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

# -*- coding: utf-8 -*-
# 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.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# 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
"""
Kernel Principal Component Analysis
===================================

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 absolute_import, division, unicode_literals

from future import standard_library
from sklearn.decomposition import KernelPCA

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

standard_library.install_aliases()


class KPCA(DimensionReduction):
    """ Kernel principal component dimension reduction algorithm. """

    def __init__(
        self,
        name="KPCA",
        n_components=5,
        fit_inverse_transform=True,
        kernel="linear",
        **parameters
    ):
        """Constructor.

        :param str name: transformer name. Default: 'KPCA'.
        :param int n_components: number of components. Default: 5.
        :param bool fit_inverse_transform: Learn the inverse transform for
            non-precomputed kernels. Default: True.
        :param str kernel: kernel name ('linear', 'poly', 'rbf',
            'sigmoid', 'cosine' or 'precomputed'). Default: 'linear'.
        :param parameters: Optional parameters for sklearn KPCA constructor.
        """
        super(KPCA, self).__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):
        """Fit transformer to data.

        :param ndarray data: data to be fitted.
        """
        self.algo.fit(data)

    def transform(self, data):
        """Transform data.

        :param ndarray data: data  to be transformed.
        :return: transformed data.
        :rtype: ndarray
        """
        return self.algo.transform(data)

    def inverse_transform(self, data):
        """Perform an inverse transform on the data.

        :param ndarray data: data to be inverse transformed.
        :return: inverse transformed data.
        :rtype: ndarray
        """
        return self.algo.inverse_transform(data)