Skll

- Fixed all known remaining compatibility issues with Python 3.
- Fixed bug in `skll.metrics.kappa` which would raise an exception if full
range of ratings was not seen in both `y_true` and `y_pred`. Also added a
unit test to prevent future regressions.
- Added missing configuration file that would cause a unit test to fail.
- Slightly refactored `skll.Learner._create_estimator` to make it a lot
simpler to add new learners/estimators in the future.
- Fixed a bug in handling of sparse matrices that would cause a crash if
the number of features in the training and the test set were not the same.
Also added a corresponding unit test to prevent future regressions.
- We now require the backported configparser module for Python 2.7 to make
maintaining compatibility with both 2.x and 3.x a lot easier.

- Fixed bug introduced in v0.9.9 that broke "predict" mode.

- Automatically generate a result summary file with all results for experiment in one TSV.
- Fixed bug where printing predictions to file would cause a crash with some learners.
- Run unit tests for Python 3.3 as well as 2.7.
- More unit tests for increased coverage.

- Fixed crash due to trying to print name of grid objective which is now a str and not a function.
- Added --version option to shell scripts.

- Can now use any objective function scikit-learn supports for tuning (i.e.,
any valid argument for scorer when instantiating GridSearchCV) in addition
to those we define.
- Removed ml_metrics dependency and we now support custom weights for kappa
(through the API only so far).
- Require's scikit-learn 0.14+.
- `accuracy`, `quadratic_weighted_kappa`, `unweighted_kappa`,
`f1_score_micro`, and `f1_score_macro` functions are no longer available
under `skll.metrics`. The accuracy and f1 score ones are no longer needed
because we just use the built-in ones. As for quadratic_weighted_kappa and
unweighted_kappa, they've been superseded by the kappa function that takes
a weights argument.
- Fixed issue where you couldn't write prediction files if you were
classifying using numeric classes.

- Fixes issue with setup.py importing from package when trying to install
it (for real this time).