weka.gui.visualize.ThresholdVisualizePanel Maven / Gradle / Ivy
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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other breaking updates.
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see
* lists all the commandline parameters
*
*
* -t file
* Dataset to process with given classifier.
*
*
* -W classname
* Full classname of classifier to run.
* Options after '--' are passed to the classifier.
* (default weka.classifiers.functions.Logistic)
*
*
* -r number
* The number of runs to perform (default 2).
*
*
* -x number
* The number of Cross-validation folds (default 10).
*
*
* -l file
* Previously saved threshold curve ARFF file.
*
*
* @param args optional commandline parameters
*/
public static void main(String[] args) {
Instances inst;
Classifier classifier;
int runs;
int folds;
String tmpStr;
boolean compute;
Instances result;
String[] options;
SingleIndex classIndex;
SingleIndex valueIndex;
int seed;
inst = null;
classifier = null;
runs = 2;
folds = 10;
compute = true;
result = null;
classIndex = null;
valueIndex = null;
seed = 1;
try {
// help?
if (Utils.getFlag('h', args)) {
System.out.println("\nOptions for "
+ ThresholdVisualizePanel.class.getName() + ":\n");
System.out.println("-h\n\tThis help.");
System.out
.println("-t \n\tDataset to process with given classifier.");
System.out
.println("-c \n\tThe class index. first and last are valid, too (default: last).");
System.out
.println("-C \n\tThe index of the class value to get the the curve for (default: first).");
System.out
.println("-W \n\tFull classname of classifier to run.\n\tOptions after '--' are passed to the classifier.\n\t(default: weka.classifiers.functions.Logistic)");
System.out
.println("-r \n\tThe number of runs to perform (default: 1).");
System.out
.println("-x \n\tThe number of Cross-validation folds (default: 10).");
System.out
.println("-S \n\tThe seed value for randomizing the data (default: 1).");
System.out
.println("-l \n\tPreviously saved threshold curve ARFF file.");
return;
}
// regular options
tmpStr = Utils.getOption('l', args);
if (tmpStr.length() != 0) {
result = new Instances(new BufferedReader(new FileReader(tmpStr)));
compute = false;
}
if (compute) {
tmpStr = Utils.getOption('r', args);
if (tmpStr.length() != 0) {
runs = Integer.parseInt(tmpStr);
} else {
runs = 1;
}
tmpStr = Utils.getOption('x', args);
if (tmpStr.length() != 0) {
folds = Integer.parseInt(tmpStr);
} else {
folds = 10;
}
tmpStr = Utils.getOption('S', args);
if (tmpStr.length() != 0) {
seed = Integer.parseInt(tmpStr);
} else {
seed = 1;
}
tmpStr = Utils.getOption('t', args);
if (tmpStr.length() != 0) {
inst = new Instances(new BufferedReader(new FileReader(tmpStr)));
inst.setClassIndex(inst.numAttributes() - 1);
}
tmpStr = Utils.getOption('W', args);
if (tmpStr.length() != 0) {
options = Utils.partitionOptions(args);
} else {
tmpStr = weka.classifiers.functions.Logistic.class.getName();
options = new String[0];
}
classifier = AbstractClassifier.forName(tmpStr, options);
tmpStr = Utils.getOption('c', args);
if (tmpStr.length() != 0) {
classIndex = new SingleIndex(tmpStr);
} else {
classIndex = new SingleIndex("last");
}
tmpStr = Utils.getOption('C', args);
if (tmpStr.length() != 0) {
valueIndex = new SingleIndex(tmpStr);
} else {
valueIndex = new SingleIndex("first");
}
}
// compute if necessary
if (compute) {
if (classIndex != null) {
classIndex.setUpper(inst.numAttributes() - 1);
inst.setClassIndex(classIndex.getIndex());
} else {
inst.setClassIndex(inst.numAttributes() - 1);
}
if (valueIndex != null) {
valueIndex.setUpper(inst.classAttribute().numValues() - 1);
}
ThresholdCurve tc = new ThresholdCurve();
EvaluationUtils eu = new EvaluationUtils();
ArrayList predictions = new ArrayList();
for (int i = 0; i < runs; i++) {
eu.setSeed(seed + i);
predictions.addAll(eu.getCVPredictions(classifier, inst, folds));
}
if (valueIndex != null) {
result = tc.getCurve(predictions, valueIndex.getIndex());
} else {
result = tc.getCurve(predictions);
}
}
// setup GUI
ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
vmc.setROCString("(Area under ROC = "
+ Utils.doubleToString(ThresholdCurve.getROCArea(result), 4) + ")");
if (compute) {
vmc.setName(result.relationName() + ". (Class value "
+ inst.classAttribute().value(valueIndex.getIndex()) + ")");
} else {
vmc.setName(result.relationName() + " (display only)");
}
PlotData2D tempd = new PlotData2D(result);
tempd.setPlotName(result.relationName());
tempd.addInstanceNumberAttribute();
vmc.addPlot(tempd);
String plotName = vmc.getName();
final JFrame jf = new JFrame("Weka Classifier Visualize: " + plotName);
jf.setSize(500, 400);
jf.getContentPane().setLayout(new BorderLayout());
jf.getContentPane().add(vmc, BorderLayout.CENTER);
jf.addWindowListener(new WindowAdapter() {
@Override
public void windowClosing(WindowEvent e) {
jf.dispose();
}
});
jf.setVisible(true);
} catch (Exception e) {
e.printStackTrace();
}
}
}