[Weka] Tutorial 소스

아래 소스는 연령과 성별에 따라 특정 사이트에 로그인한 횟수를 기반으로

특정 성별과 연령을 입력 했을 때 그 사람이 로그인할 횟수를 예측하는 소스입니다.

Weka를 이용하면 다양한 분야에 적용할 수 있을 듯 합니다.

[소스]
package com.alag.ci.weka.tutorial;

import weka.classifiers.Classifier;

import weka.classifiers.Evaluation;

import weka.classifiers.functions.RBFNetwork;

import weka.core.Attribute;

import weka.core.FastVector;

import weka.core.Instance;

import weka.core.Instances;

public class WEKATutorial {

    

    public static void main(String [] args) throws Exception {

        WEKATutorial wekaTut = new WEKATutorial();

        wekaTut.executeWekaTutorial();

    }

    

    private void executeWekaTutorial() throws Exception {

        FastVector allAttributes = createAttributes();

        Instances learningDataset = createLearningDataSet(allAttributes);

        Classifier predictiveModel = learnPredictiveModel( learningDataset);

        Evaluation evaluation = evaluatePredictiveModel(predictiveModel,  learningDataset);

        System.out.println(evaluation.toSummaryString());

        predictUnknownCases(learningDataset,predictiveModel);

   

        //plotData(learningDataset, predictiveModel);

    }

    

    private void predictUnknownCases(Instances learningDataset, Classifier predictiveModel) 

        throws Exception {

        Instance testMaleInstance = 

            createInstance(learningDataset,32., "male", 0) ;

        Instance testFemaleInstance = 

            createInstance(learningDataset,32., "female", 0) ;

        double malePrediction = 

            predictiveModel.classifyInstance(testMaleInstance);

        double femalePrediction = 

            predictiveModel.classifyInstance(testFemaleInstance);

        System.out.println("Predicted number of logins [age=32]: ");

        System.out.println("\tMale = " + malePrediction);

        System.out.println("\tFemale = " + femalePrediction);

    }

    private FastVector createAttributes() {

        //create the numeric attribute Age

        Attribute ageAttribute = new Attribute("age");

        //create the nominal attribute Gender

        FastVector genderAttributeValues = new FastVector(2);

        genderAttributeValues.addElement("male");

        genderAttributeValues.addElement("female");

        Attribute genderAttribute = new Attribute("gender", genderAttributeValues);

        //create the numLogins attribute

        Attribute numLoginsAttribute = new Attribute("numLogins");

        FastVector allAttributes = new FastVector(3);

        allAttributes.addElement(ageAttribute);

        allAttributes.addElement(genderAttribute);

        allAttributes.addElement(numLoginsAttribute);

        return allAttributes;

    }

    

    private Instances createLearningDataSet(FastVector allAttributes) {

        Instances trainingDataSet = 

            new Instances("wekaTutorial", allAttributes, 4); 

        trainingDataSet.setClassIndex(2);

        addInstance(trainingDataSet, 20.,"male", 5);

        addInstance(trainingDataSet, 30.,"female", 2);

        addInstance(trainingDataSet, 40.,"male", 3);

        addInstance(trainingDataSet, 35.,"female", 4);

        return trainingDataSet;

    }

    

    private void addInstance(Instances trainingDataSet, 

                             double age, 

                             String gender,

                             int numLogins)

    {        

        Instance instance = createInstance(trainingDataSet,age, gender,numLogins);

        trainingDataSet.add(instance);

    }

    

    private Instance createInstance(Instances associatedDataSet,

            double age, String gender, int numLogins) {

        // Create empty instance with three attribute values

        Instance instance = new Instance(3);

        instance.setDataset(associatedDataSet);

        instance.setValue(0, age);

        instance.setValue(1, gender);

        instance.setValue(2, numLogins);

        return instance;

    }

    

    private Classifier learnPredictiveModel(Instances learningDataset) throws Exception {

       Classifier classifier = getClassifier();

       classifier.buildClassifier(learningDataset);

       return classifier;

    }

    

    private Classifier getClassifier() {

        RBFNetwork rbfLearner = new RBFNetwork();

        rbfLearner.setNumClusters(2);

        return rbfLearner;

    }

    

    private Evaluation evaluatePredictiveModel(Classifier classifier, Instances learningDataset) throws Exception {

        Evaluation learningSetEvaluation = new Evaluation(learningDataset);

        learningSetEvaluation.evaluateModel(classifier, learningDataset);

        return learningSetEvaluation;

    }

    private void plotData(Instances learningDataset, Classifier predictiveModel) throws Exception {

        for (int i = 20;  i <= 40; i ++) {

            Instance testMaleInstance = createInstance(learningDataset,i, "male", 0) ;

            Instance testFemaleInstance = createInstance(learningDataset,i, "female", 0) ;

            System.out.println(i + ",male," + predictiveModel.classifyInstance(testMaleInstance));

            System.out.println(i + ",female," + predictiveModel.classifyInstance(testFemaleInstance));

        }

    }

}