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, org.apache.spark.internal.Logging, BucketedRandomProjectionLSHParams, LSHParams, Params, HasInputCol, HasOutputCol, HasSeed, DefaultParamsWritable, Identifiable, MLWritable

public class BucketedRandomProjectionLSH
extends Estimator<T>
implements BucketedRandomProjectionLSHParams, HasSeed

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

Modifier and Type	Method and Description
DoubleParam	bucketLength() The length of each hash bucket, a larger bucket lowers the false negative rate.
BucketedRandomProjectionLSH	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
T	fit(Dataset<?> dataset) Fits a model to the input data.
Param<String>	inputCol() Param for input column name.
static BucketedRandomProjectionLSH	load(String path)
IntParam	numHashTables() Param for the number of hash tables used in LSH OR-amplification.
Param<String>	outputCol() Param for output column name.
static MLReader<T>	read()
LongParam	seed() Param for random seed.
BucketedRandomProjectionLSH	setBucketLength(double value)
BucketedRandomProjectionLSH	setInputCol(String value)
BucketedRandomProjectionLSH	setNumHashTables(int value)
BucketedRandomProjectionLSH	setOutputCol(String value)
BucketedRandomProjectionLSH	setSeed(long 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.

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.BucketedRandomProjectionLSHParams

getBucketLength

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

getSeed

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.feature.LSHParams

getNumHashTables, 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.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

BucketedRandomProjectionLSH

public BucketedRandomProjectionLSH(String uid)

BucketedRandomProjectionLSH

public BucketedRandomProjectionLSH()

Method Detail

load

public static BucketedRandomProjectionLSH load(String path)

read

public static MLReader<T> read()

seed

public final LongParam seed()

Description copied from interface: HasSeed

Param for random seed.

Specified by:

seed in interface HasSeed

Returns:

(undocumented)

bucketLength

public DoubleParam bucketLength()

Description copied from interface: BucketedRandomProjectionLSHParams

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

Specified by:

bucketLength in interface BucketedRandomProjectionLSHParams

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 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

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)

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)

inputCol

public final Param<String> inputCol()

Description copied from interface: HasInputCol

Param for input column name.

Specified by:

inputCol in interface HasInputCol

Returns:

(undocumented)

numHashTables

public final IntParam numHashTables()

Description copied from interface: LSHParams

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.

Specified by:

numHashTables in interface LSHParams

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)