/*
* 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.olap;
import java.util.Collection;
import java.util.LinkedList;
import java.util.List;
import java.util.TreeSet;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.InputDescription;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeString;
import com.rapidminer.tools.math.AnovaCalculator;
import com.rapidminer.tools.math.SignificanceCalculationException;
import com.rapidminer.tools.math.SignificanceTestResult;
import com.rapidminer.tools.math.function.AggregationFunction;
import com.rapidminer.tools.math.function.AverageFunction;
import com.rapidminer.tools.math.function.VarianceFunction;
/**
* <p>This operator creates groups of the input example set based on
* the defined grouping attribute. For each of the groups the mean and
* variance of another attribute (the anova attribute) is calculated
* and an ANalysis Of VAriance (ANOVA) is performed. The result will
* be a significance test result for the specified significance level
* indicating if the values for the attribute are significantly different
* between the groups defined by the grouping attribute.</p>
*
* @author Ingo Mierswa
* @version $Id: GroupedANOVAOperator.java,v 1.6 2008/07/07 07:06:46 ingomierswa Exp $
*/
public class GroupedANOVAOperator extends Operator {
public static final String PARAMETER_ANOVA_ATTRIBUTE = "anova_attribute";
public static final String PARAMETER_GROUP_BY_ATTRIBUTE = "group_by_attribute";
public static final String PARAMETER_SIGNIFICANCE_LEVEL = "significance_level";
public static final String PARAMETER_ONLY_DISTINCT = "only_distinct";
public GroupedANOVAOperator(OperatorDescription desc) {
super(desc);
}
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = getInput(ExampleSet.class);
// init and checks
String attributeName = getParameterAsString(PARAMETER_ANOVA_ATTRIBUTE);
String groupByAttributeName = getParameterAsString(PARAMETER_GROUP_BY_ATTRIBUTE);
boolean onlyDistinct = getParameterAsBoolean(PARAMETER_ONLY_DISTINCT);
Attribute anovaAttribute = exampleSet.getAttributes().get(attributeName);
if (anovaAttribute == null) {
throw new UserError(this, 111, this.getParameterAsString(PARAMETER_ANOVA_ATTRIBUTE));
}
if (anovaAttribute.isNominal()) {
throw new UserError(this, 104, new Object[] { this.getParameterAsString(PARAMETER_ANOVA_ATTRIBUTE), "anova calculation" });
}
Attribute groupByAttribute = exampleSet.getAttributes().get(groupByAttributeName);
if (groupByAttribute == null) {
throw new UserError(this, 111, this.getParameterAsString(PARAMETER_GROUP_BY_ATTRIBUTE));
}
if (!groupByAttribute.isNominal()) {
throw new UserError(this, 103, new Object[] { this.getParameterAsString(PARAMETER_GROUP_BY_ATTRIBUTE), "grouping by attribute." });
}
// create anova calculator
AnovaCalculator anovaCalculator = new AnovaCalculator();
double alpha = getParameterAsDouble(PARAMETER_SIGNIFICANCE_LEVEL);
anovaCalculator.setAlpha(alpha);
// add groups
SplittedExampleSet grouped = SplittedExampleSet.splitByAttribute(exampleSet, groupByAttribute);
AggregationFunction meanFunction = new AverageFunction();
AggregationFunction varianceFunction = new VarianceFunction();
for (int i = 0; i < grouped.getNumberOfSubsets(); i++) {
grouped.selectSingleSubset(i);
double[] values = getValues(grouped, anovaAttribute, onlyDistinct);
double mean = meanFunction.calculate(values);
double variance = varianceFunction.calculate(values);
anovaCalculator.addGroup(grouped.size(), mean, variance);
}
// calculate and return result
SignificanceTestResult result = null;
try {
result = anovaCalculator.performSignificanceTest();
} catch (SignificanceCalculationException e) {
throw new UserError(this, 920, e.getMessage());
}
return new IOObject[] { result };
}
private double[] getValues(ExampleSet exampleSet, Attribute attribute, boolean onlyDistinct) {
Collection<Double> valueCollection = new LinkedList<Double>();
if (onlyDistinct)
valueCollection = new TreeSet<Double>();
for (Example e : exampleSet) {
valueCollection.add(e.getValue(attribute));
}
double[] result = new double[valueCollection.size()];
int counter = 0;
for (double d : valueCollection)
result[counter++] = d;
return result;
}
/** Indicates that the consumption of example sets can be user defined (default: no consumption). */
public InputDescription getInputDescription(Class cls) {
if (ExampleSet.class.isAssignableFrom(cls)) {
return new InputDescription(cls, false, true);
} else {
return super.getInputDescription(cls);
}
}
public Class<?>[] getInputClasses() {
return new Class[] { ExampleSet.class };
}
public Class<?>[] getOutputClasses() {
return new Class[] { SignificanceTestResult.class };
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeString(PARAMETER_ANOVA_ATTRIBUTE, "Calculate the ANOVA for this attribute based on the groups defines by " + PARAMETER_GROUP_BY_ATTRIBUTE + ".", false));
types.add(new ParameterTypeString(PARAMETER_GROUP_BY_ATTRIBUTE, "Performs a grouping by the values of the attribute with this name.", false));
types.add(new ParameterTypeDouble(PARAMETER_SIGNIFICANCE_LEVEL, "The significance level for the ANOVA calculation.", 0.0d, 1.0d, 0.05d));
types.add(new ParameterTypeBoolean(PARAMETER_ONLY_DISTINCT, "Indicates if only rows with distinct values for the aggregation attribute should be used for the calculation of the aggregation function.", false));
return types;
}
}