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* 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,
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* See the License for the specific language governing permissions and
* limitations under the License.
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package org.apache.lucene.classification;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.classification.utils.NearestFuzzyQuery;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexableField;
import org.apache.lucene.index.LeafReader;
import org.apache.lucene.index.StoredFields;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.ScoreDoc;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.search.WildcardQuery;
import org.apache.lucene.search.similarities.BM25Similarity;
import org.apache.lucene.search.similarities.Similarity;
import org.apache.lucene.util.BytesRef;
/**
* A k-Nearest Neighbor classifier based on {@link NearestFuzzyQuery}.
*
* @lucene.experimental
*/
public class KNearestFuzzyClassifier implements Classifier {
/** the name of the fields used as the input text */
private final String[] textFieldNames;
/** the name of the field used as the output text */
private final String classFieldName;
/** an {@link IndexSearcher} used to perform queries */
private final IndexSearcher indexSearcher;
/** the no. of docs to compare in order to find the nearest neighbor to the input text */
private final int k;
/**
* a {@link Query} used to filter the documents that should be used from this classifier's
* underlying {@link LeafReader}
*/
private final Query query;
private final Analyzer analyzer;
/**
* Creates a {@link KNearestFuzzyClassifier}.
*
* @param indexReader the reader on the index to be used for classification
* @param analyzer an {@link Analyzer} used to analyze unseen text
* @param similarity the {@link Similarity} to be used by the underlying {@link IndexSearcher} or
* {@code null} (defaults to {@link BM25Similarity})
* @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 k the no. of docs to select in the MLT results to find the nearest neighbor
* @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, they can
* contain boosting indication e.g. title^10
*/
public KNearestFuzzyClassifier(
IndexReader indexReader,
Similarity similarity,
Analyzer analyzer,
Query query,
int k,
String classFieldName,
String... textFieldNames) {
this.textFieldNames = textFieldNames;
this.classFieldName = classFieldName;
this.analyzer = analyzer;
this.indexSearcher = new IndexSearcher(indexReader);
this.indexSearcher.setSimilarity(
Objects.requireNonNullElseGet(similarity, BM25Similarity::new));
this.query = query;
this.k = k;
}
@Override
public ClassificationResult assignClass(String text) throws IOException {
TopDocs knnResults = knnSearch(text);
List> assignedClasses = buildListFromTopDocs(knnResults);
ClassificationResult assignedClass = null;
double maxscore = -Double.MAX_VALUE;
for (ClassificationResult cl : assignedClasses) {
if (cl.score() > maxscore) {
assignedClass = cl;
maxscore = cl.score();
}
}
return assignedClass;
}
@Override
public List> getClasses(String text) throws IOException {
TopDocs knnResults = knnSearch(text);
List> assignedClasses = buildListFromTopDocs(knnResults);
Collections.sort(assignedClasses);
return assignedClasses;
}
@Override
public List> getClasses(String text, int max) throws IOException {
TopDocs knnResults = knnSearch(text);
List> assignedClasses = buildListFromTopDocs(knnResults);
Collections.sort(assignedClasses);
return assignedClasses.subList(0, max);
}
private TopDocs knnSearch(String text) throws IOException {
BooleanQuery.Builder bq = new BooleanQuery.Builder();
NearestFuzzyQuery nearestFuzzyQuery = new NearestFuzzyQuery(analyzer);
for (String fieldName : textFieldNames) {
nearestFuzzyQuery.addTerms(text, fieldName);
}
bq.add(nearestFuzzyQuery, BooleanClause.Occur.MUST);
Query classFieldQuery = new WildcardQuery(new Term(classFieldName, "*"));
bq.add(new BooleanClause(classFieldQuery, BooleanClause.Occur.MUST));
if (query != null) {
bq.add(query, BooleanClause.Occur.MUST);
}
return indexSearcher.search(bq.build(), k);
}
/**
* build a list of classification results from search results
*
* @param topDocs the search results as a {@link TopDocs} object
* @return a {@link List} of {@link ClassificationResult}, one for each existing class
* @throws IOException if it's not possible to get the stored value of class field
*/
private List> buildListFromTopDocs(TopDocs topDocs)
throws IOException {
Map classCounts = new HashMap<>();
Map classBoosts =
new HashMap<>(); // this is a boost based on class ranking positions in topDocs
float maxScore = topDocs.totalHits.value() == 0 ? Float.NaN : topDocs.scoreDocs[0].score;
StoredFields storedFields = indexSearcher.storedFields();
for (ScoreDoc scoreDoc : topDocs.scoreDocs) {
IndexableField storableField = storedFields.document(scoreDoc.doc).getField(classFieldName);
if (storableField != null) {
BytesRef cl = new BytesRef(storableField.stringValue());
// update count
classCounts.merge(cl, 1, Integer::sum);
// update boost, the boost is based on the best score
Double totalBoost = classBoosts.get(cl);
double singleBoost = scoreDoc.score / maxScore;
if (totalBoost != null) {
classBoosts.put(cl, totalBoost + singleBoost);
} else {
classBoosts.put(cl, singleBoost);
}
}
}
List> returnList = new ArrayList<>();
List> temporaryList = new ArrayList<>();
int sumdoc = 0;
for (Map.Entry entry : classCounts.entrySet()) {
Integer count = entry.getValue();
Double normBoost =
classBoosts.get(entry.getKey()) / count; // the boost is normalized to be 0(entry.getKey().clone(), (count * normBoost) / (double) k));
sumdoc += count;
}
// correction
if (sumdoc < k) {
for (ClassificationResult cr : temporaryList) {
returnList.add(
new ClassificationResult<>(cr.assignedClass(), cr.score() * k / (double) sumdoc));
}
} else {
returnList = temporaryList;
}
return returnList;
}
@Override
public String toString() {
return "KNearestFuzzyClassifier{"
+ "textFieldNames="
+ Arrays.toString(textFieldNames)
+ ", classFieldName='"
+ classFieldName
+ '\''
+ ", k="
+ k
+ ", query="
+ query
+ ", similarity="
+ indexSearcher.getSimilarity()
+ '}';
}
}
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