org.apache.spark.ml.feature

Class StandardScaler

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<StandardScalerModel>

org.apache.spark.ml.feature.StandardScaler

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, StandardScalerParams, Params, HasInputCol, HasOutputCol, DefaultParamsWritable, Identifiable, MLWritable

public class StandardScaler
extends Estimator<StandardScalerModel>
implements StandardScalerParams, DefaultParamsWritable

Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.

The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
StandardScaler()
StandardScaler(String uid)

Method Summary

Modifier and Type	Method and Description
StandardScaler	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
StandardScalerModel	fit(Dataset<?> dataset) Fits a model to the input data.
Param<String>	inputCol() Param for input column name.
static StandardScaler	load(String path)
Param<String>	outputCol() Param for output column name.
static MLReader<T>	read()
StandardScaler	setInputCol(String value)
StandardScaler	setOutputCol(String value)
StandardScaler	setWithMean(boolean value)
StandardScaler	setWithStd(boolean value)
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.
BooleanParam	withMean() Whether to center the data with mean before scaling.
BooleanParam	withStd() Whether to scale the data to unit standard deviation.

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

fit, fit, fit, fit

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

params

Methods inherited from class Object

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

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

getWithMean, getWithStd, 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.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.Identifiable

toString

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

write

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

Constructor Detail

StandardScaler

public StandardScaler(String uid)

StandardScaler

public StandardScaler()

Method Detail

load

public static StandardScaler load(String path)

read

public static MLReader<T> read()

withMean

public BooleanParam withMean()

Description copied from interface: StandardScalerParams

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

Specified by:

withMean in interface StandardScalerParams

Returns:

(undocumented)

withStd

public BooleanParam withStd()

Description copied from interface: StandardScalerParams

Whether to scale the data to unit standard deviation. Default: true

Specified by:

withStd in interface StandardScalerParams

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)

setInputCol

public StandardScaler setInputCol(String value)

setOutputCol

public StandardScaler setOutputCol(String value)

setWithMean

public StandardScaler setWithMean(boolean value)

setWithStd

public StandardScaler setWithStd(boolean value)

fit

public StandardScalerModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<StandardScalerModel>

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 StandardScaler 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 Estimator<StandardScalerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)