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| package edu.siu.sentise;
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|
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| import java.io.FileInputStream;
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| import java.io.FileOutputStream;
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| import java.io.ObjectInputStream;
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| import java.io.ObjectOutputStream;
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|
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| import weka.classifiers.Classifier;
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| import weka.classifiers.bayes.NaiveBayesMultinomial;
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| import weka.classifiers.functions.SMO;
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| import weka.classifiers.meta.AdaBoostM1;
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| import weka.classifiers.meta.RandomCommittee;
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| import weka.classifiers.meta.RandomSubSpace;
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| import weka.classifiers.misc.InputMappedClassifier;
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| import weka.classifiers.trees.J48;
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| import weka.classifiers.trees.LMT;
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| import weka.classifiers.trees.RandomForest;
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| import weka.core.Instances;
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| import weka.classifiers.functions.MLPClassifier;
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| import weka.classifiers.functions.MultilayerPerceptron;
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| import weka.classifiers.functions.NeuralNetwork;
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|
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| public class WekaClassifierBuilder {
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|
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| public static InputMappedClassifier createClassifierFromInstance(String algo, Instances instance) {
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| try {
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| InputMappedClassifier classifier = new InputMappedClassifier();
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| classifier.setClassifier(getClassifierForAlgorithm(algo));
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| classifier.setSuppressMappingReport(true);
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| classifier.buildClassifier(instance);
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|
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| return classifier;
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| } catch (Exception e) {
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| e.printStackTrace();
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| }
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| return null;
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| }
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|
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| public static boolean storeClassfierModel(String fileName, Classifier classifier) {
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| try {
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| FileOutputStream fos = new FileOutputStream(fileName);
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| ObjectOutputStream oos = new ObjectOutputStream(fos);
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| oos.writeObject(classifier);
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| oos.close();
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| return true;
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| } catch (Exception e) {
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| e.printStackTrace();
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| }
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| return false;
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| }
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|
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| public static Classifier getSavedClassfier(String fileName) {
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| try {
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| FileInputStream fis = new FileInputStream(fileName);
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| ObjectInputStream ois = new ObjectInputStream(fis);
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| Classifier savedClassifier = (Classifier) ois.readObject();
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| System.out.println("Loaded classifier model from: " + fileName);
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| ois.close();
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|
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| return savedClassifier;
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| } catch (Exception e) {
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| e.printStackTrace();
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| }
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| return null;
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|
|
| }
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|
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| public static Classifier getClassifierForAlgorithm(String algo) {
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| if (algo.equals("NB")) {
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| System.out.println("Algorithm: Multinomial Naive Bayes.");
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| NaiveBayesMultinomial classifier = new NaiveBayesMultinomial();
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| return classifier;
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| } else if (algo.equals("DT")) {
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| System.out.println("Algorithm: Decision tree");
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| J48 classifier = new J48();
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|
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| return classifier;
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|
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| } else if (algo.equals("CNN")) {
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| System.out.println("Algorithm: Convulated Neural Network");
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| NeuralNetwork classifier = new weka.classifiers.functions.NeuralNetwork();
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|
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| try {
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| classifier.setOptions(
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| weka.core.Utils.splitOptions("--lr 0.0 -wp 1.0E-8 -mi 200 -bs 0 -th 12 -hl 50 -di 0.2 -dh 0.5 -iw 0"));
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| classifier.setDebug(false);
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|
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| } catch (Exception e) {
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|
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| }
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| return classifier;
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| }
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|
|
| else if (algo.equals("SVM")) {
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| System.out.println("Algorithm: Support Vector Machine");
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| return new SMO();
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| }
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|
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| else if (algo.equals("MLPC")) {
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| System.out.println("Algorithm: Multilayer Percptron Classifier");
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| MLPClassifier classifier =new weka.classifiers.functions.MLPClassifier();
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| classifier.setNumThreads(8);
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| }
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|
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| else if (algo.equals("SL")) {
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| System.out.println("Algorithm: Simple Logistic Regression");
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| return new weka.classifiers.functions.SimpleLogistic();
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| }
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|
|
| else if (algo.equals("KNN")) {
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| System.out.println("Algorithm: K-Nearest Neighbours");
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| return new weka.classifiers.lazy.IBk();
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| } else if (algo.equals("LMT")) {
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| LMT classifier=new LMT();
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|
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| System.out.println("Algorithm: Logistic Model Trees");
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|
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| return classifier;
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| }
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| else if (algo.equals("RF")) {
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| System.out.println("Algorithm: Random Forest");
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| RandomForest classifier = new RandomForest();
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| classifier.setNumExecutionSlots(4);
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| try {
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| classifier.setOptions(
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| weka.core.Utils.splitOptions("-P 100 -I 100 -num-slots 4 -K 0 -M 1.0 -V 0.001 -S 1"));
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| } catch (Exception e) {
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|
|
| }
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|
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| return classifier;
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| }
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|
|
| else if (algo.equals("RS")) {
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| System.out.println("Algorithm: Random Subspace");
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| RandomSubSpace classifier = new RandomSubSpace();
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| classifier.setNumExecutionSlots(4);
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| try {
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| classifier.setOptions(weka.core.Utils.splitOptions(
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| "-P 0.5 -S 1 -num-slots 1 -I 10 -W weka.classifiers.trees.REPTree -- -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0"));
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| } catch (Exception e) {
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|
|
| }
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|
|
| return classifier;
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| }
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|
|
| return null;
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|
|
| }
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|
|
| }
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|
|
