Packages

class BucketedRandomProjectionLSH extends LSH[BucketedRandomProjectionLSHModel] with BucketedRandomProjectionLSHParams with 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).

Annotations
@Since( "2.1.0" )
Source
BucketedRandomProjectionLSH.scala
Linear Supertypes
HasSeed, BucketedRandomProjectionLSHParams, LSH[BucketedRandomProjectionLSHModel], DefaultParamsWritable, MLWritable, LSHParams, HasOutputCol, HasInputCol, Estimator[BucketedRandomProjectionLSHModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. BucketedRandomProjectionLSH
  2. HasSeed
  3. BucketedRandomProjectionLSHParams
  4. LSH
  5. DefaultParamsWritable
  6. MLWritable
  7. LSHParams
  8. HasOutputCol
  9. HasInputCol
  10. Estimator
  11. PipelineStage
  12. Logging
  13. Params
  14. Serializable
  15. Serializable
  16. Identifiable
  17. AnyRef
  18. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Parameters

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

  1. 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
  2. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  3. 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
  4. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  5. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed

Members

  1. final def clear(param: Param[_]): BucketedRandomProjectionLSH.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. def copy(extra: ParamMap): BucketedRandomProjectionLSH.this.type

    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
    BucketedRandomProjectionLSHEstimatorPipelineStageParams
    Annotations
    @Since( "2.1.0" )
  3. 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
  4. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. 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
  7. def fit(dataset: Dataset[_]): BucketedRandomProjectionLSHModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    LSH → Estimator
  8. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[BucketedRandomProjectionLSHModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], paramMap: ParamMap): BucketedRandomProjectionLSHModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BucketedRandomProjectionLSHModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  11. 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
  12. 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
  13. 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
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. 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
  16. 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
  17. 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
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. 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.

  20. 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( ... )
  21. final def set[T](param: Param[T], value: T): BucketedRandomProjectionLSH.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  22. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  23. 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
    BucketedRandomProjectionLSHPipelineStage
    Annotations
    @Since( "2.1.0" )
  24. 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
    BucketedRandomProjectionLSHIdentifiable
  25. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Parameter setters

  1. def setBucketLength(value: Double): BucketedRandomProjectionLSH.this.type

    Annotations
    @Since( "2.1.0" )
  2. def setInputCol(value: String): BucketedRandomProjectionLSH.this.type

    Definition Classes
    BucketedRandomProjectionLSH → LSH
    Annotations
    @Since( "2.1.0" )
  3. def setNumHashTables(value: Int): BucketedRandomProjectionLSH.this.type

    Definition Classes
    BucketedRandomProjectionLSH → LSH
    Annotations
    @Since( "2.1.0" )
  4. def setOutputCol(value: String): BucketedRandomProjectionLSH.this.type

    Definition Classes
    BucketedRandomProjectionLSH → LSH
    Annotations
    @Since( "2.1.0" )
  5. def setSeed(value: Long): BucketedRandomProjectionLSH.this.type

    Annotations
    @Since( "2.1.0" )

Parameter getters

  1. final def getBucketLength: Double

    Definition Classes
    BucketedRandomProjectionLSHParams
  2. final def getInputCol: String

    Definition Classes
    HasInputCol
  3. final def getNumHashTables: Int

    Definition Classes
    LSHParams
  4. final def getOutputCol: String

    Definition Classes
    HasOutputCol
  5. final def getSeed: Long

    Definition Classes
    HasSeed