Add Expected Reciprocal Rank metric (#31891)

This change adds Expected Reciprocal Rank (ERR) as a ranking evaluation metric
as descriped in:

Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009).
Expected reciprocal rank for graded relevance.
Proceeding of the 18th ACM Conference on Information and Knowledge Management.
https://doi.org/10.1145/1645953.1646033

ERR is an extension of the classical reciprocal rank to the graded relevance
case and assumes a cascade browsing model. It quantifies the usefulness of a
document at rank `i` conditioned on the degree of relevance of the items at ranks
less than `i`. ERR seems to be gain traction as an alternative to (n)DCG, so it
seems like a good metric to support. Also ERR seems to be the default optimization
metric used for training in RankLib, a widely used learning to rank library.

Relates to #29653

Author: Christoph Büscher

libs/x-content/src/main/java/org/elasticsearch/common/xcontent/ConstructingObjectParser.java

@@ -294,6 +294,7 @@ public <T> void declareNamedObjects(BiConsumer<Value, List<T>> consumer, NamedOb
         }
     }
 
+    @Override
     public String getName() {
         return objectParser.getName();
     }

modules/rank-eval/src/main/java/org/elasticsearch/index/rankeval/DiscountedCumulativeGain.java

@@ -326,9 +326,9 @@ public boolean equals(Object obj) {
                 return false;
             }
             DiscountedCumulativeGain.Detail other = (DiscountedCumulativeGain.Detail) obj;
-            return (this.dcg == other.dcg &&
-                    this.idcg == other.idcg &&
-                    this.unratedDocs == other.unratedDocs);
+            return Double.compare(this.dcg, other.dcg) == 0 &&
+                   Double.compare(this.idcg, other.idcg) == 0 &&
+                   this.unratedDocs == other.unratedDocs;
         }
 
         @Override

modules/rank-eval/src/main/java/org/elasticsearch/index/rankeval/ExpectedReciprocalRank.java

