org.dmg.pmml.regression.RegressionModel Java Examples

@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
	Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.DOUBLE);

	List<MiningModel> miningModels = new ArrayList<>();

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

	for(int i = 0, rows = categoricalLabel.size(), columns = (trees.size() / rows); i < rows; i++){
		MiningModel miningModel = createMiningModel(FortranMatrixUtil.getRow(trees, rows, columns, i), numIteration, segmentSchema)
			.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));

		miningModels.add(miningModel);
	}

	return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
 

static
public <V extends Number> Value<V> normalizeBinaryLogisticClassificationResult(RegressionModel.NormalizationMethod normalizationMethod, Value<V> value){

	switch(normalizationMethod){
		case NONE:
			return value.restrict(Numbers.DOUBLE_ZERO, Numbers.DOUBLE_ONE);
		case LOGIT:
			return value.inverseLogit();
		case PROBIT:
			return value.inverseProbit();
		case CLOGLOG:
			return value.inverseCloglog();
		case LOGLOG:
			return value.inverseLoglog();
		case CAUCHIT:
			return value.inverseCauchit();
		default:
			throw new IllegalArgumentException();
	}
}
 

static
public <V extends Number> Value<V> normalizeRegressionResult(RegressionModel.NormalizationMethod normalizationMethod, Value<V> value){

	switch(normalizationMethod){
		case NONE:
			return value;
		case SOFTMAX:
		case LOGIT:
			return value.inverseLogit();
		case EXP:
			return value.exp();
		case PROBIT:
			return value.inverseProbit();
		case CLOGLOG:
			return value.inverseCloglog();
		case LOGLOG:
			return value.inverseLoglog();
		case CAUCHIT:
			return value.inverseCauchit();
		default:
			throw new IllegalArgumentException();
	}
}
 

public static MiningModel encodeMiningModel(List<List<RegressionTree>> regTrees, float base_score, Schema schema){
    Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.FLOAT), schema.getFeatures());

    List<MiningModel> miningModels = new ArrayList<>();

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


    int numClasses = regTrees.size();
    for (int l=0;l<numClasses;l++){
        MiningModel miningModel = createMiningModel(regTrees.get(l), base_score, segmentSchema)
                .setOutput(ModelUtil.createPredictedOutput(FieldName.create("class_(" + categoricalLabel.getValue(l) + ")"), OpType.CONTINUOUS, DataType.FLOAT));
        miningModels.add(miningModel);
    }

    return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
 

@Override
public Model encodeModel(Schema schema){
	RGenericVector ada = getObject();

	RGenericVector model = ada.getGenericElement("model");

	RGenericVector trees = model.getGenericElement("trees");
	RDoubleVector alpha = model.getDoubleElement("alpha");

	List<TreeModel> treeModels = encodeTreeModels(trees);

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(null))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.WEIGHTED_SUM, treeModels, alpha.getValues()))
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("adaValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 2d, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
 

@Override
public Model encodeModel(Schema schema){
	Classifier gbdt = getGBDT();
	MultiOneHotEncoder ohe = getOHE();
	LinearClassifier lr = getLR();

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

	SchemaUtil.checkSize(2, categoricalLabel);

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

	Schema segmentSchema = schema.toAnonymousSchema();

	MiningModel miningModel = GBDTUtil.encodeModel(gbdt, ohe, coef, Iterables.getOnlyElement(intercept), segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("decisionFunction"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.LOGIT, lr.hasProbabilityDistribution(), schema);
}
 

/**
 * @param path Path.
 */
public static LogisticRegressionModel load(String path) {
    try (InputStream is = new FileInputStream(new File(path))) {
        PMML pmml = PMMLUtil.unmarshal(is);

        RegressionModel logRegMdl = (RegressionModel)pmml.getModels().get(0);

        RegressionTable regTbl = logRegMdl.getRegressionTables().get(0);

