class_docstr: A variant of the Naive Bayes Classifier that performs binary
  classification with partially-labeled training sets, i.e. when only
  one class is labeled and the other is not. Assuming a prior distribution
  on the two labels, uses the unlabeled set to estimate the frequencies of
  the features.
  
  Example usage:
  ::
  
      >>> from text.classifiers import PositiveNaiveBayesClassifier
      >>> sports_sentences = ['The team dominated the game',
      ...                   'They lost the ball',
      ...                   'The game was intense',
      ...                   'The goalkeeper catched the ball',
      ...                   'The other team controlled the ball']
      >>> various_sentences = ['The President did not comment',
      ...                        'I lost the keys',
      ...                        'The team won the game',
      ...                        'Sara has two kids',
      ...                        'The ball went off the court',
      ...                        'They had the ball for the whole game',
      ...                        'The show is over']
      >>> classifier = PositiveNaiveBayesClassifier(positive_set=sports_sentences,
      ...                                           unlabeled_set=various_sentences)
      >>> classifier.classify("My team lost the game")
      True
      >>> classifier.classify("And now for something completely different.")
      False
  
  
  :param positive_set: A collection of strings that have the positive label.
  :param unlabeled_set: A collection of unlabeled strings.
  :param feature_extractor: A feature extractor function.
  :param positive_prob_prior: A prior estimate of the probability of the
      label ``True``.
  
  .. versionadded:: 0.7.0
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code: def __repr__(self):
      class_name = self.__class__.__name__
      return "<{cls} trained on {n_pos} labeled and {n_unlabeled} unlabeled instances>".format(
          cls=class_name,
          n_pos=len(self.positive_set),
          n_unlabeled=len(self.unlabeled_set),
      )
  
imports: from __future__ import absolute_import
  from itertools import chain
  import nltk
  from textblob.compat import basestring
  from textblob.decorators import cached_property
  from textblob.exceptions import FormatError
  from textblob.tokenizers import word_tokenize
  from textblob.utils import strip_punc, is_filelike
  import textblob.formats
label_desc: Use image node_id 2 for calling the PositiveNaiveBayesClassifier obj's underlying member method code with example usage: obj.__repr__() and returns: str