# HG changeset patch # User eschen42 # Date 1520271617 18000 # Node ID c415b7dc6f375a24497580575c638ff437e08b54 # Parent 02cafb660b725130aa86e0eba2c1fcbd3fad42a4 planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 3e916537da6bb37e6f3927d7a11e98e0ab6ef5ec diff -r 02cafb660b72 -r c415b7dc6f37 README --- a/README Wed Aug 09 18:06:55 2017 -0400 +++ b/README Mon Mar 05 12:40:17 2018 -0500 @@ -1,2 +1,5 @@ -# w4mkmeans_galaxy_wrapper -Planemo-based galaxy-tool-wrapper to wrap the stats::kmeans R package for the W4M dataMatrix +# Kmeans for W4m + +Kmeans for W4m is Galaxy tool-wrapper to wrap the R stats::kmeans package +for use with the Workflow4Metabolomics flavor of Galaxy. +This tool is built with Planemo. diff -r 02cafb660b72 -r c415b7dc6f37 test-data/input_sampleMetadata.tsv --- a/test-data/input_sampleMetadata.tsv Wed Aug 09 18:06:55 2017 -0400 +++ b/test-data/input_sampleMetadata.tsv Mon Mar 05 12:40:17 2018 -0500 @@ -1,25 +1,25 @@ -sampleMetadata class polarity sampleType injectionOrder batch tissue hotelling_pval missing_pval decile_pval PCA_XSCOR.p1 PCA_XSCOR.p2 class_PLSDA_XSCOR.p1 class_PLSDA_XSCOR.p2 class_PLSDA_predictions -Y11_1_RA5_01_213 y1 positive sample 213 1 2 0.0955561581467602 1 0.0306775551319138 -2.26882060894901 1.94958116765736 -3.05527623038242 2.32594165405491 y1 -Y2_1_RB1_01_218 y2 positive sample 218 1 1 0.090775969547078 1 0.0334308308237932 -5.43790231069006 3.28509884002914 -5.09396184849057 3.13632363820691 y2 -Y4_1_RB3_01_220 y4 positive sample 220 1 1 0.0922380872343134 1 0.0343065627030201 -5.96519534532645 2.76065569212045 -5.61271725241037 2.60836215684335 y4 -Y12_1_RB4_01_221 y2 positive sample 221 1 2 0.0731025791938841 1 0.0335814402688999 -3.90074024447077 2.32567583717618 -4.06427508572245 2.47867307479093 y2 -Y1_1_RC1_01_228 y1 positive sample 228 1 1 0.948646526283138 1 0.0150153992414774 -5.79172889541087 3.18442356006801 -5.71894211121787 3.14075608795096 y1 -Y14_1_RC6_01_234 y4 positive sample 234 1 2 0.961424772615561 1 0.0889762542416943 -3.50543091786719 1.90332246047248 -3.60257250798236 1.95341548985651 y4 -Y1_2_RD1_01_239 y1 positive sample 239 1 1 0.391486624975171 1 0.419632697534464 -10.0290510611276 3.46916578350898 -9.30301404180818 3.22425994348737 y1 -Y14_2_RD2_01_240 y4 positive sample 240 1 2 0.334478686842038 1 0.471265236704114 -0.955577667004931 1.62643379077323 -1.17127923245195 1.73770179646644 y4 -Y4_2_RD3_01_241 y4 positive sample 241 1 1 0.243979127543208 1 0.115904447650611 -6.29053214398527 3.48768497975009 -5.86080882473825 3.19393908077519 y4 -Y11_2_RD7_01_246 y1 positive sample 246 1 2 0.639085015503201 1 0.496291025606805 -0.737703114796199 1.89206669195622 -1.33734677265265 2.19119862732508 y1 -Y2_2_RE4_01_253 y2 positive sample 253 1 1 0.681339414372971 1 0.713644697663014 -4.43122798643441 2.59136016132011 -4.21959323228049 2.4942150403311 y2 -Y12_2_RE6_01_255 y2 positive sample 255 1 2 0.581861317126264 1 0.446040669279691 -3.42333388673909 2.19844489197916 -3.40077262161465 2.21882800112511 y2 -Y14_3_GA2_01_260 y4 positive sample 260 1 2 0.792323381194401 1 0.812319191661791 -2.61403564986014 1.9025507158402 -2.90132077481451 2.05744453719897 y4 -Y2_3_GA4_01_264 y2 positive sample 264 1 1 0.278347988263537 1 0.668405316795454 -6.19672954480257 4.11371745717593 -5.72942704887795 3.94423530839635 y2 -Y1_3_GA6_01_266 y1 positive sample 266 1 1 0.303133108610158 1 0.521065147801524 -5.91283168480956 2.57721868167528 -5.8128281040434 2.56623732055011 y1 -Y4_3_GA7_01_267 y4 positive sample 267 1 1 0.204620420161485 1 0.53551459376182 -5.81862869528986 3.42191037440281 -5.37442797934098 3.22465790741629 y4 -Y12_3_GB1_01_270 y2 positive sample 270 1 2 0.7747649633382 1 