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annotate w4mkmeans_routines.R @ 2:c415b7dc6f37 draft default tip

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planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 3e916537da6bb37e6f3927d7a11e98e0ab6ef5ec
author eschen42
date Mon, 05 Mar 2018 12:40:17 -0500
parents 02cafb660b72
children
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1 ##------------------------------------------------------------------------------------------------------
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2 ## these are the batch-independent and file-structure-independent routines to support the w4mkmeans tool
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3 ##------------------------------------------------------------------------------------------------------
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4
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5 library(parallel)
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6
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7 w4mkmeans_usage <- function() {
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8 return (
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9 c(
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10 "w4mkmeans: bad input.",
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11 " contract:",
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12 " required - caller will provide an environment comprising:",
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13 " log_print - a logging function with the signature function(x, ...) expecting strings as x and ...",
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14 " variableMetadata - the corresponding W4M data.frame having feature metadata",
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15 " sampleMetdata - the corresponding W4M data.frame having sample metadata",
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16 " dataMatrix - the corresponding W4M matrix",
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17 " slots - the number of parallel slots for calculating kmeans",
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18 " optional - environment may comprise:",
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19 " kfeatures - an array of integers, the k's to apply for clustering by feature (default, empty array)",
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20 " ksamples - an array of integers, the k's to apply for clustering by sample (default, empty array)",
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21 " iter_max - the maximum number of iterations when calculating a cluster (default = 20)",
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22 " nstart - how many random sets of centers should be chosen (default = 20)",
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23 " algorithm - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)",
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24 " categorical_prefix - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)",
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25 " ",
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26 " this routine will return a list comprising:",
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27 " variableMetadata - the input variableMetadata data.frame with updates, if any",
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28 " sampleMetadata - the input sampleMetadata data.frame with updates, if any",
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29 " scores - an array of strings, each representing a line of a tsv having the following header:",
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30 " clusterOn TAB k TAB totalSS TAB betweenSS TAB proportion"
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31 )
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32 )
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33 }
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34
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35 w4mkmeans <- function(env) {
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36 # abort if 'env' is null or is not an environment
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37 if ( is.null(env) || ! is.environment(env) ) {
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38 lapply(w4mkmeans_usage(),print)
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39 }
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40 # extract parameters from 'env'
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41 log_action <- env$log_print
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42 # supply default arguments
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43 if ( ! exists("iter_max" , env) ) env$iter_max <- 20
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44 if ( ! exists("nstart" , env) ) env$nstart <- 20
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45 if ( ! exists("algorithm" , env) ) env$algorithm <- 'Hartigan-Wong'
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46 if ( ! exists("categorical_prefix", env) ) env$categorical_prefix <- 'c'
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47 if ( ! exists("ksamples" , env) ) env$ksamples <- c()
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48 if ( ! exists("kfeatures" , env) ) env$kfeatures <- c()
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49 # check mandatory arguments
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50 expected <- c(
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51 "log_print"
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52 , "variableMetadata"
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53 , "sampleMetadata"
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54 , "dataMatrix"
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55 , "slots"
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56 )
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57 missing_from_env <- setdiff(expected, (ls(env)))
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58 if ( length(missing_from_env) > 0 ) {
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59 print(paste(c('expected environment members not found: ', as.character(missing_from_env)), collapse = ", "))
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60 lapply(w4mkmeans_usage(),log_action)
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61 stop("w4mkmeans: contract has been broken")
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62 }
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63 # extract parameters from 'env'
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64 log_action <- env$log_print
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65 scores <- c( "clusterOn\tk\ttotalSS\tbetweenSS\tproportion" )
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66 sampleMetadata <- env$sampleMetadata
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67 featureMetadata <- env$variableMetadata
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68 slots <- env$slots
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69 positive_ints <- function(a, what) {
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70 i <- as.integer(a) # may introduce NAs by coercion
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71 i <- i[!is.na(i)] # eliminate NAs
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72 i <- i[i > 0] # eliminate non-positive integers
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73 i <- unique(sort(i)) # eliminate redundancy and disorder
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74 if (length(a)!=length(i)) {
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75 log_action("Some values for '", what, "' were skipped where not unique, not positive, or not convertible to an integer.")
