Source code for gemseo.mlearning.classification.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: Francois Gallard, Matthias De Lozzo, Syver Doving Agdestein
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""The Support Vector Machine algorithm for classification.

This module implements the SVMClassifier class.
A support vector machine (SVM) passes the data through a kernel
in order to increase its dimension
and thereby make the classes linearly separable.

Dependence
----------
The classifier relies on the SVC class
of the `scikit-learn library <https://scikit-learn.org/stable/modules/
generated/sklearn.svm.SVC.html>`_.
"""

from __future__ import annotations

from typing import TYPE_CHECKING
from typing import Callable
from typing import ClassVar

from sklearn.svm import SVC

from gemseo.mlearning.classification.classification import BaseMLClassificationAlgo
from gemseo.utils.seeder import SEED

if TYPE_CHECKING:
    from collections.abc import Iterable

    from numpy import ndarray

    from gemseo.datasets.io_dataset import IODataset
    from gemseo.mlearning.core.ml_algo import TransformerType


[docs] class SVMClassifier(BaseMLClassificationAlgo): """The Support Vector Machine algorithm for classification.""" SHORT_ALGO_NAME: ClassVar[str] = "SVM" LIBRARY: ClassVar[str] = "scikit-learn" def __init__( self, data: IODataset, transformer: TransformerType = BaseMLClassificationAlgo.IDENTITY, input_names: Iterable[str] | None = None, output_names: Iterable[str] | None = None, C: float = 1.0, # noqa: N803 kernel: str | Callable | None = "rbf", probability: bool = False, random_state: int | None = SEED, **parameters: int | float | bool | str | None, ) -> None: """ Args: C: The inverse L2 regularization parameter. Higher values give less regularization. kernel: The name of the kernel or a callable for the SVM. Examples: "linear", "poly", "rbf", "sigmoid", "precomputed" or a callable. probability: Whether to enable the probability estimates. The algorithm is faster if set to False. random_state: The random state passed to the random number generator. Use an integer for reproducible results. """ # noqa: D205, D212, D415 super().__init__( data, transformer=transformer, input_names=input_names, output_names=output_names, C=C, kernel=kernel, probability=probability, random_state=random_state, **parameters, ) self.algo = SVC( C=C, kernel=kernel, probability=probability, random_state=random_state, **parameters, ) def _fit( self, input_data: ndarray, output_data: ndarray, ) -> None: self.algo.fit(input_data, output_data.ravel()) def _predict( self, input_data: ndarray, ) -> ndarray: return self.algo.predict(input_data).astype(int).reshape((len(input_data), -1)) def _predict_proba_soft( self, input_data: ndarray, ) -> ndarray: if not self.parameters["probability"]: msg = ( "SVMClassifier soft probability prediction is only available if the " "parameter 'probability' is set to True." ) raise NotImplementedError(msg) return self.algo.predict_proba(input_data)[..., None]