Package edu.isi.karma.modeling.alignment

Examples of edu.isi.karma.modeling.alignment.ModelEvaluation

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      }

      Map<String, SemanticModel> models =
          new TreeMap<String, SemanticModel>();

      ModelEvaluation me;
      models.put("1-correct model", correctModel);
      if (topHypotheses != null)
        for (int k = 0; k < topHypotheses.size(); k++) {

          SortableSemanticModel m = topHypotheses.get(k);

          me = m.evaluate(correctModel);

          String label = "candidate" + k +
              m.getSteinerNodes().getScoreDetailsString() +
              "cost:" + roundDecimals(m.getCost(), 6) +
              //                "-distance:" + me.getDistance() +
              "-precision:" + me.getPrecision() +
              "-recall:" + me.getRecall();

          models.put(label, m);

          if (k == 0) { // first rank model
            System.out.println("precision: " + me.getPrecision() +
                ", recall: " + me.getRecall() +
                ", time: " + elapsedTimeSec);
            logger.info("precision: " + me.getPrecision() +
                ", recall: " + me.getRecall() +
                ", time: " + elapsedTimeSec);
            String s = newSource.getName() + me.getPrecision() + "\t" + me.getRecall() + "\t" + elapsedTimeSec;
            resultFile.println(s);

          }
        }
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        //            new SemanticModel(m).writeJson(Params.MODEL_DIR +
        //                newSource.getName() + fileExt);
        //           
        //          }

        ModelEvaluation me;
        models.put("1-correct model", correctModel);
        if (topHypotheses != null)
          for (int k = 0; k < topHypotheses.size(); k++) {

            SortableSemanticModel m = topHypotheses.get(k);

            me = m.evaluate(correctModel);

            String label = "candidate" + k +
                m.getSteinerNodes().getScoreDetailsString() +
                "cost:" + roundDecimals(m.getCost(), 6) +
                //                "-distance:" + me.getDistance() +
                "-precision:" + me.getPrecision() +
                "-recall:" + me.getRecall();

            models.put(label, m);

            if (k == 0) { // first rank model
              System.out.println("number of known models: " + numberOfKnownModels +
                  ", precision: " + me.getPrecision() +
                  ", recall: " + me.getRecall() +
                  ", time: " + elapsedTimeSec);
              logger.info("number of known models: " + numberOfKnownModels +
                  ", precision: " + me.getPrecision() +
                  ", recall: " + me.getRecall() +
                  ", time: " + elapsedTimeSec);
//              resultFile.println("number of known models \t precision \t recall");
//              resultFile.println(numberOfKnownModels + "\t" + me.getPrecision() + "\t" + me.getRecall());
              String s = me.getPrecision() + "\t" + me.getRecall() + "\t" + elapsedTimeSec;
              if (resultsArray[numberOfKnownModels + 2].length() > 0)
                resultsArray[numberOfKnownModels + 2].append(" \t ");
              resultsArray[numberOfKnownModels + 2].append(s);

//              resultFile.println(me.getPrecision() + "\t" + me.getRecall() + "\t" + elapsedTimeSec);
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        //            new SemanticModel(m).writeJson(Params.MODEL_DIR +
        //                newSource.getName() + fileExt);
        //           
        //          }

        ModelEvaluation me;
        models.put("1-correct model", correctModel);
        if (topHypotheses != null)
          for (int k = 0; k < topHypotheses.size(); k++) {

            SortableSemanticModel_Old m = topHypotheses.get(k);

            me = m.evaluate(correctModel);

            String label = "candidate" + k +
                m.getSteinerNodes().getScoreDetailsString() +
                "cost:" + roundTwoDecimals(m.getCost()) +
                //                "-distance:" + me.getDistance() +
                "-precision:" + me.getPrecision() +
                "-recall:" + me.getRecall();

            models.put(label, m);

            if (k == 0) { // first rank model
              System.out.println("number of known models: " + numberOfKnownModels +
                  ", precision: " + me.getPrecision() +
                  ", recall: " + me.getRecall() +
                  ", time: " + elapsedTimeSec);
              logger.info("number of known models: " + numberOfKnownModels +
                  ", precision: " + me.getPrecision() +
                  ", recall: " + me.getRecall() +
                  ", time: " + elapsedTimeSec);
//              resultFile.println("number of known models \t precision \t recall");
//              resultFile.println(numberOfKnownModels + "\t" + me.getPrecision() + "\t" + me.getRecall());
              String s = me.getPrecision() + "\t" + me.getRecall() + "\t" + elapsedTimeSec;
              if (resultsArray[numberOfKnownModels + 2].length() > 0)
                resultsArray[numberOfKnownModels + 2].append(" \t ");
              resultsArray[numberOfKnownModels + 2].append(s);

//              resultFile.println(me.getPrecision() + "\t" + me.getRecall() + "\t" + elapsedTimeSec);
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Related Classes of edu.isi.karma.modeling.alignment.ModelEvaluation

  • edu.isi.karma.modeling.alignment.learner.ModelLearner
  • edu.isi.karma.modeling.alignment.learner.ModelLearner_Old
  • edu.isi.karma.research.lod.LODModelLearner
  • java.text.DecimalFormat
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