.

Type

str

parameters

The parameters of the transformer.

Type

str

Parameters
  • **parameters (Union[float,int,bool]) – The optional parameters for sklearn PCA constructor.

  • name (str) –

    By default it is set to PLS.

  • n_components (int) –

    By default it is set to 5.

Return type

None

Attributes:

CROSSED

components

The principal components.

n_components

The number of components.

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, other_data)

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 = True
property components

The principal components.

compute_jacobian(data)[source]

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)[source]

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.

Returns

A deepcopy of the current instance.

Return type

gemseo.mlearning.transform.transformer.Transformer

fit(data, other_data)[source]

Fit the transformer to the data.

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

  • other_data (numpy.ndarray) –

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)[source]

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 n_components

The number of components.

transform(data)[source]

Transform the data.

Parameters

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

Returns

The transformed data.

Return type

numpy.ndarray