Source code for asreview.models.svm

# Copyright 2019 The ASReview Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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from sklearn.svm import SVC

from asreview.models.base import BaseTrainModel
from asreview.utils import _set_class_weight


[docs]class SVMModel(BaseTrainModel): "Support Vector Machine SKLearn model." name = "svm" def __init__(self, gamma="auto", class_weight=0.249, C=15.4, kernel="linear", random_state=None): """Initialize the SKLearn SVM model. 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, RandomState State of the RNG. """ super(SVMModel, 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) 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