mirror of https://github.com/apache/lucene.git
LUCENE-5214: add FreeTextSuggester
git-svn-id: https://svn.apache.org/repos/asf/lucene/dev/trunk@1528517 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Michael McCandless
parent
691ec53476
commit
b50a2edbb2
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@ -80,6 +80,11 @@ New Features
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on best effort which was not user-friendly.
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(Uwe Schindler, Robert Muir)
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* LUCENE-5214: Add new FreeTextSuggester, to predict the next word
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using a simple ngram language model. This is useful for the "long
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tail" suggestions, when a primary suggester fails to find a
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suggestion. (Mike McCandless)
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Bug Fixes
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* LUCENE-4998: Fixed a few places to pass IOContext.READONCE instead
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@ -383,7 +383,7 @@ public final class BytesRefHash {
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return ids[findHash(bytes, code)];
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}
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private final int findHash(BytesRef bytes, int code) {
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private int findHash(BytesRef bytes, int code) {
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assert bytesStart != null : "bytesStart is null - not initialized";
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// final position
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int hashPos = code & hashMask;
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@ -578,7 +578,7 @@ public final class BytesRefHash {
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}
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/** A simple {@link BytesStartArray} that tracks
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* memory allocation using a private {@link AtomicLong}
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* memory allocation using a private {@link Counter}
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* instance. */
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public static class DirectBytesStartArray extends BytesStartArray {
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// TODO: can't we just merge this w/
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@ -238,11 +238,16 @@ public final class Util {
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}
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}
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private static class FSTPath<T> {
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/** Represents a path in TopNSearcher.
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*
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* @lucene.experimental
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*/
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public static class FSTPath<T> {
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public FST.Arc<T> arc;
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public T cost;
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public final IntsRef input;
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/** Sole constructor */
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public FSTPath(T cost, FST.Arc<T> arc, IntsRef input) {
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this.arc = new FST.Arc<T>().copyFrom(arc);
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this.cost = cost;
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@ -300,7 +305,7 @@ public final class Util {
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}
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// If back plus this arc is competitive then add to queue:
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private void addIfCompetitive(FSTPath<T> path) {
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protected void addIfCompetitive(FSTPath<T> path) {
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assert queue != null;
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@ -399,6 +404,7 @@ public final class Util {
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if (queue == null) {
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// Ran out of paths
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//System.out.println(" break queue=null");
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break;
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}
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@ -408,6 +414,7 @@ public final class Util {
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if (path == null) {
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// There were less than topN paths available:
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//System.out.println(" break no more paths");
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break;
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}
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@ -478,6 +485,7 @@ public final class Util {
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//System.out.println(" done!: " + path);
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T finalOutput = fst.outputs.add(path.cost, path.arc.output);
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if (acceptResult(path.input, finalOutput)) {
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//System.out.println(" add result: " + path);
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results.add(new MinResult<T>(path.input, finalOutput));
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} else {
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rejectCount++;
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@ -761,10 +769,10 @@ public final class Util {
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* Ensures an arc's label is indeed printable (dot uses US-ASCII).
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*/
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private static String printableLabel(int label) {
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if (label >= 0x20 && label <= 0x7d) {
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if (label != 0x22 && label != 0x5c) { // " OR \
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return Character.toString((char) label);
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}
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// Any ordinary ascii character, except for " or \, are
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// printed as the character; else, as a hex string:
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if (label >= 0x20 && label <= 0x7d && label != 0x22 && label != 0x5c) { // " OR \
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return Character.toString((char) label);
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}
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return "0x" + Integer.toHexString(label);
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}
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@ -0,0 +1,766 @@
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package org.apache.lucene.search.suggest.analyzing;
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// TODO
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// - test w/ syns
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// - add pruning of low-freq ngrams?
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import java.io.File;
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import java.io.IOException;
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import java.io.InputStream;
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import java.io.OutputStream;
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//import java.io.PrintWriter;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.Comparator;
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import java.util.HashSet;
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import java.util.List;
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import java.util.Random;
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import java.util.Set;
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import org.apache.lucene.analysis.Analyzer;
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import org.apache.lucene.analysis.AnalyzerWrapper;
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import org.apache.lucene.analysis.TokenStream;
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import org.apache.lucene.analysis.shingle.ShingleFilter;
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import org.apache.lucene.analysis.tokenattributes.OffsetAttribute;
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import org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute;
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import org.apache.lucene.analysis.tokenattributes.PositionLengthAttribute;
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import org.apache.lucene.analysis.tokenattributes.TermToBytesRefAttribute;
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import org.apache.lucene.codecs.CodecUtil;
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import org.apache.lucene.document.Document;
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import org.apache.lucene.document.Field;
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import org.apache.lucene.document.FieldType;
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import org.apache.lucene.document.TextField;
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import org.apache.lucene.index.DirectoryReader;
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import org.apache.lucene.index.FieldInfo.IndexOptions;
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import org.apache.lucene.index.IndexReader;
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import org.apache.lucene.index.IndexWriter;
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import org.apache.lucene.index.IndexWriterConfig;
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import org.apache.lucene.index.MultiFields;
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import org.apache.lucene.index.Terms;
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import org.apache.lucene.index.TermsEnum;
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import org.apache.lucene.search.spell.TermFreqIterator;
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import org.apache.lucene.search.spell.TermFreqPayloadIterator;
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import org.apache.lucene.search.suggest.Lookup;
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import org.apache.lucene.search.suggest.Sort;
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import org.apache.lucene.store.ByteArrayDataInput;
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import org.apache.lucene.store.DataInput;
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import org.apache.lucene.store.DataOutput;
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import org.apache.lucene.store.Directory;
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import org.apache.lucene.store.FSDirectory;
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import org.apache.lucene.store.InputStreamDataInput;
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import org.apache.lucene.store.OutputStreamDataOutput;
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import org.apache.lucene.util.BytesRef;
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import org.apache.lucene.util.CharsRef;
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import org.apache.lucene.util.IOUtils;
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import org.apache.lucene.util.IntsRef;
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import org.apache.lucene.util.UnicodeUtil;
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import org.apache.lucene.util.Version;
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import org.apache.lucene.util.fst.Builder;
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import org.apache.lucene.util.fst.FST.Arc;
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import org.apache.lucene.util.fst.FST.BytesReader;
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import org.apache.lucene.util.fst.FST;
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import org.apache.lucene.util.fst.Outputs;
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import org.apache.lucene.util.fst.PositiveIntOutputs;
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import org.apache.lucene.util.fst.Util.MinResult;
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import org.apache.lucene.util.fst.Util;
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/**
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* Builds an ngram model from the text sent to {@link
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* #build} and predicts based on the last grams-1 tokens in
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* the request sent to {@link #lookup}. This tries to
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* handle the "long tail" of suggestions for when the
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* incoming query is a never before seen query string.
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*
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* <p>Likely this suggester would only be used as a
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* fallback, when the primary suggester fails to find
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* any suggestions.
