├── img ├── error.png ├── iters.png ├── opt_error.png ├── opt_iters.png ├── test_times.png ├── test_errors.png ├── 2d-sample-data.png ├── 3d-sample-data.png ├── test_errors_20.png ├── test_times-error.png ├── test_times-error_2.png ├── test_times-error_3.png ├── test_times-error_4.png ├── test_times-error_5.png ├── test_times-error_8.png ├── test_times-error_10.png ├── test_times-error_12.png ├── test_times-error_15.png ├── test_times-error_18.png ├── test_times-error_20.png └── test_errors_20_annotated.png ├── costTest.bash ├── .gitignore ├── output ├── error.csv └── test.csv ├── timer.py ├── plotResult.r ├── plotDPopt.r ├── plotSampleScatter.r ├── plotTest.r ├── createTestData.py ├── LICENSE ├── DPopt.py ├── input ├── c3_s20_f2.csv ├── c4_s100_f3.csv ├── c5_s100_f3.csv └── c4_s300_f2.csv ├── test.py ├── README └── cluster.py /img/error.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /costTest.bash: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | cd /Users/scotthendrickson/IdeaProjects/Python-DP-Means-Clustering 4 | 5 | for i in 2 3 4 5 8 10 12 15 18 20; do 6 | echo "calculating min errors in $i iterations..." 7 | cmd="./test.py -i$i | tee ./output/test.$i.csv | grep -v Inter > ./output/test.csv" 8 | eval $cmd 9 | ./plotTest.r 10 | mv ./img/test_times-error.png ./img/test_times-error_$i.png 11 | done 12 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.py[co] 2 | 3 | # Packages 4 | *.egg 5 | *.egg-info 6 | dist 7 | build 8 | eggs 9 | parts 10 | bin 11 | var 12 | sdist 13 | develop-eggs 14 | .installed.cfg 15 | 16 | # output files 17 | *_error.csv 18 | *_result.csv 19 | test.*.csv 20 | 21 | # Installer logs 22 | pip-log.txt 23 | 24 | # Unit test / coverage reports 25 | .coverage 26 | .tox 27 | 28 | #Translations 29 | *.mo 30 | 31 | #Mr Developer 32 | .mr.developer.cfg 33 | 34 | .DS_Store 35 | -------------------------------------------------------------------------------- /output/error.csv: -------------------------------------------------------------------------------- 1 | 0,2.605374130102773,2.7651365749269905 2 | 1,1.6917802094910792,2.238615606965474 3 | 2,1.6373989942013074,2.165969680901868 4 | 3,1.6263732525986454,2.1421832470706628 5 | 4,1.6222419033389868,2.1260371395564697 6 | 5,1.6192950181145158,2.113266231114027 7 | 6,1.6174839819008446,2.109779672231068 8 | 7,1.6157278419578598,2.102679268137146 9 | 8,1.6148313395540854,2.0978624400635573 10 | 9,1.6146165532476915,2.094700564677894 11 | 10,1.6142080012384852,2.0930483147786183 12 | 11,1.6138091515822164,2.092016132556209 13 | 12,1.6137769254691285,2.09157429960118 14 | -------------------------------------------------------------------------------- /timer.py: -------------------------------------------------------------------------------- 1 | from timeit import default_timer 2 | import sys 3 | 4 | class Timer(object): 5 | def __init__(self, verbose=False): 6 | self.verbose = verbose 7 | self.timer = default_timer 8 | 9 | def __enter__(self): 10 | self.start = self.timer() 11 | return self 12 | 13 | def __exit__(self, *args): 14 | end = self.timer() 15 | self.elapsed_secs = end - self.start 16 | self.elapsed = self.elapsed_secs * 1000 # millisecs 17 | if self.verbose: 18 | sys.stderr.write('elapsed time: %f ms\n' % self.elapsed) 19 | else: 20 | print self.elapsed 21 | -------------------------------------------------------------------------------- /plotResult.r: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env Rscript 2 | 3 | library(ggplot2) 4 | library(gridExtra) 5 | library(reshape2) 6 | 7 | X <- read.delim("./output/result.csv", sep=",", header=FALSE) 8 | X$cluster <- as.factor(X$V3) 9 | summary(X) 10 | p <-ggplot(data=X) + 11 | geom_point(aes(V1, V2, shape = cluster, color = cluster)) + 12 | facet_wrap( ~ V4) + 13 | ggsave(file="./img/iters.png", width=8, height=8, dpi=100) 14 | 15 | Y <- read.delim("./output/error.csv", sep=",", header=FALSE) 16 | #summary(Y) 17 | colnames(Y) <- c("Iteration","TrainErr","XValidErr") 18 | YY <- melt(Y, id=("Iteration")) 19 | summary(YY) 20 | p1 <-ggplot(data=YY) + 21 | geom_point(aes(Iteration,value, color=variable)) + 22 | geom_line(aes(Iteration, value, color=variable)) + 23 | scale_y_log10() + 24 | ylab("Normalized Error") 25 | ggsave(file="./img/error.png", width=4, height=4, dpi=100) 26 | -------------------------------------------------------------------------------- /plotDPopt.r: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env Rscript 2 | 3 | library(ggplot2) 4 | library(gridExtra) 5 | library(reshape2) 6 | 7 | X <- read.delim("./output/opt_result.csv", sep=",", header=FALSE) 8 | X$cluster <- as.factor(X$V3) 9 | summary(X) 10 | p <-ggplot(data=X) + 11 | geom_point(aes(V1, V2, shape = cluster, color = cluster)) + 12 | facet_wrap( ~ V4) + 13 | ggsave(file="./img/opt_iters.png", width=8, height=8, dpi=100) 14 | 15 | Y <- read.delim("./output/opt_error.csv", sep=",", header=FALSE) 16 | #summary(Y) 17 | colnames(Y) <- c("Iteration","TrainErr","XValidErr") 18 | YY <- melt(Y, id=("Iteration")) 19 | summary(YY) 20 | p1 <-ggplot(data=YY) + 21 | geom_point(aes(Iteration,value, color=variable)) + 22 | geom_line(aes(Iteration, value, color=variable)) + 23 | scale_y_log10() + 24 | ylab("Normalized Error") 25 | ggsave(file="./img/opt_error.png", width=4, height=4, dpi=100) 26 | -------------------------------------------------------------------------------- /plotSampleScatter.r: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env Rscript 2 | 3 | library(ggplot2) 4 | library(gridExtra) 5 | 6 | X <- read.delim("./input/c5_s100_f3.csv", sep=",", header=FALSE) 7 | summary(X) 8 | 9 | p1 <-ggplot(data=X, aes(V1,V2)) + 10 | geom_point() + 11 | xlab("feature 1") + 12 | ylab("feature 2") 13 | 14 | p2 <-ggplot(data=X, aes(V3,V2)) + 15 | geom_point() + 16 | xlab("feature 3") + 17 | ylab("feature 2") 18 | 19 | p3 <-ggplot(data=X, aes(V1,V3)) + 20 | geom_point() + 21 | xlab("feature 1") + 22 | ylab("feature 3") 23 | 24 | png(filename = "./img/3d-sample-data.png", width=600, height=600, unit="px") 25 | print( 26 | grid.arrange(p1, p2, p3, ncol = 2) 27 | ) 28 | dev.off() 29 | 30 | # 31 | X <- read.delim("./input/c3_s20_f2.csv", sep=",", header=FALSE) 32 | summary(X) 33 | p <-ggplot(data=X, aes(V1,V2)) + 34 | geom_point() + 35 | ggsave(file="./img/2d-sample-data.png", width=5, height=4, dpi=100) 36 | -------------------------------------------------------------------------------- /plotTest.r: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env Rscript 2 | 3 | library(ggplot2) 4 | library(gridExtra) 5 | library(reshape2) 6 | 7 | X <- read.delim("./output/test.csv", sep=",", header=FALSE) 8 | colnames(X) <- c("mthd","parameter","error","time") 9 | X$method <- as.factor(X$mthd) 10 | 11 | summary(X) 12 | 13 | p <-ggplot(data=X, aes(parameter, error)) + 14 | geom_point(aes(color=method)) + 15 | geom_line(aes(color=method)) + 16 | scale_y_log10() + 17 | xlab("Parameter") + 18 | ylab("Normalized Error") 19 | ggsave(file="./img/test_errors.png", width=5, height=4, dpi=100) 20 | 21 | p1 <-ggplot(data=X, aes(parameter, time)) + 22 | geom_point(aes(color = method)) + 23 | geom_line(aes(color = method)) + 24 | xlab("Parameter") + 25 | ylab("Time") 26 | ggsave(file="./img/test_times.png", width=5, height=4, dpi=100) 27 | 28 | p2 <-ggplot(data=X, aes(error, time)) + 29 | geom_point(aes(color = method)) + 30 | #geom_line(aes(color = method)) + 31 | geom_smooth(aes(color=method), method = "lm") + 32 | scale_y_log10() + 33 | scale_x_log10() + 34 | xlab("Error") + 35 | ylab("Time") 36 | ggsave(file="./img/test_times-error.png", width=5, height=4, dpi=100) 37 | 38 | -------------------------------------------------------------------------------- /createTestData.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | import random 4 | import csv 5 | import sys 6 | 7 | def createTestData(nSample, nFeatures, nClusters): 8 | scaleMu = 10. 9 | scaleSig = 0.5 + float(options.overlap) * scaleMu * nFeatures/nClusters 10 | for i in range(0, nClusters): 11 | mu = [random.random() * scaleMu - scaleMu/2 for j in range(0,nFeatures)] 12 | sig = [(0.75 * random.random() + 0.25) * scaleSig for j in range(0,nFeatures)] 13 | # 14 | res = [[random.gauss(mu[i], sig[i]) for x in range(0, nSample)] for i in range(0, nFeatures)] 15 | for i in range(0,nSample): 16 | yield [res[j][i] for j in range(0, nFeatures)] 17 | 18 | if __name__ == '__main__': 19 | from optparse import OptionParser 20 | parser = OptionParser() 21 | parser.add_option("-s", "--sample-size", dest="sample", default=10, 22 | help="Sample size per cluster") 23 | parser.add_option("-f", "--features", dest="features", default=2, 24 | help="Number of features") 25 | parser.add_option("-c", "--clusters", dest="clusters", default=2, 26 | help="Sample size") 27 | parser.add_option("-o", "--overlap", dest="overlap", default=0.0, 28 | help="0 - distinct, 1 - scale = sig") 29 | (options, args) = parser.parse_args() 30 | wtr = csv.writer(sys.stdout) 31 | for x in createTestData(int(options.sample), int(options.features), int(options.clusters)): 32 | wtr.writerow(x) 33 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2013,2014 by Scott Hendrickson 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are met: 6 | 7 | 1. Redistributions of source code must retain the above copyright notice, this 8 | list of conditions and the following disclaimer. 9 | 10 | 2. Redistributions in binary form must reproduce the above copyright notice, 11 | this list of conditions and the following disclaimer in the documentation 12 | and/or other materials provided with the distribution. 13 | 14 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 15 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 16 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 17 | ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE 18 | LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 19 | CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 20 | SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 21 | INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 22 | CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 23 | ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 24 | POSSIBILITY OF SUCH DAMAGE. 25 | -------------------------------------------------------------------------------- /output/test.csv: -------------------------------------------------------------------------------- 1 | k-means, 1 , 10.115582301 , 669.871091843 2 | k-means, 2 , 3.23959804589 , 2026.11088753 3 | k-means, 3 , 1.97460618086 , 3961.11297607 4 | k-means, 4 , 1.59596381525 , 6816.23601913 5 | k-means, 5 , 1.33080683704 , 11120.5599308 6 | k-means, 6 , 1.11692006606 , 13592.5810337 7 | k-means, 7 , 0.962702157439 , 13542.6220894 8 | k-means, 8 , 0.844396469959 , 14799.451828 9 | k-means, 9 , 0.751034395905 , 18954.9930096 10 | k-means, 10 , 0.69851604084 , 17583.9669704 11 | dp-means, 0.9 , 46.9833596554 , 46788.5539532 12 | dp-means, 1.20439098899 , 46.0136401996 , 46590.6238556 13 | dp-means, 1.61173072706 , 39.0130940506 , 34036.3729 14 | dp-means, 2.15683773816 , 32.8541641137 , 27543.4191227 15 | dp-means, 2.88630659616 , 23.9560880948 , 13070.0771809 16 | dp-means, 3.86249072874 , 17.1087539015 , 13815.7219887 17 | dp-means, 5.16883225415 , 17.4811029433 , 5286.26990318 18 | dp-means, 6.91699443387 , 17.0735869136 , 1983.09993744 19 | dp-means, 9.2564064078 , 19.3719887088 , 1523.28109741 20 | dp-means, 12.3870360755 , 22.5026183765 , 889.410018921 21 | dp-means, 16.5764829218 , 26.6920652228 , 667.338848114 22 | dp-means, 22.1828518445 , 32.2984341455 , 667.797803879 23 | dp-means, 29.6853631906 , 39.8009454916 , 660.581111908 24 | dp-means, 39.7253154795 , 49.8408977805 , 661.388158798 25 | dp-means, 53.1609022202 , 63.2764845212 , 663.712024689 26 | dp-means, 71.1405684449 , 81.2561507459 , 659.132957458 27 | dp-means, 95.2011773182 , 105.316759619 , 665.764093399 28 | dp-means, 127.399377892 , 137.514960193 , 659.416913986 29 | dp-means, 170.48740304 , 180.602985341 , 658.109903336 30 | dp-means, 228.148324396 , 238.263906697 , 666.985034943 31 | -------------------------------------------------------------------------------- /DPopt.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import cluster 3 | import sys 4 | import csv 5 | import math 6 | import random 7 | import scipy.optimize 8 | 9 | # function to minimize 10 | def g(l, returnObject=False): 11 | minError = sys.maxint 12 | for i in range(0,iters): 13 | k1 = cluster.dpmeans(res, l, xVal) 14 | err, xerr = k1.run() 15 | if xerr < minError: 16 | minError = xerr 17 | kmin = k1 18 | if returnObject: 19 | return minError, k1 20 | return minError 21 | 22 | #parameters 23 | iters = 8 # iterations in search for min 24 | maxClusters = 12 # used for setting minimum lambda 25 | xValFrac = 0.2 # 20% of data for xVal 26 | 27 | # Read data from standard in 28 | res = [] 29 | for row in csv.reader(sys.stdin): 30 | res.append([float(x) for x in row]) 31 | nFeatures = len(res[0]) 32 | minx, maxx = [sys.maxint for i in range(0,nFeatures)], [-sys.maxint for i in range(0,nFeatures)] 33 | for r in res: 34 | idx = 0 35 | for i in minx: 36 | if r[idx] < i: 37 | minx[idx] = r[idx] 38 | idx += 1 39 | idx = 0 40 | for i in maxx: 41 | if r[idx] > i: 42 | maxx[idx] = r[idx] 43 | idx += 1 44 | dataSpread = max([abs(x - y) for x, y in zip(maxx, minx)]) 45 | # quick and dirty min scale is average dist if data was along dataspread 46 | dataGrain = dataSpread/maxClusters 47 | # make sure data is in random order 48 | random.shuffle(res) 49 | # set aside for cross-validation 50 | xVal = int(xValFrac*len(res)) 51 | 52 | optLambda = scipy.optimize.brent(g, 53 | brack=(1./dataSpread, 1./dataGrain), 54 | tol=1e-4, 55 | full_output=0, 56 | maxiter=100) 57 | 58 | e,k = g(optLambda, returnObject=True) 59 | wrtr = csv.writer(open("./output/opt_result.csv","wb")) 60 | for x in k.getOutput(): 61 | wrtr.writerow(x) 62 | eWrtr = csv.writer(open("./output/opt_error.csv","wb")) 63 | for x in k.getErrors(): 64 | eWrtr.writerow(x) 65 | print "lambda: %2.5f\n with error: %2.5f\n"%(optLambda,e) 66 | 67 | -------------------------------------------------------------------------------- /input/c3_s20_f2.csv: -------------------------------------------------------------------------------- 1 | -2.8167369830384317,-0.9828595370686912 2 | -3.588281838632481,-4.041194288064502 3 | -2.561910765908436,-2.252817329417915 4 | -2.974270051168319,-2.202135649653358 5 | -4.045841203397948,-2.368597231716389 6 | -3.635122007567597,-3.15591728921731 7 | -3.178348901819658,-3.8401871455494385 8 | -3.1775923553471186,-2.2214784807886154 9 | -3.963162816119008,-3.6385951288815255 10 | -4.81779934009132,-2.528615887585795 11 | -4.488249146278856,-3.0791651323676077 12 | -3.740442401502619,-2.341228115585079 13 | -4.690452080478854,-2.404820967699472 14 | -4.340303400953691,-4.111171624534848 15 | -4.710130321222081,-3.5500533166185844 16 | -2.923319625016947,-3.101517525732272 17 | -4.153284249347453,-3.49600562452302 18 | -4.99705593479799,-1.3067521513991072 19 | -4.096137549113846,-3.318167900008268 20 | -3.8694866555096654,-2.21255296627442 21 | -2.9027727389933786,0.8637396919813872 22 | -3.25917453594928,0.22293980201930014 23 | -2.0154423786638604,1.0724883288391975 24 | -1.507640174302362,-0.8809584284821697 25 | -1.8528991323995176,-0.6559021972474459 26 | -2.5767273740204972,-0.013874623641317085 27 | -2.192917510643365,-0.8616369964900834 28 | -1.9417493340262253,0.3053547833682613 29 | -2.8021748626559253,1.7147127934209396 30 | -2.0942030893650934,2.0047203436303143 31 | -2.2709739477618323,1.0742131971395943 32 | -2.6467740637773662,0.3778931355432681 33 | -2.053771205744675,0.5324738830099182 34 | -3.4131610854901044,0.8683008304899438 35 | -2.072213614506647,1.0299139787961784 36 | -2.727950210872145,1.336170491994921 37 | -3.1648657756618763,-0.8578136694071175 38 | -2.9794786420042856,1.5475880961754545 39 | -3.178452104234517,0.6936822571386221 40 | -1.902528650081976,0.18123386574355088 41 | 1.7837875663120017,-3.1726699200249477 42 | 1.3948814266640777,-2.845004573439183 43 | 1.716974893150411,-3.011484187461642 44 | 1.842874262598796,-3.722460724650701 45 | 2.196684174390947,-2.5470720919590875 46 | 2.163219263490912,-2.515049116729154 47 | 1.423672489652454,-3.2786176364852095 48 | 1.6869460313149462,-2.5274280855055693 49 | 1.4246074335792802,-3.4569381756423345 50 | 2.1924960650682728,-1.9344099416026204 51 | 2.0125315842743925,-2.910051153583743 52 | 1.7697336066389193,-2.0520531188124123 53 | 1.5160129151783324,-3.053521423524953 54 | 1.5428143004378665,-2.9550568716681465 55 | 1.1067354447168727,-2.6749233737808766 56 | 2.223892560189723,-2.5797307523439192 57 | 1.8811387655270533,-2.896256181974136 58 | 1.614055605804688,-2.2805966899443777 59 | 1.6042314772546085,-2.582882303979417 60 | 1.4236338798221948,-2.354495855304643 61 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import cluster 3 | import sys 4 | import csv 5 | import math 6 | import timer 7 | import random 8 | from optparse import OptionParser 9 | parser = OptionParser() 10 | 11 | parser.add_option("-f", "--file", dest="file", default="./input/c4_s300_f2.csv", 12 | help="Input file name") 13 | parser.add_option("-i", "--iterations", dest="iter", default=20, 14 | help="Iterations to use in searching for min error. Default 20.") 15 | (options, args) = parser.parse_args() 16 | 17 | filename = options.file 18 | iters = int(options.iter) 19 | 20 | maxClusters = 10 21 | 22 | # Read data 23 | res = [] 24 | for row in csv.reader(open(filename,"rb")): 25 | res.append([float(x) for x in row]) 26 | nFeatures = len(res[0]) 27 | minx, maxx = [sys.maxint for i in range(0,nFeatures)], [-sys.maxint for i in range(0,nFeatures)] 28 | for r in res: 29 | idx = 0 30 | for i in minx: 31 | if r[idx] < i: 32 | minx[idx] = r[idx] 33 | idx += 1 34 | idx = 0 35 | for i in maxx: 36 | if r[idx] > i: 37 | maxx[idx] = r[idx] 38 | idx += 1 39 | dataSpread = max([abs(x - y) for x, y in zip(maxx, minx)]) 40 | 41 | # write results 42 | def writeFile( postfix, k): 43 | fn = filename.split('/')[-1] 44 | fn += "_" + str(postfix) 45 | wrtr = csv.writer(open("./output/"+fn+"_result.csv","wb")) 46 | for x in k.getOutput(): 47 | wrtr.writerow(x) 48 | eWrtr = csv.writer(open("./output/"+fn+"_error.csv","wb")) 49 | for x in k.getErrors(): 50 | eWrtr.writerow(x) 51 | 52 | # make sure data is in random order 53 | random.shuffle(res) 54 | # 20 percent set aside for cross-validation 55 | xval = int(0.20*len(res)) 56 | 57 | for c in range(1,maxClusters+1): 58 | minError = sys.maxint 59 | with timer.Timer(): 60 | for i in range(0,iters): 61 | k1 = cluster.kmeans(res, c, xval) 62 | err, xerr = k1.run() 63 | if xerr < minError: 64 | minError = xerr 65 | writeFile("k-%d-%f1.4"%(c,err), k1) 66 | print 'k-means,',i,',',c,',',minError,',Inter' 67 | sys.stderr.write("kmeans clusters: %d iter: %d \n"%(c,i)) 68 | print 'k-means,',c,',',minError,',', 69 | 70 | minl = math.log(0.9) 71 | maxl = math.log(math.sqrt(2.0) * dataSpread) 72 | dl = (maxl-minl)/float(maxClusters) 73 | for l in [math.exp(minl + i*dl) for i in range(0, 2*maxClusters)]: 74 | minError = sys.maxint 75 | with timer.Timer(): 76 | for i in range(0,iters): 77 | k1 = cluster.dpmeans(res, l, xval) 78 | err, xerr = k1.run() 79 | if xerr < minError: 80 | minError = xerr 81 | writeFile("k-%d-%f1.4"%(c,err), k1) 82 | print 'dp-means,',i,',',l,',',minError,',Inter' 83 | sys.stderr.write("dpmeans lambda: %2.5f iter: %d \n"%(c,i)) 84 | print 'dp-means,',l,',',minError,',', 85 | -------------------------------------------------------------------------------- /README: -------------------------------------------------------------------------------- 1 | Python-DP-Means-Clustering 2 | ========================== 3 | 4 | Comparing DP-means and K-means clustering algorithms 5 | 6 | "cluster.py" has implementations of k-means and dp-means clustering algorithms. 7 | Implementations were intended to be straight-forward, understandable and give 8 | full output for diagnostics, rather than optimized implmentations. 9 | 10 | For more information on the dp-means, see Revisiting k-means: New Algorithms 11 | via Bayesian Nonparametrics at http://arxiv.org/abs/1111.0352/ 12 | 13 | CLUSTERING 14 | ========== 15 | 16 | > ./cluster.py -h 17 | Usage: cluster.py [options] 18 | 19 | Options: 20 | -h, --help show this help message and exit 21 | -k CLUSTERS, --kmeans-clusters=CLUSTERS 22 | If present, use kmeans with number of clusters 23 | specified 24 | -l LAM, --lamda=LAM If preset, use dpmeans with lambda parameters 25 | specified 26 | -x XVAL, --cross-validate=XVAL 27 | Number of records to hold out for cross validations. 28 | Data will be random-ordered for you. 29 | -s, --cross-validate-stop 30 | Stop when cross-validation error rises. 31 | 32 | > cat input/c3_s20_f2.csv | ./cluster.py -k2 33 | Tolerance reached at step 8 34 | Iterations completed: 8 35 | Final error: 2.994711 36 | elapsed time: 6.582022 ms 37 | 38 | > cat input/c3_s20_f2.csv | ./cluster.py -k2 39 | Tolerance reached at step 3 40 | Iterations completed: 3 41 | Final error: 2.994711 42 | elapsed time: 2.923965 ms 43 | 44 | > cat input/c3_s20_f2.csv | ./cluster.py -l2 45 | Tolerance reached at step 4 46 | Iterations completed: 4 47 | Final error: 0.520926 48 | elapsed time: 5.388021 ms 49 | 50 | > cat input/c3_s20_f2.csv | ./cluster.py -l5 51 | Tolerance reached at step 2 52 | Iterations completed: 2 53 | Final error: 2.994711 54 | elapsed time: 2.490044 ms 55 | 56 | To plot errors for the last run (dp-means in this case), use "plotResult.r" 57 | This script reads ./output/results.csv and ./output/error.csv. 58 | 59 | > ./plotResult.r 60 | Loading required package: methods 61 | Loading required package: grid 62 | V1 V2 V3 V4 cluster 63 | Min. :-4.997 Min. :-4.11117 Min. :0.000 Iter-0 :65 0:103 64 | 1st Qu.:-3.413 1st Qu.:-3.01148 1st Qu.:0.000 Iter-1 :65 1: 90 65 | Median :-2.562 Median :-2.34123 Median :2.000 Iter-2 :65 2: 59 66 | Mean :-1.606 Mean :-1.68030 Mean :1.918 Iter-3 :65 3: 12 67 | 3rd Qu.: 1.425 3rd Qu.:-0.01388 3rd Qu.:4.000 Iter-4 :65 4:126 68 | Max. : 2.224 Max. : 2.00472 Max. :4.000 Iter-Final:65 69 | V1 V2 70 | Min. :0 Min. :0.5209 71 | 1st Qu.:1 1st Qu.:0.5209 72 | Median :2 Median :0.5388 73 | Mean :2 Mean :0.6489 74 | 3rd Qu.:3 3rd Qu.:0.5777 75 | Max. :4 Max. :1.0860 76 | 77 | See training output images created in ./img/iters.png and ./img/error.png 78 | 79 | OPTIMAL DP-MEANS 80 | ================ 81 | Finds the optimal value of lambda only from data. 82 | 83 | cat input/c4_s300_f2.csv | ./DPopt.py 84 | 85 | ... 86 | Final error: 18.510049 87 | Final cross-validation error: 18.223702 88 | Tolerance reached at step 6 89 | Iterations completed: 6 90 | Final error: 14.329098 91 | Final cross-validation error: 14.262425 92 | lambda: 5.48775 93 | with error: 14.26242 94 | 95 | Code holds back 20% of data for training optimization. 96 | 97 | There are no parameters to set unless you anticipate more than the default max 98 | number of clusters (set in code). 99 | 100 | CREATE TEST DATA 101 | ================ 102 | > ./createTestData.py -h 103 | Usage: createTestData.py [options] 104 | 105 | Options: 106 | -h, --help show this help message and exit 107 | -s SAMPLE, --sample-size=SAMPLE 108 | Sample size per cluster 109 | -f FEATURES, --features=FEATURES 110 | Number of features 111 | -c CLUSTERS, --clusters=CLUSTERS 112 | Sample size 113 | -o OVERLAP, --overlap=OVERLAP 114 | 0 - distinct, 1 - scale = sig 115 | 116 | > ./createTestData.py -s6 -c1 -f3 117 | 2.80484810546906,-5.107337369680055,1.7687444192348534 118 | 4.045291632153071,-4.955840347993885,1.5936351799326172 119 | 3.503220395140305,-5.008280722637208,1.5695863487866264 120 | 3.2134872837791812,-4.809839458886229,1.3158740999089755 121 | 3.8383496901618197,-4.745260338782687,1.74511375801971 122 | 3.3736868708580805,-5.2559718245077045,1.4113521104252063 123 | 124 | CLUSTERING TESTS 125 | ================ 126 | 127 | Example test run on data set with 3 features, 100 points per cluster, with 4 clusters. 128 | 129 | > ./test.py | tee output/test.all.csv | grep -v Inter > output/test.csv 130 | ... 131 | Tolerance reached at step 1 132 | Iterations completed: 1 133 | Final error: 0.091798 134 | Tolerance reached at step 1 135 | Iterations completed: 1 136 | Final error: 0.091798 137 | Tolerance reached at step 1 138 | Iterations completed: 1 139 | Final error: 0.091798 140 | Tolerance reached at step 1 141 | Iterations completed: 1 142 | Final error: 0.091798 143 | Tolerance reached at step 1 144 | Iterations completed: 1 145 | Final error: 0.091798 146 | Tolerance reached at step 1 147 | Iterations completed: 1 148 | Final error: 0.091798 149 | ... 150 | 151 | > ./test.py -h 152 | Usage: test.py [options] 153 | 154 | Options: 155 | -h, --help show this help message and exit 156 | -f FILE, --file=FILE Input file name 157 | -i ITER, --iterations=ITER 158 | Iterations to use in searching for min error. Default 159 | 20. 160 | 161 | 162 | Plot the test results, 163 | 164 | > ./plotTest.r 165 | Loading required package: methods 166 | Loading required package: grid 167 | V1 V2 V3 V4 168 | dp-means:12 Min. : 0.5774 Min. : 1.046 Min. : 345.6 169 | k-means :12 1st Qu.: 2.7424 1st Qu.: 2.722 1st Qu.: 1438.7 170 | Median : 4.8094 Median : 3.758 Median : 4243.7 171 | Mean : 5.1264 Mean : 7.159 Mean : 4779.7 172 | 3rd Qu.: 6.9462 3rd Qu.: 6.242 3rd Qu.: 5946.4 173 | Max. :12.0000 Max. :33.695 Max. :12496.4 174 | method 175 | dp-means:12 176 | k-means :12 177 | 178 | See ./img/test_errors.png and ./img/test_times.png for comparative error and times 179 | for k-means and dp-means. 180 | 181 | NOTE: lambda is chosen based on relevant scale of the data. In this example, the data set 182 | was created to fall between -5 and 5, so the range is 10. The maximum lambda is there 10, 183 | while the smallest lambda could be chosen as the smallest expected cluster size. 184 | -------------------------------------------------------------------------------- /cluster.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | from pprint import pprint 3 | import sys 4 | import random 5 | import math 6 | import timer 7 | 8 | class kmeans(object): 9 | 10 | def __init__(self, _X, _k, _xVal = 0, _stop=False): 11 | # X is sample size lists of dim length 12 | # 13 | # _xVal is the number of records to hold out cross-validation. 14 | # To use this you must randomize input data! 15 | # 16 | # Setting _stop=True causes iteration to stop when out of cross-validate 17 | # error starts to rise. 18 | # 19 | self.nFeatures = len(_X[0]) 20 | self.xValSize = _xVal 21 | self.allSize = len(_X) 22 | self.size = self.allSize - self.xValSize 23 | self.X = _X 24 | self.k = _k 25 | self.stop = _stop 26 | # Initialize group memebership 27 | self.dataClusterId = [-1 for i in range(0, self.allSize)] # index of group for each data pair 28 | self.clusters = {} 29 | idx = 0 30 | # initialize to k random data points 31 | # don't assign x-val as a strat center 32 | for i in random.sample(range(0, self.size), self.k): 33 | self.clusters[idx] = self.X[i] 34 | idx += 1 35 | # output records 36 | self.record = [] 37 | self.errorRecord = [] 38 | 39 | def dSquared(self, x, y): 40 | dist2 = 0.0 41 | for j,k in zip(x,y): 42 | dist2 += (j - k)**2 43 | return dist2 44 | 45 | def error(self): 46 | res = 0.0 47 | for i in range(0, self.size): 48 | res += self.dSquared(self.X[i], self.clusters[self.dataClusterId[i]]) 49 | # error on non training data 50 | res1 = 0.0 51 | err1 = 0.0 52 | for i in range(self.size, self.allSize): 53 | res1 += self.dSquared(self.X[i], self.clusters[self.dataClusterId[i]]) 54 | if res1 > 0.0: 55 | err1 = res1/self.xValSize 56 | return res/self.size, err1 57 | 58 | def nearestCluster(self, x): 59 | cmin = sys.maxint 60 | cidx = -sys.maxint 61 | for j in self.clusters: 62 | dist = math.sqrt(self.dSquared(x, self.clusters[j])) 63 | if dist < cmin: # record closest centroid 64 | cmin = dist 65 | cidx = j 66 | return cidx, cmin 67 | 68 | def assign(self): 69 | for i in range(0, self.allSize): 70 | self.dataClusterId[i], dmin = self.nearestCluster(self.X[i]) 71 | 72 | def updateClusters(self): 73 | ctemp = {} # dim sums by cluster 74 | for j in range(0, self.k): 75 | ctemp[j] = [] 76 | for k in range(0, self.nFeatures): 77 | ctemp[j].append(0.0) # init sums 78 | ctemp[j].append(0) # init counter 79 | # only calculate clusters on training, not cross-validation set 80 | for i in range(0,self.size): 81 | for j in range(0, self.nFeatures): 82 | ctemp[self.dataClusterId[i]][j] += self.X[i][j] 83 | ctemp[self.dataClusterId[i]][self.nFeatures] += 1 # count 84 | for c in self.clusters: 85 | if ctemp[c][self.nFeatures] <> 0: 86 | self.clusters[c] = [ ctemp[c][k]/ctemp[c][self.nFeatures] for k in range(0,self.nFeatures)] 87 | else: 88 | # no members in this cluster 89 | pass 90 | return 91 | 92 | def run(self, nmax = 100, eps = 1e-7): 93 | prev = 0.0 94 | prevXVal = float(sys.maxint) 95 | for iter in range(0,nmax): 96 | # update assignments 97 | self.assign() 98 | # calculate error 99 | err, errXVal = self.error() 100 | # 101 | if self.stop and errXVal - prevXVal >= 0.0: 102 | sys.stderr.write("Cross-validation error increasing at step %d\n"%iter) 103 | break 104 | prevXVal = errXVal 105 | # 106 | if abs(err-prev) < eps: 107 | sys.stderr.write("Tolerance reached at step %d\n"%iter) 108 | break 109 | prev = err 110 | # going on... 111 | self.errorRecord.append((iter, err, errXVal)) 112 | self.output(str(iter)) 113 | self.updateClusters() 114 | sys.stderr.write("Iterations completed: %d\n"%iter) 115 | sys.stderr.write("Final error: %f\n"%prev) 116 | sys.stderr.write("Final cross-validation error: %f\n"%prevXVal) 117 | # This is a step past stop if using cross-validation... 118 | self.output("Final") 119 | return err, errXVal 120 | 121 | def output(self, iter): 122 | for i in range(0,self.size): 123 | self.record.append([str(y) for y in self.X[i]] + [str(self.dataClusterId[i])] + ["Iter-%s"%iter]) 124 | for i in range(self.size, self.allSize): 125 | self.record.append([str(y) for y in self.X[i]] + [str(self.dataClusterId[i])] + ["Xval-Iter-%s"%iter]) 126 | for k in self.clusters: 127 | self.record.append([str(y) for y in self.clusters[k]] + [str(k)] + ["Cent-Iter-%s"%iter]) 128 | 129 | def getOutput(self): 130 | for x in self.record: 131 | yield x 132 | 133 | def getErrors(self): 134 | for x in self.errorRecord: 135 | yield x 136 | 137 | 138 | class dpmeans(kmeans): 139 | def __init__(self, _X, _lam = 1, _xVal = 0, _stop=False): 140 | # init k-means with 1 cluster 141 | kmeans.__init__(self, _X, 1, _xVal, _stop) 142 | self.lam = _lam 143 | 144 | def assign(self): 145 | for i in range(0, self.size): 146 | cidx, dmin = self.nearestCluster(self.X[i]) 147 | if dmin > self.lam: 148 | self.k += 1 149 | self.clusters[self.k-1] = self.X[i] 150 | self.dataClusterId[i] = self.k - 1 151 | else: 152 | self.dataClusterId[i] = cidx 153 | # don't create new clusters on cross-validation data 154 | for i in range(self.size, self.allSize): 155 | self.dataClusterId[i], dmin = self.nearestCluster(self.X[i]) 156 | 157 | def error(self): 158 | err, xValErr = kmeans.error(self) 159 | return err + self.lam * self.k, xValErr + self.lam * self.k 160 | 161 | 162 | if __name__ == '__main__': 163 | import csv 164 | from optparse import OptionParser 165 | parser = OptionParser() 166 | parser.add_option("-k", "--kmeans-clusters", dest="clusters", default=None, 167 | help="If present, use kmeans with number of clusters specified") 168 | parser.add_option("-l", "--lamda", dest="lam", default=None, 169 | help="If preset, use dpmeans with lambda parameters specified") 170 | parser.add_option("-x", "--cross-validate", dest="xVal", default=0, 171 | help="Number of records to hold out for cross validations. Data will be random-ordered for you.") 172 | parser.add_option("-s", "--cross-validate-stop", dest="xValStop", default=False, action="store_true", 173 | help="Stop when cross-validation error rises.") 174 | (options, args) = parser.parse_args() 175 | 176 | res = [] 177 | for row in csv.reader(sys.stdin): 178 | res.append([float(x) for x in row]) 179 | random.shuffle(res) 180 | xVal = int(options.xVal) 181 | xValStop = False 182 | if xVal > 0 and options.xValStop: 183 | xValStop = True 184 | if options.clusters is not None: 185 | k1 = kmeans(res, int(options.clusters), xVal, xValStop) 186 | elif options.lam is not None: 187 | k1 = dpmeans(res, float(options.lam), xVal, xValStop) 188 | else: 189 | sys.stderr.write("Use -h for help.\n") 190 | sys.exit() 191 | 192 | # 193 | with timer.Timer(verbose=True): 194 | k1.run() 195 | # 196 | wrtr = csv.writer(open("./output/result.csv","wb")) 197 | for x in k1.getOutput(): 198 | wrtr.writerow(x) 199 | eWrtr = csv.writer(open("./output/error.csv","wb")) 200 | for x in k1.getErrors(): 201 | eWrtr.writerow(x) 202 | -------------------------------------------------------------------------------- /input/c4_s100_f3.csv: -------------------------------------------------------------------------------- 1 | 2.654682743616464,1.8404167629025876,2.6064240260102043 2 | 1.8473602885468043,2.6537084984018406,2.511138673029868 3 | 5.247772814708676,1.250733657680343,2.5123593786069964 4 | 4.883490874674957,2.4844758867166847,1.7741168443602826 5 | 2.8017989831095536,2.5302043023303327,1.2999040362142837 6 | 4.132963619595802,0.10814537278229963,1.976974725331975 7 | 4.086415195983251,1.861131735176499,1.6770365692857139 8 | 3.4878134045114417,2.8617294095213968,1.922854917705191 9 | 3.6315223231630966,2.138302506943739,1.3645863468110586 10 | 3.762975130259414,1.5534655542900433,1.9335961264938106 11 | 4.102303847764124,1.1176051054232656,2.3820896255320876 12 | 4.382371403635078,1.0510330870841904,2.1871241493475075 13 | 4.744731464902117,1.5431294222730125,1.9468604942638525 14 | 4.849093033785677,1.6479676673240164,2.11370054247637 15 | 4.858317450311864,0.7639420854462916,1.4315133072915618 16 | 4.759814329333844,1.7178383391615777,1.6281810306524314 17 | 2.900369751022794,1.3789351745330625,2.128728330087438 18 | 5.216596682222466,2.249859492058456,1.9607230120587467 19 | 5.657281464801925,1.6641411305874356,1.7602794681169 20 | 5.855499056718844,1.7054013066249063,2.2766035665326485 21 | 4.361260689853053,0.9860431089804957,1.7793077105746962 22 | 3.592313014450811,2.4789653818923725,1.442617378431892 23 | 5.361000381615639,2.3909501711943113,1.6293023141670213 24 | 5.462870462317561,1.712810973210546,1.7916896577628383 25 | 3.766582002086249,2.304666359455248,1.7605732649446626 26 | 3.8849170554774064,1.1350130940995866,1.8902405172340897 27 | 4.2705222433539545,1.7947160474089277,2.0829253480281524 28 | 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