Class/Object

org.apache.spark.ml.feature

BucketedRandomProjectionLSHModel

Related Docs: object BucketedRandomProjectionLSHModel | package feature

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class BucketedRandomProjectionLSHModel extends LSHModel[BucketedRandomProjectionLSHModel] with BucketedRandomProjectionLSHParams

:: Experimental ::

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
@Experimental() @Since( "2.1.0" )
Source
BucketedRandomProjectionLSH.scala
Linear Supertypes
BucketedRandomProjectionLSHParams, LSHModel[BucketedRandomProjectionLSHModel], MLWritable, LSHParams, HasOutputCol, HasInputCol, Model[BucketedRandomProjectionLSHModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. BucketedRandomProjectionLSHModel
  2. BucketedRandomProjectionLSHParams
  3. LSHModel
  4. MLWritable
  5. LSHParams
  6. HasOutputCol
  7. HasInputCol
  8. Model
  9. Transformer
  10. PipelineStage
  11. Logging
  12. Params
  13. Serializable
  14. Serializable
  15. Identifiable
  16. AnyRef
  17. Any
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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. def approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int): Dataset[_]

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    Overloaded method for approxNearestNeighbors.

    Overloaded method for approxNearestNeighbors. Use "distCol" as default distCol.

    Definition Classes
    LSHModel
  6. def approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int, distCol: String): Dataset[_]

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    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.

  7. def approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double): Dataset[_]

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    Overloaded method for approxSimilarityJoin.

    Overloaded method for approxSimilarityJoin. Use "distCol" as default distCol.

    Definition Classes
    LSHModel
  8. def approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double, distCol: String): Dataset[_]

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    Join two dataset to approximately find all pairs of rows whose distance are smaller than the threshold.

    Join two dataset 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 result row and the key.

    returns

    A joined dataset containing pairs of rows. The original rows are in columns "datasetA" and "datasetB", and a distCol is added to show the distance of each pair.

    Definition Classes
    LSHModel
  9. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  10. val bucketLength: DoubleParam

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    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
  11. final def clear(param: Param[_]): BucketedRandomProjectionLSHModel.this.type

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    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  12. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  13. def copy(extra: ParamMap): BucketedRandomProjectionLSHModel.this.type

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    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
    BucketedRandomProjectionLSHModelModelTransformerPipelineStageParams
    Annotations
    @Since( "2.1.0" )
  14. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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
  15. final def defaultCopy[T <: Params](extra: ParamMap): T

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    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
  16. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  18. def explainParam(param: Param[_]): String

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    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
  19. def explainParams(): String

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    Explains all params of this instance.

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

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

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap

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    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
  22. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. final def get[T](param: Param[T]): Option[T]

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  24. final def getBucketLength: Double

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    Definition Classes
    BucketedRandomProjectionLSHParams
  25. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  26. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  27. final def getInputCol: String

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    Definition Classes
    HasInputCol
  28. final def getNumHashTables: Int

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    Definition Classes
    LSHParams
  29. final def getOrDefault[T](param: Param[T]): T

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    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
  30. final def getOutputCol: String

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    Definition Classes
    HasOutputCol
  31. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  32. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  33. def hasParam(paramName: String): Boolean

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    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
  34. def hasParent: Boolean

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  35. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  36. def hashDistance(x: Seq[Vector], y: Seq[Vector]): Double

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    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[org.apache.spark.ml]
    Definition Classes
    BucketedRandomProjectionLSHModel → LSHModel
    Annotations
    @Since( "2.1.0" )
  37. val hashFunction: (Vector) ⇒ Array[Vector]

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    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[org.apache.spark.ml]
    Definition Classes
    BucketedRandomProjectionLSHModel → LSHModel
    Annotations
    @Since( "2.1.0" )
  38. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    protected
    Definition Classes
    Logging
  39. final val inputCol: Param[String]

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    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  40. final def isDefined(param: Param[_]): Boolean

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    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
  41. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  42. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  43. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  44. def keyDistance(x: Vector, y: Vector): Double

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    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[org.apache.spark.ml]
    Definition Classes
    BucketedRandomProjectionLSHModel → LSHModel
    Annotations
    @Since( "2.1.0" )
  45. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  46. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  47. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  48. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  49. def logError(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  50. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  51. def logInfo(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  52. def logName: String

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    protected
    Definition Classes
    Logging
  53. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  54. def logTrace(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  55. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  56. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  57. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  58. final def notify(): Unit

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    Definition Classes
    AnyRef
  59. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  60. final val numHashTables: IntParam

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    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
  61. final val outputCol: Param[String]

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    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  62. lazy val params: Array[Param[_]]

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    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.

  63. var parent: Estimator[BucketedRandomProjectionLSHModel]

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    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.

  64. def save(path: String): Unit

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    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( ... )
  65. final def set(paramPair: ParamPair[_]): BucketedRandomProjectionLSHModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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    protected
    Definition Classes
    Params
  66. final def set(param: String, value: Any): BucketedRandomProjectionLSHModel.this.type

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    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
  67. final def set[T](param: Param[T], value: T): BucketedRandomProjectionLSHModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  68. final def setDefault(paramPairs: ParamPair[_]*): BucketedRandomProjectionLSHModel.this.type

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    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
  69. final def setDefault[T](param: Param[T], value: T): BucketedRandomProjectionLSHModel.this.type

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    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
    Definition Classes
    Params
  70. def setParent(parent: Estimator[BucketedRandomProjectionLSHModel]): BucketedRandomProjectionLSHModel

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  71. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  72. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  73. def transform(dataset: Dataset[_]): DataFrame

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    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    LSHModel → Transformer
  74. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    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" )
  75. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    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()
  76. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: 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.

    Definition Classes
    LSHModel → PipelineStage
  77. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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()
  78. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    BucketedRandomProjectionLSHModelIdentifiable
  79. final def validateAndTransformSchema(schema: StructType): StructType

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    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
  80. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  81. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  82. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  83. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    BucketedRandomProjectionLSHModelMLWritable
    Annotations
    @Since( "2.1.0" )

Inherited from BucketedRandomProjectionLSHParams

Inherited from LSHModel[BucketedRandomProjectionLSHModel]

Inherited from MLWritable

Inherited from LSHParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

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.

Members

Parameter getters