LUCENE-7838 - removed dep from sandbox, created a minimal FLT version specific for knn classification

dev-tools/idea/lucene/classification/classification.iml

@@ -19,6 +19,5 @@
     <orderEntry type="module" module-name="analysis-common" />
     <orderEntry type="module" module-name="grouping" />
     <orderEntry type="module" module-name="misc" />
-    <orderEntry type="module" module-name="sandbox" />
   </component>
 </module>

lucene/classification/build.xml

@@ -28,7 +28,6 @@
     <path refid="base.classpath"/>
     <pathelement path="${queries.jar}"/>
     <pathelement path="${grouping.jar}"/>
-    <pathelement path="${sandbox.jar}"/>
     <pathelement path="${analyzers-common.jar}"/>
   </path>
 
@@ -38,18 +37,17 @@
     <path refid="test.base.classpath"/>
   </path>
 
-  <target name="compile-core" depends="jar-sandbox,jar-grouping,jar-queries,jar-analyzers-common,common.compile-core" />
+  <target name="compile-core" depends="jar-grouping,jar-queries,jar-analyzers-common,common.compile-core" />
 
   <target name="jar-core" depends="common.jar-core" />
 
-  <target name="javadocs" depends="javadocs-sandbox,javadocs-grouping,compile-core,check-javadocs-uptodate"
+  <target name="javadocs" depends="javadocs-grouping,compile-core,check-javadocs-uptodate"
           unless="javadocs-uptodate-${name}">
     <invoke-module-javadoc>
       <links>
         <link href="../queries"/>
         <link href="../analyzers-common"/>
         <link href="../grouping"/>
-        <link href="../sandbox"/>
       </links>
     </invoke-module-javadoc>
   </target>

lucene/classification/src/java/org/apache/lucene/classification/KNearestFuzzyClassifier.java

@@ -25,11 +25,11 @@ import java.util.List;
 import java.util.Map;
 
 import org.apache.lucene.analysis.Analyzer;
+import org.apache.lucene.classification.utils.NearestFuzzyQuery;
 import org.apache.lucene.index.IndexReader;
 import org.apache.lucene.index.IndexableField;
 import org.apache.lucene.index.LeafReader;
 import org.apache.lucene.index.Term;
-import org.apache.lucene.sandbox.queries.FuzzyLikeThisQuery;
 import org.apache.lucene.search.BooleanClause;
 import org.apache.lucene.search.BooleanQuery;
 import org.apache.lucene.search.IndexSearcher;
@@ -42,7 +42,7 @@ import org.apache.lucene.search.similarities.Similarity;
 import org.apache.lucene.util.BytesRef;
 
 /**
- * A k-Nearest Neighbor classifier based on {@link FuzzyLikeThisQuery}.
+ * A k-Nearest Neighbor classifier based on {@link NearestFuzzyQuery}.
  *
  * @lucene.experimental
  */
@@ -51,27 +51,27 @@ public class KNearestFuzzyClassifier implements Classifier<BytesRef> {
   /**
    * the name of the fields used as the input text
    */
-  protected final String[] textFieldNames;
+  private final String[] textFieldNames;
 
   /**
    * the name of the field used as the output text
    */
-  protected final String classFieldName;
+  private final String classFieldName;
 
   /**
    * an {@link IndexSearcher} used to perform queries
    */
-  protected final IndexSearcher indexSearcher;
+  private final IndexSearcher indexSearcher;
 
   /**
    * the no. of docs to compare in order to find the nearest neighbor to the input text
    */
-  protected final int k;
+  private final int k;
 
   /**
    * a {@link Query} used to filter the documents that should be used from this classifier's underlying {@link LeafReader}
    */
-  protected final Query query;
+  private final Query query;
   private final Analyzer analyzer;
 
   /**
@@ -145,11 +145,11 @@ public class KNearestFuzzyClassifier implements Classifier<BytesRef> {
 
