Booster

代码

package machineLearning.adaboosting;import weka.core.Instances;import java.io.FileReader;
import java.util.Arrays;/*** ClassName: WeightedInstances* Package: machineLearning.adaboosting* Description:Weighted instances.** @Author: luv_x_c* @Create: 2023/8/13 15:12*/
public class WeightedInstances extends Instances {/*** Just the requirement of some classes, any number is ok.*/private static final long serialVersionUID = 11087456L;/*** Weights.*/private double[] weights;/*** The first constructor.** @param paraFileReader The given reader to read data from file.*/public WeightedInstances(FileReader paraFileReader) throws Exception {super(paraFileReader);setClassIndex(numAttributes() - 1);//Initialize weights.weights = new double[numInstances()];double tempAverage = 1.0 / numInstances();Arrays.fill(weights, tempAverage);System.out.println("Instances weights are: " + Arrays.toString(weights));}//Of the first constructor/*** The second constructor.** @param paraInstances The given instances.*/public WeightedInstances(Instances paraInstances) {super(paraInstances);setClassIndex(numAttributes() - 1);// Initialize weights.weights = new double[numInstances()];double tempAverage = 1.0 / numInstances();Arrays.fill(weights, tempAverage);System.out.println("Instances weights are: " + Arrays.toString(weights));}//Of the second constructor/*** Getter.** @param paraIndex The given index.* @return The weight of the given index.*/public double getWeight(int paraIndex) {return weights[paraIndex];}//Of getWeight/*** Adjust the weights.** @param paraCorrectArray Indicate which instance have been correctly classify.* @param paraAlpha        The weight of the last classifier.*/public void adjustWeights(boolean[] paraCorrectArray, double paraAlpha) {//Step1. Calculate alpha.double tempIncrease = Math.exp(paraAlpha);// Step2. Adjust.double tempWeightsSum = 0;for (int i = 0; i < weights.length; i++) {if (paraCorrectArray[i]) {weights[i] /= tempIncrease;} else {weights[i] *= tempIncrease;}//Of iftempWeightsSum += weights[i];}//Of for i// Step3. Normalize.for (int i = 0; i < weights.length; i++) {weights[i] /= tempWeightsSum;}//Of for iSystem.out.println("After adjusting, instances weights are: " + Arrays.toString(weights));}//Of adjustWeights/*** Test the method.*/public void adjustWeightsTest() {boolean[] tempCorrectArray = new boolean[numInstances()];Arrays.fill(tempCorrectArray, true);double tempWeightError = 0.3;adjustWeights(tempCorrectArray, tempWeightError);System.out.println("After adjusting: ");System.out.println(toString());}// Of adjustWeightsTest@Overridepublic String toString() {String resultString ="I am a weighted Instances object.\r\n" + " I have " + numInstances() + " " +"instances and " + (numAttributes() - 1) + " conditional attributes.\r\n" +" My weights are: " + Arrays.toString(weights) + "\r\n" +" My data are：\r\n" + super.toString();return resultString;}//Of toString/*** @param args Not provided.*/public static void main(String[] args) {WeightedInstances tempWeightInstances = null;String tempFileName = "E:\\java_code\\data\\sampledata\\iris.arff";try {FileReader fileReader = new FileReader(tempFileName);tempWeightInstances = new WeightedInstances(fileReader);fileReader.close();} catch (Exception ee) {System.out.println("Cannot read the file: " + tempFileName + "\r\n" + ee);System.exit(0);}//Of trySystem.out.println(tempWeightInstances.toString());tempWeightInstances.adjustWeightsTest();}//Of main
}//OF class WeightedInstances

package machineLearning.adaboosting;import weka.core.Instance;import java.io.FileReader;
import java.util.Arrays;/*** ClassName: StumpClassifier* Package: machineLearning.adaboosting* Description:The stump classifier.** @Author: luv_x_c* @Create: 2023/8/17 20:37*/
