/*
* 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.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.LearnerCapability;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
/**
* <p>This operator learns decision trees from both nominal and numerical data.
* Decision trees are powerful classification methods which often can also
* easily be understood. This decision tree learner works similar to Quinlan's
* C4.5 or CART.</p>
*
* <p>The actual type of the tree is determined by the criterion, e.g. using
* gain_ratio or Gini for CART / C4.5.</p>
*
* @rapidminer.index C4.5
* @rapidminer.index CART
*
* @author Sebastian Land, Ingo Mierswa
* @version $Id: DecisionTreeLearner.java,v 1.14 2008/05/09 19:22:53 ingomierswa Exp $
*/
public class DecisionTreeLearner extends AbstractTreeLearner {
/** The parameter name for the maximum tree depth. */
public static final String PARAMETER_MAXIMAL_DEPTH = "maximal_depth";
/** The parameter name for "The confidence level used for pruning." */
public static final String PARAMETER_CONFIDENCE = "confidence";
/** The parameter name for "Disables the pruning and delivers an unpruned tree." */
public static final String PARAMETER_NO_PRUNING = "no_pruning";
public DecisionTreeLearner(OperatorDescription description) {
super(description);
}
public Pruner getPruner() throws OperatorException {
if (!getParameterAsBoolean(PARAMETER_NO_PRUNING)) {
return new PessimisticPruner(getParameterAsDouble(PARAMETER_CONFIDENCE), new DecisionTreeLeafCreator());
} else {
return null;
}
}
public List<Terminator> getTerminationCriteria(ExampleSet exampleSet) throws OperatorException {
List<Terminator> result = new LinkedList<Terminator>();
result.add(new SingleLabelTermination());
result.add(new NoAttributeLeftTermination());
result.add(new EmptyTermination());
int maxDepth = getParameterAsInt(PARAMETER_MAXIMAL_DEPTH);
if (maxDepth <= 0) {
maxDepth = exampleSet.size();
}
result.add(new MaxDepthTermination(maxDepth));
return result;
}
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_MAXIMAL_DEPTH, "The maximum tree depth (-1: no bound)", -1, Integer.MAX_VALUE, 10);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_CONFIDENCE, "The confidence level used for the pessimistic error calculation of pruning.", 0.0000001, 0.5, 0.25);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeBoolean(PARAMETER_NO_PRUNING, "Disables the pruning and delivers an unpruned tree.", false));
return types;
}
}