Package org.apache.spark.ml.feature

Class BucketedRandomProjectionLSH

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.feature.LSH<BucketedRandomProjectionLSHModel>

org.apache.spark.ml.feature.BucketedRandomProjectionLSH

All Implemented Interfaces:

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

public class BucketedRandomProjectionLSH
extends org.apache.spark.ml.feature.LSH<BucketedRandomProjectionLSHModel>
implements org.apache.spark.ml.feature.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

Nested Class Summary

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

org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor	Description
BucketedRandomProjectionLSH()
BucketedRandomProjectionLSH(String uid)

Method Summary

Modifier and Type	Method	Description
DoubleParam	bucketLength()
BucketedRandomProjectionLSH	copy(ParamMap extra)	Creates a copy of this instance with the same UID and some extra params.
static BucketedRandomProjectionLSH	load(String path)
static MLReader<T>	read()
final 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.feature.LSH

fit, getInputCol, getNumHashTables, getOutputCol, inputCol, numHashTables, org$apache$spark$ml$feature$LSHParams$_setter_$numHashTables_$eq, org$apache$spark$ml$param$shared$HasInputCol$_setter_$inputCol_$eq, org$apache$spark$ml$param$shared$HasOutputCol$_setter_$outputCol_$eq, outputCol, save, validateAndTransformSchema, write

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 java.lang.Object

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

Methods inherited from interface org.apache.spark.ml.feature.BucketedRandomProjectionLSHParams

getBucketLength, org$apache$spark$ml$feature$BucketedRandomProjectionLSHParams$_setter_$bucketLength_$eq

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

getSeed

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.internal.Logging

initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, estimateMatadataSize, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Constructor Details

BucketedRandomProjectionLSH

public BucketedRandomProjectionLSH(String uid)

BucketedRandomProjectionLSH

public BucketedRandomProjectionLSH()

Method Details

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()

Specified by:

bucketLength in interface org.apache.spark.ml.feature.BucketedRandomProjectionLSHParams

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)

Overrides:

setInputCol in class org.apache.spark.ml.feature.LSH<BucketedRandomProjectionLSHModel>

setOutputCol

public BucketedRandomProjectionLSH setOutputCol(String value)

Overrides:

setOutputCol in class org.apache.spark.ml.feature.LSH<BucketedRandomProjectionLSHModel>

setNumHashTables

public BucketedRandomProjectionLSH setNumHashTables(int value)

Overrides:

setNumHashTables in class org.apache.spark.ml.feature.LSH<BucketedRandomProjectionLSHModel>

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)