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/*
* 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.cmdline;
import java.io.OutputStream;
import java.io.PrintStream;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import opennlp.tools.util.Span;
import opennlp.tools.util.eval.EvaluationMonitor;
/**
* Note: Do not use this class, internal use only!
*/
public abstract class EvaluationErrorPrinter implements EvaluationMonitor {
protected PrintStream printStream;
protected EvaluationErrorPrinter(OutputStream outputStream) {
this.printStream = new PrintStream(outputStream);
}
// for the sentence detector
protected void printError(Span[] references, Span[] predictions,
T referenceSample, T predictedSample, String sentence) {
List falseNegatives = new ArrayList<>();
List falsePositives = new ArrayList<>();
findErrors(references, predictions, falseNegatives, falsePositives);
if (falsePositives.size() + falseNegatives.size() > 0) {
printSamples(referenceSample, predictedSample);
printErrors(falsePositives, falseNegatives, sentence);
}
}
// for namefinder, chunker...
protected void printError(String id, Span[] references, Span[] predictions,
T referenceSample, T predictedSample, String[] sentenceTokens) {
List falseNegatives = new ArrayList<>();
List falsePositives = new ArrayList<>();
findErrors(references, predictions, falseNegatives, falsePositives);
if (falsePositives.size() + falseNegatives.size() > 0) {
if (id != null) {
printStream.println("Id: {" + id + "}");
}
printSamples(referenceSample, predictedSample);
printErrors(falsePositives, falseNegatives, sentenceTokens);
}
}
protected void printError(Span[] references, Span[] predictions,
T referenceSample, T predictedSample, String[] sentenceTokens) {
printError(null, references, predictions, referenceSample, predictedSample, sentenceTokens);
}
// for pos tagger
protected void printError(String[] references, String[] predictions,
T referenceSample, T predictedSample, String[] sentenceTokens) {
List filteredDoc = new ArrayList<>();
List filteredRefs = new ArrayList<>();
List filteredPreds = new ArrayList<>();
for (int i = 0; i < references.length; i++) {
if (!references[i].equals(predictions[i])) {
filteredDoc.add(sentenceTokens[i]);
filteredRefs.add(references[i]);
filteredPreds.add(predictions[i]);
}
}
if (filteredDoc.size() > 0) {
printSamples(referenceSample, predictedSample);
printErrors(filteredDoc, filteredRefs, filteredPreds);
}
}
// for others
protected void printError(T referenceSample, T predictedSample) {
printSamples(referenceSample, predictedSample);
printStream.println();
}
/**
* Auxiliary method to print tag errors
*
* @param filteredDoc
* the document tokens which were tagged wrong
* @param filteredRefs
* the reference tags
* @param filteredPreds
* the predicted tags
*/
private void printErrors(List filteredDoc, List filteredRefs,
List filteredPreds) {
printStream.println("Errors: {");
printStream.println("Tok: Ref | Pred");
printStream.println("---------------");
for (int i = 0; i < filteredDoc.size(); i++) {
printStream.println(filteredDoc.get(i) + ": " + filteredRefs.get(i)
+ " | " + filteredPreds.get(i));
}
printStream.println("}\n");
}
/**
* Auxiliary method to print span errors
*
* @param falsePositives
* false positives span
* @param falseNegatives
* false negative span
* @param doc
* the document text
*/
private void printErrors(List falsePositives,
List falseNegatives, String doc) {
printStream.println("False positives: {");
for (Span span : falsePositives) {
printStream.println(span.getCoveredText(doc));
}
printStream.println("} False negatives: {");
for (Span span : falseNegatives) {
printStream.println(span.getCoveredText(doc));
}
printStream.println("}\n");
}
/**
* Auxiliary method to print span errors
*
* @param falsePositives
* false positives span
* @param falseNegatives
* false negative span
* @param toks
* the document tokens
*/
private void printErrors(List falsePositives,
List falseNegatives, String[] toks) {
printStream.println("False positives: {");
printStream.println(print(falsePositives, toks));
printStream.println("} False negatives: {");
printStream.println(print(falseNegatives, toks));
printStream.println("}\n");
}
/**
* Auxiliary method to print spans
*
* @param spans
* the span list
* @param toks
* the tokens array
* @return the spans as string
*/
private String print(List spans, String[] toks) {
return Arrays.toString(Span.spansToStrings(
spans.toArray(new Span[spans.size()]), toks));
}
/**
* Auxiliary method to print expected and predicted samples.
*
* @param referenceSample
* the reference sample
* @param predictedSample
* the predicted sample
*/
private void printSamples(S referenceSample, S predictedSample) {
String details = "Expected: {\n" + referenceSample + "}\nPredicted: {\n"
+ predictedSample + "}";
printStream.println(details);
}
/**
* Outputs falseNegatives and falsePositives spans from the references and
* predictions list.
*
* @param references
* @param predictions
* @param falseNegatives
* [out] the false negatives list
* @param falsePositives
* [out] the false positives list
*/
private void findErrors(Span[] references, Span[] predictions,
List falseNegatives, List falsePositives) {
falseNegatives.addAll(Arrays.asList(references));
falsePositives.addAll(Arrays.asList(predictions));
for (int referenceIndex = 0; referenceIndex < references.length; referenceIndex++) {
Span referenceName = references[referenceIndex];
for (int predictedIndex = 0; predictedIndex < predictions.length; predictedIndex++) {
if (referenceName.equals(predictions[predictedIndex])) {
// got it, remove from fn and fp
falseNegatives.remove(referenceName);
falsePositives.remove(predictions[predictedIndex]);
}
}
}
}
public void correctlyClassified(T reference, T prediction) {
// do nothing
}
public abstract void missclassified(T reference, T prediction) ;
}
