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LUCNE-4818 - added boolean perceptron classifier 

git-svn-id: https://svn.apache.org/repos/asf/lucene/dev/trunk@1519590 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Tommaso Teofili 2013-09-03 08:10:19 +00:00
parent 1b9c6a24d4
commit 05940e34ab
9 changed files with 398 additions and 54 deletions
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226
lucene/classification/src/java/org/apache/lucene/classification/BooleanPerceptronClassifier.java Normal file
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@ -0,0 +1,226 @@
/*
* 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.lucene.classification;
import java.io.IOException;
import java.io.StringReader;
import java.util.Map;
import java.util.SortedMap;
import java.util.TreeMap;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.index.AtomicReader;
import org.apache.lucene.index.MultiFields;
import org.apache.lucene.index.StorableField;
import org.apache.lucene.index.StoredDocument;
import org.apache.lucene.index.Terms;
import org.apache.lucene.index.TermsEnum;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.MatchAllDocsQuery;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.IntsRef;
import org.apache.lucene.util.fst.Builder;
import org.apache.lucene.util.fst.FST;
import org.apache.lucene.util.fst.PositiveIntOutputs;
import org.apache.lucene.util.fst.Util;
/**
* A perceptron (see <code>http://en.wikipedia.org/wiki/Perceptron</code>) based
* <code>Boolean</code> {@link org.apache.lucene.classification.Classifier}. The
* weights are calculated using
* {@link org.apache.lucene.index.TermsEnum#totalTermFreq} both on a per field
* and a per document basis and then a corresponding
* {@link org.apache.lucene.util.fst.FST} is used for class assignment.
*
* @lucene.experimental
*/
public class BooleanPerceptronClassifier implements Classifier<Boolean> {
private Double threshold;
private final Integer batchSize;
private Terms textTerms;
private Analyzer analyzer;
private String textFieldName;
private FST<Long> fst;
/**
* Create a {@link BooleanPerceptronClassifier}
*
* @param threshold
* the binary threshold for perceptron output evaluation
*/
public BooleanPerceptronClassifier(Double threshold, Integer batchSize) {
this.threshold = threshold;
this.batchSize = batchSize;
}
/**
* Default constructor, no batch updates of FST, perceptron threshold is
* calculated via underlying index metrics during
* {@link #train(org.apache.lucene.index.AtomicReader, String, String, org.apache.lucene.analysis.Analyzer)
* training}
*/
public BooleanPerceptronClassifier() {
batchSize = 1;
}
/**
* {@inheritDoc}
*/
@Override
public ClassificationResult<Boolean> assignClass(String text)
throws IOException {
if (textTerms == null) {
throw new IOException("You must first call Classifier#train");
}
Long output = 0l;
TokenStream tokenStream = analyzer.tokenStream(textFieldName,
new StringReader(text));
CharTermAttribute charTermAttribute = tokenStream
.addAttribute(CharTermAttribute.class);
tokenStream.reset();
while (tokenStream.incrementToken()) {
String s = charTermAttribute.toString();
Long d = Util.get(fst, new BytesRef(s));
if (d != null) {
output += d;
}
}
tokenStream.end();
tokenStream.close();
return new ClassificationResult<>(output >= threshold, output.doubleValue());
}
/**
* {@inheritDoc}
*/
@Override
public void train(AtomicReader atomicReader, String textFieldName,
String classFieldName, Analyzer analyzer) throws IOException {
this.textTerms = MultiFields.getTerms(atomicReader, textFieldName);
if (textTerms == null) {
throw new IOException(new StringBuilder(
"term vectors need to be available for field ").append(textFieldName)
.toString());
}
this.analyzer = analyzer;
this.textFieldName = textFieldName;
if (threshold == null || threshold == 0d) {
// automatic assign a threshold
long sumDocFreq = atomicReader.getSumDocFreq(textFieldName);
if (sumDocFreq != -1) {
this.threshold = (double) sumDocFreq / 2d;
} else {
throw new IOException(
"threshold cannot be assigned since term vectors for field "
+ textFieldName + " do not exist");
}
}
// TODO : remove this map as soon as we have a writable FST
SortedMap<String,Double> weights = new TreeMap<>();
TermsEnum reuse = textTerms.iterator(null);
BytesRef textTerm;
while ((textTerm = reuse.next()) != null) {
weights.put(textTerm.utf8ToString(), (double) reuse.totalTermFreq());
}
updateFST(weights);
IndexSearcher indexSearcher = new IndexSearcher(atomicReader);
int batchCount = 0;
// do a *:* search and use stored field values
for (ScoreDoc scoreDoc : indexSearcher.search(new MatchAllDocsQuery(),
Integer.MAX_VALUE).scoreDocs) {
StoredDocument doc = indexSearcher.doc(scoreDoc.doc);
// assign class to the doc
ClassificationResult<Boolean> classificationResult = assignClass(doc
.getField(textFieldName).stringValue());
Boolean assignedClass = classificationResult.getAssignedClass();
// get the expected result
StorableField field = doc.getField(classFieldName);
Boolean correctClass = Boolean.valueOf(field.stringValue());
long modifier = correctClass.compareTo(assignedClass);
if (modifier != 0) {
reuse = updateWeights(atomicReader, reuse, scoreDoc.doc, assignedClass,
weights, modifier, batchCount % batchSize == 0);
}
batchCount++;
}
weights.clear(); // free memory while waiting for GC
}
private TermsEnum updateWeights(AtomicReader atomicReader, TermsEnum reuse,
int docId, Boolean assignedClass, SortedMap<String,Double> weights,
double modifier, boolean updateFST) throws IOException {
TermsEnum cte = textTerms.iterator(reuse);
// get the doc term vectors
Terms terms = atomicReader.getTermVector(docId, textFieldName);
if (terms == null) {
throw new IOException("term vectors must be stored for field "
+ textFieldName);
}
TermsEnum termsEnum = terms.iterator(null);
BytesRef term;
while ((term = termsEnum.next()) != null) {
cte.seekExact(term);
if (assignedClass != null) {
long termFreqLocal = termsEnum.totalTermFreq();
// update weights
Long previousValue = Util.get(fst, term);
String termString = term.utf8ToString();
weights.put(termString, previousValue + modifier * termFreqLocal);
}
}
if (updateFST) {
updateFST(weights);
}
reuse = cte;
return reuse;
}
private void updateFST(SortedMap<String,Double> weights) throws IOException {
PositiveIntOutputs outputs = PositiveIntOutputs.getSingleton();
Builder<Long> fstBuilder = new Builder<>(FST.INPUT_TYPE.BYTE1, outputs);
BytesRef scratchBytes = new BytesRef();
IntsRef scratchInts = new IntsRef();
for (Map.Entry<String,Double> entry : weights.entrySet()) {
scratchBytes.copyChars(entry.getKey());
fstBuilder.add(Util.toIntsRef(scratchBytes, scratchInts), entry
.getValue().longValue());
}
fst = fstBuilder.finish();
}
}

