Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/21513#discussion_r194244592
--- 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), \
--- End diff --
Users do not know that we make `long` an alias of `int` in PySpark + Python
3.
I think in both Py2 and 3, PySpark infers Python int/long as long type in
DataFrame. Could you help verify? If that is the case, we can drop
`(long)(...)` here. If not, we can cast the columns to long type after creating
the DataFrame.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
