Project details for python weka wrapper

Screenshot python weka wrapper 0.2.1

by fracpete - January 5, 2015, 00:30:01 CET [ Project Homepage BibTeX Download ]

view ( today), download ( today ), 0 subscriptions


A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls. Offers all major APIs, like data generators, loaders, savers, filters, classifiers, clusterers, attribute selection, associations and experiments. Weka packages can be listed/installed/uninstalled as well. It does not provide any graphical frontend, but some basic plotting and graph visualizations are available through matplotlib and pygraphviz.

Changes to previous version:
  • 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)
BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Arff, Csv, Libsvm, Xrff
Tags: Machine Learning, Weka
Archive: download here


No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.