4
lucene/classification/src/java/org/apache/lucene/classification/KNearestNeighborClassifier.java
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@ -59,7 +59,7 @@ public class KNearestNeighborClassifier implements Classifier<BytesRef> {
@Override
public ClassificationResult<BytesRef> assignClass(String text) throws IOException {
if (mlt == null) {
throw new IOException("You must first call Classifier#train first");
throw new IOException("You must first call Classifier#train");
}
Query q = mlt.like(new StringReader(text), textFieldName);
TopDocs topDocs = indexSearcher.search(q, k);
@ -71,7 +71,6 @@ public class KNearestNeighborClassifier implements Classifier<BytesRef> {
Map<BytesRef, Integer> classCounts = new HashMap<BytesRef, Integer>();
for (ScoreDoc scoreDoc : topDocs.scoreDocs) {
BytesRef cl = new BytesRef(indexSearcher.doc(scoreDoc.doc).getField(classFieldName).stringValue());
if (cl != null) {
Integer count = classCounts.get(cl);
if (count != null) {
classCounts.put(cl, count + 1);
@ -79,7 +78,6 @@ public class KNearestNeighborClassifier implements Classifier<BytesRef> {
classCounts.put(cl, 1);
}
}
}
double max = 0;
BytesRef assignedClass = new BytesRef();
for (BytesRef cl : classCounts.keySet()) {

