/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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.List;
import org.apache.commons.lang.ArrayUtils;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.MappedExampleSet;
import com.rapidminer.example.table.DataRowFactory;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.OperatorVersion;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.annotation.ResourceConsumptionEstimator;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.MDInteger;
import com.rapidminer.operator.preprocessing.MaterializeDataInMemory;
import com.rapidminer.operator.validation.IteratingPerformanceAverage;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.parameter.conditions.EqualTypeCondition;
import com.rapidminer.tools.OperatorResourceConsumptionHandler;
import com.rapidminer.tools.RandomGenerator;
/**
* This operator constructs a bootstrapped sample from the given example set. That means that a
* sampling with replacement will be performed. The usual sample size is the number of original
* examples. This operator also offers the possibility to create the inverse example set, i.e. an
* example set containing all examples which are not part of the bootstrapped example set. This
* inverse example set might be used for a bootstrapped validation (together with an
* {@link IteratingPerformanceAverage} operator.
*
* @author Ingo Mierswa, Tobias Malbrecht
*/
public class BootstrappingOperator extends AbstractSamplingOperator {
public static final String PARAMETER_SAMPLE = "sample";
public static final String[] SAMPLE_MODES = { "absolute", "relative" };
public static final int SAMPLE_ABSOLUTE = 0;
public static final int SAMPLE_RELATIVE = 1;
/** The parameter name for "The fraction of examples which should be sampled" */
public static final String PARAMETER_SAMPLE_SIZE = "sample_size";
/** The parameter name for "This ratio determines the size of the new example set." */
public static final String PARAMETER_SAMPLE_RATIO = "sample_ratio";
public static final String PARAMETER_USE_WEIGHTS = "use_weights";
private static final OperatorVersion VERSION_6_4_0 = new OperatorVersion(6, 4, 0);
public BootstrappingOperator(OperatorDescription description) {
super(description);
}
@Override
protected MDInteger getSampledSize(ExampleSetMetaData emd) throws UndefinedParameterError {
switch (getParameterAsInt(PARAMETER_SAMPLE)) {
case SAMPLE_ABSOLUTE:
return new MDInteger(getParameterAsInt(PARAMETER_SAMPLE_SIZE));
case SAMPLE_RELATIVE:
MDInteger number = emd.getNumberOfExamples();
number.multiply(getParameterAsDouble(PARAMETER_SAMPLE_RATIO));
return number;
default:
return new MDInteger();
}
}
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
int size = exampleSet.size();
// cannot bootstrap without any examples
if (size < 1) {
throw new UserError(this, 117);
}
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
switch (getParameterAsInt(PARAMETER_SAMPLE)) {
case SAMPLE_ABSOLUTE:
size = getParameterAsInt(PARAMETER_SAMPLE_SIZE);
break;
case SAMPLE_RELATIVE:
size = (int) Math.round(exampleSet.size() * getParameterAsDouble(PARAMETER_SAMPLE_RATIO));
break;
}
int[] mapping = null;
if (getParameterAsBoolean(PARAMETER_USE_WEIGHTS) && exampleSet.getAttributes().getWeight() != null) {
mapping = MappedExampleSet.createWeightedBootstrappingMapping(exampleSet, size, random);
} else {
mapping = MappedExampleSet.createBootstrappingMapping(exampleSet, size, random);
}
// create and materialize example set
ExampleSet mappedExampleSet = new MappedExampleSet(exampleSet, mapping, true);
if (getCompatibilityLevel().isAbove(VERSION_6_4_0)) {
int type = DataRowFactory.TYPE_DOUBLE_ARRAY;
if (exampleSet.size() > 0) {
type = exampleSet.getExampleTable().getDataRow(0).getType();
}
mappedExampleSet = MaterializeDataInMemory.materializeExampleSet(mappedExampleSet, type);
}
return mappedExampleSet;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeCategory(PARAMETER_SAMPLE, "Determines how the amount of data is specified.",
SAMPLE_MODES, SAMPLE_RELATIVE);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_SAMPLE_SIZE, "The number of examples which should be sampled", 1,
Integer.MAX_VALUE, 100);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_SAMPLE, SAMPLE_MODES, true, SAMPLE_ABSOLUTE));
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_SAMPLE_RATIO, "This ratio determines the size of the new example set.",
0.0d, Double.POSITIVE_INFINITY, 1.0d);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_SAMPLE, SAMPLE_MODES, true, SAMPLE_RELATIVE));
type.setExpert(false);
types.add(type);
type = new ParameterTypeBoolean(PARAMETER_USE_WEIGHTS,
"If checked, example weights will be considered during the bootstrapping if such weights are present.", true);
type.setExpert(false);
types.add(type);
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
@Override
public OperatorVersion[] getIncompatibleVersionChanges() {
return (OperatorVersion[]) ArrayUtils.addAll(super.getIncompatibleVersionChanges(),
new OperatorVersion[] { VERSION_6_4_0 });
}
@Override
public ResourceConsumptionEstimator getResourceConsumptionEstimator() {
return OperatorResourceConsumptionHandler.getResourceConsumptionEstimator(getInputPort(),
BootstrappingOperator.class, null);
}
}