        Vector coefficients = new DenseVector(regTbl.getNumericPredictors().size());

        for (int i = 0; i < regTbl.getNumericPredictors().size(); i++)
            coefficients.set(i, regTbl.getNumericPredictors().get(i).getCoefficient());

        double interceptor = regTbl.getIntercept();

        return new LogisticRegressionModel(coefficients, interceptor);
    }
    catch (IOException | JAXBException | SAXException e) {
        e.printStackTrace();
    }

    return null;
}
 

@Override
public MiningModel encodeMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){
	Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.FLOAT);

	Transformation transformation = new FunctionTransformation(PMMLFunctions.THRESHOLD){

		@Override
		public FieldName getName(FieldName name){
			return FieldName.create("hinge(" + name + ")");
		}

		@Override
		public Expression createExpression(FieldRef fieldRef){
			Apply apply = (Apply)super.createExpression(fieldRef);

			apply.addExpressions(PMMLUtil.createConstant(0f));

			return apply;
		}
	};

	MiningModel miningModel = createMiningModel(trees, weights, base_score, ntreeLimit, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT, transformation));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.NONE, true, schema);
}
 

@Override
public MiningModel encodeMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){
	Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.FLOAT);

	List<MiningModel> miningModels = new ArrayList<>();

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

	for(int i = 0, columns = categoricalLabel.size(), rows = (trees.size() / columns); i < columns; i++){
		MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(trees, rows, columns, i), (weights != null) ? CMatrixUtil.getColumn(weights, rows, columns, i) : null, base_score, ntreeLimit, segmentSchema)
			.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.FLOAT));

		miningModels.add(miningModel);
	}

	return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
 

@Override
public MiningModel encodeModel(Schema schema){
	LinearSVCModel model = getTransformer();

	Transformation transformation = new AbstractTransformation(){

		@Override
		public Expression createExpression(FieldRef fieldRef){
			return PMMLUtil.createApply(PMMLFunctions.THRESHOLD)
				.addExpressions(fieldRef, PMMLUtil.createConstant(model.getThreshold()));
		}
	};

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

	Model linearModel = LinearModelUtil.createRegression(this, model.coefficients(), model.intercept(), segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("margin"), OpType.CONTINUOUS, DataType.DOUBLE, transformation));

	return MiningModelUtil.createBinaryLogisticClassification(linearModel, 1d, 0d, RegressionModel.NormalizationMethod.NONE, false, schema);
}
 

@Override
public MiningModel encodeModel(Schema schema){
	GBTClassificationModel model = getTransformer();

	String lossType = model.getLossType();
	switch(lossType){
		case "logistic":
			break;
		default:
			throw new IllegalArgumentException("Loss function " + lossType + " is not supported");
	}

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

	List<TreeModel> treeModels = TreeModelUtil.encodeDecisionTreeEnsemble(this, segmentSchema);

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(segmentSchema.getLabel()))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.WEIGHTED_SUM, treeModels, Doubles.asList(model.treeWeights())))
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbtValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 2d, 0d, RegressionModel.NormalizationMethod.LOGIT, false, schema);
}
 

@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);
}
 

@Test
public void computeOrdinalProbabilities(){
	ValueMap<String, Float> values = new ValueMap<>();
	values.put("loud", new FloatValue(0.2f));
	values.put("louder", new FloatValue(0.7f));
	values.put("insane", new FloatValue(1f));

	RegressionModelUtil.computeOrdinalProbabilities(RegressionModel.NormalizationMethod.NONE, values);

	assertEquals(new FloatValue(0.2f - 0f), values.get("loud"));
	assertEquals(new FloatValue(0.7f - 0.2f), values.get("louder"));
	assertEquals(new FloatValue(1f - 0.7f), values.get("insane"));
}
 

@Test
public void computeMultinomialProbabilities(){
	ValueMap<String, Float> values = new ValueMap<>();
	values.put("red", new FloatValue(0.3f));
	values.put("yellow", new FloatValue(0.5f));
	values.put("green", new FloatValue(Float.MAX_VALUE));

