XMoreLikeThis.java

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 *    http://www.apache.org/licenses/LICENSE-2.0
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/**
 * Copyright 2004-2005 The Apache Software Foundation.
 *
 * Licensed 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.elasticsearch.common.lucene.search;

import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.*;
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.similarities.DefaultSimilarity;
import org.apache.lucene.search.similarities.TFIDFSimilarity;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.CharsRef;
import org.apache.lucene.util.PriorityQueue;
import org.apache.lucene.util.UnicodeUtil;
import org.elasticsearch.common.io.FastStringReader;

import java.io.IOException;
import java.io.Reader;
import java.util.*;


/**
 * Generate "more like this" similarity queries.
 * Based on this mail:
 * <code><pre>
 * Lucene does let you access the document frequency of terms, with IndexReader.docFreq().
 * Term frequencies can be computed by re-tokenizing the text, which, for a single document,
 * is usually fast enough.  But looking up the docFreq() of every term in the document is
 * probably too slow.
 * <p/>
 * You can use some heuristics to prune the set of terms, to avoid calling docFreq() too much,
 * or at all.  Since you're trying to maximize a tf*idf score, you're probably most interested
 * in terms with a high tf. Choosing a tf threshold even as low as two or three will radically
 * reduce the number of terms under consideration.  Another heuristic is that terms with a
 * high idf (i.e., a low df) tend to be longer.  So you could threshold the terms by the
 * number of characters, not selecting anything less than, e.g., six or seven characters.
 * With these sorts of heuristics you can usually find small set of, e.g., ten or fewer terms
 * that do a pretty good job of characterizing a document.
 * <p/>
 * It all depends on what you're trying to do.  If you're trying to eek out that last percent
 * of precision and recall regardless of computational difficulty so that you can win a TREC
 * competition, then the techniques I mention above are useless.  But if you're trying to
 * provide a "more like this" button on a search results page that does a decent job and has
 * good performance, such techniques might be useful.
 * <p/>
 * An efficient, effective "more-like-this" query generator would be a great contribution, if
 * anyone's interested.  I'd imagine that it would take a Reader or a String (the document's
 * text), analyzer Analyzer, and return a set of representative terms using heuristics like those
 * above.  The frequency and length thresholds could be parameters, etc.
 * <p/>
 * Doug
 * </pre></code>
 * <p/>
 * <p/>
 * <p/>
 * <h3>Initial Usage</h3>
 * <p/>
 * This class has lots of options to try to make it efficient and flexible.
 * The simplest possible usage is as follows. The bold
 * fragment is specific to this class.
 * <p/>
 * <pre class="prettyprint">
 * <p/>
 * IndexReader ir = ...
 * IndexSearcher is = ...
 * <p/>
 * MoreLikeThis mlt = new MoreLikeThis(ir);
 * Reader target = ... // orig source of doc you want to find similarities to
 * Query query = mlt.like( target);
 * <p/>
 * Hits hits = is.search(query);
 * // now the usual iteration thru 'hits' - the only thing to watch for is to make sure
 * //you ignore the doc if it matches your 'target' document, as it should be similar to itself
 * <p/>
 * </pre>
 * <p/>
 * Thus you:
 * <ol>
 * <li> do your normal, Lucene setup for searching,
 * <li> create a MoreLikeThis,
 * <li> get the text of the doc you want to find similarities to
 * <li> then call one of the like() calls to generate a similarity query
 * <li> call the searcher to find the similar docs
 * </ol>
 * <p/>
 * <h3>More Advanced Usage</h3>
 * <p/>
 * You may want to use {@link #setFieldNames setFieldNames(...)} so you can examine
 * multiple fields (e.g. body and title) for similarity.
 * <p/>
 * <p/>
 * Depending on the size of your index and the size and makeup of your documents you
 * may want to call the other set methods to control how the similarity queries are
 * generated:
 * <ul>
 * <li> {@link #setMinTermFreq setMinTermFreq(...)}
 * <li> {@link #setMinDocFreq setMinDocFreq(...)}
 * <li> {@link #setMaxDocFreq setMaxDocFreq(...)}
 * <li> {@link #setMaxDocFreqPct setMaxDocFreqPct(...)}
 * <li> {@link #setMinWordLen setMinWordLen(...)}
 * <li> {@link #setMaxWordLen setMaxWordLen(...)}
 * <li> {@link #setMaxQueryTerms setMaxQueryTerms(...)}
 * <li> {@link #setMaxNumTokensParsed setMaxNumTokensParsed(...)}
 * <li> {@link #setStopWords setStopWord(...)}
 * </ul>
 * <p/>
 * <hr>
 * <pre>
 * Changes: Mark Harwood 29/02/04
 * Some bugfixing, some refactoring, some optimisation.
 * - bugfix: retrieveTerms(int docNum) was not working for indexes without a termvector -added missing code
 * - bugfix: No significant terms being created for fields with a termvector - because
 * was only counting one occurrence per term/field pair in calculations(ie not including frequency info from TermVector)
 * - refactor: moved common code into isNoiseWord()
 * - optimise: when no termvector support available - used maxNumTermsParsed to limit amount of tokenization
 * </pre>
 */
public final class XMoreLikeThis {