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*
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* <p>Note that the weight for each suggestion is unused,
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* and the suggestions are the analyzed forms (so your
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* analysis process should normally be very "light").
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*
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* <p>This uses the stupid backoff language model to smooth
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* scores across ngram models; see
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* "Large language models in machine translation",
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* http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.76.1126
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* for details.
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*
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* <p> From {@link #lookup}, the key of each result is the
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* ngram token; the value is Long.MAX_VALUE * score (fixed
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* point, cast to long). Divide by Long.MAX_VALUE to get
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* the score back, which ranges from 0.0 to 1.0.
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*
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* onlyMorePopular is unused.
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*
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* @lucene.experimental
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*/
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public class FreeTextSuggester extends Lookup {
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/** Codec name used in the header for the saved model. */
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public final static String CODEC_NAME = "freetextsuggest";
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/** Initial version of the the saved model file format. */
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public final static int VERSION_START = 0;
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/** Current version of the the saved model file format. */
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public final static int VERSION_CURRENT = VERSION_START;
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/** By default we use a bigram model. */
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public static final int DEFAULT_GRAMS = 2;
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// In general this could vary with gram, but the
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// original paper seems to use this constant:
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/** The constant used for backoff smoothing; during
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* lookup, this means that if a given trigram did not
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* occur, and we backoff to the bigram, the overall score
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* will be 0.4 times what the bigram model would have
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* assigned. */
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public final static double ALPHA = 0.4;
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/** Holds 1gram, 2gram, 3gram models as a single FST. */
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private FST<Long> fst;
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/**
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* Analyzer that will be used for analyzing suggestions at
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* index time.
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*/
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private final Analyzer indexAnalyzer;
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private long totTokens;
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/**
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* Analyzer that will be used for analyzing suggestions at
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* query time.
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*/
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private final Analyzer queryAnalyzer;
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// 2 = bigram, 3 = trigram
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private final int grams;
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private final byte separator;
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/** The default character used to join multiple tokens
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* into a single ngram token. The input tokens produced
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* by the analyzer must not contain this character. */
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public static final byte DEFAULT_SEPARATOR = 0x1e;
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/** Instantiate, using the provided analyzer for both
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* indexing and lookup, using bigram model by default. */
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public FreeTextSuggester(Analyzer analyzer) {
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this(analyzer, analyzer, DEFAULT_GRAMS);
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}
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/** Instantiate, using the provided indexing and lookup
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* analyzers, using bigram model by default. */
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public FreeTextSuggester(Analyzer indexAnalyzer, Analyzer queryAnalyzer) {
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this(indexAnalyzer, queryAnalyzer, DEFAULT_GRAMS);
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}
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/** Instantiate, using the provided indexing and lookup
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* analyzers, with the specified model (2
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* = bigram, 3 = trigram, etc.). */
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public FreeTextSuggester(Analyzer indexAnalyzer, Analyzer queryAnalyzer, int grams) {
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this(indexAnalyzer, queryAnalyzer, grams, DEFAULT_SEPARATOR);
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}
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/** Instantiate, using the provided indexing and lookup
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* analyzers, and specified model (2 = bigram, 3 =
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* trigram ,etc.). The separator is passed to {@link
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* ShingleFilter#setTokenSeparator} to join multiple
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* tokens into a single ngram token; it must be an ascii
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* (7-bit-clean) byte. No input tokens should have this
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* byte, otherwise {@code IllegalArgumentException} is
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* thrown. */
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public FreeTextSuggester(Analyzer indexAnalyzer, Analyzer queryAnalyzer, int grams, byte separator) {
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this.grams = grams;
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this.indexAnalyzer = addShingles(indexAnalyzer);
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this.queryAnalyzer = addShingles(queryAnalyzer);
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if (grams < 1) {
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throw new IllegalArgumentException("grams must be >= 1");
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}
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if ((separator & 0x80) != 0) {
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throw new IllegalArgumentException("separator must be simple ascii character");
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}
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this.separator = separator;
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}
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/** Returns byte size of the underlying FST. */
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public long sizeInBytes() {
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if (fst == null) {
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return 0;
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}
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return fst.sizeInBytes();
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}
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private static class AnalyzingComparator implements Comparator<BytesRef> {
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private final ByteArrayDataInput readerA = new ByteArrayDataInput();
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private final ByteArrayDataInput readerB = new ByteArrayDataInput();
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private final BytesRef scratchA = new BytesRef();
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private final BytesRef scratchB = new BytesRef();
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@Override
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public int compare(BytesRef a, BytesRef b) {
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readerA.reset(a.bytes, a.offset, a.length);
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readerB.reset(b.bytes, b.offset, b.length);
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// By token:
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scratchA.length = readerA.readShort();
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scratchA.bytes = a.bytes;
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scratchA.offset = readerA.getPosition();
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scratchB.bytes = b.bytes;
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scratchB.length = readerB.readShort();
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scratchB.offset = readerB.getPosition();
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int cmp = scratchA.compareTo(scratchB);
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if (cmp != 0) {
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return cmp;
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}
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readerA.skipBytes(scratchA.length);
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readerB.skipBytes(scratchB.length);
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// By length (smaller surface forms sorted first):
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cmp = a.length - b.length;
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if (cmp != 0) {
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return cmp;
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}
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// By surface form:
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scratchA.offset = readerA.getPosition();
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scratchA.length = a.length - scratchA.offset;
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scratchB.offset = readerB.getPosition();
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scratchB.length = b.length - scratchB.offset;
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return scratchA.compareTo(scratchB);
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}
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}
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private Analyzer addShingles(final Analyzer other) {
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if (grams == 1) {
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return other;
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} else {
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// TODO: use ShingleAnalyzerWrapper?
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// Tack on ShingleFilter to the end, to generate token ngrams:
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return new AnalyzerWrapper(other.getReuseStrategy()) {
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@Override
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protected Analyzer getWrappedAnalyzer(String fieldName) {
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return other;
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}
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@Override
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protected TokenStreamComponents wrapComponents(String fieldName, TokenStreamComponents components) {
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ShingleFilter shingles = new ShingleFilter(components.getTokenStream(), 2, grams);
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shingles.setTokenSeparator(Character.toString((char) separator));
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return new TokenStreamComponents(components.getTokenizer(), shingles);
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}
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};
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}
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}
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@Override
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public void build(TermFreqIterator iterator) throws IOException {
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build(iterator, IndexWriterConfig.DEFAULT_RAM_BUFFER_SIZE_MB);
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}
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/** Build the suggest index, using up to the specified
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* amount of temporary RAM while building. Note that
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* the weights for the suggestions are ignored. */
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public void build(TermFreqIterator iterator, double ramBufferSizeMB) throws IOException {
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if (iterator instanceof TermFreqPayloadIterator) {
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throw new IllegalArgumentException("payloads are not supported");
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}
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String prefix = getClass().getSimpleName();
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File directory = Sort.defaultTempDir();
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// TODO: messy ... java7 has Files.createTempDirectory
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// ... but 4.x is java6:
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File tempIndexPath = null;
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Random random = new Random();
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while (true) {
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tempIndexPath = new File(directory, prefix + ".index." + random.nextInt(Integer.MAX_VALUE));
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if (tempIndexPath.mkdir()) {
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break;
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}
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}
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Directory dir = FSDirectory.open(tempIndexPath);
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IndexWriterConfig iwc = new IndexWriterConfig(Version.LUCENE_46, indexAnalyzer);
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iwc.setOpenMode(IndexWriterConfig.OpenMode.CREATE);
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iwc.setRAMBufferSizeMB(ramBufferSizeMB);
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IndexWriter writer = new IndexWriter(dir, iwc);
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FieldType ft = new FieldType(TextField.TYPE_NOT_STORED);
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// TODO: if only we had IndexOptions.TERMS_ONLY...
