org.apache.spark.ml.feature

Class Bucketizer

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.feature.Bucketizer

All Implemented Interfaces:

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

public final class Bucketizer
extends Model<Bucketizer>
implements HasHandleInvalid, HasInputCol, HasOutputCol, HasInputCols, HasOutputCols, DefaultParamsWritable

Bucketizer maps a column of continuous features to a column of feature buckets.

Since 2.3.0, Bucketizer can map multiple columns at once by setting the inputCols parameter. Note that when both the inputCol and inputCols parameters are set, an Exception will be thrown. The splits parameter is only used for single column usage, and splitsArray is for multiple columns.

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
Bucketizer()
Bucketizer(String uid)

Method Summary

Modifier and Type	Method and Description
Bucketizer	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
double[]	getSplits()
double[][]	getSplitsArray()
Param<String>	handleInvalid() Param for how to handle invalid entries containing NaN values.
Param<String>	inputCol() Param for input column name.
StringArrayParam	inputCols() Param for input column names.
static Bucketizer	load(String path)
Param<String>	outputCol() Param for output column name.
StringArrayParam	outputCols() Param for output column names.
static MLReader<T>	read()
Bucketizer	setHandleInvalid(String value)
Bucketizer	setInputCol(String value)
Bucketizer	setInputCols(String[] value)
Bucketizer	setOutputCol(String value)
Bucketizer	setOutputCols(String[] value)
Bucketizer	setSplits(double[] value)
Bucketizer	setSplitsArray(double[][] value)
DoubleArrayParam	splits() Parameter for mapping continuous features into buckets.
DoubleArrayArrayParam	splitsArray() Parameter for specifying multiple splits parameters.
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.

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.param.shared.HasHandleInvalid

getHandleInvalid

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.HasInputCols

getInputCols

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

getOutputCols

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.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

Bucketizer

public Bucketizer(String uid)

Bucketizer

public Bucketizer()

Method Detail

load

public static Bucketizer load(String path)

read

public static MLReader<T> read()

outputCols

public final StringArrayParam outputCols()

Description copied from interface: HasOutputCols

Param for output column names.

Specified by:

outputCols in interface HasOutputCols

Returns:

(undocumented)

inputCols

public final StringArrayParam inputCols()

Description copied from interface: HasInputCols

Param for input column names.

Specified by:

inputCols in interface HasInputCols

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)

splits

public DoubleArrayParam splits()

Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. Splits should be of length greater than or equal to 3 and strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; otherwise, values outside the splits specified will be treated as errors.

See also handleInvalid, which can optionally create an additional bucket for NaN values.

Returns:

(undocumented)

getSplits

public double[] getSplits()

setSplits

public Bucketizer setSplits(double[] value)

setInputCol

public Bucketizer setInputCol(String value)

setOutputCol

public Bucketizer setOutputCol(String value)

handleInvalid

public Param<String> handleInvalid()

Param for how to handle invalid entries containing NaN values. Values outside the splits will always be treated as errors. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Note that in the multiple column case, the invalid handling is applied to all columns. That said for 'error' it will throw an error if any invalids are found in any column, for 'skip' it will skip rows with any invalids in any columns, etc. Default: "error"

Specified by:

handleInvalid in interface HasHandleInvalid

Returns:

(undocumented)

setHandleInvalid

public Bucketizer setHandleInvalid(String value)

splitsArray

public DoubleArrayArrayParam splitsArray()

Parameter for specifying multiple splits parameters. Each element in this array can be used to map continuous features into buckets.

Returns:

(undocumented)

getSplitsArray

public double[][] getSplitsArray()

setSplitsArray

public Bucketizer setSplitsArray(double[][] value)

setInputCols

public Bucketizer setInputCols(String[] value)

setOutputCols

public Bucketizer setOutputCols(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 Bucketizer 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<Bucketizer>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object