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* 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.
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package org.apache.solr.ltr;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;
import org.apache.lucene.document.FloatDocValuesField;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.index.RandomIndexWriter;
import org.apache.lucene.index.ReaderUtil;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Scorer;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.search.Weight;
import org.apache.lucene.store.Directory;
import org.apache.lucene.util.LuceneTestCase;
import org.apache.solr.core.SolrResourceLoader;
import org.apache.solr.ltr.feature.Feature;
import org.apache.solr.ltr.feature.ValueFeature;
import org.apache.solr.ltr.model.LTRScoringModel;
import org.apache.solr.ltr.model.ModelException;
import org.apache.solr.ltr.model.TestLinearModel;
import org.apache.solr.ltr.norm.IdentityNormalizer;
import org.apache.solr.ltr.norm.Normalizer;
import org.apache.solr.ltr.norm.NormalizerException;
import org.junit.Test;
public class TestLTRScoringQuery extends LuceneTestCase {
public final static SolrResourceLoader solrResourceLoader = new SolrResourceLoader();
private IndexSearcher getSearcher(IndexReader r) {
final IndexSearcher searcher = newSearcher(r, false, false);
return searcher;
}
private static List<Feature> makeFeatures(int[] featureIds) {
final List<Feature> features = new ArrayList<>();
for (final int i : featureIds) {
Map<String,Object> params = new HashMap<String,Object>();
params.put("value", i);
final Feature f = Feature.getInstance(solrResourceLoader,
ValueFeature.class.getCanonicalName(),
"f" + i, params);
f.setIndex(i);
features.add(f);
}
return features;
}
private static List<Feature> makeFilterFeatures(int[] featureIds) {
final List<Feature> features = new ArrayList<>();
for (final int i : featureIds) {
Map<String,Object> params = new HashMap<String,Object>();
params.put("value", i);
final Feature f = Feature.getInstance(solrResourceLoader,
ValueFeature.class.getCanonicalName(),
"f" + i, params);
f.setIndex(i);
features.add(f);
}
return features;
}
private static Map<String,Object> makeFeatureWeights(List<Feature> features) {
final Map<String,Object> nameParams = new HashMap<String,Object>();
final HashMap<String,Double> modelWeights = new HashMap<String,Double>();
for (final Feature feat : features) {
modelWeights.put(feat.getName(), 0.1);
}
nameParams.put("weights", modelWeights);
return nameParams;
}
private LTRScoringQuery.ModelWeight performQuery(TopDocs hits,
IndexSearcher searcher, int docid, LTRScoringQuery model) throws IOException,
ModelException {
final List<LeafReaderContext> leafContexts = searcher.getTopReaderContext()
.leaves();
final int n = ReaderUtil.subIndex(hits.scoreDocs[0].doc, leafContexts);
final LeafReaderContext context = leafContexts.get(n);
final int deBasedDoc = hits.scoreDocs[0].doc - context.docBase;
final Weight weight = searcher.createNormalizedWeight(model, true);
final Scorer scorer = weight.scorer(context);
// rerank using the field final-score
scorer.iterator().advance(deBasedDoc);
scorer.score();
// assertEquals(42.0f, score, 0.0001);
// assertTrue(weight instanceof AssertingWeight);
// (AssertingIndexSearcher)
assertTrue(weight instanceof LTRScoringQuery.ModelWeight);
final LTRScoringQuery.ModelWeight modelWeight = (LTRScoringQuery.ModelWeight) weight;
return modelWeight;
}
@Test
public void testLTRScoringQueryEquality() throws ModelException {
final List<Feature> features = makeFeatures(new int[] {0, 1, 2});
final List<Normalizer> norms =
new ArrayList<Normalizer>(
Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
final List<Feature> allFeatures = makeFeatures(
new int[] {0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
final Map<String,Object> modelParams = makeFeatureWeights(features);
final LTRScoringModel algorithm1 = TestLinearModel.createLinearModel(
"testModelName",
features, norms, "testStoreName", allFeatures, modelParams);
final LTRScoringQuery m0 = new LTRScoringQuery(algorithm1);
final HashMap<String,String[]> externalFeatureInfo = new HashMap<>();
externalFeatureInfo.put("queryIntent", new String[] {"company"});
externalFeatureInfo.put("user_query", new String[] {"abc"});
final LTRScoringQuery m1 = new LTRScoringQuery(algorithm1, externalFeatureInfo, false, null);
final HashMap<String,String[]> externalFeatureInfo2 = new HashMap<>();
externalFeatureInfo2.put("user_query", new String[] {"abc"});
externalFeatureInfo2.put("queryIntent", new String[] {"company"});
int totalPoolThreads = 10, numThreadsPerRequest = 10;
LTRThreadModule threadManager = new LTRThreadModule(totalPoolThreads, numThreadsPerRequest);
final LTRScoringQuery m2 = new LTRScoringQuery(algorithm1, externalFeatureInfo2, false, threadManager);
// Models with same algorithm and efis, just in different order should be the same
assertEquals(m1, m2);
assertEquals(m1.hashCode(), m2.hashCode());
// Models with same algorithm, but different efi content should not match
assertFalse(m1.equals(m0));
assertFalse(m1.hashCode() == m0.hashCode());
final LTRScoringModel algorithm2 = TestLinearModel.createLinearModel(
