/*
* 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 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.math.Matrix;
import org.apache.mahout.math.SequentialAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.classifier.evaluation.Auc;
import java.io.BufferedReader;
import java.io.File;
import java.io.OutputStreamWriter;
import java.io.PrintWriter;
import java.util.Locale;
public final class RunLogistic {
private static String inputFile;
private static String modelFile;
private static boolean showAuc;
private static boolean showScores;
private static boolean showConfusion;
private RunLogistic() {
}
public static void main(String[] args) throws Exception {
mainToOutput(args, new PrintWriter(new OutputStreamWriter(System.out, Charsets.UTF_8), true));
}
static void mainToOutput(String[] args, PrintWriter output) throws Exception {
if (parseArgs(args)) {
if (!showAuc && !showConfusion && !showScores) {
showAuc = true;
showConfusion = true;
}
Auc collector = new Auc();
LogisticModelParameters lmp = LogisticModelParameters.loadFrom(new File(modelFile));
CsvRecordFactory csv = lmp.getCsvRecordFactory();
OnlineLogisticRegression lr = lmp.createRegression();
BufferedReader in = TrainLogistic.open(inputFile);
String line = in.readLine();
csv.firstLine(line);
line = in.readLine();
if (showScores) {
output.println("\"target\",\"model-output\",\"log-likelihood\"");
}
while (line != null) {
Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
int target = csv.processLine(line, v);
double score = lr.classifyScalar(v);
if (showScores) {
output.printf(Locale.ENGLISH, "%d,%.3f,%.6f%n", target, score, lr.logLikelihood(target, v));
}
collector.add(target, score);
line = in.readLine();
}
if (showAuc) {
output.printf(Locale.ENGLISH, "AUC = %.2f%n", collector.auc());
}
if (showConfusion) {
Matrix m = collector.confusion();
output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f, %.1f]]%n",
m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
m = collector.entropy();
output.printf(Locale.ENGLISH, "entropy: [[%.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 training data")
.create();
Option modelFileOption = builder.withLongName("model")
.withRequired(true)
.withArgument(argumentBuilder.withName("model").withMaximum(1).create())
.withDescription("where to get a model")
.create();
Group normalArgs = new GroupBuilder()
.withOption(help)
.withOption(quiet)
.withOption(auc)
.withOption(scores)
.withOption(confusion)
.withOption(inputFileOption)
.withOption(modelFileOption)
.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);
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);
}
}