/**
* Buildclassifier selects a classifier from the set of classifiers by minimising error on the
* training data.
*
* @param data the training data to be used for generating the boosted classifier.
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifiers.length == 0) {
throw new Exception("No base classifiers have been set!");
}
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
newData.randomize(new Random(m_Seed));
if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1))
newData.stratify(m_NumXValFolds);
Instances train = newData; // train on all data by default
Instances test = newData; // test on training data by default
Classifier bestClassifier = null;
int bestIndex = -1;
double bestPerformance = Double.NaN;
int numClassifiers = m_Classifiers.length;
for (int i = 0; i < numClassifiers; i++) {
Classifier currentClassifier = getClassifier(i);
Evaluation evaluation;
if (m_NumXValFolds > 1) {
evaluation = new Evaluation(newData);
for (int j = 0; j < m_NumXValFolds; j++) {
train = newData.trainCV(m_NumXValFolds, j);
test = newData.testCV(m_NumXValFolds, j);
currentClassifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(currentClassifier, test);
}
} else {
currentClassifier.buildClassifier(train);
evaluation = new Evaluation(train);
evaluation.evaluateModel(currentClassifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println(
"Error rate: "
+ Utils.doubleToString(error, 6, 4)
+ " for classifier "
+ currentClassifier.getClass().getName());
}
if ((i == 0) || (error < bestPerformance)) {
bestClassifier = currentClassifier;
bestPerformance = error;
bestIndex = i;
}
}
m_ClassifierIndex = bestIndex;
m_Classifier = bestClassifier;
if (m_NumXValFolds > 1) {
m_Classifier.buildClassifier(newData);
}
}
/**
* 用分类器测试
*
* @param trainFileName
* @param testFileName
*/
public static void classify(String trainFileName, String testFileName) {
try {
File inputFile = new File(fileName + trainFileName); // 训练语料文件
ArffLoader atf = new ArffLoader();
atf.setFile(inputFile);
Instances instancesTrain = atf.getDataSet(); // 读入训练文件
// 设置类标签类
inputFile = new File(fileName + testFileName); // 测试语料文件
atf.setFile(inputFile);
Instances instancesTest = atf.getDataSet(); // 读入测试文件
instancesTest.setClassIndex(instancesTest.numAttributes() - 1);
instancesTrain.setClassIndex(instancesTrain.numAttributes() - 1);
classifier = (Classifier) Class.forName(CLASSIFIERNAME).newInstance();
classifier.buildClassifier(instancesTrain);
Evaluation eval = new Evaluation(instancesTrain);
// 第一个为一个训练过的分类器，第二个参数是在某个数据集上评价的数据集
eval.evaluateModel(classifier, instancesTest);
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString());
System.out.println(eval.toMatrixString());
System.out.println("precision is :" + (1 - eval.errorRate()));
} catch (Exception e) {
e.printStackTrace();
}
}
public static void run(String[] args) throws Exception {
/**
* *************************************************
*
* @param args[0]: train arff path
* @param args[1]: test arff path
*/
DataSource source = new DataSource(args[0]);
Instances data = source.getDataSet();
data.setClassIndex(data.numAttributes() - 1);
NaiveBayes model = new NaiveBayes();
model.buildClassifier(data);
// Evaluation:
Evaluation eval = new Evaluation(data);
Instances testData = new DataSource(args[1]).getDataSet();
testData.setClassIndex(testData.numAttributes() - 1);
eval.evaluateModel(model, testData);
System.out.println(model.toString());
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
System.out.println("======\nConfusion Matrix:");
double[][] confusionM = eval.confusionMatrix();
for (int i = 0; i < confusionM.length; ++i) {
for (int j = 0; j < confusionM[i].length; ++j) {
System.out.format("%10s ", confusionM[i][j]);
}
System.out.print("\n");
}
}
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
Instances isTrainingSet = createSet(4);
Instance instance1 = createInstance(new double[] {1, 0.7, 0.1, 0.7}, "S1", isTrainingSet);
Instance instance2 = createInstance(new double[] {0.1, 0.2, 1, 0.3}, "S2", isTrainingSet);
Instance instance22 = createInstance(new double[] {0, 0, 0, 0}, "S3", isTrainingSet);
isTrainingSet.add(instance1);
isTrainingSet.add(instance2);
isTrainingSet.add(instance22);
Instances isTestingSet = createSet(4);
Instance instance3 = createInstance(new double[] {1, 0.7, 0.1, 0.7}, "S1", isTrainingSet);
Instance instance4 = createInstance(new double[] {0.1, 0.2, 1, 0.3}, "S2", isTrainingSet);
isTestingSet.add(instance3);
isTestingSet.add(instance4);
// Create a naïve bayes classifier
Classifier cModel = (Classifier) new BayesNet(); // M5P
cModel.buildClassifier(isTrainingSet);
// Test the model
Evaluation eTest = new Evaluation(isTrainingSet);
eTest.evaluateModel(cModel, isTestingSet);
// Print the result à la Weka explorer:
String strSummary = eTest.toSummaryString();
System.out.println(strSummary);
// Get the likelihood of each classes
// fDistribution[0] is the probability of being “positive”
// fDistribution[1] is the probability of being “negative”
double[] fDistribution = cModel.distributionForInstance(instance4);
for (int i = 0; i < fDistribution.length; i++) {
System.out.println(fDistribution[i]);
}
}
/**
* Creates an evaluation overview of the built classifier.
*
* @return the panel to be displayed as result evaluation view for the current decision point
*/
protected JPanel createEvaluationVisualization(Instances data) {
// build text field to display evaluation statistics
JTextPane statistic = new JTextPane();
try {
// build evaluation statistics
Evaluation evaluation = new Evaluation(data);
evaluation.evaluateModel(myClassifier, data);
statistic.setText(
evaluation.toSummaryString()
+ "\n\n"
+ evaluation.toClassDetailsString()
+ "\n\n"
+ evaluation.toMatrixString());
} catch (Exception ex) {
ex.printStackTrace();
return createMessagePanel("Error while creating the decision tree evaluation view");
}
statistic.setFont(new Font("Courier", Font.PLAIN, 14));
statistic.setEditable(false);
statistic.setCaretPosition(0);
JPanel resultViewPanel = new JPanel();
resultViewPanel.setLayout(new BoxLayout(resultViewPanel, BoxLayout.PAGE_AXIS));
resultViewPanel.add(new JScrollPane(statistic));
return resultViewPanel;
}
/**
* Finds the best parameter combination. (recursive for each parameter being optimised).
*
* @param depth the index of the parameter to be optimised at this level
* @param trainData the data the search is based on
* @param random a random number generator
* @throws Exception if an error occurs
*/
protected void findParamsByCrossValidation(int depth, Instances trainData, Random random)
throws Exception {
if (depth < m_CVParams.size()) {
CVParameter cvParam = (CVParameter) m_CVParams.elementAt(depth);
double upper;
switch ((int) (cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
case 1:
upper = m_NumAttributes;
break;
case 2:
upper = m_TrainFoldSize;
break;
default:
upper = cvParam.m_Upper;
break;
}
double increment = (upper - cvParam.m_Lower) / (cvParam.m_Steps - 1);
for (cvParam.m_ParamValue = cvParam.m_Lower;
cvParam.m_ParamValue <= upper;
cvParam.m_ParamValue += increment) {
findParamsByCrossValidation(depth + 1, trainData, random);
}
} else {
Evaluation evaluation = new Evaluation(trainData);
// Set the classifier options
String[] options = createOptions();
if (m_Debug) {
System.err.print("Setting options for " + m_Classifier.getClass().getName() + ":");
for (int i = 0; i < options.length; i++) {
System.err.print(" " + options[i]);
}
System.err.println("");
}
((OptionHandler) m_Classifier).setOptions(options);
for (int j = 0; j < m_NumFolds; j++) {
// We want to randomize the data the same way for every
// learning scheme.
