PowerIterationClustering¶
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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.See also
>>> 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
New in version 2.4.0.
Methods
Attributes
Methods Documentation
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assignClusters
(dataset)[source]¶ Run the PIC algorithm and returns a cluster assignment for each input vertex.
- Parameters
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 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
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.
New in version 2.4.0.
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
extra – extra param values
- Returns
merged param map
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getMaxIter
()¶ Gets the value of maxIter or its default value.
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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.
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getParam
(paramName)¶ Gets a param by its name.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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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.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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dstCol
= Param(parent='undefined', name='dstCol', doc='Name of the input column for destination vertex IDs.')¶
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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'.")¶
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k
= Param(parent='undefined', name='k', doc='The number of clusters to create. Must be > 1.')¶
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maxIter
= Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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srcCol
= Param(parent='undefined', name='srcCol', doc='Name of the input column for source vertex IDs.')¶
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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.')¶
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