--- /dev/null
+/**
+ * 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.apache.lucene.search.similar;
+
+import java.io.File;
+import java.io.FileReader;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.io.PrintStream;
+import java.io.Reader;
+import java.io.StringReader;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.Map;
+import java.util.Set;
+
+import org.apache.lucene.analysis.Analyzer;
+import org.apache.lucene.analysis.TokenStream;
+import org.apache.lucene.analysis.standard.StandardAnalyzer;
+import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
+import org.apache.lucene.document.Document;
+import org.apache.lucene.index.IndexReader;
+import org.apache.lucene.index.Term;
+import org.apache.lucene.index.TermFreqVector;
+import org.apache.lucene.search.BooleanClause;
+import org.apache.lucene.search.BooleanQuery;
+import org.apache.lucene.search.DefaultSimilarity;
+import org.apache.lucene.search.IndexSearcher;
+import org.apache.lucene.search.Query;
+import org.apache.lucene.search.ScoreDoc;
+import org.apache.lucene.search.Similarity;
+import org.apache.lucene.search.TermQuery;
+import org.apache.lucene.search.TopDocs;
+import org.apache.lucene.store.FSDirectory;
+import org.apache.lucene.util.PriorityQueue;
+import org.apache.lucene.util.Version;
+
+/**
+ * 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.
+ *
+ * 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.
+ *
+ * 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.
+ *
+ * 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.
+ *
+ * Doug
+ * </pre></code>
+ *
+ *
+ * <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.
+ *
+ * <pre class="prettyprint">
+ *
+ * IndexReader ir = ...
+ * IndexSearcher is = ...
+ *
+ * MoreLikeThis mlt = new MoreLikeThis(ir);
+ * Reader target = ... // orig source of doc you want to find similarities to
+ * Query query = mlt.like( target);
+ *
+ * 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
+ *
+ * </pre>
+ *
+ * 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>
+ *
+ * <h3>More Advanced Usage</h3>
+ *
+ * You may want to use {@link #setFieldNames setFieldNames(...)} so you can
+ * examine multiple fields (e.g. body and title) for similarity.
+ * <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>
+ *
+ * <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 MoreLikeThis {
+
+ /**
+ * 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;
+
+ /**
+ * Default analyzer to parse source doc with.
+ *
+ * @see #getAnalyzer
+ * @deprecated This default will be removed in Lucene 4.0 (with the default
+ * being null). If you are not using term vectors, explicitly set
+ * your analyzer instead.
+ */
+ @Deprecated
+ public static final Analyzer DEFAULT_ANALYZER = new StandardAnalyzer(
+ Version.LUCENE_CURRENT);
+
+ /**
+ * 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 = DEFAULT_ANALYZER;
+
+ /**
+ * 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 Similarity 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
+ */
+ public float getBoostFactor() {
+ return boostFactor;
+ }
+
+ /**
+ * Sets the boost factor to use when boosting terms
+ *
+ * @param boostFactor
+ */
+ public void setBoostFactor(float boostFactor) {
+ this.boostFactor = boostFactor;
+ }
+
+ /**
+ * Constructor requiring an IndexReader.
+ */
+ public MoreLikeThis(IndexReader ir) {
+ this(ir, new DefaultSimilarity());
+ }
+
+ public MoreLikeThis(IndexReader ir, Similarity sim) {
+ this.ir = ir;
+ this.similarity = sim;
+ }
+
+ public Similarity getSimilarity() {
+ return similarity;
+ }
+
+ public void setSimilarity(Similarity similarity) {
+ this.similarity = similarity;
+ }
+
+ /**
+ * Returns an analyzer that will be used to parse source doc with. The default
+ * analyzer is the {@link #DEFAULT_ANALYZER}.
+ *
+ * @return the analyzer that will be used to parse source doc with.
+ * @see #DEFAULT_ANALYZER
+ */
+ 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 org.apache.lucene.analysis.StopFilter#makeStopSet
+ * StopFilter.makeStopSet()
+ * @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 = ir
+ .getFieldNames(IndexReader.FieldOption.INDEXED);
+ fieldNames = fields.toArray(new String[fields.size()]);
+ }
+
+ return createQuery(retrieveTerms(docNum));
+ }
+
+ /**
+ * Return a query that will return docs like the passed file.
+ *
+ * @return a query that will return docs like the passed file.
