Source code for gemseo.mlearning.classification.svm

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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
"""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.

The classifier relies on the SVC class
of the `scikit-learn library <
from __future__ import annotations

from typing import Callable
from typing import ClassVar
from typing import Final
from typing import Iterable

from numpy import ndarray
from numpy import newaxis
from sklearn.svm import SVC

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

[docs]class SVMClassifier(MLClassificationAlgo): """The Support Vector Machine algorithm for classification.""" SHORT_ALGO_NAME: ClassVar[str] = "SVM" LIBRARY: Final[str] = "scikit-learn" def __init__( self, data: IODataset, transformer: TransformerType = MLClassificationAlgo.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, **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. """ # noqa: D205, D212, D415 super().__init__( data, transformer=transformer, input_names=input_names, output_names=output_names, C=C, kernel=kernel, probability=probability, **parameters, ) self.algo = SVC(C=C, kernel=kernel, probability=probability, **parameters) def _fit( self, input_data: ndarray, output_data: ndarray, ) -> None:, output_data.ravel()) def _predict( self, input_data: ndarray, ) -> ndarray: return self.algo.predict(input_data)[:, newaxis].astype(int) def _predict_proba_soft( self, input_data: ndarray, ) -> ndarray: if not self.parameters["probability"]: raise NotImplementedError( "SVMClassifier soft probability prediction is only available if the " "parameter 'probability' is set to True." ) return self.algo.predict_proba(input_data)[..., None]