text: # Copyright 2019 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.
  
  import logging
  
  from sklearn.naive_bayes import MultinomialNB
  
  from asreview.models.classifiers.base import BaseTrainClassifier
  
  
  class NaiveBayesClassifier(BaseTrainClassifier):
      """Naive Bayes classifier
  
      Naive Bayes classifier. Only works in combination with the
      :class:`asreview.models.feature_extraction.Tfidf` feature extraction model.
      Though relatively simplistic, seems to work quite well on a wide range of
      datasets.
  
      The naive Bayes classifier is an implementation based
      on the sklearn multinomial naive Bayes classifier.
  
      Arguments
      ---------
      alpha : float, default=3.822
          Additive (Laplace/Lidstone) smoothing parameter
          (0 for no smoothing).
      """
  
      name = "nb"
  
      def __init__(self, alpha=3.822):
  
          super(NaiveBayesClassifier, self).__init__()
          self.alpha = alpha
          self._model = MultinomialNB(alpha=alpha)
          logging.debug(self._model)
  
      def full_hyper_space(self):
          from hyperopt import hp
          hyper_choices = {}
          hyper_space = {
              "mdl_alpha": hp.lognormal("mdl_alpha", 0, 1),
          }
          return hyper_space, hyper_choices
  