/**
* @param targetTerm
* @param sourceFile
* @param trainingAlgo
* @param outputFileClassifier
* @param outputFileResults
* @param termWindowSize
* @param pipe
* @return
*/
private static List<ClassificationResult> runTrainingAndClassification(
String targetTerm,
String sourceFile,
String trainingAlgo,
String outputFileClassifier,
String outputFileResults,
int termWindowSize,
Pipe pipe,
boolean useCollocationalVector) {
// Read in concordance file and create list of Mallet training instances
// TODO: Remove duplication of code (see execConvertToMalletFormat(...))
String vectorType = useCollocationalVector ? "coll" : "bow";
InstanceList instanceList =
readConcordanceFileToInstanceList(
targetTerm, sourceFile, termWindowSize, pipe, useCollocationalVector);
// Creating splits for training and testing
double[] proportions = {0.9, 0.1};
InstanceList[] splitLists = instanceList.split(proportions);
InstanceList trainingList = splitLists[0];
InstanceList testList = splitLists[1];
// Train the classifier
ClassifierTrainer classifierTrainer = getClassifierTrainerForAlgorithm(trainingAlgo);
Classifier classifier = classifierTrainer.train(trainingList);
if (classifier.getLabelAlphabet()
!= null) { // TODO: Make sure this is not null in RandomClassifier
System.out.println("Labels:\n" + classifier.getLabelAlphabet());
System.out.println(
"Size of data alphabet (= type count of training list): "
+ classifier.getAlphabet().size());
}
// Run tests and get results
Trial trial = new Trial(classifier, testList);
List<ClassificationResult> results = new ArrayList<ClassificationResult>();
for (int i = 0; i < classifier.getLabelAlphabet().size(); i++) {
Label label = classifier.getLabelAlphabet().lookupLabel(i);
ClassificationResult result =
new MalletClassificationResult(
trainingAlgo,
targetTerm,
vectorType,
label.toString(),
termWindowSize,
trial,
sourceFile);
results.add(result);
System.out.println(result.toString());
}
// Save classifier
saveClassifierToFile(outputFileClassifier, classifier, trainingAlgo, termWindowSize);
return results;
}
public void printPerformance(InstanceList instanceList, String description) {
System.out.println(description);
System.out.println("Accuracy: " + classifier.getAccuracy(instanceList));
LabelAlphabet labelAlphabet = classifier.getLabelAlphabet();
Iterator iterator = labelAlphabet.iterator();
while (iterator.hasNext()) {
Object label = iterator.next();
double p = classifier.getPrecision(instanceList, label);
double r = classifier.getRecall(instanceList, label);
double f1 = classifier.getF1(instanceList, label);
System.out.println("Precision[" + label + "] = " + p);
System.out.println("Recall[" + label + "] = " + r);
System.out.println("F1[" + label + "] = " + f1);
System.out.println("");
}
ExtendedTrial trial = new ExtendedTrial(classifier, instanceList);
System.out.println("Overall performance\n=====");
System.out.println("Precision: " + trial.getPrecision());
System.out.println("Recall: " + trial.getRecall());
System.out.println("F1: " + trial.getF1());
System.out.println("Fall-out: " + trial.getFallOut());
trial.showErrors();
}