Examples with LTRScoringModel org.apache.solr.ltr.model.LTRScoringModel used on opensource projects

Example 1 with LTRScoringModel

use of org.apache.solr.ltr.model.LTRScoringModel in project lucene-solr by apache.

the class TestLTRScoringQuery method testLTRScoringQuery.

@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();
}

Example 2 with LTRScoringModel

use of org.apache.solr.ltr.model.LTRScoringModel in project lucene-solr by apache.

the class TestLTRReRankingPipeline method testDifferentTopN.

@Ignore
@Test
public void testDifferentTopN() throws IOException {
    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 oz oz oz oz 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));
    doc.add(newTextField("field", "wizard oz oz oz oz the", Field.Store.NO));
    doc.add(new FloatDocValuesField("final-score", 2.0f));
    w.addDocument(doc);
    doc = new Document();
    doc.add(newStringField("id", "2", Field.Store.YES));
    doc.add(newTextField("field", "wizard oz oz oz the the ", Field.Store.NO));
    doc.add(new FloatDocValuesField("final-score", 3.0f));
    w.addDocument(doc);
    doc = new Document();
    doc.add(newStringField("id", "3", Field.Store.YES));
    doc.add(newTextField("field", "wizard oz oz the the the the ", Field.Store.NO));
    doc.add(new FloatDocValuesField("final-score", 4.0f));
    w.addDocument(doc);
    doc = new Document();
    doc.add(newStringField("id", "4", Field.Store.YES));
    doc.add(newTextField("field", "wizard oz the the the the the the", Field.Store.NO));
    doc.add(new FloatDocValuesField("final-score", 5.0f));
    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
    TopDocs hits = searcher.search(bqBuilder.build(), 10);
    assertEquals(5, hits.totalHits);
    assertEquals("0", searcher.doc(hits.scoreDocs[0].doc).get("id"));
    assertEquals("1", searcher.doc(hits.scoreDocs[1].doc).get("id"));
    assertEquals("2", searcher.doc(hits.scoreDocs[2].doc).get("id"));
    assertEquals("3", searcher.doc(hits.scoreDocs[3].doc).get("id"));
    assertEquals("4", searcher.doc(hits.scoreDocs[4].doc).get("id"));
    final List<Feature> features = makeFieldValueFeatures(new int[] { 0, 1, 2 }, "final-score");
    final List<Normalizer> norms = new ArrayList<Normalizer>(Collections.nCopies(features.size(), IdentityNormalizer.INSTANCE));
    final List<Feature> allFeatures = makeFieldValueFeatures(new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }, "final-score");
    final LTRScoringModel ltrScoringModel = TestLinearModel.createLinearModel("test", features, norms, "test", allFeatures, null);
    final LTRRescorer rescorer = new LTRRescorer(new LTRScoringQuery(ltrScoringModel));
    // rerank @ 0 should not change the order
    hits = rescorer.rescore(searcher, hits, 0);
    assertEquals("0", searcher.doc(hits.scoreDocs[0].doc).get("id"));
    assertEquals("1", searcher.doc(hits.scoreDocs[1].doc).get("id"));
    assertEquals("2", searcher.doc(hits.scoreDocs[2].doc).get("id"));
    assertEquals("3", searcher.doc(hits.scoreDocs[3].doc).get("id"));
    assertEquals("4", searcher.doc(hits.scoreDocs[4].doc).get("id"));
    for (int topN = 1; topN <= 5; topN++) {
        log.info("rerank {} documents ", topN);
        hits = searcher.search(bqBuilder.build(), 10);
        final ScoreDoc[] slice = new ScoreDoc[topN];
        System.arraycopy(hits.scoreDocs, 0, slice, 0, topN);
        hits = new TopDocs(hits.totalHits, slice, hits.getMaxScore());
        hits = rescorer.rescore(searcher, hits, topN);
        for (int i = topN - 1, j = 0; i >= 0; i--, j++) {
            log.info("doc {} in pos {}", searcher.doc(hits.scoreDocs[j].doc).get("id"), j);
            assertEquals(i, Integer.parseInt(searcher.doc(hits.scoreDocs[j].doc).get("id")));
            assertEquals(i + 1, hits.scoreDocs[j].score, 0.00001);
        }
    }
    r.close();
    dir.close();
}

Example 3 with LTRScoringModel

use of org.apache.solr.ltr.model.LTRScoringModel in project lucene-solr by apache.

the class TestLTRScoringQuery method testLTRScoringQueryEquality.

