/
processComparisons.R
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/
processComparisons.R
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reverse_norm_MIWAE = function(x,norm_means,norm_sds){
xnew=matrix(nrow=nrow(x),ncol=ncol(x))
for(i in 1:ncol(xnew)){
xnew[,i]=(x[,i]*(norm_sds[i]))+norm_means[i]
}
return(xnew)
}
NRMSE = function(x,xhat,Missing){
x=as.matrix(x);xhat=as.matrix(xhat);Missing=as.matrix(Missing)
#x = (x-colMeans(x))/apply(x,2,sd)
#xhat = (xhat-colMeans(x))/apply(x,2,sd)
# Missing=1 --> observed
MSE=rep(NA,ncol(x))
RMSE=rep(NA,ncol(x))
NRMSE=rep(NA,ncol(x))
for(j in 1:ncol(x)){
if(all(Missing[,j]==1)){next}
norm_term = (max(x[Missing[,j]==0,j])-min(x[Missing[,j]==0,j])) # in case denom is 0
# norm_term = (max(x[,j])-min(x[,j]))
# norm_term = sd(x[,j])
# norm_term = sd(x[Missing[,j]==0,j])
MSE[j] = mean((x[Missing[,j]==0,j]-xhat[Missing[,j]==0,j])^2)
RMSE[j] = sqrt(MSE[j])
NRMSE[j] = RMSE[j]/norm_term
}
MSE=mean(MSE,na.rm=T); RMSE=mean(RMSE,na.rm=T); NRMSE=mean(NRMSE,na.rm=T)
# MSE = mean((x[Missing==0]-xhat[Missing==0])^2)
# RMSE = sqrt(MSE)
# NRMSE = RMSE / sd(x[Missing==0])
L1 = mean(abs(x[Missing==0]-xhat[Missing==0]))
L2 = mean((x[Missing==0]-xhat[Missing==0])^2)
return(list(MSE=MSE,RMSE=RMSE,NRMSE=NRMSE,L1=L1,L2=L2))
}
overlap_hists=function(x1,x2,x3=NULL,lab1="Truth",lab2="Imputed",lab3="...",
title="MNAR Missing Values, Truth vs Imputed, Missing column"){
library(ggplot2)
x1=data.frame(value=x1); x1$status=lab1
x2=data.frame(value=x2); x2$status=lab2
if(!is.null(x3)){x3=data.frame(value=x3); x3$status=lab3; df=rbind(x1,x2,x3)
}else{df = rbind(x1,x2)}
p = ggplot(df,aes(value,fill=status)) + geom_density(alpha=0.2, adjust=1/5) + ggtitle(title) + xlim(quantile(df$value,c(0.01,0.99),na.rm=T))
print(p)
}
output_file.name=function(dir_name,method=c("IMIWAE_unsup","NIMIWAE_unsup","MIWAE","HIVAE","VAEAC","MEAN","MF","MICE_unsup"),
mechanism,miss_pct,arch,rdeponz, init){
if(grepl("^NIMIWAE", method)){
yesrdeponz = if(rdeponz){"rzT"}else{"rzF"}
if(init=="default"){NIM_pref = ""} else if(init=="alt"){NIM_pref="alt_init/"}
file.name=sprintf("%s/%sres_NIMIWAE_%s_%d_%s_%s",
dir_name,NIM_pref,method,mechanism,miss_pct,arch,yesrdeponz)
}else if(grepl("^IMIWAE",method)){
yesrdeponz = if(rdeponz){"rzT"}else{"rzF"}
file.name=sprintf("%s/Ignorable/res_NIMIWAE_%s_%d_%s_%s",
dir_name,method,mechanism,miss_pct,arch,yesrdeponz)
}else{
file.name=sprintf("%s/res_%s_%s_%d",
dir_name,method,mechanism,miss_pct) # for miwae, default = Normal (StudentT can be done later)
}
print(file.name)
file.name=sprintf("%s.RData",file.name)
return(file.name)
}
process_results=function(data.file.name, file.name, method=c("IMIWAE_unsup","NIMIWAE_unsup","MIWAE","HIVAE","VAEAC","MEAN","MF","MICE_unsup")){
call_name=match.call()
# load data and split into training/valid/test sets
load(data.file.name)
## turn this back on (1/3)
if(is.null(g)){ # turned saving g back on, but for prev results (seed locked)
ratios=c(train = .8, valid = .2)
set.seed(333)
g = sample(cut(
seq(nrow(data)),
nrow(data)*cumsum(c(0,ratios)),
labels = names(ratios)
))
}
classes = fit_data$classes
P=ncol(data); N=nrow(data)
if(grepl("_sup",method)){