+ * @deprecated use {@link #like(Reader, String)} instead */
+ @Deprecated
+ public Query like(File f) throws IOException {
+ if (fieldNames == null) {
+ // gather list of valid fields from lucene
+ Collection<String> fields = ir
+ .getFieldNames(IndexReader.FieldOption.INDEXED);
+ fieldNames = fields.toArray(new String[fields.size()]);
+ }
+
+ return like(new FileReader(f));
+ }
+
+ /**
+ * Return a query that will return docs like the passed URL.
+ *
+ * @return a query that will return docs like the passed URL.
+ * @deprecated use {@link #like(Reader, String)} instead */
+ @Deprecated
+ public Query like(URL u) throws IOException {
+ return like(new InputStreamReader(u.openConnection().getInputStream()));
+ }
+
+ /**
+ * Return a query that will return docs like the passed stream.
+ *
+ * @return a query that will return docs like the passed stream.
+ * @deprecated use {@link #like(Reader, String)} instead */
+ @Deprecated
+ public Query like(java.io.InputStream is) throws IOException {
+ return like(new InputStreamReader(is));
+ }
+
+ /** @deprecated use {@link #like(Reader, String)} instead */
+ @Deprecated
+ public Query like(Reader r) throws IOException {
+ return createQuery(retrieveTerms(r, fieldNames[0]));
+ }
+
+ /**
+ * Return a query that will return docs like the passed Reader.
+ *
+ * @return a query that will return docs like the passed Reader.
+ */
+ public Query like(Reader r, String fieldName) throws IOException {
+ return createQuery(retrieveTerms(r, fieldName));
+ }
+
+ /**
+ * Create the More like query from a PriorityQueue
+ */
+ private Query createQuery(PriorityQueue<Object[]> q) {
+ BooleanQuery query = new BooleanQuery();
+ Object cur;
+ int qterms = 0;
+ float bestScore = 0;
+
+ while (((cur = q.pop()) != null)) {
+ Object[] ar = (Object[]) cur;
+ TermQuery tq = new TermQuery(new Term((String) ar[1], (String) ar[0]));
+
+ if (boost) {
+ if (qterms == 0) {
+ bestScore = ((Float) ar[2]).floatValue();
+ }
+ float myScore = ((Float) ar[2]).floatValue();
+
+ tq.setBoost(boostFactor * myScore / bestScore);
+ }
+
+ try {
+ query.add(tq, BooleanClause.Occur.SHOULD);
+ } catch (BooleanQuery.TooManyClauses ignore) {
+ break;
+ }
+
+ qterms++;
+ if (maxQueryTerms > 0 && qterms >= maxQueryTerms) {
+ 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<Object[]> createQueue(Map<String,Int> words)
+ throws IOException {
+ // have collected all words in doc and their freqs
+ int numDocs = ir.numDocs();
+ FreqQ res = new FreqQ(words.size()); // will order words by score
+
+ Iterator<String> it = words.keySet().iterator();
+ while (it.hasNext()) { // for every word
+ String word = it.next();
+
+ 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 (int i = 0; i < fieldNames.length; i++) {
+ int freq = ir.docFreq(new Term(fieldNames[i], word));
+ topField = (freq > docFreq) ? fieldNames[i] : 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;
+
+ // only really need 1st 3 entries, other ones are for troubleshooting
+ res.insertWithOverflow(new Object[] {word, // the word
+ topField, // the top field
+ Float.valueOf(score), // overall score
+ Float.valueOf(idf), // idf
+ Integer.valueOf(docFreq), // freq in all docs
+ Integer.valueOf(tf)});
+ }
+ return res;
+ }
+
+ /**
+ * Describe the parameters that control how the "more like this" query is
+ * formed.
