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1 ########################################################
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2 #
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3 # creation date : 25/10/16
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4 # last modification : 25/10/16
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5 # author : Dr Nicolas Beaume
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6 #
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7 ########################################################
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8
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9 suppressWarnings(suppressMessages(library(GA)))
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10 library("miscTools")
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11 library(rpart)
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12 suppressWarnings(suppressMessages(library(randomForest)))
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13 library(e1071)
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14 suppressWarnings(suppressMessages(library(glmnet)))
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48
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15 options(warn=-1)
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46
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16 ############################ helper functions #######################
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17
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18 ##### Genetic algorithm
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48
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19
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20 # compute r2 by computing the classic formula
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21 # compare the sum of square difference from target to prediciton
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22 # to the sum of square difference from target to the mean of the target
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23 r2 <- function(target, prediction) {
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24 sst <- sum((target-mean(target))^2)
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25 ssr <- sum((target-prediction)^2)
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26 return(1-ssr/sst)
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27 }
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28
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46
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29 optimizeOneIndividual <- function(values, trueValue) {
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30 # change the value into a function
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31 f <- function(w) {sum(values * w/sum(w))}
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32 fitness <- function(x) {1/abs(trueValue-f(x))}
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33 resp <- ga(type = "real-valued", fitness = fitness, min = rep(0, length(values)), max = rep(1, length(values)),
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34 maxiter = 1000, monitor = NULL, keepBest = T)
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35 resp@solution <- resp@solution/sum(resp@solution)
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36 return(resp)
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37 }
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38
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39 optimizeWeight <- function(values, trueValue, n=1000) {
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40 fitnessAll <- function(w) {
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41 predicted <- apply(values, 1, weightedPrediction.vec, w)
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42 return(mean(r2(trueValue, predicted)))
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43 #return(mean(1/abs(trueValue-predicted)))
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44 }
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45 resp <- ga(type = "real-valued", fitness = fitnessAll, min = rep(0, ncol(values)), max = rep(1, ncol(values)),
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46 maxiter = n, monitor = NULL, keepBest = T)
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47 resp@solution <- resp@solution/sum(resp@solution)
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48 return(resp)
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49 }
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50
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51 weightedPrediction <- function(classifiers, w) {
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52 if(length(w) > ncol(classifiers)) {
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53 warning("more weights than classifiers, extra weigths are ignored")
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54 w <- w[1:ncol(classifiers)]
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55 } else if(length(w) < ncol(classifiers)) {
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56 warning("less weights than classifiers, extra classifiers are ignored")
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57 classifiers <- classifiers[,1:length(w)]
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58 }
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59 prediction <- NULL
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60 prediction <- c(prediction, apply(classifiers, 1, weightedPrediction.vec, w))
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61 return(prediction)
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62 }
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63
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64 weightedPrediction.vec <- function(values, w) {
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65 return(sum(values * w/sum(w)))
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66 }
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67
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68 ##### meta-decision tree
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69
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70 tuneTree <- function(data, target) {
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71 data <- data.frame(data, target=target)
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72 size <- nrow(data)
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73 xerror <- NULL
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74 split <- 1:ceiling(size/5)
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75 leafSize <- 1:ceiling(size/10)
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76 xerror <- matrix(rep(-1, length(split)*length(leafSize)), ncol=length(leafSize))
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77 cp <- matrix(rep(-1, length(split)*length(leafSize)), ncol=length(leafSize))
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78 for(i in 1:length(split)) {
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79 for(j in 1:length(leafSize)) {
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80 op <- list(minsplit=split[i], minbucket=leafSize[j])
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81 tree <- rpart(target ~., data=data, control=op, method="anova")
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82 xerror[i,j] <- tree$cptable[which.min(tree$cptable[,"xerror"]),"xerror"]
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83 cp[i,j] <- tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"]
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84 }
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85 }
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86 index <- which(xerror==min(xerror), arr.ind = T)
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87 op <- list(minsplit=split[index[1]], minbucket=leafSize[index[2]], cp=cp[index[1], index[2]])
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88 return(op)
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89 }
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90
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91 ###### meta-LASSO
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92 # create fold by picking at random row indexes
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93 createFolds <- function(nbObs, n) {
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94 # pick indexes
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95 index <- sample(1:n, size=nbObs, replace = T)
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96 # populate folds
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97 folds <- NULL
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98 for(i in 1:n) {
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99 folds <- c(folds, list(which(index==i)))
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100 }
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101 return(folds)