Instances train = trainData.trainCV(m_NumFolds, j, new Random(1));
Instances test = trainData.testCV(m_NumFolds, j);
m_Classifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(m_Classifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println("Cross-validated error rate: " + Utils.doubleToString(error, 6, 4));
}
if ((m_BestPerformance == -99) || (error < m_BestPerformance)) {
m_BestPerformance = error;
m_BestClassifierOptions = createOptions();
}
}
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String[] argv) {
try {
System.out.println(Evaluation.evaluateModel(new Decorate(), argv));
} catch (Exception e) {
System.err.println(e.getMessage());
}
}
/**
* Main method for testing this class
*
* @param argv options
*/
public static void main(String[] argv) {
try {
System.out.println(Evaluation.evaluateModel(new UnivariateLinearRegression(), argv));
} catch (Exception e) {
System.out.println(e.getMessage());
e.printStackTrace();
}
}
/** evaluates the classifier */
@Override
public void evaluate() throws Exception {
// evaluate classifier and print some statistics
if (_test.classIndex() == -1) _test.setClassIndex(_test.numAttributes() - 1);
Evaluation eval = new Evaluation(_train);
eval.evaluateModel(_cl, _test);
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
System.out.println(eval.toMatrixString());
}
public Evaluation evaluateClassifier(Instances trainInstances, Instances testInstances) {
try {
Evaluation eval = new Evaluation(trainInstances);
eval.evaluateModel(bayesNet, testInstances);
return eval;
} catch (Exception e) {
System.err.println(e.getMessage());
e.printStackTrace();
return null;
}
}
private double[] makePredictions(
Classifier classifier, Instances validationSet, Evaluation evaluation) {
double[] predictions = null;
try {
predictions = evaluation.evaluateModel(classifier, validationSet);
} catch (ArrayIndexOutOfBoundsException e) {
throw new ClassifierPredictionException(
"Error applying the trained classifier to the train instances. The number of features of the instance exceeds the number of features the classifier was trained on.",
e);
} catch (Exception e) {
throw new ClassifierPredictionException(
"Error applying the trained classifier to the test instances.", e);
}
return predictions;
}
public static Double runClassify(String trainFile, String testFile) {
double predictOrder = 0.0;
double trueOrder = 0.0;
try {
String trainWekaFileName = trainFile;
String testWekaFileName = testFile;
Instances train = DataSource.read(trainWekaFileName);
Instances test = DataSource.read(testWekaFileName);
train.setClassIndex(0);
test.setClassIndex(0);
train.deleteAttributeAt(8);
test.deleteAttributeAt(8);
train.deleteAttributeAt(6);
test.deleteAttributeAt(6);
train.deleteAttributeAt(5);
test.deleteAttributeAt(5);
train.deleteAttributeAt(4);
test.deleteAttributeAt(4);
// AdditiveRegression classifier = new AdditiveRegression();
// NaiveBayes classifier = new NaiveBayes();
RandomForest classifier = new RandomForest();
// LibSVM classifier = new LibSVM();
classifier.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(classifier, test);
System.out.println(eval.toSummaryString("\nResults\n\n", true));
// System.out.println(eval.toClassDetailsString());
// System.out.println(eval.toMatrixString());
int k = 892;
for (int i = 0; i < test.numInstances(); i++) {
predictOrder = classifier.classifyInstance(test.instance(i));
trueOrder = test.instance(i).classValue();
System.out.println((k++) + "," + (int) predictOrder);
}
} catch (Exception e) {
e.printStackTrace();
}
return predictOrder;
}
/**
* SVM trainer
*
* @param dataTrain
* @param dataTest
*/
public static void trainModelLibSVM(Instances dataTrain, Instances dataTest) {
try {
LibSVM classifier = new LibSVM();
CVParameterSelection ps = new CVParameterSelection();
ps.setClassifier(classifier);
ps.setNumFolds(5); // using 5-fold CV
// ps.addCVParameter("C 0.1 0.5 5");
// build and output best options
ps.buildClassifier(dataTrain);
Evaluation eval = new Evaluation(dataTrain);
eval.evaluateModel(ps, dataTest);
System.out.println("Results of the set :::::::::::::::::::::: ");
System.out.println(
"Percentage of correctly classified instances : "
+ eval.pctCorrect()
+ "\n"
+ "Percentage of incorrectly classified instances : "
+ eval.pctIncorrect());
System.out.println("No of correct predictions : " + eval.correct());
System.out.println("TRUTHFUL");
System.out.println(
"Precision : "
+ eval.precision(0)
+ "\n"
+ "Recall : "
+ eval.recall(0)
+ "\n"
+ "F measure/score : "
+ eval.fMeasure(0));
System.out.println("DECEPTIVE");
System.out.println(
"Precision : "
+ eval.precision(0)
+ "\n"
+ "Recall : "
+ eval.recall(1)
+ "\n"
+ "F measure/score : "
+ eval.fMeasure(1));
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
public static void trainModel(Instances dataTrain, Instances dataTest) {
try {
LibLINEAR classifier = new LibLINEAR();
classifier.setBias(10);
classifier.buildClassifier(dataTrain);
Evaluation eval = new Evaluation(dataTrain);
eval.evaluateModel(classifier, dataTest);
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
/**
* Utility method for fast 5-fold cross validation of a naive bayes model
*
* @param fullModel a <code>NaiveBayesUpdateable</code> value
* @param trainingSet an <code>Instances</code> value
* @param r a <code>Random</code> value
* @return a <code>double</code> value
* @exception Exception if an error occurs
*/
public static double crossValidate(
NaiveBayesUpdateable fullModel, Instances trainingSet, Random r) throws Exception {
// make some copies for fast evaluation of 5-fold xval
Classifier[] copies = AbstractClassifier.makeCopies(fullModel, 5);
Evaluation eval = new Evaluation(trainingSet);
// make some splits
for (int j = 0; j < 5; j++) {
Instances test = trainingSet.testCV(5, j);
// unlearn these test instances
for (int k = 0; k < test.numInstances(); k++) {
test.instance(k).setWeight(-test.instance(k).weight());
((NaiveBayesUpdateable) copies[j]).updateClassifier(test.instance(k));
// reset the weight back to its original value
test.instance(k).setWeight(-test.instance(k).weight());
}
eval.evaluateModel(copies[j], test);
}
return eval.incorrect();
}
@Override
protected Evaluation test() {
working = true;
System.out.println("Agent " + getLocalName() + ": Testing...");
// evaluate classifier and print some statistics
Evaluation eval = null;
try {
eval = new Evaluation(train);
eval.evaluateModel(cls, test);
System.out.println(
eval.toSummaryString(getLocalName() + " agent: " + "\nResults\n=======\n", false));
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
working = false;
return eval;
} // end test
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();
}
}
