/*
* 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.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.ParameterTypeStringCategory;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.Tools;
/**
* This is the abstract super class for all decision tree learners. The actual
* type of the tree is determined by the criterion, e.g. using gain_ratio or Gini
* for CART / C4.5 and chi_squared for CHAID.
*
* @author Sebastian Land, Ingo Mierswa
* @version $Id: AbstractTreeLearner.java,v 1.9 2008/08/20 17:58:26 ingomierswa Exp $
*/
public abstract class AbstractTreeLearner extends AbstractLearner {
/** The parameter name for "Specifies the used criterion for selecting attributes and numerical splits." */
public static final String PARAMETER_CRITERION = "criterion";
/** The parameter name for "The minimal size of all leaves." */
public static final String PARAMETER_MINIMAL_SIZE_FOR_SPLIT = "minimal_size_for_split";
/** The parameter name for "The minimal size of all leaves." */
public static final String PARAMETER_MINIMAL_LEAF_SIZE = "minimal_leaf_size";
/** The parameter name for the minimal gain. */
public static final String PARAMETER_MINIMAL_GAIN = "minimal_gain";
public static final String[] CRITERIA_NAMES = {
"gain_ratio",
"information_gain",
"gini_index",
"accuracy"
};
public static final Class[] CRITERIA_CLASSES = {
GainRatioCriterion.class,
InfoGainCriterion.class,
GiniIndexCriterion.class,
AccuracyCriterion.class
};
public static final int CRITERION_GAIN_RATIO = 0;
public static final int CRITERION_INFO_GAIN = 1;
public static final int CRITERION_GINI_INDEX = 2;
public static final int CRITERION_ACCURACY = 3;
public AbstractTreeLearner(OperatorDescription description) {
super(description);
}
/** Returns all termination criteria. */
public abstract List<Terminator> getTerminationCriteria(ExampleSet exampleSet) throws OperatorException;
/** Returns the pruner for this tree learner. If this method returns null,
* pruning will be disabled. */
public abstract Pruner getPruner() throws OperatorException;
/** The split preprocessing is applied before each new split
* The default implementation does nothing and simply returns
* the given example set. Subclasses might want to override this
* in order to perform some data preprocessing like random subset
* selections.*/
public SplitPreprocessing getSplitPreprocessing() {
return null;
}
public Model learn(ExampleSet eSet) throws OperatorException {
ExampleSet exampleSet = (ExampleSet)eSet.clone();
// create tree builder
TreeBuilder builder = new TreeBuilder(createCriterion(),
getTerminationCriteria(exampleSet),
getPruner(),
getSplitPreprocessing(),
getParameterAsInt(PARAMETER_MINIMAL_SIZE_FOR_SPLIT),
getParameterAsInt(PARAMETER_MINIMAL_LEAF_SIZE),
getParameterAsDouble(PARAMETER_MINIMAL_GAIN));
// learn tree
Tree root = builder.learnTree(exampleSet);
// create and return model
return new TreeModel(exampleSet, root);
}
protected Criterion createCriterion() throws UndefinedParameterError {
String criterionName = getParameterAsString(PARAMETER_CRITERION);
Class criterionClass = null;
for (int i = 0; i < CRITERIA_NAMES.length; i++) {
if (CRITERIA_NAMES[i].equals(criterionName)) {
criterionClass = CRITERIA_CLASSES[i];
}
}
if ((criterionClass == null) && (criterionName != null)) {
try {
criterionClass = Tools.classForName(criterionName);
} catch (ClassNotFoundException e) {
logWarning("Cannot find criterion '"+criterionName+"' and cannot instantiate a class with this name. Using gain ratio criterion instead.");
}
}
if (criterionClass != null) {
try {
return (Criterion)criterionClass.newInstance();
} catch (InstantiationException e) {
logWarning("Cannot instantiate criterion class '"+criterionClass.getName()+"'. Using gain ratio criterion instead.");
return new GainRatioCriterion();
} catch (IllegalAccessException e) {
logWarning("Cannot access criterion class '"+criterionClass.getName()+"'. Using gain ratio criterion instead.");
return new GainRatioCriterion();
}
} else {
log("No relevance criterion defined, using gain ratio...");
return new GainRatioCriterion();
}
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeStringCategory(PARAMETER_CRITERION, "Specifies the used criterion for selecting attributes and numerical splits.", CRITERIA_NAMES, CRITERIA_NAMES[CRITERION_INFO_GAIN]);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_MINIMAL_SIZE_FOR_SPLIT, "The minimal size of a node in order to allow a split.", 1, Integer.MAX_VALUE, 4);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_MINIMAL_LEAF_SIZE, "The minimal size of all leaves.", 1, Integer.MAX_VALUE, 2);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeDouble(PARAMETER_MINIMAL_GAIN, "The minimal gain which must be achieved in order to produce a split.", 0.0d, Double.POSITIVE_INFINITY, 0.0d));
return types;
}
}