@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type =
new ParameterTypeInt(
PARAMETER_DEGREE,
"Specifies the degree of the local fitted polynomial. Please keep in mind, that a higher degree than 2 will increase calculation time extremely and probably suffer from overfitting.",
0,
Integer.MAX_VALUE,
2);
type.setExpert(false);
types.add(type);
type =
new ParameterTypeDouble(
PARAMETER_RIDGE,
"Specifies the ridge factor. This factor is used to penalize high coefficients. In order to aviod overfitting this might be increased.",
0,
Double.POSITIVE_INFINITY,
0.000000001);
types.add(type);
type =
new ParameterTypeBoolean(
PARAMETER_USE_ROBUST_ESTIMATION,
"If checked, a reweighting of the examples is performed in order to downweight outliers",
false);
type.setExpert(false);
types.add(type);
type =
new ParameterTypeBoolean(
PARAMETER_USE_EXAMPLE_WEIGHTS,
"Indicates if example weights should be used if present in the given example set.",
true);
type.registerDependencyCondition(
new BooleanParameterCondition(this, PARAMETER_USE_ROBUST_ESTIMATION, false, false));
types.add(type);
type =
new ParameterTypeInt(
LocalPolynomialExampleWeightingOperator.PARAMETER_NUMBER_OF_ITERATIONS,
"The number of iterations performed for weight calculation.",
1,
Integer.MAX_VALUE,
20);
type.registerDependencyCondition(
new BooleanParameterCondition(this, PARAMETER_USE_ROBUST_ESTIMATION, false, true));
types.add(type);
types.addAll(DistanceMeasures.getParameterTypesForNumericals(this));
types.addAll(Neighborhoods.getParameterTypes(this));
types.addAll(SmoothingKernels.getParameterTypes(this));
return types;
}
@Override
public Model learn(ExampleSet exampleSet) throws OperatorException {
DistanceMeasure measure = DistanceMeasures.createMeasure(this);
measure.init(exampleSet);
GeometricDataCollection<RegressionData> data = new LinearList<RegressionData>(measure);
// check if weights should be used
boolean useWeights = getParameterAsBoolean(PARAMETER_USE_EXAMPLE_WEIGHTS);
// check if robust estimate should be performed: Then calculate weights and use it anyway
if (getParameterAsBoolean(PARAMETER_USE_ROBUST_ESTIMATION)) {
useWeights = true;
LocalPolynomialExampleWeightingOperator weightingOperator;
try {
weightingOperator =
OperatorService.createOperator(LocalPolynomialExampleWeightingOperator.class);
exampleSet = weightingOperator.doWork((ExampleSet) exampleSet.clone(), this);
} catch (OperatorCreationException e) {
throw new UserError(this, 904, "LocalPolynomialExampleWeighting", e.getMessage());
}
}
Attributes attributes = exampleSet.getAttributes();
Attribute label = attributes.getLabel();
Attribute weightAttribute = attributes.getWeight();
for (Example example : exampleSet) {
double[] values = new double[attributes.size()];
double labelValue = example.getValue(label);
double weight = 1d;
if (weightAttribute != null && useWeights) {
weight = example.getValue(weightAttribute);
}
// filter out examples without influence
if (weight > 0d) {
// copying example values
int i = 0;
for (Attribute attribute : attributes) {
values[i] = example.getValue(attribute);
i++;
}
// inserting into geometric data collection
data.add(values, new RegressionData(values, labelValue, weight));
}
}
return new LocalPolynomialRegressionModel(
exampleSet,
data,
Neighborhoods.createNeighborhood(this),
SmoothingKernels.createKernel(this),
getParameterAsInt(PARAMETER_DEGREE),
getParameterAsDouble(PARAMETER_RIDGE));
}