public class StumpClassifier extends SimpleClassifier {/*** The best cut for the current attribute on weightInstances.*/double bestCut;/*** The class label for attribute value less than bestCut.*/int leftLeafLabel;/*** The class label for attribute value no less than bestCut.*/int rightLeafLabel;/*** The only constructor.** @param paraWeightedInstances The given instances.*/public StumpClassifier(WeightedInstances paraWeightedInstances) {super(paraWeightedInstances);}//Of the only constructorpublic void train() {//Step1. Randomly choose an attribute.selectedAttribute = random.nextInt(numConditions);//Step2. Find all attributes values and sort.double[] tempValuesArray = new double[numInstances];for (int i = 0; i < tempValuesArray.length; i++) {tempValuesArray[i] = weightedInstances.instance(i).value(selectedAttribute);}//Of for iArrays.sort(tempValuesArray);//Step3. Initialize, classify all instances as the same with the original cut.int tempNumLabels = numClasses;double[] tempLabelCountArray = new double[tempNumLabels];int tempCurrentLabel;//Step3.1 Scan all labels to obtain their counts.for (int i = 0; i < numInstances; i++) {// The label of the ith instancetempCurrentLabel = (int) weightedInstances.instance(i).classValue();tempLabelCountArray[tempCurrentLabel] += weightedInstances.getWeight(i);}//Of for i//Step3.2 Find the label with the maximal count.double tempMaxCorrect = 0;int tempBestLabel = -1;for (int i = 0; i < tempLabelCountArray.length; i++) {if (tempMaxCorrect < tempLabelCountArray[i]) {tempMaxCorrect = tempLabelCountArray[i];tempBestLabel = i;}//Of if}//Of for i//Steep3.3 The cut is a little  smaller than the minimal value.bestCut = tempValuesArray[0] - 0.1;leftLeafLabel = tempBestLabel;rightLeafLabel = tempBestLabel;// Step4. Check candidate cuts one by one.// Step4.1 To handle multi-class data, left and right.double tempCut;double[][] tempLabelCountMatrix = new double[2][tempNumLabels];for (int i = 0; i < tempValuesArray.length - 1; i++) {// Step4.1 Some attribute values are identical, ignore them.if (tempValuesArray[i] == tempValuesArray[i + 1]) {continue;}//Of iftempCut = (tempValuesArray[i] + tempValuesArray[i + 1]) / 2;// Step4.2 Scan all labels to obtain their counts wrt, the cut .// Initialize again since it is used many times.for (int j = 0; j < 2; j++) {for (int k = 0; k < tempNumLabels; k++) {tempLabelCountMatrix[j][k] = 0;}//Of for k}//Of for jfor (int j = 0; j < numInstances; j++) {// The label of the jth instance.tempCurrentLabel = (int) weightedInstances.instance(j).classValue();if (weightedInstances.instance(j).value(selectedAttribute) < tempCut) {tempLabelCountMatrix[0][tempCurrentLabel] += weightedInstances.getWeight(j);} else {tempLabelCountMatrix[1][tempCurrentLabel] += weightedInstances.getWeight(j);}//Of if}//Of for j// Step4.3 Left leafdouble tempLeftMaxCorrect = 0;int tempLeftBestLabel = 0;for (int j = 0; j < tempLabelCountMatrix[0].length; j++) {if (tempLeftMaxCorrect < tempLabelCountMatrix[0][j]) {tempLeftMaxCorrect = tempLabelCountMatrix[0][j];tempLeftBestLabel = j;}//Of if}//Of for j// Step 4.4 Right leafdouble tempRightMaxCorrect = 0;int tempRightBestLabel = 0;for (int j = 0; j < tempLabelCountMatrix[0].length; j++) {if (tempRightMaxCorrect < tempLabelCountMatrix[1][j]) {tempRightMaxCorrect = tempLabelCountMatrix[1][j];tempRightBestLabel = j;}//Of if}//Of for j// Step 4.5 Compare with the current bestif (tempMaxCorrect < tempLeftMaxCorrect + tempRightMaxCorrect) {tempMaxCorrect = tempLeftMaxCorrect + tempRightMaxCorrect;bestCut = tempCut;leftLeafLabel = tempLeftBestLabel;rightLeafLabel = tempRightBestLabel;}//Of if}//Of for iSystem.out.println("Attribute = " + selectedAttribute + ", cut =" + bestCut + ", " +"leftLeafLabel = " + leftLeafLabel + ", rightLeafLabel" + rightLeafLabel);}//Of train@Overridepublic int classify(Instance paraInstance) {int resultLabel = -1;if (paraInstance.value(selectedAttribute) < bestCut) {resultLabel = leftLeafLabel;} else {resultLabel = rightLeafLabel;}//Of ifreturn resultLabel;}//Of classify@Overridepublic String toString() {return "I am a stump classifier.\r\n" + "I choose attribute #" + selectedAttribute+ " with cut value " + bestCut + ".\r\n" + "The left and right leaf labels are " + leftLeafLabel+ " and " + rightLeafLabel + ", respectively.\r\n" + "My weighted error is: " + computeWeightedError()+ ".\r\n" + "My weighted accuracy is : " + computeTrainingAccuracy() + ".";}//Of toString/*** For unit test.** @param args Not used.*/public static void main(String[] args) {WeightedInstances tempWeightedInstance = null;String tempFileName = "E:\\java_code\\data\\sampledata\\iris.arff";try {FileReader fileReader = new FileReader(tempFileName);tempWeightedInstance = new WeightedInstances(fileReader);fileReader.close();} catch (Exception e) {System.out.println("Cannot read the file: " + tempFileName + "\r\n" + e);System.exit(0);}//OF tryStumpClassifier tempClassifier = new StumpClassifier(tempWeightedInstance);tempClassifier.train();System.out.println(tempClassifier);System.out.println(Arrays.toString(tempClassifier.computeCorrectnessArray()));}//OF main
