/*
* JGAAP -- a graphical program for stylometric authorship attribution
* Copyright (C) 2009,2011 by Patrick Juola
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as
* published by the Free Software Foundation, either version 3 of the
* License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/**
*
*/
package com.jgaap.classifiers;
import static org.junit.Assert.*;
import java.util.ArrayList;
import java.util.List;
import org.junit.Test;
import com.jgaap.generics.AnalyzeException;
import com.jgaap.util.Document;
import com.jgaap.util.Event;
import com.jgaap.util.EventSet;
import com.jgaap.util.Pair;
/**
* @author Amanda Kroft
*
*/
public class WEKALinearRegressionTest {
/**
* Test method for {@link
* com.jgaap.classifiers.WEKAJ48DecisionTree#analyze(com.jgaap.generics.EventSet,
* List<EventSet>)}.
* @throws AnalyzeException
*/
@Test
public void testAnalyze() throws AnalyzeException {
//Notes: Needs at least two docs per author, otherwise results become strange
//Test 1
//Create known texts
EventSet known1 = new EventSet();
EventSet known2 = new EventSet();
EventSet known3 = new EventSet();
EventSet known4 = new EventSet();
known1.addEvent(new Event("mary", null));
known1.addEvent(new Event("had", null));
known1.addEvent(new Event("a", null));
known1.addEvent(new Event("little", null));
known1.addEvent(new Event("lamb", null));
//known1.setAuthor("Mary");
known3.addEvent(new Event("mary", null));
known3.addEvent(new Event("had", null));
known3.addEvent(new Event("a", null));
known3.addEvent(new Event("small", null));
known3.addEvent(new Event("lamb", null));
//known3.setAuthor("Mary");
known2.addEvent(new Event("peter", null));
known2.addEvent(new Event("piper", null));
known2.addEvent(new Event("picked", null));
known2.addEvent(new Event("a", null));
known2.addEvent(new Event("peck", null));
//known2.setAuthor("Peter");
known4.addEvent(new Event("peter", null));
known4.addEvent(new Event("piper", null));
known4.addEvent(new Event("collected", null));
known4.addEvent(new Event("a", null));
known4.addEvent(new Event("peck", null));
//known4.setAuthor("Peter");
List<Document> knowns = new ArrayList<Document>();
Document knownDocument1 = new Document();
knownDocument1.setAuthor("Mary");
knownDocument1.addEventSet(null, known1);
knowns.add(knownDocument1);
Document knownDocument2 = new Document();
knownDocument2.setAuthor("Peter");
knownDocument2.addEventSet(null, known2);
knowns.add(knownDocument2);
Document knownDocument3 = new Document();
knownDocument3.setAuthor("Mary");
knownDocument3.addEventSet(null, known3);
knowns.add(knownDocument3);
Document knownDocument4 = new Document();
knownDocument4.setAuthor("Peter");
knownDocument4.addEventSet(null, known4);
knowns.add(knownDocument4);
//Create unknown text
EventSet unknown1 = new EventSet();
unknown1.addEvent(new Event("mary", null));
unknown1.addEvent(new Event("had", null));
unknown1.addEvent(new Event("a", null));
unknown1.addEvent(new Event("little", null));
unknown1.addEvent(new Event("beta", null));
Document unknownDocument = new Document();
unknownDocument.addEventSet(null, unknown1);
//Classify unknown based on the knowns
WEKALinearRegression classifier = new WEKALinearRegression();
List<List<Pair<String, Double>>> t = new ArrayList<List<Pair<String,Double>>>();
classifier.train(knowns);
t.add(classifier.analyze(unknownDocument));
System.out.println(t.toString());
//Assert that the authors match
assertTrue(t.get(0).get(0).getFirst().equals("Mary"));
// Test 2 - Test equal likelihood
EventSet unknown2 = new EventSet();
unknown2.addEvent(new Event("piper", null));
unknown2.addEvent(new Event("mary", null));
unknown2.addEvent(new Event("a", null));
unknown2.addEvent(new Event("peter", null));
unknown2.addEvent(new Event("had", null));
Document unknownDocument2 = new Document();
unknownDocument2.addEventSet(null, unknown2);
t = new ArrayList<List<Pair<String,Double>>>();
classifier.train(knowns);
t.add(classifier.analyze(unknownDocument2));
System.out.println(t.toString());
assertTrue(Math.abs(t.get(0).get(0).getSecond()-0.5)<.1 && Math.abs(t.get(0).get(1).getSecond()-0.5)<.1);
// Test 3 - more training documents
EventSet known5 = new EventSet();
EventSet known6 = new EventSet();
known5.addEvent(new Event("she", null));
known5.addEvent(new Event("sells", null));
known5.addEvent(new Event("seashells", null));
known5.addEvent(new Event("by", null));
known5.addEvent(new Event("seashore", null));
//known5.setAuthor("Susie");
known6.addEvent(new Event("susie", null));
known6.addEvent(new Event("sells", null));
known6.addEvent(new Event("shells", null));
known6.addEvent(new Event("by", null));
known6.addEvent(new Event("seashore", null));
//known6.setAuthor("Susie");
Document knownDocument5 = new Document();
knownDocument5.setAuthor("Susie");
knownDocument5.addEventSet(null, known5);
knowns.add(knownDocument5);
Document knownDocument6 = new Document();
knownDocument6.setAuthor("Susie");
knownDocument6.addEventSet(null, known6);
knowns.add(knownDocument6);
t = new ArrayList<List<Pair<String,Double>>>();
classifier.train(knowns);
t.add(classifier.analyze(unknownDocument));
System.out.println(t.toString());
assertTrue(t.get(0).get(0).getFirst().equals("Mary"));
}
//TODO: Test 4 - test documents/author requirements and exception handling
}