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ft.setIndexOptions(IndexOptions.DOCS_AND_FREQS);
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ft.setOmitNorms(true);
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ft.freeze();
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Document doc = new Document();
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Field field = new Field("body", "", ft);
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doc.add(field);
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totTokens = 0;
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IndexReader reader = null;
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boolean success = false;
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try {
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while (true) {
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BytesRef surfaceForm = iterator.next();
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if (surfaceForm == null) {
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break;
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}
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field.setStringValue(surfaceForm.utf8ToString());
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writer.addDocument(doc);
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}
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reader = DirectoryReader.open(writer, false);
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Terms terms = MultiFields.getTerms(reader, "body");
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if (terms == null) {
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throw new IllegalArgumentException("need at least one suggestion");
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}
|
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// Move all ngrams into an FST:
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TermsEnum termsEnum = terms.iterator(null);
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Outputs<Long> outputs = PositiveIntOutputs.getSingleton();
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Builder<Long> builder = new Builder<Long>(FST.INPUT_TYPE.BYTE1, outputs);
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IntsRef scratchInts = new IntsRef();
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while (true) {
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BytesRef term = termsEnum.next();
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if (term == null) {
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break;
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}
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int ngramCount = countGrams(term);
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if (ngramCount > grams) {
|
||||
throw new IllegalArgumentException("tokens must not contain separator byte; got token=" + term + " but gramCount=" + ngramCount + ", which is greater than expected max ngram size=" + grams);
|
||||
}
|
||||
if (ngramCount == 1) {
|
||||
totTokens += termsEnum.totalTermFreq();
|
||||
}
|
||||
|
||||
builder.add(Util.toIntsRef(term, scratchInts), encodeWeight(termsEnum.totalTermFreq()));
|
||||
}
|
||||
|
||||
fst = builder.finish();
|
||||
if (fst == null) {
|
||||
throw new IllegalArgumentException("need at least one suggestion");
|
||||
}
|
||||
//System.out.println("FST: " + fst.getNodeCount() + " nodes");
|
||||
|
||||
/*
|
||||
PrintWriter pw = new PrintWriter("/x/tmp/out.dot");
|
||||
Util.toDot(fst, pw, true, true);
|
||||
pw.close();
|
||||
*/
|
||||
|
||||
success = true;
|
||||
} finally {
|
||||
try {
|
||||
if (success) {
|
||||
IOUtils.close(writer, reader);
|
||||
} else {
|
||||
IOUtils.closeWhileHandlingException(writer, reader);
|
||||
}
|
||||
} finally {
|
||||
for(String file : dir.listAll()) {
|
||||
File path = new File(tempIndexPath, file);
|
||||
if (path.delete() == false) {
|
||||
throw new IllegalStateException("failed to remove " + path);
|
||||
}
|
||||
}
|
||||
|
||||
if (tempIndexPath.delete() == false) {
|
||||
throw new IllegalStateException("failed to remove " + tempIndexPath);
|
||||
}
|
||||
|
||||
dir.close();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean store(OutputStream output) throws IOException {
|
||||
DataOutput out = new OutputStreamDataOutput(output);
|
||||
CodecUtil.writeHeader(out, CODEC_NAME, VERSION_CURRENT);
|
||||
out.writeByte(separator);
|
||||
out.writeVInt(grams);
|
||||
out.writeVLong(totTokens);
|
||||
fst.save(out);
|
||||
return true;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean load(InputStream input) throws IOException {
|
||||
DataInput in = new InputStreamDataInput(input);
|
||||
CodecUtil.checkHeader(in, CODEC_NAME, VERSION_START, VERSION_START);
|
||||
byte separatorOrig = in.readByte();
|
||||
if (separatorOrig != separator) {
|
||||
throw new IllegalStateException("separator=" + separator + " is incorrect: original model was built with separator=" + separatorOrig);
|
||||
}
|
||||
int gramsOrig = in.readVInt();
|
||||
if (gramsOrig != grams) {
|
||||
throw new IllegalStateException("grams=" + grams + " is incorrect: original model was built with grams=" + gramsOrig);
|
||||
}
|
||||
totTokens = in.readVLong();
|
||||
|
||||
fst = new FST<Long>(in, PositiveIntOutputs.getSingleton());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
@Override
|
||||
public List<LookupResult> lookup(final CharSequence key, /* ignored */ boolean onlyMorePopular, int num) {
|
||||
try {
|
||||
return lookup(key, num);
|
||||
} catch (IOException ioe) {
|
||||
// bogus:
|
||||
throw new RuntimeException(ioe);
|
||||
}
|
||||
}
|
||||
|
||||
private int countGrams(BytesRef token) {
|
||||
int count = 1;
|
||||
for(int i=0;i<token.length;i++) {
|
||||
if (token.bytes[token.offset + i] == separator) {
|
||||
count++;
|
||||
}
|
||||
}
|
||||
|
||||
return count;
|
||||
}
|
||||
|
||||
/** Retrieve suggestions. */
|
||||
public List<LookupResult> lookup(final CharSequence key, int num) throws IOException {
|
||||
TokenStream ts = queryAnalyzer.tokenStream("", key.toString());
|
||||
TermToBytesRefAttribute termBytesAtt = ts.addAttribute(TermToBytesRefAttribute.class);
|
||||
OffsetAttribute offsetAtt = ts.addAttribute(OffsetAttribute.class);
|
||||
PositionLengthAttribute posLenAtt = ts.addAttribute(PositionLengthAttribute.class);
|
||||
PositionIncrementAttribute posIncAtt = ts.addAttribute(PositionIncrementAttribute.class);
|
||||
ts.reset();
|
||||
|
||||
BytesRef[] lastTokens = new BytesRef[grams];
|
||||
//System.out.println("lookup: key='" + key + "'");
|
||||
|
||||
// Run full analysis, but save only the
|
||||
// last 1gram, last 2gram, etc.:
|
||||
BytesRef tokenBytes = termBytesAtt.getBytesRef();
|
||||
int maxEndOffset = -1;
|
||||
boolean sawRealToken = false;
|
||||
while(ts.incrementToken()) {
|
||||
termBytesAtt.fillBytesRef();
|
||||
sawRealToken |= tokenBytes.length > 0;
|
||||
// TODO: this is somewhat iffy; today, ShingleFilter
|
||||
// sets posLen to the gram count; maybe we should make
|
||||
// a separate dedicated att for this?
