34a889db85
I Implemented the KMeans API for spark.ml Pipelines. But it doesn't include clustering abstractions for spark.ml (SPARK-7610). It would fit for another issues. And I'll try it later, since we are trying to add the hierarchical clustering algorithms in another issue. Thanks. [SPARK-7879] KMeans API for spark.ml Pipelines - ASF JIRA https://issues.apache.org/jira/browse/SPARK-7879 Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #6756 from yu-iskw/SPARK-7879 and squashes the following commits: be752de [Yu ISHIKAWA] Add assertions a14939b [Yu ISHIKAWA] Fix the dashed line's length in pyspark.ml.rst 4c61693 [Yu ISHIKAWA] Remove the test about whether "features" and "prediction" columns exist or not in Python fb2417c [Yu ISHIKAWA] Use getInt, instead of get f397be4 [Yu ISHIKAWA] Switch the comparisons. ca78b7d [Yu ISHIKAWA] Add the Scala docs about the constraints of each parameter. effc650 [Yu ISHIKAWA] Using expertSetParam and expertGetParam c8dc6e6 [Yu ISHIKAWA] Remove an unnecessary test 19a9d63 [Yu ISHIKAWA] Include spark.ml.clustering to python tests 1abb19c [Yu ISHIKAWA] Add the statements about spark.ml.clustering into pyspark.ml.rst f8338bc [Yu ISHIKAWA] Add the placeholders in Python 4a03003 [Yu ISHIKAWA] Test for contains in Python 6566c8b [Yu ISHIKAWA] Use `get`, instead of `apply` 288e8d5 [Yu ISHIKAWA] Using `contains` to check the column names 5a7d574 [Yu ISHIKAWA] Renamce `validateInitializationMode` to `validateInitMode` and remove throwing exception 97cfae3 [Yu ISHIKAWA] Fix the type of return value of `KMeans.copy` e933723 [Yu ISHIKAWA] Remove the default value of seed from the Model class 978ee2c [Yu ISHIKAWA] Modify the docs of KMeans, according to mllib's KMeans 2ec80bc [Yu ISHIKAWA] Fit on 1 line e186be1 [Yu ISHIKAWA] Make a few variables, setters and getters be expert ones b2c205c [Yu ISHIKAWA] Rename the method `getInitializationSteps` to `getInitSteps` and `setInitializationSteps` to `setInitSteps` in Scala and Python f43f5b4 [Yu ISHIKAWA] Rename the method `getInitializationMode` to `getInitMode` and `setInitializationMode` to `setInitMode` in Scala and Python 3cb5ba4 [Yu ISHIKAWA] Modify the description about epsilon and the validation 4fa409b [Yu ISHIKAWA] Add a comment about the default value of epsilon 2f392e1 [Yu ISHIKAWA] Make some variables `final` and Use `IntParam` and `DoubleParam` 19326f8 [Yu ISHIKAWA] Use `udf`, instead of callUDF 4d2ad1e [Yu ISHIKAWA] Modify the indentations 0ae422f [Yu ISHIKAWA] Add a test for `setParams` 4ff7913 [Yu ISHIKAWA] Add "ml.clustering" to `javacOptions` in SparkBuild.scala 11ffdf1 [Yu ISHIKAWA] Use `===` and the variable 220a176 [Yu ISHIKAWA] Set a random seed in the unit testing 92c3efc [Yu ISHIKAWA] Make the points for a test be fewer c758692 [Yu ISHIKAWA] Modify the parameters of KMeans in Python 6aca147 [Yu ISHIKAWA] Add some unit testings to validate the setter methods 687cacc [Yu ISHIKAWA] Alias mllib.KMeans as MLlibKMeans in KMeansSuite.scala a4dfbef [Yu ISHIKAWA] Modify the last brace and indentations 5bedc51 [Yu ISHIKAWA] Remve an extra new line 444c289 [Yu ISHIKAWA] Add the validation for `runs` e41989c [Yu ISHIKAWA] Modify how to validate `initStep` 7ea133a [Yu ISHIKAWA] Change how to validate `initMode` 7991e15 [Yu ISHIKAWA] Add a validation for `k` c2df35d [Yu ISHIKAWA] Make `predict` private 93aa2ff [Yu ISHIKAWA] Use `withColumn` in `transform` d3a79f7 [Yu ISHIKAWA] Remove the inhefited docs e9532e1 [Yu ISHIKAWA] make `parentModel` of KMeansModel private 8559772 [Yu ISHIKAWA] Remove the `paramMap` parameter of KMeans 6684850 [Yu ISHIKAWA] Rename `initializationSteps` to `initSteps` 99b1b96 [Yu ISHIKAWA] Rename `initializationMode` to `initMode` 79ea82b [Yu ISHIKAWA] Modify the parameters of KMeans docs 6569bcd [Yu ISHIKAWA] Change how to set the default values with `setDefault` 20a795a [Yu ISHIKAWA] Change how to set the default values with `setDefault` 11c2a12 [Yu ISHIKAWA] Limit the imports badb481 [Yu ISHIKAWA] Alias spark.mllib.{KMeans, KMeansModel} f80319a [Yu ISHIKAWA] Rebase mater branch and add copy methods 85d92b1 [Yu ISHIKAWA] Add `KMeans.setPredictionCol` aa9469d [Yu ISHIKAWA] Fix a python test suite error caused by python 3.x c2d6bcb [Yu ISHIKAWA] ADD Java test suites of the KMeans API for spark.ml Pipeline 598ed2e [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Python 63ad785 [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Scala
207 lines
7 KiB
Python
207 lines
7 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from pyspark.ml.util import keyword_only
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from pyspark.ml.wrapper import JavaEstimator, JavaModel
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from pyspark.ml.param.shared import *
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from pyspark.mllib.common import inherit_doc
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from pyspark.mllib.linalg import _convert_to_vector
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__all__ = ['KMeans', 'KMeansModel']
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class KMeansModel(JavaModel):
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"""
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Model fitted by KMeans.
