- switched to underscores in project name
- requiring python-weka-wrapper3 >= 0.3.0 now (jpype-based)
- using scikit-learn instead of deprecated sklearn dependency for scikit-learn (#10)
- WekaEstimator (module sklweka.classifiers) now has a custom score method that distinguishes between classification and regression to return the correct score.
- renamed data to X and targets to y, since some sklearn schemes use named arguments
- added dummy argument sample_weight=None to fit, score and fit_predict methods
- fixed: when supplying Classifier or JBObject instead of classname/options, classname/options now get determined automatically
- method to_instance (module: sklweka.dataset) now performs correct missing value check
- method to_nominal_labels (module: sklweka.dataset) generates nicer labels now
- WekaEstimator (module sklweka.classifiers) and WekaCluster (module sklweka.clusters) now allow specifying how many labels a particular nominal attribute or class attribute has (to avoid error message like Cannot handle unary class attribute! if there is only one label present in a particular split)
- the to_nominal_attributes method in the sklearn.dataset module requires now the indices parameter (incorrectly declared as optional); can parse a range string now as well
- fixed the fit, set_params and __str__ methods fo the MakeNominal transformer (module sklweka.preprocessing)
- WekaEstimator (module sklweka.classifiers) and WekaCluster (module sklweka.clusters) now allow specifying which attributes to turn into nominal ones, which avoids having to manually convert the data (either as list with 0-based indices or range string with 1-based indices)
- set_params methods now ignore empty dictionaries
- fixed sorting of labels in to_instances method in module sklweka.dataset
- redoing X when no class present in load_arff method (module sklweka.dataset)
- added load_dataset method in module sklweka.dataset that uses Weka to load the data before converting it into sklearn data structures (slower, but more flexible)
- added support for generating data via Weka's data generators
- requiring python-weka-wrapper3 version 0.2.1 at least in order to offer pickle support
- initial release