0.698966513767803 -3.0854085700971 1.67899209632345 -3.14572560562774 1.75648836353689 y2 -Y11_3_GC3_01_283 y1 positive sample 283 1 2 0.918803505111851 1 0.396638581468035 -1.16946485386388 1.66851916844539 -1.7032576378218 1.94860768310552 y1 -Y14_4_GC7_01_287 y4 positive sample 287 1 2 0.577273975934045 1 0.14919566995266 -1.24666389579168 2.84891525888206 -1.58652468539139 2.90364189714377 y4 -Y11_4_GD8_01_299 y1 positive sample 299 1 2 0.31302025978985 1 0.426766355892969 -2.15936901108787 1.66989335813642 -2.66042240568943 1.95509478589954 y1 -Y2_4_GE1_01_300 y2 positive sample 300 1 1 0.0338929937565918 1 0.419149865807458 -5.76080121045973 3.47845733452933 -5.39212267305567 3.34452446158071 y2 -Y12_4_GE2_01_304 y2 positive sample 304 1 2 0.130905883509031 1 0.59698349195307 -4.15585900988913 3.22702525356271 -4.28382960930699 3.34874792551519 y2 -Y1_4_GE3_01_305 y1 positive sample 305 1 1 0.129479197101219 1 0.618449187175638 -2.9921645121991 3.14523577730793 -2.85639638001866 3.08197032598192 y1 -Y4_4_GE7_01_309 y4 positive sample 309 1 1 0.758837157578886 1 0.339564008612217 -5.95949084754462 3.20317151028856 -5.46675286145534 3.04585537553442 y4 +sampleMetadata class polarity sampleType injectionOrder batch tissue hotelling_pval missing_pval decile_pval +Y11_1_RA5_01_213 y1 positive sample 213 1 2 0.0955561581467602 1 0.0306775551319138 +Y2_1_RB1_01_218 y2 positive sample 218 1 1 0.090775969547078 1 0.0334308308237932 +Y4_1_RB3_01_220 y4 positive sample 220 1 1 0.0922380872343134 1 0.0343065627030201 +Y12_1_RB4_01_221 y2 positive sample 221 1 2 0.0731025791938841 1 0.0335814402688999 +Y1_1_RC1_01_228 y1 positive sample 228 1 1 0.948646526283138 1 0.0150153992414774 +Y14_1_RC6_01_234 y4 positive sample 234 1 2 0.961424772615561 1 0.0889762542416943 +Y1_2_RD1_01_239 y1 positive sample 239 1 1 0.391486624975171 1 0.419632697534464 +Y14_2_RD2_01_240 y4 positive sample 240 1 2 0.334478686842038 1 0.471265236704114 +Y4_2_RD3_01_241 y4 positive sample 241 1 1 0.243979127543208 1 0.115904447650611 +Y11_2_RD7_01_246 y1 positive sample 246 1 2 0.639085015503201 1 0.496291025606805 +Y2_2_RE4_01_253 y2 positive sample 253 1 1 0.681339414372971 1 0.713644697663014 +Y12_2_RE6_01_255 y2 positive sample 255 1 2 0.581861317126264 1 0.446040669279691 +Y14_3_GA2_01_260 y4 positive sample 260 1 2 0.792323381194401 1 0.812319191661791 +Y2_3_GA4_01_264 y2 positive sample 264 1 1 0.278347988263537 1 0.668405316795454 +Y1_3_GA6_01_266 y1 positive sample 266 1 1 0.303133108610158 1 0.521065147801524 +Y4_3_GA7_01_267 y4 positive sample 267 1 1 0.204620420161485 1 0.53551459376182 +Y12_3_GB1_01_270 y2 positive sample 270 1 2 0.7747649633382 1 0.698966513767803 +Y11_3_GC3_01_283 y1 positive sample 283 1 2 0.918803505111851 1 0.396638581468035 +Y14_4_GC7_01_287 y4 positive sample 287 1 2 0.577273975934045 1 0.14919566995266 +Y11_4_GD8_01_299 y1 positive sample 299 1 2 0.31302025978985 1 0.426766355892969 +Y2_4_GE1_01_300 y2 positive sample 300 1 1 0.0338929937565918 1 0.419149865807458 +Y12_4_GE2_01_304 y2 positive sample 304 1 2 0.130905883509031 1 0.59698349195307 +Y1_4_GE3_01_305 y1 positive sample 305 1 1 0.129479197101219 1 0.618449187175638 +Y4_4_GE7_01_309 y4 positive sample 309 1 1 0.758837157578886 1 0.339564008612217 diff -r 02cafb660b72 -r c415b7dc6f37 test-data/input_variableMetadata.tsv --- a/test-data/input_variableMetadata.tsv Wed Aug 09 18:06:55 2017 -0400 +++ b/test-data/input_variableMetadata.tsv Mon Mar 05 12:40:17 2018 -0500 @@ -1,50 +1,50 @@ -variableMetadata namecustom mz mzmin mzmax rt rtmin rtmax npeaks my_blank pool y0 y1 y2 y3 y4 y5 y6 y7 y8 y9 isotopes adduct pcgroup CV.samp CV.pool CV.ind blank_mean blank_sd blank_CV sample_mean sample_sd sample_CV