com.microsoft.azure.synapse.ml.lightgbm
LightGBMRankerModel
Companion object LightGBMRankerModel
class LightGBMRankerModel extends RankerModel[Vector, LightGBMRankerModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable with SynapseMLLogging
Model produced by LightGBMRanker.
- Alphabetic
- By Inheritance
- LightGBMRankerModel
- SynapseMLLogging
- ComplexParamsWritable
- MLWritable
- LightGBMPredictionParams
- LightGBMModelMethods
- LightGBMModelParams
- Wrappable
- DotnetWrappable
- RWrappable
- PythonWrappable
- BaseWrappable
- RankerModel
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Value Members
-
final
def
clear(param: Param[_]): LightGBMRankerModel.this.type
- Definition Classes
- Params
-
def
copy(extra: ParamMap): LightGBMRankerModel
- Definition Classes
- LightGBMRankerModel → Model → Transformer → PipelineStage → Params
-
def
dotnetAdditionalMethods: String
- Definition Classes
- DotnetWrappable
-
def
explainParam(param: Param[_]): String
- Definition Classes
- Params
-
def
explainParams(): String
- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
-
final
val
featuresCol: Param[String]
- Definition Classes
- HasFeaturesCol
-
val
featuresShapCol: Param[String]
- Definition Classes
- LightGBMPredictionParams
-
final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getBoosterBestIteration(): Int
Public method to get the best iteration from the booster.
Public method to get the best iteration from the booster.
- returns
The best iteration, if early stopping was triggered.
- Definition Classes
- LightGBMModelMethods
-
def
getBoosterNumClasses(): Int
Public method to get the number of classes from the booster.
Public method to get the number of classes from the booster.
- returns
The number of classes.
- Definition Classes
- LightGBMModelMethods
-
def
getBoosterNumFeatures(): Int
Public method to get the number of features from the booster.
Public method to get the number of features from the booster.
- returns
The number of features.
- Definition Classes
- LightGBMModelMethods
-
def
getBoosterNumTotalIterations(): Int
Public method to get the total number of iterations trained.
Public method to get the total number of iterations trained.
- returns
The total number of iterations trained.
- Definition Classes
- LightGBMModelMethods
-
def
getBoosterNumTotalModel(): Int
Public method to get the total number of models trained.
Public method to get the total number of models trained. Note this may be larger than the number of iterations, since in multiclass a model is trained per class for each iteration.
- returns
The total number of models.
- Definition Classes
- LightGBMModelMethods
-
final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getDenseFeatureShaps(features: Array[Double]): Array[Double]
Public method for pyspark API to get the dense local SHAP feature importance values for an instance.
Public method for pyspark API to get the dense local SHAP feature importance values for an instance.
- features
The local instance or row to compute the SHAP values for.
- returns
The local feature importance values.
- Definition Classes
- LightGBMModelMethods
-
def
getFeatureImportances(importanceType: String): Array[Double]
Public method to get the global feature importance values.
Public method to get the global feature importance values.
- importanceType
split or gini
- returns
The global feature importance values.
- Definition Classes
- LightGBMModelMethods
-
def
getFeatureShaps(features: Vector): Array[Double]
Public method to get the vector local SHAP feature importance values for an instance.
Public method to get the vector local SHAP feature importance values for an instance.
- features
The local instance or row to compute the SHAP values for.
- returns
The local feature importance values.
- Definition Classes
- LightGBMModelMethods
-
final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
-
def
getFeaturesShapCol: String
- Definition Classes
- LightGBMPredictionParams
-
final
def
getLabelCol: String
- Definition Classes
- HasLabelCol
-
def
getLeafPredictionCol: String
- Definition Classes
- LightGBMPredictionParams
-
def
getLightGBMBooster: LightGBMBooster
- Definition Classes
- LightGBMModelParams
-
def
getModel: LightGBMBooster
Alias for same method
-
def
getNativeModel(): String
Gets the native model serialized representation as a string.
Gets the native model serialized representation as a string.
