Computación de Rowmeans para cada combinación de columnas -- performance campo con combinatorics campo con r camp codereview Relacionados El problema

Computing rowMeans for every combination of columns


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problema

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Los datos:

      pre1 <-structure(list(A = c(0.0276, 0.0165, 0.0113, 0.0229, 0.0113,  0.0151, 0.015, 0.0122, 0.0113, 0.0113, 0.0113, 0.0113), B = c(0.0884,  0.0135, 0.0001, 0.0523, 0, 0.0069, 0.0069, 0.0007, 0, 0, 0, 0 ), C = c(0.04, 0.0155, 0.0065, 0.0291, 0.0065, 0.0128, 0.0127,  0.0078, 0.0065, 0.0065, 0.0065, 0.0065), D = c(0.0897, 0.014,  0.0001, 0.0546, 0, 0.0073, 0.0073, 0.0007, 0, 0, 0, 0), E = c(0.0911,  0.0129, 0, 0.0537, 0, 0.0065, 0.0065, 0.0006, 0, 0, 0, 0), F = c(0.0891,  0.0134, 0, 0.0529, 0, 0.0069, 0.007, 0.0006, 0, 0, 0, 0), G = c(0.0921,  0.0118, 0, 0.0536, 0, 0.0035, 0.004, 0.0001, 0, 0, 0, 0), H = c(0.0906,  0.0168, 0, 0.0631, 0, 0.0024, 0.0032, 0.0001, 0, 0, 0, 0), I = c(0.0922,  0.0156, 0, 0.0625, 0, 0.0024, 0.0031, 0, 0, 0, 0, 0), J = c(0.1115,  0.0052, 0.0006, 0.0458, 0.0006, 0.005, 0.005, 0.0007, 0.0006,  0.0006, 0.0006, 0.0006), K = c(0.0892, 0.0128, 0, 0.0514, 0,  0.0073, 0.0072, 0.0006, 0, 0, 0, 0), L = c(0.0895, 0.009, 0.0002,  0.0515, 0.0002, 0.0055, 0.0052, 0.0008, 0.0002, 0.0002, 0.0002,  0.0002), M = c(0.0887, 0.0135, 0.0001, 0.0525, 0, 0.0068, 0.0069,  0.0007, 0, 0, 0, 0), N = c(0.0892, 0.0128, 0, 0.0514, 0, 0.0073,  0.0072, 0.0006, 0, 0, 0, 0), O = c(0.087, 0.0133, 0.0001, 0.0511,  0.0001, 0.0072, 0.0072, 0.0007, 0.0001, 0.0001, 0.0001, 0.0001 ), P = c(0.0875, 0.011, 0, 0.0492, 0, 0.002, 0.0027, 0.0002,  0, 0, 0, 0), Q = c(0.0893, 0.0126, 0, 0.0518, 0, 0.0063, 0.0063,  0.0004, 0, 0, 0, 0), R = c(0.0763, 0.0142, 0.0006, 0.0494, 0.0003,  0.0018, 0.0027, 0.0007, 0.0002, 0.0002, 0.0003, 0.0002), S = c(0.0176,  0.0173, 0.0172, 0.0175, 0.0172, 0.0173, 0.0172, 0.0172, 0.0172,  0.0172, 0.0172, 0.0172)), .Names = c("A", "B", "C", "D", "E",  "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R",  "S"), row.names = c(NA, -12L), class = "data.frame")  obs<- c(0.0958964144767174, 0.00112749085522926, 0, 0.0538084597701149,  0, 0, 0, 0, 0, 0, 0, 0)   

La lista de combinaciones de columnas:

  library(foreach) xcomb <- foreach(z=1:ncol(pre1), .combine=c) %do% {    combn(names(pre1), z, simplify=FALSE) }   

el bucle:

precaución

El siguiente código tarda 2 minutos en ejecutar en mi PC (de hecho, 9988776655544332 tiene 23 columnas y toma más de 37 minutos para ejecutar):

    library(plyr)   res <- ldply(xcomb, function(l) {    pred <- rowMeans(pre1[l])    rmse <- sqrt(mean((obs-pred)^2))    if(rmse<0.00345){     me <- mean(obs-pred)     smape <- sum(abs(pred-obs))/sum(pred+obs)     data.frame(combin=paste(names(pre1[l]),collapse="-"),me = me, rmse = rmse, smape = smape)   }  })   