blankMean_over_sampleMean pool_mean pool_sd pool_CV poolCV_over_sampleCV class_kruskal_fdr class_kruskal_sig class_kruskal_y1.y0_dif class_kruskal_y2.y0_dif class_kruskal_y3.y0_dif class_kruskal_y4.y0_dif class_kruskal_y5.y0_dif class_kruskal_y6.y0_dif class_kruskal_y7.y0_dif class_kruskal_y8.y0_dif class_kruskal_y9.y0_dif class_kruskal_y2.y1_dif class_kruskal_y3.y1_dif class_kruskal_y4.y1_dif class_kruskal_y5.y1_dif class_kruskal_y6.y1_dif 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1.10257987395527 +M263T323 M263T323.29 263 263 263 323.286376953125 317.427062988281 324.498107910156 95 0 10 8 7 3 1 8 7 5 8 2 3 [8][M]+ 25 1.24549325872202 0.169688974744568 1 4856126.41100817 6417229.02375924 1.32147075274076 3269419.74893288 4306448.22482372 1.31719037490653 1.48531751317437 2850327.25225239 3833675.62917697 1.34499490405795 1.02110896775524 diff -r 02cafb660b72 -r c415b7dc6f37 w4m_general_purpose_routines.R --- a/w4m_general_purpose_routines.R Wed Aug 09 18:06:55 2017 -0400 +++ b/w4m_general_purpose_routines.R Mon Mar 05 12:40:17 2018 -0500 @@ -1,3 +1,48 @@ +##----------------------------------------------- +## helper functions for error detection/reporting +##----------------------------------------------- + +# ISO 8601 date ref: https://en.wikipedia.org/wiki/ISO_8601 +iso_date <- function() { + format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z") +} + +# log-printing to stderr +log_print <- function(x, ...) { + cat( + sep="" + , file=stderr() + , iso_date() + , " " + , c(x, ...) + , "\n" + ) +} + +# format error for logging +format_error <- function(e) { + paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ") +} + +# tryCatchFunc produces a list +# func - a function that takes no arguments +# On success of func(), tryCatchFunc produces +# list(success = TRUE, value = func(), msg = "") +# On failure of func(), tryCatchFunc produces +# list(success = FALSE, value = NA, msg = "the error message") +tryCatchFunc <- function(func) { + retval <- NULL + tryCatch( + expr = { + retval <- ( list( success = TRUE, value = func(), msg = "" ) ) + } + , error = function(e) { + retval <<- list( success = FALSE, value = NA, msg = format_error(e) ) + } + ) + return (retval) +} + # prepare.data.matrix - Prepare x.datamatrix for multivariate statistical analaysis (MVA) # - Motivation: # - Selection: @@ -7,7 +52,7 @@ # - If so, set the argument 'exclude.features' to a vector of feature names # - Renaming samples: # - You may want to rename several samples from your analysis: -# - If so, set the argument 'sample.rename.function' to a function accepting a vector +# - If so, set the argument 'sample.rename.function' to a function accepting a vector # of sample names and producing a vector of strings of equivalent length # - MVA is confounded by missing values. # - By default, this function imputes missing values as zero. @@ -19,7 +64,7 @@ # - By default, this function performs an eigth-root transformation: # - Any root-tranformation has the advantage of never being negative. # - Calculation of the eight-root is four times faster in my hands than log10. -# - However, it has the disadvantage that calculation of fold-differences +# - However, it has the disadvantage that calculation of fold-differences # is not additive as with log-transformation. # - Rather, you must divide the values and raise to the eighth power. # - For a different transformation, set the 'data.transformation' argument @@ -107,6 +152,13 @@ } , en = new.env() ) { + # log to environment + if ( !exists("log", envir = en) ) { + en$log <- c() + } + enlog <- function(s) { en$log <- c(en$log, s); s } + #enlog("foo") + # MatVar - Compute variance of rows or columns of a matrix # ref: http://stackoverflow.com/a/25100036 # For row variance, dim == 1, for col variance, dim == 2 @@ -137,11 +189,9 @@ nonzero.var <- function(x) { if (nrow(x) == 0) { - print(str(x)) stop("matrix has no rows") } if (ncol(x) == 0) { - print(str(x)) stop("matrix has no columns") } if ( is.numeric(x) ) { @@ -153,7 +203,7 @@ row.names <- attr(nonzero.rows,"names") x <- x[ row.names, , drop = FALSE ] } - + # exclude any columns with zero variance column.vars <- MatVar(x, dim = 2) nonzero.column.vars <- column.vars > 0 @@ -170,10 +220,13 @@ stop("FATAL ERROR - prepare.data.matrix was called with null x.matrix") } + enlog("prepare.data.matrix - get matrix") + en$xpre <- x <- x.matrix # exclude any samples as indicated if ( !is.null(exclude.features) ) { + enlog("prepare.data.matrix - exclude any samples as indicated") my.colnames <- colnames(x) my.col.diff <- setdiff(my.colnames, exclude.features) x <- x[ , my.col.diff , drop = FALSE ] @@ -181,6 +234,7 @@ # exclude any features as indicated if ( !is.null(exclude.samples) ) { + enlog("prepare.data.matrix - exclude any features as indicated") my.rownames <- rownames(x) my.row.diff <- setdiff(my.rownames, exclude.samples) x <- x[ my.row.diff, , drop = FALSE ] @@ -188,20 +242,25 @@ # rename rows if desired if ( !is.null(sample.rename.function) ) { + enlog("prepare.data.matrix - rename rows if desired") renamed <- sample.rename.function(x) rownames(x) <- renamed } + enlog("prepare.data.matrix - save redacted x.datamatrix to environment") + # save redacted x.datamatrix to environment en$redacted.data.matrix <- x # impute values missing from the x.datamatrix if ( !is.null(data.imputation) ) { + enlog("prepare.data.matrix - impute values missing from the x.datamatrix") x <- data.imputation(x) } # perform transformation if desired if ( !is.null(data.transformation) ) { + enlog("prepare.data.matrix - perform transformation") x <- data.transformation(x) } else { x <- x @@ -209,6 +268,7 @@ # purge rows and columns that have zero variance if ( is.numeric(x) ) { + enlog("prepare.data.matrix - purge rows and columns that have zero variance") x <- nonzero.var(x) } @@ -218,66 +278,4 @@ return(x) } - -##----------------------------------------------- -## helper functions for error detection/reporting -##----------------------------------------------- - -# log-printing to stderr -log_print <- function(x, ...) { - cat( - format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z") - , " " - , c(x, ...) - , "\n" - , sep="" - , file=stderr() - ) -} - -# tryCatchFunc produces a list -# On success of expr(), tryCatchFunc produces -# list(success TRUE, value = expr(), msg = "") -# On failure of expr(), tryCatchFunc produces -# list(success = FALSE, value = NA, msg = "the error message") -tryCatchFunc <- function(expr) { - # format error for logging - format_error <- function(e) { - paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ") - } - my_expr <- expr - retval <- NULL - tryCatch( - expr = { - retval <- ( list( success = TRUE, value = my_expr(), msg = "" ) ) - } - , error = function(e) { - retval <<- list( success = FALSE, value = NA, msg = format_error(e) ) - } - ) - return (retval) -} - -# tryCatchProc produces a list -# On success of expr(), tryCatchProc produces -# list(success TRUE, msg = "") -# On failure of expr(), tryCatchProc produces -# list(success = FALSE, msg = "the error message") -tryCatchProc <- function(expr) { - # format error for logging - format_error <- function(e) { - paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ") - } - retval <- NULL - tryCatch( - expr = { - expr() - retval <- ( list( success = TRUE, msg = "" ) ) - } - , error = function(e) { - retval <<- list( success = FALSE, msg = format_error(e) ) - } - ) - return (retval) -} - +# vim: sw=2 ts=2 et : diff -r 02cafb660b72 -r c415b7dc6f37 w4mkmeans.xml --- a/w4mkmeans.xml Wed Aug 09 18:06:55 2017 -0400 +++ b/w4mkmeans.xml Mon Mar 05 12:40:17 2018 -0500 @@ -1,9 +1,11 @@ - - Calculate K-means for W4M dataMatrix features or samples + + Calculate K-means for W4m dataMatrix features or samples - r-base + r-base r-batch + libssh2 + krb5 @@ -27,19 +29,18 @@ slots "\${GALAXY_SLOTS:-1}" variableMetadata_out '$variableMetadata_out' variable_metadata_path '$variableMetadata_in' - ; echo exit code $? ]]> - - - - + + + + - - - + + + @@ -48,9 +49,9 @@ - - - + + + @@ -60,17 +61,17 @@ - - + + - - - - + + + + @@ -79,57 +80,38 @@ - + 10.1016/j.biocel.2017.07.002 - + 10.1093/bioinformatics/btu813 - + 10.2307/2346830 10.1109/TIT.1982.1056489 diff -r 02cafb660b72 -r c415b7dc6f37 w4mkmeans_routines.R --- a/w4mkmeans_routines.R Wed Aug 09 18:06:55 2017 -0400 +++ b/w4mkmeans_routines.R Mon Mar 05 12:40:17 2018 -0500 @@ -4,11 +4,11 @@ library(parallel) -w4kmeans_usage <- function() { - return ( +w4mkmeans_usage <- function() { + return ( c( "w4mkmeans: bad input.", - "# contract:", + " contract:", " required - caller will provide an environment comprising:", " log_print - a logging function with the signature function(x, ...) expecting strings as x and ...", " variableMetadata - the corresponding W4M data.frame having feature metadata", @@ -18,8 +18,8 @@ " optional - environment may comprise:", " kfeatures - an array of integers, the k's to apply for clustering by feature (default, empty array)", " ksamples - an array of integers, the k's to apply for clustering by sample (default, empty array)", - " iter.max - the maximum number of iterations when calculating a cluster (default = 10)", - " nstart - how many random sets of centers should be chosen (default = 1)", + " iter_max - the maximum number of iterations when calculating a cluster (default = 20)", + " nstart - how many random sets of centers should be chosen (default = 20)", " algorithm - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)", " categorical_prefix - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)", " ", @@ -35,13 +35,15 @@ w4mkmeans <- function(env) { # abort if 'env' is null or is not an environment if ( is.null(env) || ! is.environment(env) ) { - lapply(w4kmeans_usage(),print) - } + lapply(w4mkmeans_usage(),print) + } + # extract parameters from 'env' + log_action <- env$log_print # supply default arguments - if ( ! exists("iter.max" , env) ) env$iter.max <- 10 - if ( ! exists("nstart" , env) ) env$nstart <- 1 + if ( ! exists("iter_max" , env) ) env$iter_max <- 20 + if ( ! exists("nstart" , env) ) env$nstart <- 20 if ( ! exists("algorithm" , env) ) env$algorithm <- 'Hartigan-Wong' - if ( ! exists("categorical_prefix", env) ) env$categorical_prefix <- 'k' + if ( ! exists("categorical_prefix", env) ) env$categorical_prefix <- 'c' if ( ! exists("ksamples" , env) ) env$ksamples <- c() if ( ! exists("kfeatures" , env) ) env$kfeatures <- c() # check mandatory arguments @@ -55,11 +57,11 @@ missing_from_env <- setdiff(expected, (ls(env))) if ( length(missing_from_env) > 0 ) { print(paste(c('expected environment members not found: ', as.character(missing_from_env)), collapse = ", ")) - lapply(w4kmeans_usage(),print) + lapply(w4mkmeans_usage(),log_action) stop("w4mkmeans: contract has been broken") - } + } # extract parameters from 'env' - failure_action <- env$log_print + log_action <- env$log_print scores <- c( "clusterOn\tk\ttotalSS\tbetweenSS\tproportion" ) sampleMetadata <- env$sampleMetadata featureMetadata <- env$variableMetadata @@ -70,39 +72,79 @@ i <- i[i > 0] # eliminate non-positive integers i <- unique(sort(i)) # eliminate redundancy and disorder if (length(a)!