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102 }
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103
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104 searchParamLASSO <- function(genotype, phenotype, alpha=seq(0,1,0.1), n=7) {
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105 folds <- createFolds(nrow(genotype), n = n)
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106 acc <- NULL
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107 indexAlpha <- 1
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108 for(a in alpha) {
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109 curAcc <- NULL
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110 for(i in 1:n) {
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111 train <- genotype[-folds[[i]],]
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112 test <- genotype[folds[[i]],]
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113 phenoTrain <- phenotype[-folds[[i]]]
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114 phenoTest <- phenotype[folds[[i]]]
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115 cv <- cv.glmnet(x=as.matrix(train), y=phenoTrain, alpha=a)
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116 model <- glmnet(x=as.matrix(train), y=phenoTrain, alpha=a, lambda = cv$lambda.1se)
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117 pred <- predict(model, test, type = "response")
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118 curAcc <- c(curAcc, r2(phenoTest, pred))
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119 }
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120 acc <- c(acc, mean(curAcc))
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121 }
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122 names(acc) <- alpha
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123 return(as.numeric(names(acc)[which.max(acc)]))
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124 }
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125
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126 ###### meta-random forest
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127
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128 searchParamRF <- function(genotype, phenotype, rangeNtree, mtry=ncol(genotype)) {
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129 n <- ceiling(nrow(genotype)/3)
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130 indexTest <- sample(1:nrow(genotype), size=n)
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131 train <- genotype[-indexTest,]
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132 test <- genotype[indexTest,]
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133 phenoTrain <- phenotype[-indexTest]
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134 phenoTest <- phenotype[indexTest]
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135 acc <- NULL
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136 indexNtree <- 1
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137 for(ntree in rangeNtree) {
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138 model <- randomForest(x=train, y=phenoTrain, ntree = ntree, mtry = mtry)
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139 pred <- predict(model, test)
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140 acc <- c(acc, r2(phenoTest, pred))
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141 }
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142 names(acc) <- rangeNtree
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143 best <- which.max(acc)
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144 return(as.numeric(names(acc)[best]))
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145 }
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146
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147 ###### meta-SVM
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148 searchParamSVM <- function(train, target, kernel="radial") {
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149 # tuning parameters then train
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150 model <- NULL
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151 switch(kernel,
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152 sigmoid={
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153 tune <- tune.svm(train, target, gamma = 10^(-6:-1), cost = 10^(0:2), kernel="sigmoid");
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154 g <- tune$best.parameters[[1]];
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155 c <- tune$best.parameters[[2]];
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156 model <- svm(x=train, y=target, gamma = g, cost = c, kernel = "sigmoid")},
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157 linear={
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158 tune <- tune.svm(train, target, cost = 10^(0:2), kernel="linear");
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159 c <- tune$best.parameters[[1]];
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160 model <- svm(x=train, y=target, cost = c, kernel = "linear")},
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161 polynomial={
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162 tune <- tune.svm(train, target, gamma = 10^(-6:-1), cost = 10^(0:2), degree = 0:4, coef0 = 0:3, kernel="polynomial");
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163 d <- tune$best.parameters[[1]];
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164 g <- tune$best.parameters[[2]];
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165 coef <- tune$best.parameters[[3]];
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166 c <- tune$best.parameters[[4]];
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167 model <- svm(x=train, y=target, gamma = g, cost = c, kernel = "polynomial", degree = d, coef0 = coef)},
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168 {
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169 tune <- tune.svm(train, target, gamma = 10^(-6:-1), cost = 10^(0:3), kernel="radial");
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170 g <- tune$best.parameters[[1]];
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171 c <- tune$best.parameters[[2]];
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172 model <- svm(x=train, y=target, gamma = g, cost = c, kernel = "radial")}
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173 )
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174 return(model)
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175 }
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176
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177 #################### upper level functions #####################
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178
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179 aggregateDT <- function(classifiers, target=NULL, prediction=F, model=NULL, out) {
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180 if(!prediction) {
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181 treeParam <- tuneTree(classifiers, target)
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182 data <- data.frame(classifiers, target)
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183 model <- rpart(target ~., data=data, method = "anova", control = treeParam)
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184 model <- prune(model, cp=treeParam["cp"])
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185 saveRDS(model, out)
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186 } else {
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187 saveRDS(predict(model, data.frame(classifiers)), out)
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188 }
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189 }
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190
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191 aggregateGeneticMean <- function(classifiers, target=NULL, prediction=F, model=NULL, out){
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192 if(!prediction) {
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193 opt <- optimizeWeight(values = classifiers, trueValue = target)
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194 saveRDS(opt@solution, out)
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195 # evaluation of the method
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196 } else {
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197 saveRDS(weightedPrediction.vec(classifiers, model), out)
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198 }
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199 }
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200
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201 aggregateLASSO <- function(classifiers, target=NULL, prediction=F, model=NULL, alpha=NULL, out) {
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202 if(!prediction) {
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203 alpha <- searchParamLASSO(classifiers, target)
