/**
* 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.rules;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.ExampleSetUtilities;
import com.rapidminer.operator.learner.SimplePredictionModel;
import com.rapidminer.report.Readable;
import com.rapidminer.tools.Tools;
/**
* The basic rule model.
*
* @author Sebastian Land, Ingo Mierswa
*/
public class RuleModel extends SimplePredictionModel implements Readable {
private static final long serialVersionUID = 7792658268037025366L;
private List<Rule> rules = new ArrayList<>();
public RuleModel(ExampleSet exampleSet) {
super(exampleSet, ExampleSetUtilities.SetsCompareOption.ALLOW_SUPERSET,
ExampleSetUtilities.TypesCompareOption.ALLOW_SAME_PARENTS);
}
@Override
public String getName() {
return "RuleModel";
}
@Override
public double predict(Example example) {
for (Rule rule : rules) {
if (rule.coversExample(example)) {
double[] confidences = rule.getConfidences();
for (int index = 0; index < confidences.length; index++) {
example.setConfidence(getLabel().getMapping().mapIndex(index), confidences[index]);
}
return getLabel().getMapping().getIndex(rule.getLabel());
}
}
return Double.NaN; // return unknown if no rule exists
}
public double getPrediction(Example example) {
for (Rule rule : rules) {
if (rule.coversExample(example)) {
double label = getLabel().getMapping().getIndex(rule.getLabel());
return label;
}
}
return Double.NaN; // return unknown if no rule exists
}
public void addRule(Rule rule) {
this.rules.add(rule);
}
public void addRules(Collection<Rule> newRules) {
this.rules.addAll(newRules);
}
public List<Rule> getRules() {
return this.rules;
}
@Override
public String toString() {
StringBuffer buffer = new StringBuffer();
int correct = 0;
int wrong = 0;
for (Rule rule : rules) {
buffer.append(rule.toString());
buffer.append(Tools.getLineSeparator());
int label = getLabel().getMapping().getIndex(rule.getLabel());
int[] frequencies = rule.getFrequencies();
if (frequencies != null) {
for (int i = 0; i < frequencies.length; i++) {
if (i == label) {
correct += frequencies[i];
} else {
wrong += frequencies[i];
}
}
}
}
buffer.append(Tools.getLineSeparator());
buffer.append("correct: " + correct + " out of " + (correct + wrong) + " training examples.");
return buffer.toString();
}
public int getNumberOfReadables() {
return 1;
}
public Readable getReadable(int index) {
return this;
}
}