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org.dmg.pmml.anomaly_detection.AnomalyDetectionModel Maven / Gradle / Ivy
package org.dmg.pmml.anomaly_detection;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.annotation.JsonPropertyOrder;
import com.fasterxml.jackson.annotation.JsonRootName;
import com.fasterxml.jackson.annotation.JsonSubTypes;
import com.fasterxml.jackson.annotation.JsonTypeInfo;
import jakarta.xml.bind.annotation.XmlAttribute;
import jakarta.xml.bind.annotation.XmlElement;
import jakarta.xml.bind.annotation.XmlElements;
import jakarta.xml.bind.annotation.XmlRootElement;
import jakarta.xml.bind.annotation.XmlType;
import org.dmg.pmml.Extension;
import org.dmg.pmml.HasExtensions;
import org.dmg.pmml.LocalTransformations;
import org.dmg.pmml.MathContext;
import org.dmg.pmml.MiningFunction;
import org.dmg.pmml.MiningSchema;
import org.dmg.pmml.ModelVerification;
import org.dmg.pmml.Output;
import org.dmg.pmml.PMMLObject;
import org.dmg.pmml.Visitor;
import org.dmg.pmml.VisitorAction;
import org.jpmml.model.MissingAttributeException;
import org.jpmml.model.MissingElementException;
import org.jpmml.model.annotations.ValueConstructor;
@XmlRootElement(name = "AnomalyDetectionModel", namespace = "http://www.dmg.org/PMML-4_4")
@XmlType(name = "", propOrder = {
"extensions",
"miningSchema",
"output",
"localTransformations",
"modelVerification",
"model",
"meanClusterDistances"
})
@org.jpmml.model.annotations.Added((org.dmg.pmml.Version.PMML_4_4))
@JsonRootName("AnomalyDetectionModel")
@JsonPropertyOrder({
"modelName",
"algorithmName",
"miningFunction",
"algorithmType",
"sampleDataSize",
"scorable",
"mathContext",
"extensions",
"miningSchema",
"output",
"localTransformations",
"modelVerification",
"model",
"meanClusterDistances"
})
public class AnomalyDetectionModel
extends org.dmg.pmml.Model
implements HasExtensions
{
@XmlAttribute(name = "modelName")
@JsonProperty("modelName")
private String modelName;
@XmlAttribute(name = "algorithmName")
@JsonProperty("algorithmName")
private String algorithmName;
@XmlAttribute(name = "functionName", required = true)
@JsonProperty("functionName")
private MiningFunction miningFunction;
@XmlAttribute(name = "algorithmType", required = true)
@JsonProperty("algorithmType")
private String algorithmType;
@XmlAttribute(name = "sampleDataSize")
@JsonProperty("sampleDataSize")
private String sampleDataSize;
@XmlAttribute(name = "isScorable")
@JsonProperty("isScorable")
private Boolean scorable;
@XmlAttribute(name = "x-mathContext")
@org.jpmml.model.annotations.Added((org.dmg.pmml.Version.XPMML))
@JsonProperty("x-mathContext")
private MathContext mathContext;
@XmlElement(name = "Extension", namespace = "http://www.dmg.org/PMML-4_4")
@JsonProperty("Extension")
private List extensions;
@XmlElement(name = "MiningSchema", namespace = "http://www.dmg.org/PMML-4_4", required = true)
@JsonProperty("MiningSchema")
private MiningSchema miningSchema;
@XmlElement(name = "Output", namespace = "http://www.dmg.org/PMML-4_4")
@JsonProperty("Output")
private Output output;
@XmlElement(name = "LocalTransformations", namespace = "http://www.dmg.org/PMML-4_4")
@JsonProperty("LocalTransformations")
private LocalTransformations localTransformations;
@XmlElement(name = "ModelVerification", namespace = "http://www.dmg.org/PMML-4_4")
@JsonProperty("ModelVerification")
private ModelVerification modelVerification;
@XmlElements({
@XmlElement(name = "AnomalyDetectionModel", namespace = "http://www.dmg.org/PMML-4_4", type = AnomalyDetectionModel.class),
@XmlElement(name = "AssociationModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.association.AssociationModel.class),
@XmlElement(name = "BayesianNetworkModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.bayesian_network.BayesianNetworkModel.class),
@XmlElement(name = "BaselineModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.baseline.BaselineModel.class),
@XmlElement(name = "ClusteringModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.clustering.ClusteringModel.class),
@XmlElement(name = "GaussianProcessModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.gaussian_process.GaussianProcessModel.class),
@XmlElement(name = "GeneralRegressionModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.general_regression.GeneralRegressionModel.class),
@XmlElement(name = "MiningModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.mining.MiningModel.class),
@XmlElement(name = "NaiveBayesModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.naive_bayes.NaiveBayesModel.class),
@XmlElement(name = "NearestNeighborModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.nearest_neighbor.NearestNeighborModel.class),
