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* 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
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package org.apache.lucene.classification;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.MultiFields;
import org.apache.lucene.index.Term;
import org.apache.lucene.index.Terms;
import org.apache.lucene.index.TermsEnum;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.search.TotalHitCountCollector;
import org.apache.lucene.util.BytesRef;
/**
* A simplistic Lucene based NaiveBayes classifier, with caching feature, see
* <code>http://en.wikipedia.org/wiki/Naive_Bayes_classifier</code>
* <p>
* This is NOT an online classifier.
*
* @lucene.experimental
*/
public class CachingNaiveBayesClassifier extends SimpleNaiveBayesClassifier {
//for caching classes this will be the classification class list
private final ArrayList<BytesRef> cclasses = new ArrayList<>();
// it's a term-inmap style map, where the inmap contains class-hit pairs to the
// upper term
private final Map<String, Map<BytesRef, Integer>> termCClassHitCache = new HashMap<>();
// the term frequency in classes
private final Map<BytesRef, Double> classTermFreq = new HashMap<>();
private boolean justCachedTerms;
private int docsWithClassSize;
/**
* Creates a new NaiveBayes classifier with inside caching. If you want less memory usage you could call
* {@link #reInitCache(int, boolean) reInitCache()}.
*
* @param indexReader the reader on the index to be used for classification
* @param analyzer an {@link Analyzer} used to analyze unseen text
* @param query a {@link Query} to eventually filter the docs used for training the classifier, or {@code null}
* if all the indexed docs should be used
* @param classFieldName the name of the field used as the output for the classifier
* @param textFieldNames the name of the fields used as the inputs for the classifier
*/
public CachingNaiveBayesClassifier(IndexReader indexReader, Analyzer analyzer, Query query, String classFieldName, String... textFieldNames) {
super(indexReader, analyzer, query, classFieldName, textFieldNames);
// building the cache
try {
reInitCache(0, true);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
protected List<ClassificationResult<BytesRef>> assignClassNormalizedList(String inputDocument) throws IOException {
String[] tokenizedText = tokenize(inputDocument);
List<ClassificationResult<BytesRef>> assignedClasses = calculateLogLikelihood(tokenizedText);
// normalization
// The values transforms to a 0-1 range
ArrayList<ClassificationResult<BytesRef>> asignedClassesNorm = super.normClassificationResults(assignedClasses);
return asignedClassesNorm;
}
private List<ClassificationResult<BytesRef>> calculateLogLikelihood(String[] tokenizedText) throws IOException {
// initialize the return List
ArrayList<ClassificationResult<BytesRef>> ret = new ArrayList<>();
for (BytesRef cclass : cclasses) {
ClassificationResult<BytesRef> cr = new ClassificationResult<>(cclass, 0d);
ret.add(cr);
}
// for each word
for (String word : tokenizedText) {
// search with text:word for all class:c
Map<BytesRef, Integer> hitsInClasses = getWordFreqForClassess(word);
// for each class
for (BytesRef cclass : cclasses) {
Integer hitsI = hitsInClasses.get(cclass);
// if the word is out of scope hitsI could be null
int hits = 0;
if (hitsI != null) {
hits = hitsI;
}
// num : count the no of times the word appears in documents of class c(+1)
double num = hits + 1; // +1 is added because of add 1 smoothing
// den : for the whole dictionary, count the no of times a word appears in documents of class c (+|V|)
double den = classTermFreq.get(cclass) + docsWithClassSize;
// P(w|c) = num/den
double wordProbability = num / den;
// modify the value in the result list item
int removeIdx = -1;
int i = 0;
for (ClassificationResult<BytesRef> cr : ret) {
if (cr.getAssignedClass().equals(cclass)) {
removeIdx = i;
break;
}
i++;
}
if (removeIdx >= 0) {
ClassificationResult<BytesRef> toRemove = ret.get(removeIdx);
