Surprise

=============

Date: 03/05/17

Enhancements
------------

* Added FAQ in the doc
* Added the possibility to retrieve the k nearest neighbors of a user or an
item.
* Changed the dumping process a bit (see API changes). Plus, dumps can now be
loaded.
* Added possibility to build a testset from the ratings of a training set
* Added inner-to-raw id conversion in the Trainset class
* The r_ui parameter of the predict() method is now optional

Fixes
-----
* Fixed verbosity of the evaluate function
* Corrected prediction when only user (or only item) is unknown in SVD and NMF
algorithms. Thanks to kenoung!
* Corrected factor vectors initialization of SVD algorithms. Thanks to
adideshp!

API Changes
-----------

* The dump() method now dumps a list of predition (optional) and an algorithm
(optional as well). The algorithm is now a real algorithm object. The
trainset is not dumped anymore as it is already part of the algorithm anyway.
* The dump() method is now part of the dump namespace, and not the global
namespace (so it is accessed by surprise.dump.dump)

=============

Date: 04/01/17

Just a minor change so that README.md is converted to rst for better rendering
on PyPI.

=============

Date: 02/01/17

Enhancements
------------

* Added the GridSearch feature, by Maher
* Added a 'clip' option to the predict() method
* Added NMF algorithm
* Added entry point for better command line usage.
* Added CoClustering algorithm.
* Added SlopeOne algorithm.
* Added Probabilistic Matrix Factorization as an option SVD
* Cythonized Baseline Computation

Other
-----

* Surprise is now a scikit!
* Changed license to BSD
* Six is now a dependency

=============

Date: 22/11/16

* Changed name from recsys to surprise
* Improved printing of accuracy measures.
* Added version number.
* Rewrote the the __main__.py

=============

Date: 15/11/16

Enhancements
------------

* Added notebooks for comparing and evaluating algorithm performances
* Better use of setup.py
* Added a min_support parameter to the similarity measures.
* Added a min_k parameter to the KNN algorithms.
* The similarity matrix and baselines are now returned.
* You can now train on a whole training set without test set.
* The estimate method can return a tuple with prediction details.
* Added SVD and SVD++ algorithms.
* Removed all the x/y vs user/item stuff. That was useless for most algorithms.


API Changes
-----------

* Removed the property decorator for many iterators.
* It's now up to the algorithms to decide if they can or cannot make a
prediction.

=============

Date: 25/10/16

* Added support for Python 2