Java tutorial
/* * RapidMiner * * Copyright (C) 2001-2008 by Rapid-I and the contributors * * Complete list of developers available at our web site: * * http://rapid-i.com * * 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.rapidminer.operator.learner.clustering.clusterer; import java.util.Iterator; import java.util.LinkedList; import java.util.List; import weka.clusterers.Clusterer; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import com.rapidminer.example.Attribute; import com.rapidminer.example.Example; import com.rapidminer.example.ExampleSet; import com.rapidminer.operator.OperatorDescription; import com.rapidminer.operator.OperatorException; import com.rapidminer.operator.UserError; import com.rapidminer.operator.learner.clustering.ClusterModel; import com.rapidminer.operator.learner.clustering.DefaultCluster; import com.rapidminer.operator.learner.clustering.FlatCrispClusterModel; import com.rapidminer.parameter.ParameterType; import com.rapidminer.tools.WekaInstancesAdaptor; import com.rapidminer.tools.WekaTools; /** * This operator performs the Weka clustering scheme with the same name. The operator expects an example set containing ids and returns a * FlatClusterModel or directly annotates the examples with a cluster attribute. Please note: Currently only clusterers that produce a partition of * items are supported. * * @author Ingo Mierswa * @version $Id: GenericWekaClusteringAdaptor.java,v 1.8 2008/05/09 19:22:49 ingomierswa Exp $ */ public class GenericWekaClusteringAdaptor extends AbstractFlatClusterer implements TechnicalInformationHandler { public static final String[] WEKA_CLUSTERERS = WekaTools.getWekaClasses(Clusterer.class); /** The list with the weka parameters. */ private List<ParameterType> wekaParameters = new LinkedList<ParameterType>(); public GenericWekaClusteringAdaptor(OperatorDescription description) { super(description); } public ClusterModel createClusterModel(ExampleSet exampleSet) throws OperatorException { weka.clusterers.Clusterer clusterer = getWekaClusterer( WekaTools.getWekaParametersFromTypes(this, wekaParameters)); weka.core.Instances instances = WekaTools.toWekaInstances(exampleSet, "ClusterInstances", WekaInstancesAdaptor.CLUSTERING); try { clusterer.buildClusterer(instances); WekaCluster wekaCluster = new WekaCluster(exampleSet, clusterer); exampleSet = wekaCluster.apply(exampleSet); } catch (Exception e) { throw new UserError(this, e, 905, new Object[] { getOperatorClassName(), e }); } ClusterModel clusterModel = createWekaBasedClusterModel(exampleSet); return clusterModel; } private ClusterModel createWekaBasedClusterModel(ExampleSet exampleSet) { Attribute idAttribute = exampleSet.getAttributes().getId(); Attribute clusterAttribute = exampleSet.getAttributes().getCluster(); // create cluster models FlatCrispClusterModel result = new FlatCrispClusterModel(); Iterator<String> i = clusterAttribute.getMapping().getValues().iterator(); while (i.hasNext()) { String name = i.next(); DefaultCluster cluster = new DefaultCluster(name); cluster.setDescription(name); result.addCluster(cluster); } Iterator<Example> er = exampleSet.iterator(); while (er.hasNext()) { Example e = er.next(); DefaultCluster cluster = (DefaultCluster) result.getClusterById(e.getValueAsString(clusterAttribute)); cluster.addObject(e.getValueAsString(idAttribute)); } return result; } /** Returns the Weka classifier based on the subtype of this operator. */ private Clusterer getWekaClusterer(String[] parameters) throws OperatorException { String prefixName = getOperatorClassName(); String actualName = prefixName.substring(WekaTools.WEKA_OPERATOR_PREFIX.length()); String clustererName = null; for (int i = 0; i < WEKA_CLUSTERERS.length; i++) { if (WEKA_CLUSTERERS[i].endsWith(actualName)) { clustererName = WEKA_CLUSTERERS[i]; break; } } Clusterer clusterer = null; try { clusterer = weka.clusterers.Clusterer.forName(clustererName, parameters); } catch (Exception e) { throw new UserError(this, e, 904, new Object[] { clustererName, e }); } return clusterer; } public TechnicalInformation getTechnicalInformation() { try { Clusterer clusterer = getWekaClusterer(null); if (clusterer instanceof TechnicalInformationHandler) return ((TechnicalInformationHandler) clusterer).getTechnicalInformation(); else return null; } catch (OperatorException e) { return null; } } public List<ParameterType> getParameterTypes() { List<ParameterType> types = super.getParameterTypes(); weka.clusterers.Clusterer clusterer = null; try { // parameters must be null, not an empty String[0] array! clusterer = getWekaClusterer(null); } catch (OperatorException e) { throw new RuntimeException( "Cannot instantiate Weka clusterer " + getOperatorClassName() + ": " + e.getMessage()); } wekaParameters = new LinkedList<ParameterType>(); if ((clusterer != null) && (clusterer instanceof weka.core.OptionHandler)) { WekaTools.addParameterTypes((weka.core.OptionHandler) clusterer, types, wekaParameters, false, null); } return types; } }