# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ["SVMClassifier"]
from sklearn.svm import SVC
from asreview.models.classifiers.base import BaseTrainClassifier
from asreview.models.classifiers.utils import _set_class_weight
[docs]
class SVMClassifier(BaseTrainClassifier):
"""
Support vector machine classifier (``svm``).
The Support Vector Machine classifier is an implementation based
on the sklearn Support Vector Machine classifier.
Arguments
---------
gamma: str
Gamma parameter of the SVM model.
class_weight: float
class_weight of the inclusions.
C: float
C parameter of the SVM model.
kernel: str
SVM kernel type.
random_state: int, asreview.utils.SeededRandomState
State of the RNG.
"""
name = "svm"
label = "Support vector machine"
def __init__(
self,
gamma="auto",
class_weight=0.249,
C=15.4,
kernel="linear",
random_state=None,
):
super(SVMClassifier, self).__init__()
self.gamma = gamma
self.class_weight = class_weight
self.C = C
self.kernel = kernel
self._random_state = random_state
self._model = SVC(
kernel=kernel,
C=C,
class_weight=_set_class_weight(class_weight),
random_state=random_state,
gamma=gamma,
probability=True,
)
[docs]
def full_hyper_space(self):
from hyperopt import hp
hyper_choices = {
"mdl_gamma": ["auto", "scale"],
"mdl_kernel": ["linear", "rbf", "poly", "sigmoid"],
}
hyper_space = {
"mdl_gamma": hp.choice("mdl_gamma", hyper_choices["mdl_gamma"]),
"mdl_kernel": hp.choice("mdl_kernel", hyper_choices["mdl_kernel"]),
"mdl_C": hp.lognormal("mdl_C", 0, 2),
"mdl_class_weight": hp.lognormal("mdl_class_weight", 0, 1),
}
return hyper_space, hyper_choices