//    static {
//        assert Version.CURRENT.luceneVersion == org.apache.lucene.util.Version.LUCENE_4_9: "Remove this class once we upgrade to Lucene 5.0";
//    }

    /**
     * Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
     *
     * @see #getMaxNumTokensParsed
     */
    public static final int DEFAULT_MAX_NUM_TOKENS_PARSED = 5000;

    /**
     * Ignore terms with less than this frequency in the source doc.
     *
     * @see #getMinTermFreq
     * @see #setMinTermFreq
     */
    public static final int DEFAULT_MIN_TERM_FREQ = 2;

    /**
     * Ignore words which do not occur in at least this many docs.
     *
     * @see #getMinDocFreq
     * @see #setMinDocFreq
     */
    public static final int DEFAULT_MIN_DOC_FREQ = 5;

    /**
     * Ignore words which occur in more than this many docs.
     *
     * @see #getMaxDocFreq
     * @see #setMaxDocFreq
     * @see #setMaxDocFreqPct
     */
    public static final int DEFAULT_MAX_DOC_FREQ = Integer.MAX_VALUE;

    /**
     * Boost terms in query based on score.
     *
     * @see #isBoost
     * @see #setBoost
     */
    public static final boolean DEFAULT_BOOST = false;

    /**
     * Default field names. Null is used to specify that the field names should be looked
     * up at runtime from the provided reader.
     */
    public static final String[] DEFAULT_FIELD_NAMES = new String[]{"contents"};

    /**
     * Ignore words less than this length or if 0 then this has no effect.
     *
     * @see #getMinWordLen
     * @see #setMinWordLen
     */
    public static final int DEFAULT_MIN_WORD_LENGTH = 0;

    /**
     * Ignore words greater than this length or if 0 then this has no effect.
     *
     * @see #getMaxWordLen
     * @see #setMaxWordLen
     */
    public static final int DEFAULT_MAX_WORD_LENGTH = 0;

    /**
     * Default set of stopwords.
     * If null means to allow stop words.
     *
     * @see #setStopWords
     * @see #getStopWords
     */
    public static final Set<?> DEFAULT_STOP_WORDS = null;

    /**
     * Current set of stop words.
     */
    private Set<?> stopWords = DEFAULT_STOP_WORDS;

    /**
     * Return a Query with no more than this many terms.
     *
     * @see BooleanQuery#getMaxClauseCount
     * @see #getMaxQueryTerms
     * @see #setMaxQueryTerms
     */
    public static final int DEFAULT_MAX_QUERY_TERMS = 25;

    /**
     * Analyzer that will be used to parse the doc.
     */
    private Analyzer analyzer = null;

    /**
     * Ignore words less frequent that this.
     */
    private int minTermFreq = DEFAULT_MIN_TERM_FREQ;

    /**
     * Ignore words which do not occur in at least this many docs.
     */
    private int minDocFreq = DEFAULT_MIN_DOC_FREQ;

    /**
     * Ignore words which occur in more than this many docs.
     */
    private int maxDocFreq = DEFAULT_MAX_DOC_FREQ;

    /**
     * Should we apply a boost to the Query based on the scores?
     */
    private boolean boost = DEFAULT_BOOST;

    /**
     * Field name we'll analyze.
     */
    private String[] fieldNames = DEFAULT_FIELD_NAMES;

    /**
     * The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
     */
    private int maxNumTokensParsed = DEFAULT_MAX_NUM_TOKENS_PARSED;

    /**
     * Ignore words if less than this len.
     */
    private int minWordLen = DEFAULT_MIN_WORD_LENGTH;

