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/*
* Copyright (C) 2018 Southern Illinois University Carbondale, SoftSearch Lab
*
* Author: Amiangshu Bosu
*
* Licensed under GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.gnu.org/licenses/lgpl.html
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package edu.siu.sentise;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import weka.classifiers.Classifier;
import weka.classifiers.bayes.NaiveBayesMultinomial;
import weka.classifiers.functions.SMO;
import weka.classifiers.meta.AdaBoostM1;
import weka.classifiers.meta.RandomCommittee;
import weka.classifiers.meta.RandomSubSpace;
import weka.classifiers.misc.InputMappedClassifier;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.LMT;
import weka.classifiers.trees.RandomForest;
import weka.core.Instances;
import weka.classifiers.functions.MLPClassifier;
import weka.classifiers.functions.MultilayerPerceptron;
import weka.classifiers.functions.NeuralNetwork;
public class WekaClassifierBuilder {
public static InputMappedClassifier createClassifierFromInstance(String algo, Instances instance) {
try {
InputMappedClassifier classifier = new InputMappedClassifier();
classifier.setClassifier(getClassifierForAlgorithm(algo));
classifier.setSuppressMappingReport(true);
classifier.buildClassifier(instance);
return classifier;
} catch (Exception e) {
e.printStackTrace();
}
return null;
}
public static boolean storeClassfierModel(String fileName, Classifier classifier) {
try {
FileOutputStream fos = new FileOutputStream(fileName);
ObjectOutputStream oos = new ObjectOutputStream(fos);
oos.writeObject(classifier);
oos.close();
return true;
} catch (Exception e) {
e.printStackTrace();
}
return false;
}
public static Classifier getSavedClassfier(String fileName) {
try {
FileInputStream fis = new FileInputStream(fileName);
ObjectInputStream ois = new ObjectInputStream(fis);
Classifier savedClassifier = (Classifier) ois.readObject();
System.out.println("Loaded classifier model from: " + fileName);
ois.close();
return savedClassifier;
} catch (Exception e) {
e.printStackTrace();
}
return null;
}
public static Classifier getClassifierForAlgorithm(String algo) {
if (algo.equals("NB")) {
System.out.println("Algorithm: Multinomial Naive Bayes.");
NaiveBayesMultinomial classifier = new NaiveBayesMultinomial();
return classifier;
} else if (algo.equals("DT")) {
System.out.println("Algorithm: Decision tree");
J48 classifier = new J48();
return classifier;
} else if (algo.equals("CNN")) {
System.out.println("Algorithm: Convulated Neural Network");
NeuralNetwork classifier = new weka.classifiers.functions.NeuralNetwork();
try {
classifier.setOptions(
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"));
classifier.setDebug(false);
} catch (Exception e) {
}
return classifier;
}
else if (algo.equals("SVM")) {
System.out.println("Algorithm: Support Vector Machine");
return new SMO();
}
else if (algo.equals("MLPC")) {
System.out.println("Algorithm: Multilayer Percptron Classifier");
MLPClassifier classifier =new weka.classifiers.functions.MLPClassifier();
classifier.setNumThreads(8);
}
else if (algo.equals("SL")) {
System.out.println("Algorithm: Simple Logistic Regression");
return new weka.classifiers.functions.SimpleLogistic();
}
else if (algo.equals("KNN")) {
System.out.println("Algorithm: K-Nearest Neighbours");
return new weka.classifiers.lazy.IBk();
} else if (algo.equals("LMT")) {
LMT classifier=new LMT();
System.out.println("Algorithm: Logistic Model Trees");
return classifier;
}
else if (algo.equals("RF")) {
System.out.println("Algorithm: Random Forest");
RandomForest classifier = new RandomForest();
classifier.setNumExecutionSlots(4);
try {
classifier.setOptions(
weka.core.Utils.splitOptions("-P 100 -I 100 -num-slots 4 -K 0 -M 1.0 -V 0.001 -S 1"));
} catch (Exception e) {
}
return classifier;
}
else if (algo.equals("RS")) {
System.out.println("Algorithm: Random Subspace");
RandomSubSpace classifier = new RandomSubSpace();
classifier.setNumExecutionSlots(4);
try {
classifier.setOptions(weka.core.Utils.splitOptions(
"-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"));
} catch (Exception e) {
}
return classifier;
}
return null;
}
}
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