Skll

- 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unweighted_kappa, they've been superseded by the kappa function that takes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).

- You can now include feature files that don't have class labels in your
featuresets. At least one feature file has to have a label though,
because we only support supervised learning so far.
- **Important:** If you're using TSV files in your experiments, you should
either name the class label column 'y' or use the new `tsv_label` option
in your configuration file to specify the name of the label column. This
was necessary to support feature files without labels.
- Fixed an issue with how version number was being imported in setup.py that
would prevent installation if you didn't already have the prereqs
installed on your machine.
- Made random seeds smaller to fix crash on 32-bit machines. This means that
experiments run with previous versions of skll will yield slightly
different results if you re-run them with v0.9.5+.
- Added `megam_to_csv` for converting .megam files to CSV/TSV files.
- Fixed a potential rounding problem with `csv_to_megam` that could slightly
change feature values in conversion process.
- Cleaned up test_skll.py a little bit.
- Updated documentation to include missing fields that can be specified in
config files.