LSI documentation

docs/src/models/lsi_dispatcher.rst

@@ -5,4 +5,5 @@
     :synopsis: Dispatcher for distributed LSI
     :members:
     :inherited-members:
-
+    :undoc-members:
+    :show-inheritance:

docs/src/models/lsi_worker.rst

@@ -5,4 +5,5 @@
     :synopsis: Worker for distributed LSI
     :members:
     :inherited-members:
-
+    :undoc-members:
+    :show-inheritance:

gensim/models/basemodel.py

@@ -1,24 +1,51 @@
 class BaseTopicModel(object):
     def print_topic(self, topicno, topn=10):
-        """
-        Return a single topic as a formatted string. See `show_topic()` for parameters.
+        """Get a single topic as a formatted string.
+
+        Parameters
+        ----------
+        topicno : int
+            Topic id.
+        topn : int
+            Number of words from topic that will be used.
 
-        >>> lsimodel.print_topic(10, topn=5)
-        '-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + -0.174 * "functor" + -0.168 * "operator"'
+        Returns
+        -------
+        str
+            String representation of topic, like '-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + ... '.
 
         """
         return ' + '.join(['%.3f*"%s"' % (v, k) for k, v in self.show_topic(topicno, topn)])
 
     def print_topics(self, num_topics=20, num_words=10):
-        """Alias for `show_topics()` that prints the `num_words` most
-        probable words for `topics` number of topics to log.
-        Set `topics=-1` to print all topics."""
+        """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
+        -------
+        list of (int, list of (str, float))
+            Sequence with (topic_id, [(word, value), ... ]).
+
+        """
         return self.show_topics(num_topics=num_topics, num_words=num_words, log=True)
 
     def get_topics(self):
-        """
-        Returns:
-            np.ndarray: `num_topics` x `vocabulary_size` array of floats which represents
-            the term topic matrix learned during inference.
+        """Get words X topics matrix.
+
+        Returns
+        --------
+        numpy.ndarray:
+            The term topic matrix learned during inference, shape (`num_topics`, `vocabulary_size`).
+
+        Raises
+        ------
+        NotImplementedError
+
         """
         raise NotImplementedError