@XmlElement(name = "NeuralNetwork", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.neural_network.NeuralNetwork.class),
@XmlElement(name = "RegressionModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.regression.RegressionModel.class),
@XmlElement(name = "RuleSetModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.rule_set.RuleSetModel.class),
@XmlElement(name = "SequenceModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.sequence.SequenceModel.class),
@XmlElement(name = "Scorecard", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.scorecard.Scorecard.class),
@XmlElement(name = "SupportVectorMachineModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.support_vector_machine.SupportVectorMachineModel.class),
@XmlElement(name = "TextModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.text.TextModel.class),
@XmlElement(name = "TimeSeriesModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.time_series.TimeSeriesModel.class),
@XmlElement(name = "TreeModel", namespace = "http://www.dmg.org/PMML-4_4", type = org.dmg.pmml.tree.TreeModel.class)
})
@JsonProperty("Model")
@JsonTypeInfo(include = JsonTypeInfo.As.WRAPPER_OBJECT, use = JsonTypeInfo.Id.NAME)
@JsonSubTypes({
@JsonSubTypes.Type(name = "AnomalyDetectionModel", value = org.dmg.pmml.anomaly_detection.AnomalyDetectionModel.class),
@JsonSubTypes.Type(name = "AssociationModel", value = org.dmg.pmml.association.AssociationModel.class),
@JsonSubTypes.Type(name = "BayesianNetworkModel", value = org.dmg.pmml.bayesian_network.BayesianNetworkModel.class),
@JsonSubTypes.Type(name = "BaselineModel", value = org.dmg.pmml.baseline.BaselineModel.class),
@JsonSubTypes.Type(name = "ClusteringModel", value = org.dmg.pmml.clustering.ClusteringModel.class),
@JsonSubTypes.Type(name = "GaussianProcessModel", value = org.dmg.pmml.gaussian_process.GaussianProcessModel.class),
@JsonSubTypes.Type(name = "GeneralRegressionModel", value = org.dmg.pmml.general_regression.GeneralRegressionModel.class),
@JsonSubTypes.Type(name = "MiningModel", value = org.dmg.pmml.mining.MiningModel.class),
@JsonSubTypes.Type(name = "NaiveBayesModel", value = org.dmg.pmml.naive_bayes.NaiveBayesModel.class),
@JsonSubTypes.Type(name = "NearestNeighborModel", value = org.dmg.pmml.nearest_neighbor.NearestNeighborModel.class),
@JsonSubTypes.Type(name = "NeuralNetwork", value = org.dmg.pmml.neural_network.NeuralNetwork.class),
@JsonSubTypes.Type(name = "RegressionModel", value = org.dmg.pmml.regression.RegressionModel.class),
@JsonSubTypes.Type(name = "RuleSetModel", value = org.dmg.pmml.rule_set.RuleSetModel.class),
@JsonSubTypes.Type(name = "SequenceModel", value = org.dmg.pmml.sequence.SequenceModel.class),
@JsonSubTypes.Type(name = "Scorecard", value = org.dmg.pmml.scorecard.Scorecard.class),
@JsonSubTypes.Type(name = "SupportVectorMachineModel", value = org.dmg.pmml.support_vector_machine.SupportVectorMachineModel.class),
@JsonSubTypes.Type(name = "TextModel", value = org.dmg.pmml.text.TextModel.class),
@JsonSubTypes.Type(name = "TimeSeriesModel", value = org.dmg.pmml.time_series.TimeSeriesModel.class),
@JsonSubTypes.Type(name = "TreeModel", value = org.dmg.pmml.tree.TreeModel.class)
})
private org.dmg.pmml.Model model;
@XmlElement(name = "MeanClusterDistances", namespace = "http://www.dmg.org/PMML-4_4")
@JsonProperty("MeanClusterDistances")
private MeanClusterDistances meanClusterDistances;
private final static Boolean DEFAULT_SCORABLE = true;
private final static long serialVersionUID = 67371270L;
public AnomalyDetectionModel() {
}
@ValueConstructor
public AnomalyDetectionModel(
@org.jpmml.model.annotations.Property("miningFunction")
MiningFunction miningFunction,
@org.jpmml.model.annotations.Property("algorithmType")
String algorithmType,
@org.jpmml.model.annotations.Property("miningSchema")
MiningSchema miningSchema,
@org.jpmml.model.annotations.Property("model")
org.dmg.pmml.Model model) {
this.miningFunction = miningFunction;
this.algorithmType = algorithmType;
this.miningSchema = miningSchema;
this.model = model;
}
@Override
public String getModelName() {
return modelName;
}
@Override
public AnomalyDetectionModel setModelName(
@org.jpmml.model.annotations.Property("modelName")
String modelName) {
this.modelName = modelName;
return this;
}
@Override
public String getAlgorithmName() {
return algorithmName;
}
@Override
public AnomalyDetectionModel setAlgorithmName(
@org.jpmml.model.annotations.Property("algorithmName")
String algorithmName) {
this.algorithmName = algorithmName;
return this;
}
@Override
public MiningFunction requireMiningFunction() {
if (this.miningFunction == null) {
