Examples of org.apache.mahout.classifier.evaluation.Auc.auc()

Package org.apache.mahout.classifier.evaluation

Class org.apache.mahout.classifier.evaluation.Auc

Examples of org.apache.mahout.classifier.evaluation.Auc.auc()

org.apache.mahout.classifier.evaluation.Auc.auc()
Allocates a new data-structure for accumulating information about AUC and a few other accuracy measures. @param threshold The threshold to use in computing the confusion matrix.

        if (pass % 5 == 0) {
          Auc eval = new Auc(0.5);
          for (TelephoneCall testCall : test) {
            eval.add(testCall.getTarget(), lr.classifyScalar(testCall.asVector()));
          }
          System.out.printf("%d, %.4f, %.4f\n", pass, lr.currentLearningRate(), eval.auc());
        }
      }


    }
  }

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            double probabilityOfCorrectInstance = classifierPrediction.get(labelPredictionWeight.getLabel());
            double score = 0;
            score = (trueValue == 1) ? probabilityOfCorrectInstance: 1 - probabilityOfCorrectInstance;
            auc.add(trueValue, score);
        }
        return 1 - auc.auc();
    }
}

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        Auc auc = new Auc();
        for(WeightedAUCCrossValLossFunction.AUCData aucData : aucDataList) {
            auc.add("test1".equals(aucData.getClassification()) ? 1 : 0, aucData.getProbability());
        }


        double mahoutAucLoss = 1.0 - auc.auc();
        //These aren't matching exactly, but the difference is minimal
        double acceptableDifference = 0.000000000001;
        Assert.assertTrue(Math.abs(mahoutAucLoss - aucCrossValLoss) < acceptableDifference);
    }
}

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