mirror of https://github.com/apache/lucene.git
LUCENE-7838 - added knn classifier based on flt
This commit is contained in:
Tommaso Teofili
parent
afd70a48cc
commit
bd9e32d358
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@ -16,8 +16,9 @@
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<orderEntry type="module" scope="TEST" module-name="lucene-test-framework" />
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<orderEntry type="module" module-name="lucene-core" />
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<orderEntry type="module" module-name="queries" />
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<orderEntry type="module" scope="TEST" module-name="analysis-common" />
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<orderEntry type="module" module-name="analysis-common" />
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<orderEntry type="module" module-name="grouping" />
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<orderEntry type="module" module-name="misc" />
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<orderEntry type="module" module-name="sandbox" />
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</component>
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</module>
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@ -28,6 +28,8 @@
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<path refid="base.classpath"/>
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<pathelement path="${queries.jar}"/>
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<pathelement path="${grouping.jar}"/>
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<pathelement path="${sandbox.jar}"/>
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<pathelement path="${analyzers-common.jar}"/>
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</path>
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<path id="test.classpath">
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@ -36,16 +38,18 @@
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<path refid="test.base.classpath"/>
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</path>
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<target name="compile-core" depends="jar-grouping,jar-queries,jar-analyzers-common,common.compile-core" />
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<target name="compile-core" depends="jar-sandbox,jar-grouping,jar-queries,jar-analyzers-common,common.compile-core" />
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<target name="jar-core" depends="common.jar-core" />
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<target name="javadocs" depends="javadocs-grouping,compile-core,check-javadocs-uptodate"
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<target name="javadocs" depends="javadocs-sandbox,javadocs-grouping,compile-core,check-javadocs-uptodate"
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unless="javadocs-uptodate-${name}">
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<invoke-module-javadoc>
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<links>
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<link href="../queries"/>
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<link href="../analyzers/common"/>
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<link href="../grouping"/>
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<link href="../sandbox"/>
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</links>
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</invoke-module-javadoc>
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</target>
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@ -0,0 +1,225 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.lucene.classification;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.Collections;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import org.apache.lucene.analysis.Analyzer;
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import org.apache.lucene.index.IndexReader;
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import org.apache.lucene.index.IndexableField;
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import org.apache.lucene.index.LeafReader;
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import org.apache.lucene.index.Term;
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import org.apache.lucene.sandbox.queries.FuzzyLikeThisQuery;
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import org.apache.lucene.search.BooleanClause;
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import org.apache.lucene.search.BooleanQuery;
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import org.apache.lucene.search.IndexSearcher;
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import org.apache.lucene.search.Query;
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import org.apache.lucene.search.ScoreDoc;
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import org.apache.lucene.search.TopDocs;
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import org.apache.lucene.search.WildcardQuery;
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import org.apache.lucene.search.similarities.BM25Similarity;
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import org.apache.lucene.search.similarities.ClassicSimilarity;
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import org.apache.lucene.search.similarities.Similarity;
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import org.apache.lucene.util.BytesRef;
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/**
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* A k-Nearest Neighbor classifier based on {@link FuzzyLikeThisQuery}.
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*
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* @lucene.experimental
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*/
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public class KNearestFuzzyClassifier implements Classifier<BytesRef> {
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/**
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* the name of the fields used as the input text
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*/
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protected final String[] textFieldNames;
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/**
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* the name of the field used as the output text
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*/
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protected final String classFieldName;
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/**
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* an {@link IndexSearcher} used to perform queries
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*/
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protected final IndexSearcher indexSearcher;
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/**
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* the no. of docs to compare in order to find the nearest neighbor to the input text
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*/
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protected final int k;
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/**
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* a {@link Query} used to filter the documents that should be used from this classifier's underlying {@link LeafReader}
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*/
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protected final Query query;
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private final Analyzer analyzer;
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/**
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* Creates a {@link KNearestFuzzyClassifier}.
