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- Description:
A thin Python3 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 check_for_modified_class_attribute method to FilterClassifier class
- added complete_classname method to weka.core.classes module, which allows completion of partial classnames like .J48 to weka.classifiers.trees.J48; if there is a unique match; JavaObject.new_instance and JavaObject.check_type now make use of this functionality, allowing for instantiations like Classifier(cls=".J48")
- jvm.start(system_cp=True) no longer fails with a KeyError: 'CLASSPATH' if there is no CLASSPATH environment variable defined
- Libraries mtl.jar, core.jar and arpack_combined_all.jar were added as is to the weka.jar in the 3.9.1 release instead of adding their content to it. Repackaged weka.jar to fix this issue.
- BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Arff, Csv, Libsvm, Xrff
- Tags: Machine Learning, Weka
- Archive: download here
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