Package opennlp.tools.util.eval

Examples of opennlp.tools.util.eval.FMeasure

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    ChunkerEvaluationMonitor listener = new ChunkEvaluationErrorListener(stream);
    ChunkerEvaluator evaluator = new ChunkerEvaluator(dummyChunker, listener);

    evaluator.evaluate(expectedSample);

    FMeasure fm = evaluator.getFMeasure();

    assertEquals(0.8d, fm.getPrecisionScore(), DELTA);
    assertEquals(0.875d, fm.getRecallScore(), DELTA);

    assertNotSame(stream.toString().length(), 0);

  }
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        stream);
    ChunkerEvaluator evaluator = new ChunkerEvaluator(dummyChunker, listener);

    evaluator.evaluate(expectedSample);

    FMeasure fm = evaluator.getFMeasure();

    assertEquals(1d, fm.getPrecisionScore(), DELTA);
    assertEquals(1d, fm.getRecallScore(), DELTA);

    assertEquals(stream.toString().length(), 0);

  }
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    Span[] goldConsts = getConstituencySpans(Parse.parseParse(goldParseString));

    String testParseString = "(TOP (S (NP (NNS Sales) (NNS executives)) (VP (VBD were) (VP (VBG examing) (NP (DT the) (NNS figures)) (PP (IN with) (NP (JJ great) (NN care) (NN yesterday))) ))  (. .) ))";
    Span[] testConsts = getConstituencySpans(Parse.parseParse(testParseString));

    FMeasure measure = new FMeasure();
    measure.updateScores(goldConsts, testConsts);

    // Expected output:
    // Precision: 0.42857142857142855
    // Recall: 0.375
    // F-Measure: 0.39999999999999997

    System.out.println(measure.toString());
  }
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          if (preds[i].equals(tag)) {
            prediction.add(new Span(i, i + 1));
          }
        }
        if (!this.tagFMeasure.containsKey(tag)) {
          this.tagFMeasure.put(tag, new FMeasure());
        }
        // populate the fmeasure
        this.tagFMeasure.get(tag).updateScores(
            reference.toArray(new Span[reference.size()]),
            prediction.toArray(new Span[prediction.size()]));
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          if (preds[i].equals(tag)) {
            prediction.add(new Span(i, i + 1));
          }
        }
        if (!this.tagFMeasure.containsKey(tag)) {
          this.tagFMeasure.put(tag, new FMeasure());
        }
        // populate the fmeasure
        this.tagFMeasure.get(tag).updateScores(
            reference.toArray(new Span[reference.size()]),
            prediction.toArray(new Span[prediction.size()]));
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      } catch (IOException e) {
        // sorry that this can fail
      }
    }

    FMeasure result = validator.getFMeasure();

    System.out.println(result.toString());
  }
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      } catch (IOException e) {
        // sorry that this can fail
      }
    }

    FMeasure result = validator.getFMeasure();

    System.out.println(result.toString());
  }
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        // sorry that this can fail
      }
    }

    if (detailedFMeasureListener == null) {
      FMeasure result = validator.getFMeasure();
      System.out.println(result.toString());
    } else {
      System.out.println(detailedFMeasureListener.toString());
    }
  }
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        nameFinder, new NameEvaluationErrorListener());
    ObjectStream<NameSample> sample = createSample();

    evaluator.evaluate(sample);
    sample.close();
    FMeasure fmeasure = evaluator.getFMeasure();

    assertTrue(fmeasure.getFMeasure() == 1);
    assertTrue(fmeasure.getRecallScore() == 1);
  }
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      } catch (IOException e) {
        // sorry that this can fail
      }
    }
   
    FMeasure result = validator.getFMeasure();
   
    System.out.println(result.toString());
  }
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Related Classes of opennlp.tools.util.eval.FMeasure

  • opennlp.tools.chunker.ChunkerEvaluatorTest
  • opennlp.tools.cmdline.chunker.ChunkerCrossValidatorTool
  • opennlp.tools.cmdline.doccat.DoccatFineGrainedReportListener$Stats
  • opennlp.tools.cmdline.postag.POSTaggerFineGrainedReportListener$Stats
  • opennlp.tools.cmdline.sentdetect.SentenceDetectorCrossValidatorTool
  • opennlp.tools.cmdline.tokenizer.TokenizerCrossValidatorTool
  • opennlp.tools.namefind.DictionaryNameFinderEvaluatorTest
  • opennlp.tools.parser.ParserEvaluator
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