	RegressionModelUtil.computeMultinomialProbabilities(RegressionModel.NormalizationMethod.NONE, values);

	assertEquals(new FloatValue(0.3f), values.get("red"));
	assertEquals(new FloatValue(0.5f), values.get("yellow"));
	assertEquals(new FloatValue(1f - (0.3f + 0.5f)), values.get("green"));
}
 

@Test
public void computeBinomialProbabilities(){
	ValueMap<String, Float> values = new ValueMap<>();
	values.put("yes", new FloatValue(0.3f));
	values.put("no", new FloatValue(Float.MAX_VALUE));

	RegressionModelUtil.computeBinomialProbabilities(RegressionModel.NormalizationMethod.NONE, values);

	assertEquals(new FloatValue(0.3f), values.get("yes"));
	assertEquals(new FloatValue(1f - 0.3f), values.get("no"));
}
 

@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
	Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.DOUBLE);

	MiningModel miningModel = createMiningModel(trees, numIteration, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, BinomialLogisticRegression.this.sigmoid_, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
 

public RegressionModelEvaluator(PMML pmml, RegressionModel regressionModel){
	super(pmml, regressionModel);

	if(!regressionModel.hasRegressionTables()){
		throw new MissingElementException(regressionModel, PMMLElements.REGRESSIONMODEL_REGRESSIONTABLES);
	}
}
 

@Test
public void marshal() throws Exception {
	PMML pmml = new PMML(Version.PMML_4_4.getVersion(), new Header(), new DataDictionary());

	RegressionModel regressionModel = new RegressionModel()
		.addRegressionTables(new RegressionTable());

	pmml.addModels(regressionModel);

	JAXBContext context = JAXBContextFactory.createContext(new Class[]{org.dmg.pmml.ObjectFactory.class, org.dmg.pmml.regression.ObjectFactory.class}, null);

	Marshaller marshaller = context.createMarshaller();

	String string;

	try(ByteArrayOutputStream os = new ByteArrayOutputStream()){
		marshaller.marshal(pmml, os);

		string = os.toString("UTF-8");
	}

	assertTrue(string.contains("<PMML xmlns=\"http://www.dmg.org/PMML-4_4\""));
	assertTrue(string.contains(" version=\"4.4\">"));
	assertTrue(string.contains("<RegressionModel>"));
	assertTrue(string.contains("</RegressionModel>"));
	assertTrue(string.contains("</PMML>"));
}
 

@Test
public void inspectTypeAnnotations(){
	PMML pmml = createPMML();

	assertVersionRange(pmml, Version.PMML_3_0, Version.PMML_4_4);

	pmml.addModels(new AssociationModel(),
		//new ClusteringModel(),
		//new GeneralRegressionModel(),
		//new MiningModel(),
		new NaiveBayesModel(),
		new NeuralNetwork(),
		new RegressionModel(),
		new RuleSetModel(),
		new SequenceModel(),
		//new SupportVectorMachineModel(),
		new TextModel(),
		new TreeModel());

	assertVersionRange(pmml, Version.PMML_3_0, Version.PMML_4_4);

	pmml.addModels(new TimeSeriesModel());

	assertVersionRange(pmml, Version.PMML_4_0, Version.PMML_4_4);

	pmml.addModels(new BaselineModel(),
		new Scorecard(),
		new NearestNeighborModel());

	assertVersionRange(pmml, Version.PMML_4_1, Version.PMML_4_4);

	pmml.addModels(new BayesianNetworkModel(),
		new GaussianProcessModel());

	assertVersionRange(pmml, Version.PMML_4_3, Version.PMML_4_4);
}
 

@Test
public void copyState(){
	PMML pmml = new PMML(Version.PMML_4_4.getVersion(), new Header(), new DataDictionary());

	// Initialize a live list instance
	pmml.getModels();

	CustomPMML customPmml = new CustomPMML();