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*
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* @param indexReader the reader on the index to be used for classification
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* @param analyzer an {@link Analyzer} used to analyze unseen text
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* @param similarity the {@link Similarity} to be used by the underlying {@link IndexSearcher} or {@code null}
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* (defaults to {@link BM25Similarity})
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* @param query a {@link Query} to eventually filter the docs used for training the classifier, or {@code null}
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* if all the indexed docs should be used
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* @param k the no. of docs to select in the MLT results to find the nearest neighbor
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* @param classFieldName the name of the field used as the output for the classifier
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* @param textFieldNames the name of the fields used as the inputs for the classifier, they can contain boosting indication e.g. title^10
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*/
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public KNearestFuzzyClassifier(IndexReader indexReader, Similarity similarity, Analyzer analyzer, Query query, int k,
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String classFieldName, String... textFieldNames) {
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this.textFieldNames = textFieldNames;
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this.classFieldName = classFieldName;
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this.analyzer = analyzer;
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this.indexSearcher = new IndexSearcher(indexReader);
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if (similarity != null) {
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this.indexSearcher.setSimilarity(similarity);
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} else {
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this.indexSearcher.setSimilarity(new BM25Similarity());
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}
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this.query = query;
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this.k = k;
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}
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/**
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* {@inheritDoc}
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*/
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@Override
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public ClassificationResult<BytesRef> assignClass(String text) throws IOException {
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TopDocs knnResults = knnSearch(text);
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List<ClassificationResult<BytesRef>> assignedClasses = buildListFromTopDocs(knnResults);
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ClassificationResult<BytesRef> assignedClass = null;
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double maxscore = -Double.MAX_VALUE;
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for (ClassificationResult<BytesRef> cl : assignedClasses) {
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if (cl.getScore() > maxscore) {
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assignedClass = cl;
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maxscore = cl.getScore();
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}
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}
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return assignedClass;
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}
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/**
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* {@inheritDoc}
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*/
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@Override
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public List<ClassificationResult<BytesRef>> getClasses(String text) throws IOException {
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TopDocs knnResults = knnSearch(text);
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List<ClassificationResult<BytesRef>> assignedClasses = buildListFromTopDocs(knnResults);
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Collections.sort(assignedClasses);
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return assignedClasses;
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}
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/**
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* {@inheritDoc}
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*/
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@Override
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public List<ClassificationResult<BytesRef>> getClasses(String text, int max) throws IOException {
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TopDocs knnResults = knnSearch(text);
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List<ClassificationResult<BytesRef>> assignedClasses = buildListFromTopDocs(knnResults);
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Collections.sort(assignedClasses);
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return assignedClasses.subList(0, max);
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}
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private TopDocs knnSearch(String text) throws IOException {
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BooleanQuery.Builder bq = new BooleanQuery.Builder();
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FuzzyLikeThisQuery fuzzyLikeThisQuery = new FuzzyLikeThisQuery(300, analyzer);
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for (String fieldName : textFieldNames) {
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fuzzyLikeThisQuery.addTerms(text, fieldName, 1f, 2); // TODO: make this parameters configurable
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}
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bq.add(fuzzyLikeThisQuery, BooleanClause.Occur.MUST);
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Query classFieldQuery = new WildcardQuery(new Term(classFieldName, "*"));
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bq.add(new BooleanClause(classFieldQuery, BooleanClause.Occur.MUST));
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if (query != null) {
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bq.add(query, BooleanClause.Occur.MUST);
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}
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return indexSearcher.search(bq.build(), k);
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}
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/**
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* build a list of classification results from search results
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*
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* @param topDocs the search results as a {@link TopDocs} object
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* @return a {@link List} of {@link ClassificationResult}, one for each existing class
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* @throws IOException if it's not possible to get the stored value of class field
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*/
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protected List<ClassificationResult<BytesRef>> buildListFromTopDocs(TopDocs topDocs) throws IOException {
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Map<BytesRef, Integer> classCounts = new HashMap<>();
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Map<BytesRef, Double> classBoosts = new HashMap<>(); // this is a boost based on class ranking positions in topDocs
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float maxScore = topDocs.getMaxScore();
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for (ScoreDoc scoreDoc : topDocs.scoreDocs) {
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IndexableField storableField = indexSearcher.doc(scoreDoc.doc).getField(classFieldName);
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if (storableField != null) {
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BytesRef cl = new BytesRef(storableField.stringValue());
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//update count
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Integer count = classCounts.get(cl);
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if (count != null) {