}//Of class StumpClassifier

package machineLearning.adaboosting;import weka.core.Instance;import java.util.Random;/*** ClassName: SimpleClassifier* Package: machineLearning.adaboosting* Description:The super class of any simple classifier.** @Author: luv_x_c* @Create: 2023/8/14 13:43*/
public abstract class SimpleClassifier {/*** The index of the current attribute.*/int selectedAttribute;/*** Weighted data.*/WeightedInstances weightedInstances;/*** The accuracy on the training set.*/double trainingAccuracy;/*** The number of instances.*/int numInstances;/*** The number of instances.*/int numClasses;/*** The number of conditional attributes.*/int numConditions;Random random = new Random();/*** The first constructor.** @param paraWeightedInstances The given instances.*/public SimpleClassifier(WeightedInstances paraWeightedInstances) {weightedInstances = paraWeightedInstances;numConditions = weightedInstances.numAttributes() - 1;numInstances = weightedInstances.numInstances();numClasses = weightedInstances.classAttribute().numValues();}// Of the first constructor/*** Train the classifier.*/public abstract void train();/*** Classify an instance.** @param paraInstance The given instance.* @return Predicted label.*/public abstract int classify(Instance paraInstance);/*** Which instance in the  training set are correctly classified.** @return The correctness array.*/public boolean[] computeCorrectnessArray() {boolean[] resultCorrectnessArray = new boolean[weightedInstances.numInstances()];for (int i = 0; i < resultCorrectnessArray.length; i++) {Instance tempInstance = weightedInstances.instance(i);if ((int) (tempInstance.classValue()) == classify(tempInstance)) {resultCorrectnessArray[i] = true;}// OF if}//Of for ireturn resultCorrectnessArray;}//Of computeCorrectnessArray/*** Compute the accuracy on the training set.** @return The training accuracy.*/public double computeTrainingAccuracy() {double tempCorrect = 0;boolean[] tempCorrectnessArray = computeCorrectnessArray();for (int i = 0; i < tempCorrectnessArray.length; i++) {if (tempCorrectnessArray[i]) {tempCorrect++;}//Of if}//Of for idouble resultAccuracy = tempCorrect / tempCorrectnessArray.length;return resultAccuracy;}//Of computeTrainingAccuracypublic double computeWeightedError(){double resultError=0;boolean[]tempCorrectnessArray=computeCorrectnessArray();for (int i = 0; i < tempCorrectnessArray.length; i++) {if(!tempCorrectnessArray[i]){resultError+=weightedInstances.getWeight(i);}//Of if}//Of for iif(resultError<1e-6){resultError=1e-6;}//Of ifreturn resultError;}//Of computeWeightedError
}//Of class SimpleClassifier

package machineLearning.adaboosting;import weka.core.Instance;
import weka.core.Instances;import java.io.FileReader;
import java.io.IOException;/*** ClassName: Booster* Package: machineLearning.adaboosting* Description: The booster which ensembles base classifiers.** @Author: luv_x_c* @Create: 2023/8/18 14:34*/
public class Booster {/*** Classifiers.*/SimpleClassifier[] classifiers;/*** Number of classifiers.*/int numClassifiers;/*** Whether stop after the training error is 0.*/boolean stopAfterConverge = false;/*** The weights of classifier.*/double[] classifierWeights;/*** The training data.*/Instances trainingData;/*** The testing data.*/Instances testingData;/*** The first constructor. The testing set is the same as the training data.** @param paraTrainingFileName The data file name.*/public Booster(String paraTrainingFileName) {// Step1. Read the training set.try {FileReader fileReader = new FileReader(paraTrainingFileName);trainingData = new Instances(fileReader);fileReader.close();} catch (IOException e) {System.out.println("Cannot read the file: " + paraTrainingFileName + "\r\n" + e);System.exit(0);}//Of try// Step2. Set the last attribute as the class indextrainingData.setClassIndex(trainingData.numAttributes() - 1);// Step3. The testing data is the same as the training datatestingData = trainingData;stopAfterConverge = true;System.out.println("****************Data**********\r\n" + trainingData);}//Of the first constructor/*** Set the number of base classifier, and allocate space for them.