第一周-调用weka算法进行数据挖掘

第一周-调用weka算法进行数据挖掘

• 简单数据集data.txt

  @relation weather
  
  @attribute outlook {sunny, overcast, rainy}
  @attribute temperature numeric
  @attribute humidity numeric
  @attribute windy {TRUE, FALSE}
  @attribute play {yes, no}
  
  @data
  sunny,85,85,FALSE,no
  sunny,80,90,TRUE,no
  overcast,83,86,FALSE,yes
  rainy,70,96,FALSE,yes
  rainy,68,80,FALSE,yes
  rainy,65,70,TRUE,no
  overcast,64,65,TRUE,yes
  sunny,72,95,FALSE,no
  sunny,69,70,FALSE,yes
  rainy,75,80,FALSE,yes
  sunny,75,70,TRUE,yes
  overcast,72,90,TRUE,yes
  overcast,81,75,FALSE,yes
  rainy,71,91,TRUE,no
  

• 在eclipse中新建项目导入weka.jar包，调用weka中的一些算法。
  
  package test;

  import java.io.BufferedReader;
  import java.io.FileNotFoundException;
  import java.io.FileReader;
  import weka.classifiers.Classifier;
  import weka.classifiers.Evaluation;
  import weka.classifiers.evaluation.NominalPrediction;
  import weka.classifiers.rules.DecisionTable;
  import weka.classifiers.rules.PART;
  import weka.classifiers.trees.DecisionStump;
  import weka.classifiers.trees.J48;
  import weka.core.FastVector;
  import weka.core.Instances;
  
  @SuppressWarnings("deprecation")
  public class WeakTest {
  	public static BufferedReader readDataFile(String filename) {
  		BufferedReader inputReader = null;
  
  		try {
  			inputReader = new BufferedReader(new FileReader(filename));
  		} catch (FileNotFoundException ex) {
  			System.err.println("File not found: " + filename);
  		}
  
  		return inputReader;
  	}
  
  	public static Evaluation classify(Classifier model,
  			Instances trainingSet, Instances testingSet) throws Exception {
  		Evaluation evaluation = new Evaluation(trainingSet);
  
  		model.buildClassifier(trainingSet);
  		evaluation.evaluateModel(model, testingSet);
  
  		return evaluation;
  	}
  
  	public static double calculateAccuracy(FastVector predictions) {
  		double correct = 0;
  
  		for (int i = 0; i < predictions.size(); i++) {
  			NominalPrediction np = (NominalPrediction) predictions.elementAt(i);
  			if (np.predicted() == np.actual()) {
  				correct++;
  			}
  		}
  
  		return 100 * correct / predictions.size();
  	}
  
  	public static Instances[][] crossValidationSplit(Instances data, int numberOfFolds) {
  		Instances[][] split = new Instances[2][numberOfFolds];
  
  		for (int i = 0; i < numberOfFolds; i++) {
  			split[0][i] = data.trainCV(numberOfFolds, i);
  			split[1][i] = data.testCV(numberOfFolds, i);
  		}
  
  		return split;
  	}
  
  	public static void main(String[] args) throws Exception {
  		BufferedReader datafile = readDataFile("E:\\yuce/data.txt");
  
  		Instances data = new Instances(datafile);
  		data.setClassIndex(data.numAttributes() - 1);
  
  		// Do 10-split cross validation
  		Instances[][] split = crossValidationSplit(data, 10);
  
  		// Separate split into training and testing arrays
  		Instances[] trainingSplits = split[0];
  		Instances[] testingSplits = split[1];
  
  		// Use a set of classifiers
  		Classifier[] models = {
  				new J48(), // a decision tree
  				new PART(),
  				new DecisionTable(),//decision table majority classifier
  				new DecisionStump() //one-level decision tree
  		};
  
  		// Run for each model
  		for (int j = 0; j < models.length; j++) {
  
  			// Collect every group of predictions for current model in a FastVector
  			FastVector predictions = new FastVector();
  
  			// For each training-testing split pair, train and test the classifier
  			for (int i = 0; i < trainingSplits.length; i++) {
  				Evaluation validation = classify(models[j], trainingSplits[i], testingSplits[i]);
  
  				predictions.appendElements(validation.predictions());
  
  				// Uncomment to see the summary for each training-testing pair.
  				//System.out.println(models[j].toString());
  			}
  
  			// Calculate overall accuracy of current classifier on all splits
  			double accuracy = calculateAccuracy(predictions);
  
  			// Print current classifier's name and accuracy in a complicated,
  			// but nice-looking way.
  			System.out.println("Accuracy of " + models[j].getClass().getSimpleName() + ": "
  					+ String.format("%.2f%%", accuracy)
  					+ "\n---------------------------------");
  		}
  
  	}
  }
  

• 运行截图