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From jkbradley <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-11944][PYSPARK][MLLIB] python mllib.clu...
Date Fri, 08 Jan 2016 23:10:23 GMT
Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10150#discussion_r49249961
  
    --- 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:
    --- End diff --
    
    inherit from object


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