    /**
     * Ignore words if greater than this len.
     */
    private int maxWordLen = DEFAULT_MAX_WORD_LENGTH;

    /**
     * Don't return a query longer than this.
     */
    private int maxQueryTerms = DEFAULT_MAX_QUERY_TERMS;

    /**
     * For idf() calculations.
     */
    private TFIDFSimilarity similarity;// = new DefaultSimilarity();

    /**
     * IndexReader to use
     */
    private final IndexReader ir;

    /**
     * Boost factor to use when boosting the terms
     */
    private float boostFactor = 1;

    /**
     * Returns the boost factor used when boosting terms
     *
     * @return the boost factor used when boosting terms
     * @see #setBoostFactor(float)
     */
    public float getBoostFactor() {
        return boostFactor;
    }

    /**
     * Sets the boost factor to use when boosting terms
     *
     * @see #getBoostFactor()
     */
    public void setBoostFactor(float boostFactor) {
        this.boostFactor = boostFactor;
    }

    /**
     * Constructor requiring an IndexReader.
     */
    public XMoreLikeThis(IndexReader ir) {
        this(ir, new DefaultSimilarity());
    }

    public XMoreLikeThis(IndexReader ir, TFIDFSimilarity sim) {
        this.ir = ir;
        this.similarity = sim;
    }


    public TFIDFSimilarity getSimilarity() {
        return similarity;
    }

    public void setSimilarity(TFIDFSimilarity similarity) {
        this.similarity = similarity;
    }

    /**
     * Returns an analyzer that will be used to parse source doc with. The default analyzer
     * is not set.
     *
     * @return the analyzer that will be used to parse source doc with.
     */
    public Analyzer getAnalyzer() {
        return analyzer;
    }

    /**
     * Sets the analyzer to use. An analyzer is not required for generating a query with the
     * {@link #like(int)} method, all other 'like' methods require an analyzer.
     *
     * @param analyzer the analyzer to use to tokenize text.
     */
    public void setAnalyzer(Analyzer analyzer) {
        this.analyzer = analyzer;
    }

    /**
     * Returns the frequency below which terms will be ignored in the source doc. The default
     * frequency is the {@link #DEFAULT_MIN_TERM_FREQ}.
     *
     * @return the frequency below which terms will be ignored in the source doc.
     */
    public int getMinTermFreq() {
        return minTermFreq;
    }

    /**
     * Sets the frequency below which terms will be ignored in the source doc.
     *
     * @param minTermFreq the frequency below which terms will be ignored in the source doc.
     */
    public void setMinTermFreq(int minTermFreq) {
        this.minTermFreq = minTermFreq;
    }

    /**
     * Returns the frequency at which words will be ignored which do not occur in at least this
     * many docs. The default frequency is {@link #DEFAULT_MIN_DOC_FREQ}.
     *
     * @return the frequency at which words will be ignored which do not occur in at least this
     *         many docs.
     */
    public int getMinDocFreq() {
        return minDocFreq;
    }

    /**
     * Sets the frequency at which words will be ignored which do not occur in at least this
     * many docs.
     *
     * @param minDocFreq the frequency at which words will be ignored which do not occur in at
     * least this many docs.
     */
    public void setMinDocFreq(int minDocFreq) {
        this.minDocFreq = minDocFreq;
    }

    /**
     * Returns the maximum frequency in which words may still appear.
     * Words that appear in more than this many docs will be ignored. The default frequency is
     * {@link #DEFAULT_MAX_DOC_FREQ}.
     *
     * @return get the maximum frequency at which words are still allowed,
     *         words which occur in more docs than this are ignored.
     */
    public int getMaxDocFreq() {
        return maxDocFreq;
    }

    /**
     * Set the maximum frequency in which words may still appear. Words that appear
     * in more than this many docs will be ignored.
     *
     * @param maxFreq the maximum count of documents that a term may appear
     * in to be still considered relevant
     */
    public void setMaxDocFreq(int maxFreq) {
        this.maxDocFreq = maxFreq;
    }

    /**
     * Set the maximum percentage in which words may still appear. Words that appear
     * in more than this many percent of all docs will be ignored.
     *
     * @param maxPercentage the maximum percentage of documents (0-100) that a term may appear
     * in to be still considered relevant
     */
    public void setMaxDocFreqPct(int maxPercentage) {
        this.maxDocFreq = maxPercentage * ir.numDocs() / 100;
    }