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76 }
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77 return (i) # return results, if any
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78 }
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79 ksamples <- positive_ints(env$ksamples , "ksamples")
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80 kfeatures <- positive_ints(env$kfeatures, "kfeatures")
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81
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82 log_action("w4mkmeans: preparing data matrix")
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83 # prepare data matrix (normalize, eliminate zero-variance rows, etc.; no transformation)
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84 dm_en <- new.env()
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85 dm_en$log <- c()
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86 preparation_result <- tryCatchFunc(function(){
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87 dm <- prepare.data.matrix(
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88 x.matrix = env$dataMatrix
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89 , data.transformation = function(x) { x }
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90 , en = dm_en
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91 )
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92 my_log <- dm_en$log
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93 for ( i in 1:length(my_log) ) {
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94 log_action(my_log[i])
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95 }
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96 dm
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97 })
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98 if ( !preparation_result$success ) {
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99 postmortem <- paste("prepare.data.matrix failed:", preparation_result$msg)
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100 log_action(postmortem)
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101 stop(postmortem)
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102 }
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103
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104 env$preparedDataMatrix <- preparation_result$value
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105
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106 log_action("w4mkmeans: determining evaluation mode")
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107
0
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108 myLapply <- parLapply
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109 cl <- NULL
2
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110 tryCatch(
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111 expr = {
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112 outfile <- ""
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113 # outfile: Where to direct the stdout and stderr connection output from the workers.
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114 # - "" indicates no redirection (which may only be useful for workers on the local machine).
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115 # - Defaults to ‘/dev/null’ (‘nul:’ on Windows).
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116 # - The other possibility is a file path on the worker's host.
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117 # - Files will be opened in append mode, as all workers log to the same file.
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118 cl <- makePSOCKcluster( names = slots, outfile = outfile )
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119 }
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120 , error = function(e) {
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121 log_action(sprintf("w4kmeans: falling back to serial evaluation because makePSOCKcluster(names = %d) threw an exception", slots))
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122 # mimic parLapply, but without parallelization (as a last resort)
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123 myLapply <<- function(cl, ...) lapply(...)
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124 }
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125 )
0
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126 if ( identical(myLapply, parLapply) ) {
2
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127 log_action(sprintf("w4mkmeans: using parallel evaluation with %d slots", slots))
0
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128 # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster."
2
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129 # note varlist: names of references must be passed to the cluster so that they can be resolved
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130 clusterExport(
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131 cl = cl
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132 , varlist = c(
2
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133 "tryCatchFunc" # required by calc_kmeans_one_dimension_one_k
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134 , "format_error" # required by tryCatchFunc when errors are caught
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135 , "iso_date" # required by log_print
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136 , "log_print" # required by calc_kmeans_one_dimension_one_k
0
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137 )
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138 )
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139 final <- function(cl) {
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140 # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster."
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141 if ( !is.null(cl) ) {
2
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142 log_action("w4mkmeans: stopping cluster used for parallel evaluation")
0
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143 stopCluster(cl)
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144 }
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145 }
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146 } else {
2
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147 log_action("w4mkmeans: using sequential evaluation (one slot)")
0
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148 final <- function(cl) { }
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149 }
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150
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151 tryCatch(
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152 expr = {
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153 # These myLapply calls produce lists of lists of results:
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154 # - The outer list has no keys and its members are accessed by index
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155 # - The inner list has keys "clusters" and "scores"
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156
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157 # for each $i in ksamples, append column 'k$i' to data frame sampleMetadata
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158 ksamples_length <- length(ksamples)
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159 if ( ksamples_length > 0 ) {
2
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160 smpl_result_list <- myLapply(
0
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161 cl = cl
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162 , ksamples
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163 , calc_kmeans_one_dimension_one_k
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164 , env = env
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165 , dimension = "samples"
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166 )
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167 for ( i in 1:ksamples_length ) {
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168 result <- smpl_result_list[[i]]
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169 if (result$success) {
1
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170 sampleMetadata[sprintf("k%d",ksamples[i])] <- sprintf("%s%d", env$categorical_prefix, result$value$clusters)
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171 scores <- c(scores, result$value$scores)
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172 }
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173 }
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174 }
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175
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176 # for each $i in kfeatures, append column 'k$i' to data frame featureMetadata
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177 kfeatures_length <- length(kfeatures)
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178 if ( kfeatures_length > 0 ) {
2
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179 feat_result_list <- myLapply(
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180 cl = cl
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181 , kfeatures
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182 , calc_kmeans_one_dimension_one_k
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183 , env = env
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184 , dimension = "features"
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185 )
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186 for ( i in 1:kfeatures_length ) {
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187 result <- feat_result_list[[i]]
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188 if (result$success) {
1
