Docs for word2vec.py forwarding functions and one more

gensim/models/word2vec.py

@@ -355,6 +355,9 @@ class Word2Vec(utils.SaveLoad):
     """
     Class for training, using and evaluating neural networks described in https://code.google.com/p/word2vec/
 
+    If you're finished training a model (=no more updates, only querying)
+    then switch to the :mod:`gensim.models.KeyedVectors` instance in wv
+
     The model can be stored/loaded via its `save()` and `load()` methods, or stored/loaded in a format
     compatible with the original word2vec implementation via `wv.save_word2vec_format()` and `KeyedVectors.load_word2vec_format()`.
 
@@ -1076,6 +1079,11 @@ def worker_loop():
         return sentence_scores[:sentence_count]
 
     def clear_sims(self):
+        """
+        Removes all L2-normalized vectors for words from the model.
+        You will have to recompute them using init_sims method.
+        """
+
         self.wv.syn0norm = None
 
     def update_weights(self):
@@ -1181,33 +1189,103 @@ def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='ut
         logger.info("merged %d vectors into %s matrix from %s" % (overlap_count, self.wv.syn0.shape, fname))
 
     def most_similar(self, positive=[], negative=[], topn=10, restrict_vocab=None, indexer=None):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.most_similar`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.most_similar(positive, negative, topn, restrict_vocab, indexer)
 
     def wmdistance(self, document1, document2):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.wmdistance`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.wmdistance(document1, document2)
 
     def most_similar_cosmul(self, positive=[], negative=[], topn=10):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.most_similar_cosmul`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.most_similar_cosmul(positive, negative, topn)
 
     def similar_by_word(self, word, topn=10, restrict_vocab=None):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.similar_by_word`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.similar_by_word(word, topn, restrict_vocab)
 
     def similar_by_vector(self, vector, topn=10, restrict_vocab=None):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.similar_by_vector`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.similar_by_vector(vector, topn, restrict_vocab)
 
     def doesnt_match(self, words):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.doesnt_match`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.doesnt_match(words)
 
     def __getitem__(self, words):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.__getitem__`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.__getitem__(words)
 
     def __contains__(self, word):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.__contains__`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.__contains__(word)
 
     def similarity(self, w1, w2):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.similarity`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.similarity(w1, w2)
 
     def n_similarity(self, ws1, ws2):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.n_similarity`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.n_similarity(ws1, ws2)
 
     def predict_output_word(self, context_words_list, topn=10):
@@ -1270,9 +1348,23 @@ def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_inse
 
     @staticmethod
     def log_evaluate_word_pairs(pearson, spearman, oov, pairs):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.log_evaluate_word_pairs`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return KeyedVectors.log_evaluate_word_pairs(pearson, spearman, oov, pairs)
 
     def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, case_insensitive=True, dummy4unknown=False):
+        """
+        Please refer to the documentation for
+        `gensim.models.KeyedVectors.evaluate_word_pairs`
+        This is just a forwarding function.
+        In the future please use the `gensim.models.KeyedVectors` instance in wv
+        """
+
         return self.wv.evaluate_word_pairs(pairs, delimiter, restrict_vocab, case_insensitive, dummy4unknown)
 
     def __str__(self):