/**
* Emit the result from a sub process wrapper back up to whoever called us
*
* @param res The classifier result.
*/
@Override
protected void _processResults(ClassifierResult res) {
System.out.print(
"SubProcessWrapper: Time(" + res.getTime() + ") Score(" + res.getScore() + ")");
String outputFilePrefix = mProperties.getProperty("modelOutputFilePrefix", null);
if (outputFilePrefix != null) {
try {
if (res.getAttributeSelection() != null) {
weka.core.SerializationHelper.write(
outputFilePrefix + ".attributeselection", res.getAttributeSelection());
} else {
File oldFile = new File(outputFilePrefix + ".attributeselection");
if (oldFile.exists()) oldFile.delete();
}
weka.core.SerializationHelper.write(outputFilePrefix + ".model", res.getClassifier());
} catch (Exception e) {
throw new RuntimeException(e);
}
}
}
public static void main(String args[]) throws Exception {
ArffLoader trainLoader = new ArffLoader();
trainLoader.setSource(new File("src/train.arff"));
trainLoader.setRetrieval(Loader.BATCH);
Instances trainDataSet = trainLoader.getDataSet();
weka.core.Attribute trainAttribute = trainDataSet.attribute("class");
trainDataSet.setClass(trainAttribute);
// trainDataSet.deleteStringAttributes();
NaiveBayes classifier = new NaiveBayes();
final double startTime = System.currentTimeMillis();
classifier.buildClassifier(trainDataSet);
final double endTime = System.currentTimeMillis();
double executionTime = (endTime - startTime) / (1000.0);
System.out.println("Total execution time: " + executionTime);
SerializationHelper.write("NaiveBayes.model", classifier);
System.out.println("Saved trained model to classifier.model");
}
// 输入问题，输出问题所属类型。
public double classifyByBayes(String question) throws Exception {
double label = -1;
List<Question> questionID = questionDAO.getQuestionIDLabeled();
// 定义数据格式
Attribute att1 = new Attribute("法律政策");
Attribute att2 = new Attribute("位置交通");
Attribute att3 = new Attribute("风水");
Attribute att4 = new Attribute("房价");
Attribute att5 = new Attribute("楼层");
Attribute att6 = new Attribute("户型");
Attribute att7 = new Attribute("小区配套");
Attribute att8 = new Attribute("贷款");
Attribute att9 = new Attribute("买房时机");
Attribute att10 = new Attribute("开发商");
FastVector labels = new FastVector();
labels.addElement("1");
labels.addElement("2");
labels.addElement("3");
labels.addElement("4");
labels.addElement("5");
labels.addElement("6");
labels.addElement("7");
labels.addElement("8");
labels.addElement("9");
labels.addElement("10");
Attribute att11 = new Attribute("类别", labels);
FastVector attributes = new FastVector();
attributes.addElement(att1);
attributes.addElement(att2);
attributes.addElement(att3);
attributes.addElement(att4);
attributes.addElement(att5);
attributes.addElement(att6);
attributes.addElement(att7);
attributes.addElement(att8);
attributes.addElement(att9);
attributes.addElement(att10);
attributes.addElement(att11);
Instances dataset = new Instances("Test-dataset", attributes, 0);
dataset.setClassIndex(10);
Classifier classifier = null;
if (!new File("naivebayes.model").exists()) {
// 添加数据
double[] values = new double[11];
for (int i = 0; i < questionID.size(); i++) {
for (int m = 0; m < 11; m++) {
values[m] = 0;
}
int whitewordcount = 0;
whitewordcount = questionDAO.getHitWhiteWordNum(questionID.get(i).getId());
if (whitewordcount != 0) {
List<QuestionWhiteWord> questionwhiteword =
questionDAO.getHitQuestionWhiteWord(questionID.get(i).getId());
for (int j = 0; j < questionwhiteword.size(); j++) {
values[getAttIndex(questionwhiteword.get(j).getWordId()) - 1]++;
}
for (int m = 0; m < 11; m++) {
values[m] = values[m] / whitewordcount;
}
}
values[10] = questionID.get(i).getType() - 1;
Instance inst = new Instance(1.0, values);
dataset.add(inst);
}
// 构造分类器
classifier = new NaiveBayes();
classifier.buildClassifier(dataset);
SerializationHelper.write("naivebayes.model", classifier);
} else {
classifier = (Classifier) SerializationHelper.read("naivebayes.model");
}
System.out.println("*************begin evaluation*******************");