public static void execSVM(String expName) {
try {
FileWriter outFile = null;
PrintWriter out = null;
outFile = new FileWriter(expName + "-SVM.results");
out = new PrintWriter(outFile);
DateFormat dateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss");
ProcessTweets tweetsProcessor = null;
System.out.println("***************************************");
System.out.println("***\tEXECUTING TEST\t" + expName + "***");
System.out.println("+++++++++++++++++++++++++++++++++++++++");
out.println("***************************************");
out.println("***\tEXECUTING TEST\t" + expName + "***");
out.println("+++++++++++++++++++++++++++++++++++++++");
out.println("4-Generate classifier " + dateFormat.format(new Date()));
Classifier cls = null;
DataSource sourceTrain = new DataSource(expName + "-train.arff");
Instances dataTrain = sourceTrain.getDataSet();
if (dataTrain.classIndex() == -1) dataTrain.setClassIndex(dataTrain.numAttributes() - 1);
// Entreno el clasificador
// cls = new weka.classifiers.functions.LibSVM();
int clase = dataTrain.numAttributes() - 1;
cls = new weka.classifiers.bayes.ComplementNaiveBayes();
dataTrain.setClassIndex(clase);
cls.buildClassifier(dataTrain);
ObjectOutputStream oos =
new ObjectOutputStream(new FileOutputStream(expName + "-SVM.classifier"));
oos.writeObject(cls);
oos.flush();
oos.close();
DataSource sourceTest = new DataSource(expName + "-test.arff");
Instances dataTest = sourceTest.getDataSet();
dataTest.setClassIndex(clase);
Evaluation eval = new Evaluation(dataTest);
eval.evaluateModel(cls, dataTest);
// Ahora calculo los valores precision, recall y fmeasure. Además saco las matrices de
// confusion
float precision = 0;
float recall = 0;
float fmeasure = 0;
int topeTopics = 8;
for (int ind = 0; ind < topeTopics; ind++) {
precision += eval.precision(ind);
recall += eval.recall(ind);
fmeasure += eval.fMeasure(ind);
}
precision = precision / topeTopics;
recall = recall / topeTopics;
fmeasure = fmeasure / topeTopics;
System.out.println("++++++++++++++ CNB ++++++++++++++++++++");
System.out.println(eval.toMatrixString());
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.printf("Precision: %.3f\n", precision);
System.out.printf("Recall: %.3f\n", recall);
System.out.printf("F-measure: %.3f\n", fmeasure);
System.out.println("***************************************");
out.println("++++++++++++++ CNB ++++++++++++++++++++");
out.println(eval.toMatrixString());
out.println("+++++++++++++++++++++++++++++++++++++++");
out.printf("Precision: %.3f\n", precision);
out.printf("Recall: %.3f\n", recall);
out.printf("F-measure: %.3f\n", fmeasure);
out.println("***************************************");
// OTRO CLASIFICADOR ZeroR
cls = new weka.classifiers.rules.ZeroR();
dataTrain.setClassIndex(clase);
cls.buildClassifier(dataTrain);
eval = new Evaluation(dataTest);
eval.evaluateModel(cls, dataTest);
precision = 0;
recall = 0;
fmeasure = 0;
for (int ind = 0; ind < topeTopics; ind++) {
precision += eval.precision(ind);
recall += eval.recall(ind);
fmeasure += eval.fMeasure(ind);
}
precision = precision / topeTopics;
recall = recall / topeTopics;
fmeasure = fmeasure / topeTopics;
System.out.println("++++++++++++++ ZEROR ++++++++++++++++++++");
System.out.println(eval.toMatrixString());
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.printf("Precision: %.3f\n", precision);
System.out.printf("Recall: %.3f\n", recall);
System.out.printf("F-measure: %.3f\n", fmeasure);
System.out.println("***************************************");
out.println("++++++++++++++ ZEROR ++++++++++++++++++++");
out.println(eval.toMatrixString());
out.println("+++++++++++++++++++++++++++++++++++++++");
out.printf("Precision: %.3f\n", precision);
out.printf("Recall: %.3f\n", recall);
out.printf("F-measure: %.3f\n", fmeasure);
out.println("***************************************");
// OTRO CLASIFICADOR J48
/*
cls = new weka.classifiers.trees.J48();
dataTrain.setClassIndex(clase);
cls.buildClassifier(dataTrain);
eval = new Evaluation(dataTest);
eval.evaluateModel(cls, dataTest);
precision=0;
recall=0;
fmeasure=0;
for(int ind=0; ind<topeTopics; ind++)
{
precision += eval.precision(ind);
recall += eval.recall(ind);
fmeasure += eval.fMeasure(ind);
}
precision = precision / topeTopics;
recall = recall / topeTopics;
fmeasure = fmeasure / topeTopics;
System.out.println("++++++++++++++ J48 ++++++++++++++++++++");
System.out.println(eval.toMatrixString());
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.printf("Precision: %.3f\n", precision);
System.out.printf("Recall: %.3f\n", recall);
System.out.printf("F-measure: %.3f\n", fmeasure);
System.out.println("***************************************");
out.println("++++++++++++++ J48 ++++++++++++++++++++");
out.println(eval.toMatrixString());
out.println("+++++++++++++++++++++++++++++++++++++++");
out.printf("Precision: %.3f\n", precision);
out.printf("Recall: %.3f\n", recall);
out.printf("F-measure: %.3f\n", fmeasure);
out.println("***************************************");
//OTRO SMO
cls = new weka.classifiers.functions.SMO();
dataTrain.setClassIndex(clase);
cls.buildClassifier(dataTrain);
eval = new Evaluation(dataTest);
eval.evaluateModel(cls, dataTest);
precision=0;
recall=0;
fmeasure=0;
for(int ind=0; ind<topeTopics; ind++)
{
precision += eval.precision(ind);
recall += eval.recall(ind);
fmeasure += eval.fMeasure(ind);
}
precision = precision / topeTopics;
recall = recall / topeTopics;
fmeasure = fmeasure / topeTopics;
System.out.println("++++++++++++++ SMO ++++++++++++++++++++");
System.out.println(eval.toMatrixString());
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.printf("Precision: %.3f\n", precision);
System.out.printf("Recall: %.3f\n", recall);
System.out.printf("F-measure: %.3f\n", fmeasure);
System.out.println("***************************************");
out.println("++++++++++++++ SMO ++++++++++++++++++++");
out.println(eval.toMatrixString());
out.println("+++++++++++++++++++++++++++++++++++++++");
out.printf("Precision: %.3f\n", precision);
out.printf("Recall: %.3f\n", recall);
out.printf("F-measure: %.3f\n", fmeasure);
out.println("***************************************");
*/
out.flush();
out.close();
dataTest.delete();
dataTrain.delete();
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
/**
* Gets the results for the supplied train and test datasets. Now performs a deep copy of the
* classifier before it is built and evaluated (just in case the classifier is not initialized
* properly in buildClassifier()).
*
* @param train the training Instances.
* @param test the testing Instances.
* @return the results stored in an array. The objects stored in the array may be Strings,
* Doubles, or null (for the missing value).