=length(i)) { - failure_action("Some values for '", what, "' were skipped where not unique, not positive, or not convertible to an integer.") + log_action("Some values for '", what, "' were skipped where not unique, not positive, or not convertible to an integer.") } return (i) # return results, if any } ksamples <- positive_ints(env$ksamples , "ksamples") kfeatures <- positive_ints(env$kfeatures, "kfeatures") + log_action("w4mkmeans: preparing data matrix") + # prepare data matrix (normalize, eliminate zero-variance rows, etc.; no transformation) + dm_en <- new.env() + dm_en$log <- c() + preparation_result <- tryCatchFunc(function(){ + dm <- prepare.data.matrix( + x.matrix = env$dataMatrix + , data.transformation = function(x) { x } + , en = dm_en + ) + my_log <- dm_en$log + for ( i in 1:length(my_log) ) { + log_action(my_log[i]) + } + dm + }) + if ( !preparation_result$success ) { + postmortem <- paste("prepare.data.matrix failed:", preparation_result$msg) + log_action(postmortem) + stop(postmortem) + } + + env$preparedDataMatrix <- preparation_result$value + + log_action("w4mkmeans: determining evaluation mode") + myLapply <- parLapply - # uncomment the next line to mimic parLapply, but without parallelization (for testing/experimentation) - # myLapply <- function(cl, ...) lapply(...) cl <- NULL + tryCatch( + expr = { + outfile <- "" + # outfile: Where to direct the stdout and stderr connection output from the workers. + # - "" indicates no redirection (which may only be useful for workers on the local machine). + # - Defaults to ‘/dev/null’ (‘nul:’ on Windows). + # - The other possibility is a file path on the worker's host. + # - Files will be opened in append mode, as all workers log to the same file. + cl <- makePSOCKcluster( names = slots, outfile = outfile ) + } + , error = function(e) { + log_action(sprintf("w4kmeans: falling back to serial evaluation because makePSOCKcluster(names = %d) threw an exception", slots)) + # mimic parLapply, but without parallelization (as a last resort) + myLapply <<- function(cl, ...) lapply(...) + } + ) if ( identical(myLapply, parLapply) ) { - failure_action(sprintf("w4mkmeans: using parallel evaluation with %d slots", slots)) - failure_action(names(cl)) - cl <- makePSOCKcluster(names = slots) + log_action(sprintf("w4mkmeans: using parallel evaluation with %d slots", slots)) # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster." + # note varlist: names of references must be passed to the cluster so that they can be resolved clusterExport( cl = cl , varlist = c( - "tryCatchFunc" - , "calc_kmeans_one_dimension_one_k" - , "prepare.data.matrix" + "tryCatchFunc" # required by calc_kmeans_one_dimension_one_k + , "format_error" # required by tryCatchFunc when errors are caught + , "iso_date" # required by log_print + , "log_print" # required by calc_kmeans_one_dimension_one_k ) ) final <- function(cl) { # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster." if ( !is.null(cl) ) { - failure_action("w4mkmeans: stopping cluster used for parallel evaluation") + log_action("w4mkmeans: stopping cluster used for parallel evaluation") stopCluster(cl) } } } else { - failure_action("w4mkmeans: using sequential evaluation (1 slot)") + log_action("w4mkmeans: using sequential evaluation (one slot)") final <- function(cl) { } } @@ -115,7 +157,7 @@ # for each $i in ksamples, append column 'k$i' to data frame sampleMetadata ksamples_length <- length(ksamples) if ( ksamples_length > 0 ) { - smpl_result_list <- myLapply( + smpl_result_list <- myLapply( cl = cl , ksamples , calc_kmeans_one_dimension_one_k @@ -134,7 +176,7 @@ # for each $i in kfeatures, append column 'k$i' to data frame