|
||||
int gramCount = posLenAtt.getPositionLength();
|
||||
|
||||
assert gramCount <= grams;
|
||||
|
||||
// Safety: make sure the recalculated count "agrees":
|
||||
if (countGrams(tokenBytes) != gramCount) {
|
||||
throw new IllegalArgumentException("tokens must not contain separator byte; got token=" + tokenBytes + " but gramCount=" + gramCount + " does not match recalculated count=" + countGrams(tokenBytes));
|
||||
}
|
||||
maxEndOffset = Math.max(maxEndOffset, offsetAtt.endOffset());
|
||||
lastTokens[gramCount-1] = BytesRef.deepCopyOf(tokenBytes);
|
||||
}
|
||||
ts.end();
|
||||
|
||||
if (!sawRealToken) {
|
||||
throw new IllegalArgumentException("no tokens produced by analyzer, or the only tokens were empty strings");
|
||||
}
|
||||
|
||||
// Carefully fill last tokens with _ tokens;
|
||||
// ShingleFilter appraently won't emit "only hole"
|
||||
// tokens:
|
||||
int endPosInc = posIncAtt.getPositionIncrement();
|
||||
|
||||
// Note this will also be true if input is the empty
|
||||
// string (in which case we saw no tokens and
|
||||
// maxEndOffset is still -1), which in fact works out OK
|
||||
// because we fill the unigram with an empty BytesRef
|
||||
// below:
|
||||
boolean lastTokenEnded = offsetAtt.endOffset() > maxEndOffset || endPosInc > 0;
|
||||
ts.close();
|
||||
//System.out.println("maxEndOffset=" + maxEndOffset + " vs " + offsetAtt.endOffset());
|
||||
|
||||
if (lastTokenEnded) {
|
||||
//System.out.println(" lastTokenEnded");
|
||||
// If user hit space after the last token, then
|
||||
// "upgrade" all tokens. This way "foo " will suggest
|
||||
// all bigrams starting w/ foo, and not any unigrams
|
||||
// starting with "foo":
|
||||
for(int i=grams-1;i>0;i--) {
|
||||
BytesRef token = lastTokens[i-1];
|
||||
if (token == null) {
|
||||
continue;
|
||||
}
|
||||
token.grow(token.length+1);
|
||||
token.bytes[token.length] = separator;
|
||||
token.length++;
|
||||
lastTokens[i] = token;
|
||||
}
|
||||
lastTokens[0] = new BytesRef();
|
||||
}
|
||||
|
||||
Arc<Long> arc = new Arc<Long>();
|
||||
|
||||
BytesReader bytesReader = fst.getBytesReader();
|
||||
|
||||
// Try highest order models first, and if they return
|
||||
// results, return that; else, fallback:
|
||||
double backoff = 1.0;
|
||||
|
||||
List<LookupResult> results = new ArrayList<LookupResult>(num);
|
||||
|
||||
// We only add a given suffix once, from the highest
|
||||
// order model that saw it; for subsequent lower order
|
||||
// models we skip it:
|
||||
final Set<BytesRef> seen = new HashSet<BytesRef>();
|
||||
|
||||
for(int gram=grams-1;gram>=0;gram--) {
|
||||
BytesRef token = lastTokens[gram];
|
||||
// Don't make unigram predictions from empty string:
|
||||
if (token == null || (token.length == 0 && key.length() > 0)) {
|
||||
// Input didn't have enough tokens:
|
||||
//System.out.println(" gram=" + gram + ": skip: not enough input");
|
||||
continue;
|
||||
}
|
||||
|
||||
if (endPosInc > 0 && gram <= endPosInc) {
|
||||
// Skip hole-only predictions; in theory we
|
||||
// shouldn't have to do this, but we'd need to fix
|
||||
// ShingleFilter to produce only-hole tokens:
|
||||
//System.out.println(" break: only holes now");
|
||||
break;
|
||||
}
|
||||
|
||||
//System.out.println("try " + (gram+1) + " gram token=" + token.utf8ToString());
|
||||
|
||||
// TODO: we could add fuzziness here
|
||||
// match the prefix portion exactly
|
||||
//Pair<Long,BytesRef> prefixOutput = null;
|
||||
Long prefixOutput = null;
|
||||
try {
|
||||
prefixOutput = lookupPrefix(fst, bytesReader, token, arc);
|
||||
} catch (IOException bogus) {
|
||||
throw new RuntimeException(bogus);
|
||||
}
|
||||
//System.out.println(" prefixOutput=" + prefixOutput);
|
||||
|
||||
if (prefixOutput == null) {
|
||||
// This model never saw this prefix, e.g. the
|
||||
// trigram model never saw context "purple mushroom"
|
||||
backoff *= ALPHA;
|
||||
continue;
|
||||
}
|
||||
|
||||
// TODO: we could do this division at build time, and
|
||||
// bake it into the FST?
