/*
* RapidMiner
*
* Copyright (C) 2001-2011 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;
import java.util.Collection;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.annotation.ResourceConsumptionEstimator;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.SetRelation;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeAttribute;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.ParameterTypeList;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.OperatorResourceConsumptionHandler;
import com.rapidminer.tools.RandomGenerator;
import com.rapidminer.tools.math.container.Range;
/**
* This operator adds random attributes and white noise to the data. New random
* attributes are simply filled with random data which is not correlated to the
* label at all. Additionally, this operator might add noise to the label
* attribute or to the regular attributes. In case of a numerical label the
* given <code>label_noise</code> is the percentage of the label range which
* defines the standard deviation of normal distributed noise which is added to
* the label attribute. For nominal labels the parameter
* <code>label_noise</code> defines the probability to randomly change the
* nominal label value. In case of adding noise to regular attributes the
* parameter <code>default_attribute_noise</code> simply defines the standard
* deviation of normal distributed noise without using the attribute value
* range. Using the parameter list it is possible to set different noise levels
* for different attributes. However, it is not possible to add noise to nominal
* attributes.
*
* @author Ingo Mierswa
*/
public class NoiseOperator extends PreprocessingOperator {
/** The parameter name for "Adds this number of random attributes." */
public static final String PARAMETER_RANDOM_ATTRIBUTES = "random_attributes";
/** The parameter name for "Add this percentage of a numerical label range as a normal distributed noise or probability for a nominal label change." */
public static final String PARAMETER_LABEL_NOISE = "label_noise";
/** The parameter name for "The standard deviation of the default attribute noise." */
public static final String PARAMETER_DEFAULT_ATTRIBUTE_NOISE = "default_attribute_noise";
/** The parameter name for "List of noises for each attributes." */
public static final String PARAMETER_NOISE = "noise";
/** The parameter name for "Offset added to the values of each random attribute" */
public static final String PARAMETER_OFFSET = "offset";
/** The parameter name for "Linear factor multiplicated with the values of each random attribute" */
public static final String PARAMETER_LINEAR_FACTOR = "linear_factor";
public NoiseOperator(OperatorDescription description) {
super(description);
}
@Override
public PreprocessingModel createPreprocessingModel(ExampleSet exampleSet) throws OperatorException {
exampleSet.recalculateAllAttributeStatistics();
String[] attributeNames = new String[getParameterAsInt(PARAMETER_RANDOM_ATTRIBUTES)];
for (int i = 0; i < getParameterAsInt(PARAMETER_RANDOM_ATTRIBUTES); i++) {
attributeNames[i] = AttributeFactory.createName("random");
}
return new NoiseModel(exampleSet, RandomGenerator.getRandomGenerator(this), getParameterList(PARAMETER_NOISE), getParameterAsDouble(PARAMETER_DEFAULT_ATTRIBUTE_NOISE), getParameterAsDouble(PARAMETER_LABEL_NOISE), getParameterAsDouble(PARAMETER_OFFSET), getParameterAsDouble(PARAMETER_LINEAR_FACTOR), attributeNames);
}
@Override
protected ExampleSetMetaData modifyMetaData(ExampleSetMetaData exampleSetMetaData) throws UndefinedParameterError {
AttributeMetaData label= exampleSetMetaData.getLabelMetaData();
if (label != null) {
if (label.isNumerical() && getParameterAsDouble(PARAMETER_LABEL_NOISE) > 0)
label.setValueSetRelation(SetRelation.SUPERSET);
}
double defaultNoise = getParameterAsDouble(PARAMETER_DEFAULT_ATTRIBUTE_NOISE);
if (defaultNoise > 0) {
for (AttributeMetaData amd : exampleSetMetaData.getAllAttributes()) {
if (!amd.isSpecial()) {
if (amd.isNumerical()) {
amd.setValueSetRelation(SetRelation.SUPERSET);
}
}
}
}
int numberOfRandomAttributes = getParameterAsInt(PARAMETER_RANDOM_ATTRIBUTES);
for (int i = 0; i < numberOfRandomAttributes; i++) {
AttributeMetaData amd = new AttributeMetaData("random" + ((i == 0) ? "" : i + ""), Ontology.REAL);
amd.setValueRange(new Range(Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY), SetRelation.SUBSET);
exampleSetMetaData.addAttribute(amd);
}
return exampleSetMetaData;
}
/**
* This method isn't used anymore, since the calling super function is overridden.
*/
@Override
protected Collection<AttributeMetaData> modifyAttributeMetaData(ExampleSetMetaData emd, AttributeMetaData amd) {
return null;
}
@Override
public boolean isSupportingAttributeRoles() {
return true;
}
/** This operator does not support view creation proper. Hence hide the parameter */
@Override
public boolean isSupportingView() {
return false;
}
@Override
public Class<? extends PreprocessingModel> getPreprocessingModelClass() {
return NoiseModel.class;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_RANDOM_ATTRIBUTES, "Adds this number of random attributes.", 0, Integer.MAX_VALUE, 0);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_LABEL_NOISE, "Add this percentage of a numerical label range as a normal distributed noise or probability for a nominal label change.", 0.0d, Double.POSITIVE_INFINITY, 0.05d);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeDouble(PARAMETER_DEFAULT_ATTRIBUTE_NOISE, "The standard deviation of the default attribute noise.", 0.0d, Double.POSITIVE_INFINITY, 0.0d));
types.add(new ParameterTypeList(PARAMETER_NOISE, "List of noises for each attributes.",
new ParameterTypeAttribute("attribute", "To this attribute noise is added.", getExampleSetInputPort()),
new ParameterTypeDouble(PARAMETER_NOISE, "The strength of gaussian noise, which is added to this attribute.", 0.0d, Double.POSITIVE_INFINITY, 0.05d)));
type = new ParameterTypeDouble(PARAMETER_OFFSET, "Offset added to the values of each random attribute", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0d);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_LINEAR_FACTOR, "Linear factor multiplicated with the values of each random attribute", 0.0d, Double.POSITIVE_INFINITY, 1.0d);
types.add(type);
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
@Override
protected int[] getFilterValueTypes() {
return new int[] { Ontology.ATTRIBUTE_VALUE };
}
@Override
public ResourceConsumptionEstimator getResourceConsumptionEstimator() {
return OperatorResourceConsumptionHandler.getResourceConsumptionEstimator(getInputPort(), NoiseOperator.class, attributeSelector);
}
}