/*
* 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.List;
import org.apache.lucene.analysis.MockAnalyzer;
import org.apache.lucene.classification.BM25NBClassifier;
import org.apache.lucene.classification.BooleanPerceptronClassifier;
import org.apache.lucene.classification.CachingNaiveBayesClassifier;
import org.apache.lucene.classification.ClassificationResult;
import org.apache.lucene.classification.ClassificationTestBase;
import org.apache.lucene.classification.Classifier;
import org.apache.lucene.classification.KNearestFuzzyClassifier;
import org.apache.lucene.classification.KNearestNeighborClassifier;
import org.apache.lucene.classification.SimpleNaiveBayesClassifier;
import org.apache.lucene.index.LeafReader;
import org.apache.lucene.util.BytesRef;
import org.junit.Test;
/**
* Tests for {@link ConfusionMatrixGenerator}
*/
public class ConfusionMatrixGeneratorTest extends ClassificationTestBase<Object> {
@Test
public void testGetConfusionMatrix() throws Exception {
LeafReader reader = null;
try {
MockAnalyzer analyzer = new MockAnalyzer(random());
reader = getSampleIndex(analyzer);
Classifier<BytesRef> classifier = new Classifier<BytesRef>() {
@Override
public ClassificationResult<BytesRef> assignClass(String text) throws IOException {
return new ClassificationResult<>(new BytesRef(), 1 / (1 + Math.exp(-random().nextInt())));
}
@Override
public List<ClassificationResult<BytesRef>> getClasses(String text) throws IOException {
return null;
}
@Override
public List<ClassificationResult<BytesRef>> getClasses(String text, int max) throws IOException {
return null;
}
};
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
classifier, categoryFieldName, textFieldName, -1);
assertNotNull(confusionMatrix);
assertNotNull(confusionMatrix.getLinearizedMatrix());
assertEquals(7, confusionMatrix.getNumberOfEvaluatedDocs());
double avgClassificationTime = confusionMatrix.getAvgClassificationTime();
assertTrue(avgClassificationTime >= 0d );
double accuracy = confusionMatrix.getAccuracy();
assertTrue(accuracy >= 0d);
assertTrue(accuracy <= 1d);
double precision = confusionMatrix.getPrecision();
assertTrue(precision >= 0d);
assertTrue(precision <= 1d);
double recall = confusionMatrix.getRecall();
assertTrue(recall >= 0d);
assertTrue(recall <= 1d);
double f1Measure = confusionMatrix.getF1Measure();
assertTrue(f1Measure >= 0d);
assertTrue(f1Measure <= 1d);
} finally {
if (reader != null) {
reader.close();
}
}
}
@Test
public void testGetConfusionMatrixWithSNB() throws Exception {
LeafReader reader = null;
try {
MockAnalyzer analyzer = new MockAnalyzer(random());
reader = getSampleIndex(analyzer);
Classifier<BytesRef> classifier = new SimpleNaiveBayesClassifier(reader, analyzer, null, categoryFieldName, textFieldName);
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
classifier, categoryFieldName, textFieldName, -1);
checkCM(confusionMatrix);
} finally {
if (reader != null) {
reader.close();
}
}
}
private void checkCM(ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix) {
assertNotNull(confusionMatrix);
assertNotNull(confusionMatrix.getLinearizedMatrix());
assertEquals(7, confusionMatrix.getNumberOfEvaluatedDocs());
assertTrue(confusionMatrix.getAvgClassificationTime() >= 0d);
double accuracy = confusionMatrix.getAccuracy();
assertTrue(accuracy >= 0d);
assertTrue(accuracy <= 1d);
double precision = confusionMatrix.getPrecision();
assertTrue(precision >= 0d);
assertTrue(precision <= 1d);
double recall = confusionMatrix.getRecall();
assertTrue(recall >= 0d);
assertTrue(recall <= 1d);
double f1Measure = confusionMatrix.getF1Measure();
assertTrue(f1Measure >= 0d);
assertTrue(f1Measure <= 1d);
}
@Test
public void testGetConfusionMatrixWithBM25NB() throws Exception {
LeafReader reader = null;
try {
MockAnalyzer analyzer = new MockAnalyzer(random());
reader = getSampleIndex(analyzer);
Classifier<BytesRef> classifier = new BM25NBClassifier(reader, analyzer, null, categoryFieldName, textFieldName);
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
classifier, categoryFieldName, textFieldName, -1);
checkCM(confusionMatrix);
} finally {
if (reader != null) {
reader.close();
}
}
}
@Test
public void testGetConfusionMatrixWithCNB() throws Exception {
LeafReader reader = null;
try {
MockAnalyzer analyzer = new MockAnalyzer(random());
reader = getSampleIndex(analyzer);
Classifier<BytesRef> classifier = new CachingNaiveBayesClassifier(reader, analyzer, null, categoryFieldName, textFieldName);
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
classifier, categoryFieldName, textFieldName, -1);
checkCM(confusionMatrix);
} finally {
if (reader != null) {
reader.close();
}
}
}
@Test
public void testGetConfusionMatrixWithKNN() throws Exception {
LeafReader reader = null;
try {
MockAnalyzer analyzer = new MockAnalyzer(random());
reader = getSampleIndex(analyzer);
Classifier<BytesRef> classifier = new KNearestNeighborClassifier(reader, null, analyzer, null, 1, 0, 0, categoryFieldName, textFieldName);
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
classifier, categoryFieldName, textFieldName, -1);
checkCM(confusionMatrix);
} finally {
if (reader != null) {
reader.close();
}
}
}
@Test
public void testGetConfusionMatrixWithFLTKNN() throws Exception {
LeafReader reader = null;
try {
MockAnalyzer analyzer = new MockAnalyzer(random());
reader = getSampleIndex(analyzer);
Classifier<BytesRef> classifier = new KNearestFuzzyClassifier(reader, null, analyzer, null, 1, categoryFieldName, textFieldName);
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
classifier, categoryFieldName, textFieldName, -1);
checkCM(confusionMatrix);
} finally {
if (reader != null) {
reader.close();
}
}
}
@Test
public void testGetConfusionMatrixWithBP() throws Exception {
LeafReader reader = null;
try {
MockAnalyzer analyzer = new MockAnalyzer(random());
reader = getSampleIndex(analyzer);
Classifier<Boolean> classifier = new BooleanPerceptronClassifier(reader, analyzer, null, 1, null, booleanFieldName, textFieldName);
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
classifier, booleanFieldName, textFieldName, -1);
checkCM(confusionMatrix);
assertTrue(confusionMatrix.getPrecision("true") >= 0d);
assertTrue(confusionMatrix.getPrecision("true") <= 1d);
assertTrue(confusionMatrix.getPrecision("false") >= 0d);
assertTrue(confusionMatrix.getPrecision("false") <= 1d);
assertTrue(confusionMatrix.getRecall("true") >= 0d);
assertTrue(confusionMatrix.getRecall("true") <= 1d);
assertTrue(confusionMatrix.getRecall("false") >= 0d);
assertTrue(confusionMatrix.getRecall("false") <= 1d);
assertTrue(confusionMatrix.getF1Measure("true") >= 0d);
assertTrue(confusionMatrix.getF1Measure("true") <= 1d);
assertTrue(confusionMatrix.getF1Measure("false") >= 0d);
assertTrue(confusionMatrix.getF1Measure("false") <= 1d);
} finally {
if (reader != null) {
reader.close();
}
}
}
}