abstract class AutoTrainedModel[TrainedModel <: Model[TrainedModel]] extends Model[TrainedModel] with ComplexParamsWritable with HasLabelCol with HasFeaturesCol
Defines common inheritance and functions across auto trained models.
Linear Supertypes
Known Subclasses
Ordering
- Alphabetic
- By Inheritance
Inherited
- AutoTrainedModel
- HasFeaturesCol
- HasLabelCol
- ComplexParamsWritable
- MLWritable
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
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Visibility
- Public
- All
Instance Constructors
- new AutoTrainedModel()
Abstract Value Members
-
abstract
def
copy(extra: ParamMap): TrainedModel
- Definition Classes
- Model → Transformer → PipelineStage → Params
-
abstract
def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
abstract
def
transformSchema(schema: StructType): StructType
- Definition Classes
- PipelineStage
-
abstract
val
uid: String
- Definition Classes
- Identifiable
Concrete Value Members
-
final
def
clear(param: Param[_]): AutoTrainedModel.this.type
- Definition Classes
- Params
-
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
-
val
featuresCol: Param[String]
The name of the features column
The name of the features column
- Definition Classes
- HasFeaturesCol
-
final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
-
def
getLabelCol: String
- Definition Classes
- HasLabelCol
-
def
getLastStage: Transformer
Retrieve the underlying model.
Retrieve the underlying model.
- returns
The underlying model.
- def getModel: PipelineModel
-
final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
-
def
getParamMap: ParamMap
Retrieve the param map from the underlying model.
Retrieve the param map from the underlying model.
- returns
The param map from the underlying model.
-
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
-
val
labelCol: Param[String]
The name of the label column
The name of the label column
- Definition Classes
- HasLabelCol
- val model: TransformerParam
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[TrainedModel]
- Definition Classes
- Model
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set[T](param: Param[T], value: T): AutoTrainedModel.this.type
- Definition Classes
- Params
-
def
setFeaturesCol(value: String): AutoTrainedModel.this.type
- Definition Classes
- HasFeaturesCol
-
def
setLabelCol(value: String): AutoTrainedModel.this.type
- Definition Classes
- HasLabelCol
- def setModel(v: PipelineModel): AutoTrainedModel.this.type
-
def
setParent(parent: Estimator[TrainedModel]): TrainedModel
- Definition Classes
- Model
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
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
write: MLWriter
- Definition Classes
- ComplexParamsWritable → MLWritable