Github user freeman-lab commented on a diff in the pull request:
https://github.com/apache/spark/pull/2906#discussion_r22634203
--- 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):
+ """Cut nodes and leaves in a cluster tree by a dendrogram height.
+ :param height: a threshold to cut a cluster tree
+ """
+ ser = PickleSerializer()
+ model = self._java_model.cut(height)
+ bytes = self._sc._jvm.SerDe.dumps(model.getCenters())
+ centers = ser.loads(str(bytes))
+ return HierarchicalClusteringModel(self._sc, model, [c.toArray()
for c in centers])
+
+ def sum_of_variance(self):
+ """Gets the sum of variance of all clusters.
+ :return: sum of variance of all clusters
+ """
+ ser = PickleSerializer()
+ model = self._java_model
+ bytes = self._sc._jvm.SerDe.dumps(model.getSumOfVariance())
+ variance = ser.loads(str(bytes))
+ return variance
+
+ def to_merge_list(self):
+ """Extract an array for dendrogram
+
+ the array is fit for SciPy's dendrogram
+ :return: an array which is fit for scipy's dendrogram
+ """
+ ser = PickleSerializer()
+ model = self._java_model
+ bytes = self._sc._jvm.SerDe.dumps(model.toMergeList())
+ centers = ser.loads(str(bytes))
+ return array([c.toArray() for c in centers])
+
+
+class HierarchicalClustering(object):
+
+ @classmethod
+ def train(cls, rdd, k,
+ subIterations=20, numRetries=10, epsilon=1.0e-4,
randomSeed=1, randomRange=0.1):
+ """Train a hierarchical clustering model."""
+ sc = rdd.context
+ ser = PickleSerializer()
+ # cache serialized data to avoid objects over head in JVM
+ cached =
rdd.map(_convert_to_vector)._reserialize(AutoBatchedSerializer(ser)).cache()
+ model = sc._jvm.PythonMLLibAPI().trainHierarchicalClusteringModel(
+ _to_java_object_rdd(cached), k,
+ subIterations, numRetries, epsilon, randomSeed, randomRange)
+ bytes = sc._jvm.SerDe.dumps(model.getCenters())
+ centers = ser.loads(str(bytes))
+ # TODO: because centers are SparseVector, wel will change them
numpy.array.
--- End diff --
Did you mean convert from `DenseVector`? That's currently the output. I
agree it would be good to change to `numpy.array`, especially because after
calling `cut` the output is an array of `numpy.array`s. The two behaviors
should be consistent, as calling `cut` should not change the type.
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