PowerIterationClustering

class pyspark.ml.clustering.PowerIterationClustering(*, k: int = 2, maxIter: int = 20, initMode: str = 'random', srcCol: str = 'src', dstCol: str = 'dst', weightCol: Optional[str] = None)

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.

New in version 2.4.0.

Notes

See Wikipedia on Spectral clustering

Examples

>>> data = [(1, 0, 0.5),
...         (2, 0, 0.5), (2, 1, 0.7),
...         (3, 0, 0.5), (3, 1, 0.7), (3, 2, 0.9),
...         (4, 0, 0.5), (4, 1, 0.7), (4, 2, 0.9), (4, 3, 1.1),
...         (5, 0, 0.5), (5, 1, 0.7), (5, 2, 0.9), (5, 3, 1.1), (5, 4, 1.3)]
>>> df = spark.createDataFrame(data).toDF("src", "dst", "weight").repartition(1)
>>> pic = PowerIterationClustering(k=2, weightCol="weight")
>>> pic.setMaxIter(40)
PowerIterationClustering...
>>> assignments = pic.assignClusters(df)
>>> assignments.sort(assignments.id).show(truncate=False)
+---+-------+
|id |cluster|
+---+-------+
|0  |0      |
|1  |0      |
|2  |0      |
|3  |0      |
|4  |0      |
|5  |1      |
+---+-------+
...
>>> pic_path = temp_path + "/pic"
>>> pic.save(pic_path)
>>> pic2 = PowerIterationClustering.load(pic_path)
>>> pic2.getK()
2
>>> pic2.getMaxIter()
40
>>> pic2.assignClusters(df).take(6) == assignments.take(6)
True

Methods

assignClusters(dataset)	Run the PIC algorithm and returns a cluster assignment for each input vertex.
clear(param)	Clears a param from the param map if it has been explicitly set.
copy([extra])	Creates a copy of this instance with the same uid and some extra params.
explainParam(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()	Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([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 < user-supplied values < extra.
getDstCol()	Gets the value of dstCol or its default value.
getInitMode()	Gets the value of initMode or its default value.
getK()	Gets the value of k or its default value.
getMaxIter()	Gets the value of maxIter or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)	Gets a param by its name.
getSrcCol()	Gets the value of srcCol or its default value.
getWeightCol()	Gets the value of weightCol or its default value.
hasDefault(param)	Checks whether a param has a default value.
hasParam(paramName)	Tests whether this instance contains a param with a given (string) name.
isDefined(param)	Checks whether a param is explicitly set by user or has a default value.
isSet(param)	Checks whether a param is explicitly set by user.
load(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
read()	Returns an MLReader instance for this class.
save(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)	Sets a parameter in the embedded param map.
setDstCol(value)	Sets the value of dstCol.
setInitMode(value)	Sets the value of initMode.
setK(value)	Sets the value of k.
setMaxIter(value)	Sets the value of maxIter.
setParams(self, \*[, k, maxIter, initMode, …])	Sets params for PowerIterationClustering.
setSrcCol(value)	Sets the value of srcCol.
setWeightCol(value)	Sets the value of weightCol.
write()	Returns an MLWriter instance for this ML instance.

Attributes

dstCol
initMode
k
maxIter
params	Returns all params ordered by name.
srcCol
weightCol

Methods Documentation

assignClusters(dataset: pyspark.sql.dataframe.DataFrame) → pyspark.sql.dataframe.DataFrame

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

Parameters

datasetpyspark.sql.DataFrame

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 s,,ij,, which must be nonnegative. This is a symmetric matrix and hence s,,ij,, = s,,ji,,. For any (i, j) with nonzero similarity, there should be either (i, j, s,,ij,,) or (j, i, s,,ji,,) in the input. Rows with i = j are ignored, because we assume s,,ij,, = 0.0.

Returns

pyspark.sql.DataFrame

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

New in version 2.4.0: ..

clear(param: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional

Extra parameters to copy to the new instance

Returns

JavaParams

Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → 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 < user-supplied values < extra.

Parameters

extradict, optional

extra param values

Returns

dict

merged param map

getDstCol() → str

Gets the value of dstCol or its default value.

New in version 2.4.0.

getInitMode() → str

Gets the value of initMode or its default value.

New in version 2.4.0.

getK() → int

Gets the value of k or its default value.

New in version 2.4.0.

getMaxIter() → int

Gets the value of maxIter or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getSrcCol() → str

Gets the value of srcCol or its default value.

New in version 2.4.0.

getWeightCol() → str

Gets the value of weightCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setDstCol(value: str) → pyspark.ml.clustering.PowerIterationClustering

Sets the value of dstCol.

New in version 2.4.0.

setInitMode(value: str) → pyspark.ml.clustering.PowerIterationClustering

Sets the value of initMode.

New in version 2.4.0.

setK(value: int) → pyspark.ml.clustering.PowerIterationClustering

Sets the value of k.

New in version 2.4.0.

setMaxIter(value: int) → pyspark.ml.clustering.PowerIterationClustering

Sets the value of maxIter.

New in version 2.4.0.

setParams(self, \*, k=2, maxIter=20, initMode="random", srcCol="src", dstCol="dst", weightCol=None)

Sets params for PowerIterationClustering.

New in version 2.4.0.

setSrcCol(value: str) → pyspark.ml.clustering.PowerIterationClustering

Sets the value of srcCol.

New in version 2.4.0.

setWeightCol(value: str) → pyspark.ml.clustering.PowerIterationClustering

Sets the value of weightCol.

New in version 2.4.0.

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

dstCol = Param(parent='undefined', name='dstCol', doc='Name of the input column for destination vertex IDs.')

initMode = Param(parent='undefined', name='initMode', doc="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. Supported options: 'random' and 'degree'.")

k = Param(parent='undefined', name='k', doc='The number of clusters to create. Must be > 1.')

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

srcCol = Param(parent='undefined', name='srcCol', doc='Name of the input column for source vertex IDs.')

weightCol = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')

DistributedLDAModel pyspark.ml.functions.array_to_vector