|
||||
|
||||
// Denominator for computing scores from current
|
||||
// model's predictions:
|
||||
long contextCount = totTokens;
|
||||
|
||||
BytesRef lastTokenFragment = null;
|
||||
|
||||
for(int i=token.length-1;i>=0;i--) {
|
||||
if (token.bytes[token.offset+i] == separator) {
|
||||
BytesRef context = new BytesRef(token.bytes, token.offset, i);
|
||||
Long output = Util.get(fst, Util.toIntsRef(context, new IntsRef()));
|
||||
assert output != null;
|
||||
contextCount = decodeWeight(output);
|
||||
lastTokenFragment = new BytesRef(token.bytes, token.offset + i + 1, token.length - i - 1);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
final BytesRef finalLastToken;
|
||||
|
||||
if (lastTokenFragment == null) {
|
||||
finalLastToken = BytesRef.deepCopyOf(token);
|
||||
} else {
|
||||
finalLastToken = BytesRef.deepCopyOf(lastTokenFragment);
|
||||
}
|
||||
assert finalLastToken.offset == 0;
|
||||
|
||||
CharsRef spare = new CharsRef();
|
||||
|
||||
// complete top-N
|
||||
MinResult<Long> completions[] = null;
|
||||
try {
|
||||
|
||||
// Because we store multiple models in one FST
|
||||
// (1gram, 2gram, 3gram), we must restrict the
|
||||
// search so that it only considers the current
|
||||
// model. For highest order model, this is not
|
||||
// necessary since all completions in the FST
|
||||
// must be from this model, but for lower order
|
||||
// models we have to filter out the higher order
|
||||
// ones:
|
||||
|
||||
// Must do num+seen.size() for queue depth because we may
|
||||
// reject up to seen.size() paths in acceptResult():
|
||||
Util.TopNSearcher<Long> searcher = new Util.TopNSearcher<Long>(fst, num, num+seen.size(), weightComparator) {
|
||||
|
||||
BytesRef scratchBytes = new BytesRef();
|
||||
|
||||
@Override
|
||||
protected void addIfCompetitive(Util.FSTPath<Long> path) {
|
||||
if (path.arc.label != separator) {
|
||||
//System.out.println(" keep path: " + Util.toBytesRef(path.input, new BytesRef()).utf8ToString() + "; " + path + "; arc=" + path.arc);
|
||||
super.addIfCompetitive(path);
|
||||
} else {
|
||||
//System.out.println(" prevent path: " + Util.toBytesRef(path.input, new BytesRef()).utf8ToString() + "; " + path + "; arc=" + path.arc);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
protected boolean acceptResult(IntsRef input, Long output) {
|
||||
Util.toBytesRef(input, scratchBytes);
|
||||
finalLastToken.grow(finalLastToken.length + scratchBytes.length);
|
||||
int lenSav = finalLastToken.length;
|
||||
finalLastToken.append(scratchBytes);
|
||||
//System.out.println(" accept? input='" + scratchBytes.utf8ToString() + "'; lastToken='" + finalLastToken.utf8ToString() + "'; return " + (seen.contains(finalLastToken) == false));
|
||||
boolean ret = seen.contains(finalLastToken) == false;
|
||||
|
||||
finalLastToken.length = lenSav;
|
||||
return ret;
|
||||
}
|
||||
};
|
||||
|
||||
// since this search is initialized with a single start node
|
||||
// it is okay to start with an empty input path here
|
||||
searcher.addStartPaths(arc, prefixOutput, true, new IntsRef());
|
||||
|
||||
completions = searcher.search();
|
||||
} catch (IOException bogus) {
|
||||
throw new RuntimeException(bogus);
|
||||
}
|
||||
|
||||
int prefixLength = token.length;
|
||||
|
||||
BytesRef suffix = new BytesRef(8);
|
||||
//System.out.println(" " + completions.length + " completions");
|
||||
|
||||
nextCompletion:
|
||||
for (MinResult<Long> completion : completions) {
|
||||
token.length = prefixLength;
|
||||
// append suffix
|
||||
Util.toBytesRef(completion.input, suffix);
|
||||
token.append(suffix);
|
||||
|
||||
//System.out.println(" completion " + token.utf8ToString());
|
||||
|
||||
// Skip this path if a higher-order model already
|
||||
// saw/predicted its last token:
|
||||
BytesRef lastToken = token;
|
||||
for(int i=token.length-1;i>=0;i--) {
|
||||
if (token.bytes[token.offset+i] == separator) {
|
||||
assert token.length-i-1 > 0;
|
||||
lastToken = new BytesRef(token.bytes, token.offset+i+1, token.length-i-1);
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (seen.contains(lastToken)) {
|
||||
//System.out.println(" skip dup " + lastToken.utf8ToString());
|
||||
continue nextCompletion;
|
||||
}
|
||||
seen.add(BytesRef.deepCopyOf(lastToken));
|
||||
spare.grow(token.length);
|
||||
UnicodeUtil.UTF8toUTF16(token, spare);
|
||||
LookupResult result = new LookupResult(spare.toString(), (long) (Long.MAX_VALUE * backoff * ((double) decodeWeight(completion.output)) / contextCount));
|
||||
results.add(result);
|
||||
assert results.size() == seen.size();
|
||||
//System.out.println(" add result=" + result);
|
||||
}
|
||||
backoff *= ALPHA;
|
||||
}
|
||||
|
||||
Collections.sort(results, new Comparator<LookupResult>() {
|
||||
@Override
|
||||
public int compare(LookupResult a, LookupResult b) {
|
||||
if (a.value > b.value) {
|
||||
return -1;
|
||||
} else if (a.value < b.value) {
|
||||
return 1;
|
||||
} else {
|
||||
// Tie break by UTF16 sort order:
|
||||
return ((String) a.key).compareTo((String) b.key);
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
if (results.size() > num) {
|
||||
results.subList(num, results.size()).clear();
|
||||
}
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
/** weight -> cost */
|
||||
private long encodeWeight(long ngramCount) {
|
||||
return Long.MAX_VALUE - ngramCount;
|
||||
}
|
||||
|
||||
/** cost -> weight */
|
||||
//private long decodeWeight(Pair<Long,BytesRef> output) {
|
||||
private long decodeWeight(Long output) {
|
||||
assert output != null;
|
||||
return (int)(Long.MAX_VALUE - output);
|
||||
}
|
||||
|
||||
// NOTE: copied from WFSTCompletionLookup & tweaked
|
||||
private Long lookupPrefix(FST<Long> fst, FST.BytesReader bytesReader,
|
||||
BytesRef scratch, Arc<Long> arc) throws /*Bogus*/IOException {
|
||||
|
||||
Long output = fst.outputs.getNoOutput();
|
||||
|
||||
fst.getFirstArc(arc);
|
||||
|
||||
byte[] bytes = scratch.bytes;
|
||||
int pos = scratch.offset;
|
||||
int end = pos + scratch.length;
|
||||
while (pos < end) {
|
||||
if (fst.findTargetArc(bytes[pos++] & 0xff, arc, arc, bytesReader) == null) {
|
||||
return null;
|
||||
} else {
|
||||
output = fst.outputs.add(output, arc.output);
|
||||
}
|
||||
}
|
||||
|
||||
return output;
|
||||
}
|
||||
|
||||
static final Comparator<Long> weightComparator = new Comparator<Long> () {
|
||||
@Override
|
||||
public int compare(Long left, Long right) {
|
||||
return left.compareTo(right);
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* Returns the weight associated with an input string,
|
||||
* or null if it does not exist.
|
||||
*/
|
||||
public Object get(CharSequence key) {
|
||||
throw new UnsupportedOperationException();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,576 @@
|
|||
package org.apache.lucene.search.suggest.analyzing;
|
||||
|
||||
/*
|
||||
* 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.
