opennlp.tools.doccat.DocumentCategorizerEvaluator Maven / Gradle / Ivy
/*
* 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 opennlp.tools.doccat;
import opennlp.tools.tokenize.TokenSample;
import opennlp.tools.util.eval.Evaluator;
import opennlp.tools.util.eval.Mean;
/**
* The {@link DocumentCategorizerEvaluator} measures the performance of
* the given {@link DocumentCategorizer} with the provided reference
* {@link DocumentSample}s.
*
* @see DocumentCategorizer
* @see DocumentSample
*/
public class DocumentCategorizerEvaluator extends Evaluator {
private DocumentCategorizer categorizer;
private Mean accuracy = new Mean();
/**
* Initializes the current instance.
*
* @param categorizer the document categorizer instance
*/
public DocumentCategorizerEvaluator(DocumentCategorizer categorizer,
DoccatEvaluationMonitor ... listeners) {
super(listeners);
this.categorizer = categorizer;
}
/**
* Evaluates the given reference {@link DocumentSample} object.
*
* This is done by categorizing the document from the provided
* {@link DocumentSample}. The detected category is then used
* to calculate and update the score.
*
* @param sample the reference {@link TokenSample}.
*/
public DocumentSample processSample(DocumentSample sample) {
String[] document = sample.getText();
double[] probs = categorizer.categorize(document);
String cat = categorizer.getBestCategory(probs);
if (sample.getCategory().equals(cat)) {
accuracy.add(1);
}
else {
accuracy.add(0);
}
return new DocumentSample(cat, sample.getText());
}
/**
* Retrieves the accuracy of provided {@link DocumentCategorizer}.
*
* accuracy = correctly categorized documents / total documents
*
* @return the accuracy
*/
public double getAccuracy() {
return accuracy.mean();
}
public long getDocumentCount() {
return accuracy.count();
}
/**
* Represents this objects as human readable {@link String}.
*/
@Override
public String toString() {
return "Accuracy: " + accuracy.mean() + "\n" +
"Number of documents: " + accuracy.count();
}
}