Evaluation evaluation = new Evaluation(dataset);
evaluation.evaluateModel(classifier, dataset); // 按道理说，这里应该使用另一份数据，而不是训练集data。
System.out.println(evaluation.toSummaryString());
// 分类
System.out.println("*************begin classification*******************");
Instance subject = new Instance(1.0, getQuestionVector(question));
subject.setDataset(dataset);
label = classifier.classifyInstance(subject);
System.out.println("label: " + label);
// double dis[]=classifier.distributionForInstance(inst);
// for(double i:dis){
// System.out.print(i+" ");
// }
System.out.println(questionID.size());
return label + 1;
}
public void saveModel(String filename) throws Exception {
SerializationHelper.write(filename, classifier);
}
@Override
public Void doInBackground() {
BufferedReader reader;
try {
publish("Reading data...");
reader = new BufferedReader(new FileReader("cross_validation_data.arff"));
final Instances trainingdata = new Instances(reader);
reader.close();
// setting class attribute
trainingdata.setClassIndex(13);
trainingdata.randomize(new Random(1));
long startTime = System.nanoTime();
publish("Training Naive Bayes Classifier...");
NaiveBayes nb = new NaiveBayes();
startTime = System.nanoTime();
nb.buildClassifier(trainingdata);
double runningTimeNB = (System.nanoTime() - startTime) / 1000000;
runningTimeNB /= 1000;
// saving the naive bayes model
weka.core.SerializationHelper.write("naivebayes.model", nb);
System.out.println("running time" + runningTimeNB);
publish("Done training NB.\nEvaluating NB using 10-fold cross-validation...");
evalNB = new Evaluation(trainingdata);
evalNB.crossValidateModel(nb, trainingdata, 10, new Random(1));
publish("Done evaluating NB.");
// System.out.println(evalNB.toSummaryString("\nResults for Naive Bayes\n======\n", false));
MultilayerPerceptron mlp = new MultilayerPerceptron();
mlp.setOptions(Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"));
publish("Training ANN...");
startTime = System.nanoTime();
mlp.buildClassifier(trainingdata);
long runningTimeANN = (System.nanoTime() - startTime) / 1000000;
runningTimeANN /= 1000;
// saving the MLP model
weka.core.SerializationHelper.write("mlp.model", mlp);
publish("Done training ANN.\nEvaluating ANN using 10-fold cross-validation...");
evalANN = new Evaluation(trainingdata);
evalANN.evaluateModel(mlp, trainingdata);
// evalMLP.crossValidateModel(mlp, trainingdata, 10, new Random(1));
publish("Done evaluating ANN.");
publish("Training SVM...");
SMO svm = new SMO();
startTime = System.nanoTime();
svm.buildClassifier(trainingdata);
long runningTimeSVM = (System.nanoTime() - startTime) / 1000000;
runningTimeSVM /= 1000;
weka.core.SerializationHelper.write("svm.model", svm);
publish("Done training SVM.\nEvaluating SVM using 10-fold cross-validation...");
evalSVM = new Evaluation(trainingdata);
evalSVM.evaluateModel(svm, trainingdata);
publish("Done evaluating SVM.");
Platform.runLater(
new Runnable() {
@Override
public void run() {
bc.getData()
.get(0)
.getData()
.get(0)
.setYValue(evalANN.correct() / trainingdata.size() * 100);
bc.getData()
.get(0)
.getData()
.get(1)
.setYValue(evalSVM.correct() / trainingdata.size() * 100);
bc.getData()
.get(0)
.getData()
.get(2)
.setYValue(evalNB.correct() / trainingdata.size() * 100);
for (int i = 0; i < NUM_CLASSES; i++) {
lineChart.getData().get(0).getData().get(i).setYValue(evalANN.recall(i) * 100);
lineChart.getData().get(1).getData().get(i).setYValue(evalSVM.recall(i) * 100);
lineChart.getData().get(2).getData().get(i).setYValue(evalNB.recall(i) * 100);
}
}
});
panel.fillConfTable(evalSVM.confusionMatrix());
summaryTable.setValueAt(evalANN.correct() / trainingdata.size() * 100., 0, 1);
summaryTable.setValueAt(evalSVM.correct() / trainingdata.size() * 100, 0, 2);
summaryTable.setValueAt(evalNB.correct() / trainingdata.size() * 100, 0, 3);
summaryTable.setValueAt(runningTimeANN, 1, 1);
summaryTable.setValueAt(runningTimeSVM, 1, 2);
summaryTable.setValueAt(runningTimeNB, 1, 3);
} catch (Exception e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
return null;
}