How to use

ref

method

in

org.jpmml.converter.ContinuousFeature

@Override
public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
  UFunc func = getFunc();
  if(func == null){
    return features;
  }
  List<Feature> result = new ArrayList<>();
  for(int i = 0; i < features.size(); i++){
    ContinuousFeature continuousFeature = (features.get(i)).toContinuousFeature();
    DerivedField derivedField = encoder.ensureDerivedField(FeatureUtil.createName(func.getName(), continuousFeature), OpType.CONTINUOUS, DataType.DOUBLE, () -> encodeUFunc(func, continuousFeature.ref()));
    result.add(new ContinuousFeature(encoder, derivedField));
  }
  return result;
}

@Override
public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
  Number threshold = getThreshold();
  List<Feature> result = new ArrayList<>();
  for(int i = 0; i < features.size(); i++){
    Feature feature = features.get(i);
    ContinuousFeature continuousFeature = feature.toContinuousFeature();
    // "($name <= threshold) ? 0 : 1"
    Apply apply = PMMLUtil.createApply("threshold", continuousFeature.ref(), PMMLUtil.createConstant(threshold));
    DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("binarizer", continuousFeature), apply);
    result.add(new ContinuousFeature(encoder, derivedField));
  }
  return result;
}

@Override
public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
  List<? extends Number> scale = getScale();
  ClassDictUtil.checkSize(features, scale);
  List<Feature> result = new ArrayList<>();
  for(int i = 0; i < features.size(); i++){
    Feature feature = features.get(i);
    Number value = scale.get(i);
    if(ValueUtil.isOne(value)){
      result.add(feature);
      continue;
    }
    ContinuousFeature continuousFeature = feature.toContinuousFeature();
    // "$name / scale"
    Apply apply = PMMLUtil.createApply("/", continuousFeature.ref(), PMMLUtil.createConstant(value));
    DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("max_abs_scaler", continuousFeature), apply);
    result.add(new ContinuousFeature(encoder, derivedField));
  }
  return result;
}

Apply apply = PMMLUtil.createApply("/", continuousFeature.ref(), PMMLUtil.createConstant(factor));

@Override
public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
  List<? extends Number> min = getMin();
  List<? extends Number> scale = getScale();
  ClassDictUtil.checkSize(features, min, scale);
  List<Feature> result = new ArrayList<>();
  for(int i = 0; i < features.size(); i++){
    Feature feature = features.get(i);
    Number minValue = min.get(i);
    Number scaleValue = scale.get(i);
    if(ValueUtil.isOne(scaleValue) && ValueUtil.isZero(minValue)){
      result.add(feature);
      continue;
    }
    ContinuousFeature continuousFeature = feature.toContinuousFeature();
    // "($name * scale) + min"
    Expression expression = continuousFeature.ref();
    if(!ValueUtil.isOne(scaleValue)){
      expression = PMMLUtil.createApply("*", expression, PMMLUtil.createConstant(scaleValue));
    } // End if
    if(!ValueUtil.isZero(minValue)){
      expression = PMMLUtil.createApply("+", expression, PMMLUtil.createConstant(minValue));
    }
    DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("mix_max_scaler", continuousFeature), expression);
    result.add(new ContinuousFeature(encoder, derivedField));
  }
  return result;
}

  @Override
  public ContinuousFeature toContinuousFeature(){
    Supplier<Apply> applySupplier = () -> {
      Feature feature = getFeature();
      Number factor = getFactor();
      return PMMLUtil.createApply("*", (feature.toContinuousFeature()).ref(), PMMLUtil.createConstant(factor));
    };
    return toContinuousFeature(name, DataType.DOUBLE, applySupplier);
  }
};

ContinuousFeature continuousFeature = feature.toContinuousFeature();
Expression expression = PMMLUtil.createApply("/", continuousFeature.ref(), PMMLUtil.createConstant(maxAbsUnzero));

Expression expression = continuousFeature.ref();

Expression expression = continuousFeature.ref();

Expression expression = continuousFeature.ref();

  @Override
  public List<Feature> encodeFeatures(SparkMLEncoder encoder){
    Binarizer transformer = getTransformer();

    Feature feature = encoder.getOnlyFeature(transformer.getInputCol());

    ContinuousFeature continuousFeature = feature.toContinuousFeature();

    Apply apply = new Apply("if")
      .addExpressions(PMMLUtil.createApply("lessOrEqual", continuousFeature.ref(), PMMLUtil.createConstant(transformer.getThreshold())))
      .addExpressions(PMMLUtil.createConstant(0d), PMMLUtil.createConstant(1d));

    DerivedField derivedField = encoder.createDerivedField(formatName(transformer), OpType.CATEGORICAL, DataType.DOUBLE, apply);

    return Collections.singletonList(new CategoricalFeature(encoder, derivedField, Arrays.asList("0", "1")));
  }
}

double min = originalMin.apply(i);
Expression expression = PMMLUtil.createApply("/", PMMLUtil.createApply("-", continuousFeature.ref(), PMMLUtil.createConstant(min)), PMMLUtil.createConstant(max - min));

ContinuousFeature continuousFeature = feature.toContinuousFeature();
expression = PMMLUtil.createApply("-", continuousFeature.ref(), PMMLUtil.createConstant(meanValue));

Expression expression = continuousFeature.ref();

Expression expression = continuousFeature.ref();