2015-07-17 21:30:04 -04:00
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#
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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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__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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2015-08-13 02:04:59 -04:00
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class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed):
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"""
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2015-08-12 16:24:18 -04:00
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K-means clustering with support for multiple parallel runs and a k-means++ like initialization
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mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
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they are executed together with joint passes over the data for efficiency.
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2015-07-17 21:30:04 -04:00
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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(k=2, seed=1)
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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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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, featuresCol="features", predictionCol="prediction", k=2,
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initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
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"""
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__init__(self, featuresCol="features", predictionCol="prediction", k=2, \
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initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
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"""
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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.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, initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20)
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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, featuresCol="features", predictionCol="prediction", k=2,
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initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
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"""
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setParams(self, featuresCol="features", predictionCol="prediction", k=2, \
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initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
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2015-07-17 21:30:04 -04:00
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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 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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