Java Instances.randomize方法代碼示例

import weka.core.Instances; //導入方法依賴的package包/類
public static void main(String[] args){
    GRules GRulesLearner = new GRules();
    
    try {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("data/car.arff");
    Instances originalDataset = source.getDataSet();
    
    // split data 80/20
    int trainSize = (int) Math.round(originalDataset.numInstances() * 0.8
        / 100);
    int testSize = originalDataset.numInstances() - trainSize;
    Instances train = new Instances(originalDataset, 0, trainSize);
    Instances test = new Instances(originalDataset, trainSize, testSize);
    
    train.randomize(new java.util.Random(0));
    
    // train the rules learner
    List<List<Term>> rightSideRules = GRulesLearner.learnRightSide(train);
    List<Rule> completedRules = GRulesLearner.induceCompleteRules(rightSideRules, originalDataset);
    
    // try to predict an instance
    System.out.println("A testing instance: ");
    System.out.println(test.get(10));
    System.out.println("A rule covered the instance: ");
    System.out.println(GRulesLearner.predict(test.get(10), completedRules).nicePrint());
    
    } catch (Exception ex) {
        System.err.println(ex.toString());
    }
}
 

import weka.core.Instances; //導入方法依賴的package包/類
/**<p>Generate Random sample according to random seed on Desktop, each sample has the same distribution of InTrace/OutTrace
 *  and have <b>SIZE</b> instances.
 * </p>
 * @param path original arff file to be sampled in path
 * @param rand random seed
 * @param num the number of selection
 * */
public static void generateARFF(String path, int rand, int num) throws Exception{
	/*** original dataset reading */
	Instances data = DataSource.read(path);
	data.setClassIndex(data.numAttributes()-1);
	
	/*** randomize the dataset */
	data.randomize(new Random(rand));
	
	/*** dataIn to save instances of InTrace class */
	Instances dataIn = new Instances("dataIn", InsMerge.getStandAttrs(), 1);
	dataIn.setClassIndex(dataIn.numAttributes() - 1);

	/*** dataOut to save instances of OutTrace class */
	Instances dataOut = new Instances("dataOut", InsMerge.getStandAttrs(), 1);
	dataIn.setClassIndex(dataIn.numAttributes() - 1);
	
	/*** add OutTrace instances into dataOut */
	for(int i=0; i<data.numInstances(); i++){
		if(data.get(i).stringValue(data.get(i).classAttribute()).equals("OutTrace")){
			dataOut.add(data.get(i));
		}
	}
	
	/** add InTrace instances into dataIn */
	for(int i=0; i<data.numInstances(); i++){
		if(data.get(i).stringValue(data.get(i).classAttribute()).equals("InTrace")){
			dataIn.add(data.get(i));
		}
	}
	
	/*** get the In/Out ratio in original dataset */
	int inTrace = dataIn.numInstances();
	int outTrace = dataOut.numInstances();
	double ratioI = inTrace*1.0/(outTrace + inTrace);
	
	/*** expected number to select from original dataset*/
	int intrace = (int) (num * ratioI);
	int outtrace = num - intrace;
	
	/** create new generated dataset train*/
	Instances train = new Instances("dataIn", InsMerge.getStandAttrs(), 1);
	train.setClassIndex(train.numAttributes() - 1);

	/** train get X instances from dataIn*/
	for(int i=0; i<intrace; i++){
		train.add(dataIn.get(i));
	}
	
	/** train get Y instances from dataOut*/
	for(int j=0; j<outtrace; j++){
		train.add(dataOut.get(j));
	}
	
	/** save the dataset in path, we save the arff into D:/Users/LEE/Desktop/New_Data/XXX.arff */
	String filename = "files/generated/" + filterName(path) + rand + ".arff";
	DataSink.write(filename, train);

}
 

import weka.core.Instances; //導入方法依賴的package包/類
/***
	 * <p>Using Feature Selection method to get 10 folds results in the given project</p>
	 * @param path project path
	 * @param sel label means we use the Feature Selection method
	 * @throws Exception 
	 */
	public static void showFolds(String path, int k, int flag) throws Exception{
			
		Instances data1 = DataSource.read(path);
		data1.setClassIndex(data1.numAttributes()-1);
		if(!data1.classAttribute().isNominal()) // in case of noisy data, return
			return;
		
		/** Feature Selection: Correlation */
		CorrelationAttributeEval evall = new CorrelationAttributeEval();
		Ranker ranker = new Ranker();
		AttributeSelection selector = new AttributeSelection();		
		selector.setEvaluator(evall);
		selector.setSearch(ranker);
		selector.setInputFormat(data1);
		data1 = Filter.useFilter(data1, selector);

		/** Randomize and stratify the dataset*/
		data1.randomize(new Random(1)); 	
		data1.stratify(10);	// 10 folds
		
		double[][] matrix = new double[10][7];	
		
		for(int i=0; i<10; i++){ // To calculate the results in each fold
			
			Instances test = data1.testCV(10, i);
			Instances train = data1.trainCV(10, i);
			
			/** SMOTE */
			SMOTE smote = new SMOTE();
			smote.setInputFormat(train);
			train = Filter.useFilter(train, smote);

			/** C4.5 */
			J48 rf = new J48();
//			RandomForest rf = new RandomForest();
//			rf.setNumIterations(300);
			rf.buildClassifier(train);
			
			Evaluation eval = new Evaluation(train);
			eval.evaluateModel(rf, test); 
					
			matrix[i][6] = 1-eval.errorRate();
			
			matrix[i][0] = eval.precision(0);
			
			matrix[i][1] = eval.recall(0);
			
			matrix[i][2] = eval.fMeasure(0);
			
			matrix[i][3] = eval.precision(1);
			
			matrix[i][4] = eval.recall(1);
			
			matrix[i][5] = eval.fMeasure(1);
			
		}
		
		for(int i=0;i<10;i++){
			for(int j=0;j<7;j++){
				System.out.printf("%15.8f", matrix[i][j]);
			}
			System.out.println("");
		}
	}
 

import weka.core.Instances; //導入方法依賴的package包/類
/** 
 * Randomize the dataset
 * @param data
 * @return
 */
public static Instances random(Instances data) {
	data.randomize(new Random());
	return data;
}