/*
* 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.learner.tree;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorCreationException;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.LearnerCapability;
import com.rapidminer.operator.learner.SimplePredictionModel;
import com.rapidminer.operator.learner.meta.SimpleVoteModel;
import com.rapidminer.operator.preprocessing.sampling.AbstractBootstrapping;
import com.rapidminer.operator.preprocessing.sampling.Bootstrapping;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.OperatorService;
/**
* This operators learns a random forest. The resulting forest model contains serveral
* single random tree models.
*
* @author Ingo Mierswa
* @version $Id: RandomForestLearner.java,v 1.6 2008/05/09 19:22:53 ingomierswa Exp $
*/
public class RandomForestLearner extends AbstractLearner {
/** The parameter name for the number of trees. */
public static final String PARAMETER_NUMBER_OF_TREES = "number_of_trees";
public RandomForestLearner(OperatorDescription description) {
super(description);
}
public Model learn(ExampleSet exampleSet) throws OperatorException {
// create random tree learner and set parameters
RandomTreeLearner randomTreeLearner = null;
try {
randomTreeLearner = OperatorService.createOperator(RandomTreeLearner.class);
List<ParameterType> parameters = randomTreeLearner.getParameterTypes();
for (ParameterType parameter : parameters) {
Object value = getParameter(parameter.getKey());
if (value != null)
randomTreeLearner.setParameter(parameter.getKey(), value.toString());
}
} catch (OperatorCreationException e) {
throw new OperatorException(getName() + ": cannot construct random tree learner: " + e.getMessage());
}
Bootstrapping bootstrapping = null;
try {
bootstrapping = OperatorService.createOperator(Bootstrapping.class);
bootstrapping.setParameter(AbstractBootstrapping.PARAMETER_SAMPLE_RATIO, "1.0");
} catch (OperatorCreationException e) {
throw new OperatorException(getName() + ": cannot construct random tree learner: " + e.getMessage());
}
// learn base models
List<SimplePredictionModel> baseModels = new LinkedList<SimplePredictionModel>();
int numberOfTrees = getParameterAsInt(PARAMETER_NUMBER_OF_TREES);
for (int i = 0; i < numberOfTrees; i++) {
TreeModel model = (TreeModel)randomTreeLearner.learn((ExampleSet)exampleSet.clone());
baseModels.add(model);
}
// create and return model
return new SimpleVoteModel(exampleSet, baseModels);
}
public boolean supportsCapability(LearnerCapability capability) {
if (capability == com.rapidminer.operator.learner.LearnerCapability.BINOMINAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.POLYNOMINAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.NUMERICAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.POLYNOMINAL_CLASS)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.BINOMINAL_CLASS)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.WEIGHTED_EXAMPLES)
return true;
return false;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_OF_TREES, "The number of learned random trees.", 1, Integer.MAX_VALUE, 10);
type.setExpert(false);
types.add(type);
// add random tree parameters
try {
Operator randomTreeLearner = OperatorService.createOperator(RandomTreeLearner.class);
List<ParameterType> innerParameters = randomTreeLearner.getParameterTypes();
for (ParameterType innerType : innerParameters) {
if (innerType.getKey().equals(DecisionTreeLearner.PARAMETER_NO_PRUNING)) {
innerType.setDefaultValue(false);
} else {
if (!innerType.getKey().equals(DecisionTreeLearner.PARAMETER_CONFIDENCE) && !innerType.getKey().equals("keep_example_set"))
types.add(innerType);
}
}
} catch (OperatorCreationException e) {
logWarning("Cannot create random tree learner: " + e.getMessage());
}
return types;
}
}