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204 cv <- cv.glmnet(x=as.matrix(classifiers), y=target, alpha=alpha)
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205 model <- glmnet(x=as.matrix(classifiers), y=target, alpha=alpha, lambda = cv$lambda.1se)
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206 saveRDS(model, out)
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207 } else {
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208 saveRDS(predict(model, classifiers), out)
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209 }
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210 }
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211
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212 aggregateRF <- function(classifiers, target=NULL, model=NULL, ntree=NULL, prediction=F, out) {
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213 if(!prediction) {
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214 ntree <- searchParamRF(genotype = classifiers, phenotype = target,
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215 rangeNtree = seq(100, 1000, 100))
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216 model <- randomForest(x=classifiers, y=target, ntree = ntree, mtry = ncol(classifiers))
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217 saveRDS(model, out)
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218 } else {
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219 saveRDS(predict(model, classifiers), out)
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220 }
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221 }
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222
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223 aggregateSVM <- function(classifiers, target=NULL, prediction=F,
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224 model=NULL, c=NULL, g=NULL, d=NULL, coef=NULL, kernel="radial", out) {
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225 if(!prediction) {
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226 model <- searchParamSVM(train = classifiers, target = target, kernel = kernel)
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227 saveRDS(model, out)
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228 } else {
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229 saveRDS(predict(model, classifiers), out)
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230 }
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231 }
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232
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233 ################################### main #############################
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234 # # load argument
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235 cmd <- commandArgs(T)
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236 source(cmd[1])
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237 # check if evaluation is required
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238 evaluation <- F
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239 if(as.integer(doEvaluation) == 1) {
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240 evaluation <- T
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241 con = file(folds)
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242 folds <- readLines(con = con, n = 1, ok=T)
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243 close(con)
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244 folds <- readRDS(folds)
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245 }
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246 # check for model
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247 if(model == "None") {
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248 model <- NULL
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249 prediction <- F
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250 } else {
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251 prediction <- T
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252 con = file(model)
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253 model <- readLines(con = con, n = 1, ok=T)
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254 close(con)
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255 model <- readRDS(model)
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256 }
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257 # load classifiers and phenotype
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258 classifiers <- NULL
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259 classifNames <- NULL
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260 if(lassoPred !="None"){
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261 classifiers <- c(classifiers, lassoPred)
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262 classifNames <- c(classifNames, "lasso")
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263 }
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264 if(rrBLUPPred !="None"){
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265 classifiers <- c(classifiers, rrBLUPPred)
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266 classifNames <- c(classifNames, "rrBLUP")
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267 }
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268 if(rfPred !="None"){
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269 classifiers <- c(classifiers, rfPred)
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270 classifNames <- c(classifNames, "rf")
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271 }
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272 if(svmPred !="None"){
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273 classifiers <- c(classifiers, svmPred)
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274 classifNames <- c(classifNames, "svm")
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275 }
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276 classifPrediction <- NULL
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277 for(classif in classifiers) {
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278 classifPrediction <- c(classifPrediction, list(read.table(classif, sep="\t", h=T)))
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279 }
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280 classifPrediction <- data.frame(classifPrediction)
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281 colnames(classifPrediction) <- classifNames
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282 # phenotype is written as a table (in columns) but it must be sent as a vector for mixed.solve
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283 phenotype <- read.table(phenotype, sep="\t", h=T)[,1]
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284 out <- paste(out, ".rds", sep = "")
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285 # aggregate !
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286 switch(method,
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287 geneticMean={
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288 aggregateGeneticMean(classifiers = classifPrediction, target = phenotype,
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289 out = out, prediction = prediction, model=model)
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290 },
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291 dt={
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292 aggregateDT(classifiers = classifPrediction, target = phenotype,
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293 out = out, prediction = prediction, model=model)
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294 },
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295 lasso={
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48
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296 aggregateLASSO(classifiers = data.matrix(classifPrediction), target = phenotype,
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297 out = out, prediction = prediction, model=model)
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298 },
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299 rf={
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300 aggregateRF(classifiers = classifPrediction, target = phenotype,
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301 out = out, prediction = prediction, model=model)
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302 },
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303 # svm
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304 {aggregateSVM(classifiers = classifPrediction, target = phenotype, kernel = kernel,
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305 out = out, prediction = prediction, model = model)}
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306 )
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307 # return path of the result file to galaxy
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308 cat(paste(out, "\n", sep="")) |