    /**
     * Returns whether to boost terms in query based on "score" or not. The default is
     * {@link #DEFAULT_BOOST}.
     *
     * @return whether to boost terms in query based on "score" or not.
     * @see #setBoost
     */
    public boolean isBoost() {
        return boost;
    }

    /**
     * Sets whether to boost terms in query based on "score" or not.
     *
     * @param boost true to boost terms in query based on "score", false otherwise.
     * @see #isBoost
     */
    public void setBoost(boolean boost) {
        this.boost = boost;
    }

    /**
     * Returns the field names that will be used when generating the 'More Like This' query.
     * The default field names that will be used is {@link #DEFAULT_FIELD_NAMES}.
     *
     * @return the field names that will be used when generating the 'More Like This' query.
     */
    public String[] getFieldNames() {
        return fieldNames;
    }

    /**
     * Sets the field names that will be used when generating the 'More Like This' query.
     * Set this to null for the field names to be determined at runtime from the IndexReader
     * provided in the constructor.
     *
     * @param fieldNames the field names that will be used when generating the 'More Like This'
     * query.
     */
    public void setFieldNames(String[] fieldNames) {
        this.fieldNames = fieldNames;
    }

    /**
     * Returns the minimum word length below which words will be ignored. Set this to 0 for no
     * minimum word length. The default is {@link #DEFAULT_MIN_WORD_LENGTH}.
     *
     * @return the minimum word length below which words will be ignored.
     */
    public int getMinWordLen() {
        return minWordLen;
    }

    /**
     * Sets the minimum word length below which words will be ignored.
     *
     * @param minWordLen the minimum word length below which words will be ignored.
     */
    public void setMinWordLen(int minWordLen) {
        this.minWordLen = minWordLen;
    }

    /**
     * Returns the maximum word length above which words will be ignored. Set this to 0 for no
     * maximum word length. The default is {@link #DEFAULT_MAX_WORD_LENGTH}.
     *
     * @return the maximum word length above which words will be ignored.
     */
    public int getMaxWordLen() {
        return maxWordLen;
    }

    /**
     * Sets the maximum word length above which words will be ignored.
     *
     * @param maxWordLen the maximum word length above which words will be ignored.
     */
    public void setMaxWordLen(int maxWordLen) {
        this.maxWordLen = maxWordLen;
    }

    /**
     * Set the set of stopwords.
     * Any word in this set is considered "uninteresting" and ignored.
     * Even if your Analyzer allows stopwords, you might want to tell the MoreLikeThis code to ignore them, as
     * for the purposes of document similarity it seems reasonable to assume that "a stop word is never interesting".
     *
     * @param stopWords set of stopwords, if null it means to allow stop words
     * @see #getStopWords
     */
    public void setStopWords(Set<?> stopWords) {
        this.stopWords = stopWords;
    }

    /**
     * Get the current stop words being used.
     *
     * @see #setStopWords
     */
    public Set<?> getStopWords() {
        return stopWords;
    }


    /**
     * Returns the maximum number of query terms that will be included in any generated query.
     * The default is {@link #DEFAULT_MAX_QUERY_TERMS}.
     *
     * @return the maximum number of query terms that will be included in any generated query.
     */
    public int getMaxQueryTerms() {
        return maxQueryTerms;
    }

    /**
     * Sets the maximum number of query terms that will be included in any generated query.
     *
     * @param maxQueryTerms the maximum number of query terms that will be included in any
     * generated query.
     */
    public void setMaxQueryTerms(int maxQueryTerms) {
        this.maxQueryTerms = maxQueryTerms;
    }

    /**
     * @return The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
     * @see #DEFAULT_MAX_NUM_TOKENS_PARSED
     */
    public int getMaxNumTokensParsed() {
        return maxNumTokensParsed;
    }

    /**
     * @param i The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
     */
    public void setMaxNumTokensParsed(int i) {
        maxNumTokensParsed = i;
    }


    /**
     * Return a query that will return docs like the passed lucene document ID.
     *
     * @param docNum the documentID of the lucene doc to generate the 'More Like This" query for.
     * @return a query that will return docs like the passed lucene document ID.
     */
    public Query like(int docNum) throws IOException {
        if (fieldNames == null) {
            // gather list of valid fields from lucene
            Collection<String> fields = MultiFields.getIndexedFields(ir);
            fieldNames = fields.toArray(new String[fields.size()]);
        }

        return createQuery(retrieveTerms(docNum));
    }

    /**
     * Return a query that will return docs like the passed Readers.
     * This was added in order to treat multi-value fields.
     *
     * @return a query that will return docs like the passed Readers.
     */
    public Query like(String fieldName, Reader... readers) throws IOException {
        Map<String, Int> words = new HashMap<>();
        for (Reader r : readers) {
            addTermFrequencies(r, words, fieldName);
        }
        return createQuery(createQueue(words));
    }