   private TopDocs knnSearch(String text) throws IOException {
     BooleanQuery.Builder bq = new BooleanQuery.Builder();
-    FuzzyLikeThisQuery fuzzyLikeThisQuery = new FuzzyLikeThisQuery(300, analyzer);
+    NearestFuzzyQuery nearestFuzzyQuery = new NearestFuzzyQuery(analyzer);
     for (String fieldName : textFieldNames) {
-      fuzzyLikeThisQuery.addTerms(text, fieldName, 1f, 2); // TODO: make this parameters configurable
+      nearestFuzzyQuery.addTerms(text, fieldName);
     }
-    bq.add(fuzzyLikeThisQuery, BooleanClause.Occur.MUST);
+    bq.add(nearestFuzzyQuery, BooleanClause.Occur.MUST);
     Query classFieldQuery = new WildcardQuery(new Term(classFieldName, "*"));
     bq.add(new BooleanClause(classFieldQuery, BooleanClause.Occur.MUST));
     if (query != null) {
@@ -165,7 +165,7 @@ public class KNearestFuzzyClassifier implements Classifier<BytesRef> {
    * @return a {@link List} of {@link ClassificationResult}, one for each existing class
    * @throws IOException if it's not possible to get the stored value of class field
    */
-  protected List<ClassificationResult<BytesRef>> buildListFromTopDocs(TopDocs topDocs) throws IOException {
+  private List<ClassificationResult<BytesRef>> buildListFromTopDocs(TopDocs topDocs) throws IOException {
     Map<BytesRef, Integer> classCounts = new HashMap<>();
     Map<BytesRef, Double> classBoosts = new HashMap<>(); // this is a boost based on class ranking positions in topDocs
     float maxScore = topDocs.getMaxScore();
@@ -174,12 +174,7 @@ public class KNearestFuzzyClassifier implements Classifier<BytesRef> {
       if (storableField != null) {
         BytesRef cl = new BytesRef(storableField.stringValue());
         //update count
-        Integer count = classCounts.get(cl);
-        if (count != null) {
-          classCounts.put(cl, count + 1);
-        } else {
-          classCounts.put(cl, 1);
-        }
+        classCounts.merge(cl, 1, (a, b) -> a + b);
         //update boost, the boost is based on the best score
         Double totalBoost = classBoosts.get(cl);
         double singleBoost = scoreDoc.score / maxScore;

lucene/classification/src/java/org/apache/lucene/classification/utils/NearestFuzzyQuery.java

