Class/Object

org.apache.spark.ml.clustering

PowerIterationClustering

Related Docs: object PowerIterationClustering | package clustering

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class PowerIterationClustering extends PowerIterationClusteringParams with DefaultParamsWritable

:: Experimental :: Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen. From the abstract: PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data.

This class is not yet an Estimator/Transformer, use assignClusters method to run the PowerIterationClustering algorithm.

Annotations
@Since( "2.4.0" ) @Experimental()
Source
PowerIterationClustering.scala
See also

Spectral clustering (Wikipedia)

Linear Supertypes
DefaultParamsWritable, MLWritable, PowerIterationClusteringParams, HasWeightCol, HasMaxIter, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. PowerIterationClustering
  2. DefaultParamsWritable
  3. MLWritable
  4. PowerIterationClusteringParams
  5. HasWeightCol
  6. HasMaxIter
  7. Params
  8. Serializable
  9. Serializable
  10. Identifiable
  11. AnyRef
  12. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new PowerIterationClustering()

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    Annotations
    @Since( "2.4.0" )

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. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. def assignClusters(dataset: Dataset[_]): DataFrame

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    Run the PIC algorithm and returns a cluster assignment for each input vertex.

    Run the PIC algorithm and returns a cluster assignment for each input vertex.

    dataset

    A dataset with columns src, dst, weight representing the affinity matrix, which is the matrix A in the PIC paper. Suppose the src column value is i, the dst column value is j, the weight column value is similarity sij which must be nonnegative. This is a symmetric matrix and hence sij = sji. For any (i, j) with nonzero similarity, there should be either (i, j, sij) or (j, i, sji) in the input. Rows with i = j are ignored, because we assume sij = 0.0.

    returns

    A dataset that contains columns of vertex id and the corresponding cluster for the id. The schema of it will be:

    • id: Long
    • cluster: Int
    Annotations
    @Since( "2.4.0" )
  7. final def clear(param: Param[_]): PowerIterationClustering.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
  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def copy(extra: ParamMap): PowerIterationClustering

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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
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  10. 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
  11. 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
  12. val dstCol: Param[String]

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    Name of the input column for destination vertex IDs.

    Name of the input column for destination vertex IDs. Default: "dst"

    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  13. final def eq(arg0: AnyRef): Boolean

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

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    Definition Classes
    AnyRef → Any
  15. 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
  16. def explainParams(): String

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    Explains all params of this instance.

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

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

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  18. 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
  19. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. 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
  21. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  22. 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
  23. def getDstCol: String

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    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  24. def getInitMode: String

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    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  25. def getK: Int

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    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  26. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  27. 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
  28. 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
  29. def getSrcCol: String

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    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  30. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  31. 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
  32. 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
  33. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  34. final val initMode: Param[String]

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    Param for the initialization algorithm.

    Param for the initialization algorithm. This can be either "random" to use a random vector as vertex properties, or "degree" to use a normalized sum of similarities with other vertices. Default: random.

    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  35. 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
  36. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  37. 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
  38. final val k: IntParam

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    The number of clusters to create (k).

    The number of clusters to create (k). Must be > 1. Default: 2.

    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  39. final val maxIter: IntParam

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    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  40. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  43. 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.

  44. 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( ... )
  45. final def set(paramPair: ParamPair[_]): PowerIterationClustering.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  46. final def set(param: String, value: Any): PowerIterationClustering.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
  47. final def set[T](param: Param[T], value: T): PowerIterationClustering.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
  48. final def setDefault(paramPairs: ParamPair[_]*): PowerIterationClustering.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
  49. final def setDefault[T](param: Param[T], value: T): PowerIterationClustering.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
  50. def setDstCol(value: String): PowerIterationClustering.this.type

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    Annotations
    @Since( "2.4.0" )
  51. def setInitMode(value: String): PowerIterationClustering.this.type

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    Annotations
    @Since( "2.4.0" )
  52. def setK(value: Int): PowerIterationClustering.this.type

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    Annotations
    @Since( "2.4.0" )
  53. def setMaxIter(value: Int): PowerIterationClustering.this.type

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    Annotations
    @Since( "2.4.0" )
  54. def setSrcCol(value: String): PowerIterationClustering.this.type

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    Annotations
    @Since( "2.4.0" )
  55. def setWeightCol(value: String): PowerIterationClustering.this.type

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    Annotations
    @Since( "2.4.0" )
  56. val srcCol: Param[String]

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    Param for the name of the input column for source vertex IDs.

    Param for the name of the input column for source vertex IDs. Default: "src"

    Definition Classes
    PowerIterationClusteringParams
    Annotations
    @Since( "2.4.0" )
  57. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  58. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  59. 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
    PowerIterationClusteringIdentifiable
    Annotations
    @Since( "2.4.0" )
  60. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  63. final val weightCol: Param[String]

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    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  64. 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
    DefaultParamsWritableMLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from PowerIterationClusteringParams

Inherited from HasWeightCol

Inherited from HasMaxIter

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

(expert-only) Parameters

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

(expert-only) Parameter setters

(expert-only) Parameter getters