	ReflectionUtil.copyState(pmml, customPmml);

	assertSame(pmml.getVersion(), customPmml.getVersion());
	assertSame(pmml.getHeader(), customPmml.getHeader());
	assertSame(pmml.getDataDictionary(), customPmml.getDataDictionary());

	assertFalse(pmml.hasModels());
	assertFalse(customPmml.hasModels());

	pmml.addModels(new RegressionModel());

	assertTrue(pmml.hasModels());
	assertTrue(customPmml.hasModels());

	assertSame(pmml.getModels(), customPmml.getModels());

	try {
		ReflectionUtil.copyState(customPmml, pmml);

		fail();
	} catch(IllegalArgumentException iae){
		// Ignored
	}
}
 

@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
	Schema segmentSchema = schema.toAnonymousSchema();

	MiningModel miningModel = super.encodeMiningModel(trees, numIteration, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createRegression(miningModel, RegressionModel.NormalizationMethod.EXP, schema);
}
 

private MiningModel encodeBinaryClassification(List<TreeModel> treeModels, Double initF, double coefficient, Schema schema){
	Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.DOUBLE);

	MiningModel miningModel = createMiningModel(treeModels, initF, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, -coefficient, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
 

@Override
public MiningModel encodeMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){
	Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.FLOAT);

	MiningModel miningModel = createMiningModel(trees, weights, base_score, ntreeLimit, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT));

	return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
 

@Override
public MiningModel encodeMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){
	Schema segmentSchema = schema.toAnonymousSchema();

	MiningModel miningModel = createMiningModel(trees, weights, base_score, ntreeLimit, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT));

	return MiningModelUtil.createRegression(miningModel, RegressionModel.NormalizationMethod.LOGIT, schema);
}
 

@Override
public RegressionModel encodeModel(Schema schema){
	List<? extends Number> constant = getConstant();

	Number intercept = Iterables.getOnlyElement(constant);

	return RegressionModelUtil.createRegression(Collections.emptyList(), Collections.emptyList(), intercept.doubleValue(), null, schema);
}
 

@Override
public RegressionModel encodeModel(Schema schema){
	List<? extends Number> coef = getCoef();
	List<? extends Number> intercept = getIntercept();

	return RegressionModelUtil.createRegression(schema.getFeatures(), coef, Iterables.getOnlyElement(intercept), null, schema);
}
 

@Override
public RegressionModel encodeModel(Schema schema){
	RegressionModel regressionModel = super.encodeModel(schema)
		.setNormalizationMethod(RegressionModel.NormalizationMethod.EXP);

	return regressionModel;
}
 

@Override
public MiningModel encodeMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){
	Schema segmentSchema = schema.toAnonymousSchema();

	MiningModel miningModel = createMiningModel(trees, weights, base_score, ntreeLimit, segmentSchema)
		.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT));

	return MiningModelUtil.createRegression(miningModel, RegressionModel.NormalizationMethod.EXP, schema);
}
 

@Override
public RegressionModel encodeModel(TensorFlowEncoder encoder){
	DataField dataField = encoder.createDataField(FieldName.create("_target"), OpType.CONTINUOUS, DataType.FLOAT);

	Label label = new ContinuousLabel(dataField);

	RegressionModel regressionModel = encodeRegressionModel(encoder)
		.setMiningFunction(MiningFunction.REGRESSION)
		.setMiningSchema(ModelUtil.createMiningSchema(label));

	return regressionModel;
}
 

private Model encodeMultinomialModel(Schema schema){
	String sklearnVersion = getSkLearnVersion();
	int[] shape = getCoefShape();

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

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

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

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

	// See https://github.com/scikit-learn/scikit-learn/issues/6848
	boolean corrected = (sklearnVersion != null && SkLearnUtil.compareVersion(sklearnVersion, "0.21") >= 0);

	if(!corrected){
		return super.encodeModel(schema);
	} // End if

	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.NONE, segmentSchema)
				.setOutput(ModelUtil.createPredictedOutput(FieldName.create("decisionFunction(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));

			regressionModels.add(regressionModel);
		}

		return MiningModelUtil.createClassification(regressionModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
	} else

	{
		throw new IllegalArgumentException();
	}
}