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