@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());
}

Example 4 with LTRScoringModel

use of org.apache.solr.ltr.model.LTRScoringModel in project lucene-solr by apache.

the class TestRerankBase method createModelFromFiles.

public static LTRScoringModel createModelFromFiles(String modelFileName, String featureFileName, String featureStoreName) throws ModelException, Exception {
    URL url = TestRerankBase.class.getResource("/modelExamples/" + modelFileName);
    final String modelJson = FileUtils.readFileToString(new File(url.toURI()), "UTF-8");
    final ManagedModelStore ms = getManagedModelStore();
    url = TestRerankBase.class.getResource("/featureExamples/" + featureFileName);
    final String featureJson = FileUtils.readFileToString(new File(url.toURI()), "UTF-8");
    Object parsedFeatureJson = null;
    try {
        parsedFeatureJson = ObjectBuilder.fromJSON(featureJson);
    } catch (final IOException ioExc) {
        throw new ModelException("ObjectBuilder failed parsing json", ioExc);
    }
    final ManagedFeatureStore fs = getManagedFeatureStore();
    // fs.getFeatureStore(null).clear();
    // is this safe??
    fs.doDeleteChild(null, featureStoreName);
    // based on my need to call this I dont think that
    // "getNewManagedFeatureStore()"
    // is actually returning a new feature store each time
    fs.applyUpdatesToManagedData(parsedFeatureJson);
    // can we skip this and just use fs directly below?
    ms.setManagedFeatureStore(fs);
    final LTRScoringModel ltrScoringModel = ManagedModelStore.fromLTRScoringModelMap(solrResourceLoader, mapFromJson(modelJson), ms.getManagedFeatureStore());
    ms.addModel(ltrScoringModel);
    return ltrScoringModel;
}

Example 5 with LTRScoringModel

use of org.apache.solr.ltr.model.LTRScoringModel in project lucene-solr by apache.

the class TestSelectiveWeightCreation method testScoringQueryWeightCreation.

@Test
public void testScoringQueryWeightCreation() throws IOException, ModelException {
    final Directory dir = newDirectory();
    final RandomIndexWriter w = new RandomIndexWriter(random(), dir);
    Document doc = new Document();
    doc.add(newStringField("id", "10", 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", "11", 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("10", searcher.doc(hits.scoreDocs[0].doc).get("id"));
    assertEquals("11", 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 });
    final List<Normalizer> norms = new ArrayList<>();
    for (int k = 0; k < features.size(); ++k) {
        norms.add(IdentityNormalizer.INSTANCE);
    }
    // when features are NOT requested in the response, only the modelFeature weights should be created
    final LTRScoringModel ltrScoringModel1 = TestLinearModel.createLinearModel("test", features, norms, "test", allFeatures, makeFeatureWeights(features));
    LTRScoringQuery.ModelWeight modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc, // features not requested in response
    new LTRScoringQuery(ltrScoringModel1, false));
    LTRScoringQuery.FeatureInfo[] featuresInfo = modelWeight.getFeaturesInfo();
    assertEquals(features.size(), modelWeight.getModelFeatureValuesNormalized().length);
    int validFeatures = 0;
    for (int i = 0; i < featuresInfo.length; ++i) {
        if (featuresInfo[i] != null && featuresInfo[i].isUsed()) {
            validFeatures += 1;
        }
    }
    assertEquals(validFeatures, features.size());
    // when features are requested in the response, weights should be created for all features
    final LTRScoringModel ltrScoringModel2 = TestLinearModel.createLinearModel("test", features, norms, "test", allFeatures, makeFeatureWeights(features));
    modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc, // features requested in response
    new LTRScoringQuery(ltrScoringModel2, true));
    featuresInfo = modelWeight.getFeaturesInfo();
    assertEquals(features.size(), modelWeight.getModelFeatureValuesNormalized().length);
    assertEquals(allFeatures.size(), modelWeight.getExtractedFeatureWeights().length);
    validFeatures = 0;
    for (int i = 0; i < featuresInfo.length; ++i) {
        if (featuresInfo[i] != null && featuresInfo[i].isUsed()) {
            validFeatures += 1;
        }
    }
    assertEquals(validFeatures, allFeatures.size());
    assertU(delI("10"));
    assertU(delI("11"));
    r.close();
    dir.close();
}