"testModelName2",
features, norms, "testStoreName", allFeatures, modelParams);
final LTRScoringQuery m3 = new LTRScoringQuery(algorithm2);
assertFalse(m1.equals(m3));
assertFalse(m1.hashCode() == m3.hashCode());
final LTRScoringModel algorithm3 = TestLinearModel.createLinearModel(
"testModelName",
features, norms, "testStoreName3", allFeatures, modelParams);
final LTRScoringQuery m4 = new LTRScoringQuery(algorithm3);
assertFalse(m1.equals(m4));
assertFalse(m1.hashCode() == m4.hashCode());
}
@Test
public void testLTRScoringQuery() throws IOException, ModelException {
final Directory dir = newDirectory();
final RandomIndexWriter w = new RandomIndexWriter(random(), dir);
Document doc = new Document();
doc.add(newStringField("id", "0", Field.Store.YES));
doc.add(newTextField("field", "wizard the the the the the oz",
Field.Store.NO));
doc.add(new FloatDocValuesField("final-score", 1.0f));
w.addDocument(doc);
doc = new Document();
doc.add(newStringField("id", "1", Field.Store.YES));
// 1 extra token, but wizard and oz are close;
doc.add(newTextField("field", "wizard oz the the the the the the",
Field.Store.NO));
doc.add(new FloatDocValuesField("final-score", 2.0f));
w.addDocument(doc);
final IndexReader r = w.getReader();
w.close();
// Do ordinary BooleanQuery:
final BooleanQuery.Builder bqBuilder = new BooleanQuery.Builder();
bqBuilder.add(new TermQuery(new Term("field", "wizard")), BooleanClause.Occur.SHOULD);
bqBuilder.add(new TermQuery(new Term("field", "oz")), BooleanClause.Occur.SHOULD);
final IndexSearcher searcher = getSearcher(r);
// first run the standard query
final TopDocs hits = searcher.search(bqBuilder.build(), 10);
assertEquals(2, hits.totalHits);
assertEquals("0", searcher.doc(hits.scoreDocs[0].doc).get("id"));
assertEquals("1", searcher.doc(hits.scoreDocs[1].doc).get("id"));
List<Feature> features = makeFeatures(new int[] {0, 1, 2});
final List<Feature> allFeatures = makeFeatures(new int[] {0, 1, 2, 3, 4, 5,
6, 7, 8, 9});
List<Normalizer> norms =
new ArrayList<Normalizer>(
Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
LTRScoringModel ltrScoringModel = TestLinearModel.createLinearModel("test",
features, norms, "test", allFeatures,
makeFeatureWeights(features));
LTRScoringQuery.ModelWeight modelWeight = performQuery(hits, searcher,
hits.scoreDocs[0].doc, new LTRScoringQuery(ltrScoringModel));
assertEquals(3, modelWeight.getModelFeatureValuesNormalized().length);
for (int i = 0; i < 3; i++) {
assertEquals(i, modelWeight.getModelFeatureValuesNormalized()[i], 0.0001);
}
int[] posVals = new int[] {0, 1, 2};
int pos = 0;
for (LTRScoringQuery.FeatureInfo fInfo:modelWeight.getFeaturesInfo()) {
if (fInfo == null){
continue;
}
assertEquals(posVals[pos], fInfo.getValue(), 0.0001);
assertEquals("f"+posVals[pos], fInfo.getName());
pos++;
}
final int[] mixPositions = new int[] {8, 2, 4, 9, 0};
features = makeFeatures(mixPositions);
norms =
new ArrayList<Normalizer>(
Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
ltrScoringModel = TestLinearModel.createLinearModel("test",
features, norms, "test", allFeatures, makeFeatureWeights(features));
modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc,
new LTRScoringQuery(ltrScoringModel));
assertEquals(mixPositions.length,
modelWeight.getModelFeatureWeights().length);
for (int i = 0; i < mixPositions.length; i++) {
assertEquals(mixPositions[i],
modelWeight.getModelFeatureValuesNormalized()[i], 0.0001);
}
final ModelException expectedModelException = new ModelException("no features declared for model test");
final int[] noPositions = new int[] {};
features = makeFeatures(noPositions);
norms =
new ArrayList<Normalizer>(
Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
try {
ltrScoringModel = TestLinearModel.createLinearModel("test",
features, norms, "test", allFeatures, makeFeatureWeights(features));
fail("unexpectedly got here instead of catching "+expectedModelException);
modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc,
new LTRScoringQuery(ltrScoringModel));
assertEquals(0, modelWeight.getModelFeatureWeights().length);
} catch (ModelException actualModelException) {
assertEquals(expectedModelException.toString(), actualModelException.toString());
}
// test normalizers
features = makeFilterFeatures(mixPositions);
final Normalizer norm = new Normalizer() {
@Override
public float normalize(float value) {
return 42.42f;
}
@Override
public LinkedHashMap<String,Object> paramsToMap() {
return null;
}
@Override
protected void validate() throws NormalizerException {
}
};
norms =
new ArrayList<Normalizer>(
Collections.nCopies(features.size(),norm));
final LTRScoringModel normMeta = TestLinearModel.createLinearModel("test",
features, norms, "test", allFeatures,
makeFeatureWeights(features));
modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc,
new LTRScoringQuery(normMeta));
normMeta.normalizeFeaturesInPlace(modelWeight.getModelFeatureValuesNormalized());
assertEquals(mixPositions.length,
modelWeight.getModelFeatureWeights().length);
for (int i = 0; i < mixPositions.length; i++) {
assertEquals(42.42f, modelWeight.getModelFeatureValuesNormalized()[i], 0.0001);
}
r.close();
dir.close();
}
}