/*
* 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.learner.tree;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorCreationException;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.preprocessing.sampling.BootstrappingOperator;
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 several
* single random tree models.
*
* @author Ingo Mierswa, Sebastian Land
*/
public class RandomForestLearner extends RandomTreeLearner {
/** 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);
}
@Override
public Class<? extends PredictionModel> getModelClass() {
return RandomForestModel.class;
}
@Override
public Model learn(ExampleSet exampleSet) throws OperatorException {
BootstrappingOperator bootstrapping = null;
try {
bootstrapping = OperatorService.createOperator(BootstrappingOperator.class);
bootstrapping.setParameter(BootstrappingOperator.PARAMETER_USE_WEIGHTS, "false");
bootstrapping.setParameter(BootstrappingOperator.PARAMETER_SAMPLE_RATIO, "1.0");
} catch (OperatorCreationException e) {
throw new OperatorException(getName() + ": cannot construct random tree learner: " + e.getMessage());
}
// learn base models
List<TreeModel> baseModels = new LinkedList<TreeModel>();
int numberOfTrees = getParameterAsInt(PARAMETER_NUMBER_OF_TREES);
for (int i = 0; i < numberOfTrees; i++) {
TreeModel model = (TreeModel)super.learn(bootstrapping.apply(exampleSet));
model.setSource(getName());
baseModels.add(model);
}
// create and return model
return new RandomForestModel(exampleSet, baseModels);
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
if (capability == com.rapidminer.operator.OperatorCapability.BINOMINAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.OperatorCapability.POLYNOMINAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.OperatorCapability.NUMERICAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.OperatorCapability.POLYNOMINAL_LABEL)
return true;
if (capability == com.rapidminer.operator.OperatorCapability.BINOMINAL_LABEL)
return true;
if (capability == com.rapidminer.operator.OperatorCapability.WEIGHTED_EXAMPLES)
return false;
return false;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = new LinkedList<ParameterType>();
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);
types.addAll(super.getParameterTypes());
return types;
}
}