Package org.apache.ctakes.ytex.kernel.model

Examples of org.apache.ctakes.ytex.kernel.model.ClassifierInstanceEvaluation

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        // get instance id corresponding to this line
        long instanceId = instanceIds.size() > nLine ? instanceIds
            .get(nLine) : nLine;
        nLine++;
        // allocate instanceEval
        ClassifierInstanceEvaluation instanceEval = new ClassifierInstanceEvaluation();
        // parse out predicted class from output predictions
        String predictTokens[] = wsPattern.split(predictionLine);
        String classIdPredicted = predictTokens[0];
        String classIdTarget = extractFirstToken(instanceLine,
            wsPattern);
        // parse out target class from input data file
        instanceEval.setTargetClassId(Integer.parseInt(classIdTarget));
        instanceEval.setPredictedClassId(Integer
            .parseInt(classIdPredicted));
        instanceEval.setInstanceId(instanceId);
        instanceEval.setClassifierEvaluation(eval);
        // add the instance to the map
        eval.getClassifierInstanceEvaluations().put(instanceId,
            instanceEval);
        // parse class id probabilities
        if (storeProbabilities && predictTokens.length > 1) {
          for (int i = 1; i < predictTokens.length; i++) {
            instanceEval.getClassifierInstanceProbabilities().put(
                classIds.get(i - 1),
                Double.parseDouble(predictTokens[i]));
          }
        }
      }
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      int targetClassId = classInfo.get(2).intValue();
      // if we are storing unlabeled instance ids, save this instance
      // evaluation
      // else only store it if this is a test instance id - save it
      if (storeUnlabeled || !train) {
        ClassifierInstanceEvaluation cie = new ClassifierInstanceEvaluation();
        cie.setClassifierEvaluation(ce);
        cie.setInstanceId(instanceId);
        cie.setPredictedClassId(classIds[i]);
        if (targetClassId != 0)
          cie.setTargetClassId(targetClassId);
        // add the instance eval to the parent
        ce.getClassifierInstanceEvaluations().put(instanceId, cie);
      }
    }
  }
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      boolean train = classInfo.isTrain();
      // if we are storing unlabeled instance ids, save this instance
      // evaluation
      // else only store it if this is a test instance id - save it
      if (storeUnlabeled || !train) {
        ClassifierInstanceEvaluation cie = new ClassifierInstanceEvaluation();
        cie.setClassifierEvaluation(ce);
        cie.setInstanceId(classInfo.getInstanceId());
        cie.setPredictedClassId(classNameToIdMap.get(predictedClassNames[i]));
        int targetClassId = classNameToIdMap.get(classInfo.getTargetClassName());
        if (targetClassId != 0)
          cie.setTargetClassId(targetClassId);
        // add the instance eval to the parent
        ce.getClassifierInstanceEvaluations().put(cie.getInstanceId(), cie);
      }
    }
  }
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        nLine++;
        int classIdTarget = Integer.parseInt(extractFirstToken(
            instanceLine, wsPattern));
        // only store unlabeled instances if configured to do so
        if (bStoreUnlabeled || classIdTarget != 0) {
          ClassifierInstanceEvaluation result = new ClassifierInstanceEvaluation();
          // target class id is null for unlabeled instances
          result.setTargetClassId(classIdTarget == 0 ? null
              : classIdTarget);
          int classIdPredicted = 0;
          try {
            double dPredict = Double.parseDouble(predictionLine);
            if (dPredict > 0)
              classIdPredicted = 1;
            else
              classIdPredicted = -1;
          } catch (NumberFormatException nfe) {
            System.err.println("error parsing:" + predictionLine);
            nfe.printStackTrace(System.err);
          }
          result.setPredictedClassId(classIdPredicted);
          result.setInstanceId(instanceId);
          result.setClassifierEvaluation(eval);
          eval.getClassifierInstanceEvaluations().put(instanceId,
              result);
        }
      }
    } finally {
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    @Override
    public void importInstanceResult(Integer instanceNumber,
        List<String> instanceKey, String task, int classAuto,
        int classGold, List<Double> predictions) {
      ClassifierInstanceEvaluation ci = new ClassifierInstanceEvaluation();
      if (instanceKey.size() > 0) {
        try {
          ci.setInstanceId(Integer.parseInt(instanceKey.get(0)));
        } catch (NumberFormatException nfe) {
        }
      } else {
        ci.setInstanceId(instanceNumber);
      }
      ci.setPredictedClassId(classAuto);
      ci.setTargetClassId(classGold);
      if (storeProbabilities && predictions != null
          && predictions.size() > 0) {
        for (int i = 0; i < predictions.size(); i++) {
          ci.getClassifierInstanceProbabilities().put(i,
              predictions.get(i));
        }
      }
      ci.setClassifierEvaluation(classifierEvaluation);
      sessionFactory.getCurrentSession().save(ci);
    }
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Related Classes of org.apache.ctakes.ytex.kernel.model.ClassifierInstanceEvaluation

  • org.apache.ctakes.ytex.kernel.BaseClassifierEvaluationParser
  • org.apache.ctakes.ytex.libsvm.LibSVMParser
  • org.apache.ctakes.ytex.svmlight.SVMLightParser
  • org.apache.ctakes.ytex.weka.WekaResultsImporterImpl$ClassifierEvaluationInstanceImporter
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