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prediction_loops.R
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prediction_loops.R
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####
## DEFINE DATA
####
param <- list()
# param$fMRI <- list()
# param$fMRI$df_name <- dd0
# param$fMRI$nTrainSize <- 16
# param$fMRI$splitType_set <- c('winter', 'summer', 'first', 'random')
# param$fMRI$predvar <- c("angry_lamy", "angry_ramy", "fear_lamy", "fear_ramy", "neutral_lamy", "neutral_ramy")
# param$fMRI$df_name[,param$fMRI$predvar] <- scale(param$fMRI$df_name[,param$fMRI$predvar])
# param$rsfMRI <- list()
# param$rsfMRI$df_name <- dd_rs
# param$rsfMRI$nTrainSize <- 16
# param$rsfMRI$splitType_set <- c('winter', 'summer', 'first', 'random')
# param$rsfMRI$predvar <- paste0('DefaultMode', seq(6))
# param$rsfMRI$df_name[,param$rsfMRI$predvar] <- scale(param$rsfMRI$df_name[,param$rsfMRI$predvar])
#
# param$SB <- list()
# param$SB$df_name <- dd_sb
# param$SB$nTrainSize <- 5
# param$SB$splitType_set <- c('winter', 'summer', 'first', 'random')
# param$SB$predvar <- c('sb.hb', 'sb.neo')
# param$SB$df_name[,param$SB$predvar] <- scale(param$SB$df_name[,param$SB$predvar])
#
# param$DASB <- list()
# param$DASB$df_name <- dd_dasb
# param$DASB$nTrainSize <- 15
# param$DASB$splitType_set <- c('winter', 'summer', 'first', 'random')
# param$DASB$predvar <- c('dasb.hb', 'dasb.neo')
# param$DASB$df_name[,param$DASB$predvar] <- scale(param$DASB$df_name[,param$DASB$predvar])
#
# param$srt <- list()
# param$srt$df_name <- dd_np.srt
# param$srt$nTrainSize <- 20
# param$srt$splitType_set <- c('winter', 'summer', 'first', 'random')
# param$srt$predvar <- c("srt")
# param$srt$df_name[,param$srt$predvar] <- scale(param$srt$df_name[,param$srt$predvar])
#
# param$sdmt_lns <- list()
# param$sdmt_lns$df_name <- dd_np.sdmt_lns
# param$sdmt_lns$nTrainSize <- 20
# param$sdmt_lns$splitType_set <- c('winter', 'summer', 'first', 'random')
# param$sdmt_lns$predvar <- c('sdmt.correct','lns')
# param$sdmt_lns$df_name[,param$sdmt_lns$predvar] <- scale(param$sdmt_lns$df_name[,param$sdmt_lns$predvar])
# param$mixed1 <- list()
# param$mixed1$df_name <- dd_np.all
# param$mixed1$nTrainSize <- 18
# param$mixed1$splitType_set <- c('winter', 'summer', 'random')
# param$mixed1$predvar <- c("neopir.neuroticism","neopir.extraversion","neopir.openness","neopir.agreeableness","neopir.conscientiousness", 'sdmt.correct', 'lns', 'srt')
# param$mixed1$df_name[,param$mixed1$predvar] <- scale(param$mixed1$df_name[,param$mixed1$predvar])
# param$mixed2 <- list()
# param$mixed2$df_name <- dd_fmri_np
# param$mixed2$nTrainSize <- 8
# param$mixed2$splitType_set <- c('winter', 'summer', 'random')
# param$mixed2$predvar <- c("neopir.neuroticism","neopir.extraversion","neopir.openness","neopir.agreeableness","neopir.conscientiousness", 'sdmt.correct', 'lns', 'srt', "angry_lamy", "angry_ramy", "fear_lamy", "fear_ramy", "neutral_lamy", "neutral_ramy")
