Spark 3.0.0 ScalaDoc - org.apache.spark.ml.feature.MinMaxScalerModel

final def !=(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def ##(): Int

Definition Classes: AnyRef → Any

final def $[T](param: Param[T]): T

An alias for getOrDefault().

Attributes: protected
Definition Classes: Params

final def ==(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def asInstanceOf[T0]: T0

Definition Classes: Any

final def clear(param: Param[_]): MinMaxScalerModel.this.type

Clears the user-supplied value for the input param.

Definition Classes: Params

def clone(): AnyRef

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @native() @throws( ... )

def copy(extra: ParamMap): MinMaxScalerModel

Creates a copy of this instance with the same UID and some extra params.

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Definition Classes: MinMaxScalerModel → Model → Transformer → PipelineStage → Params
Annotations: @Since( "1.5.0" )

def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

Copies param values from this instance to another instance for params shared by them.

This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

to: the target instance, which should work with the same set of default Params as this source instance
extra: extra params to be copied to the target's paramMap
returns: the target instance with param values copied

Attributes: protected
Definition Classes: Params

final def defaultCopy[T <: Params](extra: ParamMap): T

Default implementation of copy with extra params.

Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes: protected
Definition Classes: Params

final def eq(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def equals(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def explainParam(param: Param[_]): String

Explains a param.

param: input param, must belong to this instance.
returns: a string that contains the input param name, doc, and optionally its default value and the user-supplied value

Definition Classes: Params

def explainParams(): String

Explains all params of this instance.

Explains all params of this instance. See explainParam().

Definition Classes: Params

final def extractParamMap(): ParamMap

extractParamMap with no extra values.

Definition Classes: Params

final def extractParamMap(extra: ParamMap): ParamMap

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

Definition Classes: Params

def finalize(): Unit

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @throws( classOf[java.lang.Throwable] )

final def get[T](param: Param[T]): Option[T]

Optionally returns the user-supplied value of a param.

Definition Classes: Params

final def getClass(): Class[_]

Definition Classes: AnyRef → Any
Annotations: @native()

final def getDefault[T](param: Param[T]): Option[T]

Gets the default value of a parameter.

Definition Classes: Params

final def getInputCol: String

Definition Classes: HasInputCol

def getMax: Double

Definition Classes: MinMaxScalerParams

def getMin: Double

Definition Classes: MinMaxScalerParams

final def getOrDefault[T](param: Param[T]): T

Gets the value of a param in the embedded param map or its default value.

Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

Definition Classes: Params

final def getOutputCol: String

Definition Classes: HasOutputCol

def getParam(paramName: String): Param[Any]

Gets a param by its name.

Definition Classes: Params

final def hasDefault[T](param: Param[T]): Boolean

Tests whether the input param has a default value set.

Definition Classes: Params

def hasParam(paramName: String): Boolean

Tests whether this instance contains a param with a given name.

Definition Classes: Params

def hasParent: Boolean

Indicates whether this Model has a corresponding parent.

Definition Classes: Model

def hashCode(): Int

Definition Classes: AnyRef → Any
Annotations: @native()

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

Attributes: protected
Definition Classes: Logging

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes: protected
Definition Classes: Logging

final val inputCol: Param[String]

Param for input column name.

Definition Classes: HasInputCol

final def isDefined(param: Param[_]): Boolean

Checks whether a param is explicitly set or has a default value.

Definition Classes: Params

final def isInstanceOf[T0]: Boolean

Definition Classes: Any

final def isSet(param: Param[_]): Boolean

Checks whether a param is explicitly set.

Definition Classes: Params

def isTraceEnabled(): Boolean

Attributes: protected
Definition Classes: Logging

def log: Logger

Attributes: protected
Definition Classes: Logging

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logDebug(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logError(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logInfo(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logName: String

Attributes: protected
Definition Classes: Logging

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logTrace(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logWarning(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

val max: DoubleParam

upper bound after transformation, shared by all features Default: 1.0

Definition Classes: MinMaxScalerParams

val min: DoubleParam

lower bound after transformation, shared by all features Default: 0.0

Definition Classes: MinMaxScalerParams

final def ne(arg0: AnyRef): Boolean

Definition Classes: AnyRef

final def notify(): Unit

Definition Classes: AnyRef
Annotations: @native()

final def notifyAll(): Unit

Definition Classes: AnyRef
Annotations: @native()

val originalMax: Vector

Annotations: @Since( "2.0.0" )

val originalMin: Vector

Annotations: @Since( "2.0.0" )

final val outputCol: Param[String]

Param for output column name.

