Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6756#discussion_r34096466
--- Diff: python/pyspark/ml/clustering.py ---
@@ -0,0 +1,202 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark.ml.util import keyword_only
+from pyspark.ml.wrapper import JavaEstimator, JavaModel
+from pyspark.ml.param.shared import *
+from pyspark.mllib.common import inherit_doc
+from pyspark.mllib.linalg import _convert_to_vector
+
+__all__ = ['KMeans', 'KMeansModel']
+
+
+class KMeansModel(JavaModel):
+ """
+ Model fitted by KMeans.
+ """
+
+ def clusterCenters(self):
+ """Get the cluster centers, represented as a list of NumPy
arrays."""
+ return [c.toArray() for c in self._call_java("clusterCenters")]
+
+
+@inherit_doc
+class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
+ """
+ K-means Clustering
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
+ ... (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
+ >>> df = sqlContext.createDataFrame(data, ["features"])
+ >>> kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol("features")
+ >>> model = kmeans.fit(df)
+ >>> centers = model.clusterCenters()
+ >>> len(centers)
+ 2
+ >>> transformed = model.transform(df)
+ >>> (transformed.columns)[0] == 'features'
+ True
+ >>> (transformed.columns)[1] == 'prediction'
+ True
+ >>> rows = sorted(transformed.collect(), key = lambda r: r[0])
+ >>> rows[0].prediction == rows[1].prediction
+ True
+ >>> rows[2].prediction == rows[3].prediction
+ True
+ >>> kmeans.setParams("features")
+ Traceback (most recent call last):
+ ...
+ TypeError: Method setParams forces keyword arguments.
+ """
+
+ @keyword_only
+ def __init__(self, k=2, maxIter=20, runs=1, epsilon=1e-4,
initMode="k-means||", initStep=5):
+ super(KMeans, self).__init__()
+ self._java_obj =
self._new_java_obj("org.apache.spark.ml.clustering.KMeans", self.uid)
+ self.k = Param(self, "k", "number of clusters to create")
+ self.epsilon = Param(self, "epsilon",
+ "distance threshold within which " +
+ "we've consider centers to have converged")
+ self.runs = Param(self, "runs", "number of runs of the algorithm
to execute in parallel")
+ self.seed = Param(self, "seed", "random seed")
+ self.initMode = Param(self, "initMode",
+ "the initialization algorithm. This can be
either \"random\" to " +
+ "choose random points as initial cluster
centers, or \"k-means||\" " +
+ "to use a parallel variant of k-means++")
+ self.initSteps = Param(self, "initSteps", "steps for k-means
initialization mode")
+ self._setDefault(k=2, maxIter=20, runs=1, epsilon=1e-4,
initMode="k-means||", initSteps=5)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ def _create_model(self, java_model):
+ return KMeansModel(java_model)
+
+ @keyword_only
+ def setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4,
initMode="k-means||", initSteps=5):
+ """
+ setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4,
initMode="k-means||", initSteps=5):
+
+ Sets params for KMeans.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def setK(self, value):
+ """
+ Sets the value of :py:attr:`k`.
+
+ >>> algo = KMeans().setK(10)
+ >>> algo.getK()
+ 10
+ """
+ self._paramMap[self.k] = value
+ return self
+
+ def getK(self):
+ """
+ Gets the value of `k`
+ """
+ return self.getOrDefault(self.k)
+
+ def setEpsilon(self, value):
+ """
+ Sets the value of :py:attr:`epsilon`.
+
+ >>> algo = KMeans().setEpsilon(1e-5)
+ >>> abs(algo.getEpsilon() - 1e-5) < 1e-5
+ True
+ """
+ self._paramMap[self.epsilon] = value
+ return self
+
+ def getEpsilon(self):
+ """
+ Gets the value of `epsilon`
+ """
+ return self.getOrDefault(self.epsilon)
+
+ def setRuns(self, value):
+ """
+ Sets the value of :py:attr:`runs`.
+
+ >>> algo = KMeans().setRuns(10)
+ >>> algo.getRuns()
+ 10
+ """
+ self._paramMap[self.runs] = value
+ return self
+
+ def getRuns(self):
+ """
+ Gets the value of `runs`
+ """
+ return self.getOrDefault(self.runs)
+
+ def setInitializationMode(self, value):
--- End diff --
For all of these params & getters/setters, please rename "initialization"
to "init"
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