## supervised methods
# data = cbind(data, classes)
data = cbind(data, as.numeric(as.factor(classes)))
Missing = cbind(Missing, rep(1, nrow(Missing))) # include 1's for classes (fully observed)
}
datas = split(data.frame(data), g) # split by $train, $test, and $valid
Missings = split(data.frame(Missing), g)
if(!is.null(classes)){ classess = split(classes,g) }else{classess = list()}
## turn this back on (2/3)
if(is.null(datas$test)){
datas$test = datas$train
Missings$test = Missings$train
classess$test = classess$train
}
norm_means=colMeans(datas$train); norm_sds=apply(datas$train,2,sd)
# MIWAE and NIMIWAE only
load(file.name)
print(file.name)
# fit=eval(parse(text=paste("res",method,sep="_")))
results2 = list() # for imputation
#xhat=reverse_norm_MIWAE(fit$xhat,norm_means,norm_sds) # already reversed
if(grepl("NIMIWAE",method) | grepl("^IMIWAE",method)){
xhat=res_NIMIWAE$xhat
time = res_NIMIWAE$time
}else if(grepl("MIWAE",method)){
xhat=res_MIWAE$xhat_rev
time = res_MIWAE$time
}else if(grepl("HIVAE",method)){
xhat=res_HIVAE$data_reconstructed
time = res_HIVAE$time
}else if(grepl("VAEAC",method)){
xhat_all = res_VAEAC$result # this method reverses normalization intrinsically
# average imputations
xhat = matrix(nrow=nrow(datas$test),ncol=ncol(datas$test))
n_imputations = res_VAEAC$train_params$n_imputations
for(i in 1:nrow(datas$test)){
xhat[i,]=colMeans(xhat_all[((i-1)*n_imputations+1):(i*n_imputations),])
}
time = res_VAEAC$time
}else if(grepl("MEAN",method)){
# xhat = fit$xhat_rev
if(is.null(res_MEAN$xhat_rev)){res_MEAN$xhat_rev = reverse_norm_MIWAE(res_MEAN$xhat_mean,norm_means,norm_sds)}
xhat = res_MEAN$xhat_rev
time = res_MEAN$time
}else if(grepl("MF",method)){
# xhat = fit$xhat_rev
if(is.null(res_MF$xhat_rev)){res_MF$xhat_rev = reverse_norm_MIWAE(res_MF$xhat_mf,norm_means,norm_sds)}
xhat = res_MF$xhat_rev
time = res_MF$time
}else if(grepl("MICE",method)){
xhat=res_MICE$xhat
time = res_MICE$time
}
## turn this back on (3/3)
if(all(dim(xhat) != dim(datas$test))){print("dim(xhat):"); print(dim(xhat)); print("dim(test data):"); print(dim(datas$test)); stop("Dimensions wrong")}
# check same xhat:
print("Mean Squared Error (Observed): should be 0")
print(mean((xhat[Missings$test==1] - datas$test[Missings$test==1])^2)) # should be 0
print("Mean Squared Error (Missing):")
print(mean((xhat[Missings$test==0] - datas$test[Missings$test==0])^2))
# Imputation metrics
imputation_metrics=NRMSE(x=datas$test, xhat=xhat, Missing=Missings$test)
#imputation_metrics=NRMSE(x=xfull, xhat=xhat, Missing=Missings$test)
# Other metrics (names aren't consistent)
#LB=fit$LB; time=fit$time
results = c(unlist(imputation_metrics))
# ratio = 0.8; n_train = floor(ratio*nrow(xhat)); n_test = nrow(xhat) - n_train
# idx = c( rep(T, n_train) , rep(F, n_test) )
#
# fit_pred_imputed = predict_classes(X_train=as.matrix(xhat[idx,]), y_train=classess$test[idx], X_test=as.matrix(xhat[!idx,]), y_test=classess$test[!idx])
# fit_pred_true = predict_classes(X_train=as.matrix(datas$test[idx,]), y_train=classess$test[idx], X_test=as.matrix(datas$test[!idx,]), y_test=classess$test[!idx])
#
# fits_pred = list(imputed = fit_pred_imputed,
# true = fit_pred_true)
# fit$fits_pred = fits_pred
return(list(results=results, call=call_name, time = time))