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classCounts.put(cl, count + 1);
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} else {
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classCounts.put(cl, 1);
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}
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//update boost, the boost is based on the best score
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Double totalBoost = classBoosts.get(cl);
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double singleBoost = scoreDoc.score / maxScore;
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if (totalBoost != null) {
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classBoosts.put(cl, totalBoost + singleBoost);
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} else {
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classBoosts.put(cl, singleBoost);
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}
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}
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}
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List<ClassificationResult<BytesRef>> returnList = new ArrayList<>();
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List<ClassificationResult<BytesRef>> temporaryList = new ArrayList<>();
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int sumdoc = 0;
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for (Map.Entry<BytesRef, Integer> entry : classCounts.entrySet()) {
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Integer count = entry.getValue();
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Double normBoost = classBoosts.get(entry.getKey()) / count; //the boost is normalized to be 0<b<1
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temporaryList.add(new ClassificationResult<>(entry.getKey().clone(), (count * normBoost) / (double) k));
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sumdoc += count;
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}
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//correction
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if (sumdoc < k) {
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for (ClassificationResult<BytesRef> cr : temporaryList) {
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returnList.add(new ClassificationResult<>(cr.getAssignedClass(), cr.getScore() * k / (double) sumdoc));
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}
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} else {
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returnList = temporaryList;
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}
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return returnList;
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}
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@Override
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public String toString() {
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return "KNearestFuzzyClassifier{" +
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"textFieldNames=" + Arrays.toString(textFieldNames) +
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", classFieldName='" + classFieldName + '\'' +
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", k=" + k +
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", query=" + query +
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", similarity=" + indexSearcher.getSimilarity(true) +
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'}';
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}
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}
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@ -121,7 +121,7 @@ public class DatasetSplitter {
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int b = 0;
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// iterate over existing documents
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for (GroupDocs group : topGroups.groups) {
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for (GroupDocs<Object> group : topGroups.groups) {
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int totalHits = group.totalHits;
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double testSize = totalHits * testRatio;
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int tc = 0;
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@ -0,0 +1,124 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.lucene.classification;
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import org.apache.lucene.analysis.Analyzer;
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import org.apache.lucene.analysis.MockAnalyzer;
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import org.apache.lucene.analysis.Tokenizer;
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import org.apache.lucene.analysis.core.KeywordTokenizer;
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import org.apache.lucene.analysis.ngram.EdgeNGramTokenFilter;
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import org.apache.lucene.analysis.reverse.ReverseStringFilter;
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import org.apache.lucene.classification.utils.ConfusionMatrixGenerator;
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import org.apache.lucene.index.LeafReader;
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import org.apache.lucene.index.MultiFields;
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import org.apache.lucene.index.Term;
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import org.apache.lucene.index.Terms;
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import org.apache.lucene.index.TermsEnum;
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import org.apache.lucene.search.TermQuery;
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import org.apache.lucene.util.BytesRef;
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import org.junit.Test;
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/**
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* Testcase for {@link KNearestFuzzyClassifier}
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*/
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public class KNearestFuzzyClassifierTest extends ClassificationTestBase<BytesRef> {
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@Test
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public void testBasicUsage() throws Exception {
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LeafReader leafReader = null;
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try {
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MockAnalyzer analyzer = new MockAnalyzer(random());
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leafReader = getSampleIndex(analyzer);
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Classifier<BytesRef> classifier = new KNearestFuzzyClassifier(leafReader, null, analyzer, null, 3, categoryFieldName, textFieldName);
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checkCorrectClassification(classifier, TECHNOLOGY_INPUT, TECHNOLOGY_RESULT);
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checkCorrectClassification(classifier, POLITICS_INPUT, POLITICS_RESULT);
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} finally {
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if (leafReader != null) {
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leafReader.close();
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}
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}
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}
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@Test
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public void testBasicUsageWithQuery() throws Exception {
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LeafReader leafReader = null;
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try {
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MockAnalyzer analyzer = new MockAnalyzer(random());
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leafReader = getSampleIndex(analyzer);
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TermQuery query = new TermQuery(new Term(textFieldName, "not"));
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Classifier<BytesRef> classifier = new KNearestFuzzyClassifier(leafReader, null, analyzer, query, 3, categoryFieldName, textFieldName);
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checkCorrectClassification(classifier, TECHNOLOGY_INPUT, TECHNOLOGY_RESULT);
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} finally {
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if (leafReader != null) {
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leafReader.close();
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}
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}
