| /* |
| * 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; |
| |
| import java.util.List; |
| import org.apache.lucene.analysis.Analyzer; |
| import org.apache.lucene.analysis.MockAnalyzer; |
| import org.apache.lucene.analysis.en.EnglishAnalyzer; |
| import org.apache.lucene.classification.utils.ConfusionMatrixGenerator; |
| import org.apache.lucene.index.LeafReader; |
| import org.apache.lucene.index.MultiTerms; |
| import org.apache.lucene.index.Term; |
| import org.apache.lucene.index.Terms; |
| import org.apache.lucene.index.TermsEnum; |
| import org.apache.lucene.search.TermQuery; |
| import org.apache.lucene.search.similarities.BM25Similarity; |
| import org.apache.lucene.search.similarities.LMDirichletSimilarity; |
| import org.apache.lucene.util.BytesRef; |
| import org.junit.Test; |
| |
| /** Testcase for {@link KNearestNeighborClassifier} */ |
| public class TestKNearestNeighborClassifier extends ClassificationTestBase<BytesRef> { |
| |
| @Test |
| public void testBasicUsage() throws Exception { |
| LeafReader leafReader = null; |
| try { |
| MockAnalyzer analyzer = new MockAnalyzer(random()); |
| leafReader = getSampleIndex(analyzer); |
| checkCorrectClassification( |
| new KNearestNeighborClassifier( |
| leafReader, null, analyzer, null, 1, 0, 0, categoryFieldName, textFieldName), |
| TECHNOLOGY_INPUT, |
| TECHNOLOGY_RESULT); |
| checkCorrectClassification( |
| new KNearestNeighborClassifier( |
| leafReader, |
| new LMDirichletSimilarity(), |
| analyzer, |
| null, |
| 1, |
| 0, |
| 0, |
| categoryFieldName, |
| textFieldName), |
| TECHNOLOGY_INPUT, |
| TECHNOLOGY_RESULT); |
| ClassificationResult<BytesRef> resultDS = |
| checkCorrectClassification( |
| new KNearestNeighborClassifier( |
| leafReader, |
| new BM25Similarity(), |
| analyzer, |
| null, |
| 3, |
| 2, |
| 1, |
| categoryFieldName, |
| textFieldName), |
| TECHNOLOGY_INPUT, |
| TECHNOLOGY_RESULT); |
| ClassificationResult<BytesRef> resultLMS = |
| checkCorrectClassification( |
| new KNearestNeighborClassifier( |
| leafReader, |
| new LMDirichletSimilarity(), |
| analyzer, |
| null, |
| 3, |
| 2, |
| 1, |
| categoryFieldName, |
| textFieldName), |
| TECHNOLOGY_INPUT, |
| TECHNOLOGY_RESULT); |
| assertTrue(resultDS.getScore() != resultLMS.getScore()); |
| } finally { |
| if (leafReader != null) { |
| leafReader.close(); |
| } |
| } |
| } |
| |
| /** |
| * This test is for the scenario where in the first topK results from the MLT query, we have the |
| * same number of results per class. But the results for a class have a better ranking in |
| * comparison with the results of the second class. So we would expect a greater score for the |
| * best ranked class. |
| * |
| * @throws Exception if any error happens |
| */ |
| @Test |
| public void testRankedClasses() throws Exception { |
| LeafReader leafReader = null; |
| try { |
| Analyzer analyzer = new EnglishAnalyzer(); |
| leafReader = getSampleIndex(analyzer); |
| KNearestNeighborClassifier knnClassifier = |
| new KNearestNeighborClassifier( |
| leafReader, null, analyzer, null, 6, 1, 1, categoryFieldName, textFieldName); |
| List<ClassificationResult<BytesRef>> classes = |
| knnClassifier.getClasses(STRONG_TECHNOLOGY_INPUT); |
| assertTrue(classes.get(0).getScore() > classes.get(1).getScore()); |
| checkCorrectClassification(knnClassifier, STRONG_TECHNOLOGY_INPUT, TECHNOLOGY_RESULT); |
| } finally { |
| if (leafReader != null) { |
| leafReader.close(); |
| } |
| } |
| } |
| |
