PowerIterationClustering¶
-
class
pyspark.ml.clustering.PowerIterationClustering(*, k=2, maxIter=20, initMode='random', srcCol='src', dstCol='dst', weightCol=None)[source]¶ 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.
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.
Gets the value of
dstColor its default value.Gets the value of
initModeor its default value.getK()Gets the value of
kor its default value.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.
Gets the value of
srcColor its default value.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
Returns all params ordered by name.
Methods Documentation
-
assignClusters(dataset)[source]¶ Run the PIC algorithm and returns a cluster assignment for each input vertex.
- Parameters
- dataset
pyspark.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.
- dataset
- Returns
pyspark.sql.DataFrameA 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)¶ Clears a param from the param map if it has been explicitly set.
-
copy(extra=None)¶ 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
JavaParamsCopy of this instance
-
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=None)¶ 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
-
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. Raises an error if neither is set.
-
getParam(paramName)¶ Gets a param by its name.
-
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.
-
classmethod
load(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
-
classmethod
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.
-
setParams(self, \*, k=2, maxIter=20, initMode="random", srcCol="src", dstCol="dst", weightCol=None)[source]¶ Sets params for PowerIterationClustering.
New in version 2.4.0.
-
write()¶ 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 typeParam.
-
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.')¶
-