@@ -0,0 +1,295 @@
+/*
+ * Licensed to Elasticsearch under one or more contributor
+ * license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright
+ * ownership. Elasticsearch 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.elasticsearch.index.rankeval;
+
+import org.elasticsearch.common.Nullable;
+import org.elasticsearch.common.ParseField;
+import org.elasticsearch.common.io.stream.StreamInput;
+import org.elasticsearch.common.io.stream.StreamOutput;
+import org.elasticsearch.common.xcontent.ConstructingObjectParser;
+import org.elasticsearch.common.xcontent.XContentBuilder;
+import org.elasticsearch.common.xcontent.XContentParser;
+import org.elasticsearch.search.SearchHit;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Objects;
+import java.util.Optional;
+
+import static org.elasticsearch.common.xcontent.ConstructingObjectParser.constructorArg;
+import static org.elasticsearch.common.xcontent.ConstructingObjectParser.optionalConstructorArg;
+import static org.elasticsearch.index.rankeval.EvaluationMetric.joinHitsWithRatings;
+
+/**
+ * Implementation of the Expected Reciprocal Rank metric described in:<p>
+ *
+ * Chapelle, O., Metlzer, D., Zhang, Y., &amp; Grinspan, P. (2009).<br>
+ * Expected reciprocal rank for graded relevance.<br>
+ * Proceeding of the 18th ACM Conference on Information and Knowledge Management - CIKM ’09, 621.<br>
+ * https://doi.org/10.1145/1645953.1646033
+ */
+public class ExpectedReciprocalRank implements EvaluationMetric {
+
+    /** the default search window size */
+    private static final int DEFAULT_K = 10;
+
+    /** the search window size */
+    private final int k;
+
+    /**
+     * Optional. If set, this will be the rating for docs that are unrated in the ranking evaluation request
+     */
+    private final Integer unknownDocRating;
+
+    private final int maxRelevance;
+
+    private final double two_pow_maxRelevance;
+
+    public static final String NAME = "expected_reciprocal_rank";
+
+    public ExpectedReciprocalRank(int maxRelevance) {
+        this(maxRelevance, null, DEFAULT_K);
+    }
+
+    /**
+     * @param maxRelevance
+     *            the maximal relevance judgment in the evaluation dataset
+     * @param unknownDocRating
+     *            the rating for documents the user hasn't supplied an explicit
+     *            rating for. Can be {@code null}, in which case document is
+     *            skipped.
+     * @param k
+     *            the search window size all request use.
+     */
+    public ExpectedReciprocalRank(int maxRelevance, @Nullable Integer unknownDocRating, int k) {
+        this.maxRelevance = maxRelevance;
+        this.unknownDocRating = unknownDocRating;
+        this.k = k;
+        // we can pre-calculate the constant used in metric calculation
+        this.two_pow_maxRelevance = Math.pow(2, this.maxRelevance);
+    }
+
+    ExpectedReciprocalRank(StreamInput in) throws IOException {
+        this.maxRelevance = in.readVInt();
+        this.unknownDocRating = in.readOptionalVInt();
+        this.k = in.readVInt();
+        this.two_pow_maxRelevance = Math.pow(2, this.maxRelevance);
+    }
+
+    @Override
+    public void writeTo(StreamOutput out) throws IOException {
+        out.writeVInt(maxRelevance);
+        out.writeOptionalVInt(unknownDocRating);
+        out.writeVInt(k);
+    }
+
+    @Override
+    public String getWriteableName() {
+        return NAME;
+    }
+
+    int getK() {
+        return this.k;
+    }
+
+    int getMaxRelevance() {
+        return this.maxRelevance;
+    }
+
+    /**
+     * get the rating used for unrated documents
+     */
+    public Integer getUnknownDocRating() {
+        return this.unknownDocRating;
+    }
+
+
+    @Override
+    public Optional<Integer> forcedSearchSize() {
+        return Optional.of(k);
+    }
+
+    @Override
+    public EvalQueryQuality evaluate(String taskId, SearchHit[] hits, List<RatedDocument> ratedDocs) {
+        List<RatedSearchHit> ratedHits = joinHitsWithRatings(hits, ratedDocs);
+        if (ratedHits.size() > this.k) {
+            ratedHits = ratedHits.subList(0, k);
+        }
+        List<Integer> ratingsInSearchHits = new ArrayList<>(ratedHits.size());
+        int unratedResults = 0;
+        for (RatedSearchHit hit : ratedHits) {
+            // unknownDocRating might be null, in which case unrated will be ignored in the calculation.
+            // we still need to add them as a placeholder so the rank of the subsequent ratings is correct
+            ratingsInSearchHits.add(hit.getRating().orElse(unknownDocRating));
+            if (hit.getRating().isPresent() == false) {
+                unratedResults++;
+            }
+        }
+
+        double p = 1;
+        double err = 0;
+        int rank = 1;
+        for (Integer rating : ratingsInSearchHits) {
+            if (rating != null) {
