Source code for gemseo.mlearning.transform.dimension_reduction.kpca

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
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# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Syver Doving Agdestein
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""The Kernel Principal Component Analysis (KPCA) to reduce the dimension of a variable.

The :class:`.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 <https://scikit-learn.org/stable/modules/
generated/sklearn.decomposition.PCA.html>`_.
"""
from __future__ import annotations

from numpy import ndarray
from sklearn.decomposition import KernelPCA

from gemseo.mlearning.transform.dimension_reduction.dimension_reduction import (
    DimensionReduction,
)
from gemseo.mlearning.transform.transformer import TransformerFitOptionType


[docs]class KPCA(DimensionReduction): """Kernel principal component dimension reduction algorithm.""" def __init__( self, name: str = "KPCA", n_components: int | None = None, fit_inverse_transform: bool = True, kernel: str = "linear", **parameters: float | int | str | None, ) -> None: """ Args: 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. """ super().__init__(name, n_components=n_components, **parameters) self.algo = KernelPCA( n_components, fit_inverse_transform=fit_inverse_transform, kernel=kernel, **parameters, ) def _fit(self, data: ndarray, *args: TransformerFitOptionType) -> None: self.algo.fit(data) self.parameters["n_components"] = len(self.algo.eigenvalues_)
[docs] def transform(self, data: ndarray) -> ndarray: return self.algo.transform(data)
[docs] def inverse_transform(self, data: ndarray) -> ndarray: return self.algo.inverse_transform(data)