Java tutorial
/** * Copyright (C) 2001-2015 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); } }