* @throws Exception if a problem occurs while getting the results
*/
public Object[] getResult(Instances train, Instances test) throws Exception {
if (train.classAttribute().type() != Attribute.NUMERIC) {
throw new Exception("Class attribute is not numeric!");
}
if (m_Template == null) {
throw new Exception("No classifier has been specified");
}
ThreadMXBean thMonitor = ManagementFactory.getThreadMXBean();
boolean canMeasureCPUTime = thMonitor.isThreadCpuTimeSupported();
if (canMeasureCPUTime && !thMonitor.isThreadCpuTimeEnabled())
thMonitor.setThreadCpuTimeEnabled(true);
int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
Object[] result = new Object[RESULT_SIZE + addm + m_numPluginStatistics];
long thID = Thread.currentThread().getId();
long CPUStartTime = -1,
trainCPUTimeElapsed = -1,
testCPUTimeElapsed = -1,
trainTimeStart,
trainTimeElapsed,
testTimeStart,
testTimeElapsed;
Evaluation eval = new Evaluation(train);
m_Classifier = AbstractClassifier.makeCopy(m_Template);
trainTimeStart = System.currentTimeMillis();
if (canMeasureCPUTime) CPUStartTime = thMonitor.getThreadUserTime(thID);
m_Classifier.buildClassifier(train);
if (canMeasureCPUTime) trainCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
testTimeStart = System.currentTimeMillis();
if (canMeasureCPUTime) CPUStartTime = thMonitor.getThreadUserTime(thID);
eval.evaluateModel(m_Classifier, test);
if (canMeasureCPUTime) testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
thMonitor = null;
m_result = eval.toSummaryString();
// The results stored are all per instance -- can be multiplied by the
// number of instances to get absolute numbers
int current = 0;
result[current++] = new Double(train.numInstances());
result[current++] = new Double(eval.numInstances());
result[current++] = new Double(eval.meanAbsoluteError());
result[current++] = new Double(eval.rootMeanSquaredError());
result[current++] = new Double(eval.relativeAbsoluteError());
result[current++] = new Double(eval.rootRelativeSquaredError());
result[current++] = new Double(eval.correlationCoefficient());
result[current++] = new Double(eval.SFPriorEntropy());
result[current++] = new Double(eval.SFSchemeEntropy());
result[current++] = new Double(eval.SFEntropyGain());
result[current++] = new Double(eval.SFMeanPriorEntropy());
result[current++] = new Double(eval.SFMeanSchemeEntropy());
result[current++] = new Double(eval.SFMeanEntropyGain());
// Timing stats
result[current++] = new Double(trainTimeElapsed / 1000.0);
result[current++] = new Double(testTimeElapsed / 1000.0);
if (canMeasureCPUTime) {
result[current++] = new Double((trainCPUTimeElapsed / 1000000.0) / 1000.0);
result[current++] = new Double((testCPUTimeElapsed / 1000000.0) / 1000.0);
} else {
result[current++] = new Double(Utils.missingValue());
result[current++] = new Double(Utils.missingValue());
}
// sizes
if (m_NoSizeDetermination) {
result[current++] = -1.0;
result[current++] = -1.0;
result[current++] = -1.0;
} else {
ByteArrayOutputStream bastream = new ByteArrayOutputStream();
ObjectOutputStream oostream = new ObjectOutputStream(bastream);
oostream.writeObject(m_Classifier);
result[current++] = new Double(bastream.size());
bastream = new ByteArrayOutputStream();
oostream = new ObjectOutputStream(bastream);
oostream.writeObject(train);
result[current++] = new Double(bastream.size());
bastream = new ByteArrayOutputStream();
oostream = new ObjectOutputStream(bastream);
oostream.writeObject(test);
result[current++] = new Double(bastream.size());
}
// Prediction interval statistics
result[current++] = new Double(eval.coverageOfTestCasesByPredictedRegions());
result[current++] = new Double(eval.sizeOfPredictedRegions());
if (m_Classifier instanceof Summarizable) {
result[current++] = ((Summarizable) m_Classifier).toSummaryString();
} else {
result[current++] = null;
}
for (int i = 0; i < addm; i++) {
if (m_doesProduce[i]) {
try {
double dv =
((AdditionalMeasureProducer) m_Classifier).getMeasure(m_AdditionalMeasures[i]);
if (!Utils.isMissingValue(dv)) {
Double value = new Double(dv);
result[current++] = value;
} else {
result[current++] = null;
}
} catch (Exception ex) {
System.err.println(ex);
}
} else {
result[current++] = null;
}
}
// get the actual metrics from the evaluation object
List<AbstractEvaluationMetric> metrics = eval.getPluginMetrics();
if (metrics != null) {
for (AbstractEvaluationMetric m : metrics) {
if (m.appliesToNumericClass()) {
List<String> statNames = m.getStatisticNames();
for (String s : statNames) {
result[current++] = new Double(m.getStatistic(s));
}
}
}
}
if (current != RESULT_SIZE + addm + m_numPluginStatistics) {
throw new Error("Results didn't fit RESULT_SIZE");
}
return result;
}
public void exec(PrintWriter printer) {
try {
FileWriter outFile = null;
PrintWriter out = null;
if (printer == null) {
outFile = new FileWriter(id + ".results");
out = new PrintWriter(outFile);
} else out = printer;
DateFormat dateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss");
ProcessTweets tweetsProcessor = null;
System.out.println("***************************************");
System.out.println("***\tEXECUTING TEST\t" + id + "***");
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.println("Train size:" + traincorpus.size());
System.out.println("Test size:" + testcorpus.size());
out.println("***************************************");
out.println("***\tEXECUTING TEST\t***");
out.println("+++++++++++++++++++++++++++++++++++++++");
out.println("Train size:" + traincorpus.size());
out.println("Test size:" + testcorpus.size());
String cloneID = "";
boolean clonar = false;
if (baseline) {
System.out.println("***************************************");
System.out.println("***\tEXECUTING TEST BASELINE\t***");
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.println("Train size:" + traincorpus.size());
System.out.println("Test size:" + testcorpus.size());
out.println("***************************************");
out.println("***\tEXECUTING TEST\t***");
out.println("+++++++++++++++++++++++++++++++++++++++");
out.println("Train size:" + traincorpus.size());
out.println("Test size:" + testcorpus.size());
BaselineClassifier base = new BaselineClassifier(testcorpus, 8);
precision = base.getPrecision();
recall = base.getRecall();
fmeasure = base.getFmeasure();
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.printf("Precision: %.3f\n", precision);
System.out.printf("Recall: %.3f\n", recall);
System.out.printf("F-measure: %.3f\n", fmeasure);
System.out.println("***************************************");
out.println("+++++++++++++++++++++++++++++++++++++++");
out.printf("Precision: %.3f\n", precision);
out.printf("Recall: %.3f\n", recall);
out.printf("F-measure: %.3f\n", fmeasure);
out.println("***************************************");
out.flush();
out.close();
return;
} else {
System.out.println("Stemming: " + stemming);
System.out.println("Lematization:" + lematization);
System.out.println("URLs:" + urls);
System.out.println("Hashtags:" + hashtags);
System.out.println("Mentions:" + mentions);
System.out.println("Unigrams:" + unigrams);
System.out.println("Bigrams:" + bigrams);
System.out.println("TF:" + tf);
System.out.println("TF-IDF:" + tfidf);
out.println("Stemming: " + stemming);
out.println("Lematization:" + lematization);
out.println("URLs:" + urls);
out.println("Hashtags:" + hashtags);
out.println("Mentions:" + mentions);
out.println("Unigrams:" + unigrams);
out.println("Bigrams:" + bigrams);
out.println("TF:" + tf);
out.println("TF-IDF:" + tfidf);
}
// Si tengo los tweets procesados, me evito un nuevo proceso
System.out.println("1-Process tweets " + dateFormat.format(new Date()));
out.println("1-Process tweets " + dateFormat.format(new Date()));
List<ProcessedTweet> train = null;
String[] ids = id.split("-");
cloneID = ids[0] + "-" + (Integer.valueOf(ids[1]) + 6);
if (((Integer.valueOf(ids[1]) / 6) % 2) == 0) clonar = true;
if (new File(id + "-train.ptweets").exists()) {
train = ProcessedTweetSerialization.fromFile(id + "-train.ptweets");
tweetsProcessor =
new ProcessTweets(stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
if (lematization) {
tweetsProcessor.doLematization(train);
}
if (stemming) {
tweetsProcessor.doStemming(train);
}
} else {
tweetsProcessor =
new ProcessTweets(stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
// Esto del set training es un añadido para poder diferenciar los idiomas de las url en el
// corpus paralelo
// tweetsProcessor.setTraining(true);
train = tweetsProcessor.processTweets(traincorpus);
// tweetsProcessor.setTraining(false);
ProcessedTweetSerialization.toFile(id + "-train.ptweets", train);
/*
if (clonar)
{
File f = new File (id+"-train.ptweets");
Path p = f.toPath();
CopyOption[] options = new CopyOption[]{
StandardCopyOption.REPLACE_EXISTING,
StandardCopyOption.COPY_ATTRIBUTES
};
Files.copy(p, new File (cloneID+"-train.ptweets").toPath(), options);
Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+12)+"-train.ptweets").toPath(), options);
Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+18)+"-train.ptweets").toPath(), options);
Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+24)+"-train.ptweets").toPath(), options);
Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+30)+"-train.ptweets").toPath(), options);
}
*/
}
// Generamos las BOW. Igual que antes, si existen no las creo.
