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.sgd; import java.io.BufferedReader; import java.io.File; import java.io.IOException; import java.io.OutputStreamWriter; import java.io.PrintWriter; import java.util.Locale; import com.google.common.base.Charsets; import org.apache.commons.cli2.CommandLine; import org.apache.commons.cli2.Group; import org.apache.commons.cli2.Option; import org.apache.commons.cli2.builder.ArgumentBuilder; import org.apache.commons.cli2.builder.DefaultOptionBuilder; import org.apache.commons.cli2.builder.GroupBuilder; import org.apache.commons.cli2.commandline.Parser; import org.apache.commons.cli2.util.HelpFormatter; import org.apache.mahout.classifier.ConfusionMatrix; import org.apache.mahout.classifier.evaluation.Auc; import org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression.Wrapper; import org.apache.mahout.ep.State; import org.apache.mahout.math.Matrix; import org.apache.mahout.math.SequentialAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.stats.OnlineSummarizer; /* * Auc and averageLikelihood are always shown if possible, if the number of target value is more than 2, * then Auc and entropy matirx are not shown regardless the value of showAuc and showEntropy * the user passes, because the current implementation does not support them on two value targets. * */ public final class ValidateAdaptiveLogistic { private static String inputFile; private static String modelFile; private static String defaultCategory; private static boolean showAuc; private static boolean showScores; private static boolean showConfusion; private ValidateAdaptiveLogistic() { } public static void main(String[] args) throws IOException { mainToOutput(args, new PrintWriter(new OutputStreamWriter(System.out, Charsets.UTF_8), true)); } static void mainToOutput(String[] args, PrintWriter output) throws IOException { if (parseArgs(args)) { if (!showAuc && !showConfusion && !showScores) { showAuc = true; showConfusion = true; } Auc collector = null; AdaptiveLogisticModelParameters lmp = AdaptiveLogisticModelParameters.loadFromFile(new File(modelFile)); CsvRecordFactory csv = lmp.getCsvRecordFactory(); AdaptiveLogisticRegression lr = lmp.createAdaptiveLogisticRegression(); if (lmp.getTargetCategories().size() <= 2) { collector = new Auc(); } OnlineSummarizer slh = new OnlineSummarizer(); ConfusionMatrix cm = new ConfusionMatrix(lmp.getTargetCategories(), defaultCategory); State<Wrapper, CrossFoldLearner> best = lr.getBest(); if (best == null) { output.println("AdaptiveLogisticRegression has not be trained probably."); return; } CrossFoldLearner learner = best.getPayload().getLearner(); BufferedReader in = TrainLogistic.open(inputFile); String line = in.readLine(); csv.firstLine(line); line = in.readLine(); if (showScores) { output.println("\"target\", \"model-output\", \"log-likelihood\", \"average-likelihood\""); } while (line != null) { Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures()); //TODO: How to avoid extra target values not shown in the training process. int target = csv.processLine(line, v); double likelihood = learner.logLikelihood(target, v); double score = learner.classifyFull(v).maxValue(); slh.add(likelihood); cm.addInstance(csv.getTargetString(line), csv.getTargetLabel(target)); if (showScores) { output.printf(Locale.ENGLISH, "%8d, %.12f, %.13f, %.13f%n", target, score, learner.logLikelihood(target, v), slh.getMean()); } if (collector != null) { collector.add(target, score); } line = in.readLine(); } output.printf(Locale.ENGLISH, "\nLog-likelihood:"); output.printf(Locale.ENGLISH, "Min=%.2f, Max=%.2f, Mean=%.2f, Median=%.2f%n", slh.getMin(), slh.getMax(), slh.getMean(), slh.getMedian()); if (collector != null) { output.printf(Locale.ENGLISH, "%nAUC = %.2f%n", collector.auc()); } if (showConfusion) { output.printf(Locale.ENGLISH, "%n%s%n%n", cm.toString()); if (collector != null) { Matrix m = collector.entropy(); output.printf(Locale.ENGLISH, "Entropy Matrix: [[%.1f, %.1f], [%.1f, %.1f]]%n", m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1)); } } } } private static boolean parseArgs(String[] args) { DefaultOptionBuilder builder = new DefaultOptionBuilder(); Option help = builder.withLongName("help").withDescription("print this list").create(); Option quiet = builder.withLongName("quiet").withDescription("be extra quiet").create(); Option auc = builder.withLongName("auc").withDescription("print AUC").create(); Option confusion = builder.withLongName("confusion").withDescription("print confusion matrix").create(); Option scores = builder.withLongName("scores").withDescription("print scores").create(); ArgumentBuilder argumentBuilder = new ArgumentBuilder(); Option inputFileOption = builder.withLongName("input").withRequired(true) .withArgument(argumentBuilder.withName("input").withMaximum(1).create()) .withDescription("where to get validate data").create(); Option modelFileOption = builder.withLongName("model").withRequired(true) .withArgument(argumentBuilder.withName("model").withMaximum(1).create()) .withDescription("where to get the trained model").create(); Option defaultCagetoryOption = builder.withLongName("defaultCategory").withRequired(false) .withArgument( argumentBuilder.withName("defaultCategory").withMaximum(1).withDefault("unknown").create()) .withDescription("the default category value to use").create(); Group normalArgs = new GroupBuilder().withOption(help).withOption(quiet).withOption(auc).withOption(scores) .withOption(confusion).withOption(inputFileOption).withOption(modelFileOption) .withOption(defaultCagetoryOption).create(); Parser parser = new Parser(); parser.setHelpOption(help); parser.setHelpTrigger("--help"); parser.setGroup(normalArgs); parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130)); CommandLine cmdLine = parser.parseAndHelp(args); if (cmdLine == null) { return false; } inputFile = getStringArgument(cmdLine, inputFileOption); modelFile = getStringArgument(cmdLine, modelFileOption); defaultCategory = getStringArgument(cmdLine, defaultCagetoryOption); showAuc = getBooleanArgument(cmdLine, auc); showScores = getBooleanArgument(cmdLine, scores); showConfusion = getBooleanArgument(cmdLine, confusion); return true; } private static boolean getBooleanArgument(CommandLine cmdLine, Option option) { return cmdLine.hasOption(option); } private static String getStringArgument(CommandLine cmdLine, Option inputFile) { return (String) cmdLine.getValue(inputFile); } }