+ */
+ public String describeParams() {
+ StringBuilder sb = new StringBuilder();
+ sb.append("\t" + "maxQueryTerms : " + maxQueryTerms + "\n");
+ sb.append("\t" + "minWordLen : " + minWordLen + "\n");
+ sb.append("\t" + "maxWordLen : " + maxWordLen + "\n");
+ sb.append("\t" + "fieldNames : ");
+ String delim = "";
+ for (int i = 0; i < fieldNames.length; i++) {
+ String fieldName = fieldNames[i];
+ sb.append(delim).append(fieldName);
+ delim = ", ";
+ }
+ sb.append("\n");
+ sb.append("\t" + "boost : " + boost + "\n");
+ sb.append("\t" + "minTermFreq : " + minTermFreq + "\n");
+ sb.append("\t" + "minDocFreq : " + minDocFreq + "\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
+ */
+ public PriorityQueue<Object[]> retrieveTerms(int docNum) throws IOException {
+ Map<String,Int> termFreqMap = new HashMap<String,Int>();
+ for (int i = 0; i < fieldNames.length; i++) {
+ String fieldName = fieldNames[i];
+ TermFreqVector vector = ir.getTermFreqVector(docNum, fieldName);
+
+ // field does not store term vector info
+ if (vector == null) {
+ Document d = ir.document(docNum);
+ String text[] = d.getValues(fieldName);
+ if (text != null) {
+ for (int j = 0; j < text.length; j++) {
+ addTermFrequencies(new StringReader(text[j]), 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,
+ TermFreqVector vector) {
+ String[] terms = vector.getTerms();
+ int freqs[] = vector.getTermFrequencies();
+ for (int j = 0; j < terms.length; j++) {
+ String term = terms[j];
+
+ if (isNoiseWord(term)) {
+ continue;
+ }
+ // increment frequency
+ Int cnt = termFreqMap.get(term);
+ if (cnt == null) {
+ cnt = new Int();
+ termFreqMap.put(term, cnt);
+ cnt.x = freqs[j];
+ } else {
+ cnt.x += freqs[j];
+ }
+ }
+ }
+
+ /**
+ * 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 {
+ TokenStream ts = analyzer.reusableTokenStream(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();
+ ts.close();
+ }
+
+ /**
+ * 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;
+ }
+ if (stopWords != null && stopWords.contains(term)) {
+ return true;
+ }
+ return false;
+ }
+
+ /**
+ * 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
+ */
+ public PriorityQueue<Object[]> retrieveTerms(Reader r, String fieldName) throws IOException {
+ Map<String, Int> words = new HashMap<String, Int>();
+ addTermFrequencies(r, words, fieldName);
+ return createQueue(words);
+ }
+
+ /** @deprecated use {@link #retrieveTerms(Reader, String)} instead */
+ @Deprecated
+ public PriorityQueue<Object[]> retrieveTerms(Reader r) throws IOException {
+ return retrieveTerms(r, fieldNames[0]);
+ }
+
+ /**
+ * @see #retrieveInterestingTerms(java.io.Reader, String)
+ */
+ public String[] retrieveInterestingTerms(int docNum) throws IOException {
+ ArrayList<Object> al = new ArrayList<Object>(maxQueryTerms);
+ PriorityQueue<Object[]> pq = retrieveTerms(docNum);
+ Object cur;
+ 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 (((cur = pq.pop()) != null) && lim-- > 0) {
+ Object[] ar = (Object[]) cur;
+ al.add(ar[0]); // 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<Object>(maxQueryTerms);
+ PriorityQueue<Object[]> pq = retrieveTerms(r, fieldName);
+ Object cur;
+ 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 (((cur = pq.pop()) != null) && lim-- > 0) {
+ Object[] ar = (Object[]) cur;
+ al.add(ar[0]); // the 1st entry is the interesting word
+ }
+ String[] res = new String[al.size()];
+ return al.toArray(res);
+ }
+
+ /** @deprecated use {@link #retrieveInterestingTerms(Reader, String)} instead. */
+ @Deprecated
+ public String[] retrieveInterestingTerms(Reader r) throws IOException {
+ return retrieveInterestingTerms(r, fieldNames[0]);
+ }
+
+ /**
+ * PriorityQueue that orders words by score.
+ */
+ private static class FreqQ extends PriorityQueue<Object[]> {
+ FreqQ(int s) {
+ initialize(s);
+ }
+
+ @Override
+ protected boolean lessThan(Object[] aa, Object[] bb) {
+ Float fa = (Float) aa[2];
+ Float fb = (Float) bb[2];
+ return fa.floatValue() > fb.floatValue();
+ }
+ }
+
+ /**
+ * Use for frequencies and to avoid renewing Integers.
+ */
+ private static class Int {
+ int x;
+
+ Int() {
+ x = 1;
+ }
+ }
+
+}
fnp / pylucene.git / blobdiff