ret.add(new ClassificationResult<>(toRemove.getAssignedClass(), toRemove.getScore() + Math.log(wordProbability)));
ret.remove(removeIdx);
}
}
}
// log(P(d|c)) = log(P(w1|c))+...+log(P(wn|c))
return ret;
}
private Map<BytesRef, Integer> getWordFreqForClassess(String word) throws IOException {
Map<BytesRef, Integer> insertPoint;
insertPoint = termCClassHitCache.get(word);
// if we get the answer from the cache
if (insertPoint != null) {
if (!insertPoint.isEmpty()) {
return insertPoint;
}
}
Map<BytesRef, Integer> searched = new ConcurrentHashMap<>();
// if we dont get the answer, but it's relevant we must search it and insert to the cache
if (insertPoint != null || !justCachedTerms) {
for (BytesRef cclass : cclasses) {
BooleanQuery.Builder booleanQuery = new BooleanQuery.Builder();
BooleanQuery.Builder subQuery = new BooleanQuery.Builder();
for (String textFieldName : textFieldNames) {
subQuery.add(new BooleanClause(new TermQuery(new Term(textFieldName, word)), BooleanClause.Occur.SHOULD));
}
booleanQuery.add(new BooleanClause(subQuery.build(), BooleanClause.Occur.MUST));
booleanQuery.add(new BooleanClause(new TermQuery(new Term(classFieldName, cclass)), BooleanClause.Occur.MUST));
if (query != null) {
booleanQuery.add(query, BooleanClause.Occur.MUST);
}
TotalHitCountCollector totalHitCountCollector = new TotalHitCountCollector();
indexSearcher.search(booleanQuery.build(), totalHitCountCollector);
int ret = totalHitCountCollector.getTotalHits();
if (ret != 0) {
searched.put(cclass, ret);
}
}
if (insertPoint != null) {
// threadsafe and concurrent write
termCClassHitCache.put(word, searched);
}
}
return searched;
}
/**
* This function is building the frame of the cache. The cache is storing the
* word occurrences to the memory after those searched once. This cache can
* made 2-100x speedup in proper use, but can eat lot of memory. There is an
* option to lower the memory consume, if a word have really low occurrence in
* the index you could filter it out. The other parameter is switching between
* the term searching, if it true, just the terms in the skeleton will be
* searched, but if it false the terms whoes not in the cache will be searched
* out too (but not cached).
*
* @param minTermOccurrenceInCache Lower cache size with higher value.
* @param justCachedTerms The switch for fully exclude low occurrence docs.
* @throws IOException If there is a low-level I/O error.
*/
public void reInitCache(int minTermOccurrenceInCache, boolean justCachedTerms) throws IOException {
this.justCachedTerms = justCachedTerms;
this.docsWithClassSize = countDocsWithClass();
termCClassHitCache.clear();
cclasses.clear();
classTermFreq.clear();
// build the cache for the word
Map<String, Long> frequencyMap = new HashMap<>();
for (String textFieldName : textFieldNames) {
TermsEnum termsEnum = MultiFields.getTerms(indexReader, textFieldName).iterator();
while (termsEnum.next() != null) {
BytesRef term = termsEnum.term();
String termText = term.utf8ToString();
long frequency = termsEnum.docFreq();
Long lastfreq = frequencyMap.get(termText);
if (lastfreq != null) frequency += lastfreq;
frequencyMap.put(termText, frequency);
}
}
for (Map.Entry<String, Long> entry : frequencyMap.entrySet()) {
if (entry.getValue() > minTermOccurrenceInCache) {
termCClassHitCache.put(entry.getKey(), new ConcurrentHashMap<BytesRef, Integer>());
}
}
// fill the class list
Terms terms = MultiFields.getTerms(indexReader, classFieldName);
TermsEnum termsEnum = terms.iterator();
while ((termsEnum.next()) != null) {
cclasses.add(BytesRef.deepCopyOf(termsEnum.term()));
}
// fill the classTermFreq map
for (BytesRef cclass : cclasses) {
double avgNumberOfUniqueTerms = 0;
for (String textFieldName : textFieldNames) {
terms = MultiFields.getTerms(indexReader, textFieldName);
long numPostings = terms.getSumDocFreq(); // number of term/doc pairs
avgNumberOfUniqueTerms += numPostings / (double) terms.getDocCount();
}
int docsWithC = indexReader.docFreq(new Term(classFieldName, cclass));
classTermFreq.put(cclass, avgNumberOfUniqueTerms * docsWithC);
}
}
}