throw new MissingAttributeException(this, PMMLAttributes.ANOMALYDETECTIONMODEL_MININGFUNCTION);
}
return this.miningFunction;
}
@Override
public MiningFunction getMiningFunction() {
return miningFunction;
}
@Override
public AnomalyDetectionModel setMiningFunction(
@org.jpmml.model.annotations.Property("miningFunction")
MiningFunction miningFunction) {
this.miningFunction = miningFunction;
return this;
}
public String requireAlgorithmType() {
if (this.algorithmType == null) {
throw new MissingAttributeException(this, PMMLAttributes.ANOMALYDETECTIONMODEL_ALGORITHMTYPE);
}
return this.algorithmType;
}
public String getAlgorithmType() {
return algorithmType;
}
public AnomalyDetectionModel setAlgorithmType(
@org.jpmml.model.annotations.Property("algorithmType")
String algorithmType) {
this.algorithmType = algorithmType;
return this;
}
public String getSampleDataSize() {
return sampleDataSize;
}
public AnomalyDetectionModel setSampleDataSize(
@org.jpmml.model.annotations.Property("sampleDataSize")
String sampleDataSize) {
this.sampleDataSize = sampleDataSize;
return this;
}
@Override
public boolean isScorable() {
if (scorable == null) {
return DEFAULT_SCORABLE;
} else {
return scorable;
}
}
@Override
public AnomalyDetectionModel setScorable(
@org.jpmml.model.annotations.Property("scorable")
Boolean scorable) {
this.scorable = scorable;
return this;
}
@Override
public MathContext getMathContext() {
if (mathContext == null) {
return MathContext.DOUBLE;
} else {
return mathContext;
}
}
@Override
public AnomalyDetectionModel setMathContext(
@org.jpmml.model.annotations.Property("mathContext")
MathContext mathContext) {
this.mathContext = mathContext;
return this;
}
@Override
public boolean hasExtensions() {
return ((this.extensions!= null)&&(!this.extensions.isEmpty()));
}
@Override
public List getExtensions() {
if (extensions == null) {
extensions = new ArrayList();
}
return this.extensions;
}
@Override
public AnomalyDetectionModel addExtensions(Extension... extensions) {
getExtensions().addAll(Arrays.asList(extensions));
return this;
}
@Override
public MiningSchema requireMiningSchema() {
if (this.miningSchema == null) {
throw new MissingElementException(this, PMMLElements.ANOMALYDETECTIONMODEL_MININGSCHEMA);
}
return this.miningSchema;
}
@Override
public MiningSchema getMiningSchema() {
return miningSchema;
}
@Override
public AnomalyDetectionModel setMiningSchema(
@org.jpmml.model.annotations.Property("miningSchema")
MiningSchema miningSchema) {
this.miningSchema = miningSchema;
return this;
}
@Override
public Output getOutput() {
return output;
}
@Override
public AnomalyDetectionModel setOutput(
@org.jpmml.model.annotations.Property("output")
Output output) {
this.output = output;
return this;
}
@Override
public LocalTransformations getLocalTransformations() {
return localTransformations;
}
@Override
public AnomalyDetectionModel setLocalTransformations(
@org.jpmml.model.annotations.Property("localTransformations")
LocalTransformations localTransformations) {
this.localTransformations = localTransformations;
return this;
}
@Override
public ModelVerification getModelVerification() {
return modelVerification;
}
@Override
public AnomalyDetectionModel setModelVerification(
@org.jpmml.model.annotations.Property("modelVerification")
ModelVerification modelVerification) {
this.modelVerification = modelVerification;
return this;
}
public org.dmg.pmml.Model requireModel() {
if (this.model == null) {
throw new MissingElementException(this, PMMLElements.ANOMALYDETECTIONMODEL_MODEL);
}
return this.model;
}
public org.dmg.pmml.Model getModel() {
return model;
}
public AnomalyDetectionModel setModel(
@org.jpmml.model.annotations.Property("model")
org.dmg.pmml.Model model) {
this.model = model;
return this;
}
public MeanClusterDistances getMeanClusterDistances() {
return meanClusterDistances;
}
public AnomalyDetectionModel setMeanClusterDistances(
@org.jpmml.model.annotations.Property("meanClusterDistances")
MeanClusterDistances meanClusterDistances) {
this.meanClusterDistances = meanClusterDistances;
return this;
}
@Override
public VisitorAction accept(Visitor visitor) {
VisitorAction status = visitor.visit(this);
if (status == VisitorAction.CONTINUE) {
visitor.pushParent(this);
if ((status == VisitorAction.CONTINUE)&&hasExtensions()) {
status = PMMLObject.traverse(visitor, getExtensions());
}
if (status == VisitorAction.CONTINUE) {
status = PMMLObject.traverse(visitor, getMiningSchema(), getOutput(), getLocalTransformations(), getModelVerification(), getModel(), getMeanClusterDistances());
}
visitor.popParent();
}
if (status == VisitorAction.TERMINATE) {
return VisitorAction.TERMINATE;
}
return VisitorAction.CONTINUE;
}
}
