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/>. */ /* * WekaTrainClassifier.java * Copyright (C) 2012-2016 University of Waikato, Hamilton, New Zealand */ package adams.flow.transformer; import adams.core.MessageCollection; import adams.core.QuickInfoHelper; import adams.flow.container.WekaModelContainer; import adams.flow.core.CallableActorHelper; import adams.flow.core.CallableActorReference; 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; import adams.flow.source.WekaClassifierSetup; import weka.classifiers.UpdateableClassifier; import weka.core.Instance; import weka.core.Instances; import java.util.ArrayList; import java.util.Hashtable; import java.util.List; /** <!-- globalinfo-start --> * Trains a classifier based on the incoming dataset and outputs the built classifier alongside the training header (in a model container).<br> * Incremental training is performed, if the input are weka.core.Instance objects and the classifier implements weka.classifiers.UpdateableClassifier. * <br><br> <!-- globalinfo-end --> * <!-- flow-summary-start --> * Input/output:<br> * - accepts:<br> * weka.core.Instances<br> * weka.core.Instance<br> * - generates:<br> * adams.flow.container.WekaModelContainer<br> * <br><br> * Container information:<br> * - adams.flow.container.WekaModelContainer: Model, Header, Dataset * <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: WekaTrainClassifier * </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>-classifier <adams.flow.core.CallableActorReference> (property: classifier) * The Weka classifier to train on the input data. * default: WekaClassifierSetup * </pre> * <!-- options-end --> * * @author fracpete (fracpete at waikato dot ac dot nz) * @version $Revision$ */ public class WekaTrainClassifier extends AbstractTransformer implements ProvenanceSupporter { /** for serialization. */ private static final long serialVersionUID = -3019442578354930841L; /** the key for storing the current incremental classifier in the backup. */ public final static String BACKUP_INCREMENTALCLASSIFIER = "incremental classifier"; /** the name of the callable weka classifier. */ protected CallableActorReference m_Classifier; /** the classifier to use when training incrementally. */ protected weka.classifiers.Classifier m_IncrementalClassifier; /** whether to skip the buildClassifier call for incremental classifiers. */ protected boolean m_SkipBuild; /** * Returns a string describing the object. * * @return a description suitable for displaying in the gui */ @Override public String globalInfo() { return "Trains a classifier based on the incoming dataset and outputs the " + "built classifier alongside the training header (in a model container).\n" + "Incremental training is performed, if the input are weka.core.Instance " + "objects and the classifier implements " + UpdateableClassifier.class.getName() + "."; } /** * Adds options to the internal list of options. */ @Override public void defineOptions() { super.defineOptions(); m_OptionManager.add("classifier", "classifier", new CallableActorReference(WekaClassifierSetup.class.getSimpleName())); m_OptionManager.add("skip-build", "skipBuild", false); } /** * Sets the name of the callable classifier to use. * * @param value the name */ public void setClassifier(CallableActorReference value) { m_Classifier = value; reset(); } /** * Returns the name of the callable classifier in use. * * @return the name */ public CallableActorReference getClassifier() { return m_Classifier; } /** * 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 classifierTipText() { return "The Weka classifier to train on the input data."; } /** * Sets whether to skip the buildClassifier call for incremental classifiers. * * @param value true if to skip the buildClassifier call */ public void setSkipBuild(boolean value) { m_SkipBuild = value; reset(); } /** * Returns whether to skip the buildClassifier call for incremental classifiers. * * @return true if to skip buildClassifier */ public boolean getSkipBuild() { return m_SkipBuild; } /** * 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 skipBuildTipText() { return "If enabled, the buildClassifier call gets skipped in case of incremental classifiers, eg, if the model only needs updating after being loaded from disk."; } /** * 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; List<String> options; result = QuickInfoHelper.toString(this, "classifier", m_Classifier); options = new ArrayList<String>(); QuickInfoHelper.add(options, QuickInfoHelper.toString(this, "skipBuild", m_SkipBuild, "skip