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"""
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def clusterCenters(self):
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"""Get the cluster centers, represented as a list of NumPy arrays."""
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return [c.toArray() for c in self._call_java("clusterCenters")]
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@inherit_doc
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class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
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"""
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K-means Clustering
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>>> from pyspark.mllib.linalg import Vectors
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>>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
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... (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
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>>> df = sqlContext.createDataFrame(data, ["features"])
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>>> kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol("features")
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>>> model = kmeans.fit(df)
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>>> centers = model.clusterCenters()
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>>> len(centers)
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2
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>>> transformed = model.transform(df).select("features", "prediction")
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>>> rows = transformed.collect()
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>>> rows[0].prediction == rows[1].prediction
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True
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>>> rows[2].prediction == rows[3].prediction
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True
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"""
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# a placeholder to make it appear in the generated doc
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k = Param(Params._dummy(), "k", "number of clusters to create")
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epsilon = Param(Params._dummy(), "epsilon",
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"distance threshold within which " +
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"we've consider centers to have converged")
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runs = Param(Params._dummy(), "runs", "number of runs of the algorithm to execute in parallel")
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initMode = Param(Params._dummy(), "initMode",
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"the initialization algorithm. This can be either \"random\" to " +
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"choose random points as initial cluster centers, or \"k-means||\" " +
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"to use a parallel variant of k-means++")
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initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode")
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@keyword_only
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def __init__(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initStep=5):
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super(KMeans, self).__init__()
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self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.KMeans", self.uid)
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self.k = Param(self, "k", "number of clusters to create")
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self.epsilon = Param(self, "epsilon",
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"distance threshold within which " +
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"we've consider centers to have converged")
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self.runs = Param(self, "runs", "number of runs of the algorithm to execute in parallel")
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self.seed = Param(self, "seed", "random seed")
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self.initMode = Param(self, "initMode",
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"the initialization algorithm. This can be either \"random\" to " +
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"choose random points as initial cluster centers, or \"k-means||\" " +
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"to use a parallel variant of k-means++")
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self.initSteps = Param(self, "initSteps", "steps for k-means initialization mode")
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self._setDefault(k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5)
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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def _create_model(self, java_model):
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return KMeansModel(java_model)
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@keyword_only
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def setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
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"""
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setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
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Sets params for KMeans.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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def setK(self, value):
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"""
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Sets the value of :py:attr:`k`.
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>>> algo = KMeans().setK(10)
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>>> algo.getK()
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10
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"""
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self._paramMap[self.k] = value
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return self
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def getK(self):
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"""
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Gets the value of `k`
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"""
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return self.getOrDefault(self.k)
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def setEpsilon(self, value):
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"""
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Sets the value of :py:attr:`epsilon`.
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>>> algo = KMeans().setEpsilon(1e-5)
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>>> abs(algo.getEpsilon() - 1e-5) < 1e-5
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True
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"""
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self._paramMap[self.epsilon] = value
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return self
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def getEpsilon(self):
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"""
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Gets the value of `epsilon`
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"""
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return self.getOrDefault(self.epsilon)
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def setRuns(self, value):
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"""
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Sets the value of :py:attr:`runs`.
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>>> algo = KMeans().setRuns(10)
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>>> algo.getRuns()
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10
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"""
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self._paramMap[self.runs] = value
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return self
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def getRuns(self):
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"""
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Gets the value of `runs`
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"""
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return self.getOrDefault(self.runs)
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def setInitMode(self, value):
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"""
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Sets the value of :py:attr:`initMode`.
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>>> algo = KMeans()
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>>> algo.getInitMode()
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'k-means||'
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>>> algo = algo.setInitMode("random")
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>>> algo.getInitMode()
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'random'
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"""
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self._paramMap[self.initMode] = value
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return self
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def getInitMode(self):
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"""
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Gets the value of `initMode`
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"""
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return self.getOrDefault(self.initMode)
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def setInitSteps(self, value):
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"""
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Sets the value of :py:attr:`initSteps`.
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>>> algo = KMeans().setInitSteps(10)
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>>> algo.getInitSteps()
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10
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"""
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self._paramMap[self.initSteps] = value
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return self
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def getInitSteps(self):
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"""
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Gets the value of `initSteps`
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"""
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return self.getOrDefault(self.initSteps)
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if __name__ == "__main__":
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import doctest
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from pyspark.context import SparkContext
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from pyspark.sql import SQLContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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sc = SparkContext("local[2]", "ml.clustering tests")
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sqlContext = SQLContext(sc)
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globs['sc'] = sc
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globs['sqlContext'] = sqlContext
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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sc.stop()
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if failure_count:
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exit(-1)
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