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# Licensed under the Apache License, Version 2.0 (the "License");
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__all__ = ["NaiveBayesClassifier"]
import logging
from sklearn.naive_bayes import MultinomialNB
from asreview.models.classifiers.base import BaseTrainClassifier
[docs]
class NaiveBayesClassifier(BaseTrainClassifier):
"""Naive Bayes classifier (``nb``).
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"
label = "Naive Bayes"
def __init__(self, alpha=3.822):
super(NaiveBayesClassifier, self).__init__()
self.alpha = alpha
self._model = MultinomialNB(alpha=alpha)
logging.debug(self._model)
[docs]
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