|
||||
*/
|
||||
|
||||
import java.io.File;
|
||||
import java.io.FileInputStream;
|
||||
import java.io.FileOutputStream;
|
||||
import java.io.IOException;
|
||||
import java.io.InputStream;
|
||||
import java.io.OutputStream;
|
||||
import java.io.Reader;
|
||||
import java.util.ArrayList;
|
||||
import java.util.Collections;
|
||||
import java.util.Comparator;
|
||||
import java.util.HashMap;
|
||||
import java.util.HashSet;
|
||||
import java.util.List;
|
||||
import java.util.Locale;
|
||||
import java.util.Map;
|
||||
import java.util.Set;
|
||||
|
||||
import org.apache.lucene.analysis.Analyzer;
|
||||
import org.apache.lucene.analysis.MockAnalyzer;
|
||||
import org.apache.lucene.analysis.MockTokenizer;
|
||||
import org.apache.lucene.analysis.Tokenizer;
|
||||
import org.apache.lucene.analysis.core.StopFilter;
|
||||
import org.apache.lucene.analysis.util.CharArraySet;
|
||||
import org.apache.lucene.document.Document;
|
||||
import org.apache.lucene.search.spell.TermFreqIterator;
|
||||
import org.apache.lucene.search.suggest.Lookup.LookupResult;
|
||||
import org.apache.lucene.search.suggest.TermFreq;
|
||||
import org.apache.lucene.search.suggest.TermFreqArrayIterator;
|
||||
import org.apache.lucene.util.BytesRef;
|
||||
import org.apache.lucene.util.LineFileDocs;
|
||||
import org.apache.lucene.util.LuceneTestCase;
|
||||
import org.apache.lucene.util._TestUtil;
|
||||
import org.junit.Ignore;
|
||||
|
||||
public class TestFreeTextSuggester extends LuceneTestCase {
|
||||
|
||||
public void testBasic() throws Exception {
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("foo bar baz blah", 50),
|
||||
new TermFreq("boo foo bar foo bee", 20)
|
||||
);
|
||||
|
||||
Analyzer a = new MockAnalyzer(random());
|
||||
FreeTextSuggester sug = new FreeTextSuggester(a, a, 2, (byte) 0x20);
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
|
||||
for(int i=0;i<2;i++) {
|
||||
|
||||
// Uses bigram model and unigram backoff:
|
||||
assertEquals("foo bar/0.67 foo bee/0.33 baz/0.04 blah/0.04 boo/0.04",
|
||||
toString(sug.lookup("foo b", 10)));
|
||||
|
||||
// Uses only bigram model:
|
||||
assertEquals("foo bar/0.67 foo bee/0.33",
|
||||
toString(sug.lookup("foo ", 10)));
|
||||
|
||||
// Uses only unigram model:
|
||||
assertEquals("foo/0.33",
|
||||
toString(sug.lookup("foo", 10)));
|
||||
|
||||
// Uses only unigram model:
|
||||
assertEquals("bar/0.22 baz/0.11 bee/0.11 blah/0.11 boo/0.11",
|
||||
toString(sug.lookup("b", 10)));
|
||||
|
||||
// Try again after save/load:
|
||||
File tmpDir = _TestUtil.getTempDir("FreeTextSuggesterTest");
|
||||
tmpDir.mkdir();
|
||||
|
||||
File path = new File(tmpDir, "suggester");
|
||||
|
||||
OutputStream os = new FileOutputStream(path);
|
||||
sug.store(os);
|
||||
os.close();
|
||||
|
||||
InputStream is = new FileInputStream(path);
|
||||
sug = new FreeTextSuggester(a, a, 2, (byte) 0x20);
|
||||
sug.load(is);
|
||||
is.close();
|
||||
}
|
||||
}
|
||||
|
||||
public void testIllegalByteDuringBuild() throws Exception {
|
||||
// Default separator is INFORMATION SEPARATOR TWO
|
||||
// (0x1e), so no input token is allowed to contain it
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("foo\u001ebar baz", 50)
|
||||
);
|
||||
FreeTextSuggester sug = new FreeTextSuggester(new MockAnalyzer(random()));
|
||||
try {
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
fail("did not hit expected exception");
|
||||
} catch (IllegalArgumentException iae) {
|
||||
// expected
|
||||
}
|
||||
}
|
||||
|
||||
public void testIllegalByteDuringQuery() throws Exception {
|
||||
// Default separator is INFORMATION SEPARATOR TWO
|
||||
// (0x1e), so no input token is allowed to contain it
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("foo bar baz", 50)
|
||||
);
|
||||
FreeTextSuggester sug = new FreeTextSuggester(new MockAnalyzer(random()));
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
|
||||
try {
|
||||
sug.lookup("foo\u001eb", 10);
|
||||
fail("did not hit expected exception");
|
||||
} catch (IllegalArgumentException iae) {
|
||||
// expected
|
||||
}
|
||||
}
|
||||
|
||||
@Ignore
|
||||
public void testWiki() throws Exception {
|
||||
final LineFileDocs lfd = new LineFileDocs(null, "/lucenedata/enwiki/enwiki-20120502-lines-1k.txt", false);
|
||||
// Skip header:
|
||||
lfd.nextDoc();
|
||||
FreeTextSuggester sug = new FreeTextSuggester(new MockAnalyzer(random()));
|
||||
sug.build(new TermFreqIterator() {
|
||||
|
||||
private int count;
|
||||
|
||||
@Override
|
||||
public long weight() {
|
||||
return 1;
|
||||
}
|
||||
|
||||
@Override
|
||||
public BytesRef next() {
|
||||
Document doc;
|
||||
try {
|
||||
doc = lfd.nextDoc();
|
||||
} catch (IOException ioe) {
|
||||
throw new RuntimeException(ioe);
|
||||
}
|
||||
if (doc == null) {
|
||||
return null;
|
||||
}
|
||||
if (count++ == 10000) {
|
||||
return null;
|
||||
}
|
||||
return new BytesRef(doc.get("body"));
|
||||
}
|
||||
});
|
||||
if (VERBOSE) {
|
||||
System.out.println(sug.sizeInBytes() + " bytes");
|
||||
|
||||
List<LookupResult> results = sug.lookup("general r", 10);
|
||||
System.out.println("results:");
|
||||
for(LookupResult result : results) {
|
||||
System.out.println(" " + result);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Make sure you can suggest based only on unigram model:
|
||||
public void testUnigrams() throws Exception {
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("foo bar baz blah boo foo bar foo bee", 50)
|
||||
);
|
||||
|
||||
Analyzer a = new MockAnalyzer(random());
|
||||
FreeTextSuggester sug = new FreeTextSuggester(a, a, 1, (byte) 0x20);
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
// Sorts first by count, descending, second by term, ascending
|
||||
assertEquals("bar/0.22 baz/0.11 bee/0.11 blah/0.11 boo/0.11",
|
||||
toString(sug.lookup("b", 10)));
|
||||
}
|
||||
|
||||
// Make sure the last token is not duplicated
|
||||
public void testNoDupsAcrossGrams() throws Exception {
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("foo bar bar bar bar", 50)
|
||||
);
|
||||