** @param paraNumBaseClassifiers The number of base classifier.*/public void setNumBaseClassifiers(int paraNumBaseClassifiers) {numClassifiers = paraNumBaseClassifiers;// Step1. Allocate space for classifiersclassifiers = new SimpleClassifier[numClassifiers];// Step2. Initialize classifier weightsclassifierWeights = new double[numClassifiers];}//Of setNumBaseClassifiers/*** Train the booster.*/public void train() {// Step1. Initialize.WeightedInstances tempWeightedInstances = null;double tempError;numClassifiers = 0;// Step2. Build other classifier.for (int i = 0; i < classifiers.length; i++) {// Step2.1 Key code: Construct or adjust the weightedInstancesif (i == 0) {tempWeightedInstances = new WeightedInstances(trainingData);} else {// Adjust the weights of the datatempWeightedInstances.adjustWeights(classifiers[i - 1].computeCorrectnessArray(),classifierWeights[i - 1]);}//Of if// Step 2.2 Train the next classifier.classifiers[i] = new StumpClassifier(tempWeightedInstances);classifiers[i].train();tempError = classifiers[i].computeWeightedError();// Key code: Set the classifier weight.classifierWeights[i] = 0.5 * Math.log(1 / tempError - 1);if (classifierWeights[i] < 1e-6) {classifierWeights[i] = 0;}//Of ifSystem.out.println("Classifier #" + i + " , weighted error = " + tempError + " , " +"weight = " + classifierWeights[i] + "\r\n");numClassifiers++;// The accuracy is enough.if (stopAfterConverge) {double tempTrainAccuracy = computeTrainingAccuracy();System.out.println("The accuracy of the booster is: " + tempTrainAccuracy + "\r\n");if (tempTrainAccuracy > 0.99999) {System.out.println("Stop at the round： " + i + " due to converge.\r\n");break;}//Of if}//of if}//Of for i}//Of for train/*** Classify an instance** @param paraInstance The given instance* @return The predicted label*/public int classify(Instance paraInstance) {double[] tempLabelCountArray = new double[testingData.classAttribute().numValues()];for (int i = 0; i < numClassifiers; i++) {int tempLabel = classifiers[i].classify(paraInstance);tempLabelCountArray[tempLabel] += classifierWeights[i];}//Of for iint resultLabel = -1;double tempMax = -1;for (int i = 0; i < tempLabelCountArray.length; i++) {if (tempMax < tempLabelCountArray[i]) {tempMax = tempLabelCountArray[i];resultLabel = i;}//Of if}//Of for ireturn resultLabel;}//Of classify/*** Test the booster on the training data.** @return The classification accuracy.*/public double test() {System.out.println("Testing on " + testingData.numInstances() + " instances\r\n");return test(testingData);}//Of test/*** Test the booster.** @param paraInstances The testing set.* @return The classification accuracy.*/public double test(Instances paraInstances) {double tempCorrect = 0;paraInstances.setClassIndex(paraInstances.numAttributes() - 1);for (int i = 0; i < paraInstances.numInstances(); i++) {Instance tempInstance = paraInstances.instance(i);if (classify(tempInstance) == (int) tempInstance.classValue()) {tempCorrect++;}//Of if}//Of for idouble resultAccuracy = tempCorrect / paraInstances.numInstances();System.out.println("The accuracy is: " + resultAccuracy);return resultAccuracy;}//Of test/*** Compute the training accuracy of the booster. It is not weighted.** @return The training accuracy.*/public double computeTrainingAccuracy() {double tempCorrect = 0;for (int i = 0; i < trainingData.numInstances(); i++) {if (classify(trainingData.instance(i)) == (int) trainingData.instance(i).classValue()) {tempCorrect++;}//Of if}//Of for idouble tempAccuracy = tempCorrect / trainingData.numInstances();return tempAccuracy;}//Of computeTrainingAccuracy/*** The entrance of the program.** @param args Not used now.*/public static void main(String[] args) {System.out.println("Starting Adaboosting ..");Booster tempBooster = new Booster("E:\\java_code\\data\\sampledata\\iris.arff");tempBooster.setNumBaseClassifiers(5);tempBooster.train();System.out.println("The training accuracy is: " + tempBooster.computeTrainingAccuracy());tempBooster.test();}//Of main
}//Of class Booster

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