tensorflow_probability/examples/latent_dirichlet_allocation_edward2.py
def get_topics_strings(topics_words, alpha, vocabulary,
                       topics_to_print=10, words_per_topic=10):
  """Returns the summary of the learned topics.

  Arguments:
    topics_words: KxV tensor with topics as rows and words as columns.
    alpha: 1xK tensor of prior Dirichlet concentrations for the
        topics.
    vocabulary: A mapping of word's integer index to the corresponding string.
    topics_to_print: The number of topics with highest prior weight to
        summarize.
    words_per_topic: Number of wodrs per topic to return.

  Returns:
    summary: A np.array with strings.
  """
  alpha = np.squeeze(alpha, axis=0)
  # Use a stable sorting algorithm so that when alpha is fixed
  # we always get the same topics.
  highest_weight_topics = np.argsort(-alpha, kind="mergesort")
  top_words = np.argsort(-topics_words, axis=1)

  res = []
  for topic_idx in highest_weight_topics[:topics_to_print]:
    l = ["index={} alpha={:.2f}".format(topic_idx, alpha[topic_idx])]
    l += [vocabulary[word] for word in top_words[topic_idx, :words_per_topic]]
    res.append(" ".join(l))

  return np.array(res)
def get_topics_strings(topics_words, alpha, vocabulary,
                       topics_to_print=10, words_per_topic=10):
  """Returns the summary of the learned topics.

  Arguments:
    topics_words: KxV tensor with topics as rows and words as columns.
    alpha: 1xK tensor of prior Dirichlet concentrations for the
        topics.
    vocabulary: A mapping of word's integer index to the corresponding string.
    topics_to_print: The number of topics with highest prior weight to
        summarize.
    words_per_topic: Number of wodrs per topic to return.

  Returns:
    summary: A np.array with strings.
  """
  alpha = np.squeeze(alpha, axis=0)
  # Use a stable sorting algorithm so that when alpha is fixed
  # we always get the same topics.
  highest_weight_topics = np.argsort(-alpha, kind="mergesort")
  top_words = np.argsort(-topics_words, axis=1)

  res = []
  for topic_idx in highest_weight_topics[:topics_to_print]:
    l = ["index={} alpha={:.2f}".format(topic_idx, alpha[topic_idx])]
    l += [vocabulary[word] for word in top_words[topic_idx, :words_per_topic]]
    res.append(" ".join(l))

  return np.array(res)
def get_topics_strings ( topics_words , alpha , vocabulary , topics_to_print = 10 , words_per_topic = 10 ) : alpha = np . squeeze ( alpha , axis = 0 ) # Use a stable sorting algorithm so that when alpha is fixed # we always get the same topics. highest_weight_topics = np . argsort ( - alpha , kind = "mergesort" ) top_words = np . argsort ( - topics_words , axis = 1 ) res = [ ] for topic_idx in highest_weight_topics [ : topics_to_print ] : l = [ "index={} alpha={:.2f}" . format ( topic_idx , alpha [ topic_idx ] ) ] l += [ vocabulary [ word ] for word in top_words [ topic_idx , : words_per_topic ] ] res . append ( " " . join ( l ) ) return np . array ( res )
Returns the summary of the learned topics.

  Arguments:
    topics_words: KxV tensor with topics as rows and words as columns.
    alpha: 1xK tensor of prior Dirichlet concentrations for the
        topics.
    vocabulary: A mapping of word's integer index to the corresponding string.
    topics_to_print: The number of topics with highest prior weight to
        summarize.
    words_per_topic: Number of wodrs per topic to return.

  Returns:
    summary: A np.array with strings.
Returns the summary of the learned topics .