- Will now skip IDs that are missing from `cv_folds`/`grid_search_folds` dicts and print a warning instead of crashing. - Added additional kappa unit tests to help detect/prevent future issues. - **API change:** `model_type` is no longer a keyword argument to `Learner` constructor, and is now required. This was done to help prevent unexpected issues from defaulting to `LogisticRegression`. - No longer keep extra temporary config files around when running `run_experiment` in ablation mode.
0.12.0
- Fixed crash with kappa when given two sets of ratings that are both missing an intermediate value (e.g., `[1, 2, 4]`). - Added `summarize_results` script for creating a nice summary TSV file from a list of JSON results files. - Summary files for ablation studies now have an extra column that says which feature was removed.
0.11.0
- Added initial version of `skll_convert` script for converting between .jsonlines, .megam, and .tsv data file formats. - Fixed bug in `_megam_dict_iter` where labels for instances with all zero features were being incorrectly set to `None`. - Fixed bug in `_tsv_dict_iter` where features with zero values were being retained with values set as '0' instead of being removed completely. This caused `DictVectorizer` to create extra features, so **results may change** a little bit if you were using .tsv files. - Fixed crash with predict and train_only modes when running on the grid. - No longer use process pools to load files if `SKLL_MAX_CONCURRENT_PROCESSES` is 1. - Added more informative error message when trying to load a file without any features.
0.10.1
- Made processes non-daemonic to fix `pool.map` issue with running multiple configurations files at the same time with `run_experiment`.
0.10.0
- `run_experiment` can now take multiple configuration files. - Fixed issue where model parameters and scores were missing in `evaluate` mode
0.9.17
- Added `skll.data.convert_examples` function to convert a list dictionaries to an ExamplesTuple. - Added a new optional field to configuration file, `ids_to_floats`, to help save memory if you have a massive number of instances with numeric IDs. - Replaced `use_dense_features` and `scale_features` options with `feature_scaling`. See the [run_experiment documentation](http://skll2.readthedocs.org/en/latest/run_experiment.html#creating-configuration-files) for details.