/**
* 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.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.io.PrintWriter;
import java.util.Arrays;
import java.util.List;
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.ClassifierResult;
import org.apache.mahout.classifier.NewsgroupHelper;
import org.apache.mahout.classifier.ResultAnalyzer;
import org.apache.mahout.math.Vector;
import org.apache.mahout.vectorizer.encoders.Dictionary;
import com.google.common.collect.HashMultiset;
import com.google.common.collect.Lists;
import com.google.common.collect.Multiset;
/**
* Run the 20 news groups test data through SGD, as trained by {@link org.apache.mahout.classifier.sgd.TrainNewsGroups}.
*/
public final class TestNewsGroups {
private String inputFile;
private String modelFile;
private TestNewsGroups() {
}
public static void main(String[] args) throws IOException {
TestNewsGroups runner = new TestNewsGroups();
if (runner.parseArgs(args)) {
runner.run(new PrintWriter(new OutputStreamWriter(System.out, Charsets.UTF_8), true));
}
}
public void run(PrintWriter output) throws IOException {
File base = new File(inputFile);
//contains the best model
OnlineLogisticRegression classifier =
ModelSerializer.readBinary(new FileInputStream(modelFile), OnlineLogisticRegression.class);
Dictionary newsGroups = new Dictionary();
Multiset<String> overallCounts = HashMultiset.create();
List<File> files = Lists.newArrayList();
for (File newsgroup : base.listFiles()) {
if (newsgroup.isDirectory()) {
newsGroups.intern(newsgroup.getName());
files.addAll(Arrays.asList(newsgroup.listFiles()));
}
}
System.out.println(files.size() + " test files");
ResultAnalyzer ra = new ResultAnalyzer(newsGroups.values(), "DEFAULT");
for (File file : files) {
String ng = file.getParentFile().getName();
int actual = newsGroups.intern(ng);
NewsgroupHelper helper = new NewsgroupHelper();
//no leak type ensures this is a normal vector
Vector input = helper.encodeFeatureVector(file, actual, 0, overallCounts);
Vector result = classifier.classifyFull(input);
int cat = result.maxValueIndex();
double score = result.maxValue();
double ll = classifier.logLikelihood(actual, input);
ClassifierResult cr = new ClassifierResult(newsGroups.values().get(cat), score, ll);
ra.addInstance(newsGroups.values().get(actual), cr);
}
output.println(ra);
}
boolean parseArgs(String[] args) {
DefaultOptionBuilder builder = new DefaultOptionBuilder();
Option help = builder.withLongName("help").withDescription("print this list").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(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 = (String) cmdLine.getValue(inputFileOption);
modelFile = (String) cmdLine.getValue(modelFileOption);
return true;
}
}