Github user freeman-lab commented on a diff in the pull request:
https://github.com/apache/spark/pull/2906#discussion_r22632265
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -88,6 +92,162 @@ def train(cls, rdd, k, maxIterations=100, runs=1,
initializationMode="k-means||"
return KMeansModel([c.toArray() for c in centers])
+class HierarchicalClusteringModel(object):
+
+ """A clustering model derived from the hierarchical clustering method.
+
+ >>> from numpy import array
+ >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
+ >>> train_rdd = sc.parallelize(data)
+ >>> model = HierarchicalClustering.train(train_rdd, 2)
+ >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0,
1.0]))
+ True
+ >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0,
8.0]))
+ True
+ >>> x = model.predict(data[0])
+ >>> type(x)
+ <type 'int'>
+ >>> predicted_rdd = model.predict(train_rdd)
+ >>> type(predicted_rdd)
+ <class 'pyspark.rdd.RDD'>
+ >>> predicted_rdd.collect() == [0, 0, 1, 1]
+ True
+ >>> sparse_data = [
+ ... SparseVector(3, {1: 1.0}),
+ ... SparseVector(3, {1: 1.1}),
+ ... SparseVector(3, {2: 1.0}),
+ ... SparseVector(3, {2: 1.1})
+ ... ]
+ >>> train_rdd = sc.parallelize(sparse_data)
+ >>> model = HierarchicalClustering.train(train_rdd, 2, numRetries=100)
+ >>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1,
0.]))
+ True
+ >>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0,
1.1]))
+ True
+ >>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
+ True
+ >>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
+ True
+ >>> x = model.predict(array([0., 1., 0.]))
+ >>> type(x)
+ <type 'int'>
+ >>> predicted_rdd = model.predict(train_rdd)
+ >>> type(predicted_rdd)
+ <class 'pyspark.rdd.RDD'>
+ >>> (predicted_rdd.collect() == [0, 0, 1, 1]
+ ... or predicted_rdd.collect() == [1, 1, 0, 0] )
+ True
+ >>> type(model.clusterCenters)
+ <type 'list'>
+ """
+
+ def __init__(self, sc, java_model, centers):
+ """
+ :param sc: Spark context
+ :param java_model: Handle to Java model object
+ :param centers: the cluster centers
+ """
+ self._sc = sc
+ self._java_model = java_model
+ self.centers = centers
+
+ def __del__(self):
+ self._sc._gateway.detach(self._java_model)
+
+ @property
+ def clusterCenters(self):
+ """Get the cluster centers, represented as a list of NumPy
arrays."""
+ return self.centers
+
+ def predict(self, x):
+ """Predict the closest cluster index
+
+ :param x: a ndarray of list, a SparseVector or RDD[SparseVector]
+ :return: the closest index or a RDD of int which means the closest
index
+ """
+ if isinstance(x, ndarray) or isinstance(x, Vector):
+ return self.__predict_by_array(x)
+ elif isinstance(x, RDD):
+ return self.__predict_by_rdd(x)
+ else:
+ print 'Invalid input data type x:' + type(x)
+
+ def __predict_by_array(self, x):
+ """Predict the closest cluster index with an ndarray or an
SparseVector
+
+ :param x: a vector
+ :return: the closest cluster index
+ """
+ ser = PickleSerializer()
+ bytes = bytearray(ser.dumps(_convert_to_vector(x)))
+ vec = self._sc._jvm.SerDe.loads(bytes)
+ result = self._java_model.predict(vec)
+ return
PickleSerializer().loads(str(self._sc._jvm.SerDe.dumps(result)))
+
+ def __predict_by_rdd(self, x):
+ """Predict the closest cluster index with a RDD
+ :param x: a RDD of vector
+ :return: a RDD of int
+ """
+ ser = PickleSerializer()
+ cached =
x.map(_convert_to_vector)._reserialize(AutoBatchedSerializer(ser)).cache()
+ rdd = _to_java_object_rdd(cached)
+ jrdd = self._java_model.predict(rdd)
+ jpyrdd = self._sc._jvm.SerDe.javaToPython(jrdd)
+ return RDD(jpyrdd, self._sc,
AutoBatchedSerializer(PickleSerializer()))
+
+ def cut(self, height):
--- End diff --
This currently breaks if an integer is passed as `height` (which is likely
to be common). For example, after creating the model from the example, I got an
error when calling `model.cut(4)` but not `model.cut(4.0)`. Probably just
recast the input here as a float.
---
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