Salida de ejemplo de la reproducibilidad:

          combin            me        rmse      smape 1          J-L -0.0015764696 0.003398144 0.09119202 2          J-P -0.0011556362 0.003281430 0.08393609 3        E-J-L -0.0016195251 0.003424861 0.08216952 4        E-J-P -0.0013389696 0.003357466 0.07727013 5        F-J-P -0.0013000807 0.003448260 0.07759392 6        G-H-J -0.0018223029 0.003387601 0.06759025 7        G-I-J -0.0018111918 0.003316197 0.06720583 8        G-J-L -0.0014473029 0.003126429 0.07643285 9        G-J-P -0.0011667474 0.003091874 0.07144020 10       G-J-Q -0.0015584140 0.003436998 0.07965237 11       G-J-R -0.0010084140 0.003394027 0.08194241 12       H-J-L -0.0017556362 0.003236715 0.06739709 13       H-J-P -0.0014750807 0.003194667 0.06236702 14       I-J-L -0.0017445251 0.003162696 0.06825156 15       I-J-P -0.0014639696 0.003108024 0.06322841 16       I-J-R -0.0013056362 0.003443850 0.07335332 17       J-L-P -0.0011000807 0.003086438 0.07352729 18       J-L-Q -0.0014917474 0.003427897 0.08172932 19       J-L-R -0.0009417474 0.003443418 0.08960742 20       J-P-Q -0.0012111918 0.003384422 0.07680148 21     B-G-J-L -0.0014598029 0.003434496 0.07700107 22     B-G-J-P -0.0012493862 0.003418938 0.07643082 23     B-J-L-P -0.0011993862 0.003432305 0.08211289 24     D-G-J-L -0.0015618862 0.003446124 0.07491208 25     D-G-J-P -0.0013514696 0.003416486 0.07432744 26     D-J-L-P -0.0013014696 0.003417190 0.07998359 27     E-G-J-L -0.0015118862 0.003349091 0.07335527 28     E-G-J-P -0.0013014696 0.003321645 0.07178900 29     E-I-J-L -0.0017348029 0.003414617 0.06724261 30     E-I-J-P -0.0015243862 0.003377062 0.06548492 31     E-J-L-P -0.0012514696 0.003320299 0.07745106 32     F-G-J-L -0.0014827196 0.003419382 0.07576092 33     F-G-J-P -0.0012723029 0.003398932 0.07518129 34     F-I-J-P -0.0014952196 0.003443578 0.06884192 35     F-J-L-P -0.0012223029 0.003408147 0.08085604 36     G-H-J-L -0.0016139696 0.003264481 0.06225090 37     G-H-J-P -0.0014035529 0.003252500 0.06138754 38     G-I-J-L -0.0016056362 0.003192294 0.06289348 39     G-I-J-P -0.0013952196 0.003173631 0.05952244 40     G-J-K-L -0.0014535529 0.003434714 0.07694082 41     G-J-K-P -0.0012431362 0.003414584 0.07636996 42     G-J-L-M -0.0014681362 0.003426825 0.07650715 43     G-J-L-N -0.0014535529 0.003434714 0.07694082 44     G-J-L-P -0.0011223029 0.003097579 0.07148546 45     G-J-L-Q -0.0014160529 0.003354734 0.07485276 46     G-J-L-R -0.0010035529 0.003420185 0.08480000 47     G-J-M-P -0.0012577196 0.003410148 0.07593315 48     G-J-N-P -0.0012431362 0.003414584 0.07636996 49     G-J-P-Q -0.0012056362 0.003340910 0.07426441 50     H-J-L-P -0.0013535529 0.003183342 0.06700815 51     H-J-P-Q -0.0014368862 0.003449975 0.06977695 52     I-J-K-P -0.0014660529 0.003434489 0.07001368 53     I-J-L-P -0.0013452196 0.003103169 0.06514130 54     I-J-L-Q -0.0016389696 0.003392922 0.06855015 55     I-J-L-R -0.0012264696 0.003444943 0.07831468 56     I-J-N-P -0.0014660529 0.003434489 0.07001368 57     I-J-P-Q -0.0014285529 0.003368587 0.06791682 58     J-K-L-P -0.0011931362 0.003432281 0.08205326 59     J-L-M-P -0.0012077196 0.003421419 0.08161247 60     J-L-N-P -0.0011931362 0.003432281 0.08205326 61     J-L-P-Q -0.0011556362 0.003353159 0.07995181 62   B-G-J-L-P -0.0011973029 0.003415942 0.07979367 63   B-I-J-L-P -0.0013756362 0.003437632 0.07466828 64   D-G-J-L-P -0.0012789696 0.003409087 0.07809595 65   E-G-I-J-L -0.0016256362 0.003415821 0.06699637 66   E-G-I-J-P -0.0014573029 0.003398820 0.06648042 67   E-G-J-L-P -0.0012389696 0.003324390 0.07606610 68   E-H-J-L-P -0.0014239696 0.003427462 0.07246184 69   E-I-J-L-P -0.0014173029 0.003357870 0.07097378 70   F-G-J-L-P -0.0012156362 0.003397033 0.07878956 71   F-I-J-L-P -0.0013939696 0.003423666 0.07367445 72   G-H-J-L-P -0.0013206362 0.003251859 0.06770566 73   G-I-J-K-P -0.0014106362 0.003447292 0.07011274 74   G-I-J-L-P -0.0013139696 0.003181862 0.06621059 75   G-I-J-L-Q -0.0015489696 0.003404413 0.06893746 76   G-I-J-N-P -0.0014106362 0.003447292 0.07011274 77   G-I-J-P-Q -0.0013806362 0.003395895 0.06843233 78   G-J-K-L-P -0.0011923029 0.003408375 0.07974553 79   G-J-L-M-P -0.0012039696 0.003407449 0.07939387 80   G-J-L-N-P -0.0011923029 0.003408375 0.07974553 81   G-J-L-P-Q -0.0011623029 0.003347930 0.07806216 82   H-I-J-L-P -0.0014989696 0.003391859 0.06651120 83   H-J-L-P-Q -0.0013473029 0.003434350 0.07443353 84   I-J-K-L-P -0.0013706362 0.003419123 0.07461949 85   I-J-L-M-P -0.0013823029 0.003431058 0.07427245 86   I-J-L-N-P -0.0013706362 0.003419123 0.07461949 87   I-J-L-P-Q -0.0013406362 0.003363572 0.07294166 88 E-G-H-J-L-P -0.0013848029 0.003448137 0.07213946 89 E-G-I-J-L-P -0.0013792474 0.003387181 0.07089756 90 F-G-I-J-L-P -0.0013598029 0.003443722 0.07315096 91 G-H-I-J-L-P -0.0014473029 0.003420080 0.06695391 92 G-I-J-K-L-P -0.0013403585 0.003437713 0.07393901 93 G-I-J-L-N-P -0.0013403585 0.003437713 0.07393901 94 G-I-J-L-P-Q -0.0013153585 0.003392820 0.07253884   
Original en ingles