+                double probR = probabilityOfRelevance(rating);
+                err = err + (p * probR / rank);
+                p = p * (1 - probR);
+            }
+            rank++;
+        }
+
+        EvalQueryQuality evalQueryQuality = new EvalQueryQuality(taskId, err);
+        evalQueryQuality.addHitsAndRatings(ratedHits);
+        evalQueryQuality.setMetricDetails(new Detail(unratedResults));
+        return evalQueryQuality;
+    }
+
+    double probabilityOfRelevance(Integer rating) {
+        return (Math.pow(2, rating) - 1) / this.two_pow_maxRelevance;
+    }
+
+    private static final ParseField K_FIELD = new ParseField("k");
+    private static final ParseField UNKNOWN_DOC_RATING_FIELD = new ParseField("unknown_doc_rating");
+    private static final ParseField MAX_RELEVANCE_FIELD = new ParseField("maximum_relevance");
+    private static final ConstructingObjectParser<ExpectedReciprocalRank, Void> PARSER = new ConstructingObjectParser<>("dcg", false,
+            args -> {
+                int maxRelevance = (Integer) args[0];
+                Integer optK = (Integer) args[2];
+                return new ExpectedReciprocalRank(maxRelevance, (Integer) args[1],
+                        optK == null ? DEFAULT_K : optK);
+            });
+
+
+    static {
+        PARSER.declareInt(constructorArg(), MAX_RELEVANCE_FIELD);
+        PARSER.declareInt(optionalConstructorArg(), UNKNOWN_DOC_RATING_FIELD);
+        PARSER.declareInt(optionalConstructorArg(), K_FIELD);
+    }
+
+    public static ExpectedReciprocalRank fromXContent(XContentParser parser) {
+        return PARSER.apply(parser, null);
+    }
+
+    @Override
+    public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
+        builder.startObject();
+        builder.startObject(NAME);
+        builder.field(MAX_RELEVANCE_FIELD.getPreferredName(), this.maxRelevance);
+        if (unknownDocRating != null) {
+            builder.field(UNKNOWN_DOC_RATING_FIELD.getPreferredName(), this.unknownDocRating);
+        }
+        builder.field(K_FIELD.getPreferredName(), this.k);
+        builder.endObject();
+        builder.endObject();
+        return builder;
+    }
+
+    @Override
+    public final boolean equals(Object obj) {
+        if (this == obj) {
+            return true;
+        }
+        if (obj == null || getClass() != obj.getClass()) {
+            return false;
+        }
+        ExpectedReciprocalRank other = (ExpectedReciprocalRank) obj;
+        return this.k == other.k &&
+                this.maxRelevance == other.maxRelevance
+                && Objects.equals(unknownDocRating, other.unknownDocRating);
+    }
+
+    @Override
+    public final int hashCode() {
+        return Objects.hash(unknownDocRating, k, maxRelevance);
+    }
+
+    public static final class Detail implements MetricDetail {
+
+        private static ParseField UNRATED_FIELD = new ParseField("unrated_docs");
+        private final int unratedDocs;
+
+        Detail(int unratedDocs) {
+            this.unratedDocs = unratedDocs;
+        }
+
+        Detail(StreamInput in) throws IOException {
+            this.unratedDocs = in.readVInt();
+        }
+
+        @Override
+        public
+        String getMetricName() {
+            return NAME;
+        }
+
+        @Override
+        public XContentBuilder innerToXContent(XContentBuilder builder, Params params) throws IOException {
+            return builder.field(UNRATED_FIELD.getPreferredName(), this.unratedDocs);
+        }
+
+        private static final ConstructingObjectParser<Detail, Void> PARSER = new ConstructingObjectParser<>(NAME, true, args -> {
+            return new Detail((Integer) args[0]);
+        });
+
+        static {
+            PARSER.declareInt(constructorArg(), UNRATED_FIELD);
+        }
+
+        public static Detail fromXContent(XContentParser parser) {
+            return PARSER.apply(parser, null);
+        }
+
+        @Override
+        public void writeTo(StreamOutput out) throws IOException {
+            out.writeVInt(this.unratedDocs);
+        }
+
+        @Override
+        public String getWriteableName() {
+            return NAME;
+        }
+
+        /**
+         * @return the number of unrated documents in the search results
+         */
+        public Object getUnratedDocs() {
+            return this.unratedDocs;
+        }
+
+        @Override
+        public boolean equals(Object obj) {
+            if (this == obj) {
+                return true;
+            }
+            if (obj == null || getClass() != obj.getClass()) {
+                return false;
+            }
+            ExpectedReciprocalRank.Detail other = (ExpectedReciprocalRank.Detail) obj;
+            return this.unratedDocs == other.unratedDocs;
+        }
+
+        @Override
+        public int hashCode() {
+            return Objects.hash(this.unratedDocs);
+        }
+    }
+}
+

