private static void evaluateClassifier(Classifier c, Instances trainData, Instances testData)
throws Exception {
System.err.println(
"INFO: Starting split validation to predict '"
+ trainData.classAttribute().name()
+ "' using '"
+ c.getClass().getCanonicalName()
+ ":"
+ Arrays.toString(c.getOptions())
+ "' (#train="
+ trainData.numInstances()
+ ",#test="
+ testData.numInstances()
+ ") ...");
if (trainData.classIndex() < 0) throw new IllegalStateException("class attribute not set");
c.buildClassifier(trainData);
Evaluation eval = new Evaluation(testData);
eval.useNoPriors();
double[] predictions = eval.evaluateModel(c, testData);
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
// write predictions to file
{
System.err.println("INFO: Writing predictions to file ...");
Writer out = new FileWriter("prediction.trec");
writePredictionsTrecEval(predictions, testData, 0, trainData.classIndex(), out);
out.close();
}
// write predicted distributions to CSV
{
System.err.println("INFO: Writing predicted distributions to CSV ...");
Writer out = new FileWriter("predicted_distribution.csv");
writePredictedDistributions(c, testData, 0, out);
out.close();
}
}
/** outputs some data about the classifier */
public String toString() {
StringBuffer result;
result = new StringBuffer();
result.append("Weka - Demo\n===========\n\n");
result.append(
"Classifier...: "
+ m_Classifier.getClass().getName()
+ " "
+ Utils.joinOptions(m_Classifier.getOptions())
+ "\n");
if (m_Filter instanceof OptionHandler)
result.append(
"Filter.......: "
+ m_Filter.getClass().getName()
+ " "
+ Utils.joinOptions(((OptionHandler) m_Filter).getOptions())
+ "\n");
else result.append("Filter.......: " + m_Filter.getClass().getName() + "\n");
result.append("Training file: " + m_TrainingFile + "\n");
result.append("\n");
result.append(m_Classifier.toString() + "\n");
result.append(m_Evaluation.toSummaryString() + "\n");
try {
result.append(m_Evaluation.toMatrixString() + "\n");
} catch (Exception e) {
e.printStackTrace();
}
try {
result.append(m_Evaluation.toClassDetailsString() + "\n");
} catch (Exception e) {
e.printStackTrace();
}
return result.toString();
}
static void evaluateClassifier(Classifier c, Instances data, int folds) throws Exception {
System.err.println(
"INFO: Starting crossvalidation to predict '"
+ data.classAttribute().name()
+ "' using '"
+ c.getClass().getCanonicalName()
+ ":"
+ Arrays.toString(c.getOptions())
+ "' ...");
StringBuffer sb = new StringBuffer();
Evaluation eval = new Evaluation(data);
eval.crossValidateModel(c, data, folds, new Random(1), sb, new Range("first"), Boolean.FALSE);
// write predictions to file
{
Writer out = new FileWriter("cv.log");
out.write(sb.toString());
out.close();
}
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}