org.apache.spark.ml.feature

Class BucketedRandomProjectionLSH

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.feature.BucketedRandomProjectionLSH

All Implemented Interfaces:

java.io.Serializable, Params, DefaultParamsWritable, Identifiable, MLWritable

public class BucketedRandomProjectionLSH
extends Estimator<T>

:: Experimental ::

This BucketedRandomProjectionLSH implements Locality Sensitive Hashing functions for Euclidean distance metrics.

The input is dense or sparse vectors, each of which represents a point in the Euclidean distance space. The output will be vectors of configurable dimension. Hash values in the same dimension are calculated by the same hash function.

References:

1. Wikipedia on Stable Distributions

2. Wang, Jingdong et al. "Hashing for similarity search: A survey." arXiv preprint arXiv:1408.2927 (2014).

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
BucketedRandomProjectionLSH()
BucketedRandomProjectionLSH(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
static DoubleParam	bucketLength()
DoubleParam	bucketLength() The length of each hash bucket, a larger bucket lowers the false negative rate.
static Params	clear(Param<?> param)
BucketedRandomProjectionLSH	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static T	fit(Dataset<?> dataset)
T	fit(Dataset<?> dataset) Fits a model to the input data.
static M	fit(Dataset<?> dataset, ParamMap paramMap)
static scala.collection.Seq<M>	fit(Dataset<?> dataset, ParamMap[] paramMaps)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static <T> scala.Option<T>	get(Param<T> param)
static double	getBucketLength()
double	getBucketLength()
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getInputCol()
String	getInputCol()
static int	getNumHashTables()
int	getNumHashTables()
static <T> T	getOrDefault(Param<T> param)
static String	getOutputCol()
String	getOutputCol()
static Param<Object>	getParam(String paramName)
static long	getSeed()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static Param<String>	inputCol()
Param<String>	inputCol() Param for input column name.
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static BucketedRandomProjectionLSH	load(String path)
static IntParam	numHashTables()
IntParam	numHashTables() Param for the number of hash tables used in LSH OR-amplification.
static Param<String>	outputCol()
Param<String>	outputCol() Param for output column name.
static Param<?>[]	params()
static void	save(String path)
static LongParam	seed()
static <T> Params	set(Param<T> param, T value)
BucketedRandomProjectionLSH	setBucketLength(double value)
BucketedRandomProjectionLSH	setInputCol(String value)
BucketedRandomProjectionLSH	setNumHashTables(int value)
BucketedRandomProjectionLSH	setOutputCol(String value)
BucketedRandomProjectionLSH	setSeed(long value)
static String	toString()
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema) Transform the Schema for LSH
static MLWriter	write()

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

fit, fit, fit, fit

Methods inherited from class Object

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

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

Constructor Detail

BucketedRandomProjectionLSH

public BucketedRandomProjectionLSH(String uid)

BucketedRandomProjectionLSH

public BucketedRandomProjectionLSH()

Method Detail

load

public static BucketedRandomProjectionLSH load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    scala.collection.Seq<ParamPair<?>> otherParamPairs)

fit

public static M fit(Dataset<?> dataset,
    ParamMap paramMap)

fit

public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                          ParamMap[] paramMaps)

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    ParamPair<?>... otherParamPairs)

inputCol

public static final Param<String> inputCol()

getInputCol

public static final String getInputCol()

outputCol

public static final Param<String> outputCol()

getOutputCol

public static final String getOutputCol()

numHashTables

public static final IntParam numHashTables()

getNumHashTables

public static final int getNumHashTables()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

write

public static MLWriter write()

fit

public static T fit(Dataset<?> dataset)

bucketLength

public static DoubleParam bucketLength()

getBucketLength

public static final double getBucketLength()

seed

public static final LongParam seed()

getSeed

public static final long getSeed()

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 BucketedRandomProjectionLSH setInputCol(String value)

setOutputCol

public BucketedRandomProjectionLSH setOutputCol(String value)

setNumHashTables

public BucketedRandomProjectionLSH setNumHashTables(int value)

setBucketLength

public BucketedRandomProjectionLSH setBucketLength(double value)

setSeed

public BucketedRandomProjectionLSH setSeed(long value)

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 BucketedRandomProjectionLSH 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<BucketedRandomProjectionLSHModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

bucketLength

public DoubleParam bucketLength()

The length of each hash bucket, a larger bucket lowers the false negative rate. The number of buckets will be (max L2 norm of input vectors) / bucketLength.

If input vectors are normalized, 1-10 times of pow(numRecords, -1/inputDim) would be a reasonable value

Returns:

(undocumented)

getBucketLength

public double getBucketLength()

fit

public T fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<T extends org.apache.spark.ml.feature.LSHModel<T>>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

numHashTables

public IntParam numHashTables()

Param for the number of hash tables used in LSH OR-amplification.

LSH OR-amplification can be used to reduce the false negative rate. Higher values for this param lead to a reduced false negative rate, at the expense of added computational complexity.

Returns:

(undocumented)

getNumHashTables

public int getNumHashTables()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema)

Transform the Schema for LSH

Parameters:

schema - The schema of the input dataset without outputCol.

Returns:

A derived schema with outputCol added.

inputCol

public Param<String> inputCol()

Param for input column name.

Returns:

(undocumented)

getInputCol

public String getInputCol()

outputCol

public Param<String> outputCol()

Param for output column name.

Returns:

(undocumented)

getOutputCol

public String getOutputCol()