# param$mixed2$df_name[,param$mixed2$predvar] <- scale(param$mixed2$df_name[,param$mixed2$predvar])
# param$mixed3 <- list()
# param$mixed3$df_name <- dd_rs_faces
# param$mixed3$nTrainSize <- 16
# param$mixed3$splitType_set <- c('winter')#, 'summer', 'random')
# param$mixed3$predvar <- c("angry_lamy", "angry_ramy", "fear_lamy", "fear_ramy", "neutral_lamy", "neutral_ramy", paste0('DefaultMode.', seq(6)))
# param$mixed3$df_name[,param$mixed3$predvar] <- scale(param$mixed3$df_name[,param$mixed3$predvar])
####
## ANALYSIS
####
# Output directory
if (Sys.getenv("LOGNAME") == 'mganz'){
top <- '/data1/Ganz/Project14/Rresults'
} else {
top <- '/data1/patrick/fmri/hvi_trio/sad_classification/'
}
# n random splits
rsplit <- 100
# n permutations
perm <- 1000
startTime <- Sys.time()
print(startTime)
# Performance measures
measure_set <- c('specificity', 'sensitivity', 'accuracy')
for (name in names(param)){
nTrainSize <- param[[name]]$nTrainSize
dd <- param[[name]][['df_name']]
predvar <- param[[name]][['predvar']]
for (splitType in param[[name]][['splitType_set']]){
print(paste0('Working on: ', name, ', ', splitType))
### Derive observed accuracy
## rF
rF_rsplit <- mclapply(seq(rsplit), function(i) {
# set.seed(i)
fx_model(fx_sample(dd, nTrainSize, splitType, predvar=predvar), predvar=predvar, model.type = 'rF')},
mc.cores = 20)
# Contingency table across splits
rF_rsplitTable <- fx_cTable(rF_rsplit)
# Performance measures across splits
rF_rsplitPerf <- fx_modelPerf(rF_rsplitTable, make.c_table = F)
## logistic
logistic_rsplit <- mclapply(seq(rsplit), function(i) {
set.seed(i)
fx_model(fx_sample(dd, nTrainSize, splitType, predvar=predvar), predvar=predvar, model.type = 'logistic')},
mc.cores = 20)
# Contingency table across splits
logistic_rsplitTable <- fx_cTable(logistic_rsplit)
# Performance measures across splits
logistic_rsplitPerf <- fx_modelPerf(logistic_rsplitTable, make.c_table = F)
## svm
svm_rsplit <- mclapply(seq(rsplit), function(i) {
set.seed(i)
fx_model(fx_sample(dd, nTrainSize, splitType, predvar=predvar), predvar=predvar, model.type = 'svm')},
mc.cores = 20)
# Contingency table across splits
svm_rsplitTable <- fx_cTable(svm_rsplit)
# Performance measures across splits
svm_rsplitPerf <- fx_modelPerf(svm_rsplitTable, make.c_table = F)
### Derive null distribution
permOutput <- lapply(seq(perm), function(i){
if (i %% 100 == 0){
print(paste0('Reached perm: ', i, ' at ', Sys.time()))
}
# Set seed so that null distribution is reproducible and consistent across models
set.seed(i)
dd.perm <- fx_scramble(param[[name]]$df_name)
tmp <- mclapply(seq(rsplit), function(j) {
# Set seed so that rsplit of scrambled data is reproducible and consistent across models
set.seed(j)
dd.sample <- fx_sample(dd.perm, nTrainSize, splitType, predvar=predvar)
# Model performance
logisticObj <- fx_model(dd.sample, predvar=predvar, model.type = 'logistic')
