Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/21513#discussion_r194244595
--- Diff: python/pyspark/ml/clustering.py ---
@@ -1156,6 +1157,213 @@ def getKeepLastCheckpoint(self):
return self.getOrDefault(self.keepLastCheckpoint)
+@inherit_doc
+class PowerIterationClustering(HasMaxIter, HasWeightCol, JavaParams,
JavaMLReadable,
+ JavaMLWritable):
+ """
+ .. note:: Experimental
+
+ Power Iteration Clustering (PIC), a scalable graph clustering
algorithm developed by
+ <a href=http://www.icml2010.org/papers/387.pdf>Lin and Cohen</a>. 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
:py:func:`assignClusters` method
+ to run the PowerIterationClustering algorithm.
+
+ .. seealso:: `Wikipedia on Spectral clustering \
+ <http://en.wikipedia.org/wiki/Spectral_clustering>`_
+
+ >>> data = [((long)(1), (long)(0), 0.5), \
+ ((long)(2), (long)(0), 0.5), \
+ ((long)(2), (long)(1), 0.7), \
+ ((long)(3), (long)(0), 0.5), \
+ ((long)(3), (long)(1), 0.7), \
+ ((long)(3), (long)(2), 0.9), \
+ ((long)(4), (long)(0), 0.5), \
+ ((long)(4), (long)(1), 0.7), \
+ ((long)(4), (long)(2), 0.9), \
+ ((long)(4), (long)(3), 1.1), \
+ ((long)(5), (long)(0), 0.5), \
+ ((long)(5), (long)(1), 0.7), \
+ ((long)(5), (long)(2), 0.9), \
+ ((long)(5), (long)(3), 1.1), \
+ ((long)(5), (long)(4), 1.3)]
+ >>> df = spark.createDataFrame(data).toDF("src", "dst", "weight")
+ >>> pic = PowerIterationClustering()
--- End diff --
If we only keep one example, we should use keyword args:
~~~python
pic = PowerIterationClustering(k=2, maxIter=40, weightCol="weight")
assignments = pic.assignClusters(df)
~~~
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