/**
* 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.learner.meta;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.learner.rules.Rule;
import com.rapidminer.operator.learner.rules.RuleModel;
import com.rapidminer.operator.learner.tree.Edge;
import com.rapidminer.operator.learner.tree.SplitCondition;
import com.rapidminer.operator.learner.tree.Tree;
import com.rapidminer.operator.learner.tree.TreeModel;
import com.rapidminer.operator.ports.metadata.PredictionModelMetaData;
import com.rapidminer.operator.ports.metadata.SimplePrecondition;
import java.util.Iterator;
/**
* This meta learner uses an inner tree learner and creates a rule model from the learned decision
* tree.
*
* @author Ingo Mierswa
*/
public class Tree2RuleConverter extends AbstractMetaLearner {
public Tree2RuleConverter(OperatorDescription description) {
super(description);
innerModelSink.addPrecondition(new SimplePrecondition(innerModelSink, new PredictionModelMetaData(TreeModel.class)));
}
@Override
public Model learn(ExampleSet exampleSet) throws OperatorException {
Model innerModel = applyInnerLearner(exampleSet);
TreeModel treeModel = null;
if (innerModel instanceof TreeModel) {
treeModel = (TreeModel) innerModel;
} else {
throw new UserError(this, 127, "the inner learner must produce a tree model.");
}
Tree tree = treeModel.getRoot();
RuleModel ruleModel = new RuleModel(exampleSet);
addRules(ruleModel, new Rule(), tree);
return ruleModel;
}
private void addRules(RuleModel ruleModel, Rule currentRule, Tree tree) {
if (tree.isLeaf()) {
currentRule.setLabel(tree.getLabel());
int[] frequencies = new int[ruleModel.getLabel().getMapping().size()];
int index = 0;
for (String labelValue : ruleModel.getLabel().getMapping().getValues()) {
frequencies[index++] = tree.getCount(labelValue);
}
currentRule.setFrequencies(frequencies);
ruleModel.addRule(currentRule);
} else {
Iterator<Edge> e = tree.childIterator();
while (e.hasNext()) {
Edge edge = e.next();
SplitCondition condition = edge.getCondition();
Tree child = edge.getChild();
Rule clonedRule = (Rule) currentRule.clone();
clonedRule.addTerm(condition);
addRules(ruleModel, clonedRule, child);
}
}
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
switch (capability) {
case BINOMINAL_ATTRIBUTES:
case POLYNOMINAL_ATTRIBUTES:
case NUMERICAL_ATTRIBUTES:
case POLYNOMINAL_LABEL:
case BINOMINAL_LABEL:
case WEIGHTED_EXAMPLES:
case MISSING_VALUES:
return true;
default:
return false;
}
}
}