org.apache.spark.ml.feature

Class RobustScalerModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<RobustScalerModel>

org.apache.spark.ml.feature.RobustScalerModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, RobustScalerParams, Params, HasInputCol, HasOutputCol, HasRelativeError, Identifiable, MLWritable

public class RobustScalerModel
extends Model<RobustScalerModel>
implements RobustScalerParams, MLWritable

Model fitted by RobustScaler.

param: range quantile range for each original column during fitting param: median median value for each original column during fitting

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method and Description
RobustScalerModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	inputCol() Param for input column name.
static RobustScalerModel	load(String path)
DoubleParam	lower() Lower quantile to calculate quantile range, shared by all features Default: 0.25
Vector	median()
Param<String>	outputCol() Param for output column name.
Vector	range()
static MLReader<RobustScalerModel>	read()
DoubleParam	relativeError() Param for the relative target precision for the approximate quantile algorithm.
RobustScalerModel	setInputCol(String value)
RobustScalerModel	setOutputCol(String value)
String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.
String	uid() An immutable unique ID for the object and its derivatives.
DoubleParam	upper() Upper quantile to calculate quantile range, shared by all features Default: 0.75
BooleanParam	withCentering() Whether to center the data with median before scaling.
BooleanParam	withScaling() Whether to scale the data to quantile range.
MLWriter	write() Returns an MLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.feature.RobustScalerParams

getLower, getUpper, getWithCentering, getWithScaling, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCol

getInputCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol

getOutputCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasRelativeError

getRelativeError

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Method Detail

read

public static MLReader<RobustScalerModel> read()

load

public static RobustScalerModel load(String path)

lower

public DoubleParam lower()

Description copied from interface: RobustScalerParams

Lower quantile to calculate quantile range, shared by all features Default: 0.25

Specified by:

lower in interface RobustScalerParams

Returns:

(undocumented)

upper

public DoubleParam upper()

Description copied from interface: RobustScalerParams

Upper quantile to calculate quantile range, shared by all features Default: 0.75

Specified by:

upper in interface RobustScalerParams

Returns:

(undocumented)

withCentering

public BooleanParam withCentering()

Description copied from interface: RobustScalerParams

Whether to center the data with median before scaling. It will build a dense output, so take care when applying to sparse input. Default: false

Specified by:

withCentering in interface RobustScalerParams

Returns:

(undocumented)

withScaling

public BooleanParam withScaling()

Description copied from interface: RobustScalerParams

Whether to scale the data to quantile range. Default: true

Specified by:

withScaling in interface RobustScalerParams

Returns:

(undocumented)

relativeError

public final DoubleParam relativeError()

Description copied from interface: HasRelativeError

Param for the relative target precision for the approximate quantile algorithm. Must be in the range [0, 1].

Specified by:

relativeError in interface HasRelativeError

Returns:

(undocumented)

outputCol

public final Param<String> outputCol()

Description copied from interface: HasOutputCol

Param for output column name.

Specified by:

outputCol in interface HasOutputCol

Returns:

(undocumented)

inputCol

public final Param<String> inputCol()

Description copied from interface: HasInputCol

Param for input column name.

Specified by:

inputCol in interface HasInputCol

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

range

public Vector range()

median

public Vector median()

setInputCol

public RobustScalerModel setInputCol(String value)

setOutputCol

public RobustScalerModel setOutputCol(String value)

transform

public Dataset<Row> transform(Dataset<?> dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public RobustScalerModel copy(ParamMap extra)

Description copied from interface: 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().

Specified by:

copy in interface Params

Specified by:

copy in class Model<RobustScalerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object