Package org.data2semantics.proppred.learners.evaluation

Examples of org.data2semantics.proppred.learners.evaluation.F1

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    boolean inference = true;


    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

   
   
    for (int i : depths) { 
      resTable.newRow("WL RDF, depth="+i); 
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    resTable.setDigits(3);
   
   
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
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    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

    List<Double> targets = EvaluationUtils.createTarget(labels);

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
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    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

    List<Double> targets = EvaluationUtils.createTarget(labels);

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
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    return new Accuracy().computeScore(targetA, pred);
  }
 
  @Out(name="f1")
  public double getF1() {
    return new F1().computeScore(targetA, pred);
  }
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    // --------------
    // Learning Algorithm settings
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Error());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    svmParms.setNumFolds(10);
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    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
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    linParms.setSplitFraction((float) 0.7);
    linParms.setDoCrossValidation(false);

    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());


    ResultsTable resTable = new ResultsTable();
    resTable.setDigits(2);
    //resTable.setManWU(0.05);
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    // --------------
    // Learning Algorithm settings
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Error());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    svmParms.setNumFolds(10);
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    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Error());
    evalFuncs.add(new F1());

    List<Double> target = EvaluationUtils.createTarget(labels);

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Error());
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