Definition Classes: HasOutputCol

lazy val params: Array[Param[_]]

Returns all params sorted by their names.

Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

Definition Classes: Params
Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

var parent: Estimator[MinMaxScalerModel]

The parent estimator that produced this model.

Definition Classes: Model
Note: For ensembles' component Models, this value can be null.

def save(path: String): Unit

Saves this ML instance to the input path, a shortcut of write.save(path).

Definition Classes: MLWritable
Annotations: @Since( "1.6.0" ) @throws( ... )

final def set(paramPair: ParamPair[_]): MinMaxScalerModel.this.type

Sets a parameter in the embedded param map.

Attributes: protected
Definition Classes: Params

final def set(param: String, value: Any): MinMaxScalerModel.this.type

Sets a parameter (by name) in the embedded param map.

Attributes: protected
Definition Classes: Params

final def set[T](param: Param[T], value: T): MinMaxScalerModel.this.type

Sets a parameter in the embedded param map.

Definition Classes: Params

final def setDefault(paramPairs: ParamPair[_]*): MinMaxScalerModel.this.type

Sets default values for a list of params.

Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

paramPairs: a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes: protected
Definition Classes: Params

final def setDefault[T](param: Param[T], value: T): MinMaxScalerModel.this.type

Sets a default value for a param.

param: param to set the default value. Make sure that this param is initialized before this method gets called.
value: the default value

Attributes: protected
Definition Classes: Params

def setInputCol(value: String): MinMaxScalerModel.this.type

Annotations: @Since( "1.5.0" )

def setMax(value: Double): MinMaxScalerModel.this.type

Annotations: @Since( "1.5.0" )

def setMin(value: Double): MinMaxScalerModel.this.type

Annotations: @Since( "1.5.0" )

def setOutputCol(value: String): MinMaxScalerModel.this.type

Annotations: @Since( "1.5.0" )

def setParent(parent: Estimator[MinMaxScalerModel]): MinMaxScalerModel

Sets the parent of this model (Java API).

Definition Classes: Model

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

Definition Classes: AnyRef

def toString(): String

Definition Classes: Identifiable → AnyRef → Any

def transform(dataset: Dataset[_]): DataFrame

Transforms the input dataset.

Definition Classes: MinMaxScalerModel → Transformer
Annotations: @Since( "2.0.0" )

def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

Transforms the dataset with provided parameter map as additional parameters.

dataset: input dataset
paramMap: additional parameters, overwrite embedded params
returns: transformed dataset

Definition Classes: Transformer
Annotations: @Since( "2.0.0" )

def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Transforms the dataset with optional parameters

dataset: input dataset
firstParamPair: the first param pair, overwrite embedded params
otherParamPairs: other param pairs, overwrite embedded params
returns: transformed dataset

Definition Classes: Transformer
Annotations: @Since( "2.0.0" ) @varargs()

def transformSchema(schema: StructType): StructType

:: DeveloperApi ::

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes: MinMaxScalerModel → PipelineStage
Annotations: @Since( "1.5.0" )

def transformSchema(schema: StructType, logging: Boolean): StructType

:: DeveloperApi ::

Derives the output schema from the input schema and parameters, optionally with logging.

This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

Attributes: protected
Definition Classes: PipelineStage
Annotations: @DeveloperApi()

val uid: String

An immutable unique ID for the object and its derivatives.

Definition Classes: MinMaxScalerModel → Identifiable
Annotations: @Since( "1.5.0" )

def validateAndTransformSchema(schema: StructType): StructType

Validates and transforms the input schema.

Attributes: protected
Definition Classes: MinMaxScalerParams

final def wait(): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

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

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long): Unit

Definition Classes: AnyRef
Annotations: @native() @throws( ... )

def write: MLWriter

Returns an MLWriter instance for this ML instance.

Definition Classes: MinMaxScalerModel → MLWritable
Annotations: @Since( "1.6.0" )

Packages

Feature transformers

MinMaxScalerModel

Companion object MinMaxScalerModel

class MinMaxScalerModel extends Model[MinMaxScalerModel] with MinMaxScalerParams with MLWritable

Value Members

Inherited from MLWritable

Inherited from MinMaxScalerParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Model[MinMaxScalerModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Members

Parameter setters

Parameter getters

Packages

Feature transformers

MinMaxScalerModel 

Companion object MinMaxScalerModel

class MinMaxScalerModel extends Model[MinMaxScalerModel] with MinMaxScalerParams with MLWritable

Value Members

Inherited from MLWritable

Inherited from MinMaxScalerParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Model[MinMaxScalerModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Members

Parameter setters

Parameter getters

MinMaxScalerModel