- Definition Classes
- LightGBMModelMethods
-
def
getNumIterations: Int
- Definition Classes
- LightGBMModelParams
-
final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
-
def
getParamInfo(p: Param[_]): ParamInfo[_]
- Definition Classes
- BaseWrappable
-
def
getPredictDisableShapeCheck: Boolean
- Definition Classes
- LightGBMPredictionParams
-
final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
-
def
getSparseFeatureShaps(size: Int, indices: Array[Int], values: Array[Double]): Array[Double]
Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.
Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.
- returns
The local feature importance values.
- Definition Classes
- LightGBMModelMethods
-
def
getStartIteration: Int
- Definition Classes
- LightGBMModelParams
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
-
def
hasParent: Boolean
- Definition Classes
- Model
-
final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
- Params
-
final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
-
final
val
labelCol: Param[String]
- Definition Classes
- HasLabelCol
-
val
leafPredictionCol: Param[String]
- Definition Classes
- LightGBMPredictionParams
-
val
lightGBMBooster: LightGBMBoosterParam
- Definition Classes
- LightGBMModelParams
-
def
logClass(featureName: String): Unit
- Definition Classes
- SynapseMLLogging
-
def
logFit[T](f: ⇒ T, columns: Int): T
- Definition Classes
- SynapseMLLogging
-
def
logTransform[T](f: ⇒ T, columns: Int): T
- Definition Classes
- SynapseMLLogging
-
def
logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
- Definition Classes
- SynapseMLLogging
-
def
makeDotnetFile(conf: CodegenConfig): Unit
- Definition Classes
- DotnetWrappable
-
def
makePyFile(conf: CodegenConfig): Unit
- Definition Classes
- PythonWrappable
-
def
makeRFile(conf: CodegenConfig): Unit
- Definition Classes
- RWrappable
-
def
numFeatures: Int
- Definition Classes
- LightGBMRankerModel → PredictionModel
-
val
numIterations: IntParam
- Definition Classes
- LightGBMModelParams
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[LightGBMRankerModel]
- Definition Classes
- Model
-
def
predict(features: Vector): Double
- Definition Classes
- LightGBMRankerModel → PredictionModel
-
val
predictDisableShapeCheck: BooleanParam
- Definition Classes
- LightGBMPredictionParams
-
final
val
predictionCol: Param[String]
- Definition Classes
- HasPredictionCol
-
def
pyAdditionalMethods: String
- Definition Classes
- PythonWrappable
-
def
pyInitFunc(): String
- Definition Classes
- PythonWrappable
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
def
saveNativeModel(filename: String, overwrite: Boolean): Unit
Saves the native model serialized representation to file.
Saves the native model serialized representation to file.
- filename
The name of the file to save the model to
- overwrite
Whether to overwrite if the file already exists
- Definition Classes
- LightGBMModelMethods
-
final
def
set[T](param: Param[T], value: T): LightGBMRankerModel.this.type
- Definition Classes
- Params
-
def
setFeaturesCol(value: String): LightGBMRankerModel
- Definition Classes
- PredictionModel
-
def
setFeaturesShapCol(value: String): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMPredictionParams
-
def
setLeafPredictionCol(value: String): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMPredictionParams
-
def
setLightGBMBooster(value: LightGBMBooster): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMModelParams
-
def
setNumIterations(value: Int): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMModelParams
-
def
setParent(parent: Estimator[LightGBMRankerModel]): LightGBMRankerModel
- Definition Classes
- Model
-
def
setPredictDisableShapeCheck(value: Boolean): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMPredictionParams
-
def
setPredictionCol(value: String): LightGBMRankerModel
- Definition Classes
- PredictionModel
-
def
setStartIteration(value: Int): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMModelParams
-
val
startIteration: IntParam
- Definition Classes
- LightGBMModelParams
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
transform(dataset: Dataset[_]): DataFrame
Adds additional Leaf Index and SHAP columns if specified.
Adds additional Leaf Index and SHAP columns if specified.
- dataset
input dataset
- returns
transformed dataset
- Definition Classes
- LightGBMRankerModel → PredictionModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
def
transformSchema(schema: StructType): StructType
- Definition Classes
- PredictionModel → PipelineStage
-
val
uid: String
- Definition Classes
- LightGBMRankerModel → SynapseMLLogging → Identifiable
-
def
write: MLWriter
- Definition Classes
- ComplexParamsWritable → MLWritable