5995ada96b
The following items are added to Python kmeans: kmeans - setEpsilon, setInitializationSteps KMeansModel - computeCost, k Author: Hrishikesh Subramonian <hrishikesh.subramonian@flytxt.com> Closes #5647 from FlytxtRnD/newPyKmeansAPI and squashes the following commits: b9e451b [Hrishikesh Subramonian] set seed to fixed value in doc test 5fd3ced [Hrishikesh Subramonian] doc test corrections 20b3c68 [Hrishikesh Subramonian] python 3 fixes 4d4e695 [Hrishikesh Subramonian] added arguments in python tests 21eb84c [Hrishikesh Subramonian] Python Kmeans - setEpsilon, setInitializationSteps, k and computeCost added.
234 lines
8.8 KiB
Python
234 lines
8.8 KiB
Python
#
|
|
# 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.
|
|
#
|
|
|
|
import sys
|
|
import array as pyarray
|
|
|
|
if sys.version > '3':
|
|
xrange = range
|
|
|
|
from numpy import array
|
|
|
|
from pyspark import RDD
|
|
from pyspark import SparkContext
|
|
from pyspark.mllib.common import callMLlibFunc, callJavaFunc, _py2java, _java2py
|
|
from pyspark.mllib.linalg import SparseVector, _convert_to_vector
|
|
from pyspark.mllib.stat.distribution import MultivariateGaussian
|
|
from pyspark.mllib.util import Saveable, Loader, inherit_doc
|
|
|
|
__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture']
|
|
|
|
|
|
@inherit_doc
|
|
class KMeansModel(Saveable, Loader):
|
|
|
|
"""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",
|
|
... seed=50, initializationSteps=5, epsilon=1e-4)
|
|
>>> 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.k
|
|
2
|
|
>>> model.computeCost(sc.parallelize(data))
|
|
2.0000000000000004
|
|
>>> 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||",
|
|
... seed=50, initializationSteps=5, epsilon=1e-4)
|
|
>>> 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
|
|
>>> isinstance(model.clusterCenters, list)
|
|
True
|
|
>>> import os, tempfile
|
|
>>> path = tempfile.mkdtemp()
|
|
>>> model.save(sc, path)
|
|
>>> sameModel = KMeansModel.load(sc, path)
|
|
>>> sameModel.predict(sparse_data[0]) == model.predict(sparse_data[0])
|
|
True
|
|
>>> try:
|
|
... os.removedirs(path)
|
|
... except OSError:
|
|
... pass
|
|
"""
|
|
|
|
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
|
|
|
|
@property
|
|
def k(self):
|
|
"""Total number of clusters."""
|
|
return len(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
|
|
|
|
def computeCost(self, rdd):
|
|
"""
|
|
Return the K-means cost (sum of squared distances of points to
|
|
their nearest center) for this model on the given data.
|
|
"""
|
|
cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector),
|
|
[_convert_to_vector(c) for c in self.centers])
|
|
return cost
|
|
|
|
def save(self, sc, path):
|
|
java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers])
|
|
java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers)
|
|
java_model.save(sc._jsc.sc(), path)
|
|
|
|
@classmethod
|
|
def load(cls, sc, path):
|
|
java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel.load(sc._jsc.sc(), path)
|
|
return KMeansModel(_java2py(sc, java_model.clusterCenters()))
|
|
|
|
|
|
class KMeans(object):
|
|
|
|
@classmethod
|
|
def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
|
|
seed=None, initializationSteps=5, epsilon=1e-4):
|
|
"""Train a k-means clustering model."""
|
|
model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
|
|
runs, initializationMode, seed, initializationSteps, epsilon)
|
|
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),
|
|
_convert_to_vector(self.weights), means, sigmas)
|
|
return membership_matrix.map(lambda x: pyarray.array('d', x))
|
|
|
|
|
|
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()
|