docstr: Given a new set of documents (supplied as texts or doc_topics), the score method
  uses a One-Class classifier to score documents based on similarity to a
  seed set of documents (where seed set is computed by train_scorer() method).
  
  Higher scores indicate a higher degree of similarity.
  Positive values represent a binary decision of similar.
  Negative values represent a binary decision of dissimlar.
  In practice, negative scores closer to zer will also be simlar as One-Class
  classifiers are more strict than traditional binary classifiers.
  Documents with negative scores closer to zero are good candidates for
  inclusion in a training set for binary classification (e.g., active labeling).
  
  NOTE: The score method currently employs the use of LocalOutLierFactor, which
  means you should not try to score documents that were used in training. Only
  new, unseen documents should be scored for similarity.
  
  Args:
      texts(list of str): list of document texts.  Mutually-exclusive with <doc_topics>
      doc_topics(ndarray): pre-computed topic distribution for each document in texts.
                           Mutually-exclusive with <texts>.
  Returns:
      list of floats:  larger values indicate higher degree of similarity
                       positive values indicate a binary decision of similar
                       negative values indicate binary decision of dissimilar
                       In practice, negative scores closer to zero will also
                       be similar as One-class classifiers are more strict
                       than traditional binary classifiers.
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code: def score(self, texts=None, doc_topics=None):
      """
      Given a new set of documents (supplied as texts or doc_topics), the score method
      uses a One-Class classifier to score documents based on similarity to a
      seed set of documents (where seed set is computed by train_scorer() method).
  
      Higher scores indicate a higher degree of similarity.
      Positive values represent a binary decision of similar.
      Negative values represent a binary decision of dissimlar.
      In practice, negative scores closer to zer will also be simlar as One-Class
      classifiers are more strict than traditional binary classifiers.
      Documents with negative scores closer to zero are good candidates for
      inclusion in a training set for binary classification (e.g., active labeling).
  
      NOTE: The score method currently employs the use of LocalOutLierFactor, which
      means you should not try to score documents that were used in training. Only
      new, unseen documents should be scored for similarity.
  
      Args:
          texts(list of str): list of document texts.  Mutually-exclusive with <doc_topics>
          doc_topics(ndarray): pre-computed topic distribution for each document in texts.
                               Mutually-exclusive with <texts>.
      Returns:
          list of floats:  larger values indicate higher degree of similarity
                           positive values indicate a binary decision of similar
                           negative values indicate binary decision of dissimilar
                           In practice, negative scores closer to zero will also
                           be similar as One-class classifiers are more strict
                           than traditional binary classifiers.
  
      """
      # error-checks
      if texts is not None and doc_topics is not None:
          raise ValueError(
              "texts is mutually-exclusive with doc_topics"
          )
      if texts is None and doc_topics is None:
          raise ValueError("One of texts or doc_topics is required.")
      if texts is not None and type(texts) not in [list, np.ndarray]:
          raise ValueError(
              "texts must be either a list or numpy ndarray"
          )
      if doc_topics is not None and type(doc_topics) not in [
          np.ndarray
      ]:
          raise ValueError("doc_topics must be a np.ndarray")
  
      x_test = doc_topics
      if texts:
          x_test = self.predict(texts)
      return self.scorer.decision_function(x_test)
  
imports: import time
  from ..None import utils
  from ..imports import *
  from .None import preprocessor
  from .None import textutils
label_desc: Use image node_id 19 for calling the TopicModel obj's underlying member method code with example usage: obj.score(texts, doc_topics) and returns: self