Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/10150#discussion_r49253296
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -38,13 +38,120 @@
from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader,
JavaSaveable
from pyspark.streaming import DStream
-__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel',
'GaussianMixture',
- 'PowerIterationClusteringModel', 'PowerIterationClustering',
- 'StreamingKMeans', 'StreamingKMeansModel',
+__all__ = ['BisectingKMeansModel', 'BisectingKMeans', 'KMeansModel',
'KMeans',
+ 'GaussianMixtureModel', 'GaussianMixture',
'PowerIterationClusteringModel',
+ 'PowerIterationClustering', 'StreamingKMeans',
'StreamingKMeansModel',
'LDA', 'LDAModel']
@inherit_doc
+class BisectingKMeansModel(JavaModelWrapper):
+ """
+ .. note:: Experimental
+
+ A clustering model derived from the bisecting k-means method.
+
+ >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2)
+ >>> bskm = BisectingKMeans()
+ >>> model = bskm.train(sc.parallelize(data), k=4)
+ >>> p = array([0.0, 0.0])
+ >>> model.predict(p) == model.predict(p)
+ True
+ >>> model.predict(sc.parallelize([p])).first() == model.predict(p)
+ True
+ >>> model.k
+ 4
+ >>> model.computeCost(array([0.0, 0.0]))
+ 0.0
+ >>> model.k == len(model.clusterCenters)
+ True
+ >>> model = bskm.train(sc.parallelize(data), k=2)
+ >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0,
1.0]))
+ True
+ >>> model.k
+ 2
+
+ .. versionadded:: 2.0.0
+ """
+
+ @property
+ @since('2.0.0')
+ def clusterCenters(self):
+ """Get the cluster centers, represented as a list of NumPy
arrays."""
+ return [c.toArray() for c in self.call("clusterCenters")]
+
+ @property
+ @since('2.0.0')
+ def k(self):
+ """Get the number of clusters"""
+ return self.call("k")
+
+ @since('2.0.0')
+ def predict(self, x):
+ """
+ Find the cluster to which x belongs in this model.
+
+ :param x: Either the point to determine the cluster for or an RDD
of points to determine
+ the clusters for.
+ """
+ if isinstance(x, RDD):
+ vecs = x.map(_convert_to_vector)
+ return self.call("predict", vecs)
+
+ x = _convert_to_vector(x)
+ return self.call("predict", x)
+
+ @since('2.0.0')
+ def computeCost(self, point):
+ """
+ Return the Bisecting K-means cost (sum of squared distances of
points to
+ their nearest center) for this model on the given data.
+
+ :param point: the point to compute the cost to
+ """
+ return self.call("computeCost", _convert_to_vector(point))
+
+
+class BisectingKMeans:
+ """
+ .. note:: Experimental
+
+ A bisecting k-means algorithm based on the paper "A comparison of
document clustering
--- End diff --
Are we sure on the 74? Looking at pep8/pep257 it says 72 (although we
extended the length for code lines so maybe we changed that too)? We could try
and add a lint rule for this maybe in the future.
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