rFObj <- fx_model(dd.sample, predvar=predvar, model.type = 'rf')
svmObj <- fx_model(dd.sample, predvar=predvar, model.type = 'svm')
# Return objects for each model type, for each rsplit
return(list(logisticObj = logisticObj, rFObj = rFObj, svmObj = svmObj))},
mc.cores = 20)
# Compute average model performance measures across each rsplit
logisticPerf <- fx_modelPerf(fx_cTable(lapply(seq(rsplit), function(i) tmp[[i]]$logisticObj)), make.c_table = F)
rfPerf <- fx_modelPerf(fx_cTable(lapply(seq(rsplit), function(i) tmp[[i]]$rFObj)), make.c_table = F)
svmPerf <- fx_modelPerf(fx_cTable(lapply(seq(rsplit), function(i) tmp[[i]]$svmObj)), make.c_table = F)
# Return model performance measures for each model type
return(list(logisticPerf = logisticPerf, rfPerf = rfPerf, svmPerf = svmPerf))
})
# Visualize observed model performance against model-specific null distributions
modelPerm.types <- c('logisticPerf', 'rfPerf', 'svmPerf')
modelObs.types <- c('logistic_rsplitPerf', 'rF_rsplitPerf', 'svm_rsplitPerf')
modelTypes <- c('logistic', 'rF', 'svm')
nModelTypes <- length(modelTypes)
pdf(paste0(top, name, '_', splitType, '_', nTrainSize, '_allModels.pdf'))
for (j in seq(nModelTypes)){
for(measure in measure_set){
nullDistribution <- unlist(sapply(seq(perm), function(i) permOutput[[i]][[modelPerm.types[j]]][measure]))
fx_nullComparison(nullDistribution, get(modelObs.types[j]), measure = measure, model.type = modelTypes[j])
}
}
dev.off()
}
}
####
## BOOTSTRAP (not polished)
####
# boot_repeat <- 100
# boot_result <- data.frame(log.spe = rep(0,boot_repeat), log.sen = rep(0,boot_repeat), log.acc = rep(0,boot_repeat), rf.spe = rep(0,boot_repeat), rf.sen = rep(0,boot_repeat), rf.acc = rep(0,boot_repeat), svm.spe = rep(0,boot_repeat), svm.sen = rep(0,boot_repeat), svm.acc = rep(0,boot_repeat))
#
# for (k in seq(boot_repeat)){
# for (name in names(param)){
#
# nTrainSize <- param[[name]]$nTrainSize
# dd <- param[[name]][['df_name']]
# predvar <- param[[name]][['predvar']]
#
# for (splitType in param[[name]][['splitType_set']]){
#
# print(paste0('Working on: ', k))
#
# ### Derive observed accuracy
#
# ## rF
# rF_rsplit <- mclapply(seq(rsplit), function(i) {
# set.seed(i)
# fx_model(fx_sample(dd, nTrainSize, splitType, predvar=predvar), predvar=predvar, model.type = 'rF')},
# mc.cores = 20)
# # Contingency table across splits
# rF_rsplitTable <- fx_cTable(rF_rsplit)
# # Performance measures across splits
# rF_rsplitPerf <- fx_modelPerf(rF_rsplitTable, make.c_table = F)
#
# boot_result[k, 'rf.spe'] <- rF_rsplitPerf$specificity
# boot_result[k, 'rf.sen'] <- rF_rsplitPerf$sensitivity
# boot_result[k, 'rf.acc'] <- rF_rsplitPerf$accuracy
#
# ## logistic
# logistic_rsplit <- mclapply(seq(rsplit), function(i) {
# set.seed(i)
# fx_model(fx_sample(dd, nTrainSize, splitType, predvar=predvar), predvar=predvar, model.type = 'logistic')},
# mc.cores = 20)
# # Contingency table across splits
# logistic_rsplitTable <- fx_cTable(logistic_rsplit)