The data:

    pre1 <-structure(list(A = c(0.0276, 0.0165, 0.0113, 0.0229, 0.0113,  0.0151, 0.015, 0.0122, 0.0113, 0.0113, 0.0113, 0.0113), B = c(0.0884,  0.0135, 0.0001, 0.0523, 0, 0.0069, 0.0069, 0.0007, 0, 0, 0, 0 ), C = c(0.04, 0.0155, 0.0065, 0.0291, 0.0065, 0.0128, 0.0127,  0.0078, 0.0065, 0.0065, 0.0065, 0.0065), D = c(0.0897, 0.014,  0.0001, 0.0546, 0, 0.0073, 0.0073, 0.0007, 0, 0, 0, 0), E = c(0.0911,  0.0129, 0, 0.0537, 0, 0.0065, 0.0065, 0.0006, 0, 0, 0, 0), F = c(0.0891,  0.0134, 0, 0.0529, 0, 0.0069, 0.007, 0.0006, 0, 0, 0, 0), G = c(0.0921,  0.0118, 0, 0.0536, 0, 0.0035, 0.004, 0.0001, 0, 0, 0, 0), H = c(0.0906,  0.0168, 0, 0.0631, 0, 0.0024, 0.0032, 0.0001, 0, 0, 0, 0), I = c(0.0922,  0.0156, 0, 0.0625, 0, 0.0024, 0.0031, 0, 0, 0, 0, 0), J = c(0.1115,  0.0052, 0.0006, 0.0458, 0.0006, 0.005, 0.005, 0.0007, 0.0006,  0.0006, 0.0006, 0.0006), K = c(0.0892, 0.0128, 0, 0.0514, 0,  0.0073, 0.0072, 0.0006, 0, 0, 0, 0), L = c(0.0895, 0.009, 0.0002,  0.0515, 0.0002, 0.0055, 0.0052, 0.0008, 0.0002, 0.0002, 0.0002,  0.0002), M = c(0.0887, 0.0135, 0.0001, 0.0525, 0, 0.0068, 0.0069,  0.0007, 0, 0, 0, 0), N = c(0.0892, 0.0128, 0, 0.0514, 0, 0.0073,  0.0072, 0.0006, 0, 0, 0, 0), O = c(0.087, 0.0133, 0.0001, 0.0511,  0.0001, 0.0072, 0.0072, 0.0007, 0.0001, 0.0001, 0.0001, 0.0001 ), P = c(0.0875, 0.011, 0, 0.0492, 0, 0.002, 0.0027, 0.0002,  0, 0, 0, 0), Q = c(0.0893, 0.0126, 0, 0.0518, 0, 0.0063, 0.0063,  0.0004, 0, 0, 0, 0), R = c(0.0763, 0.0142, 0.0006, 0.0494, 0.0003,  0.0018, 0.0027, 0.0007, 0.0002, 0.0002, 0.0003, 0.0002), S = c(0.0176,  0.0173, 0.0172, 0.0175, 0.0172, 0.0173, 0.0172, 0.0172, 0.0172,  0.0172, 0.0172, 0.0172)), .Names = c("A", "B", "C", "D", "E",  "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R",  "S"), row.names = c(NA, -12L), class = "data.frame")  obs<- c(0.0958964144767174, 0.00112749085522926, 0, 0.0538084597701149,  0, 0, 0, 0, 0, 0, 0, 0) 

The list of column combinations:

library(foreach) xcomb <- foreach(z=1:ncol(pre1), .combine=c) %do% {    combn(names(pre1), z, simplify=FALSE) } 

The loop:

CAUTION

The following code takes 2 minutes to run on my PC (in fact, pre1 has 23 columns and takes more than 37 minutes to run):

  library(plyr)   res <- ldply(xcomb, function(l) {    pred <- rowMeans(pre1[l])    rmse <- sqrt(mean((obs-pred)^2))    if(rmse<0.00345){     me <- mean(obs-pred)     smape <- sum(abs(pred-obs))/sum(pred+obs)     data.frame(combin=paste(names(pre1[l]),collapse="-"),me = me, rmse = rmse, smape = smape)   }  }) 

Example output of the reproducible:

        combin            me        rmse      smape 1          J-L -0.0015764696 0.003398144 0.09119202 2          J-P -0.0011556362 0.003281430 0.08393609 3        E-J-L -0.0016195251 0.003424861 0.08216952 4        E-J-P -0.0013389696 0.003357466 0.07727013 5        F-J-P -0.0013000807 0.003448260 0.07759392 6        G-H-J -0.0018223029 0.003387601 0.06759025 7        G-I-J -0.0018111918 0.003316197 0.06720583 8        G-J-L -0.0014473029 0.003126429 0.07643285 9        G-J-P -0.0011667474 0.003091874 0.07144020 10       G-J-Q -0.0015584140 0.003436998 0.07965237 11       G-J-R -0.0010084140 0.003394027 0.08194241 12       H-J-L -0.0017556362 0.003236715 0.06739709 13       H-J-P -0.0014750807 0.003194667 0.06236702 14       I-J-L -0.0017445251 0.003162696 0.06825156 15       I-J-P -0.0014639696 0.003108024 0.06322841 16       I-J-R -0.0013056362 0.003443850 0.07335332 17       J-L-P -0.0011000807 0.003086438 0.07352729 18       J-L-Q -0.0014917474 0.003427897 0.08172932 19       J-L-R -0.0009417474 0.003443418 0.08960742 20       J-P-Q -0.0012111918 0.003384422 0.07680148 21     B-G-J-L -0.0014598029 0.003434496 0.07700107 22     B-G-J-P -0.0012493862 0.003418938 0.07643082 23     B-J-L-P -0.0011993862 0.003432305 0.08211289 24     D-G-J-L -0.0015618862 0.003446124 0.07491208 25     D-G-J-P -0.0013514696 0.003416486 0.07432744 26     D-J-L-P -0.0013014696 0.003417190 0.07998359 27     E-G-J-L -0.0015118862 0.003349091 0.07335527 28     E-G-J-P -0.0013014696 0.003321645 0.07178900 29     E-I-J-L -0.0017348029 0.003414617 0.06724261 30     E-I-J-P -0.0015243862 0.003377062 0.06548492 31     E-J-L-P -0.0012514696 0.003320299 0.07745106 32     F-G-J-L -0.0014827196 0.003419382 0.07576092 33     F-G-J-P -0.0012723029 0.003398932 0.07518129 34     F-I-J-P -0.0014952196 0.003443578 0.06884192 35     F-J-L-P -0.0012223029 0.003408147 0.08085604 36     G-H-J-L -0.0016139696 0.003264481 0.06225090 37     G-H-J-P -0.0014035529 0.003252500 0.06138754 38     G-I-J-L -0.0016056362 0.003192294 0.06289348 39     G-I-J-P -0.0013952196 0.003173631 0.05952244 40     G-J-K-L -0.0014535529 0.003434714 0.07694082 41     G-J-K-P -0.0012431362 0.003414584 0.07636996 42     G-J-L-M -0.0014681362 0.003426825 0.07650715 43     G-J-L-N -0.0014535529 0.003434714 0.07694082 44     G-J-L-P -0.0011223029 0.003097579 0.07148546 45     G-J-L-Q -0.0014160529 0.003354734 0.07485276 46     G-J-L-R -0.0010035529 0.003420185 0.08480000 47     G-J-M-P -0.0012577196 0.003410148 0.07593315 48     G-J-N-P -0.0012431362 0.003414584 0.07636996 49     G-J-P-Q -0.0012056362 0.003340910 0.07426441 50     H-J-L-P -0.0013535529 0.003183342 0.06700815 51     H-J-P-Q -0.0014368862 0.003449975 0.06977695 52     I-J-K-P -0.0014660529 0.003434489 0.07001368 53     I-J-L-P -0.0013452196 0.003103169 0.06514130 54     I-J-L-Q -0.0016389696 0.003392922 0.06855015 55     I-J-L-R -0.0012264696 0.003444943 0.07831468 56     I-J-N-P -0.0014660529 0.003434489 0.07001368 57     I-J-P-Q -0.0014285529 0.003368587 0.06791682 58     J-K-L-P -0.0011931362 0.003432281 0.08205326 59     J-L-M-P -0.0012077196 0.003421419 0.08161247 60     J-L-N-P -0.0011931362 0.003432281 0.08205326 61     J-L-P-Q -0.0011556362 0.003353159 0.07995181 62   B-G-J-L-P -0.0011973029 0.003415942 0.07979367 63   B-I-J-L-P -0.0013756362 0.003437632 0.07466828 64   D-G-J-L-P -0.0012789696 0.003409087 0.07809595 65   E-G-I-J-L -0.0016256362 0.003415821 0.06699637 66   E-G-I-J-P -0.0014573029 0.003398820 0.06648042 67   E-G-J-L-P -0.0012389696 0.003324390 0.07606610 68   E-H-J-L-P -0.0014239696 0.003427462 0.07246184 69   E-I-J-L-P -0.0014173029 0.003357870 0.07097378 70   F-G-J-L-P -0.0012156362 0.003397033 0.07878956 71   F-I-J-L-P -0.0013939696 0.003423666 0.07367445 72   G-H-J-L-P -0.0013206362 0.003251859 0.06770566 73   G-I-J-K-P -0.0014106362 0.003447292 0.07011274 74   G-I-J-L-P -0.0013139696 0.003181862 0.06621059 75   G-I-J-L-Q -0.0015489696 0.003404413 0.06893746 76   G-I-J-N-P -0.0014106362 0.003447292 0.07011274 77   G-I-J-P-Q -0.0013806362 0.003395895 0.06843233 78   G-J-K-L-P -0.0011923029 0.003408375 0.07974553 79   G-J-L-M-P -0.0012039696 0.003407449 0.07939387 80   G-J-L-N-P -0.0011923029 0.003408375 0.07974553 81   G-J-L-P-Q -0.0011623029 0.003347930 0.07806216 82   H-I-J-L-P -0.0014989696 0.003391859 0.06651120 83   H-J-L-P-Q -0.0013473029 0.003434350 0.07443353 84   I-J-K-L-P -0.0013706362 0.003419123 0.07461949 85   I-J-L-M-P -0.0013823029 0.003431058 0.07427245 86   I-J-L-N-P -0.0013706362 0.003419123 0.07461949 87   I-J-L-P-Q -0.0013406362 0.003363572 0.07294166 88 E-G-H-J-L-P -0.0013848029 0.003448137 0.07213946 89 E-G-I-J-L-P -0.0013792474 0.003387181 0.07089756 90 F-G-I-J-L-P -0.0013598029 0.003443722 0.07315096 91 G-H-I-J-L-P -0.0014473029 0.003420080 0.06695391 92 G-I-J-K-L-P -0.0013403585 0.003437713 0.07393901 93 G-I-J-L-N-P -0.0013403585 0.003437713 0.07393901 94 G-I-J-L-P-Q -0.0013153585 0.003392820 0.07253884 
        
