use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.
the class PMMLMiningSchemaTranslator method writeMiningSchema.
/**
* Writes the MiningSchema based upon the fields of the passed
* {@link PMMLPortObjectSpec}.
*
* @param portSpec based upon this port object spec the mining schema is
* written
* @param model the PMML model element to write the mining schema to
*/
public static void writeMiningSchema(final PMMLPortObjectSpec portSpec, final XmlObject model) {
MiningSchema miningSchema = MiningSchema.Factory.newInstance();
// avoid duplicate entries
Set<String> learningNames = new HashSet<String>(portSpec.getLearningFields());
Set<String> targetNames = new HashSet<String>(portSpec.getTargetFields());
for (String colName : portSpec.getLearningFields()) {
if (!targetNames.contains(colName)) {
MiningField miningField = miningSchema.addNewMiningField();
miningField.setName(colName);
miningField.setInvalidValueTreatment(INVALIDVALUETREATMENTMETHOD.AS_IS);
// don't write usageType = active (is default)
}
}
// add all fields referenced in local transformations
for (String colName : portSpec.getPreprocessingFields()) {
if (!learningNames.contains(colName) && !targetNames.contains(colName)) {
MiningField miningField = miningSchema.addNewMiningField();
miningField.setName(colName);
miningField.setInvalidValueTreatment(INVALIDVALUETREATMENTMETHOD.AS_IS);
// don't write usageType = active (is default)
}
}
// target columns = predicted
for (String colName : portSpec.getTargetFields()) {
MiningField miningField = miningSchema.addNewMiningField();
miningField.setName(colName);
miningField.setInvalidValueTreatment(INVALIDVALUETREATMENTMETHOD.AS_IS);
miningField.setUsageType(FIELDUSAGETYPE.TARGET);
}
/* Unfortunately the PMML models have no common base class. Therefore
* a cast to the specific type is necessary for being able to add the
* mining schema. */
SchemaType type = model.schemaType();
if (AssociationModel.type.equals(type)) {
((AssociationModel) model).setMiningSchema(miningSchema);
} else if (ClusteringModel.type.equals(type)) {
((ClusteringModel) model).setMiningSchema(miningSchema);
} else if (GeneralRegressionModel.type.equals(type)) {
((GeneralRegressionModel) model).setMiningSchema(miningSchema);
} else if (MiningModel.type.equals(type)) {
((MiningModel) model).setMiningSchema(miningSchema);
} else if (NaiveBayesModel.type.equals(type)) {
((NaiveBayesModel) model).setMiningSchema(miningSchema);
} else if (NeuralNetwork.type.equals(type)) {
((NeuralNetwork) model).setMiningSchema(miningSchema);
} else if (RegressionModel.type.equals(type)) {
((RegressionModel) model).setMiningSchema(miningSchema);
} else if (RuleSetModel.type.equals(type)) {
((RuleSetModel) model).setMiningSchema(miningSchema);
} else if (SequenceModel.type.equals(type)) {
((SequenceModel) model).setMiningSchema(miningSchema);
} else if (SupportVectorMachineModel.type.equals(type)) {
((SupportVectorMachineModel) model).setMiningSchema(miningSchema);
} else if (TextModel.type.equals(type)) {
((TextModel) model).setMiningSchema(miningSchema);
} else if (TimeSeriesModel.type.equals(type)) {
((TimeSeriesModel) model).setMiningSchema(miningSchema);
} else if (TreeModel.type.equals(type)) {
((TreeModel) model).setMiningSchema(miningSchema);
} else if (NearestNeighborModel.type.equals(type)) {
((NearestNeighborModel) model).setMiningSchema(miningSchema);
}
}
use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.
the class PMMLPortObject method moveDerivedFields.
/**
* Moves the content of the transformation dictionary to local
* transformations.
* @param type the type of model to move the derived fields to
* @return the {@link LocalTransformations} element containing the moved
* derived fields or an empty local transformation object if nothing
* has to be moved
*/
private LocalTransformations moveDerivedFields(final SchemaType type) {
PMML pmml = m_pmmlDoc.getPMML();
TransformationDictionary transDict = pmml.getTransformationDictionary();
LocalTransformations localTrans = LocalTransformations.Factory.newInstance();
if (transDict == null) {
// nothing to be moved
return localTrans;
}
localTrans.setDerivedFieldArray(transDict.getDerivedFieldArray());
localTrans.setExtensionArray(transDict.getExtensionArray());
/*
* Unfortunately the PMML models have no common base class. Therefore a
* cast to the specific type is necessary for being able to add the
* mining schema.
*/
boolean known = true;
if (AssociationModel.type.equals(type)) {
AssociationModel model = pmml.getAssociationModelArray(0);
model.setLocalTransformations(localTrans);
} else if (ClusteringModel.type.equals(type)) {
ClusteringModel model = pmml.getClusteringModelArray(0);
model.setLocalTransformations(localTrans);
} else if (GeneralRegressionModel.type.equals(type)) {
GeneralRegressionModel model = pmml.getGeneralRegressionModelArray(0);
model.setLocalTransformations(localTrans);
} else if (MiningModel.type.equals(type)) {
MiningModel model = pmml.getMiningModelArray(0);
model.setLocalTransformations(localTrans);
} else if (NaiveBayesModel.type.equals(type)) {
NaiveBayesModel model = pmml.getNaiveBayesModelArray(0);
model.setLocalTransformations(localTrans);
} else if (NeuralNetwork.type.equals(type)) {
NeuralNetwork model = pmml.getNeuralNetworkArray(0);
model.setLocalTransformations(localTrans);
} else if (RegressionModel.type.equals(type)) {
RegressionModel model = pmml.getRegressionModelArray(0);
model.setLocalTransformations(localTrans);
} else if (RuleSetModel.type.equals(type)) {
RuleSetModel model = pmml.getRuleSetModelArray(0);
model.setLocalTransformations(localTrans);
} else if (SequenceModel.type.equals(type)) {
SequenceModel model = pmml.getSequenceModelArray(0);
model.setLocalTransformations(localTrans);
} else if (SupportVectorMachineModel.type.equals(type)) {
SupportVectorMachineModel model = pmml.getSupportVectorMachineModelArray(0);
model.setLocalTransformations(localTrans);
} else if (TextModel.type.equals(type)) {
TextModel model = pmml.getTextModelArray(0);
model.setLocalTransformations(localTrans);
} else if (TimeSeriesModel.type.equals(type)) {
TimeSeriesModel model = pmml.getTimeSeriesModelArray(0);
model.setLocalTransformations(localTrans);
} else if (TreeModel.type.equals(type)) {
TreeModel model = pmml.getTreeModelArray(0);
model.setLocalTransformations(localTrans);
} else {
if (type != null) {
LOGGER.error("Could not move TransformationDictionary to " + "unsupported model of type \"" + type + "\".");
}
known = false;
}
if (known) {
// remove derived fields from TransformationDictionary
transDict.setDerivedFieldArray(new DerivedField[0]);
transDict.setExtensionArray(new ExtensionDocument.Extension[0]);
}
return localTrans;
}
use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.
the class ClassificationGBTModelExporter method addSoftmaxRegression.
private void addSoftmaxRegression(final Segment segment) {
RegressionModel reg = segment.addNewRegressionModel();
reg.setFunctionName(MININGFUNCTION.CLASSIFICATION);
reg.setNormalizationMethod(REGRESSIONNORMALIZATIONMETHOD.SOFTMAX);
addAggregationMiningScheme(reg);
addRegressionOutputs(reg);
addRegressionTables(reg);
}
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