@@ -0,0 +1,333 @@
+/*
+ * 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.
+ */
+package org.apache.lucene.classification.utils;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.Objects;
+
+import org.apache.lucene.analysis.Analyzer;
+import org.apache.lucene.analysis.TokenStream;
+import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
+import org.apache.lucene.index.IndexReader;
+import org.apache.lucene.index.LeafReaderContext;
+import org.apache.lucene.index.MultiFields;
+import org.apache.lucene.index.Term;
+import org.apache.lucene.index.TermContext;
+import org.apache.lucene.index.Terms;
+import org.apache.lucene.index.TermsEnum;
+import org.apache.lucene.search.BooleanClause;
+import org.apache.lucene.search.BooleanQuery;
+import org.apache.lucene.search.BoostAttribute;
+import org.apache.lucene.search.BoostQuery;
+import org.apache.lucene.search.FuzzyTermsEnum;
+import org.apache.lucene.search.MaxNonCompetitiveBoostAttribute;
+import org.apache.lucene.search.Query;
+import org.apache.lucene.search.TermQuery;
+import org.apache.lucene.util.AttributeSource;
+import org.apache.lucene.util.BytesRef;
+import org.apache.lucene.util.PriorityQueue;
+import org.apache.lucene.util.automaton.LevenshteinAutomata;
+
+/**
+ * Simplification of FuzzyLikeThisQuery, to be used in the context of KNN classification.
+ */
+public class NearestFuzzyQuery extends Query {
+
+  private final ArrayList<FieldVals> fieldVals = new ArrayList<>();
+  private final Analyzer analyzer;
+
+  // fixed parameters
+  private static final int MAX_VARIANTS_PER_TERM = 50;
+  private static final float MIN_SIMILARITY = 1f;
+  private static final int PREFIX_LENGTH = 2;
+  private static final int MAX_NUM_TERMS = 300;
+
+  /**
+   * Default constructor
+   *
+   * @param analyzer the analyzer used to proecss the query text
+   */
+  public NearestFuzzyQuery(Analyzer analyzer) {
+    this.analyzer = analyzer;
+  }
+
+  static class FieldVals {
+    final String queryString;
+    final String fieldName;
+    final int maxEdits;
+    final int prefixLength;
+
+    FieldVals(String name, int maxEdits, String queryString) {
+      this.fieldName = name;
+      this.maxEdits = maxEdits;
+      this.queryString = queryString;
+      this.prefixLength = NearestFuzzyQuery.PREFIX_LENGTH;
+    }
+
+    @Override
+    public int hashCode() {
+      final int prime = 31;
+      int result = 1;
+      result = prime * result
+          + ((fieldName == null) ? 0 : fieldName.hashCode());
+      result = prime * result + maxEdits;
+      result = prime * result + prefixLength;
+      result = prime * result
+          + ((queryString == null) ? 0 : queryString.hashCode());
+      return result;
+    }
+
+    @Override
+    public boolean equals(Object obj) {
+      if (this == obj)
+        return true;
+      if (obj == null)
+        return false;
+      if (getClass() != obj.getClass())
+        return false;
+      FieldVals other = (FieldVals) obj;
+      if (fieldName == null) {
+        if (other.fieldName != null)
+          return false;
+      } else if (!fieldName.equals(other.fieldName))
+        return false;
+      if (maxEdits != other.maxEdits) {
+        return false;
+      }
+      if (prefixLength != other.prefixLength)
+        return false;
+      if (queryString == null) {
+        if (other.queryString != null)
+          return false;
+      } else if (!queryString.equals(other.queryString))
+        return false;
+      return true;
+    }
+
+
+  }
+
+  /**
+   * Adds user input for "fuzzification"
+   *
+   * @param queryString The string which will be parsed by the analyzer and for which fuzzy variants will be parsed
+   */
+  public void addTerms(String queryString, String fieldName) {
+    int maxEdits = (int) MIN_SIMILARITY;
+    if (maxEdits != MIN_SIMILARITY) {
+      throw new IllegalArgumentException("MIN_SIMILARITY must integer value between 0 and " + LevenshteinAutomata.MAXIMUM_SUPPORTED_DISTANCE + ", inclusive; got " + MIN_SIMILARITY);
+    }
+    fieldVals.add(new FieldVals(fieldName, maxEdits, queryString));
+  }
+
+
+  private void addTerms(IndexReader reader, FieldVals f, ScoreTermQueue q) throws IOException {
+    if (f.queryString == null) return;
+    final Terms terms = MultiFields.getTerms(reader, f.fieldName);
+    if (terms == null) {
+      return;
+    }
+    try (TokenStream ts = analyzer.tokenStream(f.fieldName, f.queryString)) {
+      CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class);
+
+      int corpusNumDocs = reader.numDocs();
+      HashSet<String> processedTerms = new HashSet<>();
+      ts.reset();
+      while (ts.incrementToken()) {
+        String term = termAtt.toString();
+        if (!processedTerms.contains(term)) {
+          processedTerms.add(term);
+          ScoreTermQueue variantsQ = new ScoreTermQueue(MAX_VARIANTS_PER_TERM); //maxNum variants considered for any one term
+          float minScore = 0;
+          Term startTerm = new Term(f.fieldName, term);
+          AttributeSource atts = new AttributeSource();
+          MaxNonCompetitiveBoostAttribute maxBoostAtt =
+              atts.addAttribute(MaxNonCompetitiveBoostAttribute.class);
+          FuzzyTermsEnum fe = new FuzzyTermsEnum(terms, atts, startTerm, f.maxEdits, f.prefixLength, true);
+          //store the df so all variants use same idf
+          int df = reader.docFreq(startTerm);
+          int numVariants = 0;
+          int totalVariantDocFreqs = 0;
+          BytesRef possibleMatch;
+          BoostAttribute boostAtt =
+              fe.attributes().addAttribute(BoostAttribute.class);
+          while ((possibleMatch = fe.next()) != null) {
+            numVariants++;
+            totalVariantDocFreqs += fe.docFreq();
+            float score = boostAtt.getBoost();
+            if (variantsQ.size() < MAX_VARIANTS_PER_TERM || score > minScore) {
+              ScoreTerm st = new ScoreTerm(new Term(startTerm.field(), BytesRef.deepCopyOf(possibleMatch)), score, startTerm);
