class TDigestFIModel extends Model[TDigestFIModel] with TDigestFIModelParams
Model/Transformer for transforming input feature data into a DataFrame containing "name" and "importance" columns, mapping feature name to its computed importance.
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- TDigestFIModel
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Instance Constructors
- new TDigestFIModel(uid: String, featTD: Array[TDigest], spark: SparkSession)
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$[T](param: Param[T]): T
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def
asInstanceOf[T0]: T0
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final
def
clear(param: Param[_]): TDigestFIModel.this.type
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def
clone(): AnyRef
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def
copy(extra: ParamMap): TDigestFIModel
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- TDigestFIModel → Model → Transformer → PipelineStage → Params
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
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final
val
deviationMeasure: Param[String]
Function to measure the change resulting from randomizing a feature value.
Function to measure the change resulting from randomizing a feature value. Defaults to "auto" (detects whether model is regression or classification). Options: "auto", "dev-rate" (class), "abs-dev" (reg), "rms-dev" (reg)
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- TDigestFIModelParams
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
explainParam(param: Param[_]): String
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def
explainParams(): String
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final
def
extractParamMap(): ParamMap
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final
def
extractParamMap(extra: ParamMap): ParamMap
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final
val
featureNames: StringArrayParam
Names to use for features.
Names to use for features. Defaults to f1, f2, ...
- Definition Classes
- TDigestFIModelParams
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final
val
featuresCol: Param[String]
Column containing feature vectors.
Column containing feature vectors. Expected type is ML Vector. Defaults to "features"
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- HasFeaturesCol
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def
finalize(): Unit
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- TDigestFIModel → AnyRef
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final
def
get[T](param: Param[T]): Option[T]
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def
getClass(): Class[_]
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def
getDefault[T](param: Param[T]): Option[T]
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final
def
getDeviationMeasure: String
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- TDigestFIModelParams
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final
def
getFeatureNames: Array[String]
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- TDigestFIModelParams
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final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
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final
def
getImportanceCol: String
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- TDigestFIModelParams
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final
def
getNameCol: String
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- TDigestFIModelParams
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final
def
getOrDefault[T](param: Param[T]): T
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def
getParam(paramName: String): Param[Any]
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final
def
getTargetModel: AnyRef
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- TDigestFIModelParams
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final
def
hasDefault[T](param: Param[T]): Boolean
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def
hasParam(paramName: String): Boolean
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def
hasParent: Boolean
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def
hashCode(): Int
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- @native()
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final
val
importanceCol: Param[String]
Column name to use for feature importances.
Column name to use for feature importances. Defaults to "importance"
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- TDigestFIModelParams
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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isDefined(param: Param[_]): Boolean
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def
isTraceEnabled(): Boolean
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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def
logInfo(msg: ⇒ String): Unit
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def
logName: String
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
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def
logTrace(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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def
logWarning(msg: ⇒ String): Unit
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final
val
nameCol: Param[String]
Column name to use for feature names.
Column name to use for feature names. Defaults to "name"
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- TDigestFIModelParams
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final
def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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final
def
notifyAll(): Unit
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lazy val
params: Array[Param[_]]
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var
parent: Estimator[TDigestFIModel]
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- Model
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def
save(path: String): Unit
- Definition Classes
- MLWritable
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- @Since( "1.6.0" ) @throws( ... )
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final
def
set(paramPair: ParamPair[_]): TDigestFIModel.this.type
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final
def
set(param: String, value: Any): TDigestFIModel.this.type
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final
def
set[T](param: Param[T], value: T): TDigestFIModel.this.type
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final
def
setDefault(paramPairs: ParamPair[_]*): TDigestFIModel.this.type
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final
def
setDefault[T](param: Param[T], value: T): TDigestFIModel.this.type
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final
def
setDeviationMeasure(value: String): TDigestFIModel.this.type
- Definition Classes
- TDigestFIModelParams
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final
def
setFeatureNames(value: Array[String]): TDigestFIModel.this.type
- Definition Classes
- TDigestFIModelParams
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final
def
setFeaturesCol(value: String): TDigestFIModel.this.type
- Definition Classes
- HasFeaturesCol
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final
def
setImportanceCol(value: String): TDigestFIModel.this.type
- Definition Classes
- TDigestFIModelParams
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final
def
setNameCol(value: String): TDigestFIModel.this.type
- Definition Classes
- TDigestFIModelParams
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def
setParent(parent: Estimator[TDigestFIModel]): TDigestFIModel
- Definition Classes
- Model
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final
def
setTargetModel(value: AnyRef): TDigestFIModel.this.type
- Definition Classes
- TDigestFIModelParams
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
val
targetModel: Param[AnyRef]
A predictive model to compute variable importances against.
A predictive model to compute variable importances against. No default.
- Definition Classes
- TDigestFIModelParams
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def
toString(): String
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def
transform(data: Dataset[_]): DataFrame
- Definition Classes
- TDigestFIModel → Transformer
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
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- @Since( "2.0.0" )
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
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- Transformer
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- @Since( "2.0.0" ) @varargs()
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def
transformSchema(schema: StructType): StructType
- Definition Classes
- TDigestFIModel → PipelineStage
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def
transformSchema(schema: StructType, logging: Boolean): StructType
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- PipelineStage
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val
uid: String
- Definition Classes
- TDigestFIModel → Identifiable
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
write: MLWriter
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- DefaultParamsWritable → MLWritable