gemseo / mlearning / transform / dimension_reduction

kpca module¶

The Kernel Principal Component Analysis (KPCA) to reduce the dimension of a variable.

The KPCA class implements the KCPA wraps the KPCA from Scikit-learn.

Dependence¶

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

Classes:

KPCA([name, n_components, …])

Kernel principal component dimension reduction algorithm.

class gemseo.mlearning.transform.dimension_reduction.kpca.KPCA(name='KPCA', n_components=5, fit_inverse_transform=True, kernel='linear', **parameters)[source]¶

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

Kernel principal component dimension reduction algorithm.

Attributes

name (str) – The name of the transformer.
parameters (str) – The parameters of the transformer.
name (str) – The name of the transformer.
parameters (str) – The parameters of the transformer.

Parameters: name: A name for this transformer. n_components: The number of components of the latent space. **parameters: The parameters of the transformer. fit_inverse_transform: If True, learn the inverse transform

for non-precomputed kernels.

kernel: The name of the kernel,: either ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘cosine’ or ‘precomputed’.

**parameters: The optional parameters for sklearn KPCA constructor.

Attributes:

`CROSSED`
`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, *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¶

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.

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.
data – 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)[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