gemseo / mlearning / transformers / 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.

class gemseo.mlearning.transformers.dimension_reduction.kpca.KPCA(name='', n_components=None, fit_inverse_transform=True, kernel='linear', **parameters)[source]

Bases: BaseDimensionReduction

Kernel principal component dimension reduction algorithm.

Parameters:

name (str) –
A name for this transformer.

By default it is set to “”.
n_components (int | None) – The number of components of the latent space. If None, use the maximum number allowed by the technique, typically min(n_samples, n_features).
fit_inverse_transform (bool) –
If True, learn the inverse transform for non-precomputed kernels.

By default it is set to True.
kernel (str) –
The name of the kernel, either ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘cosine’ or ‘precomputed’.

By default it is set to “linear”.
**parameters (float | int | str | None) – The optional parameters for sklearn KPCA constructor.

inverse_transform(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

data (ndarray) – A 1D or 2D NumPy array.
*args (Any) – The positional arguments.
**kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

transform(data, *args, **kwargs)

Force a NumPy array to be 2D and evaluate the function f with it.

Parameters:

data (ndarray) – A 1D or 2D NumPy array.
*args (Any) – The positional arguments.
**kwargs (Any) – The optional arguments.

Returns:

Any kind of output; if a NumPy array, its dimension is made consistent with the shape of data.

Return type:

Any

name: str: The name of the transformer.