org.apache.spark.ml.feature

Class VarianceThresholdSelector

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.feature.VarianceThresholdSelector

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, VarianceThresholdSelectorParams, Params, HasFeaturesCol, HasOutputCol, DefaultParamsWritable, Identifiable, MLWritable

public final class VarianceThresholdSelector
extends Estimator<VarianceThresholdSelectorModel>
implements VarianceThresholdSelectorParams, DefaultParamsWritable

Feature selector that removes all low-variance features. Features with a (sample) variance not greater than the threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.

See Also:

Serialized Form

Nested Class Summary

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

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
VarianceThresholdSelector()
VarianceThresholdSelector(String uid)

Method Summary

Modifier and Type	Method and Description
VarianceThresholdSelector	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	featuresCol() Param for features column name.
VarianceThresholdSelectorModel	fit(Dataset<?> dataset) Fits a model to the input data.
static VarianceThresholdSelector	load(String path)
Param<String>	outputCol() Param for output column name.
static MLReader<T>	read()
VarianceThresholdSelector	setFeaturesCol(String value)
VarianceThresholdSelector	setOutputCol(String value)
VarianceThresholdSelector	setVarianceThreshold(double 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.
DoubleParam	varianceThreshold() Param for variance threshold.

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.VarianceThresholdSelectorParams

getVarianceThreshold

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

getFeaturesCol

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, onParamChange, 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

VarianceThresholdSelector

public VarianceThresholdSelector(String uid)

VarianceThresholdSelector

public VarianceThresholdSelector()

Method Detail

load

public static VarianceThresholdSelector load(String path)

read

public static MLReader<T> read()

varianceThreshold

public final DoubleParam varianceThreshold()

Description copied from interface: VarianceThresholdSelectorParams

Param for variance threshold. Features with a variance not greater than this threshold will be removed. The default value is 0.0.

Specified by:

varianceThreshold in interface VarianceThresholdSelectorParams

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)

featuresCol

public final Param<String> featuresCol()

Description copied from interface: HasFeaturesCol

Param for features column name.

Specified by:

featuresCol in interface HasFeaturesCol

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)

setVarianceThreshold

public VarianceThresholdSelector setVarianceThreshold(double value)

setFeaturesCol

public VarianceThresholdSelector setFeaturesCol(String value)

setOutputCol

public VarianceThresholdSelector setOutputCol(String value)

fit

public VarianceThresholdSelectorModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<VarianceThresholdSelectorModel>

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 VarianceThresholdSelector 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<VarianceThresholdSelectorModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)