Java tutorial
/** * 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(); } }