Java tutorial
/* * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU 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 General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ /** * WekaEvaluation.java * Copyright (C) 2017 University of Waikato, Hamilton, New Zealand */ package adams.gui.visualization.debug.inspectionhandler; import adams.flow.core.EvaluationStatistic; import adams.flow.core.Token; import adams.flow.transformer.WekaEvaluationValues; import nz.ac.waikato.cms.locator.ClassLocator; import weka.classifiers.Evaluation; import java.util.ArrayList; import java.util.Hashtable; import java.util.List; /** * Provides further insight into an {@link Evaluation} object. * * @author fracpete (fracpete at waikato dot ac dot nz) * @version $Revision$ */ public class WekaEvaluation extends AbstractInspectionHandler { /** * Checks whether the handler can handle the specified class. * * @param cls the class to check * @return true if the handler can handle this type of object */ @Override public boolean handles(Class cls) { return ClassLocator.isSubclass(Evaluation.class, cls); } /** * Returns further inspection values. * * @param obj the object to further inspect * @return the named inspected values */ @Override public Hashtable<String, Object> inspect(Object obj) { Hashtable<String, Object> result; Evaluation eval; WekaEvaluationValues values; boolean numeric; boolean nominal; List<EvaluationStatistic> stats; String msg; Token token; result = new Hashtable<>(); eval = (Evaluation) obj; result.put("header", eval.getHeader()); result.put("predictions", eval.predictions()); nominal = eval.getHeader().classAttribute().isNominal(); numeric = eval.getHeader().classAttribute().isNumeric(); stats = new ArrayList<>(); for (EvaluationStatistic stat : EvaluationStatistic.values()) { if (nominal && stat.isOnlyNominal()) stats.add(stat); else if (numeric && stat.isOnlyNumeric()) stats.add(stat); else if (!stat.isOnlyNumeric() && !stat.isOnlyNominal()) stats.add(stat); } values = new WekaEvaluationValues(); values.setStatisticValues(stats.toArray(new EvaluationStatistic[stats.size()])); values.input(new Token(eval)); msg = values.execute(); if (msg == null) { token = values.output(); if (token != null) result.put("statistics", token.getPayload()); } else { System.err.println(getClass().getName() + ": Failed to extract statistics:\n" + msg); } return result; } }