         
         

Lista de respuestas

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La mejor respuesta
 

Esto se puede manejar con la multiplicación de matriz. Bajo el capó, la multiplicación de matriz contiene un bucle para el bucle al igual que su código, pero es mucho más rápido ya que todo se implementa en el código precompilado.

Por lo tanto, primero calcule una matriz de cell8 y cell9 donde cada fila corresponde a una combinación y cada columna corresponde a uno de sus variables 99887766655443320 :

  var cell = sheetFrom.getRange('A1').offset(lastRow, 0);1  

Luego, su código se traduce en:

  var cell = sheetFrom.getRange('A1').offset(lastRow, 0);2  

Este código toma aproximadamente 5 ~ 6 segundos en mi máquina con 19 variables. Con 23 variables, tendrá 16 veces más combinaciones, por lo que aún debería correr en menos de 2 minutos. Con muchas más variables, probablemente se quede sin memoria intentando calcular la matriz de peso: tendrá que adaptar el código para que encuentre un buen equilibrio entre el uso de la memoria y los tiempos de cálculo.

 

This can be handled using matrix multiplication. Under the hood, matrix multiplication contains a for loop just like your code does, but it is a lot faster since it is all implemented in pre-compiled code.

So first compute a matrix of 0 and 1 where each row corresponds to a combination and each column corresponds to one of your 19 variables:

weightMatrix <- function(pre1) {    nvars <- ncol(pre1)    varnames <- colnames(pre1)    wposs <- replicate(nvars, 0:1, simplify = FALSE)    nposs <- Map(c, "", paste0("-",varnames))    weights <- data.matrix(do.call(expand.grid, wposs))    cnames  <- do.call(paste0, do.call(expand.grid, nposs))    cnames  <- sub("^-", "", cnames)    dimnames(weights) <- list(cnames, varnames)    weights <- tail(weights, -1)    return(weights) } 

Then your code translates to:

wmat <- weightMatrix(pre1) pred  <- wmat %*% t(data.matrix(pre1)) / rowSums(wmat) rmse  <- sqrt(colMeans((obs - t(pred)) ^ 2)) me    <- colMeans(obs - t(pred)) smape <- colSums(abs(t(pred) - obs)) / colSums(t(pred) + obs)  out <- data.frame(rmse, me, smape) subset(out, rmse < 0.00345) 

This code takes about 5~6 seconds on my machine with 19 variables. With 23 variables, it will have 16 times more combinations so it should still run in under 2 minutes. With many more variables, you will likely run out of memory trying to compute the weight matrix: you will have to adapt the code so it finds a good balance between memory usage and computation times.

 
 
     
     

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