System.out.println("2-Fill topics " + dateFormat.format(new Date()));
out.println("2-Fill topics " + dateFormat.format(new Date()));
TopicsList topics = null;
if (new File(id + ".topics").exists()) {
topics = TopicsSerialization.fromFile(id + ".topics");
if (tf) topics.setSelectionFeature(TopicDesc.TERM_TF);
else topics.setSelectionFeature(TopicDesc.TERM_TF_IDF);
topics.prepareTopics();
} else {
topics = new TopicsList();
if (tf) topics.setSelectionFeature(TopicDesc.TERM_TF);
else topics.setSelectionFeature(TopicDesc.TERM_TF_IDF);
System.out.println("Filling topics " + dateFormat.format(new Date()));
topics.fillTopics(train);
System.out.println("Preparing topics topics " + dateFormat.format(new Date()));
// Aquí tengo que serializar antes de preparar, porque si no no puedo calcular los tf y
// tfidf
System.out.println("Serializing topics topics " + dateFormat.format(new Date()));
/*
if (clonar)
{
TopicsSerialization.toFile(cloneID+".topics", topics);
}
*/
topics.prepareTopics();
TopicsSerialization.toFile(id + ".topics", topics);
}
System.out.println("3-Generate arff train file " + dateFormat.format(new Date()));
out.println("3-Generate arff train file " + dateFormat.format(new Date()));
// Si el fichero arff no existe, lo creo. en caso contrario vengo haciendo lo que hasta ahora,
// aprovechar trabajo previo
if (!new File(id + "-train.arff").exists()) {
BufferedWriter bw = topics.generateArffHeader(id + "-train.arff");
int tope = traincorpus.size();
if (tweetsProcessor == null)
tweetsProcessor =
new ProcessTweets(
stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
for (int indTweet = 0; indTweet < tope; indTweet++) {
topics.generateArffVector(bw, train.get(indTweet));
}
bw.flush();
bw.close();
}
// Ahora proceso los datos de test
System.out.println("5-build test dataset " + dateFormat.format(new Date()));
out.println("5-build test dataset " + dateFormat.format(new Date()));
List<ProcessedTweet> test = null;
if (new File(id + "-test.ptweets").exists())
test = ProcessedTweetSerialization.fromFile(id + "-test.ptweets");
else {
if (tweetsProcessor == null)
tweetsProcessor =
new ProcessTweets(
stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
test = tweetsProcessor.processTweets(testcorpus);
ProcessedTweetSerialization.toFile(id + "-test.ptweets", test);
/*
if (clonar)
{
File f = new File (id+"-test.ptweets");
Path p = f.toPath();
CopyOption[] options = new CopyOption[]{
StandardCopyOption.REPLACE_EXISTING,
StandardCopyOption.COPY_ATTRIBUTES
};
Files.copy(p, new File (cloneID+"-test.ptweets").toPath(), options);
}
*/
}
// Si el fichero arff no existe, lo creo. en caso contrario vengo haciendo lo que hasta ahora,
// aprovechar trabajo previo
if (!new File(id + "-test.arff").exists()) {
BufferedWriter bw = topics.generateArffHeader(id + "-test.arff");
int tope = testcorpus.size();
if (tweetsProcessor == null)
tweetsProcessor =
new ProcessTweets(
stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
for (int indTweet = 0; indTweet < tope; indTweet++) {
topics.generateArffVector(bw, test.get(indTweet));
}
bw.flush();
bw.close();
}
int topeTopics = topics.getTopicsList().size();
topics.getTopicsList().clear();
// Genero el clasificador
// FJRM 25-08-2013 Lo cambio de orden para intentar liberar la memoria de los topics y tener
// más libre
System.out.println("4-Generate classifier " + dateFormat.format(new Date()));
out.println("4-Generate classifier " + dateFormat.format(new Date()));
Classifier cls = null;
DataSource sourceTrain = null;
Instances dataTrain = null;
if (new File(id + "-MNB.classifier").exists()) {
ObjectInputStream ois = new ObjectInputStream(new FileInputStream(id + "-MNB.classifier"));
cls = (Classifier) ois.readObject();
ois.close();
} else {
sourceTrain = new DataSource(id + "-train.arff");
dataTrain = sourceTrain.getDataSet();
if (dataTrain.classIndex() == -1) dataTrain.setClassIndex(dataTrain.numAttributes() - 1);
// Entreno el clasificador
cls = new weka.classifiers.bayes.NaiveBayesMultinomial();
int clase = dataTrain.numAttributes() - 1;
dataTrain.setClassIndex(clase);
cls.buildClassifier(dataTrain);
ObjectOutputStream oos =
new ObjectOutputStream(new FileOutputStream(id + "-MNB.classifier"));
oos.writeObject(cls);
oos.flush();
oos.close();
// data.delete();//no borro para el svm
}
// Ahora evaluo el clasificador con los datos de test
System.out.println("6-Evaluate classifier MNB " + dateFormat.format(new Date()));
out.println("6-Evaluate classifier MNB" + dateFormat.format(new Date()));
DataSource sourceTest = new DataSource(id + "-test.arff");
Instances dataTest = sourceTest.getDataSet();
int clase = dataTest.numAttributes() - 1;
dataTest.setClassIndex(clase);
Evaluation eval = new Evaluation(dataTest);
eval.evaluateModel(cls, dataTest);
// Ahora calculo los valores precision, recall y fmeasure. Además saco las matrices de
// confusion
precision = 0;
recall = 0;
fmeasure = 0;
for (int ind = 0; ind < topeTopics; ind++) {
precision += eval.precision(ind);
recall += eval.recall(ind);
fmeasure += eval.fMeasure(ind);
}
precision = precision / topeTopics;
recall = recall / topeTopics;
fmeasure = fmeasure / topeTopics;
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.println(eval.toMatrixString());
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.printf("Precision: %.3f\n", precision);
System.out.printf("Recall: %.3f\n", recall);
System.out.printf("F-measure: %.3f\n", fmeasure);
System.out.println("***************************************");
out.println("+++++++++++++++++++++++++++++++++++++++");
out.println(eval.toMatrixString());
out.println("+++++++++++++++++++++++++++++++++++++++");
out.printf("Precision: %.3f\n", precision);
out.printf("Recall: %.3f\n", recall);
out.printf("F-measure: %.3f\n", fmeasure);
out.println("***************************************");
/* NO BORRAR
System.out.println("7-Evaluate classifier SVM"+dateFormat.format(new Date()));
out.println("7-Evaluate classifier SVM"+dateFormat.format(new Date()));
if (new File(id+"-SVM.classifier").exists())
{
ObjectInputStream ois = new ObjectInputStream(new FileInputStream(id+"-SVM.classifier"));
