Packages

class MinHashLSHModel extends LSHModel[MinHashLSHModel]

Model produced by MinHashLSH, where multiple hash functions are stored. Each hash function is picked from the following family of hash functions, where a_i and b_i are randomly chosen integers less than prime: h_i(x) = ((x \cdot a_i + b_i) \mod prime)

This hash family is approximately min-wise independent according to the reference.

Reference: Tom Bohman, Colin Cooper, and Alan Frieze. "Min-wise independent linear permutations." Electronic Journal of Combinatorics 7 (2000): R26.

Annotations
@Since( "2.1.0" )
Source
MinHashLSH.scala
Linear Supertypes
LSHModel[MinHashLSHModel], MLWritable, LSHParams, HasOutputCol, HasInputCol, Model[MinHashLSHModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. MinHashLSHModel
  2. LSHModel
  3. MLWritable
  4. LSHParams
  5. HasOutputCol
  6. HasInputCol
  7. Model
  8. Transformer
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. 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
  6. 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.

  7. 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
  8. 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
  9. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  10. final def clear(param: Param[_]): MinHashLSHModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  11. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  12. def copy(extra: ParamMap): MinHashLSHModel

    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
    MinHashLSHModel → Model → Transformer → PipelineStage → Params
    Annotations
    @Since( "2.1.0" )
  13. 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
  14. 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
  15. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  17. 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
  18. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  20. 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
  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. 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
  23. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. 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
  25. final def getInputCol: String

    Definition Classes
    HasInputCol
  26. final def getNumHashTables: Int

    Definition Classes
    LSHParams
  27. 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
  28. final def getOutputCol: String

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  30. 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
  31. 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
  32. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  33. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  34. 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
    MinHashLSHModel → LSHModel
    Annotations
    @Since( "2.1.0" )
  35. 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
    MinHashLSHModel → LSHModel
    Annotations
    @Since( "2.1.0" )
  36. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  37. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  38. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  39. 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
  40. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  41. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  42. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  43. 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
    MinHashLSHModel → LSHModel
    Annotations
    @Since( "2.1.0" )
  44. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  45. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  48. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  50. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  52. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  57. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  58. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  59. 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
  60. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  61. 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.

  62. var parent: Estimator[MinHashLSHModel]

    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.

  63. 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( ... )
  64. final def set(paramPair: ParamPair[_]): MinHashLSHModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  65. final def set(param: String, value: Any): MinHashLSHModel.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
  66. final def set[T](param: Param[T], value: T): MinHashLSHModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  67. final def setDefault(paramPairs: ParamPair[_]*): MinHashLSHModel.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
  68. final def setDefault[T](param: Param[T], value: T): MinHashLSHModel.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
    Definition Classes
    Params
  69. def setInputCol(value: String): MinHashLSHModel.this.type

    Definition Classes
    MinHashLSHModel → LSHModel
    Annotations
    @Since( "2.4.0" )
  70. def setOutputCol(value: String): MinHashLSHModel.this.type

    Definition Classes
    MinHashLSHModel → LSHModel
    Annotations
    @Since( "2.4.0" )
  71. def setParent(parent: Estimator[MinHashLSHModel]): MinHashLSHModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  72. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  73. def toString(): String
    Definition Classes
    MinHashLSHModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  74. def transform(dataset: Dataset[_]): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    LSHModel → Transformer
  75. 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" )
  76. 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()
  77. 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
  78. 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()
  79. 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
    MinHashLSHModel → Identifiable
  80. 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
  81. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  82. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  83. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  84. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    MinHashLSHModel → MLWritable
    Annotations
    @Since( "2.1.0" )

Inherited from LSHModel[MinHashLSHModel]

Inherited from MLWritable

Inherited from LSHParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Model[MinHashLSHModel]

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 setters

Parameter getters