Source code for gemseo_mlearning.regression.svm

# 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: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""Support vector machine for regression.

The support vector machine model relies on the ``SVR`` class of the `scikit-learn
library <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html>`_.
"""
from __future__ import annotations

import logging
from typing import ClassVar
from typing import Iterable
from typing import Mapping

from gemseo.core.dataset import Dataset
from gemseo.mlearning.core.ml_algo import TransformerType
from gemseo.mlearning.regression.regression import MLRegressionAlgo
from gemseo.utils.python_compatibility import Final
from numpy import array
from numpy import ndarray
from sklearn.svm import SVR

LOGGER = logging.getLogger(__name__)


class SVMRegressor(MLRegressionAlgo):
    """Support vector machine regressor."""

    LIBRARY: Final[str] = "scikit-learn"
    SHORT_ALGO_NAME: ClassVar[str] = "SVMRegression"

    def __init__(
        self,
        data: Dataset,
        transformer: Mapping[str, TransformerType] | None = None,
        input_names: Iterable[str] = None,
        output_names: Iterable[str] = None,
        kernel: str = "rbf",
        **parameters,
    ) -> None:
        """# noqa: D205 D212 D415
        Args:
            kernel: The kernel type to be used.
        """
        super().__init__(
            data,
            transformer=transformer,
            input_names=input_names,
            output_names=output_names,
            kernel=kernel,
            **parameters,
        )
        self.__algo = {"kernel": kernel, "parameters": parameters}
        self.algo = []

    def _fit(
        self,
        input_data: ndarray,
        output_data: ndarray,
    ) -> None:
        for _output_data in output_data.T:
            self.algo.append(
                SVR(
                    kernel=self.__algo["kernel"],
                    **self.__algo["parameters"],
                )
            )
            self.algo[-1].fit(input_data, _output_data)

    def _predict(
        self,
        input_data: ndarray,
    ) -> ndarray:
        return array([algo.predict(input_data) for algo in self.algo]).T