/*
* 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.validation;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.OperatorVersion;
import com.rapidminer.operator.ProcessStoppedException;
import com.rapidminer.operator.ValueDouble;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.CapabilityPrecondition;
import com.rapidminer.operator.ports.metadata.MDInteger;
import com.rapidminer.operator.ports.metadata.Precondition;
import com.rapidminer.operator.ports.quickfix.ParameterSettingQuickFix;
import com.rapidminer.operator.ports.quickfix.QuickFix;
import com.rapidminer.operator.visualization.ProcessLogOperator;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
import com.rapidminer.parameter.conditions.EqualTypeCondition;
import com.rapidminer.tools.RandomGenerator;
/**
* <p>
* <code>XValidation</code> encapsulates a cross-validation process. The example set {@rapidminer.math S} is split up into <var>
* number_of_validations</var> subsets {@rapidminer.math S_i}. The inner operators are applied <var>number_of_validations</var> times using
* {@rapidminer.math S_i} as the test set (input of the second inner operator) and {@rapidminer.math S\backslash S_i} training set (input of
* the first inner operator).
* </p>
*
* <p>
* The first inner operator must accept an {@link com.rapidminer.example.ExampleSet} while the second must accept an
* {@link com.rapidminer.example.ExampleSet} and the output of the first (which is in most cases a {@link com.rapidminer.operator.Model})
* and must produce a {@link com.rapidminer.operator.performance.PerformanceVector}.
* </p>
*
* <p>
* Like other validation schemes the RapidMiner cross validation can use several types of sampling for building the subsets. Linear sampling
* simply divides the example set into partitions without changing the order of the examples. Shuffled sampling build random subsets from
* the data. Stratifed sampling builds random subsets and ensures that the class distribution in the subsets is the same as in the whole
* example set.
* </p>
*
* <p>
* The cross validation operator provides several values which can be logged by means of a {@link ProcessLogOperator}. Of course the number
* of the current iteration can be logged which might be useful for ProcessLog operators wrapped inside a cross validation. Beside that, all
* performance estimation operators of RapidMiner provide access to the average values calculated during the estimation. Since the operator
* cannot ensure the names of the delivered criteria, the ProcessLog operator can access the values via the generic value names:
* </p>
* <ul>
* <li>performance: the value for the main criterion calculated by this validation operator</li>
* <li>performance1: the value of the first criterion of the performance vector calculated</li>
* <li>performance2: the value of the second criterion of the performance vector calculated</li>
* <li>performance3: the value of the third criterion of the performance vector calculated</li>
* <li>for the main criterion, also the variance and the standard deviation can be accessed where applicable.</li>
* </ul>
*
* @rapidminer.index cross-validation
* @author Ingo Mierswa
*/
public class XValidation extends ValidationChain {
/** The parameter name for "Number of subsets for the crossvalidation." */
public static final String PARAMETER_NUMBER_OF_VALIDATIONS = "number_of_validations";
/**
* The parameter name for "Set the number of validations to the number of examples. If set to true, number_of_validations is
* ignored"
*/
public static final String PARAMETER_LEAVE_ONE_OUT = "leave_one_out";
/**
* The parameter name for "Defines the sampling type of the cross validation (linear = consecutive subsets, shuffled = random
* subsets, stratified = random subsets with class distribution kept constant)"
*/
public static final String PARAMETER_SAMPLING_TYPE = "sampling_type";
/**
* The parameter name for "Indicates if only performance vectors should be averaged or all types of averagable result vectors"
*/
public static final String PARAMETER_AVERAGE_PERFORMANCES_ONLY = "average_performances_only";
private int iteration;
public XValidation(OperatorDescription description) {
super(description);
addValue(new ValueDouble("iteration", "The number of the current iteration.") {