# # Performance measures across splits
# logistic_rsplitPerf <- fx_modelPerf(logistic_rsplitTable, make.c_table = F)
#
# boot_result[k, 'log.spe'] <- logistic_rsplitPerf$specificity
# boot_result[k, 'log.sen'] <- logistic_rsplitPerf$sensitivity
# boot_result[k, 'log.acc'] <- logistic_rsplitPerf$accuracy
#
# ## svm
# svm_rsplit <- mclapply(seq(rsplit), function(i) {
# set.seed(i)
# fx_model(fx_sample(dd, nTrainSize, splitType, predvar=predvar), predvar=predvar, model.type = 'svm')},
# mc.cores = 20)
# # Contingency table across splits
# svm_rsplitTable <- fx_cTable(svm_rsplit)
# # Performance measures across splits
# svm_rsplitPerf <- fx_modelPerf(svm_rsplitTable, make.c_table = F)
#
# boot_result[k, 'svm.spe'] <- svm_rsplitPerf$specificity
# boot_result[k, 'svm.sen'] <- svm_rsplitPerf$sensitivity
# boot_result[k, 'svm.acc'] <- svm_rsplitPerf$accuracy
# }
# }
# }
#
# lapply(names(boot_result), function (i) {
# print(paste0(i, ': ', paste(quantile(boot_result[,i], probs = c(0.025, 0.975)), collapse = ',')))})
#
#
# rf.acc <- sapply(seq(rsplit), function(i) fx_modelPerf(rF_rsplit[[i]])$accuracy)
# rf.spe <- sapply(seq(rsplit), function(i) fx_modelPerf(rF_rsplit[[i]])$specificity)
# rf.sen <- sapply(seq(rsplit), function(i) fx_modelPerf(rF_rsplit[[i]])$sensitivity)
# log.acc <- sapply(seq(rsplit), function(i) fx_modelPerf(logistic_rsplit[[i]])$accuracy)
# log.spe <- sapply(seq(rsplit), function(i) fx_modelPerf(logistic_rsplit[[i]])$specificity)
# log.sen <- sapply(seq(rsplit), function(i) fx_modelPerf(logistic_rsplit[[i]])$sensitivity)
# svm.acc <- sapply(seq(rsplit), function(i) fx_modelPerf(svm_rsplit[[i]])$accuracy)
# svm.spe <- sapply(seq(rsplit), function(i) fx_modelPerf(svm_rsplit[[i]])$specificity)
# svm.sen <- sapply(seq(rsplit), function(i) fx_modelPerf(svm_rsplit[[i]])$sensitivity)
#
# pdf(paste0(top, 'melPlots.pdf'))
# hist(rf.acc, main = 'rF accuracy')
# abline(v = rF_rsplitPerf$accuracy, lwd = 2, col = 'red', lty = 2)
#
# hist(rf.spe, main = 'rF specificity')
# abline(v = rF_rsplitPerf$specificity, lwd = 2, col = 'red', lty = 2)
#
# hist(rf.sen, main = 'rF sensitivity')
# abline(v = rF_rsplitPerf$sensitivity, lwd = 2, col = 'red', lty = 2)
#
# hist(log.acc, main = 'logistic accuracy')
# abline(v = logistic_rsplitPerf$accuracy, lwd = 2, col = 'red', lty = 2)
#
# hist(log.spe, main = 'logistic specificity')
# abline(v = logistic_rsplitPerf$specificity, lwd = 2, col = 'red', lty = 2)
#
# hist(log.sen, main = 'logistic sensitivity')
# abline(v = logistic_rsplitPerf$sensitivity, lwd = 2, col = 'red', lty = 2)
#
# hist(svm.acc, main = 'svm accuracy')
# abline(v = svm_rsplitPerf$accuracy, lwd = 2, col = 'red', lty = 2)
#
# hist(svm.spe, main = 'svm specificity')
# abline(v = svm_rsplitPerf$specificity, lwd = 2, col = 'red', lty = 2)
#
# hist(svm.sen, main = 'svm sensitivity')
# abline(v = svm_rsplitPerf$sensitivity, lwd = 2, col = 'red', lty = 2)
# dev.off()