List of fMeasure() Examples

Examples of fMeasure()

statechum.analysis.learning.PrecisionRecall.ConfusionMatrix.fMeasure()
weka.classifiers.Evaluation.fMeasure()
Calculate the F-Measure with respect to a particular class. This is defined as
```
 2 * recall * precision ---------------------- recall + precision 
```
@param classIndex the index of the class to consider as "positive" @return the F-Measure

Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix.fMeasure()

        new String[]{"a","a"} // FN
        ,new String[]{"b"} // TP
    },config), from, to);
    Assert.assertEquals(1.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0.5,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.66666666,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0.25,matrix.BCR(),Configuration.fpAccuracy);
  }


  @Test

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Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix.fMeasure()

        new String[]{"a","a"} // FN
        ,new String[]{"b","b"} // FP
    },config), from, to);
    Assert.assertEquals(0.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0,matrix.BCR(),Configuration.fpAccuracy);
  }

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Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix.fMeasure()

        ,new String[]{"b","b"} // FP
        ,new String[]{"c"} // TN
    },config), from, to);
    Assert.assertEquals(0.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.5,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0.25,matrix.BCR(),Configuration.fpAccuracy);
  }
  
  /* More sequences. */

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Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix.fMeasure()

        ,new String[]{"b","b"} // FP
        ,new String[]{"c"} // TN
    },config), from, to);
    Assert.assertEquals(2./3.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(1,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.8,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.5,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0.75,matrix.BCR(),Configuration.fpAccuracy);
  }
  
}

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Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix.fMeasure()

  {
    
    final long startTime = System.nanoTime();
    ConfusionMatrix matrix = classify(sequences, from,to);
    final long duration = System.nanoTime() - startTime;
    double result = matrix.fMeasure();
    assert !Double.isNaN(result);
    return new Pair<Double,Long>(result,duration);
  }


  public static ConfusionMatrix classify(Collection<List<Label>> sequences,LearnerGraph from, LearnerGraph to)

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Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix.fMeasure()

    set.addAll(expected);
    set.removeAll(detected);
    fn = set.size();
    
    ConfusionMatrix conf = new ConfusionMatrix(tp, tn, fp, fn);
    return conf.fMeasure();
  }


  @SuppressWarnings("unused")
  private ExperimentResult getAverage(List<ExperimentResult> toPrint) 
  {

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Examples of weka.classifiers.Evaluation.fMeasure()

    result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats
    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());

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Examples of weka.classifiers.Evaluation.fMeasure()

    result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats
    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());

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Examples of weka.classifiers.Evaluation.fMeasure()

    // instead
    // we only print these metrics for binary classification
    // problems.
    output += "\tROC:" + evalModel.areaUnderROC(1);
    output += "\tPREC:" + evalModel.precision(1);
    output += "\tFSCR:" + evalModel.fMeasure(1);
  }
  System.out.println(output);
      }
    }
  }

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Examples of weka.classifiers.Evaluation.fMeasure()

    result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Timing stats
    result[current++] = new Double(trainTimeElapsed / 1000.0);
    result[current++] = new Double(testTimeElapsed / 1000.0);

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