cls = (Classifier) ois.readObject();
ois.close();
}
else
{
if (dataTrain==null)
{
sourceTrain = new DataSource(id+"-train.arff");
dataTrain = sourceTrain.getDataSet();
if (dataTrain.classIndex() == -1)
dataTrain.setClassIndex(dataTrain.numAttributes() - 1);
}
//Entreno el clasificador
cls = new weka.classifiers.functions.LibSVM();
clase = dataTrain.numAttributes()-1;
dataTrain.setClassIndex(clase);
cls.buildClassifier(dataTrain);
ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(id+"-SVM.classifier"));
oos.writeObject(cls);
oos.flush();
oos.close();
dataTrain.delete();
}
eval.evaluateModel(cls, dataTest);
precision=0;
recall=0;
fmeasure=0;
for(int ind=0; ind<topeTopics; ind++)
{
precision += eval.precision(ind);
recall += eval.recall(ind);
fmeasure += eval.fMeasure(ind);
}
precision = precision / topeTopics;
recall = recall / topeTopics;
fmeasure = fmeasure / topeTopics;
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.println(eval.toMatrixString());
System.out.println("+++++++++++++++++++++++++++++++++++++++");
System.out.printf("Precision: %.3f\n", precision);
System.out.printf("Recall: %.3f\n", recall);
System.out.printf("F-measure: %.3f\n", fmeasure);
System.out.println("***************************************");
out.println("+++++++++++++++++++++++++++++++++++++++");
out.println(eval.toMatrixString());
out.println("+++++++++++++++++++++++++++++++++++++++");
out.printf("Precision: %.3f\n", precision);
out.printf("Recall: %.3f\n", recall);
out.printf("F-measure: %.3f\n", fmeasure);
out.println("***************************************");
*/
System.out.println("Done " + dateFormat.format(new Date()));
out.println("Done " + dateFormat.format(new Date()));
if (printer == null) {
out.flush();
out.close();
}
// Intento de liberar memoria
if (dataTrain != null) dataTrain.delete();
if (dataTest != null) dataTest.delete();
if (train != null) train.clear();
if (test != null) test.clear();
if (topics != null) {
topics.getTopicsList().clear();
topics = null;
}
if (dataTest != null) dataTest.delete();
if (cls != null) cls = null;
if (tweetsProcessor != null) tweetsProcessor = null;
System.gc();
} catch (Exception e) {
e.printStackTrace();
}
}
public double getLiblinear(String path, String train, String test) {
// 本次精确度
double accuracy = 0.0;
try {
LibLINEAR c1 = new LibLINEAR();
// * String[] options=weka.core.Utils.splitOptions(
// * "-S 1 -C 1.0 -E 0.001 -B 0"); c1.setOptions(options);
ArffLoader atf = new ArffLoader();
File TraininputFile = new File(train);
atf.setFile(TraininputFile); // 训练语料文件
Instances instancesTrain = atf.getDataSet(); // 读入训练文件
instancesTrain.setClassIndex(instancesTrain.numAttributes() - 1);
File TestinputFile = new File(test);
atf.setFile(TestinputFile); // 测试语料文件
Instances instancesTest = atf.getDataSet(); // 读入测试文件
// 设置分类属性所在行号（第一行为0号），instancesTest.numAttributes()可以取得属性总数
instancesTest.setClassIndex(instancesTest.numAttributes() - 1);
c1.buildClassifier(instancesTrain); // 训练
Evaluation eval = new Evaluation(instancesTrain);
eval.evaluateModel(c1, instancesTest);
// eval.crossValidateModel(c1, instancesTrain, 10, new
// Random(1));
File newfile = new File(path + "OutLiblinear_temp" + ".txt");
BufferedWriter bufferedWriter =
new BufferedWriter(new OutputStreamWriter(new FileOutputStream(newfile), "utf-8"));
bufferedWriter.write(eval.toSummaryString() + "\r\n");
bufferedWriter.write(eval.toClassDetailsString() + "\r\n");
bufferedWriter.write(eval.toMatrixString() + "\r\n");
bufferedWriter.flush();
bufferedWriter.close();
BufferedReader bufferedReader = new BufferedReader(new FileReader(newfile));
String[] splitLineString = new String[5];
while (bufferedReader.ready()) {
bufferedReader.readLine();
String lineString = bufferedReader.readLine();
splitLineString = lineString.split(" ");
System.out.println(splitLineString[4]);
break;
}
bufferedReader.close();
// 求分类准确度
String tempLine;
BufferedReader tempBF = new BufferedReader(new FileReader(newfile));
while (tempBF.ready()) {
tempLine = tempBF.readLine();
if (tempLine.contains("Correctly Classified Instances")) {
tempLine = tempLine.substring(tempLine.lastIndexOf(".") - 2, tempLine.lastIndexOf(" "));
accuracy = Double.parseDouble(tempLine);
break;
}
}
tempBF.close();
} catch (Exception e) {
System.out.println("Can't run linlinear of weka.");
}
return accuracy;
}
public static void main(String[] args) throws Exception {
BufferedReader reader = new BufferedReader(new FileReader("spambase.arff"));
Instances data = new Instances(reader);
reader.close();
// setting class attribute
data.setClassIndex(data.numAttributes() - 1);
int i = data.numInstances();
int j = data.numAttributes() - 1;
File file = new File("tablelog.csv");
Writer output = null;
output = new BufferedWriter(new FileWriter(file));
output.write(
"%missing,auc1,correct1,fmeasure1,auc2,correct2,fmeasure2,auc3,correct3,fmeasure3\n");
Random randomGenerator = new Random();
data.randomize(randomGenerator);
int numBlock = data.numInstances() / 2;
double num0 = 0, num1 = 0, num2 = 0;
/*mdata.instance(0).setMissing(0);
mdata.deleteWithMissing(0);
System.out.println(mdata.numInstances()+","+data.numInstances());*/
// Instances traindata=null;
// Instances testdata=null;
// System.out.println(data.instance(3).stringValue(1));
for (int perc = 10; perc < 101; perc = perc + 10) {
Instances mdata = new Instances(data);
int numMissing = perc * numBlock / 100;
double y11[] = new double[2];
double y21[] = new double[2];
double y31[] = new double[2];
double y12[] = new double[2];
double y22[] = new double[2];
double y32[] = new double[2];
double y13[] = new double[2];
double y23[] = new double[2];
double y33[] = new double[2];
for (int p = 0; p < 2; p++) {
Instances traindata = mdata.trainCV(2, p);
Instances testdata = mdata.testCV(2, p);
num0 = 0;
num1 = 0;
num2 = 0;
for (int t = 0; t < numBlock; t++) {
if (traindata.instance(t).classValue() == 0) num0++;
if (traindata.instance(t).classValue() == 1) num1++;
// if (traindata.instance(t).classValue()==2) num2++;
}
// System.out.println(mdata.instance(0).classValue());
