homemadeWEKA.java Source code

Introduction

Here is the source code for homemadeWEKA.java

Source

/*
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import java.util.logging.Level;
import java.util.logging.Logger;
import weka.core.converters.ConverterUtils.DataSource;
import weka.core.Instances;
import weka.core.FastVector;
import weka.core.Attribute;
import weka.classifiers.Classifier;
import weka.classifiers.trees.J48;
import weka.classifiers.Evaluation;
import java.util.Random;
import weka.core.SerializationHelper;

/**
 *
 * @author HP
 */
public class homemadeWEKA {
    public static Instances loadData(String filename) {

        Instances data = null;
        try {
            data = DataSource.read(filename);
            if (data.classIndex() == -1)
                data.setClassIndex(data.numAttributes() - 1);
        } catch (Exception ex) {
            Logger.getLogger(homemadeWEKA.class.getName()).log(Level.SEVERE, null, ex);
        }

        return data;
    }

    public static void treeLearning(Instances data) throws Exception {
        //        Evaluation eval = new Evaluation(data);
        Classifier model_tree = (Classifier) new J48();
        model_tree.buildClassifier(data); // build classifier pruned tree, succeed
        //        String forPredictionsPrinting = null;
        //        double[] print_eval;
        //        print_eval = eval.evaluateModel(model_tree, data, forPredictionsPrinting);
        //        System.out.println(forPredictionsPrinting);
        save_model(model_tree);
        //        return model_tree; 
    }

    public static void treeLearning_crossVal(Instances data) throws Exception {
        Evaluation eval = new Evaluation(data);
        J48 tree = new J48();
        eval.crossValidateModel(tree, data, 10, new Random(1));
        save_modelWithEval(tree, eval);
        //        System.out.println(eval.toSummaryString("\nResult of tree learning with cross validation 10 folds\n \n",false));
    }

    public static void save_model(Classifier cls) throws Exception {
        SerializationHelper.write("j48.model", cls);
    }

    public static void save_modelWithEval(Classifier cls, Evaluation eval) throws Exception {
        Object[] o = new Object[2];
        o[0] = cls;
        o[1] = eval;
        SerializationHelper.writeAll("j48_10folds.model", o);
    }

    public static Classifier load_model(String mdl) throws Exception {
        Classifier cls = (Classifier) SerializationHelper.read(mdl);
        return cls;
    }

    public static void reevaluateModel(Instances data_train, Instances data_test, Classifier cls) throws Exception {
        Evaluation eval = new Evaluation(data_train);
        eval.evaluateModel(cls, data_test);
        System.out.println(eval.toSummaryString("\nResults\n\n", false));
    }

    public static void main(String[] args) throws Exception {
        Instances file_in = loadData("weather.nominal.arff"); //succeed
        treeLearning_crossVal(file_in);
        Classifier treeModel = load_model("j48_10folds.model");
        reevaluateModel(file_in, file_in, treeModel);
        //        System.out.println(tree_result.toString());
        //        System.out.println("~~~~~~~~~~~~~~~~~~~~~~");
        //        treeLearning_crossVal(file_in);
    }
}