Mercurial > repos > anmoljh > feature_selection

annotate feature_selection.R @ 0:b4d2524e79ab draft

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planemo upload commit a1f4dd8eb560c649391ada1a6bb9505893a35272
author anmoljh
date Fri, 01 Jun 2018 05:16:19 -0400
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0
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1 args <- commandArgs(T)
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2
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3 arg1 <- args[1]
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4 arg2 <- args[2]
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5 arg3 <- args[3]
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6 arg4 <- args[4]
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7 arg5 <- args[5]
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8 arg6 <- args[6]
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9 arg7 <- args[7]
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10 arg8 <- args[8]
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11 arg9 <- args[9]
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12 arg10 <- args[10]
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13 library(caret)
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14 library(doMC)
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15 load(arg1)
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16
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17 #RAWDATA <- dataX
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18 #RAWDATA$outcome <- dataY
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19
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21 ###########################
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22 Smpling <- arg9
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23
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24 if(Smpling=="downsampling")
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25 {
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26 dwnsmpl <- downSample(dataX,dataY)
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27 RAWDATA <- dwnsmpl[,1:length(dwnsmpl)-1]
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28 RAWDATA$outcome <- dwnsmpl[,length(dwnsmpl)]
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29 dataX <- RAWDATA[,1:length(dwnsmpl)-1]
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30 dataY <- RAWDATA[,"outcome"]
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31 remove("dwnsmpl")
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32 }else if(Smpling=="upsampling"){
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33 upsmpl <- upSample(dataX,dataY)
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34 RAWDATA <- upsmpl[,1:length(upsmpl)-1]
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35 RAWDATA$outcome <- upsmpl[,length(upsmpl)]
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36 dataX <- RAWDATA[,1:length(upsmpl)-1]
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37 dataY <- RAWDATA[,"outcome"]
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38 remove("upsmpl")
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39 }else {
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40 RAWDATA <- dataX
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41 RAWDATA$outcome <- dataY
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42 }
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46
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47 ##########################
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48
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49
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50 rawData <- dataX
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51 predictorNames <- names(rawData)
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52
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53 isNum <- apply(rawData[,predictorNames, drop = FALSE], 2, is.numeric)
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54 if(any(!isNum)) stop("all predictors in rawData should be numeric")
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55
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56 colRate <- apply(rawData[, predictorNames, drop = FALSE],
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57 2, function(x) mean(is.na(x)))
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58 colExclude <- colRate > 0.1
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59 if(any(colExclude)){
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60 predictorNames <- predictorNames[-which(colExclude)]
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61 rawData <- RAWDATA[, c(predictorNames,"outcome")]
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62 } else {
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63 rawData <- RAWDATA
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64 }
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65 rowRate <- apply(rawData[, predictorNames, drop = FALSE],
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66 1, function(x) mean(is.na(x)))
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67
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68
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69 rowExclude <- rowRate > 0
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70 if(any(rowExclude)){
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71 rawData <- rawData[!rowExclude, ]
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72 ##hasMissing <- apply(rawData[, predictorNames, drop = FALSE],
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73 ##1, function(x) mean(is.na(x)))
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74
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75 ############################################################################
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76
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77
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78 ###############################################################################
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79 } else {
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80 rawData <- rawData[complete.cases(rawData),]
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81
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82 }
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83
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84 set.seed(2)
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85
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86 #print(dim(dataX))
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87 #print(dim(rawData))
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88 #print(length(dataY))
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89
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90 nzv <- nearZeroVar(rawData[,1:(length(rawData) - 1)])
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91 if(length(nzv) > 0) {
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92 #nzvVars <- names(rawData)[nzv]
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93 rawData <- rawData[,-nzv]
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94 #rawData$outcome <- dataY
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95 }
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96
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97 predictorNames <- names(rawData)[names(rawData) != "outcome"]
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98
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99 dx <- rawData[,1:length(rawData)-1]
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100 dy <- rawData[,length(rawData)]
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101 corrThresh <- as.numeric(arg8)
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102 highCorr <- findCorrelation(cor(dx, use = "pairwise.complete.obs"),corrThresh)
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103 dx <- dx[, -highCorr]
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104 subsets <- seq(1,length(dx),by=5)
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105 normalization <- preProcess(dx)
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106 dx <- predict(normalization, dx)
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107 dx <- as.data.frame(dx)
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108
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109 if (arg4 == "lmFuncs"){
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110 ctrl1 <- rfeControl(functions = lmFuncs,
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111 method = arg5 ,
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112 repeats = as.numeric(arg6),
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113 number = as.numeric(arg7),
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114 verbose = FALSE)
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115 } else if(arg4 == "rfFuncs"){
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116 ctrl1 <- rfeControl(functions = rfFuncs,
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117 method = arg5 ,
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118 repeats = as.numeric(arg6),
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119 number = as.numeric(arg7),
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120 verbose = FALSE)
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121 }else if (arg4 == "treebagFuncs"){
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122 ctrl1 <- rfeControl(functions = treebagFuncs,
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123 method = arg5 ,
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124 repeats = as.numeric(arg6),
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125 number = as.numeric(arg7),
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126 verbose = FALSE)
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127 }else {
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128
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129 ctrl1 <- rfeControl(functions = nbFuncs,
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130 method = arg5 ,
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131 repeats = as.numeric(arg6),
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132 number = as.numeric(arg7),
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133 verbose = FALSE)
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134 }
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135
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136
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137
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138 if (as.numeric(arg10) == 1){
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139 Profile <- rfe(dx, dy,sizes = subsets,rfeControl = ctrl1)
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140
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141 pred11 <- predictors(Profile)
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142 save(Profile,file=arg2)
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143 dataX <- rawData[,pred11]
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144 dataY <- rawData$outcome
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145
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146 save(dataX,dataY,file=arg3)
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147 rm(dataX)
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148 rm(dataY)
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149 } else if (as.numeric(arg10) > 1){
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150 registerDoMC(cores = as.numeric(arg10))
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151
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152 Profile <- rfe(dx, dy,sizes = subsets,rfeControl = ctrl1)
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153
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154 pred11 <- predictors(Profile)
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155 save(Profile,file=arg2)
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156 dataX <- rawData[,pred11]
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157 dataY <- rawData$outcome
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158
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159 save(dataX,dataY,file=arg3)
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160 rm(dataX)
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161 rm(dataY)
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162 } else { stop("something went wrong. please see the parameters")}
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163
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164