class_docstr: Metric class for convergence evaluation. 
class_docstr_tok: ["Metric","class","for","convergence","evaluation","."]
docstr: Parameters
  ----------
  distance : {'kullback_leibler', 'hellinger', 'jaccard'}, optional
      Measure used to calculate difference between any topic pair.
  num_words : int, optional
      The number of most relevant words used if `distance == 'jaccard'`. Also used for annotating topics.
  n_ann_terms : int, optional
      Max number of words in intersection/symmetric difference between topics. Used for annotation.
  diagonal : bool, optional
      Whether we need the difference between identical topics (the diagonal of the difference matrix).
  annotation : bool, optional
      Whether the intersection or difference of words between two topics should be returned.
  normed : bool, optional
      Whether the matrix should be normalized or not.
  logger : {'shell', 'visdom'}, optional
     Monitor training process using one of the available methods. 'shell' will print the coherence value in
     the active shell, while 'visdom' will visualize the coherence value with increasing epochs using the Visdom
     visualization framework.
  viz_env : object, optional
      Visdom environment to use for plotting the graph. Unused.
  title : str, optional
      Title of the graph plot in case `logger == 'visdom'`. Unused.
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code: def __init__(
      self,
      distance="jaccard",
      num_words=100,
      n_ann_terms=10,
      diagonal=True,
      annotation=False,
      normed=True,
      logger=None,
      viz_env=None,
      title=None,
  ):
      """
  
      Parameters
      ----------
      distance : {'kullback_leibler', 'hellinger', 'jaccard'}, optional
          Measure used to calculate difference between any topic pair.
      num_words : int, optional
          The number of most relevant words used if `distance == 'jaccard'`. Also used for annotating topics.
      n_ann_terms : int, optional
          Max number of words in intersection/symmetric difference between topics. Used for annotation.
      diagonal : bool, optional
          Whether we need the difference between identical topics (the diagonal of the difference matrix).
      annotation : bool, optional
          Whether the intersection or difference of words between two topics should be returned.
      normed : bool, optional
          Whether the matrix should be normalized or not.
      logger : {'shell', 'visdom'}, optional
         Monitor training process using one of the available methods. 'shell' will print the coherence value in
         the active shell, while 'visdom' will visualize the coherence value with increasing epochs using the Visdom
         visualization framework.
      viz_env : object, optional
          Visdom environment to use for plotting the graph. Unused.
      title : str, optional
          Title of the graph plot in case `logger == 'visdom'`. Unused.
  
      """
      self.distance = distance
      self.num_words = num_words
      self.n_ann_terms = n_ann_terms
      self.diagonal = diagonal
      self.annotation = annotation
      self.normed = normed
      self.logger = logger
      self.viz_env = viz_env
      self.title = title
  
imports: import gensim
  import logging
  import copy
  import sys
  import numpy
label_desc: Use image node_id 1 to create a new ConvergenceMetric object from inherited base classes: Metric with example: obj = ConvergenceMetric(distance, num_words, n_ann_terms, diagonal, annotation, normed, logger, viz_env, title)