+              variantsQ.insertWithOverflow(st);
+              minScore = variantsQ.top().score; // maintain minScore
+            }
+            maxBoostAtt.setMaxNonCompetitiveBoost(variantsQ.size() >= MAX_VARIANTS_PER_TERM ? minScore : Float.NEGATIVE_INFINITY);
+          }
+
+          if (numVariants > 0) {
+            int avgDf = totalVariantDocFreqs / numVariants;
+            if (df == 0)//no direct match we can use as df for all variants
+            {
+              df = avgDf; //use avg df of all variants
+            }
+
+            // take the top variants (scored by edit distance) and reset the score
+            // to include an IDF factor then add to the global queue for ranking
+            // overall top query terms
+            int size = variantsQ.size();
+            for (int i = 0; i < size; i++) {
+              ScoreTerm st = variantsQ.pop();
+              if (st != null) {
+                st.score = (st.score * st.score) * idf(df, corpusNumDocs);
+                q.insertWithOverflow(st);
+              }
+            }
+          }
+        }
+      }
+      ts.end();
+    }
+  }
+
+  private float idf(int docFreq, int docCount) {
+    return (float)(Math.log((docCount+1)/(double)(docFreq+1)) + 1.0);
+  }
+
+  private Query newTermQuery(IndexReader reader, Term term) throws IOException {
+    // we build an artificial TermContext that will give an overall df and ttf
+    // equal to 1
+    TermContext context = new TermContext(reader.getContext());
+    for (LeafReaderContext leafContext : reader.leaves()) {
+      Terms terms = leafContext.reader().terms(term.field());
+      if (terms != null) {
+        TermsEnum termsEnum = terms.iterator();
+        if (termsEnum.seekExact(term.bytes())) {
+          int freq = 1 - context.docFreq(); // we want the total df and ttf to be 1
+          context.register(termsEnum.termState(), leafContext.ord, freq, freq);
+        }
+      }
+    }
+    return new TermQuery(term, context);
+  }
+
+  @Override
+  public Query rewrite(IndexReader reader) throws IOException {
+    ScoreTermQueue q = new ScoreTermQueue(MAX_NUM_TERMS);
+    //load up the list of possible terms
+    for (FieldVals f : fieldVals) {
+      addTerms(reader, f, q);
+    }
+
+    BooleanQuery.Builder bq = new BooleanQuery.Builder();
+
+    //create BooleanQueries to hold the variants for each token/field pair and ensure it
+    // has no coord factor
+    //Step 1: sort the termqueries by term/field
+    HashMap<Term, ArrayList<ScoreTerm>> variantQueries = new HashMap<>();
+    int size = q.size();
+    for (int i = 0; i < size; i++) {
+      ScoreTerm st = q.pop();
+      if (st != null) {
+        ArrayList<ScoreTerm> l = variantQueries.computeIfAbsent(st.fuzziedSourceTerm, k -> new ArrayList<>());
+        l.add(st);
+      }
+    }
+    //Step 2: Organize the sorted termqueries into zero-coord scoring boolean queries
+    for (ArrayList<ScoreTerm> variants : variantQueries.values()) {
+      if (variants.size() == 1) {
+        //optimize where only one selected variant
+        ScoreTerm st = variants.get(0);
+        Query tq = newTermQuery(reader, st.term);
+        // set the boost to a mix of IDF and score
+        bq.add(new BoostQuery(tq, st.score), BooleanClause.Occur.SHOULD);
+      } else {
+        BooleanQuery.Builder termVariants = new BooleanQuery.Builder();
+        for (ScoreTerm st : variants) {
+          // found a match
+          Query tq = newTermQuery(reader, st.term);
+          // set the boost using the ScoreTerm's score
+          termVariants.add(new BoostQuery(tq, st.score), BooleanClause.Occur.SHOULD);          // add to query
+        }
+        bq.add(termVariants.build(), BooleanClause.Occur.SHOULD);          // add to query
+      }
+    }
+    //TODO possible alternative step 3 - organize above booleans into a new layer of field-based
+    // booleans with a minimum-should-match of NumFields-1?
+    return bq.build();
+  }
+
+  //Holds info for a fuzzy term variant - initially score is set to edit distance (for ranking best
+  // term variants) then is reset with IDF for use in ranking against all other
+  // terms/fields
+  private static class ScoreTerm {
+    public final Term term;
+    public float score;
+    final Term fuzziedSourceTerm;
+
+    ScoreTerm(Term term, float score, Term fuzziedSourceTerm) {
+      this.term = term;
+      this.score = score;
+      this.fuzziedSourceTerm = fuzziedSourceTerm;
+    }
+  }
+
+  private static class ScoreTermQueue extends PriorityQueue<ScoreTerm> {
+    ScoreTermQueue(int size) {
+      super(size);
+    }
+
+    /* (non-Javadoc)
+     * @see org.apache.lucene.util.PriorityQueue#lessThan(java.lang.Object, java.lang.Object)
+     */
+    @Override
+    protected boolean lessThan(ScoreTerm termA, ScoreTerm termB) {
+      if (termA.score == termB.score)
+        return termA.term.compareTo(termB.term) > 0;
+      else
+        return termA.score < termB.score;
+    }
+
+  }
+
+  @Override
+  public String toString(String field) {
+    return null;
+  }
+
+  @Override
+  public int hashCode() {
+    int prime = 31;
+    int result = classHash();
+    result = prime * result + Objects.hashCode(analyzer);
+    result = prime * result + Objects.hashCode(fieldVals);
+    return result;
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    return sameClassAs(other) &&
+        equalsTo(getClass().cast(other));
+  }
+
+  private boolean equalsTo(NearestFuzzyQuery other) {
+    return Objects.equals(analyzer, other.analyzer) &&
+        Objects.equals(fieldVals, other.fieldVals);
+  }
+
+}

lucene/classification/src/test/org/apache/lucene/classification/KNearestFuzzyClassifierTest.java

@@ -28,7 +28,7 @@ import org.apache.lucene.util.BytesRef;
 import org.junit.Test;
 
 /**
- * Testcase for {@link KNearestFuzzyClassifier}
+ * Tests for {@link KNearestFuzzyClassifier}
  */
 public class KNearestFuzzyClassifierTest extends ClassificationTestBase<BytesRef> {