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}
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@Test
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public void testPerformance() throws Exception {
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MockAnalyzer analyzer = new MockAnalyzer(random());
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LeafReader leafReader = getRandomIndex(analyzer, 100);
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try {
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long trainStart = System.currentTimeMillis();
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Classifier<BytesRef> classifier = new KNearestFuzzyClassifier(leafReader, null, analyzer, null, 3, categoryFieldName, textFieldName);
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long trainEnd = System.currentTimeMillis();
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long trainTime = trainEnd - trainStart;
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assertTrue("training took more than 10s: " + trainTime / 1000 + "s", trainTime < 10000);
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long evaluationStart = System.currentTimeMillis();
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ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(leafReader,
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classifier, categoryFieldName, textFieldName, -1);
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assertNotNull(confusionMatrix);
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long evaluationEnd = System.currentTimeMillis();
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long evaluationTime = evaluationEnd - evaluationStart;
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assertTrue("evaluation took more than 2m: " + evaluationTime / 1000 + "s", evaluationTime < 120000);
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double avgClassificationTime = confusionMatrix.getAvgClassificationTime();
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assertTrue(5000 > avgClassificationTime);
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double accuracy = confusionMatrix.getAccuracy();
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assertTrue(accuracy >= 0d);
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assertTrue(accuracy <= 1d);
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double recall = confusionMatrix.getRecall();
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assertTrue(recall >= 0d);
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assertTrue(recall <= 1d);
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double precision = confusionMatrix.getPrecision();
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assertTrue(precision >= 0d);
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assertTrue(precision <= 1d);
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Terms terms = MultiFields.getTerms(leafReader, categoryFieldName);
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TermsEnum iterator = terms.iterator();
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BytesRef term;
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while ((term = iterator.next()) != null) {
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String s = term.utf8ToString();
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recall = confusionMatrix.getRecall(s);
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assertTrue(recall >= 0d);
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assertTrue(recall <= 1d);
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precision = confusionMatrix.getPrecision(s);
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assertTrue(precision >= 0d);
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assertTrue(precision <= 1d);
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double f1Measure = confusionMatrix.getF1Measure(s);
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assertTrue(f1Measure >= 0d);
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assertTrue(f1Measure <= 1d);
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}
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} finally {
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leafReader.close();
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}
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}
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}
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@ -21,11 +21,13 @@ import java.io.IOException;
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import java.util.List;
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import org.apache.lucene.analysis.MockAnalyzer;
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import org.apache.lucene.classification.BM25NBClassifier;
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import org.apache.lucene.classification.BooleanPerceptronClassifier;
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import org.apache.lucene.classification.CachingNaiveBayesClassifier;
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import org.apache.lucene.classification.ClassificationResult;
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import org.apache.lucene.classification.ClassificationTestBase;
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import org.apache.lucene.classification.Classifier;
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import org.apache.lucene.classification.KNearestFuzzyClassifier;
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import org.apache.lucene.classification.KNearestNeighborClassifier;
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import org.apache.lucene.classification.SimpleNaiveBayesClassifier;
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import org.apache.lucene.index.LeafReader;
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@ -94,22 +96,43 @@ public class ConfusionMatrixGeneratorTest extends ClassificationTestBase<Object>
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Classifier<BytesRef> classifier = new SimpleNaiveBayesClassifier(reader, analyzer, null, categoryFieldName, textFieldName);
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ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
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classifier, categoryFieldName, textFieldName, -1);
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assertNotNull(confusionMatrix);
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assertNotNull(confusionMatrix.getLinearizedMatrix());
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assertEquals(7, confusionMatrix.getNumberOfEvaluatedDocs());
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assertTrue(confusionMatrix.getAvgClassificationTime() >= 0d);
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double accuracy = confusionMatrix.getAccuracy();
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assertTrue(accuracy >= 0d);
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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);
|
||||
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();
|
||||
|
|
@ -126,22 +149,7 @@ public class ConfusionMatrixGeneratorTest extends ClassificationTestBase<Object>
|
|||
Classifier<BytesRef> classifier = new CachingNaiveBayesClassifier(reader, analyzer, null, categoryFieldName, textFieldName);
|
||||
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
|
||||
classifier, categoryFieldName, textFieldName, -1);
|
||||
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);
|
||||
checkCM(confusionMatrix);
|
||||
} finally {
|
||||
if (reader != null) {
|
||||
reader.close();
|
||||
|
|
@ -158,22 +166,24 @@ public class ConfusionMatrixGeneratorTest extends ClassificationTestBase<Object>
|
|||
Classifier<BytesRef> classifier = new KNearestNeighborClassifier(reader, null, analyzer, null, 1, 0, 0, categoryFieldName, textFieldName);
|
||||
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
|
||||
classifier, categoryFieldName, textFieldName, -1);
|
||||
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);
|
||||
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();
|
||||
|
|
@ -190,22 +200,7 @@ public class ConfusionMatrixGeneratorTest extends ClassificationTestBase<Object>
|
|||
Classifier<Boolean> classifier = new BooleanPerceptronClassifier(reader, analyzer, null, 1, null, booleanFieldName, textFieldName);
|
||||
ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(reader,
|
||||
classifier, booleanFieldName, textFieldName, -1);
|
||||
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);
|
||||
checkCM(confusionMatrix);
|
||||
assertTrue(confusionMatrix.getPrecision("true") >= 0d);
|
||||
assertTrue(confusionMatrix.getPrecision("true") <= 1d);
|
||||
assertTrue(confusionMatrix.getPrecision("false") >= 0d);
|
||||
|
|
|
|||
Loading…
Reference in New Issue