2
lucene/classification/src/java/org/apache/lucene/classification/SimpleNaiveBayesClassifier.java
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@ -102,7 +102,7 @@ public class SimpleNaiveBayesClassifier implements Classifier<BytesRef> {
@Override
public ClassificationResult<BytesRef> assignClass(String inputDocument) throws IOException {
if (atomicReader == null) {
throw new IOException("You must first call Classifier#train first");
throw new IOException("You must first call Classifier#train");
}
double max = 0d;
BytesRef foundClass = new BytesRef();

4
lucene/classification/src/java/org/apache/lucene/classification/package.html
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@ -17,7 +17,7 @@
<html>
<body>
Uses already seen data (the indexed documents) to classify new documents.
Currently only contains a (simplistic) Lucene based Naive Bayes classifier
and a k-Nearest Neighbor classifier
Currently only contains a (simplistic) Lucene based Naive Bayes classifier,
a k-Nearest Neighbor classifier and a Perceptron based classifier
</body>
</html>

42
lucene/classification/src/test/org/apache/lucene/classification/BooleanPerceptronClassifierTest.java Normal file
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@ -0,0 +1,42 @@
/*
* 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.lucene.classification;
import org.apache.lucene.analysis.MockAnalyzer;
import org.junit.Test;
/**
* Testcase for {@link org.apache.lucene.classification.BooleanPerceptronClassifier}
*/
public class BooleanPerceptronClassifierTest extends ClassificationTestBase<Boolean> {
@Test
public void testBasicUsage() throws Exception {
checkCorrectClassification(new BooleanPerceptronClassifier(), TECHNOLOGY_INPUT, false, new MockAnalyzer(random()), textFieldName, booleanFieldName);
}
@Test
public void testExplicitThreshold() throws Exception {
checkCorrectClassification(new BooleanPerceptronClassifier(100d, 1), TECHNOLOGY_INPUT, false, new MockAnalyzer(random()), textFieldName, booleanFieldName);
}
@Test
public void testPerformance() throws Exception {
checkPerformance(new BooleanPerceptronClassifier(), new MockAnalyzer(random()), booleanFieldName);
}
}

