BucketedRandomProjectionLSHModel

Companion object BucketedRandomProjectionLSHModel

class BucketedRandomProjectionLSHModel extends LSHModel[BucketedRandomProjectionLSHModel] with BucketedRandomProjectionLSHParams

Model produced by BucketedRandomProjectionLSH, where multiple random vectors are stored. The vectors are normalized to be unit vectors and each vector is used in a hash function: h_i(x) = floor(r_i.dot(x) / bucketLength) where r_i is the i-th random unit vector. The number of buckets will be (max L2 norm of input vectors) / bucketLength.

Annotations: @Since("2.1.0")
Source: BucketedRandomProjectionLSH.scala

Linear Supertypes

BucketedRandomProjectionLSHParams, LSHModel[BucketedRandomProjectionLSHModel], MLWritable, LSHParams, HasOutputCol, HasInputCol, Model[BucketedRandomProjectionLSHModel], Transformer, PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any

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BucketedRandomProjectionLSHModel
BucketedRandomProjectionLSHParams
LSHModel
MLWritable
LSHParams
HasOutputCol
HasInputCol
Model
Transformer
PipelineStage
Logging
Params
Serializable
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implicit class LogStringContext extends AnyRef
Definition Classes
Logging

Value Members

final def !=(arg0: Any): Boolean
Definition Classes
AnyRef → Any
final def ##: Int
Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().
Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean
Definition Classes
AnyRef → Any
def approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int): Dataset[_]
Overloaded method for approxNearestNeighbors.
Overloaded method for approxNearestNeighbors. Use "distCol" as default distCol.
Definition Classes
LSHModel
def approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int, distCol: String): Dataset[_]
Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.
Given a large dataset and an item, approximately find at most k items which have the closest distance to the item. If the outputCol is missing, the method will transform the data; if the outputCol exists, it will use the outputCol. This allows caching of the transformed data when necessary.
dataset
The dataset to search for nearest neighbors of the key.
key
Feature vector representing the item to search for.
numNearestNeighbors
The maximum number of nearest neighbors.
distCol
Output column for storing the distance between each result row and the key.
returns
A dataset containing at most k items closest to the key. A column "distCol" is added to show the distance between each row and the key.
Definition Classes
LSHModel
Note
This method is experimental and will likely change behavior in the next release.
def approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double): Dataset[_]
Overloaded method for approxSimilarityJoin.
Overloaded method for approxSimilarityJoin. Use "distCol" as default distCol.
Definition Classes
LSHModel
def approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double, distCol: String): Dataset[_]
Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold.
Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold. If the outputCol is missing, the method will transform the data; if the outputCol exists, it will use the outputCol. This allows caching of the transformed data when necessary.
datasetA
One of the datasets to join.
datasetB
Another dataset to join.
threshold
The threshold for the distance of row pairs.
distCol
Output column for storing the distance between each pair of rows.
returns
A joined dataset containing pairs of rows. The original rows are in columns "datasetA" and "datasetB", and a column "distCol" is added to show the distance between each pair.
Definition Classes
LSHModel
final def asInstanceOf[T0]: T0
Definition Classes
Any
val bucketLength: DoubleParam
The length of each hash bucket, a larger bucket lowers the false negative rate.
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
Definition Classes
BucketedRandomProjectionLSHParams
final def clear(param: Param[_]): BucketedRandomProjectionLSHModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
Definition Classes
Params
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
def copy(extra: ParamMap): BucketedRandomProjectionLSHModel
Creates a copy of this instance with the same UID and some extra 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().
Definition Classes
BucketedRandomProjectionLSHModel → Model → Transformer → PipelineStage → Params
Annotations
@Since("2.1.0")
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied
Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String
Explains a param.
Explains a param.
param
input param, must belong to this instance.
returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
Definition Classes
Params
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().
Definition Classes
Params
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.
Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Definition Classes
Params
final def get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
Definition Classes
Params
final def getBucketLength: Double
Definition Classes
BucketedRandomProjectionLSHParams
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
final def getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
Definition Classes
Params
final def getInputCol: String
Definition Classes
HasInputCol
final def getNumHashTables: Int
Definition Classes
LSHParams
final def getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
Definition Classes
Params
final def getOutputCol: String
Definition Classes
HasOutputCol
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
Definition Classes
Params
final def hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
Definition Classes
Params
def hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
Definition Classes
Params
def hasParent: Boolean
Indicates whether this Model has a corresponding parent.
Indicates whether this Model has a corresponding parent.
Definition Classes
Model
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def hashDistance(x: Array[Vector], y: Array[Vector]): Double
Calculate the distance between two different hash Vectors.
Calculate the distance between two different hash Vectors.
x
One of the hash vector.
y
Another hash vector.
returns
The distance between hash vectors x and y.
Attributes
protected[ml]
Definition Classes
BucketedRandomProjectionLSHModel → LSHModel
Annotations
@Since("2.1.0")
def hashFunction(elems: Vector): Array[Vector]
The hash function of LSH, mapping an input feature vector to multiple hash vectors.
The hash function of LSH, mapping an input feature vector to multiple hash vectors.
returns
The mapping of LSH function.
Attributes
protected[ml]
Definition Classes
BucketedRandomProjectionLSHModel → LSHModel
Annotations
@Since("2.1.0")
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit
Attributes
protected
Definition Classes
Logging
final val inputCol: Param[String]
Param for input column name.
Param for input column name.
Definition Classes
HasInputCol
final def isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
Definition Classes
Params
final def isInstanceOf[T0]: Boolean
Definition Classes
Any
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
Definition Classes
Params
def isTraceEnabled(): Boolean
Attributes
protected
Definition Classes
Logging
def keyDistance(x: Vector, y: Vector): Double
Calculate the distance between two different keys using the distance metric corresponding to the hashFunction.
Calculate the distance between two different keys using the distance metric corresponding to the hashFunction.
x
One input vector in the metric space.
y
One input vector in the metric space.
returns
The distance between x and y.
Attributes
protected[ml]
Definition Classes
BucketedRandomProjectionLSHModel → LSHModel
Annotations
@Since("2.1.0")
def log: Logger
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logName: String
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final val numHashTables: IntParam
Param for the number of hash tables used in LSH OR-amplification.
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.
Definition Classes
LSHParams
final val outputCol: Param[String]
Param for output column name.
Param for output column name.
Definition Classes
HasOutputCol
lazy val params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
Definition Classes
Params
Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
var parent: Estimator[BucketedRandomProjectionLSHModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
Definition Classes
Model
Note
For ensembles' component Models, this value can be null.
def save(path: String): Unit
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).
Definition Classes
MLWritable
Annotations
@Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
final def set(paramPair: ParamPair[_]): BucketedRandomProjectionLSHModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): BucketedRandomProjectionLSHModel.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): BucketedRandomProjectionLSHModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): BucketedRandomProjectionLSHModel.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): BucketedRandomProjectionLSHModel.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value
Attributes
protected[ml]
Definition Classes
Params
def setInputCol(value: String): BucketedRandomProjectionLSHModel.this.type
Definition Classes
BucketedRandomProjectionLSHModel → LSHModel
Annotations
@Since("2.4.0")
def setOutputCol(value: String): BucketedRandomProjectionLSHModel.this.type
Definition Classes
BucketedRandomProjectionLSHModel → LSHModel
Annotations
@Since("2.4.0")
def setParent(parent: Estimator[BucketedRandomProjectionLSHModel]): BucketedRandomProjectionLSHModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
Definition Classes
Model
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toString(): String
Definition Classes
BucketedRandomProjectionLSHModel → Identifiable → AnyRef → Any
Annotations
@Since("3.0.0")
def transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
Definition Classes
LSHModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
dataset
input dataset
paramMap
additional parameters, overwrite embedded params
returns
transformed dataset
Definition Classes
Transformer
Annotations
@Since("2.0.0")
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
dataset
input dataset
firstParamPair
the first param pair, overwrite embedded params
otherParamPairs
other param pairs, overwrite embedded params
returns
transformed dataset
Definition Classes
Transformer
Annotations
@Since("2.0.0") @varargs()
def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
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.
Definition Classes
LSHModel → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
Definition Classes
BucketedRandomProjectionLSHModel → Identifiable
final def validateAndTransformSchema(schema: StructType): StructType
Transform the Schema for LSH
Transform the Schema for LSH
schema
The schema of the input dataset without outputCol.
returns
A derived schema with outputCol added.
Attributes
protected[this]
Definition Classes
LSHParams
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
Attributes
protected
Definition Classes
Logging
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
Definition Classes
BucketedRandomProjectionLSHModel → MLWritable
Annotations
@Since("2.1.0")