featureMetadata kfeatures_length <- length(kfeatures) if ( kfeatures_length > 0 ) { - feat_result_list <- myLapply( + feat_result_list <- myLapply( cl = cl , kfeatures , calc_kmeans_one_dimension_one_k @@ -150,22 +192,24 @@ } } - return ( + return ( list( variableMetadata = featureMetadata - , sampleMetadata = sampleMetadata - , scores = scores + , sampleMetadata = sampleMetadata + , scores = scores ) ) } - , finally = final(cl) + , finally = { + final(cl) + } ) } # calculate k-means for features or samples # - recall that the dataMatrix has features in rows and samples in columns # return value: -# list(clusters = km$cluster, scores = scores) +# list(clusters = km$cluster, scores = scores) # arguments: # env: # environment having dataMatrix @@ -179,40 +223,64 @@ # abort if environment is not as expected if ( is.null(env) || ! is.environment(env) ) { stop("calc_kmeans_one_dimension_one_k - argument 'env' is not an environment") - } + } if ( ! exists("log_print", env) || ! is.function(env$log_print) ) { stop("calc_kmeans_one_dimension_one_k - argument 'env' - environment does not include log_print or it is not a function") - } + } + log_action <- env$log_print # abort if k is not as expected if ( ! is.numeric(k) ) { stop(sprintf("calc_kmeans_one_dimension_one_k - expected numeric argument 'k' but type is %s", typeof(k))) - } + } k <- as.integer(k) # abort if dimension is not as expected - if ( ! is.character(dimension) + if ( ! is.character(dimension) || ! Reduce( f =`|`, x = sapply(X = c("features","samples"), FUN = `==`, dimension), init = FALSE) ) { stop("calc_kmeans_one_dimension_one_k - argument 'dimension' is neither 'features' nor 'samples'") - } - dm <- env$dataMatrix - iter.max <- env$iter.max + } + dm <- env$preparedDataMatrix + iter_max <- env$iter_max nstart <- env$nstart algorithm <- env$algorithm dim_features <- dimension == "features" + # tryCatchFunc produces a list - # On success of expr(), tryCatchFunc produces - # list(success TRUE, value = expr(), msg = "") - # On failure of expr(), tryCatchFunc produces + # On success of func(), tryCatchFunc produces + # list(success = TRUE, value = func(), msg = "") + # On failure of func(), tryCatchFunc produces # list(success = FALSE, value = NA, msg = "the error message") - result_list <- tryCatchFunc( expr = function() { + result_list <- tryCatchFunc( func = function() { # kmeans clusters the rows; features are the columns of args_env$dataMatrix; samples, the rows # - to calculate sample-clusters, no transposition is needed because samples are rows # - to calculate feature-clusters, transposition is needed so that features will be the rows - if ( ! dim_features ) dm <- t(dm) - dm <- prepare.data.matrix( x.matrix = dm, data.transformation = function(x) { x } ) + if ( ! dim_features ) { + dm <- t(dm) + } + # need to set.seed to get reproducible results from kmeans set.seed(4567) + # do the k-means clustering - km <- kmeans( x = dm, centers = k, iter.max, nstart = nstart, algorithm = algorithm ) + withCallingHandlers( + { + km <<- kmeans( x = dm, centers = k, iter.max = iter_max, nstart = nstart, algorithm = algorithm ) + } + , warning = function(w) { + lw <- list(w) + smplwrn <- as.character(w[[1]]) + log_print( + sprintf( "Warning for %s: center = %d, nstart = %d, iter_max = %d: %s" + , if (dim_features) "features" else "samples" + , k + , nstart + , iter_max + , smplwrn + ) + ) + } + ) + + # collect the scores scores <- sprintf("%s\t%d\t%0.5e\t%0.5e\t%0.5f" , dimension @@ -221,8 +289,16 @@ , km$betweenss , km$betweenss/km$totss ) + + # return list of results list(clusters = km$cluster, scores = scores) }) + + # return either + # list(success = TRUE, value = func(), msg = "") + # or + # list(success = FALSE, value = NA, msg = "the error message") return ( result_list ) } +# vim: sw=2 ts=2 et : diff -r 02cafb660b72 -r c415b7dc6f37 w4mkmeans_wrapper.R --- a/w4mkmeans_wrapper.R Wed Aug 09 18:06:55 2017 -0400 +++ b/w4mkmeans_wrapper.R Mon Mar 05 12:40:17 2018 -0500 @@ -22,7 +22,7 @@ # slots "${GALAXY_SLOTS:-1}" \ # variableMetadata_out "$variableMetadata_out" \ # variable_metadata_path "$variableMetadata_in" -# +# # # # @@ -30,8 +30,8 @@ # # # -# -# +# +# # # # @@ -66,7 +66,7 @@ ## Computation - source general and module-specific routines ##---------------------------------------------------------- -log_print <- function(x, ...) { +log_print <- function(x, ...) { cat( format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z") , " " @@ -77,6 +77,15 @@ ) } +log_cat <- function(x, ...) { + cat( + c(x, ...) + , "\n" + , sep="" + , file=stderr() + ) +} + # log_print(sprintf("tool_directory is %s", tool_directory)) w4m_general_purpose_routines_path <- r_path("w4m_general_purpose_routines.R") @@ -85,7 +94,7 @@ log_print("cannot find file w4m_general_purpose_routines.R") q(save = "no", status = 1, runLast = TRUE) } -# log_print("sourcing ",w4m_general_purpose_routines_path) +log_print("sourcing ",w4m_general_purpose_routines_path) source(w4m_general_purpose_routines_path) if ( ! exists("prepare.data.matrix") ) { log_print("'prepare.data.matrix' was not read from file w4m_general_purpose_routines.R") @@ -164,7 +173,7 @@ expr = { # read in the sample metadata kind_string <- "sample metadata input" - smpl_metadata_input_env <- + smpl_metadata_input_env <- read_data_frame( file_path = env$sample_metadata_path , kind_string = kind_string @@ -178,7 +187,7 @@ # read in the variable metadata kind_string <- "variable metadata input" - vrbl_metadata_input_env <- + vrbl_metadata_input_env <- read_data_frame( file_path = env$variable_metadata_path , kind_string = kind_string @@ -218,7 +227,7 @@ } -read_input_failure_action <- function(x, ...) { +read_input_failure_action <- function(x, ...) { log_print("Failure reading input for '", modNamC, "' Galaxy module call") log_print(x, ...) } @@ -238,7 +247,7 @@ # Set the handler for R error-handling options( show.error.messages = F - , error = function () { + , error = function () { log_print( "Fatal error in '", modNamC, "': ", geterrmessage() ) q( "no", 1, F ) } @@ -283,7 +292,7 @@ args_env$data_matrix_path <- as.character(argVc["data_matrix_path"]) args_env$variable_metadata_path <- as.character(argVc["variable_metadata_path"]) args_env$sample_metadata_path <- as.character(argVc["sample_metadata_path"]) - + # other parameters # multi-string args - split csv: "1,2,3" -> c("1","2","3") @@ -305,20 +314,21 @@ for (member in ls(args_env)) { value <- get(member, args_env) value <- ifelse(length(value) == 1, value, sprintf("c(%s)", paste(value, collapse=", "))) - + log_print(sprintf(" - %s: %s", member, ifelse( !is.function(value) , value, "function" ))) } log_print("") ##--------------------------------------------------------- -## Computation - attempt to read input data +## Computation - attempt to read input data and process ##--------------------------------------------------------- if ( ! read_input_data(args_env, failure_action = read_input_failure_action) ) { result <- -1 } else { - log_print("Input data was read successfully.") + log_print("Input data was read.") + # attempt to process the data result <- w4mkmeans(env = args_env) - log_print("returned from call to w4mkmeans.") + log_print("Returned from call to w4mkmeans.") } if ( length(result) == 0 ) { @@ -356,7 +366,6 @@ ## Closing ##-------- - if (!file.exists(sampleMetadata_out)) { log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, sampleMetadata_out)) }