Java Code Examples for org.jpmml.converter.regression.RegressionModelUtil#createBinaryLogisticClassification()

The following examples show how to use org.jpmml.converter.regression.RegressionModelUtil#createBinaryLogisticClassification() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: LogNetConverter.java    From jpmml-r with GNU Affero General Public License v3.0 5 votes vote down vote up 
@Override
public Model encodeModel(RDoubleVector a0, RExp beta, int column, Schema schema){
	Double intercept = a0.getValue(column);
	List<Double> coefficients = getCoefficients((S4Object)beta, column);

	return RegressionModelUtil.createBinaryLogisticClassification(schema.getFeatures(), coefficients, intercept, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
Example 2
Source File: LinearModelUtil.java    From jpmml-sparkml with GNU Affero General Public License v3.0 4 votes vote down vote up 
static
public <C extends ModelConverter<?> & HasRegressionTableOptions> Model createBinaryLogisticClassification(C converter, Vector coefficients, double intercept, Schema schema){
	CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

	String representation = (String)converter.getOption(HasRegressionTableOptions.OPTION_REPRESENTATION, null);

	List<Feature> features = new ArrayList<>(schema.getFeatures());
	List<Double> featureCoefficients = new ArrayList<>(VectorUtil.toList(coefficients));

	RegressionTableUtil.simplify(converter, null, features, featureCoefficients);

	if(representation != null && (GeneralRegressionModel.class.getSimpleName()).equalsIgnoreCase(representation)){
		Object targetCategory = categoricalLabel.getValue(1);

		GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), null, null, null)
			.setLinkFunction(GeneralRegressionModel.LinkFunction.LOGIT);

		GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, featureCoefficients, intercept, targetCategory);

		return generalRegressionModel;
	}

	return RegressionModelUtil.createBinaryLogisticClassification(features, featureCoefficients, intercept, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
Example 3
Source File: LinearClassifier.java    From jpmml-sklearn with GNU Affero General Public License v3.0 2 votes vote down vote up 
@Override
public Model encodeModel(Schema schema){
	int[] shape = getCoefShape();

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

	boolean hasProbabilityDistribution = hasProbabilityDistribution();

	List<? extends Number> coef = getCoef();
	List<? extends Number> intercept = getIntercept();

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

	List<? extends Feature> features = schema.getFeatures();

	if(numberOfClasses == 1){
		SchemaUtil.checkSize(2, categoricalLabel);

		return RegressionModelUtil.createBinaryLogisticClassification(features, CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, 0), intercept.get(0), RegressionModel.NormalizationMethod.LOGIT, hasProbabilityDistribution, schema);
	} else

	if(numberOfClasses >= 3){
		SchemaUtil.checkSize(numberOfClasses, categoricalLabel);

		Schema segmentSchema = (schema.toAnonymousRegressorSchema(DataType.DOUBLE)).toEmptySchema();

		List<RegressionModel> regressionModels = new ArrayList<>();

		for(int i = 0, rows = categoricalLabel.size(); i < rows; i++){
			RegressionModel regressionModel = RegressionModelUtil.createRegression(features, CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, i), intercept.get(i), RegressionModel.NormalizationMethod.LOGIT, segmentSchema)
				.setOutput(ModelUtil.createPredictedOutput(FieldName.create("decisionFunction(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));

			regressionModels.add(regressionModel);
		}

		return MiningModelUtil.createClassification(regressionModels, RegressionModel.NormalizationMethod.SIMPLEMAX, hasProbabilityDistribution, schema);
	} else

	{
		throw new IllegalArgumentException();
	}
}