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Python wrapper for the Weka Machine Learning Workbench

Project description

The python-weka-wrapper package makes it easy to run Weka algorithms and filters from within Python. It offers access to Weka API using thin wrappers around JNI calls using the javabridge package.


0.2.2 (2015-01-05)

  • added convenience methods no_class (to unset class) and has_class (class set?) to Instances class
  • switched to using faster method objects for methods classify_instance/distribution_for_instance in Classifier class
  • switched to using faster method objects for methods cluster_instance/distribution_for_instance in Clusterer class
  • switched to using faster method objects for methods class_index, is_missing, get/set_value, get/set_string_value, weight in Instance class
  • switched to using faster method objects for methods input, output, outputformat in Filter class
  • switched to using faster method objects for methods attribute, attribute_by_name, num_attributes, num_instances, class_index, class_attribute, set_instance, get_instance, add_instance in Instances class

0.2.1 (2015-01-05)

  • added unit testing framework
  • added method refresh_cache() to weka/core/ 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/ 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)

0.2.0 (2014-12-22)

NB: This release is not backwards compatible!

  • requires JavaBridge 1.0.9 at least
  • moved from Java-like get/set (getIndex() and setIndex(int)) to nicer Python properties
  • using Python properties (also only read-only ones) wherevere possible
  • added weka.core.version for accessing the Weka version currently in use
  • added jwrapper and jclasswrapper methods to JavaObject class (the mother of all objects in python-weka-wrapper) to allow generic access to an object’s methods:
  • added convenience methods class_is_last() and class_is_first() to weka.core.Instances class
  • added convenience methods delete_last_attribute() and delete_first_attribute() to weka.core.Instances class

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