    /**
     * Return a query that will return docs like the passed Terms.
     *
     * @return a query that will return docs like the passed Terms.
     */
    public Query like(Terms... likeTerms) throws IOException {
        Map<String, Int> termFreqMap = new HashMap<>();
        for (Terms vector : likeTerms) {
            addTermFrequencies(termFreqMap, vector);
        }
        return createQuery(createQueue(termFreqMap));
    }

    /**
     * Return a query that will return docs like the passed Fields.
     *
     * @return a query that will return docs like the passed Fields.
     */
    public Query like(Fields... likeFields) throws IOException {
        // get all field names
        Set<String> fieldNames = new HashSet<>();
        for (Fields fields : likeFields) {
            for (String fieldName : fields) {
                fieldNames.add(fieldName);
            }
        }
        // to create one query per field name only
        BooleanQuery bq = new BooleanQuery();
        for (String fieldName : fieldNames) {
            Map<String, Int> termFreqMap = new HashMap<>();
            this.setFieldNames(new String[]{fieldName});
            for (Fields fields : likeFields) {
                Terms vector = fields.terms(fieldName);
                if (vector != null) {
                    addTermFrequencies(termFreqMap, vector);
                }
            }
            Query query = createQuery(createQueue(termFreqMap));
            bq.add(query, BooleanClause.Occur.SHOULD);
        }
        return bq;
    }

    /**
     * Create the More like query from a PriorityQueue
     */
    private Query createQuery(PriorityQueue<ScoreTerm> q) {
        BooleanQuery query = new BooleanQuery();
        ScoreTerm scoreTerm;
        float bestScore = -1;

        while ((scoreTerm = q.pop()) != null) {
            TermQuery tq = new TermQuery(new Term(scoreTerm.topField, scoreTerm.word));

            if (boost) {
                if (bestScore == -1) {
                    bestScore = (scoreTerm.score);
                }
                float myScore = (scoreTerm.score);
                tq.setBoost(boostFactor * myScore / bestScore);
            }

            try {
                query.add(tq, BooleanClause.Occur.SHOULD);
            }
            catch (BooleanQuery.TooManyClauses ignore) {
                break;
            }
        }
        return query;
    }

    /**
     * Create a PriorityQueue from a word->tf map.
     *
     * @param words a map of words keyed on the word(String) with Int objects as the values.
     */
    private PriorityQueue<ScoreTerm> createQueue(Map<String, Int> words) throws IOException {
        // have collected all words in doc and their freqs
        int numDocs = ir.numDocs();
        final int limit = Math.min(maxQueryTerms, words.size());
        FreqQ queue = new FreqQ(limit); // will order words by score

        for (String word : words.keySet()) { // for every word
            int tf = words.get(word).x; // term freq in the source doc
            if (minTermFreq > 0 && tf < minTermFreq) {
                continue; // filter out words that don't occur enough times in the source
            }

            // go through all the fields and find the largest document frequency
            String topField = fieldNames[0];
            int docFreq = 0;
            for (String fieldName : fieldNames) {
                int freq = ir.docFreq(new Term(fieldName, word));
                topField = (freq > docFreq) ? fieldName : topField;
                docFreq = (freq > docFreq) ? freq : docFreq;
            }

            if (minDocFreq > 0 && docFreq < minDocFreq) {
                continue; // filter out words that don't occur in enough docs
            }

            if (docFreq > maxDocFreq) {
                continue; // filter out words that occur in too many docs
            }

            if (docFreq == 0) {
                continue; // index update problem?
            }

            float idf = similarity.idf(docFreq, numDocs);
            float score = tf * idf;

            if (queue.size() < limit) {
                // there is still space in the queue
                queue.add(new ScoreTerm(word, topField, score, idf, docFreq, tf));
            } else {
                ScoreTerm term = queue.top();
                if (term.score < score) { // update the smallest in the queue in place and update the queue.
                    term.update(word, topField, score, idf, docFreq, tf);
                    queue.updateTop();
                }
            }
        }
        return queue;
    }