| /** |
| * This test is for the scenario where in the first topK results from the MLT query, we have less |
| * results for the expected class than the results for the bad class. But the results for the |
| * expected class have a better score in comparison with the results of the second class. So we |
| * would expect a greater score for the best ranked class. |
| * |
| * @throws Exception if any error happens |
| */ |
| @Test |
| public void testUnbalancedClasses() throws Exception { |
| LeafReader leafReader = null; |
| try { |
| Analyzer analyzer = new EnglishAnalyzer(); |
| leafReader = getSampleIndex(analyzer); |
| KNearestNeighborClassifier knnClassifier = |
| new KNearestNeighborClassifier( |
| leafReader, null, analyzer, null, 3, 1, 1, categoryFieldName, textFieldName); |
| List<ClassificationResult<BytesRef>> classes = |
| knnClassifier.getClasses(SUPER_STRONG_TECHNOLOGY_INPUT); |
| assertTrue(classes.get(0).getScore() > classes.get(1).getScore()); |
| checkCorrectClassification(knnClassifier, SUPER_STRONG_TECHNOLOGY_INPUT, TECHNOLOGY_RESULT); |
| } finally { |
| if (leafReader != null) { |
| leafReader.close(); |
| } |
| } |
| } |
| |
| @Test |
| public void testBasicUsageWithQuery() throws Exception { |
| LeafReader leafReader = null; |
| try { |
| MockAnalyzer analyzer = new MockAnalyzer(random()); |
| leafReader = getSampleIndex(analyzer); |
| TermQuery query = new TermQuery(new Term(textFieldName, "it")); |
| checkCorrectClassification( |
| new KNearestNeighborClassifier( |
| leafReader, null, analyzer, query, 1, 0, 0, categoryFieldName, textFieldName), |
| TECHNOLOGY_INPUT, |
| TECHNOLOGY_RESULT); |
| } finally { |
| if (leafReader != null) { |
| leafReader.close(); |
| } |
| } |
| } |
| |
| @Test |
| public void testPerformance() throws Exception { |
| MockAnalyzer analyzer = new MockAnalyzer(random()); |
| int numDocs = atLeast(10); |
| LeafReader leafReader = getRandomIndex(analyzer, numDocs); |
| try { |
| KNearestNeighborClassifier kNearestNeighborClassifier = |
| new KNearestNeighborClassifier( |
| leafReader, null, analyzer, null, 1, 1, 1, categoryFieldName, textFieldName); |
| |
| ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = |
| ConfusionMatrixGenerator.getConfusionMatrix( |
| leafReader, kNearestNeighborClassifier, categoryFieldName, textFieldName, -1); |
| assertNotNull(confusionMatrix); |
| |
| double avgClassificationTime = confusionMatrix.getAvgClassificationTime(); |
| assertTrue(avgClassificationTime >= 0); |
| |
| double accuracy = confusionMatrix.getAccuracy(); |
| assertTrue(accuracy >= 0d); |
| assertTrue(accuracy <= 1d); |
| |
| double recall = confusionMatrix.getRecall(); |
| assertTrue(recall >= 0d); |
| assertTrue(recall <= 1d); |
| |
| double precision = confusionMatrix.getPrecision(); |
| assertTrue(precision >= 0d); |
| assertTrue(precision <= 1d); |
| |
| Terms terms = MultiTerms.getTerms(leafReader, categoryFieldName); |
| TermsEnum iterator = terms.iterator(); |
| BytesRef term; |
| while ((term = iterator.next()) != null) { |
| String s = term.utf8ToString(); |
| recall = confusionMatrix.getRecall(s); |
| assertTrue(recall >= 0d); |
| assertTrue(recall <= 1d); |
| precision = confusionMatrix.getPrecision(s); |
| assertTrue(precision >= 0d); |
| assertTrue(precision <= 1d); |
| double f1Measure = confusionMatrix.getF1Measure(s); |
| assertTrue(f1Measure >= 0d); |
| assertTrue(f1Measure <= 1d); |
| } |
| } finally { |
| leafReader.close(); |
| } |
| } |
| } |