Instances trainwithmissing = new Instances(traindata);
Instances testwithmissing = new Instances(testdata);
for (int q = 0; q < j; q++) {
int r = randomGenerator.nextInt((int) i / 2);
for (int k = 0; k < numMissing; k++) {
// int r = randomGenerator.nextInt((int) i/2);
// int c = randomGenerator.nextInt(j);
trainwithmissing.instance((r + k) % numBlock).setMissing(q);
testwithmissing.instance((r + k) % numBlock).setMissing(q);
}
}
// trainwithmissing.deleteWithMissing(0);System.out.println(traindata.numInstances()+","+trainwithmissing.numInstances());
Classifier cModel =
(Classifier) new Logistic(); // try for different classifiers and datasets
cModel.buildClassifier(trainwithmissing);
Evaluation eTest1 = new Evaluation(trainwithmissing);
eTest1.evaluateModel(cModel, testdata);
// eTest.crossValidateModel(cModel,mdata,10,mdata.getRandomNumberGenerator(1));
y11[p] =
num0 / numBlock * eTest1.areaUnderROC(0)
+ num1
/ numBlock
* eTest1.areaUnderROC(1) /*+num2/numBlock*eTest1.areaUnderROC(2)*/;
y21[p] = eTest1.correct();
y31[p] =
num0 / numBlock * eTest1.fMeasure(0)
+ num1 / numBlock * eTest1.fMeasure(1) /*+num2/numBlock*eTest1.fMeasure(2)*/;
Classifier cModel2 = (Classifier) new Logistic();
cModel2.buildClassifier(traindata);
Evaluation eTest2 = new Evaluation(traindata);
eTest2.evaluateModel(cModel2, testwithmissing);
y12[p] =
num0 / numBlock * eTest2.areaUnderROC(0)
+ num1
/ numBlock
* eTest2.areaUnderROC(1) /*+num2/numBlock*eTest2.areaUnderROC(2)*/;
y22[p] = eTest2.correct();
y32[p] =
num0 / numBlock * eTest2.fMeasure(0)
+ num1 / numBlock * eTest2.fMeasure(1) /*+num2/numBlock*eTest2.fMeasure(2)*/;
Classifier cModel3 = (Classifier) new Logistic();
cModel3.buildClassifier(trainwithmissing);
Evaluation eTest3 = new Evaluation(trainwithmissing);
eTest3.evaluateModel(cModel3, testwithmissing);
y13[p] =
num0 / numBlock * eTest3.areaUnderROC(0)
+ num1
/ numBlock
* eTest3.areaUnderROC(1) /*+num2/numBlock*eTest3.areaUnderROC(2)*/;
y23[p] = eTest3.correct();
y33[p] =
num0 / numBlock * eTest3.fMeasure(0)
+ num1 / numBlock * eTest3.fMeasure(1) /*+num2/numBlock*eTest3.fMeasure(2)*/;
// System.out.println(num0+","+num1+","+num2+"\n");
}
double auc1 = (y11[0] + y11[1]) / 2;
double auc2 = (y12[0] + y12[1]) / 2;
double auc3 = (y13[0] + y13[1]) / 2;
double corr1 = (y21[0] + y21[1]) / i;
double corr2 = (y22[0] + y22[1]) / i;
double corr3 = (y23[0] + y23[1]) / i;
double fm1 = (y31[0] + y31[1]) / 2;
double fm2 = (y32[0] + y32[1]) / 2;
double fm3 = (y33[0] + y33[1]) / 2;
output.write(
perc + "," + auc1 + "," + corr1 + "," + fm1 + "," + auc2 + "," + corr2 + "," + fm2 + ","
+ auc3 + "," + corr3 + "," + fm3 + "\n"); // System.out.println(num0);
// mdata=data;
}
output.close();
}
// 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 runFilter() throws Exception {
System.out.println("filtering attributes...");
System.out.println("running weka filters and weka-libsvm");
File svmfile = new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat(".libsvm")));
LibSVMLoader libl = new LibSVMLoader();
libl.setFile(svmfile);
Instances data = libl.getDataSet();
NumericToNominal nm = new NumericToNominal(); // Converting last index
// attribute to type
// nominal from numeric
nm.setAttributeIndices("last"); // as the last index would be class
// label for the data
nm.setInputFormat(data);
filteredData = Filter.useFilter(data, nm); // filtered data stored in
// new Instances object
AttrNo = filteredData.numAttributes(); // number of attributes in given
// file
RecordNo = filteredData.numInstances(); // Number of records in given
// file
lowerBound = 0;
upperBound = AttrNo - 1;
AttributeSelection atsl = new AttributeSelection();
Ranker search = new Ranker();
InfoGainAttributeEval infog = new InfoGainAttributeEval(); // Applying
// Attribute
// Selection
// using
// InfoGain
// evaluator
// with
// Ranker
// search
atsl.setEvaluator(infog);
atsl.setSearch(search);
atsl.SelectAttributes(filteredData);
InfoGain = atsl.rankedAttributes();
SelectedAttributes = atsl.selectedAttributes();
// count non zero infoGain
int count = 0;
for (int i = 0; i < InfoGain.length; i++) {
count = (InfoGain[i][1] > 0) ? count + 1 : count;
}
System.out.println("writing attributes with non-zero InfoGain...");
FileWriter svmout =
new FileWriter(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_new.libsvm")));
for (int i = 0; i < RecordNo; i++) {
int index = 1;
svmout.write((int) filteredData.instance(i).value(filteredData.classIndex()) + " ");
for (int j = 0; j < count; j++) {
svmout.write(
index + ":" + (int) filteredData.instance(i).value((int) InfoGain[j][0]) + " ");
index++;
}
svmout.write("\n");
}
svmout.close();
// filtered
File newsvm = new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_new.libsvm")));
LibSVMLoader liblnew = new LibSVMLoader();
liblnew.setFile(newsvm);
Instances newdata = liblnew.getDataSet();
nm = new NumericToNominal(); // Converting last index attribute to type
// nominal from numeric
nm.setAttributeIndices("last"); // as the last index would be class
// label for the data
nm.setInputFormat(newdata);
Instances filteredDataNew = Filter.useFilter(newdata, nm); // filtered
// data
// stored in
// new
// Instances
// object
// test file
File newsvmtest =
new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_test.libsvm")));
LibSVMLoader libltest = new LibSVMLoader();
libltest.setFile(newsvmtest);
Instances newdatatest = libltest.getDataSet();
nm = new NumericToNominal(); // Converting last index attribute to type
// nominal from numeric
nm.setAttributeIndices("last"); // as the last index would be class
// label for the data
nm.setInputFormat(newdatatest);
Instances filteredDataTest = Filter.useFilter(newdatatest, nm); // filtered
// data
// stored
// in
// new
// Instances
// object
// weka.classifiers.functions.LibSVM -S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5
// -M 40.0 -C 1.0 -E 0.001 -P 0.1 -seed 1
String[] options = new String[1];
options[0] = "-S 0 -K 2 -D 3 -G 0.1 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -seed 1 -h 0";