}
process_results_MI = function(data.file.name, file.name, method, dir_name=""){
call_name=match.call()
# load data and split into training/valid/test sets
load(data.file.name)
## since g=NULL is being saved rn. (need to change)
if(is.null(g)){
ratios=c(train = .8, valid = .2)
set.seed(333)
g = sample(cut(
seq(nrow(data)),
nrow(data)*cumsum(c(0,ratios)),
labels = names(ratios)
))
}
classes = fit_data$classes
P=ncol(data); N=nrow(data)
# if(grepl("_sup",method)){
# ## supervised methods
# data = cbind(data, classes)
# Missing = cbind(Missing, rep(1, nrow(Missing))) # include 1's for classes (fully observed)
# }
datas = split(data.frame(data), g) # split by $train, $test, and $valid
Missings = split(data.frame(Missing), g)
classess = split(classes,g)
datas$test = datas$train
Missings$test = Missings$train
classess$test = classess$train
norm_means=colMeans(datas$train); norm_sds=apply(datas$train,2,sd)
# MIWAE and NIMIWAE only
load(file.name)
print(file.name)
# fit=eval(parse(text=paste("res",method,sep="_")))
# if(!(grepl("NIMIWAE",method) | grepl("MICE",method) )){stop("Only MICE or NIMIWAE for MI")}
results2 = list() # for imputation
#xhat=reverse_norm_MIWAE(fit$xhat,norm_means,norm_sds) # already reversed
if(grepl("NIMIWAE",method)){
xhat=res_NIMIWAE$xhat
}else if(grepl("MIWAE",method)){
xhat=res_MIWAE$xhat_rev
}else if(grepl("HIVAE",method)){
xhat=res_HIVAE$data_reconstructed
}else if(grepl("VAEAC",method)){
xhat_all = res_VAEAC$result # this method reverses normalization intrinsically
# average imputations
xhat = matrix(nrow=nrow(datas$test),ncol=ncol(datas$test))
n_imputations = res_VAEAC$train_params$n_imputations
for(i in 1:nrow(datas$test)){
xhat[i,]=colMeans(xhat_all[((i-1)*n_imputations+1):(i*n_imputations),])
}
}else if(grepl("MEAN",method)){
# xhat = fit$xhat_rev
# if(is.null(fit$xhat_rev)){fit$xhat_rev = reverse_norm_MIWAE(fit$xhat_mean,norm_means,norm_sds)}
xhat = res_MEAN$xhat_rev
}else if(grepl("MF",method)){
# xhat = fit$xhat_rev
# if(is.null(fit$xhat_rev)){fit$xhat_rev = reverse_norm_MIWAE(fit$xhat_mf,norm_means,norm_sds)}
xhat = res_MF$xhat_rev
}else if(grepl("MICE",method)){
xhat=res_MICE$xhat
}
X = datas$test[,1:P]; mask = Missings$test[,1:P] ## take out response (binary/cat) variable
if(all(dim(xhat) != dim(datas$test))){print("dim(xhat):"); print(dim(xhat)); print("dim(test data):"); print(dim(datas$test)); stop("Dimensions wrong")}
### MI + Regression
m=50
library(mice)
library(pROC)
if(grepl("NIMIWAE", method)){
res = res_NIMIWAE; rm(res_NIMIWAE)
norm_means = res$norm_means[1:P]; norm_sds = res$norm_sds[1:P]
library("data.table"); library("readr"); library("rhdf5")
# imp_weights = as.matrix(fread(sprintf("%s/IWs.csv",dir_name))) # m(n_imputations) x N
# miss_XY = as.matrix(fread(sprintf("%s/miss_XY.csv",dir_name))) # for just missing entries saved (indices also saved in this separate file)
samples = rhdf5::H5Fopen(sprintf("%s/samples.h5",dir_name)) # samples has $IWs $miss_XY and $Xm0 to $Xm#
imp_weights = t(samples$IWs) # for some reason, rhdf5 always transposes what's saved in python
miss_XY = t(samples$miss_XY)
