# -*- 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: 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 division, unicode_literals
import logging
from typing import Callable, Iterable, Optional, Union
from numpy import ndarray
from sklearn.svm import SVC
from gemseo.core.dataset import Dataset
from gemseo.mlearning.classification.classification import MLClassificationAlgo
from gemseo.mlearning.core.ml_algo import TransformerType
LOGGER = logging.getLogger(__name__)
[docs]class SVMClassifier(MLClassificationAlgo):
"""The Support Vector Machine algorithm for classification."""
LIBRARY = "scikit-learn"
ABBR = "SVM"
def __init__(
self,
data, # type: Dataset
transformer=None, # type: Optional[TransformerType]
input_names=None, # type: Optional[Iterable[str]]
output_names=None, # type: Optional[Iterable[str]]
C=1.0, # noqa: N803 # type: float
kernel="rbf", # type: Optional[str,Callable]
probability=False, # type: bool
**parameters # type: Optional[Union[int,float,bool,str]]
): # type: (...) -> None
# noqa: D205,D212,D415
"""
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.
"""
super(SVMClassifier, self).__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, # type:ndarray
output_data, # type:ndarray
): # type: (...) -> None
self.algo.fit(input_data, output_data.ravel())
def _predict(
self,
input_data, # type:ndarray
): # type: (...) -> ndarray
return self.algo.predict(input_data)[:, None].astype(int)
def _predict_proba_soft(
self,
input_data, # type: ndarray
): # type: (...)-> 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]