132
lucene/classification/src/test/org/apache/lucene/classification/ClassificationTestBase.java
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@ -27,9 +27,13 @@ import org.apache.lucene.index.SlowCompositeReaderWrapper;
import org.apache.lucene.store.Directory;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.LuceneTestCase;
import org.apache.lucene.util._TestUtil;
import org.junit.After;
import org.junit.Before;
import java.io.IOException;
import java.util.Random;
/**
* Base class for testing {@link Classifier}s
*/
@ -41,8 +45,9 @@ public abstract class ClassificationTestBase<T> extends LuceneTestCase {
public static final BytesRef TECHNOLOGY_RESULT = new BytesRef("technology");
private RandomIndexWriter indexWriter;
private String textFieldName;
private Directory dir;
String textFieldName;
String categoryFieldName;
String booleanFieldName;
@ -66,82 +71,141 @@ public abstract class ClassificationTestBase<T> extends LuceneTestCase {
}
protected void checkCorrectClassification(Classifier<T> classifier, String inputDoc, T expectedResult, Analyzer analyzer, String classFieldName) throws Exception {
AtomicReader compositeReaderWrapper = null;
protected void checkCorrectClassification(Classifier<T> classifier, String inputDoc, T expectedResult, Analyzer analyzer, String textFieldName, String classFieldName) throws Exception {
AtomicReader atomicReader = null;
try {
populateIndex(analyzer);
compositeReaderWrapper = SlowCompositeReaderWrapper.wrap(indexWriter.getReader());
classifier.train(compositeReaderWrapper, textFieldName, classFieldName, analyzer);
populateSampleIndex(analyzer);
atomicReader = SlowCompositeReaderWrapper.wrap(indexWriter.getReader());
classifier.train(atomicReader, textFieldName, classFieldName, analyzer);
ClassificationResult<T> classificationResult = classifier.assignClass(inputDoc);
assertNotNull(classificationResult.getAssignedClass());
assertEquals("got an assigned class of " + classificationResult.getAssignedClass(), expectedResult, classificationResult.getAssignedClass());
assertTrue("got a not positive score " + classificationResult.getScore(), classificationResult.getScore() > 0);
} finally {
if (compositeReaderWrapper != null)
compositeReaderWrapper.close();
if (atomicReader != null)
atomicReader.close();
}
}
private void populateIndex(Analyzer analyzer) throws Exception {
protected void checkPerformance(Classifier<T> classifier, Analyzer analyzer, String classFieldName) throws Exception {
AtomicReader atomicReader = null;
long trainStart = System.currentTimeMillis();
long trainEnd = 0l;
try {
populatePerformanceIndex(analyzer);
atomicReader = SlowCompositeReaderWrapper.wrap(indexWriter.getReader());
classifier.train(atomicReader, textFieldName, classFieldName, analyzer);
trainEnd = System.currentTimeMillis();
long trainTime = trainEnd - trainStart;
assertTrue("training took more than 2 mins : " + trainTime / 1000 + "s", trainTime < 120000);
} finally {
if (atomicReader != null)
atomicReader.close();
}
}
private void populatePerformanceIndex(Analyzer analyzer) throws IOException {
indexWriter.deleteAll();
indexWriter.commit();
FieldType ft = new FieldType(TextField.TYPE_STORED);
ft.setStoreTermVectors(true);
ft.setStoreTermVectorOffsets(true);
ft.setStoreTermVectorPositions(true);
int docs = 1000;
Random random = random();
for (int i = 0; i < docs; i++) {
boolean b = random.nextBoolean();
Document doc = new Document();
doc.add(new Field(textFieldName, createRandomString(random), ft));
doc.add(new Field(categoryFieldName, b ? "technology" : "politics", ft));
doc.add(new Field(booleanFieldName, String.valueOf(b), ft));
indexWriter.addDocument(doc, analyzer);
}
indexWriter.commit();
}
private String createRandomString(Random random) {
StringBuilder builder = new StringBuilder();
for (int i = 0; i < 20; i++) {
builder.append(_TestUtil.randomSimpleString(random, 5));
builder.append(" ");
}
return builder.toString();
}
private void populateSampleIndex(Analyzer analyzer) throws Exception {
indexWriter.deleteAll();
indexWriter.commit();
FieldType ft = new FieldType(TextField.TYPE_STORED);
ft.setStoreTermVectors(true);
ft.setStoreTermVectorOffsets(true);
ft.setStoreTermVectorPositions(true);
String text;
Document doc = new Document();
doc.add(new Field(textFieldName, "The traveling press secretary for Mitt Romney lost his cool and cursed at reporters " +
text = "The traveling press secretary for Mitt Romney lost his cool and cursed at reporters " +
"who attempted to ask questions of the Republican presidential candidate in a public plaza near the Tomb of " +
"the Unknown Soldier in Warsaw Tuesday.", ft));
"the Unknown Soldier in Warsaw Tuesday.";
doc.add(new Field(textFieldName, text, ft));
