/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.classifier;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.Collection;
import org.apache.commons.lang3.StringUtils;
import org.apache.mahout.cf.taste.impl.common.RunningAverageAndStdDev;
import org.apache.mahout.math.stats.OnlineSummarizer;
/** ResultAnalyzer captures the classification statistics and displays in a tabular manner */
public class ResultAnalyzer {
private final ConfusionMatrix confusionMatrix;
private final OnlineSummarizer summarizer;
private boolean hasLL;
/*
* === Summary ===
*
* Correctly Classified Instances 635 92.9722 % Incorrectly Classified Instances 48 7.0278 % Kappa statistic
* 0.923 Mean absolute error 0.0096 Root mean squared error 0.0817 Relative absolute error 9.9344 % Root
* relative squared error 37.2742 % Total Number of Instances 683
*/
private int correctlyClassified;
private int incorrectlyClassified;
public ResultAnalyzer(Collection<String> labelSet, String defaultLabel) {
confusionMatrix = new ConfusionMatrix(labelSet, defaultLabel);
summarizer = new OnlineSummarizer();
}
public ConfusionMatrix getConfusionMatrix() {
return this.confusionMatrix;
}
/**
*
* @param correctLabel
* The correct label
* @param classifiedResult
* The classified result
* @return whether the instance was correct or not
*/
public boolean addInstance(String correctLabel, ClassifierResult classifiedResult) {
boolean result = correctLabel.equals(classifiedResult.getLabel());
if (result) {
correctlyClassified++;
} else {
incorrectlyClassified++;
}
confusionMatrix.addInstance(correctLabel, classifiedResult);
if (classifiedResult.getLogLikelihood() != Double.MAX_VALUE) {
summarizer.add(classifiedResult.getLogLikelihood());
hasLL = true;
}
return result;
}
@Override
public String toString() {
StringBuilder returnString = new StringBuilder();
returnString.append('\n');
returnString.append("=======================================================\n");
returnString.append("Summary\n");
returnString.append("-------------------------------------------------------\n");
int totalClassified = correctlyClassified + incorrectlyClassified;
double percentageCorrect = (double) 100 * correctlyClassified / totalClassified;
double percentageIncorrect = (double) 100 * incorrectlyClassified / totalClassified;
NumberFormat decimalFormatter = new DecimalFormat("0.####");
returnString.append(StringUtils.rightPad("Correctly Classified Instances", 40)).append(": ").append(
StringUtils.leftPad(Integer.toString(correctlyClassified), 10)).append('\t').append(
StringUtils.leftPad(decimalFormatter.format(percentageCorrect), 10)).append("%\n");
returnString.append(StringUtils.rightPad("Incorrectly Classified Instances", 40)).append(": ").append(
StringUtils.leftPad(Integer.toString(incorrectlyClassified), 10)).append('\t').append(
StringUtils.leftPad(decimalFormatter.format(percentageIncorrect), 10)).append("%\n");
returnString.append(StringUtils.rightPad("Total Classified Instances", 40)).append(": ").append(
StringUtils.leftPad(Integer.toString(totalClassified), 10)).append('\n');
returnString.append('\n');
returnString.append(confusionMatrix);
returnString.append("=======================================================\n");
returnString.append("Statistics\n");
returnString.append("-------------------------------------------------------\n");
RunningAverageAndStdDev normStats = confusionMatrix.getNormalizedStats();
returnString.append(StringUtils.rightPad("Kappa", 40)).append(
StringUtils.leftPad(decimalFormatter.format(confusionMatrix.getKappa()), 10)).append('\n');
returnString.append(StringUtils.rightPad("Accuracy", 40)).append(
StringUtils.leftPad(decimalFormatter.format(confusionMatrix.getAccuracy()), 10)).append("%\n");
returnString.append(StringUtils.rightPad("Reliability", 40)).append(
StringUtils.leftPad(decimalFormatter.format(normStats.getAverage() * 100.00000001), 10)).append("%\n");
returnString.append(StringUtils.rightPad("Reliability (standard deviation)", 40)).append(
StringUtils.leftPad(decimalFormatter.format(normStats.getStandardDeviation()), 10)).append('\n');
returnString.append(StringUtils.rightPad("Weighted precision", 40)).append(
StringUtils.leftPad(decimalFormatter.format(confusionMatrix.getWeightedPrecision()), 10)).append('\n');
returnString.append(StringUtils.rightPad("Weighted recall", 40)).append(
StringUtils.leftPad(decimalFormatter.format(confusionMatrix.getWeightedRecall()), 10)).append('\n');
returnString.append(StringUtils.rightPad("Weighted F1 score", 40)).append(
StringUtils.leftPad(decimalFormatter.format(confusionMatrix.getWeightedF1score()), 10)).append('\n');
if (hasLL) {
returnString.append(StringUtils.rightPad("Log-likelihood", 30)).append("mean : ").append(
StringUtils.leftPad(decimalFormatter.format(summarizer.getMean()), 10)).append('\n');
returnString.append(StringUtils.rightPad("", 30)).append(StringUtils.rightPad("25%-ile : ", 10)).append(
StringUtils.leftPad(decimalFormatter.format(summarizer.getQuartile(1)), 10)).append('\n');
returnString.append(StringUtils.rightPad("", 30)).append(StringUtils.rightPad("75%-ile : ", 10)).append(
StringUtils.leftPad(decimalFormatter.format(summarizer.getQuartile(3)), 10)).append('\n');
}
return returnString.toString();
}
}