/*
* 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.collect.HashMultiset;
import com.google.common.collect.Lists;
import com.google.common.collect.Multiset;
import com.google.common.collect.Ordering;
import org.apache.mahout.ep.State;
import org.apache.mahout.math.Vector;
import org.apache.mahout.vectorizer.encoders.Dictionary;
import java.io.File;
import java.io.IOException;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Reads and trains an adaptive logistic regression model on the 20 newsgroups data.
* The first command line argument gives the path of the directory holding the training
* data. The optional second argument, leakType, defines which classes of features to use.
* Importantly, leakType controls whether a synthetic date is injected into the data as
* a target leak and if so, how.
* <p/>
* The value of leakType % 3 determines whether the target leak is injected according to
* the following table:
* <p/>
* <table>
* <tr><td valign='top'>0</td><td>No leak injected</td></tr>
* <tr><td valign='top'>1</td><td>Synthetic date injected in MMM-yyyy format. This will be a single token and
* is a perfect target leak since each newsgroup is given a different month</td></tr>
* <tr><td valign='top'>2</td><td>Synthetic date injected in dd-MMM-yyyy HH:mm:ss format. The day varies
* and thus there are more leak symbols that need to be learned. Ultimately this is just
* as big a leak as case 1.</td></tr>
* </table>
* <p/>
* Leaktype also determines what other text will be indexed. If leakType is greater
* than or equal to 6, then neither headers nor text body will be used for features and the leak is the only
* source of data. If leakType is greater than or equal to 3, then subject words will be used as features.
* If leakType is less than 3, then both subject and body text will be used as features.
* <p/>
* A leakType of 0 gives no leak and all textual features.
* <p/>
* See the following table for a summary of commonly used values for leakType
* <p/>
* <table>
* <tr><td><b>leakType</b></td><td><b>Leak?</b></td><td><b>Subject?</b></td><td><b>Body?</b></td></tr>
* <tr><td colspan=4><hr></td></tr>
* <tr><td>0</td><td>no</td><td>yes</td><td>yes</td></tr>
* <tr><td>1</td><td>mmm-yyyy</td><td>yes</td><td>yes</td></tr>
* <tr><td>2</td><td>dd-mmm-yyyy</td><td>yes</td><td>yes</td></tr>
* <tr><td colspan=4><hr></td></tr>
* <tr><td>3</td><td>no</td><td>yes</td><td>no</td></tr>
* <tr><td>4</td><td>mmm-yyyy</td><td>yes</td><td>no</td></tr>
* <tr><td>5</td><td>dd-mmm-yyyy</td><td>yes</td><td>no</td></tr>
* <tr><td colspan=4><hr></td></tr>
* <tr><td>6</td><td>no</td><td>no</td><td>no</td></tr>
* <tr><td>7</td><td>mmm-yyyy</td><td>no</td><td>no</td></tr>
* <tr><td>8</td><td>dd-mmm-yyyy</td><td>no</td><td>no</td></tr>
* <tr><td colspan=4><hr></td></tr>
* </table>
*/
public final class TrainNewsGroups {
private TrainNewsGroups() {
}
public static void main(String[] args) throws IOException {
File base = new File(args[0]);
Multiset<String> overallCounts = HashMultiset.create();
int leakType = 0;
if (args.length > 1) {
leakType = Integer.parseInt(args[1]);
}
Dictionary newsGroups = new Dictionary();
NewsgroupHelper helper = new NewsgroupHelper();
helper.getEncoder().setProbes(2);
AdaptiveLogisticRegression learningAlgorithm = new AdaptiveLogisticRegression(20, NewsgroupHelper.FEATURES, new L1());
learningAlgorithm.setInterval(800);
learningAlgorithm.setAveragingWindow(500);
List<File> files = Lists.newArrayList();
for (File newsgroup : base.listFiles()) {
if (newsgroup.isDirectory()) {
newsGroups.intern(newsgroup.getName());
files.addAll(Arrays.asList(newsgroup.listFiles()));
}
}
Collections.shuffle(files);
System.out.printf("%d training files\n", files.size());
SGDInfo info = new SGDInfo();
int k = 0;
for (File file : files) {
String ng = file.getParentFile().getName();
int actual = newsGroups.intern(ng);
Vector v = helper.encodeFeatureVector(file, actual, leakType, overallCounts);
learningAlgorithm.train(actual, v);
k++;
State<AdaptiveLogisticRegression.Wrapper, CrossFoldLearner> best = learningAlgorithm.getBest();
SGDHelper.analyzeState(info, leakType, k, best);
}
learningAlgorithm.close();
SGDHelper.dissect(leakType, newsGroups, learningAlgorithm, files, overallCounts);
System.out.println("exiting main");
ModelSerializer.writeBinary("/tmp/news-group.model",
learningAlgorithm.getBest().getPayload().getLearner().getModels().get(0));
List<Integer> counts = Lists.newArrayList();
System.out.printf("Word counts\n");
for (String count : overallCounts.elementSet()) {
counts.add(overallCounts.count(count));
}
Collections.sort(counts, Ordering.natural().reverse());
k = 0;
for (Integer count : counts) {
System.out.printf("%d\t%d\n", k, count);
k++;
if (k > 1000) {
break;
}
}
}
}