org.apache.spark.ml.clustering
PowerIterationClustering
Companion object PowerIterationClustering
class PowerIterationClustering extends PowerIterationClusteringParams with DefaultParamsWritable
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" )
- Source
- PowerIterationClustering.scala
- See also
- Grouped
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- By Inheritance
- PowerIterationClustering
- DefaultParamsWritable
- MLWritable
- PowerIterationClusteringParams
- HasWeightCol
- HasMaxIter
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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- Public
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Instance Constructors
-
new
PowerIterationClustering()
- Annotations
- @Since( "2.4.0" )
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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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
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
assignClusters(dataset: Dataset[_]): DataFrame
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" )
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final
def
clear(param: Param[_]): PowerIterationClustering.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( ... ) @native()
-
def
copy(extra: ParamMap): PowerIterationClustering
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
- PowerIterationClustering → Params
- Annotations
- @Since( "2.4.0" )
-
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 toparamMap. 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
-
val
dstCol: Param[String]
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" )
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): 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
extractParamMapwith no extra values.extractParamMapwith 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
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
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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
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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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
-
def
getDstCol: String
- Definition Classes
- PowerIterationClusteringParams
- Annotations
- @Since( "2.4.0" )
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def
getInitMode: String
- Definition Classes
- PowerIterationClusteringParams
- Annotations
- @Since( "2.4.0" )
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def
getK: Int
- Definition Classes
- PowerIterationClusteringParams
- Annotations
- @Since( "2.4.0" )
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final
def
getMaxIter: Int
- Definition Classes
- HasMaxIter
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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
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
def
getSrcCol: String
- Definition Classes
- PowerIterationClusteringParams
- Annotations
- @Since( "2.4.0" )
-
final
def
getWeightCol: String
- Definition Classes
- HasWeightCol
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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
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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final
val
initMode: Param[String]
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" )
-
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
-
final
val
k: IntParam
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" )
-
final
val
maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
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.
-
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( ... )
-
final
def
set(paramPair: ParamPair[_]): PowerIterationClustering.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): PowerIterationClustering.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): PowerIterationClustering.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[_]*): PowerIterationClustering.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): PowerIterationClustering.this.type
Sets a default value for a param.
-
def
setDstCol(value: String): PowerIterationClustering.this.type
- Annotations
- @Since( "2.4.0" )
-
def
setInitMode(value: String): PowerIterationClustering.this.type
- Annotations
- @Since( "2.4.0" )
-
def
setK(value: Int): PowerIterationClustering.this.type
- Annotations
- @Since( "2.4.0" )
-
def
setMaxIter(value: Int): PowerIterationClustering.this.type
- Annotations
- @Since( "2.4.0" )
-
def
setSrcCol(value: String): PowerIterationClustering.this.type
- Annotations
- @Since( "2.4.0" )
-
def
setWeightCol(value: String): PowerIterationClustering.this.type
- Annotations
- @Since( "2.4.0" )
-
val
srcCol: Param[String]
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" )
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
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
- PowerIterationClustering → Identifiable
- Annotations
- @Since( "2.4.0" )
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
val
weightCol: Param[String]
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
-
def
write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
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.