Source code for pyspark.ml.clustering

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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

__all__ = ['KMeans', 'KMeansModel']


[docs]class KMeansModel(JavaModel): """ Model fitted by KMeans. """
[docs] 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
[docs]class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed): """ K-means clustering with support for multiple parallel runs and a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested, they are executed together with joint passes over the data for efficiency. >>> 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(k=2, seed=1) >>> model = kmeans.fit(df) >>> centers = model.clusterCenters() >>> len(centers) 2 >>> transformed = model.transform(df).select("features", "prediction") >>> rows = transformed.collect() >>> rows[0].prediction == rows[1].prediction True >>> rows[2].prediction == rows[3].prediction True """ # a placeholder to make it appear in the generated doc k = Param(Params._dummy(), "k", "number of clusters to create") initMode = Param(Params._dummy(), "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++") initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode") @keyword_only def __init__(self, featuresCol="features", predictionCol="prediction", k=2, initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None): """ __init__(self, featuresCol="features", predictionCol="prediction", k=2, \ initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None) """ 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.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, initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) def _create_model(self, java_model): return KMeansModel(java_model) @keyword_only
[docs] def setParams(self, featuresCol="features", predictionCol="prediction", k=2, initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None): """ setParams(self, featuresCol="features", predictionCol="prediction", k=2, \ initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None) Sets params for KMeans. """ kwargs = self.setParams._input_kwargs return self._set(**kwargs)
[docs] 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
[docs] def getK(self): """ Gets the value of `k` """ return self.getOrDefault(self.k)
[docs] def setInitMode(self, value): """ Sets the value of :py:attr:`initMode`. >>> algo = KMeans() >>> algo.getInitMode() 'k-means||' >>> algo = algo.setInitMode("random") >>> algo.getInitMode() 'random' """ self._paramMap[self.initMode] = value return self
[docs] def getInitMode(self): """ Gets the value of `initMode` """ return self.getOrDefault(self.initMode)
[docs] def setInitSteps(self, value): """ Sets the value of :py:attr:`initSteps`. >>> algo = KMeans().setInitSteps(10) >>> algo.getInitSteps() 10 """ self._paramMap[self.initSteps] = value return self
[docs] def getInitSteps(self): """ Gets the value of `initSteps` """ return self.getOrDefault(self.initSteps)
if __name__ == "__main__": import doctest from pyspark.context import SparkContext from pyspark.sql import SQLContext globs = globals().copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.clustering tests") sqlContext = SQLContext(sc) globs['sc'] = sc globs['sqlContext'] = sqlContext (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() if failure_count: exit(-1)