    /**
     * Describe the parameters that control how the "more like this" query is formed.
     */
    public String describeParams() {
        StringBuilder sb = new StringBuilder();
        sb.append("\t").append("maxQueryTerms  : ").append(maxQueryTerms).append("\n");
        sb.append("\t").append("minWordLen     : ").append(minWordLen).append("\n");
        sb.append("\t").append("maxWordLen     : ").append(maxWordLen).append("\n");
        sb.append("\t").append("fieldNames     : ");
        String delim = "";
        for (String fieldName : fieldNames) {
            sb.append(delim).append(fieldName);
            delim = ", ";
        }
        sb.append("\n");
        sb.append("\t").append("boost          : ").append(boost).append("\n");
        sb.append("\t").append("minTermFreq    : ").append(minTermFreq).append("\n");
        sb.append("\t").append("minDocFreq     : ").append(minDocFreq).append("\n");
        return sb.toString();
    }

    /**
     * Find words for a more-like-this query former.
     *
     * @param docNum the id of the lucene document from which to find terms
     */
    private PriorityQueue<ScoreTerm> retrieveTerms(int docNum) throws IOException {
        Map<String, Int> termFreqMap = new HashMap<>();
        for (String fieldName : fieldNames) {
            final Fields vectors = ir.getTermVectors(docNum);
            final Terms vector;
            if (vectors != null) {
                vector = vectors.terms(fieldName);
            } else {
                vector = null;
            }

            // field does not store term vector info
            if (vector == null) {
                Document d = ir.document(docNum);
                IndexableField fields[] = d.getFields(fieldName);
                for (IndexableField field : fields) {
                    final String stringValue = field.stringValue();
                    if (stringValue != null) {
                        addTermFrequencies(new FastStringReader(stringValue), termFreqMap, fieldName);
                    }
                }
            } else {
                addTermFrequencies(termFreqMap, vector);
            }
        }

        return createQueue(termFreqMap);
    }

    /**
     * Adds terms and frequencies found in vector into the Map termFreqMap
     *
     * @param termFreqMap a Map of terms and their frequencies
     * @param vector List of terms and their frequencies for a doc/field
     */
    private void addTermFrequencies(Map<String, Int> termFreqMap, Terms vector) throws IOException {
        final TermsEnum termsEnum = vector.iterator(null);
        final CharsRef spare = new CharsRef();
        BytesRef text;
        while((text = termsEnum.next()) != null) {
            UnicodeUtil.UTF8toUTF16(text, spare);
            final String term = spare.toString();
            if (isNoiseWord(term)) {
                continue;
            }

            DocsEnum docs = termsEnum.docs(null, null);
            final int freq = docs.freq();

            // increment frequency
            Int cnt = termFreqMap.get(term);
            if (cnt == null) {
                cnt = new Int();
                termFreqMap.put(term, cnt);
                cnt.x = freq;
            } else {
                cnt.x += freq;
            }
        }
    }

    /**
     * Adds term frequencies found by tokenizing text from reader into the Map words
     *
     * @param r a source of text to be tokenized
     * @param termFreqMap a Map of terms and their frequencies
     * @param fieldName Used by analyzer for any special per-field analysis
     */
    private void addTermFrequencies(Reader r, Map<String, Int> termFreqMap, String fieldName)
            throws IOException {
        if (analyzer == null) {
            throw new UnsupportedOperationException("To use MoreLikeThis without " +
                    "term vectors, you must provide an Analyzer");
        }
        try (TokenStream ts = analyzer.tokenStream(fieldName, r)) {
            int tokenCount = 0;
            // for every token
            CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class);
            ts.reset();
            while (ts.incrementToken()) {
                String word = termAtt.toString();
                tokenCount++;
                if (tokenCount > maxNumTokensParsed) {
                    break;
                }
                if (isNoiseWord(word)) {
                    continue;
                }

                // increment frequency
                Int cnt = termFreqMap.get(word);
                if (cnt == null) {
                    termFreqMap.put(word, new Int());
                } else {
                    cnt.x++;
                }
            }
            ts.end();
        }
    }


    /**
     * determines if the passed term is likely to be of interest in "more like" comparisons
     *
     * @param term The word being considered
     * @return true if should be ignored, false if should be used in further analysis
     */
    private boolean isNoiseWord(String term) {
        int len = term.length();
        if (minWordLen > 0 && len < minWordLen) {
            return true;
        }
        if (maxWordLen > 0 && len > maxWordLen) {
            return true;
        }
        return stopWords != null && stopWords.contains(term);
    }


