Version 0.22.4

Fix missing import sys in run_experiment.py

Version 0.22.3

Very minor bug fix release. Changes are:

• main functions for all utility scripts now take optional argument lists to make unit testing simpler (and not require subprocesses).
• Fix another bug that was causing missing "ablated features" lists in summary files.

Version 0.22.2

Fix crash with filter_megam and join_megam due to references to old API.

Version 0.22.1

Minor bug fix release. Changes are:

• Switch to joblib.dump and joblib.load for serialization (should fix #94)
• Switch to using official drmaa-python release now that it's updated on PyPI
• Fix issue where training examples were being loaded for pre-trained models (#95)
• Change to using entry_points to generate scripts instead of scripts in setup.py, and utilities are now in a sub-package.

Version 0.22.0

This release features mostly bug fixes, but also includes a few minor features:

• Change license to BSD 3 clause. Now any of our code could be added back into scikit-learn without licensing issues.
• Add gamma to default paramater search grid for SVC (#84).
• Add --verbose flag to run_experiment to simplify debugging.
• Add support for wheel packaging.
• Fixed bug in _write_summary_file that prevented writing of summary files for --ablation_all experiments.
• Fixed SVR kernel string type issue (#87).
• Fixed fit_intercept default value issue (#88).
• Fixed incorrect error message (#86)
• Tweaked .travis.yml to make builds a little faster.

Version 0.21.0

• Added support for ElasticNet, Lasso, and LinearRegression
  learners.
• Reorganized examples, and created new example based on the Kaggle
  Titanic 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.

Version 0.20.0

• 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 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)

Version 0.19.0

• 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 readthedocs/readthedocs.org#456 has been fixed.

Version 0.18.1

• 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.

Version 0.18.0

• 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.