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xv_EMA_regression.R
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xv_EMA_regression.R
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##-------------------------------------------------------------------------------
## xv_EMA_regression: algorithm to estimate EEG maturational age (EMA), tested
## using leave-one-out cross-validation
##
## Syntax: xv_EMA_regression()
##
## Inputs:
## none
##
## Outputs:
## none
##
## REQUIRES:
## gbm (version 2.1.3)
## ggplot2 (version 2.0.0)
## plyr (version 1.8.3)
##
## and local functions:
## utils/load_feature_set.R
## utils/set_parameters_EMA.R
##
## John M. O' Toole, University College Cork
## Started: 23-02-2018
##
## last update: Time-stamp: <2019-01-10 16:20:07 (otoolej)>
##-------------------------------------------------------------------------------
xv_EMA_regression <- function(){
##-------------------------------------------------------------------
## 0. set directories; load packages and local functions
##-------------------------------------------------------------------
data_dir <- './data/'
utils_dir <- './utils/'
fin_feat_set <- paste(data_dir, 'subset_features_v3.csv', sep='')
library('ggplot2')
library('gbm')
library('plyr')
source(paste(utils_dir, 'load_feature_set.R', sep=""))
source(paste(utils_dir, 'set_parameters_EMA.R', sep=""))
##-------------------------------------------------------------------
## 1. load the parameters
## (set in set_parameters.R for details)
##-------------------------------------------------------------------
params <- set_parameters_EMA()
##-------------------------------------------------------------------
## 2. load the feature set
##-------------------------------------------------------------------
dfFeats <- load_feature_set(fin_feat_set)
all_ccodes <- unique(dfFeats$c_code)
LOG_TRANSFORM <- 1
if(LOG_TRANSFORM){
dfFeats$GA <- log( dfFeats$GA )
}
##-------------------------------------------------------------------
## 3. leave-one-out cross validation:
##-------------------------------------------------------------------
n <- 1
for(ccode in all_ccodes){
itest <- which(dfFeats$c_code %in% ccode)
itrain <- which(!(dfFeats$c_code %in% ccode))
dfSub <- droplevels(dfFeats[, !(names(dfFeats) %in% "c_code")])
if(n==1){
GA_all <- t(dfSub[itest, 'GA'])
} else {
GA_all <- rbind.fill.matrix(GA_all, t(dfSub[itest, 'GA']))
}
rowMeans(GA_all, na.rm=TRUE)
##-------------------------------------------------------------------
## a) train
##-------------------------------------------------------------------
gboost=gbm(GA ~ ., data=dfSub[itrain, ],
distribution=params$loss_fn,
n.trees=params$N_trees,
shrinkage=params$shrinkage,
interaction.depth=params$int_depth,
bag.fraction=params$bag_fraction)
##-------------------------------------------------------------------
## b) then test
##-------------------------------------------------------------------
GA_est <- predict(gboost, newdata=dfSub[itest, ],
n.trees=params$N_trees)
if(n==1){
GA_est_all <- t(as.vector(GA_est))
} else {
GA_est_all <- rbind.fill.matrix(GA_est_all, t(as.vector(GA_est)))
}
cat(sprintf('%d,', n))
n <- n + 1
}
cat(' -+|\n')
if(LOG_TRANSFORM){
GA_all <- exp(GA_all)
GA_est_all <- exp(GA_est_all)
}
##-------------------------------------------------------------------
## 4. error measures
##-------------------------------------------------------------------
cat("\n* TEST results; mean data points\n")
error_measures(c(rowMeans(GA_all, na.rm=TRUE)),
c(rowMeans(GA_est_all, na.rm=TRUE)))
cat("\n\n")
##-------------------------------------------------------------------
## 5. plot
##-------------------------------------------------------------------
DBplot <- 1
if(DBplot) plotGA_GAhat(GA_all, GA_est_all)
}
error_measures <- function(GA, GA_hat){
##-------------------------------------------------------------------
## calculate metrics to measure the distance between the actual
## and estimated gestational ages (GA)
##-------------------------------------------------------------------
ee <- (GA_hat - GA)
err_fn <- sd(ee, na.rm=TRUE)*7
ee_prc <- 100*length(which(abs(ee)<2)) / length(ee)
ee_prc1 <- 100*length(which(abs(ee)<1)) / length(ee)
cat(sprintf('** SD of error (total): %.2f (%.2f)\n',
err_fn, sd(GA, na.rm=TRUE)*7))
cat(sprintf('estimate within +/- 2 (1) week: %.2f (%.2f) %% \n',
ee_prc, ee_prc1))
## percentage of the variance explained by function:
var_data <- 1- (sum(ee^2, na.rm=TRUE)/sum((GA-mean(GA, na.rm=TRUE))^2, na.rm=TRUE))
cat(sprintf(' %% of variance explained by model: %.2f\n', 100*var_data))
## correlation between variables:
r2=cor(GA_hat, GA, use='complete.obs');
cat(sprintf('R (Pearson): %.2f\n', r2))
}
plotGA_GAhat <- function(GA, GA_hat){
##-------------------------------------------------------------------
## plot estimated GA (GA_hat) versus actual GA (GA)
## requires ggplot2 package
##-------------------------------------------------------------------
pData <- data.frame(GA=c(GA), estimate=c(GA_hat))
pData <- pData[!is.na(pData$GA), ]
pDataMean <- data.frame(GA=c(rowMeans(GA, na.rm=TRUE)),
estimate=c(rowMeans(GA_hat, na.rm=TRUE)))
points_colour <- '#094074'
(pl <- ggplot(data=pDataMean, aes(GA, estimate)) +
geom_point(colour=points_colour, size=3) +
geom_segment(aes(x=24, y=24, xend=32, yend=32)) +
geom_segment(aes(x=25, y=24, xend=32, yend=31), colour='gray56', linetype=5) +
geom_segment(aes(x=24, y=25, xend=31, yend=32), colour='gray56', linetype=5) +
geom_segment(aes(x=26, y=24, xend=32, yend=30), colour='gray56', linetype=5) +
geom_segment(aes(x=24, y=26, xend=30, yend=32), colour='gray56', linetype=5) +
geom_point(data=pDataMean, aes(GA, estimate), colour=points_colour, size=3) +
xlim(24, 32) + ylim(24, 32) +
xlab('gestational age (weeks)') + ylab('EEG maturational age (weeks)') +
theme_minimal() +
theme(text=element_text(size=16))
)
print(pl)
}