Java Code Examples for org.jpmml.converter.Feature#toContinuousFeature()

static
private Schema toTreeModelSchema(DataType dataType, Schema schema){
	Function<Feature, Feature> function = new Function<Feature, Feature>(){

		@Override
		public Feature apply(Feature feature){

			if(feature instanceof BinaryFeature){
				BinaryFeature binaryFeature = (BinaryFeature)feature;

				return binaryFeature;
			} else

			{
				ContinuousFeature continuousFeature = feature.toContinuousFeature(dataType);

				return continuousFeature;
			}
		}
	};

	return schema.toTransformedSchema(function);
}
 

static
public Feature encodeIndexFeature(Feature feature, List<?> categories, List<? extends Number> indexCategories, Number mapMissingTo, Number defaultValue, DataType dataType, SkLearnEncoder encoder){
	ClassDictUtil.checkSize(categories, indexCategories);

	encoder.toCategorical(feature.getName(), categories);

	Supplier<MapValues> mapValuesSupplier = () -> {
		MapValues mapValues = PMMLUtil.createMapValues(feature.getName(), categories, indexCategories)
			.setMapMissingTo(mapMissingTo)
			.setDefaultValue(defaultValue);

		return mapValues;
	};

	DerivedField derivedField = encoder.ensureDerivedField(FeatureUtil.createName("encoder", feature), OpType.CATEGORICAL, dataType, mapValuesSupplier);

	Feature encodedFeature = new IndexFeature(encoder, derivedField, indexCategories);

	Feature result = new CategoricalFeature(encoder, feature, categories){

		@Override
		public ContinuousFeature toContinuousFeature(){
			return encodedFeature.toContinuousFeature();
		}
	};

	return result;
}
 

private void scaleFeatures(RExpEncoder encoder){
	RGenericVector mvr = getObject();

	RDoubleVector scale = mvr.getDoubleElement("scale", false);
	if(scale == null){
		return;
	}

	List<Feature> features = encoder.getFeatures();

	if(scale.size() != features.size()){
		throw new IllegalArgumentException();
	}

	for(int i = 0; i < features.size(); i++){
		Feature feature = features.get(i);
		Double factor = scale.getValue(i);

		if(ValueUtil.isOne(factor)){
			continue;
		}

		ContinuousFeature continuousFeature = feature.toContinuousFeature();

		Apply apply = PMMLUtil.createApply(PMMLFunctions.DIVIDE, continuousFeature.ref(), PMMLUtil.createConstant(factor));

		DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("scale", feature), OpType.CONTINUOUS, DataType.DOUBLE, apply);

		features.set(i, new ContinuousFeature(encoder, derivedField));
	}
}
 

static
private Predicate encodePredicate(Feature feature, Node node, boolean left){
    FieldName name = feature.getName();
    SimplePredicate.Operator operator;
    String value;

    if(feature instanceof BinaryFeature){
        BinaryFeature binaryFeature = (BinaryFeature)feature;

        operator = (left ? SimplePredicate.Operator.NOT_EQUAL : SimplePredicate.Operator.EQUAL);
        value = binaryFeature.getValue();
    } else

    {
        ContinuousFeature continuousFeature = feature.toContinuousFeature();

        Number splitValue = node.getThreshold();

        DataType dataType = continuousFeature.getDataType();
        switch(dataType){
            case INTEGER:
                splitValue = (int)(splitValue.floatValue() + 1f);
                break;
            case FLOAT:
                break;
            default:
                throw new IllegalArgumentException();
        }

        operator = (left ? SimplePredicate.Operator.LESS_OR_EQUAL : SimplePredicate.Operator.GREATER_THAN);
        value = ValueUtil.formatValue(splitValue);
    }

    SimplePredicate simplePredicate = new SimplePredicate(name, operator)
            .setValue(value);

    return simplePredicate;
}
 

static
public <C extends ModelConverter<?> & HasXGBoostOptions> MiningModel encodeBooster(C converter, Booster booster, Schema schema){
	byte[] bytes = booster.toByteArray();

	Learner learner;

	try(InputStream is = new ByteArrayInputStream(bytes)){
		learner = XGBoostUtil.loadLearner(is);
	} catch(IOException ioe){
		throw new RuntimeException(ioe);
	}

	Function<Feature, Feature> function = new Function<Feature, Feature>(){

		@Override
		public Feature apply(Feature feature){

			if(feature instanceof BinaryFeature){
				BinaryFeature binaryFeature = (BinaryFeature)feature;

				return binaryFeature;
			} else

			{
				ContinuousFeature continuousFeature = feature.toContinuousFeature(DataType.FLOAT);

				return continuousFeature;
			}
		}
	};

