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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from numpy import array
from pyspark import RDD
from pyspark import SparkContext
from pyspark.mllib.common import callMLlibFunc, callJavaFunc
from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector
from pyspark.mllib.stat.distribution import MultivariateGaussian
__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture']
class KMeansModel(object):
"""A clustering model derived from the k-means method.
>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2)
>>> model = KMeans.train(
... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
>>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
True
>>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
True
>>> model = KMeans.train(sc.parallelize(data), 2)
>>> sparse_data = [
... SparseVector(3, {1: 1.0}),
... SparseVector(3, {1: 1.1}),
... SparseVector(3, {2: 1.0}),
... SparseVector(3, {2: 1.1})
... ]
>>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||")
>>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
True
>>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
True
>>> model.predict(sparse_data[0]) == model.predict(sparse_data[1])
True
>>> model.predict(sparse_data[2]) == model.predict(sparse_data[3])
True
>>> type(model.clusterCenters)
<type 'list'>
"""
def __init__(self, centers):
self.centers = centers
@property
def clusterCenters(self):
"""Get the cluster centers, represented as a list of NumPy arrays."""
return self.centers
def predict(self, x):
"""Find the cluster to which x belongs in this model."""
best = 0
best_distance = float("inf")
x = _convert_to_vector(x)
for i in xrange(len(self.centers)):
distance = x.squared_distance(self.centers[i])
if distance < best_distance:
best = i
best_distance = distance
return best
class KMeans(object):
@classmethod
def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||", seed=None):
"""Train a k-means clustering model."""
model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
runs, initializationMode, seed)
centers = callJavaFunc(rdd.context, model.clusterCenters)
return KMeansModel([c.toArray() for c in centers])
class GaussianMixtureModel(object):
"""A clustering model derived from the Gaussian Mixture Model method.
>>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1,
... 0.9,0.8,0.75,0.935,
... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2))
>>> model = GaussianMixture.train(clusterdata_1, 3, convergenceTol=0.0001,
... maxIterations=50, seed=10)
>>> labels = model.predict(clusterdata_1).collect()
>>> labels[0]==labels[1]
False
>>> labels[1]==labels[2]
True
>>> labels[4]==labels[5]
True
>>> clusterdata_2 = sc.parallelize(array([-5.1971, -2.5359, -3.8220,
... -5.2211, -5.0602, 4.7118,
... 6.8989, 3.4592, 4.6322,
... 5.7048, 4.6567, 5.5026,
... 4.5605, 5.2043, 6.2734]).reshape(5, 3))
>>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001,
... maxIterations=150, seed=10)
>>> labels = model.predict(clusterdata_2).collect()
>>> labels[0]==labels[1]==labels[2]
True
>>> labels[3]==labels[4]
True
"""
def __init__(self, weights, gaussians):
self.weights = weights
self.gaussians = gaussians
self.k = len(self.weights)
def predict(self, x):
"""
Find the cluster to which the points in 'x' has maximum membership
in this model.
:param x: RDD of data points.
:return: cluster_labels. RDD of cluster labels.
"""
if isinstance(x, RDD):
cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z)))
return cluster_labels
def predictSoft(self, x):
"""
Find the membership of each point in 'x' to all mixture components.
:param x: RDD of data points.
:return: membership_matrix. RDD of array of double values.
"""
if isinstance(x, RDD):
means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector),
self.weights, means, sigmas)
return membership_matrix
class GaussianMixture(object):
"""
Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
:param data: RDD of data points
:param k: Number of components
:param convergenceTol: Threshold value to check the convergence criteria. Defaults to 1e-3
:param maxIterations: Number of iterations. Default to 100
:param seed: Random Seed
"""
@classmethod
def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None):
"""Train a Gaussian Mixture clustering model."""
weight, mu, sigma = callMLlibFunc("trainGaussianMixture",
rdd.map(_convert_to_vector), k,
convergenceTol, maxIterations, seed)
mvg_obj = [MultivariateGaussian(mu[i], sigma[i]) for i in range(k)]
return GaussianMixtureModel(weight, mvg_obj)
def _test():
import doctest
globs = globals().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()
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