@Override
public double getDoubleValue() {
return iteration;
}
});
}
@Override
protected Precondition getCapabilityPrecondition() {
return new CapabilityPrecondition(this, trainingSetInput) {
@Override
protected List<QuickFix> getFixesForRegressionWhenClassificationSupported(AttributeMetaData labelMD) {
List<QuickFix> fixes = super.getFixesForRegressionWhenClassificationSupported(labelMD);
fixes.add(0, new ParameterSettingQuickFix(XValidation.this, PARAMETER_SAMPLING_TYPE, SplittedExampleSet.SHUFFLED_SAMPLING + "", "switch_to_shuffled_sampling"));
return fixes;
}
};
}
@Override
public void estimatePerformance(ExampleSet inputSet) throws OperatorException {
int number;
if (getParameterAsBoolean(PARAMETER_LEAVE_ONE_OUT)) {
number = inputSet.size();
} else {
number = getParameterAsInt(PARAMETER_NUMBER_OF_VALIDATIONS);
}
getLogger().fine("Starting " + number + "-fold cross validation");
// Split training / test set
int samplingType = getParameterAsInt(PARAMETER_SAMPLING_TYPE);
SplittedExampleSet splittedES = new SplittedExampleSet(inputSet, number, samplingType, getParameterAsBoolean(RandomGenerator.PARAMETER_USE_LOCAL_RANDOM_SEED), getParameterAsInt(RandomGenerator.PARAMETER_LOCAL_RANDOM_SEED), getCompatibilityLevel().isAtMost(SplittedExampleSet.VERSION_SAMPLING_CHANGED));
// start crossvalidation
for (iteration = 0; iteration < number; iteration++) {
performIteration(splittedES, iteration);
}
// end crossvalidation
}
protected void performIteration(SplittedExampleSet splittedES, int iteration) throws OperatorException, ProcessStoppedException {
splittedES.selectAllSubsetsBut(iteration);
learn(splittedES);
splittedES.selectSingleSubset(iteration);
evaluate(splittedES);
inApplyLoop();
}
@Override
protected MDInteger getTestSetSize(MDInteger originalSize) throws UndefinedParameterError {
if (getParameterAsBoolean(PARAMETER_LEAVE_ONE_OUT)) {
return new MDInteger(1);
} else {
return originalSize.multiply(1d / getParameterAsDouble(PARAMETER_NUMBER_OF_VALIDATIONS));
}
}
@Override
protected MDInteger getTrainingSetSize(MDInteger originalSize) throws UndefinedParameterError {
if (getParameterAsBoolean(PARAMETER_LEAVE_ONE_OUT)) {
return originalSize.add(-1);
} else {
return originalSize.multiply(1d - 1d / getParameterAsDouble(PARAMETER_NUMBER_OF_VALIDATIONS));
}
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeBoolean(PARAMETER_AVERAGE_PERFORMANCES_ONLY, "Indicates if only performance vectors should be averaged or all types of averagable result vectors", true));
types.add(new ParameterTypeBoolean(PARAMETER_LEAVE_ONE_OUT, "Set the number of validations to the number of examples. If set to true, number_of_validations is ignored", false, false));
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_OF_VALIDATIONS, "Number of subsets for the crossvalidation.", 2, Integer.MAX_VALUE, 10);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_LEAVE_ONE_OUT, false, false));
type.setExpert(false);
types.add(type);
type = new ParameterTypeCategory(PARAMETER_SAMPLING_TYPE, "Defines the sampling type of the cross validation (linear = consecutive subsets, shuffled = random subsets, stratified = random subsets with class distribution kept constant)", SplittedExampleSet.SAMPLING_NAMES, SplittedExampleSet.STRATIFIED_SAMPLING);
type.setExpert(false);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_LEAVE_ONE_OUT, false, false));
types.add(type);
for (ParameterType addType : RandomGenerator.getRandomGeneratorParameters(this)) {
addType.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_LEAVE_ONE_OUT, false, false));
addType.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_SAMPLING_TYPE, SplittedExampleSet.SAMPLING_NAMES, false, SplittedExampleSet.SHUFFLED_SAMPLING, SplittedExampleSet.STRATIFIED_SAMPLING));
types.add(addType);
}
return types;
}
@Override
public OperatorVersion[] getIncompatibleVersionChanges() {
return new OperatorVersion[] { SplittedExampleSet.VERSION_SAMPLING_CHANGED };
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
switch (capability) {
case NO_LABEL:
return false;
case NUMERICAL_LABEL:
try {
return getParameterAsInt(PARAMETER_SAMPLING_TYPE) != SplittedExampleSet.STRATIFIED_SAMPLING;
} catch (UndefinedParameterError e) {
return false;
}
default:
return true;
}
}
}