52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
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class BaseTopicModel(object):
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def print_topic(self, topicno, topn=10):
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"""Get a single topic as a formatted string.
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Parameters
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----------
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topicno : int
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Topic id.
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topn : int
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Number of words from topic that will be used.
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Returns
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-------
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str
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String representation of topic, like '-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + ... '.
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"""
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return ' + '.join(['%.3f*"%s"' % (v, k) for k, v in self.show_topic(topicno, topn)])
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def print_topics(self, num_topics=20, num_words=10):
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"""Get the most significant topics (alias for `show_topics()` method).
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Parameters
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----------
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num_topics : int, optional
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The number of topics to be selected, if -1 - all topics will be in result (ordered by significance).
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num_words : int, optional
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The number of words to be included per topics (ordered by significance).
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Returns
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-------
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list of (int, list of (str, float))
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Sequence with (topic_id, [(word, value), ... ]).
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"""
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return self.show_topics(num_topics=num_topics, num_words=num_words, log=True)
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def get_topics(self):
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"""Get words X topics matrix.
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Returns
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--------
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numpy.ndarray:
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The term topic matrix learned during inference, shape (`num_topics`, `vocabulary_size`).
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Raises
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------
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NotImplementedError
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"""
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raise NotImplementedError
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