Analyzer a = new MockAnalyzer(random());
|
||||
FreeTextSuggester sug = new FreeTextSuggester(a, a, 2, (byte) 0x20);
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
assertEquals("foo bar/1.00",
|
||||
toString(sug.lookup("foo b", 10)));
|
||||
}
|
||||
|
||||
// Lookup of just empty string produces unicode only matches:
|
||||
public void testEmptyString() throws Exception {
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("foo bar bar bar bar", 50)
|
||||
);
|
||||
Analyzer a = new MockAnalyzer(random());
|
||||
FreeTextSuggester sug = new FreeTextSuggester(a, a, 2, (byte) 0x20);
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
try {
|
||||
sug.lookup("", 10);
|
||||
fail("did not hit exception");
|
||||
} catch (IllegalArgumentException iae) {
|
||||
// expected
|
||||
}
|
||||
}
|
||||
|
||||
// With one ending hole, ShingleFilter produces "of _" and
|
||||
// we should properly predict from that:
|
||||
public void testEndingHole() throws Exception {
|
||||
// Just deletes "of"
|
||||
Analyzer a = new Analyzer() {
|
||||
@Override
|
||||
public TokenStreamComponents createComponents(String field, Reader reader) {
|
||||
Tokenizer tokenizer = new MockTokenizer(reader);
|
||||
CharArraySet stopSet = StopFilter.makeStopSet(TEST_VERSION_CURRENT, "of");
|
||||
return new TokenStreamComponents(tokenizer, new StopFilter(TEST_VERSION_CURRENT, tokenizer, stopSet));
|
||||
}
|
||||
};
|
||||
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("wizard of oz", 50)
|
||||
);
|
||||
FreeTextSuggester sug = new FreeTextSuggester(a, a, 3, (byte) 0x20);
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
assertEquals("wizard _ oz/1.00",
|
||||
toString(sug.lookup("wizard of", 10)));
|
||||
|
||||
// Falls back to unigram model, with backoff 0.4 times
|
||||
// prop 0.5:
|
||||
assertEquals("oz/0.20",
|
||||
toString(sug.lookup("wizard o", 10)));
|
||||
}
|
||||
|
||||
// If the number of ending holes exceeds the ngrams window
|
||||
// then there are no predictions, because ShingleFilter
|
||||
// does not produce e.g. a hole only "_ _" token:
|
||||
public void testTwoEndingHoles() throws Exception {
|
||||
// Just deletes "of"
|
||||
Analyzer a = new Analyzer() {
|
||||
@Override
|
||||
public TokenStreamComponents createComponents(String field, Reader reader) {
|
||||
Tokenizer tokenizer = new MockTokenizer(reader);
|
||||
CharArraySet stopSet = StopFilter.makeStopSet(TEST_VERSION_CURRENT, "of");
|
||||
return new TokenStreamComponents(tokenizer, new StopFilter(TEST_VERSION_CURRENT, tokenizer, stopSet));
|
||||
}
|
||||
};
|
||||
|
||||
Iterable<TermFreq> keys = shuffle(
|
||||
new TermFreq("wizard of of oz", 50)
|
||||
);
|
||||
FreeTextSuggester sug = new FreeTextSuggester(a, a, 3, (byte) 0x20);
|
||||
sug.build(new TermFreqArrayIterator(keys));
|
||||
assertEquals("",
|
||||
toString(sug.lookup("wizard of of", 10)));
|
||||
}
|
||||
|
||||
private static Comparator<LookupResult> byScoreThenKey = new Comparator<LookupResult>() {
|
||||
@Override
|
||||
public int compare(LookupResult a, LookupResult b) {
|
||||
if (a.value > b.value) {
|
||||
return -1;
|
||||
} else if (a.value < b.value) {
|
||||
return 1;
|
||||
} else {
|
||||
// Tie break by UTF16 sort order:
|
||||
return ((String) a.key).compareTo((String) b.key);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
public void testRandom() throws IOException {
|
||||
String[] terms = new String[_TestUtil.nextInt(random(), 2, 10)];
|
||||
Set<String> seen = new HashSet<String>();
|
||||
while (seen.size() < terms.length) {
|
||||
String token = _TestUtil.randomSimpleString(random(), 1, 5);
|
||||
if (!seen.contains(token)) {
|
||||
terms[seen.size()] = token;
|
||||
seen.add(token);
|
||||
}
|
||||
}
|
||||
|
||||
Analyzer a = new MockAnalyzer(random());
|
||||
|
||||
int numDocs = atLeast(10);
|
||||
long totTokens = 0;
|
||||
final String[][] docs = new String[numDocs][];
|
||||
for(int i=0;i<numDocs;i++) {
|
||||
docs[i] = new String[atLeast(100)];
|
||||
if (VERBOSE) {
|
||||
System.out.print(" doc " + i + ":");
|
||||
}
|
||||
for(int j=0;j<docs[i].length;j++) {
|
||||
docs[i][j] = getZipfToken(terms);
|
||||
if (VERBOSE) {
|
||||
System.out.print(" " + docs[i][j]);
|
||||
}
|
||||
}
|
||||
if (VERBOSE) {
|
||||
System.out.println();
|
||||
}
|
||||
totTokens += docs[i].length;
|
||||
}
|
||||
|
||||
int grams = _TestUtil.nextInt(random(), 1, 4);
|
||||
|
||||
if (VERBOSE) {
|
||||
System.out.println("TEST: " + terms.length + " terms; " + numDocs + " docs; " + grams + " grams");
|
||||
}
|
||||
|
||||
// Build suggester model:
|
||||
FreeTextSuggester sug = new FreeTextSuggester(a, a, grams, (byte) 0x20);
|
||||
sug.build(new TermFreqIterator() {
|
||||
int upto;
|
||||
|
||||
@Override
|
||||
public BytesRef next() {
|
||||
if (upto == docs.length) {
|
||||
return null;
|
||||
} else {
|
||||
StringBuilder b = new StringBuilder();
|
||||
for(String token : docs[upto]) {
|
||||
b.append(' ');
|
||||
b.append(token);
|
||||
}
|
||||
upto++;
|
||||
return new BytesRef(b.toString());
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public long weight() {
|
||||
return random().nextLong();
|
||||
}
|
||||
});
|
||||
|
||||
// Build inefficient but hopefully correct model:
|
||||
List<Map<String,Integer>> gramCounts = new ArrayList<Map<String,Integer>>(grams);
|
||||
for(int gram=0;gram<grams;gram++) {
|
||||
if (VERBOSE) {
|
||||
System.out.println("TEST: build model for gram=" + gram);
|
||||
}
|
||||
Map<String,Integer> model = new HashMap<String,Integer>();
|
||||
gramCounts.add(model);
|
||||
for(String[] doc : docs) {
|
||||
for(int i=0;i<doc.length-gram;i++) {
|
||||
StringBuilder b = new StringBuilder();
|
||||
for(int j=i;j<=i+gram;j++) {
|
||||
if (j > i) {
|
||||
b.append(' ');
|
||||
}
|
||||
b.append(doc[j]);
|
||||
}
|
||||
String token = b.toString();
|
||||
Integer curCount = model.get(token);
|
||||
if (curCount == null) {
|
||||
model.put(token, 1);
|
||||
} else {
|
||||
model.put(token, 1 + curCount);
|
||||
}
|
||||
if (VERBOSE) {
|
||||