doc.add(new Field(categoryFieldName, "politics", ft));
doc.add(new Field(booleanFieldName, "false", ft));
doc.add(new Field(booleanFieldName, "true", ft));
indexWriter.addDocument(doc, analyzer);
doc = new Document();
doc.add(new Field(textFieldName, "Mitt Romney seeks to assure Israel and Iran, as well as Jewish voters in the United" +
" States, that he will be tougher against Iran's nuclear ambitions than President Barack Obama.", ft));
text = "Mitt Romney seeks to assure Israel and Iran, as well as Jewish voters in the United" +
" States, that he will be tougher against Iran's nuclear ambitions than President Barack Obama.";
doc.add(new Field(textFieldName, text, ft));
doc.add(new Field(categoryFieldName, "politics", ft));
doc.add(new Field(booleanFieldName, "false", ft));
doc.add(new Field(booleanFieldName, "true", ft));
indexWriter.addDocument(doc, analyzer);
doc = new Document();
doc.add(new Field(textFieldName, "And there's a threshold question that he has to answer for the American people and " +
text = "And there's a threshold question that he has to answer for the American people and " +
"that's whether he is prepared to be commander-in-chief,\" she continued. \"As we look to the past events, we " +
"know that this raises some questions about his preparedness and we'll see how the rest of his trip goes.\"", ft));
"know that this raises some questions about his preparedness and we'll see how the rest of his trip goes.\"";
doc.add(new Field(textFieldName, text, ft));
doc.add(new Field(categoryFieldName, "politics", ft));
doc.add(new Field(booleanFieldName, "false", ft));
doc.add(new Field(booleanFieldName, "true", ft));
indexWriter.addDocument(doc, analyzer);
doc = new Document();
doc.add(new Field(textFieldName, "Still, when it comes to gun policy, many congressional Democrats have \"decided to " +
text = "Still, when it comes to gun policy, many congressional Democrats have \"decided to " +
"keep quiet and not go there,\" said Alan Lizotte, dean and professor at the State University of New York at " +
"Albany's School of Criminal Justice.", ft));
"Albany's School of Criminal Justice.";
doc.add(new Field(textFieldName, text, ft));
doc.add(new Field(categoryFieldName, "politics", ft));
doc.add(new Field(booleanFieldName, "true", ft));
indexWriter.addDocument(doc, analyzer);
doc = new Document();
text = "Standing amongst the thousands of people at the state Capitol, Jorstad, director of " +
"technology at the University of Wisconsin-La Crosse, documented the historic moment and shared it with the " +
"world through the Internet.";
doc.add(new Field(textFieldName, text, ft));
doc.add(new Field(categoryFieldName, "technology", ft));
doc.add(new Field(booleanFieldName, "false", ft));
indexWriter.addDocument(doc, analyzer);
doc = new Document();
doc.add(new Field(textFieldName, "Standing amongst the thousands of people at the state Capitol, Jorstad, director of " +
"technology at the University of Wisconsin-La Crosse, documented the historic moment and shared it with the " +
"world through the Internet.", ft));
text = "So, about all those experts and analysts who've spent the past year or so saying " +
"Facebook was going to make a phone. A new expert has stepped forward to say it's not going to happen.";
doc.add(new Field(textFieldName, text, ft));
doc.add(new Field(categoryFieldName, "technology", ft));
doc.add(new Field(booleanFieldName, "true", ft));
doc.add(new Field(booleanFieldName, "false", ft));
indexWriter.addDocument(doc, analyzer);
doc = new Document();
doc.add(new Field(textFieldName, "So, about all those experts and analysts who've spent the past year or so saying " +
"Facebook was going to make a phone. A new expert has stepped forward to say it's not going to happen.", ft));
doc.add(new Field(categoryFieldName, "technology", ft));
doc.add(new Field(booleanFieldName, "true", ft));
indexWriter.addDocument(doc, analyzer);
doc = new Document();
doc.add(new Field(textFieldName, "More than 400 million people trust Google with their e-mail, and 50 million store files" +
text = "More than 400 million people trust Google with their e-mail, and 50 million store files" +
" in the cloud using the Dropbox service. People manage their bank accounts, pay bills, trade stocks and " +
"generally transfer or store huge volumes of personal data online.", ft));
"generally transfer or store huge volumes of personal data online.";
doc.add(new Field(textFieldName, text, ft));
doc.add(new Field(categoryFieldName, "technology", ft));
doc.add(new Field(booleanFieldName, "true", ft));
doc.add(new Field(booleanFieldName, "false", ft));
indexWriter.addDocument(doc, analyzer);
indexWriter.commit();

