/*
* 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.PrintWriter;
import java.util.Locale;
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(System.out, 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.printf("%s\n",
"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.printf(Locale.ENGLISH, "\"%s\", \"%s\", \"%s\", \"%s\"\n",
"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);
}
}