/**
* 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.validation;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
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.UserError;
import com.rapidminer.operator.ValueDouble;
import com.rapidminer.operator.ports.metadata.MDInteger;
import com.rapidminer.operator.visualization.ProcessLogOperator;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.UndefinedParameterError;
/**
* <p>
* <code>BatchXValidation</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>
* In contrast to the usual cross validation operator (see {@link XValidation}) this operator does
* not (randomly) split the data itself but uses the partition defined by the special attribute
* "batch". This can be an arbitrary nominal or integer attribute where each possible
* value occurs at least once (since many learning schemes depend on this minimum number of
* examples).
* </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>
* 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>
*
* @author Ingo Mierswa
* @deprecated use the {@link #CrossValidationOperator} from the concurrency extension instead.
*/
@Deprecated
public class BatchXValidation extends ValidationChain {
/**
* 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 BatchXValidation(OperatorDescription description) {
super(description);
addValue(new ValueDouble("iteration", "The number of the current iteration.") {
@Override
public double getDoubleValue() {
return iteration;
}
});
}
@Override
public void estimatePerformance(ExampleSet inputSet) throws OperatorException {
// split by attribute
Attribute batchAttribute = inputSet.getAttributes().getSpecial(Attributes.BATCH_NAME);
if (batchAttribute == null) {
throw new UserError(this, 113, Attributes.BATCH_NAME);
}
SplittedExampleSet splittedES = SplittedExampleSet.splitByAttribute(inputSet, batchAttribute);
// start crossvalidation
if (modelOutput.isConnected()) {
getProgress().setTotal(splittedES.getNumberOfSubsets() + 1);
} else {
getProgress().setTotal(splittedES.getNumberOfSubsets());
}
getProgress().setCheckForStop(false);
for (iteration = 0; iteration < splittedES.getNumberOfSubsets(); iteration++) {
splittedES.selectAllSubsetsBut(iteration);
learn(splittedES);
splittedES.selectSingleSubset(iteration);
evaluate(splittedES);
inApplyLoop();
getProgress().step();
}
}
@Override
protected MDInteger getTestSetSize(MDInteger originalSize) throws UndefinedParameterError {
return new MDInteger();
}
@Override
protected MDInteger getTrainingSetSize(MDInteger originalSize) throws UndefinedParameterError {
return new MDInteger();
}
@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));
return types;
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
return true;
}
}