System.out.println("building classifier...");
LibSVM svm_model = new LibSVM();
svm_model.setOptions(options); // set the options
svm_model.buildClassifier(filteredData); // build classifier
DecimalFormat df = new DecimalFormat("0.00");
System.out.println("running cross validation...");
Evaluation eval = new Evaluation(filteredData);
// eval.crossValidateModel(svm_model, filteredDataNew, 10, new
// Random(1));
eval.evaluateModel(svm_model, filteredDataTest);
FileWriter results =
new FileWriter(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_results.txt")));
results.write("Classifier 1: Support Vector Machines\n");
results.write("Positive class precision: " + df.format(eval.precision(0)) + "\n");
results.write("Positive class recall: " + df.format(eval.recall(0)) + "\n");
results.write("Positive class f-score: " + df.format(eval.fMeasure(0)) + "\n");
results.write("Negative class precision: " + df.format(eval.precision(0)) + "\n");
results.write("Negative class recall: " + df.format(eval.precision(0)) + "\n");
results.write("Negative class f-score: " + df.format(eval.fMeasure(0)) + "\n");
System.out.println("generating results...");
System.out.println("*" + sentiAnalysis.outout + "*\t" + "\tPositive\tNegative\tNeutral");
System.out.println(
"Precision\t"
+ df.format(eval.precision(0))
+ "\t"
+ df.format(eval.precision(2))
+ "\t"
+ df.format(eval.precision(1)));
System.out.println(
"Recall\t"
+ df.format(eval.recall(0))
+ "\t"
+ df.format(eval.recall(2))
+ "\t"
+ df.format(eval.recall(1)));
System.out.println(
"F-score\t"
+ df.format(eval.fMeasure(0))
+ "\t"
+ df.format(eval.fMeasure(2))
+ "\t"
+ df.format(eval.fMeasure(1)));
results.close();
}
public void testModel() throws Exception {
Evaluation eval = new Evaluation(testData);
eval.evaluateModel(classifier, testData);
System.out.println(eval.toSummaryString("Results", false));
}
public QSARModel train(Instances data) throws QSARException {
// GET A UUID AND DEFINE THE TEMPORARY FILE WHERE THE TRAINING DATA
// ARE STORED IN ARFF FORMAT PRIOR TO TRAINING.
final String rand = java.util.UUID.randomUUID().toString();
final String temporaryFilePath = ServerFolders.temp + "/" + rand + ".arff";
final File tempFile = new File(temporaryFilePath);
// SAVE THE DATA IN THE TEMPORARY FILE
try {
ArffSaver dataSaver = new ArffSaver();
dataSaver.setInstances(data);
dataSaver.setDestination(new FileOutputStream(tempFile));
dataSaver.writeBatch();
if (!tempFile.exists()) {
throw new IOException("Temporary File was not created");
}
} catch (final IOException ex) {
/*
* The content of the dataset cannot be
* written to the destination file due to
* some communication issue.
*/
tempFile.delete();
throw new RuntimeException(
"Unexpected condition while trying to save the " + "dataset in a temporary ARFF file",
ex);
}
NaiveBayes classifier = new NaiveBayes();
String[] generalOptions = {
"-c",
Integer.toString(data.classIndex() + 1),
"-t",
temporaryFilePath,
/// Save the model in the following directory
"-d",
ServerFolders.models_weka + "/" + uuid
};
try {
Evaluation.evaluateModel(classifier, generalOptions);
} catch (final Exception ex) {
tempFile.delete();
throw new QSARException(
Cause.XQReg350,
"Unexpected condition while trying to train "
+ "an SVM model. Possible explanation : {"
+ ex.getMessage()
+ "}",
ex);
}
QSARModel model = new QSARModel();
model.setParams(getParameters());
model.setCode(uuid.toString());
model.setAlgorithm(YaqpAlgorithms.NAIVE_BAYES);
model.setDataset(datasetUri);
model.setModelStatus(ModelStatus.UNDER_DEVELOPMENT);
ArrayList<Feature> independentFeatures = new ArrayList<Feature>();
for (int i = 0; i < data.numAttributes(); i++) {
Feature f = new Feature(data.attribute(i).name());
if (data.classIndex() != i) {
independentFeatures.add(f);
}
}
Feature dependentFeature = new Feature(data.classAttribute().name());
Feature predictedFeature = dependentFeature;
model.setDependentFeature(dependentFeature);
model.setIndependentFeatures(independentFeatures);
model.setPredictionFeature(predictedFeature);
tempFile.delete();
return model;
}
public static void main(String[] args) throws Exception {
/*
* First we load our preditons from the CSV formatted file.
*/
CSVLoader predictCsvLoader = new CSVLoader();
predictCsvLoader.setSource(new File("predict.csv"));
/*
* Since we are not using the ARFF format here, we have to give the
* loader a little bit of information about the data types. Columns
* 3,8,10 need to be of type string and columns 1,4,11 are nominal
* types.
*/
predictCsvLoader.setStringAttributes("3,8,10");
predictCsvLoader.setNominalAttributes("1,4,11");
Instances predictDataSet = predictCsvLoader.getDataSet();
/*
* Here we set the attribute we want to test the predicitons with
*/
Attribute testAttribute = predictDataSet.attribute(0);
predictDataSet.setClass(testAttribute);
/*
* We still have to remove all string attributes before we can test
*/
predictDataSet.deleteStringAttributes();
/*
* Next we load the training data from our ARFF file
*/
ArffLoader trainLoader = new ArffLoader();
trainLoader.setSource(new File("train.arff"));
trainLoader.setRetrieval(Loader.BATCH);
Instances trainDataSet = trainLoader.getDataSet();
/*
* Now we tell the data set which attribute we want to classify, in our
* case, we want to classify the first column: survived
*/
Attribute trainAttribute = trainDataSet.attribute(0);
trainDataSet.setClass(trainAttribute);
/*
* The RandomForest implementation cannot handle columns of type string,
* so we remove them for now.
*/
trainDataSet.deleteStringAttributes();
/*
* Now we read in the serialized model from disk
*/
Classifier classifier = (Classifier) SerializationHelper.read("titanic.model");
/*
* Next we will use an Evaluation class to evaluate the performance of
* our Classifier.
*/
Evaluation evaluation = new Evaluation(trainDataSet);
evaluation.evaluateModel(classifier, predictDataSet, new Object[] {});
/*
* After we evaluate the Classifier, we write out the summary
* information to the screen.
*/
System.out.println(classifier);
System.out.println(evaluation.toSummaryString());
}