build")); result += QuickInfoHelper.flatten(options); return result; } /** * Removes entries from the backup. */ @Override protected void pruneBackup() { super.pruneBackup(); pruneBackup(BACKUP_INCREMENTALCLASSIFIER); } /** * Backs up the current state of the actor before update the variables. * * @return the backup */ @Override protected Hashtable<String, Object> backupState() { Hashtable<String, Object> result; result = super.backupState(); if (m_IncrementalClassifier != null) result.put(BACKUP_INCREMENTALCLASSIFIER, m_IncrementalClassifier); return result; } /** * Restores the state of the actor before the variables got updated. * * @param state the backup of the state to restore from */ @Override protected void restoreState(Hashtable<String, Object> state) { if (state.containsKey(BACKUP_INCREMENTALCLASSIFIER)) { m_IncrementalClassifier = (weka.classifiers.Classifier) state.get(BACKUP_INCREMENTALCLASSIFIER); state.remove(BACKUP_INCREMENTALCLASSIFIER); } super.restoreState(state); } /** * Resets the scheme. */ @Override protected void reset() { super.reset(); m_IncrementalClassifier = null; } /** * Returns the class that the consumer accepts. * * @return <!-- flow-accepts-start -->weka.core.Instances.class, weka.core.Instance.class<!-- flow-accepts-end --> */ public Class[] accepts() { return new Class[] { Instances.class, Instance.class }; } /** * Returns the class of objects that it generates. * * @return <!-- flow-generates-start -->adams.flow.container.WekaModelContainer.class<!-- flow-generates-end --> */ public Class[] generates() { return new Class[] { WekaModelContainer.class }; } /** * Returns an instance of the callable classifier. * * @return the classifier * @throws Exception if fails to obtain classifier */ protected weka.classifiers.Classifier getClassifierInstance() throws Exception { weka.classifiers.Classifier result; MessageCollection errors; errors = new MessageCollection(); result = (weka.classifiers.Classifier) CallableActorHelper.getSetup(weka.classifiers.Classifier.class, m_Classifier, this, errors); if (result == null) { if (errors.isEmpty()) throw new IllegalStateException("Failed to obtain classifier from '" + m_Classifier + "'!"); else throw new IllegalStateException( "Failed to obtain classifier from '" + m_Classifier + "':\n" + errors); } return result; } /** * Executes the flow item. * * @return null if everything is fine, otherwise error message */ @Override protected String doExecute() { String result; Instances data; Instance inst; weka.classifiers.Classifier cls; result = null; try { cls = null; if ((m_InputToken != null) && (m_InputToken.getPayload() instanceof Instances)) { cls = getClassifierInstance(); data = (Instances) m_InputToken.getPayload(); cls.buildClassifier(data); m_OutputToken = new Token(new WekaModelContainer(cls, new Instances(data, 0), data)); } else if ((m_InputToken != null) && (m_InputToken.getPayload() instanceof Instance)) { if (m_IncrementalClassifier == null) { cls = getClassifierInstance(); if (!(cls instanceof UpdateableClassifier)) result = m_Classifier + "/" + cls.getClass().getName() + " is not an incremental classifier!"; } if (result == null) { inst = (Instance) m_InputToken.getPayload(); if (m_IncrementalClassifier == null) { m_IncrementalClassifier = cls; if (m_SkipBuild) { ((UpdateableClassifier) m_IncrementalClassifier).updateClassifier(inst); } else { data = new Instances(inst.dataset(), 1); data.add((Instance) inst.copy()); m_IncrementalClassifier.buildClassifier(data); } } else { ((UpdateableClassifier) m_IncrementalClassifier).updateClassifier(inst); } m_OutputToken = new Token( new WekaModelContainer(m_IncrementalClassifier, new Instances(inst.dataset(), 0))); } } } catch (Exception e) { m_OutputToken = null; result = handleException("Failed to process data:", e); } if (m_OutputToken != null) updateProvenance(m_OutputToken); return result; } /** * Cleans up after the execution has finished. */ @Override public void wrapUp() { super.wrapUp(); m_IncrementalClassifier = null; } /** * Updates the provenance information in the provided container. * * @param cont the provenance container to update */ public void updateProvenance(ProvenanceContainer cont) { if (Provenance.getSingleton().isEnabled()) { if (m_InputToken.hasProvenance()) cont.setProvenance(m_InputToken.getProvenance().getClone()); cont.addProvenance(new ProvenanceInformation(ActorType.MODEL_GENERATOR, m_InputToken.getPayload().getClass(), this, m_OutputToken.getPayload().getClass())); } } }