	Map<String, Object> options = new LinkedHashMap<>();
	options.put(HasXGBoostOptions.OPTION_COMPACT, converter.getOption(HasXGBoostOptions.OPTION_COMPACT, false));
	options.put(HasXGBoostOptions.OPTION_NTREE_LIMIT, converter.getOption(HasXGBoostOptions.OPTION_NTREE_LIMIT, null));

	Schema xgbSchema = schema.toTransformedSchema(function);

	return learner.encodeMiningModel(options, xgbSchema);
}
 

public Schema toXGBoostSchema(Schema schema){
	Function<Feature, Feature> function = new Function<Feature, Feature>(){

		@Override
		public Feature apply(Feature feature){

			if(feature instanceof BinaryFeature){
				BinaryFeature binaryFeature = (BinaryFeature)feature;

				return binaryFeature;
			} else

			{
				ContinuousFeature continuousFeature = feature.toContinuousFeature();

				DataType dataType = continuousFeature.getDataType();
				switch(dataType){
					case INTEGER:
					case FLOAT:
						break;
					case DOUBLE:
						continuousFeature = continuousFeature.toContinuousFeature(DataType.FLOAT);
						break;
					default:
						throw new IllegalArgumentException("Expected integer, float or double data type, got " + dataType.value() + " data type");
				}

				return continuousFeature;
			}
		}
	};

	return schema.toTransformedSchema(function);
}
 

@Override
public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
	Boolean withCentering = getWithCentering();
	Boolean withScaling = getWithScaling();

	List<? extends Number> center = (withCentering ? getCenter() : null);
	List<? extends Number> scale = (withScaling ? getScale() : null);

	if(center == null && scale == null){
		return features;
	}

	ClassDictUtil.checkSize(features, center, scale);

	List<Feature> result = new ArrayList<>();

	for(int i = 0; i < features.size(); i++){
		Feature feature = features.get(i);

		Number centerValue = (withCentering ? center.get(i) : 0d);
		Number scaleValue = (withScaling ? scale.get(i) : 1d);

		if(ValueUtil.isZero(centerValue) && ValueUtil.isOne(scaleValue)){
			result.add(feature);

			continue;
		}

		ContinuousFeature continuousFeature = feature.toContinuousFeature();

		// "($name - center) / scale"
		Expression expression = continuousFeature.ref();

		if(!ValueUtil.isZero(centerValue)){
			expression = PMMLUtil.createApply(PMMLFunctions.SUBTRACT, expression, PMMLUtil.createConstant(centerValue));
		} // End if

		if(!ValueUtil.isOne(scaleValue)){
			expression = PMMLUtil.createApply(PMMLFunctions.DIVIDE, expression, PMMLUtil.createConstant(scaleValue));
		}

		DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("robust_scaler", continuousFeature), expression);

		result.add(new ContinuousFeature(encoder, derivedField));
	}

	return result;
}
 

@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(PMMLFunctions.MULTIPLY, expression, PMMLUtil.createConstant(scaleValue));
		} // End if

		if(!ValueUtil.isZero(minValue)){
			expression = PMMLUtil.createApply(PMMLFunctions.ADD, 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 List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
	Boolean withMean = getWithMean();
	Boolean withStd = getWithStd();

	List<? extends Number> mean = (withMean ? getMean() : null);
	List<? extends Number> std = (withStd ? getStd() : null);

	if(mean == null && std == null){
		return features;
	}

	ClassDictUtil.checkSize(features, mean, std);

	List<Feature> result = new ArrayList<>();

	for(int i = 0; i < features.size(); i++){
		Feature feature = features.get(i);

		Number meanValue = (withMean ? mean.get(i) : 0d);
		Number stdValue = (withStd ? std.get(i) : 1d);

		if(ValueUtil.isZero(meanValue) && ValueUtil.isOne(stdValue)){
			result.add(feature);

			continue;
		}

		ContinuousFeature continuousFeature = feature.toContinuousFeature();

		// "($name - mean) / std"
		Expression expression = continuousFeature.ref();

		if(!ValueUtil.isZero(meanValue)){
			expression = PMMLUtil.createApply(PMMLFunctions.SUBTRACT, expression, PMMLUtil.createConstant(meanValue));
		} // End if

		if(!ValueUtil.isOne(stdValue)){
			expression = PMMLUtil.createApply(PMMLFunctions.DIVIDE, expression, PMMLUtil.createConstant(stdValue));
		}

		DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("standard_scaler", continuousFeature), expression);

		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(PMMLFunctions.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){
	BSpline bspline = getBSpline();

