d29e429eeb
## What changes were proposed in this pull request? PySpark Param constructors need to pass the TypeConverter argument by name, partly to make sure it is not mistaken for the expectedType arg and partly because we will remove the expectedType arg in 2.1. In several places, this is not being done correctly. This PR changes all usages in pyspark/ml/ to keyword args. ## How was this patch tested? Existing unit tests. I will not test type conversion for every Param unless we really think it necessary. Also, if you start the PySpark shell and import classes (e.g., pyspark.ml.feature.StandardScaler), then you no longer get this warning: ``` /Users/josephkb/spark/python/pyspark/ml/param/__init__.py:58: UserWarning: expectedType is deprecated and will be removed in 2.1. Use typeConverter instead, as a keyword argument. "Use typeConverter instead, as a keyword argument.") ``` That warning came from the typeConverter argument being passes as the expectedType arg by mistake. Author: Joseph K. Bradley <joseph@databricks.com> Closes #12480 from jkbradley/typeconverter-fix.
339 lines
12 KiB
Python
339 lines
12 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 import since
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from pyspark.ml.util import *
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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__ = ['BisectingKMeans', 'BisectingKMeansModel',
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'KMeans', 'KMeansModel']
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class KMeansModel(JavaModel, JavaMLWritable, JavaMLReadable):
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"""
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Model fitted by KMeans.
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.. versionadded:: 1.5.0
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"""
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@since("1.5.0")
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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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@since("2.0.0")
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def computeCost(self, dataset):
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"""
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Return the K-means cost (sum of squared distances of points to their nearest center)
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for this model on the given data.
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"""
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return self._call_java("computeCost", dataset)
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@inherit_doc
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class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed,
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JavaMLWritable, JavaMLReadable):
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"""
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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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>>> 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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>>> model.computeCost(df)
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2.000...
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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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>>> kmeans_path = temp_path + "/kmeans"
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>>> kmeans.save(kmeans_path)
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>>> kmeans2 = KMeans.load(kmeans_path)
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>>> kmeans2.getK()
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2
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>>> model_path = temp_path + "/kmeans_model"
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>>> model.save(model_path)
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>>> model2 = KMeansModel.load(model_path)
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>>> model.clusterCenters()[0] == model2.clusterCenters()[0]
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array([ True, True], dtype=bool)
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>>> model.clusterCenters()[1] == model2.clusterCenters()[1]
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array([ True, True], dtype=bool)
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.. versionadded:: 1.5.0
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"""
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k = Param(Params._dummy(), "k", "number of clusters to create",
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typeConverter=TypeConverters.toInt)
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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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typeConverter=TypeConverters.toString)
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initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode",
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typeConverter=TypeConverters.toInt)
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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._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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@since("1.5.0")
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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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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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@since("1.5.0")
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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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"""
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self._set(k=value)
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return self
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@since("1.5.0")
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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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@since("1.5.0")
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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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"""
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self._set(initMode=value)
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return self
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@since("1.5.0")
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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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@since("1.5.0")
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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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"""
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self._set(initSteps=value)
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return self
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@since("1.5.0")
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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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class BisectingKMeansModel(JavaModel, JavaMLWritable, JavaMLReadable):
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"""
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.. note:: Experimental
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Model fitted by BisectingKMeans.
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.. versionadded:: 2.0.0
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"""
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@since("2.0.0")
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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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@since("2.0.0")
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def computeCost(self, dataset):
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"""
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Computes the sum of squared distances between the input points
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and their corresponding cluster centers.
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"""
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return self._call_java("computeCost", dataset)
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@inherit_doc
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class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasSeed,
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JavaMLWritable, JavaMLReadable):
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"""
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.. note:: Experimental
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A bisecting k-means algorithm based on the paper "A comparison of document clustering
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techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark.
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The algorithm starts from a single cluster that contains all points.
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Iteratively it finds divisible clusters on the bottom level and bisects each of them using
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k-means, until there are `k` leaf clusters in total or no leaf clusters are divisible.
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The bisecting steps of clusters on the same level are grouped together to increase parallelism.
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If bisecting all divisible clusters on the bottom level would result more than `k` leaf
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clusters, larger clusters get higher priority.
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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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>>> bkm = BisectingKMeans(k=2, minDivisibleClusterSize=1.0)
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>>> model = bkm.fit(df)
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>>> centers = model.clusterCenters()
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>>> len(centers)
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2
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>>> model.computeCost(df)
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2.000...
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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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>>> bkm_path = temp_path + "/bkm"
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>>> bkm.save(bkm_path)
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>>> bkm2 = BisectingKMeans.load(bkm_path)
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>>> bkm2.getK()
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2
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>>> model_path = temp_path + "/bkm_model"
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>>> model.save(model_path)
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>>> model2 = BisectingKMeansModel.load(model_path)
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>>> model.clusterCenters()[0] == model2.clusterCenters()[0]
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array([ True, True], dtype=bool)
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>>> model.clusterCenters()[1] == model2.clusterCenters()[1]
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array([ True, True], dtype=bool)
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.. versionadded:: 2.0.0
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"""
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k = Param(Params._dummy(), "k", "number of clusters to create",
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typeConverter=TypeConverters.toInt)
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minDivisibleClusterSize = Param(Params._dummy(), "minDivisibleClusterSize",
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"the minimum number of points (if >= 1.0) " +
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"or the minimum proportion",
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typeConverter=TypeConverters.toFloat)
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@keyword_only
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def __init__(self, featuresCol="features", predictionCol="prediction", maxIter=20,
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seed=None, k=4, minDivisibleClusterSize=1.0):
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"""
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__init__(self, featuresCol="features", predictionCol="prediction", maxIter=20, \
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seed=None, k=4, minDivisibleClusterSize=1.0)
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"""
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super(BisectingKMeans, self).__init__()
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self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.BisectingKMeans",
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self.uid)
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self._setDefault(maxIter=20, k=4, minDivisibleClusterSize=1.0)
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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@since("2.0.0")
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def setParams(self, featuresCol="features", predictionCol="prediction", maxIter=20,
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seed=None, k=4, minDivisibleClusterSize=1.0):
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"""
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setParams(self, featuresCol="features", predictionCol="prediction", maxIter=20, \
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seed=None, k=4, minDivisibleClusterSize=1.0)
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Sets params for BisectingKMeans.
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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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@since("2.0.0")
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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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"""
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self._set(k=value)
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return self
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@since("2.0.0")
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def getK(self):
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"""
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Gets the value of `k` or its default value.
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"""
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return self.getOrDefault(self.k)
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@since("2.0.0")
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def setMinDivisibleClusterSize(self, value):
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"""
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Sets the value of :py:attr:`minDivisibleClusterSize`.
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"""
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self._set(minDivisibleClusterSize=value)
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return self
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@since("2.0.0")
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def getMinDivisibleClusterSize(self):
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"""
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Gets the value of `minDivisibleClusterSize` or its default value.
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"""
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return self.getOrDefault(self.minDivisibleClusterSize)
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def _create_model(self, java_model):
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return BisectingKMeansModel(java_model)
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if __name__ == "__main__":
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import doctest
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import pyspark.ml.clustering
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from pyspark.context import SparkContext
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from pyspark.sql import SQLContext
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globs = pyspark.ml.clustering.__dict__.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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import tempfile
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temp_path = tempfile.mkdtemp()
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globs['temp_path'] = temp_path
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try:
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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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finally:
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from shutil import rmtree
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try:
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rmtree(temp_path)
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except OSError:
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pass
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if failure_count:
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exit(-1)
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