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Introduction

Introduction#

As of Weka version >3.5.8 (only developer version, not stable-3.6 branch) one can easily add graph visualization plugins in the Explorer (Classify panel). This makes it easy to implement custom visualizations, if the ones Weka offers are not sufficient.

Note: This is also covered in chapter Extending WEKA of the WEKA manual in versions later than 3.7.0 or snapshots of the developer version later than 10/01/2010.

Requirements#

  • custom visualization class must implement the following interface

    weka.gui.visualize.plugins.ErrorVisualizePlugin

  • the class must either reside in the following package (visualization classes are automatically discovered during run-time)

    weka.gui.visualize.plugins

  • or the class' package must be listed in the weka.gui.visualize.plugins.ErrorVisualizePlugin key of the weka/gui/GenericPropertiesCreator.props file.

Implementation#

The visualization interface contains the following four methods

  • getMinVersion

    This method returns the minimal version (inclusive) of Weka that is necessary to execute the plugin, e.g., 3.5.0. * getMaxVersion This method returns the maximal version (exclusive) of Weka that is necessary to execute the plugin, e.g., 3.6.0.

  • getDesignVersion

    Returns the actual version of Weka this plugin was designed for, e.g., 3.5.1

  • getVisualizeMenuItem

    The JMenuItem that is returned via this method will be added to the plugins menu in the popup in the Explorer. The ActionListener for clicking the menu item will most likely open a new frame containing the visualized data.

Examples#

The following screenshots were generated using weka.classifiers.functions.LinearRegression with default parameters on the UCI dataset bolts, using a percentage split of 66% for the training set and the remainder for testing.

Using Weka panels#

The ClassifierErrorsWeka.java example simply displays the classifier errors as the Visualize classifier errors menu item already available in Weka. It is just to demonstrate how to use existing Weka classes.

Screenshot

Using JMathtools' Boxplot#

The ClassifierErrorsMathtools.java. The relative error per prediction is displayed as vertical line.

Screenshot

Note: This display is only available for numeric classes.

Downloads#

See also#