System.out.println(" add '" + token + "' -> count=" + model.get(token));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
int lookups = atLeast(100);
|
||||
for(int iter=0;iter<lookups;iter++) {
|
||||
String[] tokens = new String[_TestUtil.nextInt(random(), 1, 5)];
|
||||
for(int i=0;i<tokens.length;i++) {
|
||||
tokens[i] = getZipfToken(terms);
|
||||
}
|
||||
|
||||
// Maybe trim last token; be sure not to create the
|
||||
// empty string:
|
||||
int trimStart;
|
||||
if (tokens.length == 1) {
|
||||
trimStart = 1;
|
||||
} else {
|
||||
trimStart = 0;
|
||||
}
|
||||
int trimAt = _TestUtil.nextInt(random(), trimStart, tokens[tokens.length-1].length());
|
||||
tokens[tokens.length-1] = tokens[tokens.length-1].substring(0, trimAt);
|
||||
|
||||
int num = _TestUtil.nextInt(random(), 1, 100);
|
||||
StringBuilder b = new StringBuilder();
|
||||
for(String token : tokens) {
|
||||
b.append(' ');
|
||||
b.append(token);
|
||||
}
|
||||
String query = b.toString();
|
||||
query = query.substring(1);
|
||||
|
||||
if (VERBOSE) {
|
||||
System.out.println("\nTEST: iter=" + iter + " query='" + query + "' num=" + num);
|
||||
}
|
||||
|
||||
// Expected:
|
||||
List<LookupResult> expected = new ArrayList<LookupResult>();
|
||||
double backoff = 1.0;
|
||||
seen = new HashSet<String>();
|
||||
|
||||
if (VERBOSE) {
|
||||
System.out.println(" compute expected");
|
||||
}
|
||||
for(int i=grams-1;i>=0;i--) {
|
||||
if (VERBOSE) {
|
||||
System.out.println(" grams=" + i);
|
||||
}
|
||||
|
||||
if (tokens.length < i+1) {
|
||||
// Don't have enough tokens to use this model
|
||||
if (VERBOSE) {
|
||||
System.out.println(" skip");
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
if (i == 0 && tokens[tokens.length-1].length() == 0) {
|
||||
// Never suggest unigrams from empty string:
|
||||
if (VERBOSE) {
|
||||
System.out.println(" skip unigram priors only");
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
// Build up "context" ngram:
|
||||
b = new StringBuilder();
|
||||
for(int j=tokens.length-i-1;j<tokens.length-1;j++) {
|
||||
b.append(' ');
|
||||
b.append(tokens[j]);
|
||||
}
|
||||
String context = b.toString();
|
||||
if (context.length() > 0) {
|
||||
context = context.substring(1);
|
||||
}
|
||||
if (VERBOSE) {
|
||||
System.out.println(" context='" + context + "'");
|
||||
}
|
||||
long contextCount;
|
||||
if (context.length() == 0) {
|
||||
contextCount = totTokens;
|
||||
} else {
|
||||
Integer count = gramCounts.get(i-1).get(context);
|
||||
if (count == null) {
|
||||
// We never saw this context:
|
||||
backoff *= FreeTextSuggester.ALPHA;
|
||||
if (VERBOSE) {
|
||||
System.out.println(" skip: never saw context");
|
||||
}
|
||||
continue;
|
||||
}
|
||||
contextCount = count;
|
||||
}
|
||||
if (VERBOSE) {
|
||||
System.out.println(" contextCount=" + contextCount);
|
||||
}
|
||||
Map<String,Integer> model = gramCounts.get(i);
|
||||
|
||||
// First pass, gather all predictions for this model:
|
||||
if (VERBOSE) {
|
||||
System.out.println(" find terms w/ prefix=" + tokens[tokens.length-1]);
|
||||
}
|
||||
List<LookupResult> tmp = new ArrayList<LookupResult>();
|
||||
for(String term : terms) {
|
||||
if (term.startsWith(tokens[tokens.length-1])) {
|
||||
if (VERBOSE) {
|
||||
System.out.println(" term=" + term);
|
||||
}
|
||||
if (seen.contains(term)) {
|
||||
if (VERBOSE) {
|
||||
System.out.println(" skip seen");
|
||||
}
|
||||
continue;
|
||||
}
|
||||
String ngram = (context + " " + term).trim();
|
||||
Integer count = model.get(ngram);
|
||||
if (count != null) {
|
||||
LookupResult lr = new LookupResult(ngram, (long) (Long.MAX_VALUE * (backoff * (double) count / contextCount)));
|
||||
tmp.add(lr);
|
||||
if (VERBOSE) {
|
||||
System.out.println(" add tmp key='" + lr.key + "' score=" + lr.value);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Second pass, trim to only top N, and fold those
|
||||
// into overall suggestions:
|
||||
Collections.sort(tmp, byScoreThenKey);
|
||||
if (tmp.size() > num) {
|
||||
tmp.subList(num, tmp.size()).clear();
|
||||
}
|
||||
for(LookupResult result : tmp) {
|
||||
String key = result.key.toString();
|
||||
int idx = key.lastIndexOf(' ');
|
||||
String lastToken;
|
||||
if (idx != -1) {
|
||||
lastToken = key.substring(idx+1);
|
||||
} else {
|
||||
lastToken = key;
|
||||
}
|
||||
if (!seen.contains(lastToken)) {
|
||||
seen.add(lastToken);
|
||||
expected.add(result);
|
||||
if (VERBOSE) {
|
||||
System.out.println(" keep key='" + result.key + "' score=" + result.value);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
backoff *= FreeTextSuggester.ALPHA;
|
||||
}
|
||||
|
||||
Collections.sort(expected, byScoreThenKey);
|
||||
|
||||
if (expected.size() > num) {
|
||||
expected.subList(num, expected.size()).clear();
|
||||
}
|
||||
|
||||
// Actual:
|
||||
List<LookupResult> actual = sug.lookup(query, num);
|
||||
|
||||
if (VERBOSE) {
|
||||
System.out.println(" expected: " + expected);
|
||||
System.out.println(" actual: " + actual);
|
||||
}
|
||||
|
||||
assertEquals(expected.toString(), actual.toString());
|
||||
}
|
||||
}
|
||||
|
||||
private static String getZipfToken(String[] tokens) {
|
||||
// Zipf-like distribution:
|
||||
for(int k=0;k<tokens.length;k++) {
|
||||
if (random().nextBoolean() || k == tokens.length-1) {
|
||||
return tokens[k];
|
||||
}
|
||||
}
|
||||
assert false;
|
||||
return null;
|
||||
}
|
||||
|
||||
private static String toString(List<LookupResult> results) {
|
||||
StringBuilder b = new StringBuilder();
|
||||
for(LookupResult result : results) {
|
||||
b.append(' ');
|
||||
b.append(result.key);
|
||||
b.append('/');
|
||||
b.append(String.format(Locale.ROOT, "%.2f", ((double) result.value)/Long.MAX_VALUE));
|
||||
}
|
||||
return b.toString().trim();
|
||||
}
|
||||
|
||||
@SafeVarargs
|
||||
private final <T> Iterable<T> shuffle(T...values) {
|
||||
final List<T> asList = new ArrayList<T>(values.length);
|
||||
for (T value : values) {
|
||||
asList.add(value);
|
||||
}
|
||||
Collections.shuffle(asList, random());
|
||||
return asList;
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue