silhouette: clustering labels have to be consecutive integers starting
Thank you very much, Martin. Warmest regards, b Em 13/05/2009, ?s 09:14, "Martin Maechler" <maechler at stat.math.ethz.ch> escreveu: !#x000a
"TS" == Tao Shi <shitao at hotmail.com> on Wed, 10 Oct 2007 06:15:53 +0000 writes:
TS> Thank you very much, Benilton and Prof. Ripley, for the TS> speedy replies! TS> Looking forward to the fix! TS> ....Tao I have finally re-stumbled onto this e-mail thread, and indeed found fixed the problem. Version 1.12.0 of 'cluster' should become visible within a few days, and will allow to call silhoutte(g, dis) on a grouping vector of k different integer values which need *not* necessarily be in 1:k. Martin Maechler, ETH Zurich
From: Prof Brian Ripley <ripley at stats.ox.ac.uk> To: Benilton Carvalho <bcarvalh at jhsph.edu> CC: Tao Shi <shitao at hotmail.com>, maechler at stat.math.ethz.ch, r-help at r-project.org Subject: Re: [R] silhouette: clustering labels have to be consecutive intergers starting from 1? Date: Wed, 10 Oct 2007 05:33:03 +0100 (BST) It is a C-level problem in package cluster: valgrind gives ==11377== Invalid write of size 8 ==11377== at 0xA4015D3: sildist (sildist.c:35) ==11377== by 0x4706D8: do_dotCode (dotcode.c:1750) This is a matter for the package maintainer (Cc:ed here), not R- help. On Tue, 9 Oct 2007, Benilton Carvalho wrote:
that happened to me with R-2.4.0 (alpha) and was fixed on R-2.4.0 (final)... http://tolstoy.newcastle.edu.au/R/e2/help/06/11/5061.html then i stopped using... now, the problem seems to be back. The same examples still apply. This fails: require(cluster) set.seed(1) x <- rnorm(100) g <- sample(2:4, 100, rep=T) for (i in 1:100){ print(i) tmp <- silhouette(g, dist(x)) } and this works: require(cluster) set.seed(1) x <- rnorm(100) g <- sample(2:4, 100, rep=T) for (i in 1:100){ print(i) tmp <- silhouette(as.integer(factor(g)), dist(x)) } and here's the sessionInfo():
sessionInfo()
R version 2.6.0 (2007-10-03) x86_64-unknown-linux-gnu locale: LC_CTYPE= en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.U TF- 8;L C_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF- 8;L C_NAME= C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_ID ENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] cluster_1.11.9 (Red Hat EL 2.6.9-42 smp - AMD opteron 848) b On Oct 9, 2007, at 8:35 PM, Tao Shi wrote:
Hi list, When I was using 'silhouette' from the 'cluster' package to calculate clustering performances, R crashed. I traced the problem to the fact that my clustering labels only have 2's and 3's. when I replaced them with 1's and 2's, the problem was solved. Is the function purposely written in this way so when I have clustering labels, "2" and "3", for example, the function somehow takes the 'missing' cluster "2" into account when it calculates silhouette widths? Thanks, ....Tao ##============================================ ## sorry about the long attachment
R.Version()
$platform [1] "i386-pc-mingw32" $arch [1] "i386" $os [1] "mingw32" $system [1] "i386, mingw32" $status [1] "" $major [1] "2" $minor [1] "5.1" $year [1] "2007" $month [1] "06" $day [1] "27" $`svn rev` [1] "42083" $language [1] "R" $version.string [1] "R version 2.5.1 (2007-06-27)"
library(cluster) cl1 ## clustering labels
[1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 [30] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [59] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [88] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [117] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [146] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [175] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [204] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
x1 ## 1-d input vector
[1] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 [6] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 [11] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 [16] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963 [21] 1.0163758 0.7657763 0.7370084 0.6999689 0.7366476 [26] 0.7883921 0.6925395 0.7729240 0.7202391 0.7910149 [31] 0.7397698 0.7958092 0.6978596 0.7350255 0.7294362 [36] 0.6125713 0.7174000 0.7413046 0.7044205 0.7568104 [41] 0.7048469 0.7334515 0.7143170 0.7002311 0.7540981 [46] 0.7627527 0.7712762 0.8193611 0.7801148 0.9061762 [51] 0.8248195 0.7932630 0.7248037 0.7423547 0.6419314 [56] 0.6001092 0.7572272 0.7631742 0.7085384 0.8710853 [61] 0.6589563 0.7464943 0.7487340 0.7751280 0.7946542 [66] 0.7666081 0.8508109 0.8314308 0.7442471 0.8006093 [71] 0.7949156 0.7852447 0.7630048 0.7104764 0.6768218 [76] 0.6806351 0.7255355 0.7431389 0.7523627 0.7670515 [81] 0.8118214 0.7215615 0.8186164 0.6941610 0.8285453 [86] 0.8395170 0.8088044 0.8182706 0.7550723 0.7948639 [91] 0.7204830 0.7109068 0.7756949 0.6837856 0.7055604 [96] 0.6126666 0.7201964 0.6849890 0.7779753 0.7845284 [101] 0.9370788 0.8242935 0.6908860 0.6446151 0.7660386 [106] 0.8141526 0.8111984 0.8624186 0.7865335 0.8213035 [111] 0.8059171 0.6735751 0.7815353 0.6972508 0.6699396 [116] 0.6293971 0.7475913 0.7700821 0.8258339 0.8096144 [121] 0.7058171 0.7516635 0.7323909 0.7229136 0.8344846 [126] 0.7205433 0.8287774 0.8322097 0.7767547 0.7402277 [131] 0.7939879 0.7797308 0.7112453 0.7091554 0.6417382 [136] 0.6369171 0.7059020 0.7496380 0.7298359 0.8202566 [141] 0.7331830 0.7344492 0.8316894 0.7323979 0.7977615 [146] 0.7841205 0.7587060 0.8056685 0.7895643 0.8140731 [151] 0.7890221 0.8016008 0.7381577 0.6936453 0.7133525 [156] 0.7121459 0.6851448 0.7946275 0.8077618 0.7899059 [161] 0.7128826 0.7546289 0.7042451 0.6606403 0.7525233 [166] 0.7527548 0.8098887 0.8254190 0.7873064 0.8139340 [171] 0.7903462 0.8377651 0.6709983 0.7423632 0.6632082 [176] 0.5676717 0.6925125 0.7077083 0.7488877 0.7630604 [181] 0.7843001 0.7524471 0.6871823 0.7144443 0.7692206 [186] 0.8690710 0.9282786 0.7844991 0.7094671 0.7578409 [191] 0.8026643 0.7759241 0.6997376 0.6167209 0.6682289 [196] 0.6572018 0.7615807 0.7415752 0.7659161 0.7040360 [201] 0.6874460 0.7052109 0.8290970 0.6915149 0.7173107 [206] 0.7848961 0.7943846 0.8437946 0.7817344 0.8867006 [211] 0.7575857 0.8390473 0.7382348 0.6789859 0.7129010 [216] 0.6938173 0.7384170 0.6747648 0.7203337 0.7278963
silhouette(cl1, dist(x1)^2) ##### CRASHED! ###### silhouette(ifelse(cl1==3,2,1), dist(x1)^2)
cluster neighbor sil_width [1,] 2 1 1.0000000 [2,] 2 1 1.0000000 [3,] 2 1 1.0000000 [4,] 2 1 1.0000000 [5,] 2 1 1.0000000 [6,] 2 1 1.0000000 [7,] 2 1 1.0000000 [8,] 2 1 1.0000000 [9,] 2 1 1.0000000 [10,] 2 1 1.0000000 [11,] 2 1 1.0000000 [12,] 2 1 1.0000000 [13,] 2 1 1.0000000 [14,] 2 1 1.0000000 [15,] 2 1 1.0000000 [16,] 2 1 1.0000000 [17,] 2 1 1.0000000 [18,] 2 1 1.0000000 [19,] 2 1 1.0000000 [20,] 2 1 1.0000000 [21,] 1 2 0.7592857 [22,] 1 2 0.9934455 [23,] 1 2 0.9937880 [24,] 1 2 0.9909544 [25,] 1 2 0.9937769 [26,] 1 2 0.9912442 [27,] 1 2 0.9900156 [28,] 1 2 0.9929499 [29,] 1 2 0.9929125 [30,] 1 2 0.9908637 [31,] 1 2 0.9938610 [32,] 1 2 0.9900958 [33,] 1 2 0.9906993 [34,] 1 2 0.9937227 [35,] 1 2 0.9934823 [36,] 1 2 0.9740954 [37,] 1 2 0.9926948 [38,] 1 2 0.9938924 [39,] 1 2 0.9914623 [40,] 1 2 0.9938250 [41,] 1 2 0.9915088 [42,] 1 2 0.9936633 [43,] 1 2 0.9924367 [44,] 1 2 0.9909855 [45,] 1 2 0.9938891 [46,] 1 2 0.9936028 [47,] 1 2 0.9930799 [48,] 1 2 0.9848568 [49,] 1 2 0.9922685 [50,] 1 2 0.9371272 [51,] 1 2 0.9832647 [52,] 1 2 0.9905154 [53,] 1 2 0.9932217 [54,] 1 2 0.9939101 [55,] 1 2 0.9810071 [56,] 1 2 0.9708675 [57,] 1 2 0.9938131 [58,] 1 2 0.9935827 [59,] 1 2 0.9918943 [60,] 1 2 0.9628701 [61,] 1 2 0.9844965 [62,] 1 2 0.9939491 [63,] 1 2 0.9939495 [64,] 1 2 0.9927610 [65,] 1 2 0.9902895 [66,] 1 2 0.9933968 [67,] 1 2 0.9734481 [68,] 1 2 0.9811285 [69,] 1 2 0.9939341 [70,] 1 2 0.9892304 [71,] 1 2 0.9902461 [72,] 1 2 0.9916649 [73,] 1 2 0.9935909 [74,] 1 2 0.9920846 [75,] 1 2 0.9876779 [76,] 1 2 0.9882868 [77,] 1 2 0.9932665 [78,] 1 2 0.9939213 [79,] 1 2 0.9939182 [80,] 1 2 0.9933699 [81,] 1 2 0.9868129 [82,] 1 2 0.9930074 [83,] 1 2 0.9850624 [84,] 1 2 0.9902300 [85,] 1 2 0.9820895 [86,] 1 2 0.9781906 [87,] 1 2 0.9875197 [88,] 1 2 0.9851569 [89,] 1 2 0.9938688 [90,] 1 2 0.9902547 [91,] 1 2 0.9929304 [92,] 1 2 0.9921257 [93,] 1 2 0.9927096 [94,] 1 2 0.9887702 [95,] 1 2 0.9915856 [96,] 1 2 0.9741195 [97,] 1 2 0.9929094 [98,] 1 2 0.9889500 [99,] 1 2 0.9924910 [100,] 1 2 0.9917552 [101,] 1 2 0.9047049 [102,] 1 2 0.9834247 [103,] 1 2 0.9897916 [104,] 1 2 0.9815845 [105,] 1 2 0.9934304 [106,] 1 2 0.9862375 [107,] 1 2 0.9869624 [108,] 1 2 0.9677353 [109,] 1 2 0.9914973 [110,] 1 2 0.9843076 [111,] 1 2 0.9881568 [112,] 1 2 0.9871393 [113,] 1 2 0.9921114 [114,] 1 2 0.9906240 [115,] 1 2 0.9865148 [116,] 1 2 0.9781846 [117,] 1 2 0.9939511 [118,] 1 2 0.9931681 [119,] 1 2 0.9829519 [120,] 1 2 0.9873341 [121,] 1 2 0.9916130 [122,] 1 2 0.9939273 [123,] 1 2 0.9936196 [124,] 1 2 0.9930999 [125,] 1 2 0.9800620 [126,] 1 2 0.9929347 [127,] 1 2 0.9820138 [128,] 1 2 0.9808614 [129,] 1 2 0.9926103 [130,] 1 2 0.9938711 [131,] 1 2 0.9903987 [132,] 1 2 0.9923097 [133,] 1 2 0.9921578 [134,] 1 2 0.9919558 [135,] 1 2 0.9809652 [136,] 1 2 0.9799023 [137,] 1 2 0.9916220 [138,] 1 2 0.9939454 [139,] 1 2 0.9935022 [140,] 1 2 0.9846059 [141,] 1 2 0.9936526 [142,] 1 2 0.9937017 [143,] 1 2 0.9810402 [144,] 1 2 0.9936199 [145,] 1 2 0.9897557 [146,] 1 2 0.9918058 [147,] 1 2 0.9937665 [148,] 1 2 0.9882099 [149,] 1 2 0.9910776 [150,] 1 2 0.9862575 [151,] 1 2 0.9911553 [152,] 1 2 0.9890393 [153,] 1 2 0.9938209 [154,] 1 2 0.9901624 [155,] 1 2 0.9923515 [156,] 1 2 0.9922418 [157,] 1 2 0.9889731 [158,] 1 2 0.9902939 [159,] 1 2 0.9877542 [160,] 1 2 0.9910280 [161,] 1 2 0.9923092 [162,] 1 2 0.9938784 [163,] 1 2 0.9914431 [164,] 1 2 0.9848184 [165,] 1 2 0.9939159 [166,] 1 2 0.9939125 [167,] 1 2 0.9872706 [168,] 1 2 0.9830805 [169,] 1 2 0.9913937 [170,] 1 2 0.9862925 [171,] 1 2 0.9909633 [172,] 1 2 0.9788584 [173,] 1 2 0.9866989 [174,] 1 2 0.9939102 [175,] 1 2 0.9853007 [176,] 1 2 0.9617883 [177,] 1 2 0.9900120 [178,] 1 2 0.9918102 [179,] 1 2 0.9939489 [180,] 1 2 0.9935882 [181,] 1 2 0.9917836 [182,] 1 2 0.9939170 [183,] 1 2 0.9892708 [184,] 1 2 0.9924478 [185,] 1 2 0.9932287 [186,] 1 2 0.9640487 [187,] 1 2 0.9150126 [188,] 1 2 0.9917589 [189,] 1 2 0.9919865 [190,] 1 2 0.9937946 [191,] 1 2 0.9888295 [192,] 1 2 0.9926884 [193,] 1 2 0.9909269 [194,] 1 2 0.9751339 [195,] 1 2 0.9862132 [196,] 1 2 0.9841566 [197,] 1 2 0.9936557 [198,] 1 2 0.9938973 [199,] 1 2 0.9934375 [200,] 1 2 0.9914201 [201,] 1 2 0.9893087 [202,] 1 2 0.9915481 [203,] 1 2 0.9819092 [204,] 1 2 0.9898774 [205,] 1 2 0.9926876 [206,] 1 2 0.9917091 [207,] 1 2 0.9903339 [208,] 1 2 0.9764847 [209,] 1 2 0.9920887 [210,] 1 2 0.9526866 [211,] 1 2 0.9938025 [212,] 1 2 0.9783714 [213,] 1 2 0.9938230 [214,] 1 2 0.9880267 [215,] 1 2 0.9923108 [216,] 1 2 0.9901850 [217,] 1 2 0.9938279 [218,] 1 2 0.9873388 [219,] 1 2 0.9929195 [220,] 1 2 0.9934017 attr(,"Ordered") [1] FALSE attr(,"call") silhouette.default(x = ifelse(cl1 == 3, 2, 1), dist = dist(x1)^2) attr(,"class") [1] "silhouette" ## other examples
set.seed(1234) cl.tmp <- rep(2:3, each=5) x.tmp <- c(rep(-1,5), abs(rnorm(5)+3)) silhouette(cl.tmp, dist(x.tmp))
cluster neighbor sil_width [1,] 2 1 NaN [2,] 2 1 NaN [3,] 2 1 NaN [4,] 2 1 NaN [5,] 2 1 NaN [6,] 3 2 -0.5736515 [7,] 3 2 -0.1557143 [8,] 3 2 -0.2922523 [9,] 3 2 -0.8340174 [10,] 3 2 -0.1511875 attr(,"Ordered") [1] FALSE attr(,"call") silhouette.default(x = cl.tmp, dist = dist(x.tmp)) attr(,"class") [1] "silhouette"
silhouette(ifelse(cl.tmp==2,1,2), dist(x.tmp))
cluster neighbor sil_width [1,] 1 2 1.0000000 [2,] 1 2 1.0000000 [3,] 1 2 1.0000000 [4,] 1 2 1.0000000 [5,] 1 2 1.0000000 [6,] 2 1 0.4136253 [7,] 2 1 0.7038917 [8,] 2 1 0.6467668 [9,] 2 1 -0.3360695 [10,] 2 1 0.7054709 attr(,"Ordered") [1] FALSE attr(,"call") silhouette.default(x = ifelse(cl.tmp == 2, 1, 2), dist = dist(x.tmp)) attr(,"class") [1] "silhouette"
silhouette(ifelse(cl.tmp==2,1,3), dist(x.tmp))
cluster neighbor sil_width [1,] 1 2 NaN [2,] 1 2 NaN [3,] 1 2 NaN [4,] 1 2 NaN [5,] 1 2 NaN [6,] 3 1 -0.7694686 [7,] 3 1 -0.8167313 [8,] 3 1 -0.6054665 [9,] 3 1 -0.9037412 [10,] 3 1 0.1875360 attr(,"Ordered") [1] FALSE attr(,"call") silhouette.default(x = ifelse(cl.tmp == 2, 1, 3), dist = dist(x.tmp)) attr(,"class") [1] "silhouette"
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