modules/rank-eval/src/test/java/org/elasticsearch/index/rankeval/DiscountedCumulativeGainTests.java

@@ -55,7 +55,7 @@ public class DiscountedCumulativeGainTests extends ESTestCase {
     /**
      * Assuming the docs are ranked in the following order:
      *
-     * rank | rel_rank | 2^(rel_rank) - 1 | log_2(rank + 1) | (2^(rel_rank) - 1) / log_2(rank + 1)
+     * rank | relevance | 2^(relevance) - 1 | log_2(rank + 1) | (2^(relevance) - 1) / log_2(rank + 1)
      * -------------------------------------------------------------------------------------------
      * 1 | 3 | 7.0 | 1.0 | 7.0 | 7.0 | 
      * 2 | 2 | 3.0 | 1.5849625007211563 | 1.8927892607143721
@@ -82,7 +82,7 @@ public void testDCGAt() {
          * Check with normalization: to get the maximal possible dcg, sort documents by
          * relevance in descending order
          *
-         * rank | rel_rank | 2^(rel_rank) - 1 | log_2(rank + 1) | (2^(rel_rank) - 1) / log_2(rank + 1)
+         * rank | relevance | 2^(relevance) - 1 | log_2(rank + 1) | (2^(relevance) - 1) / log_2(rank + 1)
          * ---------------------------------------------------------------------------------------
          * 1 | 3 | 7.0 | 1.0  | 7.0
          * 2 | 3 | 7.0 | 1.5849625007211563 | 4.416508275000202
@@ -101,7 +101,7 @@ public void testDCGAt() {
      * This tests metric when some documents in the search result don't have a
      * rating provided by the user.
      *
-     * rank | rel_rank | 2^(rel_rank) - 1 | log_2(rank + 1) | (2^(rel_rank) - 1) / log_2(rank + 1)
+     * rank | relevance | 2^(relevance) - 1 | log_2(rank + 1) | (2^(relevance) - 1) / log_2(rank + 1)
      * -------------------------------------------------------------------------------------------
      * 1 | 3 | 7.0 | 1.0 | 7.0 2 | 
      * 2 | 3.0 | 1.5849625007211563 | 1.8927892607143721
@@ -134,7 +134,7 @@ public void testDCGAtSixMissingRatings() {
          * Check with normalization: to get the maximal possible dcg, sort documents by
          * relevance in descending order
          *
-         * rank | rel_rank | 2^(rel_rank) - 1 | log_2(rank + 1) | (2^(rel_rank) - 1) / log_2(rank + 1)
+         * rank | relevance | 2^(relevance) - 1 | log_2(rank + 1) | (2^(relevance) - 1) / log_2(rank + 1)
          * ----------------------------------------------------------------------------------------
          * 1 | 3 | 7.0 | 1.0  | 7.0
          * 2 | 3 | 7.0 | 1.5849625007211563 | 4.416508275000202
@@ -154,7 +154,7 @@ public void testDCGAtSixMissingRatings() {
      * documents than search hits because we restrict DCG to be calculated at the
      * fourth position
      *
-     * rank | rel_rank | 2^(rel_rank) - 1 | log_2(rank + 1) | (2^(rel_rank) - 1) / log_2(rank + 1)
+     * rank | relevance | 2^(relevance) - 1 | log_2(rank + 1) | (2^(relevance) - 1) / log_2(rank + 1)
      * -------------------------------------------------------------------------------------------
      * 1 | 3 | 7.0 | 1.0 | 7.0 2 | 
      * 2 | 3.0 | 1.5849625007211563 | 1.8927892607143721
@@ -191,7 +191,7 @@ public void testDCGAtFourMoreRatings() {
          * Check with normalization: to get the maximal possible dcg, sort documents by
          * relevance in descending order
          *
-         * rank | rel_rank | 2^(rel_rank) - 1 | log_2(rank + 1) | (2^(rel_rank) - 1) / log_2(rank + 1)
+         * rank | relevance | 2^(relevance) - 1 | log_2(rank + 1) | (2^(relevance) - 1) / log_2(rank + 1)
          * ---------------------------------------------------------------------------------------
          * 1 | 3 | 7.0 | 1.0  | 7.0
          * 2 | 3 | 7.0 | 1.5849625007211563 | 4.416508275000202