With the unit testing framework finally in place (and fixing a number of bugs), I’ve made a new release available.
Here is the list of all the changes:
- added unit testing framework
- added method refresh_cache() to weka/core/packages.py to allow user to refresh local cache
- method get_classname in weka.core.utils now handles Python objects and class objects as well
- added convenience method get_jclass to weka.core.utils to instantiate a Java class
- added a JavaArray wrapper for arrays, which allows getting/setting elements and iterating
- added property classname to class JavaObject for easy access to classname of underlying object
- added class method parse_matlab for parsing Matlab matrix strings to CostMatrix class
- predictions method of Evaluation class now return None if predictions are discarded
- Associator.get_capabilities() method is now a property: Associator.capabilities
- added wrapper classes for Java enums: weka.core.classes.Enum
- fixed retrieval of sumSq in Stats class (used by AttributeStats)
- fixed cluster_instance method in Clusterer class
- fixed filter and clusterer properties in clusterer classes (SingleClustererEnhancer, FilteredClusterer)
- added crossvalidate_model method to ClusterEvaluation
- added get_prc method to plot/classifiers.py for calculating the area under the precision-recall curve
- Filter.filter method now handles list of Instances objects as well, applying the filter sequentially to all the datasets (allows generation of compatible train/test sets)
NB: I’ve flagged the library now as beta as well.