Fix docstrings for gensim.models.rpmodel

gensim/models/rpmodel.py

@@ -5,41 +5,54 @@
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
 
-import logging
+"""
+Objects of this class allow building and maintaining a model for Random Projections
+(also known as Random Indexing). 
 
-import numpy as np
+For theoretical background on RP, see:   Kanerva et al.: "Random indexing of text samples for Latent Semantic Analysis."
 
-from gensim import interfaces, matutils, utils
+    The main methods are:
 
+    1. constructor, which creates the random projection matrix
+    2. the [] method, which transforms a simple count representation into the TfIdf
+       space.
 
-logger = logging.getLogger('gensim.models.rpmodel')
+Model persistency is achieved via its load/save methods.
 
 
-class RpModel(interfaces.TransformationABC):
-    """
-    Objects of this class allow building and maintaining a model for Random Projections
-    (also known as Random Indexing). For theoretical background on RP, see:
+Examples:
+---------
+>>> from gensim.models import rpmmodel
+>>> rp = RpModel(corpus)
+>>> print(rp[some_doc])
+>>> rp.save('/tmp/foo.rp_model')
+"""
 
-      Kanerva et al.: "Random indexing of text samples for Latent Semantic Analysis."
+import logging
 
-    The main methods are:
+import numpy as np
 
-    1. constructor, which creates the random projection matrix
-    2. the [] method, which transforms a simple count representation into the TfIdf
-       space.
+from gensim import interfaces, matutils, utils
 
-    >>> rp = RpModel(corpus)
-    >>> print(rp[some_doc])
-    >>> rp.save('/tmp/foo.rp_model')
 
-    Model persistency is achieved via its load/save methods.
-    """
+logger = logging.getLogger('gensim.models.rpmodel')
+
+
+class RpModel(interfaces.TransformationABC):
 
     def __init__(self, corpus, id2word=None, num_topics=300):
         """
         `id2word` is a mapping from word ids (integers) to words (strings). It is
         used to determine the vocabulary size, as well as for debugging and topic
         printing. If not set, it will be determined from the corpus.
+
+
+        Parameters
+        ----------
+        corpus : interfaces.CorpusABC
+        id2word : dict of int tostring
+        num_topics : int
+
         """
         self.id2word = id2word
         self.num_topics = num_topics
@@ -52,6 +65,12 @@ def __str__(self):
     def initialize(self, corpus):
         """
         Initialize the random projection matrix.
+
+
+        Parameters
+        ----------
+        corpus : :class:`~interfaces.CorpusABC`
+
         """
         if self.id2word is None:
             logger.info("no word id mapping provided; initializing from corpus, assuming identity")
@@ -75,6 +94,16 @@ def initialize(self, corpus):
     def __getitem__(self, bow):
         """
         Return RP representation of the input vector and/or corpus.
+
+        Parameters
+        ----------
+        bow : :class:`~interfaces.CorpusABC` (iterable of documents) or list of (int, int).
+
+        Examples:
+        -------------
+        >>> rp = RpModel(corpus)
+        >>> print(rp[some_doc])
+
         """
         # if the input vector is in fact a corpus, return a transformed corpus as result
         is_corpus, bow = utils.is_corpus(bow)
@@ -96,5 +125,12 @@ def __getitem__(self, bow):
         ]
 
     def __setstate__(self, state):
+        """
+        Sets the internal state and updates freshly_loaded to True. Called when unpicked.
+
+        Parameters
+        ----------
+        state : state of the class
+        """
         self.__dict__ = state
         self.freshly_loaded = True