	ClassDictUtil.checkSize(1, features);

	Feature feature = features.get(0);

	ContinuousFeature continuousFeature = feature.toContinuousFeature();

	DefineFunction defineFunction = createBSplineFunction(bspline, encoder);

	Apply apply = PMMLUtil.createApply(defineFunction.getName())
		.addExpressions(continuousFeature.ref());

	DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("bspline", feature), apply);

	return Collections.singletonList(new ContinuousFeature(encoder, derivedField));
}
 

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

	Vector mean = transformer.mean();
	Vector std = transformer.std();

	boolean withMean = transformer.getWithMean();
	boolean withStd = transformer.getWithStd();

	List<Feature> features = encoder.getFeatures(transformer.getInputCol());

	if(withMean){
		SchemaUtil.checkSize(mean.size(), features);
	} // End if

	if(withStd){
		SchemaUtil.checkSize(std.size(), features);
	}

	List<Feature> result = new ArrayList<>();

	for(int i = 0, length = features.size(); i < length; i++){
		Feature feature = features.get(i);

		FieldName name = formatName(transformer, i, length);

		Expression expression = null;

		if(withMean){
			double meanValue = mean.apply(i);

			if(!ValueUtil.isZero(meanValue)){
				ContinuousFeature continuousFeature = feature.toContinuousFeature();

				expression = PMMLUtil.createApply(PMMLFunctions.SUBTRACT, continuousFeature.ref(), PMMLUtil.createConstant(meanValue));
			}
		} // End if

		if(withStd){
			double stdValue = std.apply(i);

			if(!ValueUtil.isOne(stdValue)){
				Double factor = (1d / stdValue);

				if(expression != null){
					expression = PMMLUtil.createApply(PMMLFunctions.MULTIPLY, expression, PMMLUtil.createConstant(factor));
				} else

				{
					feature = new ProductFeature(encoder, feature, factor){

						@Override
						public ContinuousFeature toContinuousFeature(){
							Supplier<Apply> applySupplier = () -> {
								Feature feature = getFeature();
								Number factor = getFactor();

								return PMMLUtil.createApply(PMMLFunctions.MULTIPLY, (feature.toContinuousFeature()).ref(), PMMLUtil.createConstant(factor));
							};

							return toContinuousFeature(name, DataType.DOUBLE, applySupplier);
						}
					};
				}
			}
		} // End if

		if(expression != null){
			DerivedField derivedField = encoder.createDerivedField(name, OpType.CONTINUOUS, DataType.DOUBLE, expression);

			result.add(new ContinuousFeature(encoder, derivedField));
		} else

		{
			result.add(feature);
		}
	}

	return result;
}
 

private void scaleFeatures(RExpEncoder encoder){
	RGenericVector svm = getObject();

	RDoubleVector sv = svm.getDoubleElement("SV");
	RBooleanVector scaled = svm.getBooleanElement("scaled");
	RGenericVector xScale = svm.getGenericElement("x.scale");

	RStringVector rowNames = sv.dimnames(0);
	RStringVector columnNames = sv.dimnames(1);

	List<Feature> features = encoder.getFeatures();

	if((scaled.size() != columnNames.size()) || (scaled.size() != features.size())){
		throw new IllegalArgumentException();
	}

	RDoubleVector xScaledCenter = xScale.getDoubleElement("scaled:center");
	RDoubleVector xScaledScale = xScale.getDoubleElement("scaled:scale");

	for(int i = 0; i < columnNames.size(); i++){
		String columnName = columnNames.getValue(i);

		if(!scaled.getValue(i)){
			continue;
		}

		Feature feature = features.get(i);

		Double center = xScaledCenter.getElement(columnName);
		Double scale = xScaledScale.getElement(columnName);

		if(ValueUtil.isZero(center) && ValueUtil.isOne(scale)){
			continue;
		}

		ContinuousFeature continuousFeature = feature.toContinuousFeature();

		Expression expression = continuousFeature.ref();

		if(!ValueUtil.isZero(center)){
			expression = PMMLUtil.createApply(PMMLFunctions.SUBTRACT, expression, PMMLUtil.createConstant(center));
		} // End if

		if(!ValueUtil.isOne(scale)){
			expression = PMMLUtil.createApply(PMMLFunctions.DIVIDE, expression, PMMLUtil.createConstant(scale));
		}

		DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("scale", feature), OpType.CONTINUOUS, DataType.DOUBLE, expression);

		features.set(i, new ContinuousFeature(encoder, derivedField));
	}
}
 

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

	InOutMode inputMode = getInputMode();

