weka.gui.explorer.AttributeSelectionPanel 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 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 list = new Vector();
list.add("-s");
if (search instanceof OptionHandler) {
list.add(sname + " "
+ Utils.joinOptions(((OptionHandler) search).getOptions()));
} else {
list.add(sname);
}
if (evaluator instanceof OptionHandler) {
String[] opt = ((OptionHandler) evaluator).getOptions();
for (String element : opt) {
list.add(element);
}
}
cmd =
ename + " "
+ Utils.joinOptions(list.toArray(new String[list.size()]));
// 2. filter command
weka.filters.supervised.attribute.AttributeSelection filter =
new weka.filters.supervised.attribute.AttributeSelection();
filter.setEvaluator((ASEvaluation) m_AttributeEvaluatorEditor
.getValue());
filter.setSearch((ASSearch) m_AttributeSearchEditor.getValue());
cmdFilter =
filter.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler) filter).getOptions());
// 3. meta-classifier command
weka.classifiers.meta.AttributeSelectedClassifier cls =
new weka.classifiers.meta.AttributeSelectedClassifier();
cls
.setEvaluator((ASEvaluation) m_AttributeEvaluatorEditor.getValue());
cls.setSearch((ASSearch) m_AttributeSearchEditor.getValue());
cmdClassifier =
cls.getClass().getName() + " "
+ Utils.joinOptions(cls.getOptions());
AttributeSelection eval = null;
try {
if (m_CVBut.isSelected()) {
testMode = 1;
numFolds = Integer.parseInt(m_CVText.getText());
seed = Integer.parseInt(m_SeedText.getText());
if (numFolds <= 1) {
throw new Exception("Number of folds must be greater than 1");
}
}
if (classIndex >= 0) {
inst.setClassIndex(classIndex);
}
// Output some header information
m_Log.logMessage("Started " + ename);
m_Log.logMessage("Command: " + cmd);
m_Log.logMessage("Filter command: " + cmdFilter);
m_Log.logMessage("Meta-classifier command: " + cmdClassifier);
if (m_Log instanceof TaskLogger) {
((TaskLogger) m_Log).taskStarted();
}
outBuff.append("=== Run information ===\n\n");
outBuff.append("Evaluator: " + ename);
if (evaluator instanceof OptionHandler) {
String[] o = ((OptionHandler) evaluator).getOptions();
outBuff.append(" " + Utils.joinOptions(o));
}
outBuff.append("\nSearch: " + sname);
if (search instanceof OptionHandler) {
String[] o = ((OptionHandler) search).getOptions();
outBuff.append(" " + Utils.joinOptions(o));
}
outBuff.append("\n");
outBuff.append("Relation: " + inst.relationName() + '\n');
outBuff.append("Instances: " + inst.numInstances() + '\n');
outBuff.append("Attributes: " + inst.numAttributes() + '\n');
if (inst.numAttributes() < 100) {
for (int i = 0; i < inst.numAttributes(); i++) {
outBuff.append(" " + inst.attribute(i).name()
+ '\n');
}
} else {
outBuff.append(" [list of attributes omitted]\n");
}
outBuff.append("Evaluation mode: ");
switch (testMode) {
case 0: // select using all training
outBuff.append("evaluate on all training data\n");
break;
case 1: // CV mode
outBuff.append("" + numFolds + "-fold cross-validation\n");
break;
}
outBuff.append("\n");
m_History.addResult(name, outBuff);
m_History.setSingle(name);
// Do the feature selection and output the results.
m_Log.statusMessage("Doing feature selection...");
m_History.updateResult(name);
eval = new AttributeSelection();
eval.setEvaluator(evaluator);
eval.setSearch(search);
eval.setFolds(numFolds);
eval.setSeed(seed);
if (testMode == 1) {
eval.setXval(true);
}
switch (testMode) {
case 0: // select using training
m_Log.statusMessage("Evaluating on training data...");
eval.SelectAttributes(inst);
break;
case 1: // CV mode
m_Log.statusMessage("Randomizing instances...");
Random random = new Random(seed);
inst.randomize(random);
if (inst.attribute(classIndex).isNominal()) {
m_Log.statusMessage("Stratifying instances...");
inst.stratify(numFolds);
}
for (int fold = 0; fold < numFolds; fold++) {
m_Log.statusMessage("Creating splits for fold " + (fold + 1)
+ "...");
Instances train = inst.trainCV(numFolds, fold, random);
m_Log.statusMessage("Selecting attributes using all but fold "
+ (fold + 1) + "...");
eval.selectAttributesCVSplit(train);
}
break;
default:
throw new Exception("Test mode not implemented");
}
if (testMode == 0) {
outBuff.append(eval.toResultsString());
} else {
outBuff.append(eval.CVResultsString());
}
outBuff.append("\n");
m_History.updateResult(name);
m_Log.logMessage("Finished " + ename + " " + sname);
m_Log.statusMessage("OK");
} catch (Exception ex) {
m_Log.logMessage(ex.getMessage());
m_Log.statusMessage("See error log");
} finally {
ArrayList