/*
* 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.preprocessing.sampling;
import java.util.Arrays;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeRole;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Statistics;
import com.rapidminer.example.Tools;
import com.rapidminer.example.set.SimpleExampleSet;
import com.rapidminer.example.table.DataRow;
import com.rapidminer.example.table.DoubleArrayDataRow;
import com.rapidminer.example.table.ExampleTable;
import com.rapidminer.example.table.ListDataRowReader;
import com.rapidminer.example.table.MemoryExampleTable;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.meta.WeightedPerformanceMeasures;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.RandomGenerator;
/**
* Sampling based on a learned model.
*
* @see com.rapidminer.operator.learner.meta.BayesianBoosting
* @author Martin Scholz, Ingo Mierswa
* @version $Id: ModelBasedSampling.java,v 1.14 2006/04/05 08:57:27 ingomierswa
* Exp $
*/
public class ModelBasedSampling extends Operator {
/** The parameter name for "Use the given random seed instead of global random numbers (-1: use global)." */
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
public ModelBasedSampling(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = getInput(ExampleSet.class);
Attribute weightAttr = exampleSet.getAttributes().getWeight();
if (weightAttr == null) {
weightAttr = Tools.createWeightAttribute(exampleSet);
Iterator<Example> reader = exampleSet.iterator();
while (reader.hasNext()) {
reader.next().setValue(weightAttr, 1.0d);
}
}
WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(exampleSet);
WeightedPerformanceMeasures.reweightExamples(exampleSet, wp.getContingencyMatrix(), true);
// recalc weight att statistics
exampleSet.recalculateAttributeStatistics(exampleSet.getAttributes().getWeight());
// fill new table
Attribute[] allAttributes = exampleSet.getExampleTable().getAttributes();
List<DataRow> dataList = new LinkedList<DataRow>();
Iterator<Example> reader = exampleSet.iterator();
double maxWeight = exampleSet.getStatistics(exampleSet.getAttributes().getWeight(), Statistics.MAXIMUM);
while (reader.hasNext()) {
Example example = reader.next();
if (RandomGenerator.getRandomGenerator(getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED)).nextDouble() > example.getValue(weightAttr) / maxWeight) {
example.setValue(weightAttr, 1.0d);
double[] values = new double[allAttributes.length];
for (int i = 0; i < values.length; i++)
values[i] = example.getValue(allAttributes[i]);
dataList.add(new DoubleArrayDataRow(values));
}
checkForStop();
}
List<Attribute> attributes = Arrays.asList(allAttributes);
ExampleTable exampleTable = new MemoryExampleTable(attributes, new ListDataRowReader(dataList.iterator()));
// regular attributes.
List<Attribute> regularAttributes = new LinkedList<Attribute>();
for (Attribute attribute : exampleSet.getAttributes()) {
regularAttributes.add(attribute);
}
// special attributes
ExampleSet result = new SimpleExampleSet(exampleTable, regularAttributes);
Iterator<AttributeRole> special = exampleSet.getAttributes().specialAttributes();
while (special.hasNext()) {
AttributeRole role = special.next();
result.getAttributes().setSpecialAttribute(role.getAttribute(), role.getSpecialName());
}
return new IOObject[] { result };
}
public Class<?>[] getInputClasses() {
return new Class[] { ExampleSet.class };
}
public Class<?>[] getOutputClasses() {
return new Class[] { ExampleSet.class };
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global).", -1, Integer.MAX_VALUE, -1));
return types;
}
}