Deprecated Value Members

def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Inherited from BucketedRandomProjectionLSHParams

Inherited from LSHModel[BucketedRandomProjectionLSHModel]

Inherited from MLWritable

Inherited from LSHParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Model[BucketedRandomProjectionLSHModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Packages

Feature transformers

BucketedRandomProjectionLSHModel

Companion object BucketedRandomProjectionLSHModel

class BucketedRandomProjectionLSHModel extends LSHModel[BucketedRandomProjectionLSHModel] with BucketedRandomProjectionLSHParams

Type Members

Value Members

Deprecated Value Members

Inherited from BucketedRandomProjectionLSHParams

Inherited from LSHModel[BucketedRandomProjectionLSHModel]

Inherited from MLWritable

Inherited from LSHParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Model[BucketedRandomProjectionLSHModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Members

Parameter setters

Parameter getters

Packages

Feature transformers

BucketedRandomProjectionLSHModel

Companion object BucketedRandomProjectionLSHModel

class BucketedRandomProjectionLSHModel extends LSHModel[BucketedRandomProjectionLSHModel] with BucketedRandomProjectionLSHParams

Type Members

Value Members

Deprecated Value Members

Inherited from BucketedRandomProjectionLSHParams

Inherited from LSHModel[BucketedRandomProjectionLSHModel]

Inherited from MLWritable

Inherited from LSHParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Model[BucketedRandomProjectionLSHModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Members

Parameter setters

Parameter getters

BucketedRandomProjectionLSHModel