	String[] inputCols;
	double[][] splitsArray;

	if((InOutMode.SINGLE).equals(inputMode)){
		inputCols = inputMode.getInputCols(transformer);
		splitsArray = new double[][]{transformer.getSplits()};
	} else

	if((InOutMode.MULTIPLE).equals(inputMode)){
		inputCols = inputMode.getInputCols(transformer);
		splitsArray = transformer.getSplitsArray();
	} else

	{
		throw new IllegalArgumentException();
	}

	List<Feature> result = new ArrayList<>();

	for(int i = 0; i < inputCols.length; i++){
		String inputCol = inputCols[i];
		double[] splits = splitsArray[i];

		Feature feature = encoder.getOnlyFeature(inputCol);

		ContinuousFeature continuousFeature = feature.toContinuousFeature();

		Discretize discretize = new Discretize(continuousFeature.getName())
			.setDataType(DataType.INTEGER);

		List<Integer> categories = new ArrayList<>();

		for(int j = 0; j < (splits.length - 1); j++){
			Integer category = j;

			categories.add(category);

			Interval interval = new Interval((j < (splits.length - 2)) ? Interval.Closure.CLOSED_OPEN : Interval.Closure.CLOSED_CLOSED)
				.setLeftMargin(formatMargin(splits[j]))
				.setRightMargin(formatMargin(splits[j + 1]));

			DiscretizeBin discretizeBin = new DiscretizeBin(category, interval);

			discretize.addDiscretizeBins(discretizeBin);
		}

		DerivedField derivedField = encoder.createDerivedField(formatName(transformer, i), OpType.CATEGORICAL, DataType.INTEGER, discretize);

		result.add(new IndexFeature(encoder, derivedField, categories));
	}

	return result;
}
 

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

	DenseMatrix pc = transformer.pc();

	List<Feature> features = encoder.getFeatures(transformer.getInputCol());

	MatrixUtil.checkRows(features.size(), pc);

	List<Feature> result = new ArrayList<>();

	for(int i = 0, length = transformer.getK(); i < length; i++){
		Apply apply = new Apply(PMMLFunctions.SUM);

		for(int j = 0; j < features.size(); j++){
			Feature feature = features.get(j);

			ContinuousFeature continuousFeature = feature.toContinuousFeature();

			Expression expression = continuousFeature.ref();

			Double coefficient = pc.apply(j, i);
			if(!ValueUtil.isOne(coefficient)){
				expression = PMMLUtil.createApply(PMMLFunctions.MULTIPLY, expression, PMMLUtil.createConstant(coefficient));
			}

			apply.addExpressions(expression);
		}

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

		result.add(new ContinuousFeature(encoder, derivedField));
	}

	return result;
}
 

@Override
public NaiveBayesModel encodeModel(Schema schema){
	int[] shape = getThetaShape();

	int numberOfClasses = shape[0];
	int numberOfFeatures = shape[1];

	List<? extends Number> theta = getTheta();
	List<? extends Number> sigma = getSigma();

	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

	BayesInputs bayesInputs = new BayesInputs();

	for(int i = 0; i < numberOfFeatures; i++){
		Feature feature = schema.getFeature(i);

		List<? extends Number> means = CMatrixUtil.getColumn(theta, numberOfClasses, numberOfFeatures, i);
		List<? extends Number> variances = CMatrixUtil.getColumn(sigma, numberOfClasses, numberOfFeatures, i);

		ContinuousFeature continuousFeature = feature.toContinuousFeature();

		BayesInput bayesInput = new BayesInput(continuousFeature.getName(), encodeTargetValueStats(categoricalLabel.getValues(), means, variances), null);

		bayesInputs.addBayesInputs(bayesInput);
	}

	List<Integer> classCount = getClassCount();

	BayesOutput bayesOutput = new BayesOutput(categoricalLabel.getName(), null)
		.setTargetValueCounts(encodeTargetValueCounts(categoricalLabel.getValues(), classCount));

	NaiveBayesModel naiveBayesModel = new NaiveBayesModel(0d, MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), bayesInputs, bayesOutput)
		.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));

	return naiveBayesModel;
}
 

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

	Double threshold = transformer.getThreshold();

	InOutMode inputMode = getInputMode();

	List<Feature> result = new ArrayList<>();

	String[] inputCols = inputMode.getInputCols(transformer);
	for(int i = 0; i < inputCols.length; i++){
		String inputCol = inputCols[i];

		Feature feature = encoder.getOnlyFeature(inputCol);

		ContinuousFeature continuousFeature = feature.toContinuousFeature();

