`models.basemodel` – Core TM interface¶

class gensim.models.basemodel.BaseTopicModel¶

Bases: object

get_topics()¶

Get words X topics matrix.

Returns: The term topic matrix learned during inference, shape (num_topics, vocabulary_size).
Return type: numpy.ndarray
Raises: NotImplementedError –

print_topic(topicno, topn=10)¶

Get a single topic as a formatted string.

Parameters

Returns

String representation of topic, like ‘-0.340 * “category” + 0.298 * “$M$” + 0.183 * “algebra” + … ‘.

Return type

str

print_topics(num_topics=20, num_words=10)¶

Get the most significant topics (alias for show_topics() method).

Parameters

num_topics (int, optional) – The number of topics to be selected, if -1 - all topics will be in result (ordered by significance).
num_words (int, optional) – The number of words to be included per topics (ordered by significance).

Returns

Sequence with (topic_id, [(word, value), … ]).

Return type

list of (int, list of (str, float))

models.basemodel – Core TM interface¶