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:
DimensionReduction
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, typicallymin(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.
- transform(data, *args, **kwargs)
Force a NumPy array to be 2D and evaluate the function
f
with it.
- name: str
The name of the transformer.