org.apache.spark.ml.feature

Class HashingTF

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.feature.HashingTF

All Implemented Interfaces:

java.io.Serializable, Logging, Params, HasInputCol, HasOutputCol, DefaultParamsWritable, Identifiable, MLWritable

public class HashingTF
extends Transformer
implements HasInputCol, HasOutputCol, DefaultParamsWritable

Maps a sequence of terms to their term frequencies using the hashing trick. Currently we use Austin Appleby's MurmurHash 3 algorithm (MurmurHash3_x86_32) to calculate the hash code value for the term object. Since a simple modulo is used to transform the hash function to a column index, it is advisable to use a power of two as the numFeatures parameter; otherwise the features will not be mapped evenly to the columns.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
HashingTF()
HashingTF(String uid)

Method Summary

Modifier and Type	Method and Description
BooleanParam	binary() Binary toggle to control term frequency counts.
HashingTF	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
boolean	getBinary()
int	getNumFeatures()
static HashingTF	load(String path)
IntParam	numFeatures() Number of features.
static MLReader<T>	read()
HashingTF	setBinary(boolean value)
HashingTF	setInputCol(String value)
HashingTF	setNumFeatures(int value)
HashingTF	setOutputCol(String value)
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.

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

transform, transform, transform

Methods inherited from class Object

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

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

getInputCol, inputCol

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

getOutputCol, outputCol

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

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

HashingTF

public HashingTF(String uid)

HashingTF

public HashingTF()

Method Detail

load

public static HashingTF load(String path)

read

public static MLReader<T> read()

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 HashingTF setInputCol(String value)

setOutputCol

public HashingTF setOutputCol(String value)

numFeatures

public IntParam numFeatures()

Number of features. Should be greater than 0. (default = 2^18^)

Returns:

(undocumented)

binary

public BooleanParam binary()

Binary toggle to control term frequency counts. If true, all non-zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. (default = false)

Returns:

(undocumented)

getNumFeatures

public int getNumFeatures()

setNumFeatures

public HashingTF setNumFeatures(int value)

getBinary

public boolean getBinary()

setBinary

public HashingTF setBinary(boolean 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

:: 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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public HashingTF 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 Transformer

Parameters:

extra - (undocumented)

Returns:

(undocumented)