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189 featureMetadata[sprintf("k%d",kfeatures[i])] <- sprintf("%s%d", env$categorical_prefix, result$value$clusters)
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190 scores <- c(scores, result$value$scores)
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191 }
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192 }
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193 }
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194
2
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195 return (
0
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196 list(
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197 variableMetadata = featureMetadata
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198 , sampleMetadata = sampleMetadata
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199 , scores = scores
0
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200 )
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201 )
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202 }
2
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203 , finally = {
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204 final(cl)
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205 }
0
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206 )
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207 }
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208
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209 # calculate k-means for features or samples
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210 # - recall that the dataMatrix has features in rows and samples in columns
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211 # return value:
2
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212 # list(clusters = km$cluster, scores = scores)
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213 # arguments:
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214 # env:
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215 # environment having dataMatrix
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216 # dimension:
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217 # - "samples": produce clusters column to add to the sampleMetadata table
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218 # - this is the default case
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219 # - "variables": produce clusters column to add to the variableMetadata table
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220 # k:
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221 # integer, the number of clusters to make
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222 calc_kmeans_one_dimension_one_k <- function(k, env, dimension = "samples") {
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223 # abort if environment is not as expected
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224 if ( is.null(env) || ! is.environment(env) ) {
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225 stop("calc_kmeans_one_dimension_one_k - argument 'env' is not an environment")
2
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226 }
0
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227 if ( ! exists("log_print", env) || ! is.function(env$log_print) ) {
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228 stop("calc_kmeans_one_dimension_one_k - argument 'env' - environment does not include log_print or it is not a function")
2
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229 }
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230 log_action <- env$log_print
0
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231 # abort if k is not as expected
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232 if ( ! is.numeric(k) ) {
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233 stop(sprintf("calc_kmeans_one_dimension_one_k - expected numeric argument 'k' but type is %s", typeof(k)))
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234 }
0
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235 k <- as.integer(k)
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236 # abort if dimension is not as expected
2
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237 if ( ! is.character(dimension)
0
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238 || ! Reduce( f =`|`, x = sapply(X = c("features","samples"), FUN = `==`, dimension), init = FALSE) ) {
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239 stop("calc_kmeans_one_dimension_one_k - argument 'dimension' is neither 'features' nor 'samples'")
2
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240 }
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241 dm <- env$preparedDataMatrix
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242 iter_max <- env$iter_max
0
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243 nstart <- env$nstart
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244 algorithm <- env$algorithm
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245 dim_features <- dimension == "features"
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246
0
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247 # tryCatchFunc produces a list
2
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248 # On success of func(), tryCatchFunc produces
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249 # list(success = TRUE, value = func(), msg = "")
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250 # On failure of func(), tryCatchFunc produces
0
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251 # list(success = FALSE, value = NA, msg = "the error message")
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252 result_list <- tryCatchFunc( func = function() {
0
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253 # kmeans clusters the rows; features are the columns of args_env$dataMatrix; samples, the rows
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254 # - to calculate sample-clusters, no transposition is needed because samples are rows
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255 # - to calculate feature-clusters, transposition is needed so that features will be the rows
2
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256 if ( ! dim_features ) {
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257 dm <- t(dm)
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258 }
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259
0
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260 # need to set.seed to get reproducible results from kmeans
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261 set.seed(4567)
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262
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263 # do the k-means clustering
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264 withCallingHandlers(
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265 {
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266 km <<- kmeans( x = dm, centers = k, iter.max = iter_max, nstart = nstart, algorithm = algorithm )
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267 }
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268 , warning = function(w) {
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269 lw <- list(w)
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270 smplwrn <- as.character(w[[1]])
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271 log_print(
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272 sprintf( "Warning for %s: center = %d, nstart = %d, iter_max = %d: %s"
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273 , if (dim_features) "features" else "samples"
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274 , k
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275 , nstart
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276 , iter_max
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277 , smplwrn
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278 )
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279 )
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280 }
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281 )
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282
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283 # collect the scores
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284 scores <-
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285 sprintf("%s\t%d\t%0.5e\t%0.5e\t%0.5f"
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286 , dimension
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287 , k
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288 , km$totss
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289 , km$betweenss
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290 , km$betweenss/km$totss
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291 )
2
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292
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293 # return list of results
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294 list(clusters = km$cluster, scores = scores)
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295 })
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296
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297 # return either
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298 # list(success = TRUE, value = func(), msg = "")
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299 # or
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300 # list(success = FALSE, value = NA, msg = "the error message")
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301 return ( result_list )
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302 }
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303
2
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304 # vim: sw=2 ts=2 et :