/*
* 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.hierarchical;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Tools;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.InputDescription;
import com.rapidminer.operator.OperatorChain;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.clustering.ClusterModel;
import com.rapidminer.operator.learner.clustering.ClusterNode;
import com.rapidminer.operator.learner.clustering.ClusterUtils;
import com.rapidminer.operator.learner.clustering.DefaultClusterNode;
import com.rapidminer.operator.learner.clustering.IdUtils;
import com.rapidminer.operator.learner.clustering.SimpleHierarchicalClusterModel;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeInt;
/**
* An abstract class supporting TopDown Clustering.
*
* @author Michael Wurst, Ingo Mierswa
* @version $Id: TopDownClustering.java,v 1.9 2008/09/12 10:30:12 tobiasmalbrecht Exp $
*/
public abstract class TopDownClustering extends OperatorChain {
/** The parameter name for "the maximal number of items in a cluster leaf" */
public static final String PARAMETER_MAX_LEAF_SIZE = "max_leaf_size";
/** The parameter name for "if true, a cluster id is generated as new special attribute " */
public static final String PARAMETER_ADD_CLUSTER_ATTRIBUTE = "add_cluster_attribute";
private int maxSize = 1;
private ExampleSet eset;
public TopDownClustering(OperatorDescription description) {
super(description);
}
/**
* Cluster a given set of items.
*
* @param items
* the items
* @return a List of List
*/
protected abstract List<List<String>> clusterItems(List<String> items) throws OperatorException;
public IOObject[] apply() throws OperatorException {
maxSize = getParameterAsInt(PARAMETER_MAX_LEAF_SIZE);
eset = getInput(ExampleSet.class);
Tools.checkAndCreateIds(eset);
Tools.isNonEmpty(eset);
List<String> items = new LinkedList<String>();
Iterator<Example> er = eset.iterator();
while (er.hasNext())
items.add(IdUtils.getIdFromExample(er.next()));
SimpleHierarchicalClusterModel result = new SimpleHierarchicalClusterModel();
result.setRootNode(recursiveClustering(items, "cl"));
if (getParameterAsBoolean(PARAMETER_ADD_CLUSTER_ATTRIBUTE)) {
if (!getParameterAsBoolean("keep_example_set"))
logWarning("Adding a cluster attribute makes only sense, if you keep the example set.");
else {
ClusterUtils.addClusterAttribute(eset, result);
}
}
return new IOObject[] {
result
};
}
private DefaultClusterNode recursiveClustering(List<String> items, String id) throws OperatorException {
inApplyLoop();
DefaultClusterNode result = new DefaultClusterNode(id);
int numItems = items.size();
boolean clusteringFailed = false;
if (items.size() > maxSize) {
List<List<String>> clusters = clusterItems(items);
for (int j = 0; j < clusters.size(); j++) {
List<String> itemsInSubnode = clusters.get(j);
int numItemsInSubnode = itemsInSubnode.size();
if ((numItemsInSubnode > 0) && (numItemsInSubnode < numItems)) {
ClusterNode newChild = recursiveClustering(itemsInSubnode, id + "." + j);
result.addSubNode(newChild);
} else
clusteringFailed = true;
}
if (!clusteringFailed)
return result;
else {
for (int i = 0; i < items.size(); i++)
result.addObject(items.get(i));
return result;
}
}
for (int i = 0; i < items.size(); i++)
result.addObject(items.get(i));
return result;
}
protected ExampleSet getExampleSet() {
return eset;
}
public Class<?>[] getInputClasses() {
return new Class[] { ExampleSet.class };
}
public Class<?>[] getOutputClasses() {
return new Class[] { ClusterModel.class };
}
public InputDescription getInputDescription(Class cls) {
if (ExampleSet.class.isAssignableFrom(cls)) {
return new InputDescription(cls, true, true);
} else {
return super.getInputDescription(cls);
}
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_MAX_LEAF_SIZE, "The maximal number of items in each cluster leaf", 1, Integer.MAX_VALUE, 1);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeBoolean(PARAMETER_ADD_CLUSTER_ATTRIBUTE, "Indicates if a cluster id is generated as new special attribute ", true));
return types;
}
}