Example usage for weka.classifiers.meta EnsembleSelection setOptions

Introduction

In this page you can find the example usage for weka.classifiers.meta EnsembleSelection setOptions.

Prototype

public void setOptions(String[] options) throws Exception 

Source Link

Document

Valid options are:

 -L </path/to/modelLibrary> Specifies the Model Library File, continuing the list of all models.

 -W </path/to/working/directory> Specifies the Working Directory, where all models will be stored.

 -B <numModelBags> Set the number of bags, i.e., number of iterations to run  the ensemble selection algorithm.

 -E <modelRatio> Set the ratio of library models that will be randomly chosen  to populate each bag of models.

 -V <validationRatio> Set the ratio of the training data set that will be reserved  for validation.

 -H <hillClimbIterations> Set the number of hillclimbing iterations to be performed  on each model bag.

 -I <sortInitialization> Set the the ratio of the ensemble library that the sort  initialization algorithm will be able to choose from while  initializing the ensemble for each model bag

 -X <numFolds> Sets the number of cross-validation folds.

 -P <hillclimbMettric> Specify the metric that will be used for model selection  during the hillclimbing algorithm. 
Usage
From source file:soccer.core.SimpleClassifier.java
public void evaluate() throws IOException, Exception {
    Instances data = loader.buildInstances();
    NumericToNominal toNominal = new NumericToNominal();
    toNominal.setOptions(new String[] { "-R", "5,6,8,9" });
    toNominal.setInputFormat(data);//from  w w w  .  jav  a2 s. c om
    data = Filter.useFilter(data, toNominal);
    data.setClassIndex(6);

    //        DataSink.write(ARFF_STRING, data);

    EnsembleLibrary ensembleLib = new EnsembleLibrary();
    ensembleLib.addModel("weka.classifiers.trees.J48");
    ensembleLib.addModel("weka.classifiers.bayes.NaiveBayes");
    ensembleLib.addModel("weka.classifiers.functions.SMO");
    ensembleLib.addModel("weka.classifiers.meta.AdaBoostM1");
    ensembleLib.addModel("weka.classifiers.meta.LogitBoost");
    ensembleLib.addModel("classifiers.trees.DecisionStump");
    ensembleLib.addModel("classifiers.trees.DecisionStump");
    EnsembleLibrary.saveLibrary(new File("./ensembleLib.model.xml"), ensembleLib, null);
    EnsembleSelection model = new EnsembleSelection();
    model.setOptions(new String[] { "-L", "./ensembleLib.model.xml", // </path/to/modelLibrary>"-W", path+"esTmp", // </path/to/working/directory> - 
            "-B", "10", // <numModelBags> 
            "-E", "1.0", // <modelRatio>.
            "-V", "0.25", // <validationRatio>
            "-H", "100", // <hillClimbIterations> 
            "-I", "1.0", // <sortInitialization> 
            "-X", "2", // <numFolds>
            "-P", "roc", // <hillclimbMettric>
            "-A", "forward", // <algorithm> 
            "-R", "true", // - Flag to be selected more than once
            "-G", "true", // - stops adding models when performance degrades
            "-O", "true", // - verbose output.
            "-S", "1", // <num> - Random number seed.
            "-D", "true" // - run in debug mode 
    });
    //        double resES[] = evaluate(ensambleSel);
    //        System.out.println("Ensemble Selection\n"
    //                + "\tchurn:     " + resES[0] + "\n"
    //                + "\tappetency: " + resES[1] + "\n"
    //                + "\tup-sell:   " + resES[2] + "\n"
    //                + "\toverall:   " + resES[3] + "\n");
    //        models.add(new J48());
    //        models.add(new RandomForest());
    //        models.add(new NaiveBayes());
    //        models.add(new AdaBoostM1());
    //        models.add(new Logistic());
    //        models.add(new MultilayerPerceptron());

    int FOLDS = 5;
    Evaluation eval = new Evaluation(data);
    //
    //        for (Classifier model : models) {
    eval.crossValidateModel(model, data, FOLDS, new Random(1), new Object[] {});
    System.out.println(model.getClass().getName() + "\n" + "\tRecall:    " + eval.recall(1) + "\n"
            + "\tPrecision: " + eval.precision(1) + "\n" + "\tF-measure: " + eval.fMeasure(1));
    System.out.println(eval.toSummaryString());
    //        }
    //        LogitBoost cl = new LogitBoost();
    //        cl.setOptions(new String[] {
    //            "-Q", "-I", "100", "-Z", "4", "-O", "4", "-E", "4"
    //        });
    //        cl.buildClassifier(data);
    //        Evaluation eval = new Evaluation(data);
    //        eval.crossValidateModel(cl, data, 6, new Random(1), new Object[]{});
    //        System.out.println(eval.weightedFMeasure());
    //        System.out.println(cl.graph());
    //        System.out.println(cl.globalInfo());

}