Model

abstract def attributes: ModelAttributes

Common attributes of this model

abstract def createOutputs(): ModelOutputs

Creates an object of subclass of ModelOutputs that is for writing into an output series.

abstract def localTransformations: Option[LocalTransformations]

The optional local transformations.

abstract def miningSchema: MiningSchema

abstract def modelElement: ModelElement

Model element type.

abstract def modelExplanation: Option[ModelExplanation]

abstract def modelStats: Option[ModelStats]

abstract def modelVerification: Option[ModelVerification]

abstract def output: Option[Output]

abstract val parent: Model

The parent model.

abstract def predict(values: Series): Series

Predicts values for a given data series.

abstract def targets: Option[Targets]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def algorithmName: Option[String]

The algorithm name is free-type and can be any description for the specific algorithm that produced the model.

def anyMissing(series: Series): Boolean

Returns true if there are any missing values of all input fields in the specified series.

final def asInstanceOf[T0]: T0

def candidateOutputFields: Array[OutputField]

def candidateOutputSchema: StructType

The schema of candidate outputs.

def classes(name: String): Array[Any]

Returns class labels of the specified target.

lazy val classes: Array[Any]

The class labels in a classification model.

def clone(): AnyRef

def combineOutputFields(listA: Array[OutputField], listB: Array[OutputField]): Array[OutputField]

def containInterResults: Boolean

var customOutputFields: Array[OutputField]

User-defined custom output fields, both the internal output of PMML and predefined output are ignored when the field is specified.

def dVersion: Double

Returns PMML version as a double value

def dataDictionary: DataDictionary

The data dictionary of this model.

def defaultOutputFields: Array[OutputField]

Returns all candidates output fields of this model when there is no output specified explicitly.

def encode(series: Series): DSeries

Encodes the input series.

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def extensions: Seq[Extension]

def field(name: String): Field

Returns the field of a given name.

def fieldsOfUsageType(typ: UsageType): Array[Field]

Get fields by its usage type: 'active', 'target', 'predicted', 'group' and so on

def finalize(): Unit

def functionName: MiningFunction

Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.

final def getClass(): Class[_]

def getField(name: String): Option[Field]

Returns the field of a given name, None if a field with the given name does not exist.

def hasExtensions: Boolean

def hasTarget: Boolean

def hashCode(): Int

def header: Header

The header of this model.

lazy val implicitInputDerivedFields: Array[Field]

Implicit referenced derived fields for the sub-model except ones defined in the mining schema.

def importances: Map[String, Double]

Returns importances of predictors.

def inferClasses: Array[Any]

The sub-classes can override this method to provide classes of target inside model.

lazy val inputDerivedFields: Array[Field]

Referenced derived fields.

lazy val inputFields: Array[Field]

All input fields in an array.

lazy val inputNames: Array[String]

All input names in an array.

lazy val inputSchema: StructType

The schema of inputs.

def isAssociationRules: Boolean

Tests if this is a association rules model.

def isBinary: Boolean

Tests if the target is a binary field

def isClassification(name: String): Boolean

Tests if this is a classification model of the specified target, it's applicable for multiple targets.

def isClassification: Boolean

Tests if this is a classification model.

def isClustering: Boolean

Tests if this is a clustering model.

final def isInstanceOf[T0]: Boolean

def isMixed: Boolean

Tests if this is a mixed model.

def isOrdinal: Boolean

Tests if the target is an ordinal field

def isPredictionOnly: Boolean

def isRegression(name: String): Boolean

Tests if this is a regression model of the specified target, it's applicable for multiple targets.

def isRegression: Boolean

Tests if this is a regression model.

def isScorable: Boolean

Indicates if the model is valid for scoring.

def isSequences: Boolean

Tests if this is a sequences model.

def isSubModel: Boolean

def isTimeSeries: Boolean

Tests if this is a time series model.

def isTopLevelModel: Boolean

def modelName: Option[String]

Identifies the model with a unique name in the context of the PMML file.

def multiTargets: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val nullSeries: Series

A series with all null values is returned when can not produce a result.

def numClasses(name: String): Int

Returns the number of class labels of the specified target.

lazy val numClasses: Int

The number of class labels in a classification model.

def opType(name: String): OpType

Returns optype of the specified target.

lazy val opType: OpType

When Target specifies optype then it overrides the optype attribute in a corresponding MiningField, if it exists.

def outputFields: Array[OutputField]

def outputIndex(feature: ResultFeature, value: Option[Any] = None): Int

def outputNames: Array[String]

def outputSchema: StructType

The schema of final outputs.

def postClassification(name: String = null): (Any, Map[Any, Double])

def postPredictedValue(outputs: MutablePredictedValue, name: String = null): MutablePredictedValue

def postRegression(predictedValue: Any, name: String = null): Any

def predict(it: Iterator[Series]): Iterator[Series]

def predict(json: String): String

Predicts one or multiple records in json format, there are two formats supported:

def predict(values: List[Any]): List[Any]

def predict[T](values: Array[T]): Array[Any]

Predicts values for a given Array, and the order of those values is supposed as same as the input fields list

def predict(values: (String, Any)*): Seq[(String, Any)]

Predicts values for a given list of key/value pairs.

def predict(values: Map[String, Any]): Map[String, Any]

Predicts values for a given data map of Java.

def predict(values: Map[String, Any]): Map[String, Any]

Predicts values for a given data map.

lazy val predictedValueIndex: Int

def prepare(series: Series): (Series, Boolean)

Pre-process the input series.

def probabilitiesSupported: Boolean

Tests if probabilities of categories of target can be produced by this model.

def result(series: Series, modelOutputs: ModelOutputs, fields: Array[OutputField] = Array.empty): Series

def setOutputFields(outputFields: Array[OutputField]): Model.this.type

def setParent(parent: Model): Model.this.type

def setSupplementOutput(value: Boolean): Model.this.type

def singleTarget: Boolean

def size: Int

var supplementOutput: Boolean

A flag for whether to return those predefined output fields not exist in the output element explicitly.

final def synchronized[T0](arg0: ⇒ T0): T0

lazy val targetClasses: Map[String, Array[Any]]

The class labels of all categorical targets.

lazy val targetField: Field

The first target field for the supervised model.

lazy val targetFields: Array[Field]

All target fields in an array.

def targetFieldsOfResidual: Array[Field]

Returns targets that are residual values to be computed, the input data must include target values.

def targetName: String

Name of the first target for the supervised model.

lazy val targetNames: Array[String]

All target names in an array.

def targetNamesOfResidual: Array[String]

def toString(): String

def transformationDictionary: Option[TransformationDictionary]

The optional transformation dictionary.

def unionCandidateOutputFields: Array[OutputField]

def unionOutputFields: Array[OutputField]

lazy val usedFields: Array[Field]

Setup indices to retrieve data from series faster by index instead of name, the index is immutable when model is built because the model object could run in multiple threads, so it's important make sure the model object is totally immutable.

lazy val usedSchema: StructType

The schema of used fields.

def version: String

PMML version.

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit