ee4ee02b86
PySpark MLlib ```GaussianMixtureModel``` should support single instance ```predict/predictSoft``` just like Scala do. Author: Yanbo Liang <ybliang8@gmail.com> Closes #10552 from yanboliang/spark-12603.
817 lines
29 KiB
Python
817 lines
29 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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import sys
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import array as pyarray
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import warnings
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if sys.version > '3':
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xrange = range
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basestring = str
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from math import exp, log
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from numpy import array, random, tile
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from collections import namedtuple
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from pyspark import SparkContext, since
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from pyspark.rdd import RDD, ignore_unicode_prefix
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from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, callJavaFunc, _py2java, _java2py
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from pyspark.mllib.linalg import SparseVector, _convert_to_vector, DenseVector
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from pyspark.mllib.regression import LabeledPoint
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from pyspark.mllib.stat.distribution import MultivariateGaussian
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from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader, JavaSaveable
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from pyspark.streaming import DStream
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__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture',
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'PowerIterationClusteringModel', 'PowerIterationClustering',
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'StreamingKMeans', 'StreamingKMeansModel',
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'LDA', 'LDAModel']
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@inherit_doc
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class KMeansModel(Saveable, Loader):
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"""A clustering model derived from the k-means method.
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>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2)
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>>> model = KMeans.train(
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... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random",
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... seed=50, initializationSteps=5, epsilon=1e-4)
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>>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
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True
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>>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
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True
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>>> model.k
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2
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>>> model.computeCost(sc.parallelize(data))
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2.0000000000000004
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>>> model = KMeans.train(sc.parallelize(data), 2)
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>>> sparse_data = [
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... SparseVector(3, {1: 1.0}),
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... SparseVector(3, {1: 1.1}),
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... SparseVector(3, {2: 1.0}),
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... SparseVector(3, {2: 1.1})
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... ]
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>>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||",
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... seed=50, initializationSteps=5, epsilon=1e-4)
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>>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
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True
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>>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
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True
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>>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
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True
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>>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
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True
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>>> isinstance(model.clusterCenters, list)
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True
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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> model.save(sc, path)
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>>> sameModel = KMeansModel.load(sc, path)
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>>> sameModel.predict(sparse_data[0]) == model.predict(sparse_data[0])
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True
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>>> from shutil import rmtree
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>>> try:
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... rmtree(path)
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... except OSError:
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... pass
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>>> data = array([-383.1,-382.9, 28.7,31.2, 366.2,367.3]).reshape(3, 2)
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>>> model = KMeans.train(sc.parallelize(data), 3, maxIterations=0,
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... initialModel = KMeansModel([(-1000.0,-1000.0),(5.0,5.0),(1000.0,1000.0)]))
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>>> model.clusterCenters
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[array([-1000., -1000.]), array([ 5., 5.]), array([ 1000., 1000.])]
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.. versionadded:: 0.9.0
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"""
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def __init__(self, centers):
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self.centers = centers
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@property
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@since('1.0.0')
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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 self.centers
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@property
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@since('1.4.0')
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def k(self):
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"""Total number of clusters."""
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return len(self.centers)
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@since('0.9.0')
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def predict(self, x):
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"""Find the cluster to which x belongs in this model."""
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best = 0
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best_distance = float("inf")
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if isinstance(x, RDD):
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return x.map(self.predict)
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x = _convert_to_vector(x)
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for i in xrange(len(self.centers)):
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distance = x.squared_distance(self.centers[i])
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if distance < best_distance:
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best = i
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best_distance = distance
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return best
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@since('1.4.0')
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def computeCost(self, rdd):
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"""
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Return the K-means cost (sum of squared distances of points to
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their nearest center) for this model on the given data.
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"""
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cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector),
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[_convert_to_vector(c) for c in self.centers])
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return cost
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@since('1.4.0')
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def save(self, sc, path):
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"""
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Save this model to the given path.
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"""
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java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers])
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java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers)
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java_model.save(sc._jsc.sc(), path)
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@classmethod
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@since('1.4.0')
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def load(cls, sc, path):
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"""
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Load a model from the given path.
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"""
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java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel.load(sc._jsc.sc(), path)
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return KMeansModel(_java2py(sc, java_model.clusterCenters()))
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class KMeans(object):
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"""
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.. versionadded:: 0.9.0
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"""
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@classmethod
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@since('0.9.0')
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def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
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seed=None, initializationSteps=5, epsilon=1e-4, initialModel=None):
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"""Train a k-means clustering model."""
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if runs != 1:
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warnings.warn(
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"Support for runs is deprecated in 1.6.0. This param will have no effect in 2.0.0.")
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clusterInitialModel = []
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if initialModel is not None:
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if not isinstance(initialModel, KMeansModel):
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raise Exception("initialModel is of "+str(type(initialModel))+". It needs "
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"to be of <type 'KMeansModel'>")
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clusterInitialModel = [_convert_to_vector(c) for c in initialModel.clusterCenters]
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model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
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runs, initializationMode, seed, initializationSteps, epsilon,
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clusterInitialModel)
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centers = callJavaFunc(rdd.context, model.clusterCenters)
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return KMeansModel([c.toArray() for c in centers])
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@inherit_doc
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class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader):
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"""
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.. note:: Experimental
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A clustering model derived from the Gaussian Mixture Model method.
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>>> from pyspark.mllib.linalg import Vectors, DenseMatrix
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>>> from numpy.testing import assert_equal
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>>> from shutil import rmtree
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>>> import os, tempfile
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>>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1,
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... 0.9,0.8,0.75,0.935,
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... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2), 2)
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>>> model = GaussianMixture.train(clusterdata_1, 3, convergenceTol=0.0001,
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... maxIterations=50, seed=10)
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>>> labels = model.predict(clusterdata_1).collect()
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>>> labels[0]==labels[1]
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False
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>>> labels[1]==labels[2]
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False
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>>> labels[4]==labels[5]
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True
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>>> model.predict([-0.1,-0.05])
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0
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>>> softPredicted = model.predictSoft([-0.1,-0.05])
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>>> abs(softPredicted[0] - 1.0) < 0.001
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True
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>>> abs(softPredicted[1] - 0.0) < 0.001
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True
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>>> abs(softPredicted[2] - 0.0) < 0.001
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True
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>>> path = tempfile.mkdtemp()
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>>> model.save(sc, path)
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>>> sameModel = GaussianMixtureModel.load(sc, path)
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>>> assert_equal(model.weights, sameModel.weights)
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>>> mus, sigmas = list(
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... zip(*[(g.mu, g.sigma) for g in model.gaussians]))
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>>> sameMus, sameSigmas = list(
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... zip(*[(g.mu, g.sigma) for g in sameModel.gaussians]))
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>>> mus == sameMus
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True
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>>> sigmas == sameSigmas
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True
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>>> from shutil import rmtree
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>>> try:
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... rmtree(path)
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... except OSError:
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... pass
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>>> data = array([-5.1971, -2.5359, -3.8220,
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... -5.2211, -5.0602, 4.7118,
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... 6.8989, 3.4592, 4.6322,
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... 5.7048, 4.6567, 5.5026,
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... 4.5605, 5.2043, 6.2734])
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>>> clusterdata_2 = sc.parallelize(data.reshape(5,3))
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>>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001,
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... maxIterations=150, seed=10)
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>>> labels = model.predict(clusterdata_2).collect()
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>>> labels[0]==labels[1]
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True
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>>> labels[2]==labels[3]==labels[4]
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True
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.. versionadded:: 1.3.0
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"""
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@property
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@since('1.4.0')
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def weights(self):
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"""
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Weights for each Gaussian distribution in the mixture, where weights[i] is
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the weight for Gaussian i, and weights.sum == 1.
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"""
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return array(self.call("weights"))
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@property
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@since('1.4.0')
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def gaussians(self):
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"""
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Array of MultivariateGaussian where gaussians[i] represents
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the Multivariate Gaussian (Normal) Distribution for Gaussian i.
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"""
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return [
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MultivariateGaussian(gaussian[0], gaussian[1])
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for gaussian in self.call("gaussians")]
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@property
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@since('1.4.0')
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def k(self):
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"""Number of gaussians in mixture."""
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return len(self.weights)
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@since('1.3.0')
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def predict(self, x):
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"""
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Find the cluster to which the point 'x' or each point in RDD 'x'
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has maximum membership in this model.
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:param x: vector or RDD of vector represents data points.
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:return: cluster label or RDD of cluster labels.
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"""
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if isinstance(x, RDD):
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cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z)))
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return cluster_labels
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else:
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z = self.predictSoft(x)
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return z.argmax()
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@since('1.3.0')
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def predictSoft(self, x):
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"""
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Find the membership of point 'x' or each point in RDD 'x' to all mixture components.
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:param x: vector or RDD of vector represents data points.
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:return: the membership value to all mixture components for vector 'x'
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or each vector in RDD 'x'.
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"""
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if isinstance(x, RDD):
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means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
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membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector),
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_convert_to_vector(self.weights), means, sigmas)
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return membership_matrix.map(lambda x: pyarray.array('d', x))
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else:
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return self.call("predictSoft", _convert_to_vector(x)).toArray()
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@classmethod
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@since('1.5.0')
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def load(cls, sc, path):
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"""Load the GaussianMixtureModel from disk.
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:param sc: SparkContext
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:param path: str, path to where the model is stored.
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"""
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model = cls._load_java(sc, path)
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wrapper = sc._jvm.GaussianMixtureModelWrapper(model)
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return cls(wrapper)
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class GaussianMixture(object):
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"""
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.. note:: Experimental
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Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
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:param data: RDD of data points
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:param k: Number of components
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:param convergenceTol: Threshold value to check the convergence criteria. Defaults to 1e-3
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:param maxIterations: Number of iterations. Default to 100
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:param seed: Random Seed
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:param initialModel: GaussianMixtureModel for initializing learning
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.. versionadded:: 1.3.0
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"""
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@classmethod
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@since('1.3.0')
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def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
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"""Train a Gaussian Mixture clustering model."""
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initialModelWeights = None
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initialModelMu = None
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initialModelSigma = None
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if initialModel is not None:
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if initialModel.k != k:
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raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s"
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% (initialModel.k, k))
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initialModelWeights = list(initialModel.weights)
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initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
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initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
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java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
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k, convergenceTol, maxIterations, seed,
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initialModelWeights, initialModelMu, initialModelSigma)
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return GaussianMixtureModel(java_model)
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class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader):
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"""
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.. note:: Experimental
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Model produced by [[PowerIterationClustering]].
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>>> data = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (1, 3, 1.0),
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... (2, 3, 1.0), (3, 4, 0.1), (4, 5, 1.0), (4, 15, 1.0), (5, 6, 1.0),
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... (6, 7, 1.0), (7, 8, 1.0), (8, 9, 1.0), (9, 10, 1.0), (10, 11, 1.0),
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... (11, 12, 1.0), (12, 13, 1.0), (13, 14, 1.0), (14, 15, 1.0)]
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>>> rdd = sc.parallelize(data, 2)
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>>> model = PowerIterationClustering.train(rdd, 2, 100)
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>>> model.k
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2
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>>> result = sorted(model.assignments().collect(), key=lambda x: x.id)
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>>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster
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True
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>>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster
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True
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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> model.save(sc, path)
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>>> sameModel = PowerIterationClusteringModel.load(sc, path)
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>>> sameModel.k
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2
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>>> result = sorted(model.assignments().collect(), key=lambda x: x.id)
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>>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster
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True
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>>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster
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True
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>>> from shutil import rmtree
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>>> try:
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... rmtree(path)
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... except OSError:
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... pass
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.. versionadded:: 1.5.0
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"""
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@property
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@since('1.5.0')
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def k(self):
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"""
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Returns the number of clusters.
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"""
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return self.call("k")
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@since('1.5.0')
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def assignments(self):
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"""
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Returns the cluster assignments of this model.
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"""
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return self.call("getAssignments").map(
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lambda x: (PowerIterationClustering.Assignment(*x)))
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@classmethod
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@since('1.5.0')
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def load(cls, sc, path):
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"""
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Load a model from the given path.
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"""
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model = cls._load_java(sc, path)
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wrapper = sc._jvm.PowerIterationClusteringModelWrapper(model)
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return PowerIterationClusteringModel(wrapper)
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class PowerIterationClustering(object):
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"""
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.. note:: Experimental
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Power Iteration Clustering (PIC), a scalable graph clustering algorithm
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developed by [[http://www.icml2010.org/papers/387.pdf Lin and Cohen]].
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From the abstract: PIC finds a very low-dimensional embedding of a
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dataset using truncated power iteration on a normalized pair-wise
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similarity matrix of the data.
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.. versionadded:: 1.5.0
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"""
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@classmethod
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@since('1.5.0')
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def train(cls, rdd, k, maxIterations=100, initMode="random"):
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"""
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:param rdd: an RDD of (i, j, s,,ij,,) tuples representing the
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affinity matrix, which is the matrix A in the PIC paper.
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The similarity s,,ij,, must be nonnegative.
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This is a symmetric matrix and hence s,,ij,, = s,,ji,,.
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For any (i, j) with nonzero similarity, there should be
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either (i, j, s,,ij,,) or (j, i, s,,ji,,) in the input.
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Tuples with i = j are ignored, because we assume
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s,,ij,, = 0.0.
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:param k: Number of clusters.
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:param maxIterations: Maximum number of iterations of the
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PIC algorithm.
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:param initMode: Initialization mode.
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"""
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model = callMLlibFunc("trainPowerIterationClusteringModel",
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rdd.map(_convert_to_vector), int(k), int(maxIterations), initMode)
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return PowerIterationClusteringModel(model)
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class Assignment(namedtuple("Assignment", ["id", "cluster"])):
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"""
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Represents an (id, cluster) tuple.
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|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
|
|
class StreamingKMeansModel(KMeansModel):
|
|
"""
|
|
.. note:: Experimental
|
|
|
|
Clustering model which can perform an online update of the centroids.
|
|
|
|
The update formula for each centroid is given by
|
|
|
|
* c_t+1 = ((c_t * n_t * a) + (x_t * m_t)) / (n_t + m_t)
|
|
* n_t+1 = n_t * a + m_t
|
|
|
|
where
|
|
|
|
* c_t: Centroid at the n_th iteration.
|
|
* n_t: Number of samples (or) weights associated with the centroid
|
|
at the n_th iteration.
|
|
* x_t: Centroid of the new data closest to c_t.
|
|
* m_t: Number of samples (or) weights of the new data closest to c_t
|
|
* c_t+1: New centroid.
|
|
* n_t+1: New number of weights.
|
|
* a: Decay Factor, which gives the forgetfulness.
|
|
|
|
Note that if a is set to 1, it is the weighted mean of the previous
|
|
and new data. If it set to zero, the old centroids are completely
|
|
forgotten.
|
|
|
|
:param clusterCenters: Initial cluster centers.
|
|
:param clusterWeights: List of weights assigned to each cluster.
|
|
|
|
>>> initCenters = [[0.0, 0.0], [1.0, 1.0]]
|
|
>>> initWeights = [1.0, 1.0]
|
|
>>> stkm = StreamingKMeansModel(initCenters, initWeights)
|
|
>>> data = sc.parallelize([[-0.1, -0.1], [0.1, 0.1],
|
|
... [0.9, 0.9], [1.1, 1.1]])
|
|
>>> stkm = stkm.update(data, 1.0, u"batches")
|
|
>>> stkm.centers
|
|
array([[ 0., 0.],
|
|
[ 1., 1.]])
|
|
>>> stkm.predict([-0.1, -0.1])
|
|
0
|
|
>>> stkm.predict([0.9, 0.9])
|
|
1
|
|
>>> stkm.clusterWeights
|
|
[3.0, 3.0]
|
|
>>> decayFactor = 0.0
|
|
>>> data = sc.parallelize([DenseVector([1.5, 1.5]), DenseVector([0.2, 0.2])])
|
|
>>> stkm = stkm.update(data, 0.0, u"batches")
|
|
>>> stkm.centers
|
|
array([[ 0.2, 0.2],
|
|
[ 1.5, 1.5]])
|
|
>>> stkm.clusterWeights
|
|
[1.0, 1.0]
|
|
>>> stkm.predict([0.2, 0.2])
|
|
0
|
|
>>> stkm.predict([1.5, 1.5])
|
|
1
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
def __init__(self, clusterCenters, clusterWeights):
|
|
super(StreamingKMeansModel, self).__init__(centers=clusterCenters)
|
|
self._clusterWeights = list(clusterWeights)
|
|
|
|
@property
|
|
@since('1.5.0')
|
|
def clusterWeights(self):
|
|
"""Return the cluster weights."""
|
|
return self._clusterWeights
|
|
|
|
@ignore_unicode_prefix
|
|
@since('1.5.0')
|
|
def update(self, data, decayFactor, timeUnit):
|
|
"""Update the centroids, according to data
|
|
|
|
:param data: Should be a RDD that represents the new data.
|
|
:param decayFactor: forgetfulness of the previous centroids.
|
|
:param timeUnit: Can be "batches" or "points". If points, then the
|
|
decay factor is raised to the power of number of new
|
|
points and if batches, it is used as it is.
|
|
"""
|
|
if not isinstance(data, RDD):
|
|
raise TypeError("Data should be of an RDD, got %s." % type(data))
|
|
data = data.map(_convert_to_vector)
|
|
decayFactor = float(decayFactor)
|
|
if timeUnit not in ["batches", "points"]:
|
|
raise ValueError(
|
|
"timeUnit should be 'batches' or 'points', got %s." % timeUnit)
|
|
vectorCenters = [_convert_to_vector(center) for center in self.centers]
|
|
updatedModel = callMLlibFunc(
|
|
"updateStreamingKMeansModel", vectorCenters, self._clusterWeights,
|
|
data, decayFactor, timeUnit)
|
|
self.centers = array(updatedModel[0])
|
|
self._clusterWeights = list(updatedModel[1])
|
|
return self
|
|
|
|
|
|
class StreamingKMeans(object):
|
|
"""
|
|
.. note:: Experimental
|
|
|
|
Provides methods to set k, decayFactor, timeUnit to configure the
|
|
KMeans algorithm for fitting and predicting on incoming dstreams.
|
|
More details on how the centroids are updated are provided under the
|
|
docs of StreamingKMeansModel.
|
|
|
|
:param k: int, number of clusters
|
|
:param decayFactor: float, forgetfulness of the previous centroids.
|
|
:param timeUnit: can be "batches" or "points". If points, then the
|
|
decayfactor is raised to the power of no. of new points.
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
def __init__(self, k=2, decayFactor=1.0, timeUnit="batches"):
|
|
self._k = k
|
|
self._decayFactor = decayFactor
|
|
if timeUnit not in ["batches", "points"]:
|
|
raise ValueError(
|
|
"timeUnit should be 'batches' or 'points', got %s." % timeUnit)
|
|
self._timeUnit = timeUnit
|
|
self._model = None
|
|
|
|
@since('1.5.0')
|
|
def latestModel(self):
|
|
"""Return the latest model"""
|
|
return self._model
|
|
|
|
def _validate(self, dstream):
|
|
if self._model is None:
|
|
raise ValueError(
|
|
"Initial centers should be set either by setInitialCenters "
|
|
"or setRandomCenters.")
|
|
if not isinstance(dstream, DStream):
|
|
raise TypeError(
|
|
"Expected dstream to be of type DStream, "
|
|
"got type %s" % type(dstream))
|
|
|
|
@since('1.5.0')
|
|
def setK(self, k):
|
|
"""Set number of clusters."""
|
|
self._k = k
|
|
return self
|
|
|
|
@since('1.5.0')
|
|
def setDecayFactor(self, decayFactor):
|
|
"""Set decay factor."""
|
|
self._decayFactor = decayFactor
|
|
return self
|
|
|
|
@since('1.5.0')
|
|
def setHalfLife(self, halfLife, timeUnit):
|
|
"""
|
|
Set number of batches after which the centroids of that
|
|
particular batch has half the weightage.
|
|
"""
|
|
self._timeUnit = timeUnit
|
|
self._decayFactor = exp(log(0.5) / halfLife)
|
|
return self
|
|
|
|
@since('1.5.0')
|
|
def setInitialCenters(self, centers, weights):
|
|
"""
|
|
Set initial centers. Should be set before calling trainOn.
|
|
"""
|
|
self._model = StreamingKMeansModel(centers, weights)
|
|
return self
|
|
|
|
@since('1.5.0')
|
|
def setRandomCenters(self, dim, weight, seed):
|
|
"""
|
|
Set the initial centres to be random samples from
|
|
a gaussian population with constant weights.
|
|
"""
|
|
rng = random.RandomState(seed)
|
|
clusterCenters = rng.randn(self._k, dim)
|
|
clusterWeights = tile(weight, self._k)
|
|
self._model = StreamingKMeansModel(clusterCenters, clusterWeights)
|
|
return self
|
|
|
|
@since('1.5.0')
|
|
def trainOn(self, dstream):
|
|
"""Train the model on the incoming dstream."""
|
|
self._validate(dstream)
|
|
|
|
def update(rdd):
|
|
self._model.update(rdd, self._decayFactor, self._timeUnit)
|
|
|
|
dstream.foreachRDD(update)
|
|
|
|
@since('1.5.0')
|
|
def predictOn(self, dstream):
|
|
"""
|
|
Make predictions on a dstream.
|
|
Returns a transformed dstream object
|
|
"""
|
|
self._validate(dstream)
|
|
return dstream.map(lambda x: self._model.predict(x))
|
|
|
|
@since('1.5.0')
|
|
def predictOnValues(self, dstream):
|
|
"""
|
|
Make predictions on a keyed dstream.
|
|
Returns a transformed dstream object.
|
|
"""
|
|
self._validate(dstream)
|
|
return dstream.mapValues(lambda x: self._model.predict(x))
|
|
|
|
|
|
class LDAModel(JavaModelWrapper, JavaSaveable, Loader):
|
|
|
|
""" A clustering model derived from the LDA method.
|
|
|
|
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
|
|
Terminology
|
|
- "word" = "term": an element of the vocabulary
|
|
- "token": instance of a term appearing in a document
|
|
- "topic": multinomial distribution over words representing some concept
|
|
References:
|
|
- Original LDA paper (journal version):
|
|
Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
|
|
|
|
>>> from pyspark.mllib.linalg import Vectors
|
|
>>> from numpy.testing import assert_almost_equal, assert_equal
|
|
>>> data = [
|
|
... [1, Vectors.dense([0.0, 1.0])],
|
|
... [2, SparseVector(2, {0: 1.0})],
|
|
... ]
|
|
>>> rdd = sc.parallelize(data)
|
|
>>> model = LDA.train(rdd, k=2, seed=1)
|
|
>>> model.vocabSize()
|
|
2
|
|
>>> model.describeTopics()
|
|
[([1, 0], [0.5..., 0.49...]), ([0, 1], [0.5..., 0.49...])]
|
|
>>> model.describeTopics(1)
|
|
[([1], [0.5...]), ([0], [0.5...])]
|
|
|
|
>>> topics = model.topicsMatrix()
|
|
>>> topics_expect = array([[0.5, 0.5], [0.5, 0.5]])
|
|
>>> assert_almost_equal(topics, topics_expect, 1)
|
|
|
|
>>> import os, tempfile
|
|
>>> from shutil import rmtree
|
|
>>> path = tempfile.mkdtemp()
|
|
>>> model.save(sc, path)
|
|
>>> sameModel = LDAModel.load(sc, path)
|
|
>>> assert_equal(sameModel.topicsMatrix(), model.topicsMatrix())
|
|
>>> sameModel.vocabSize() == model.vocabSize()
|
|
True
|
|
>>> try:
|
|
... rmtree(path)
|
|
... except OSError:
|
|
... pass
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
@since('1.5.0')
|
|
def topicsMatrix(self):
|
|
"""Inferred topics, where each topic is represented by a distribution over terms."""
|
|
return self.call("topicsMatrix").toArray()
|
|
|
|
@since('1.5.0')
|
|
def vocabSize(self):
|
|
"""Vocabulary size (number of terms or terms in the vocabulary)"""
|
|
return self.call("vocabSize")
|
|
|
|
@since('1.6.0')
|
|
def describeTopics(self, maxTermsPerTopic=None):
|
|
"""Return the topics described by weighted terms.
|
|
|
|
WARNING: If vocabSize and k are large, this can return a large object!
|
|
|
|
:param maxTermsPerTopic: Maximum number of terms to collect for each topic.
|
|
(default: vocabulary size)
|
|
:return: Array over topics. Each topic is represented as a pair of matching arrays:
|
|
(term indices, term weights in topic).
|
|
Each topic's terms are sorted in order of decreasing weight.
|
|
"""
|
|
if maxTermsPerTopic is None:
|
|
topics = self.call("describeTopics")
|
|
else:
|
|
topics = self.call("describeTopics", maxTermsPerTopic)
|
|
return topics
|
|
|
|
@classmethod
|
|
@since('1.5.0')
|
|
def load(cls, sc, path):
|
|
"""Load the LDAModel from disk.
|
|
|
|
:param sc: SparkContext
|
|
:param path: str, path to where the model is stored.
|
|
"""
|
|
if not isinstance(sc, SparkContext):
|
|
raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
|
|
if not isinstance(path, basestring):
|
|
raise TypeError("path should be a basestring, got type %s" % type(path))
|
|
model = callMLlibFunc("loadLDAModel", sc, path)
|
|
return LDAModel(model)
|
|
|
|
|
|
class LDA(object):
|
|
"""
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
@classmethod
|
|
@since('1.5.0')
|
|
def train(cls, rdd, k=10, maxIterations=20, docConcentration=-1.0,
|
|
topicConcentration=-1.0, seed=None, checkpointInterval=10, optimizer="em"):
|
|
"""Train a LDA model.
|
|
|
|
:param rdd: RDD of data points
|
|
:param k: Number of clusters you want
|
|
:param maxIterations: Number of iterations. Default to 20
|
|
:param docConcentration: Concentration parameter (commonly named "alpha")
|
|
for the prior placed on documents' distributions over topics ("theta").
|
|
:param topicConcentration: Concentration parameter (commonly named "beta" or "eta")
|
|
for the prior placed on topics' distributions over terms.
|
|
:param seed: Random Seed
|
|
:param checkpointInterval: Period (in iterations) between checkpoints.
|
|
:param optimizer: LDAOptimizer used to perform the actual calculation.
|
|
Currently "em", "online" are supported. Default to "em".
|
|
"""
|
|
model = callMLlibFunc("trainLDAModel", rdd, k, maxIterations,
|
|
docConcentration, topicConcentration, seed,
|
|
checkpointInterval, optimizer)
|
|
return LDAModel(model)
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
import pyspark.mllib.clustering
|
|
globs = pyspark.mllib.clustering.__dict__.copy()
|
|
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|