    /**
     * Find words for a more-like-this query former.
     * The result is a priority queue of arrays with one entry for <b>every word</b> in the document.
     * Each array has 6 elements.
     * The elements are:
     * <ol>
     * <li> The word (String)
     * <li> The top field that this word comes from (String)
     * <li> The score for this word (Float)
     * <li> The IDF value (Float)
     * <li> The frequency of this word in the index (Integer)
     * <li> The frequency of this word in the source document (Integer)
     * </ol>
     * This is a somewhat "advanced" routine, and in general only the 1st entry in the array is of interest.
     * This method is exposed so that you can identify the "interesting words" in a document.
     * For an easier method to call see {@link #retrieveInterestingTerms retrieveInterestingTerms()}.
     *
     * @param r the reader that has the content of the document
     * @param fieldName field passed to the analyzer to use when analyzing the content
     * @return the most interesting words in the document ordered by score, with the highest scoring, or best entry, first
     * @see #retrieveInterestingTerms
     */
    private PriorityQueue<ScoreTerm> retrieveTerms(Reader r, String fieldName) throws IOException {
        Map<String, Int> words = new HashMap<>();
        addTermFrequencies(r, words, fieldName);
        return createQueue(words);
    }

    /**
     * @see #retrieveInterestingTerms(java.io.Reader, String)
     */
    public String[] retrieveInterestingTerms(int docNum) throws IOException {
        ArrayList<Object> al = new ArrayList<>(maxQueryTerms);
        PriorityQueue<ScoreTerm> pq = retrieveTerms(docNum);
        ScoreTerm scoreTerm;
        int lim = maxQueryTerms; // have to be careful, retrieveTerms returns all words but that's probably not useful to our caller...
        // we just want to return the top words
        while (((scoreTerm = pq.pop()) != null) && lim-- > 0) {
            al.add(scoreTerm.word); // the 1st entry is the interesting word
        }
        String[] res = new String[al.size()];
        return al.toArray(res);
    }

    /**
     * Convenience routine to make it easy to return the most interesting words in a document.
     * More advanced users will call {@link #retrieveTerms(Reader, String) retrieveTerms()} directly.
     *
     * @param r the source document
     * @param fieldName field passed to analyzer to use when analyzing the content
     * @return the most interesting words in the document
     * @see #retrieveTerms(java.io.Reader, String)
     * @see #setMaxQueryTerms
     */
    public String[] retrieveInterestingTerms(Reader r, String fieldName) throws IOException {
        ArrayList<Object> al = new ArrayList<>(maxQueryTerms);
        PriorityQueue<ScoreTerm> pq = retrieveTerms(r, fieldName);
        ScoreTerm scoreTerm;
        int lim = maxQueryTerms; // have to be careful, retrieveTerms returns all words but that's probably not useful to our caller...
        // we just want to return the top words
        while (((scoreTerm = pq.pop()) != null) && lim-- > 0) {
            al.add(scoreTerm.word); // the 1st entry is the interesting word
        }
        String[] res = new String[al.size()];
        return al.toArray(res);
    }

    /**
     * PriorityQueue that orders words by score.
     */
    private static class FreqQ extends PriorityQueue<ScoreTerm> {
        FreqQ(int maxSize) {
            super(maxSize);
        }

        @Override
        protected boolean lessThan(ScoreTerm a, ScoreTerm b) {
            return a.score < b.score;
        }
    }

    private static class ScoreTerm {
        // only really need 1st 3 entries, other ones are for troubleshooting
        String word;
        String topField;
        float score;
        float idf;
        int docFreq;
        int tf;

        ScoreTerm(String word, String topField, float score, float idf, int docFreq, int tf) {
            this.word = word;
            this.topField = topField;
            this.score = score;
            this.idf = idf;
            this.docFreq = docFreq;
            this.tf = tf;
        }

        void update(String word, String topField, float score, float idf, int docFreq, int tf) {
            this.word = word;
            this.topField = topField;
            this.score = score;
            this.idf = idf;
            this.docFreq = docFreq;
            this.tf = tf;
        }
    }

    /**
     * Use for frequencies and to avoid renewing Integers.
     */
    private static class Int {
        int x;

        Int() {
            x = 1;
        }
    }
}