public void apply(Example example) {
   if (applicable(example)) {
     double weight = 1.0d;
     if (example.getAttributes().getWeight() != null) {
       weight = example.getWeight();
     }
     coveredWeight += weight;
     if (example.getLabel()
         == example.getAttributes().getLabel().getMapping().getPositiveIndex()) {
       positiveWeight += weight;
     }
   }
 }
Пример #2
0
  protected void prepareWeights(ExampleSet exampleSet) {
    Attribute weightAttr = exampleSet.getAttributes().getWeight();
    if (weightAttr == null) {
      this.oldWeights = null;
      com.rapidminer.example.Tools.createWeightAttribute(exampleSet);
    } else { // Back up old weights
      this.oldWeights = new double[exampleSet.size()];
      Iterator<Example> reader = exampleSet.iterator();

      for (int i = 0; (reader.hasNext() && i < oldWeights.length); i++) {
        Example example = reader.next();
        if (example != null) {
          this.oldWeights[i] = example.getWeight();
          example.setWeight(1);
        }
      }
    }
  }
Пример #3
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  private BayBoostModel retrainLastWeight(
      BayBoostModel ensemble, ExampleSet exampleSet, Vector holdOutSet) throws OperatorException {
    this.prepareExtendedBatch(exampleSet); // method fits by chance
    int modelNum = ensemble.getNumberOfModels();
    Vector<BayBoostBaseModelInfo> modelInfo = new Vector<BayBoostBaseModelInfo>();
    double[] priors = ensemble.getPriors();
    for (int i = 0; i < modelNum - 1; i++) {
      Model model = ensemble.getModel(i);
      ContingencyMatrix cm = ensemble.getContingencyMatrix(i);
      modelInfo.add(new BayBoostBaseModelInfo(model, cm));
      exampleSet = model.apply(exampleSet);
      WeightedPerformanceMeasures.reweightExamples(exampleSet, cm, false);
    }
    Model latestModel = ensemble.getModel(modelNum - 1);
    exampleSet = latestModel.apply(exampleSet);

    // quite ugly:
    double[] weights = new double[holdOutSet.size()];
    Iterator it = holdOutSet.iterator();
    int index = 0;
    while (it.hasNext()) {
      Example example = (Example) it.next();
      weights[index++] = example.getWeight();
    }
    Iterator<Example> reader = exampleSet.iterator();
    while (reader.hasNext()) {
      reader.next().setWeight(0);
    }
    it = holdOutSet.iterator();
    index = 0;
    while (it.hasNext()) {
      Example example = (Example) it.next();
      example.setWeight(weights[index++]);
    }

    WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(exampleSet);
    modelInfo.add(new BayBoostBaseModelInfo(latestModel, wp.getContingencyMatrix()));

    return new BayBoostModel(exampleSet, modelInfo, priors);
  }
Пример #4
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 public void reinitOnlineCounting(ExampleSet exampleSet) {
   // counting one time all class weights
   labelAttribute = exampleSet.getAttributes().getLabel();
   weightAttribute = exampleSet.getAttributes().getWeight();
   totalLabelWeights = new double[labelAttribute.getMapping().size()];
   totalWeight = 0d;
   if (exampleSet.getAttributes().getWeight() != null) {
     for (Example example : exampleSet) {
       double weight = example.getWeight();
       totalLabelWeights[(int) example.getValue(labelAttribute)] += weight;
     }
   } else {
     for (Example example : exampleSet) {
       totalLabelWeights[(int) example.getValue(labelAttribute)] += 1d;
     }
   }
   for (int i = 0; i < totalLabelWeights.length; i++) {
     totalWeight += totalLabelWeights[i];
   }
   // resetting online counter for subtraction
   labelWeights = new double[labelAttribute.getMapping().size()];
   weight = 0;
 }