		Apply apply = new Apply(PMMLFunctions.IF)
			.addExpressions(PMMLUtil.createApply(PMMLFunctions.LESSOREQUAL, continuousFeature.ref(), PMMLUtil.createConstant(threshold)))
			.addExpressions(PMMLUtil.createConstant(0d), PMMLUtil.createConstant(1d));

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

		result.add(new IndexFeature(encoder, derivedField, Arrays.asList(0d, 1d)));
	}

	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(PMMLFunctions.DIVIDE, continuousFeature.ref(), PMMLUtil.createConstant(value));

		DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("max_abs_scaler", continuousFeature), apply);

		result.add(new ContinuousFeature(encoder, derivedField));
	}

	return result;
}
 

@Override
public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
	int[] shape = getComponentsShape();

	int numberOfComponents = shape[0];
	int numberOfFeatures = shape[1];

	List<? extends Number> components = getComponents();
	List<? extends Number> mean = getMean();

	ClassDictUtil.checkSize(numberOfFeatures, features, mean);

	Boolean whiten = getWhiten();

	List<? extends Number> explainedVariance = (whiten ? getExplainedVariance() : null);

	ClassDictUtil.checkSize(numberOfComponents, explainedVariance);

	String id = "pca@" + String.valueOf(PCA.SEQUENCE.getAndIncrement());

	List<Feature> result = new ArrayList<>();

	for(int i = 0; i < numberOfComponents; i++){
		List<? extends Number> component = CMatrixUtil.getRow(components, numberOfComponents, numberOfFeatures, i);

		Apply apply = PMMLUtil.createApply(PMMLFunctions.SUM);

		for(int j = 0; j < numberOfFeatures; j++){
			Feature feature = features.get(j);

			Number meanValue = mean.get(j);
			Number componentValue = component.get(j);

			if(ValueUtil.isZero(meanValue) && ValueUtil.isOne(componentValue)){
				apply.addExpressions(feature.ref());

				continue;
			}

			ContinuousFeature continuousFeature = feature.toContinuousFeature();

			// "($name[i] - mean[i]) * component[i]"
			Expression expression = continuousFeature.ref();

			if(!ValueUtil.isZero(meanValue)){
				expression = PMMLUtil.createApply(PMMLFunctions.SUBTRACT, expression, PMMLUtil.createConstant(meanValue));
			} // End if

			if(!ValueUtil.isOne(componentValue)){
				expression = PMMLUtil.createApply(PMMLFunctions.MULTIPLY, expression, PMMLUtil.createConstant(componentValue));
			}

			apply.addExpressions(expression);
		}

		if(whiten){
			Number explainedVarianceValue = explainedVariance.get(i);

			if(!ValueUtil.isOne(explainedVarianceValue)){
				apply = PMMLUtil.createApply(PMMLFunctions.DIVIDE, apply, PMMLUtil.createConstant(Math.sqrt(ValueUtil.asDouble(explainedVarianceValue))));
			}
		}

		DerivedField derivedField = encoder.createDerivedField(FieldName.create(id + "[" + String.valueOf(i) + "]"), apply);

		result.add(new ContinuousFeature(encoder, derivedField));
	}

	return result;
}
 

@Override
public List<Feature> encodeFeatures(List<Feature> features, SkLearnEncoder encoder){
	int[] shape = getComponentsShape();

	int numberOfComponents = shape[0];
	int numberOfFeatures = shape[1];

	List<? extends Number> components = getComponents();

	ClassDictUtil.checkSize(numberOfFeatures, features);

	String id = "svd@" + String.valueOf(TruncatedSVD.SEQUENCE.getAndIncrement());

	List<Feature> result = new ArrayList<>();

	for(int i = 0; i < numberOfComponents; i++){
		List<? extends Number> component = CMatrixUtil.getRow(components, numberOfComponents, numberOfFeatures, i);

		Apply apply = PMMLUtil.createApply(PMMLFunctions.SUM);

		for(int j = 0; j < numberOfFeatures; j++){
			Feature feature = features.get(j);

			Number componentValue = component.get(j);

			if(ValueUtil.isOne(componentValue)){
				apply.addExpressions(feature.ref());

				continue;
			}

			ContinuousFeature continuousFeature = feature.toContinuousFeature();

			// "$name[i] * component[i]"
			Expression expression = PMMLUtil.createApply(PMMLFunctions.MULTIPLY, continuousFeature.ref(), PMMLUtil.createConstant(componentValue));

			apply.addExpressions(expression);
		}

		DerivedField derivedField = encoder.createDerivedField(FieldName.create(id + "[" + String.valueOf(i) + "]"), apply);

		result.add(new ContinuousFeature(encoder, derivedField));
	}

	return result;
}