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/>. */ /* * WekaTrainTestSetClustererEvaluator.java * Copyright (C) 2013-2014 University of Waikato, Hamilton, New Zealand */ package adams.flow.transformer; import weka.clusterers.ClusterEvaluation; import weka.core.Instances; import adams.core.QuickInfoHelper; import adams.flow.container.WekaClusterEvaluationContainer; import adams.flow.container.WekaTrainTestSetContainer; import adams.flow.core.Token; import adams.flow.provenance.ActorType; import adams.flow.provenance.Provenance; import adams.flow.provenance.ProvenanceContainer; import adams.flow.provenance.ProvenanceInformation; import adams.flow.provenance.ProvenanceSupporter; /** <!-- globalinfo-start --> * Trains a clusterer on an incoming training dataset (from a container) and then evaluates it on the test set (also from a container).<br> * The clusterer setup being used in the evaluation is a callable 'Clusterer' actor.<br> * If a class attribute is set, a classes-to-clusters evaluation is performed automatically * <br><br> <!-- globalinfo-end --> * <!-- flow-summary-start --> * Input/output:<br> * - accepts:<br> * adams.flow.container.WekaTrainTestSetContainer<br> * - generates:<br> * adams.flow.container.WekaClusterEvaluationContainer<br> * <br><br> * Container information:<br> * - adams.flow.container.WekaTrainTestSetContainer: Train, Test, Seed, FoldNumber, FoldCount<br> * - adams.flow.container.WekaClusterEvaluationContainer: Evaluation, Model, Log-likelohood * <br><br> <!-- flow-summary-end --> * <!-- options-start --> * <pre>-logging-level <OFF|SEVERE|WARNING|INFO|CONFIG|FINE|FINER|FINEST> (property: loggingLevel) * The logging level for outputting errors and debugging output. * default: WARNING * </pre> * * <pre>-name <java.lang.String> (property: name) * The name of the actor. * default: WekaTrainTestSetClustererEvaluator * </pre> * * <pre>-annotation <adams.core.base.BaseText> (property: annotations) * The annotations to attach to this actor. * default: * </pre> * * <pre>-skip <boolean> (property: skip) * If set to true, transformation is skipped and the input token is just forwarded * as it is. * default: false * </pre> * * <pre>-stop-flow-on-error <boolean> (property: stopFlowOnError) * If set to true, the flow gets stopped in case this actor encounters an error; * useful for critical actors. * default: false * </pre> * * <pre>-clusterer <adams.flow.core.CallableActorReference> (property: clusterer) * The callable clusterer actor to train and evaluate on the test data. * default: WekaClustererSetup * </pre> * * <pre>-output-model <boolean> (property: outputModel) * If enabled, the clusterer model is output as well. * default: false * </pre> * <!-- options-end --> * * @author fracpete (fracpete at waikato dot ac dot nz) * @version $Revision$ */ public class WekaTrainTestSetClustererEvaluator extends AbstractCallableWekaClustererEvaluator implements ProvenanceSupporter { /** for serialization. */ private static final long serialVersionUID = -1092101024095887007L; /** whether to output the model as well. */ protected boolean m_OutputModel; /** * Returns a string describing the object. * * @return a description suitable for displaying in the gui */ @Override public String globalInfo() { return "Trains a clusterer on an incoming training dataset (from a container) " + "and then evaluates it on the test set (also from a container).\n" + "The clusterer setup being used in the evaluation is a callable 'Clusterer' actor.\n" + "If a class attribute is set, a classes-to-clusters evaluation is " + "performed automatically"; } /** * Adds options to the internal list of options. */ @Override public void defineOptions() { super.defineOptions(); m_OptionManager.add("output-model", "outputModel", false); } /** * Returns the tip text for this property. * * @return tip text for this property suitable for * displaying in the GUI or for listing the options. */ @Override public String clustererTipText() { return "The callable clusterer actor to train and evaluate on the test data."; } /** * Sets whether to output the clusterer model as well. * * @param value true if to output model */ public void setOutputModel(boolean value) { m_OutputModel = value; reset(); } /** * Returns whether to output the clusterer model as well. * * @return true if model is output */ public boolean getOutputModel() { return m_OutputModel; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for * displaying in the GUI or for listing the options. */ public String outputModelTipText() { return "If enabled, the clusterer model is output as well."; } /** * Returns a quick info about the actor, which will be displayed in the GUI. * * @return null if no info available, otherwise short string */ @Override public String getQuickInfo() { String result; String value; result = super.getQuickInfo(); value = QuickInfoHelper.toString(this, "outputModel", m_OutputModel, "output model", ", "); if (value != null) result += value; return result; } /** * Returns the class that the consumer accepts. * * @return <!-- flow-accepts-start -->adams.flow.container.WekaTrainTestSetContainer.class<!-- flow-accepts-end --> */ @Override public Class[] accepts() { return new Class[] { WekaTrainTestSetContainer.class }; } /** * Returns the class of objects that it generates. * * @return String.class or weka.classifiers.Evaluation.class */ @Override public Class[] generates() { return new Class[] { WekaClusterEvaluationContainer.class }; } /** * Executes the flow item. * * @return null if everything is fine, otherwise error message */ @Override protected String doExecute() { String result; Instances train; Instances test; weka.clusterers.Clusterer cls; ClusterEvaluation eval; WekaTrainTestSetContainer cont; result = null; try { // cross-validate clusterer cls = getClustererInstance(); if (cls == null) throw new IllegalStateException("Clusterer '" + getClusterer() + "' not found!"); cont = (WekaTrainTestSetContainer) m_InputToken.getPayload(); train = (Instances) cont.getValue(WekaTrainTestSetContainer.VALUE_TRAIN); test = (Instances) cont.getValue(WekaTrainTestSetContainer.VALUE_TEST); cls.buildClusterer(train); eval = new ClusterEvaluation(); eval.setClusterer(cls); eval.evaluateClusterer(test, null, m_OutputModel); // broadcast result m_OutputToken = new Token(new WekaClusterEvaluationContainer(eval, cls)); } catch (Exception e) { m_OutputToken = null; result = handleException("Failed to evaluate: ", e); } if (m_OutputToken != null) updateProvenance(m_OutputToken); return result; } /** * Updates the provenance information in the provided container. * * @param cont the provenance container to update */ @Override public void updateProvenance(ProvenanceContainer cont) { if (Provenance.getSingleton().isEnabled()) { if (m_InputToken.hasProvenance()) cont.setProvenance(m_InputToken.getProvenance().getClone()); cont.addProvenance(new ProvenanceInformation(ActorType.EVALUATOR, m_InputToken.getPayload().getClass(), this, m_OutputToken.getPayload().getClass())); } } }