Version 0.22.5

Fixed a bug where command-line scripts didn't work after previous release. (This should hopefully be the last of these rapid fire releases. We will add unit tests for these in the future.)

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.