Merge branch 'release-0.12.3rc1'

.travis.yml

@@ -5,11 +5,12 @@ python:
   - "2.7"
   - "3.3"
   - "3.4"
+  - "3.5"  
 before_install:
   - wget 'https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh' -O miniconda.sh
   - chmod +x miniconda.sh
   - ./miniconda.sh -b
-  - export PATH=/home/travis/miniconda/bin:$PATH
+  - export PATH=/home/travis/miniconda2/bin:$PATH
   - conda update --yes conda
 install:
   - conda create --yes -n gensim-test python=$TRAVIS_PYTHON_VERSION pip atlas numpy scipy

CHANGELOG.txt

@@ -1,8 +1,21 @@
 Changes
 =======
 
-
-0.12.2
+NEXT
+
+* Make show_topics return value consistent across models (Christopher Corley, #448)
+  - All models with the `show_topics` method should return a list of
+    `(topic_number, topic)` tuples, where `topic` is a list of
+    `(word, probability)` tuples.
+  - This is a breaking change that affects users of the `LsiModel`, `LdaModel`,
+  and `LdaMulticore` that may be reliant on the old tuple layout of
+  `(probability, word)`.
+* Mixed integer & string document-tags (keys to doc-vectors) will work (Gordon Mohr, #491)
+  - DocvecsArray's `index2doctag` list is renamed/reinterpreted as `offset2doctag`
+  - `offset2doctag` entries map to `doctag_syn0` indexes *after* last plain-int doctag (if any)
+  - (If using only string doctags, `offset2doctag` may be interpreted same as `index2doctag`.)
+
+0.12.2, 19/09/2015
 
 * tutorial on text summarization (Ólavur Mortensen, #436)
 * more flexible vocabulary construction in word2vec & doc2vec (Philipp Dowling, #434)

README.rst

@@ -57,7 +57,7 @@ you'd run::
 For alternative modes of installation (without root privileges, development
 installation, optional install features), see the `documentation <https://radimrehurek.com/gensim/install.html>`_.
 
-This version has been tested under Python 2.6, 2.7, 3.3 and 3.4 (support for Python 2.5 was dropped in gensim 0.10.0; install gensim 0.9.1 if you *must* use Python 2.5). Gensim's github repo is hooked to `Travis CI for automated testing <https://travis-ci.org/piskvorky/gensim>`_ on every commit push and pull request.
+This version has been tested under Python 2.6, 2.7, 3.3, 3.4 and 3.5 (support for Python 2.5 was dropped in gensim 0.10.0; install gensim 0.9.1 if you *must* use Python 2.5). Gensim's github repo is hooked to `Travis CI for automated testing <https://travis-ci.org/piskvorky/gensim>`_ on every commit push and pull request.
 
 How come gensim is so fast and memory efficient? Isn't it pure Python, and isn't Python slow and greedy?
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