Skll

- Added support for `ElasticNet`, `Lasso`, and `LinearRegression`
learners.
- Reorganized examples, and created new example based on the Kaggle
[Titanic](http://www.kaggle.com/c/titanic-gettingStarted) data set.
- Added ability to easily create multiple files at once when using
`write_feature_file`. (80)
- Added support for the `.ndj` file extension for new-line delimited JSON
files. It's the same format as `.jsonlines`, just with a different name.
- Added support for comments and skipping blank lines in `.jsonlines`
files.
- Made some efficiency tweaks when creating logging messages.
- Made labels in `.results` files a little clearer for objective function
scores.
- Fixed some misleading error messages.
- Fixed issue with backward-compatibility unit test in Python 2.7.
- Fixed issue where predict mode required data to already be labelled.

- Refactored `experiments` module to remove unnecessary child processes,
and greatly simplify ablation code. This should fix issues 73 and 49.
- Deprecated `run_ablation` function, as its functionality has been folded
into `run_configuration`.
- Removed ability to run multiple configuration files in parallel, since
this lead to too many processes being created most of the time.
- Added ability to run multiple ablation experiments from the same
configuration file by adding support for multiple featuresets.
- Added `min_feature_count` value to results files, which fixes 62.
- Added more informative error messages when we run out of memory while
converting things to dense. They now say why something was converted to
dense in the first place.
- Added option to `skll_convert` for creating ARFF files that can be used
for regression in Weka. Previously, files would always contain non-numeric
labels, which would not work with Weka.
- Added ability to name relation in output ARFF files with `skll_convert`.
- Added `class_map` setting for collapsing multiple classes into one
(or just renaming them). See the
[run_experiment documentation](http://skll.readthedocs.org/en/latest/run_experiment.html#input) for details.
- Added warning when using `SVC` with `probability` flag set (2).
- Made logging much less verbose by default and switched to using
`QueueHandler` and `QueueListener` instances when dealing with
multiple processes/threads to prevent deadlocks (75).
- Added simple no-crash unit test for all learners. We check results with
some, but not all. (63)

- Added support for running ablation experiments with _all_ combinations of
features (instead of just holding out one feature at a time) via
`run_experiment --ablation_all`. As a result, we've also changed the
names of the `ablated_feature` column in result summary files to
`ablated_features`.
- Added ARFF and CSV file support across the board. As a result, all
instances of the parameter `tsv_label` have now been replaced with
`label_col`.
- Fixed issue 71.
- Fixed process leak that was causing sporadic issues.
- Removed `arff_to_megam`, `csv_to_megam`, `megan_to_arff`, and
`megam_to_csv` because they are all superseded by ARFF and CSV support
in `skll_convert`.
- Switched to using Anaconda for installing Atlas.
- Switched back to http://skll.readthedocs.org
URLs for documentation, now that rtfd/readthedocs.org456 has been fixed.

- Updated `generate_predictions` to use latest API.
- Switched to using multiprocessing-compatible logging. This should fix some
intermittent deadlocks.
- Switched to using miniconda for install Python on Travis-CI.

- Fixed crash when `modelpath` is blank and `task` is not
`cross_validate`.
- Fixed crash with `convert_examples` when given a generator.
- Refactored `skll.data`'s private `_*_dict_iter` functions to be
classes to reduce code duplication.

- Fixed crash with SVR on Python 3 from kernel type being a byte string.
- Fixed crash with DecisionTreeRegressor due to an invalid criterion being
set.