7
lucene/classification/src/test/org/apache/lucene/classification/KNearestNeighborClassifierTest.java
View File

@ -27,7 +27,12 @@ public class KNearestNeighborClassifierTest extends ClassificationTestBase<Bytes
@Test
public void testBasicUsage() throws Exception {
checkCorrectClassification(new KNearestNeighborClassifier(1), TECHNOLOGY_INPUT, TECHNOLOGY_RESULT, new MockAnalyzer(random()), categoryFieldName);
checkCorrectClassification(new KNearestNeighborClassifier(1), TECHNOLOGY_INPUT, TECHNOLOGY_RESULT, new MockAnalyzer(random()), textFieldName, categoryFieldName);
}
@Test
public void testPerformance() throws Exception {
checkPerformance(new KNearestNeighborClassifier(100), new MockAnalyzer(random()), categoryFieldName);
}
}

13
lucene/classification/src/test/org/apache/lucene/classification/SimpleNaiveBayesClassifierTest.java
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@ -21,11 +21,9 @@ import org.apache.lucene.analysis.MockAnalyzer;
import org.apache.lucene.analysis.Tokenizer;
import org.apache.lucene.analysis.core.KeywordTokenizer;
import org.apache.lucene.analysis.ngram.EdgeNGramTokenFilter;
import org.apache.lucene.analysis.ngram.EdgeNGramTokenizer;
import org.apache.lucene.analysis.reverse.ReverseStringFilter;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.LuceneTestCase;
import org.apache.lucene.util.Version;
import org.junit.Test;
import java.io.Reader;
@ -39,13 +37,13 @@ public class SimpleNaiveBayesClassifierTest extends ClassificationTestBase<Bytes
@Test
public void testBasicUsage() throws Exception {
checkCorrectClassification(new SimpleNaiveBayesClassifier(), TECHNOLOGY_INPUT, TECHNOLOGY_RESULT, new MockAnalyzer(random()), categoryFieldName);
checkCorrectClassification(new SimpleNaiveBayesClassifier(), POLITICS_INPUT, POLITICS_RESULT, new MockAnalyzer(random()), categoryFieldName);
checkCorrectClassification(new SimpleNaiveBayesClassifier(), TECHNOLOGY_INPUT, TECHNOLOGY_RESULT, new MockAnalyzer(random()), textFieldName, categoryFieldName);
checkCorrectClassification(new SimpleNaiveBayesClassifier(), POLITICS_INPUT, POLITICS_RESULT, new MockAnalyzer(random()), textFieldName, categoryFieldName);
}
@Test
public void testNGramUsage() throws Exception {
checkCorrectClassification(new SimpleNaiveBayesClassifier(), TECHNOLOGY_INPUT, TECHNOLOGY_RESULT, new NGramAnalyzer(), categoryFieldName);
checkCorrectClassification(new SimpleNaiveBayesClassifier(), TECHNOLOGY_INPUT, TECHNOLOGY_RESULT, new NGramAnalyzer(), textFieldName, categoryFieldName);
}
private class NGramAnalyzer extends Analyzer {
@ -56,4 +54,9 @@ public class SimpleNaiveBayesClassifierTest extends ClassificationTestBase<Bytes
}
}
@Test
public void testPerformance() throws Exception {
checkPerformance(new SimpleNaiveBayesClassifier(), new MockAnalyzer(random()), categoryFieldName);
}
}

6
lucene/classification/src/test/org/apache/lucene/classification/utils/DataSplitterTest.java
View File

@ -131,11 +131,17 @@ public class DataSplitterTest extends LuceneTestCase {
closeQuietly(testReader);
closeQuietly(cvReader);
} finally {
if (trainingIndex != null) {
trainingIndex.close();
}
if (testIndex != null) {
testIndex.close();
}
if (crossValidationIndex != null) {
crossValidationIndex.close();
}
}
}
private static void closeQuietly(IndexReader reader) throws IOException {
try {