# rhdf5::h5closeAll() # closing here sometimes causes issues..
N1 = res$opt_params$L * res$opt_params$M # number of initial samples (should be # samples of Z (L) * # samples of Xm (M) in testing)
N2 = m # number of importance-weighted resamples. this can be different from m (could be like 2*m too)
fname = sprintf("%s/SIR_samp%d.csv",dir_name,N2) # check to see if all SIR samples were already drawn. skip if they've already been saved
fname1 = sprintf("%s/NIMIWAE_MI_models.RData",dir_name)
if(!file.exists(fname) & !file.exists(fname1)){
fname0 = sprintf("%s/multinom_draws.csv",dir_name)
if(!file.exists(fname0)){
# t0 = Sys.time()
draws = matrix(nrow=nrow(X), ncol=N2)
# set.seed(1)
for(i in 1:ncol(imp_weights)){ ## for each observation
draws[i,] = apply(rmultinom(N2, 1, imp_weights[,i]), 2, which.max) # sample N2 times --> which imputed datasets to choose for that sample
}
# print(as.numeric(Sys.time() - t0, units="secs"))
fwrite(draws, file=fname0)
} else{ draws = as.matrix(fread(fname0))}
}
if(!file.exists(fname) & !file.exists(fname1)){
### loop of N1 --> filling in each N2 SIR samples --> speedup of 7-8 times
samps=list()
ts = Sys.time()
for(j in 1:N2){
samps[[j]] = datas$test # initialize
}
unique_draws = unique(c(draws))
for(j in 1:N1){
print(j)
if(!(j %in% unique_draws)){ next }
id = which(draws == j,arr.ind=T) # row: observation index, col: SIR sample index. current Xm j --> row goes in for SIR sample col
unique_SIRs = unique(id[,2]) # unique SIR's that we need to replace from this drawn Xm[j]
for(k in 1:length(unique_SIRs)){
id_rows = id[id[,2]==unique_SIRs[k],1] # rows of data matrix that needs to be replaced for SIR sample k, pertaining to this Xm
# samps[[k]][id_rows,][mask[id_rows,]==0] = Xms[[j]][((miss_XY[,1]+1) %in% id_rows)]
# samps[[k]][id_rows,][mask[id_rows,]==0] = t( eval(parse(text = sprintf("samples$Xm%d",j-1))) )[((miss_XY[,1]+1) %in% id_rows)]
# t(samps[[k]][id_rows,])[t(mask[id_rows,])==0] = t( eval(parse(text = sprintf("samples$Xm%d",j-1))) )[((miss_XY[,1]+1) %in% id_rows)]
## can't replace transpose of matrix --> make dummy matrix that is transpose we want to replace, replace, then untranspose.
dummy = t(samps[[k]][id_rows,])
dummy[t(mask[id_rows,])==0] = t( eval(parse(text = sprintf("samples$Xm%d",j-1))) )[((miss_XY[,1]+1) %in% id_rows)]
samps[[k]][id_rows,] = t(dummy)
}
}
t2 = as.numeric(Sys.time()-ts, units="secs")
for(j in 1:N2){
fname0 = sprintf("%s/SIR_samp%d.csv",dir_name,j)
fwrite(samps[[j]], file=fname0)
}
} else{
samps = list()
for(j in 1:N2){
fname0 = sprintf("%s/SIR_samp%d.csv",dir_name,j)
samps[[j]] = as.matrix(fread(fname0))
}
}
###### fname1: models seem to be too large to load on 32GB ram...
# if(!file.exists(fname1)){
reg_fit = list(); AUCs=rep(NA,m)
for(i in 1:m){
print(paste("Fitting model", i, "of", m))
dat = data.frame(cbind(samps[[i]], y=classess$test)) # append back the response variable
reg_fit[[i]] = glm(as.factor(y) ~ 1 + ., data=dat, family=binomial(link="logit"))
# if(intercept){ reg_fit[[i]] = glm(as.factor(y) ~ 1 + ., data=dat, family=binomial(link="logit")) # check imps[[1]] for response variable name in dat
# }else{ reg_fit[[i]] = glm(as.factor(y) ~ 0 + ., data=dat, family=binomial(link="logit")) } # check imps[[1]] for response variable name in dat
samps[[i]] = NA; rm(dat) # remove these memories
gc()
}
# save(list=c("reg_fit","AUCs"),file=fname1)
# } else{
# gc(); load(fname1)
# }
}else if(grepl("MICE", method)){
reg_fit = list()
AUCs=rep(NA,m)
for(i in 1:res_MICE$m){
## Easy way to use full model, response is last variable in each completed dataset
## Include an intercept
print(paste("Fitting model", i, "of", m))
if(grepl("_sup",method)){
dat = complete(res_MICE,i); colnames(dat)[ncol(dat)] = "y"
} else{
dat = data.frame(cbind(complete(res_MICE,i), y=classess$test))
}
reg_fit[[i]] = glm(as.factor(y) ~ 1 + ., data=dat, family=binomial(link="logit"))
# if(intercept){ reg_fit[[i]] = glm(as.factor(y) ~ 1 + ., data=dat, family=binomial(link="logit"))
# }else{ reg_fit[[i]] = glm(as.factor(y) ~ 0 + ., data=dat, family=binomial(link="logit")) }
rm(dat); gc()
## No intercept
# fit[[i]] = glm(update(imp$formulas[length(imp$formulas)][[1]], as.factor(.) ~ .),
# data=complete(imp,i), family=binomial(link="logit"))
}
}else{
dat = data.frame(cbind(xhat,classess$test)); colnames(dat)[ncol(dat)] = "y"
reg_fit = glm(as.factor(y) ~ 1 + ., data = dat, family=binomial(link="logit"))
# reg_fit = if(intercept){ glm(as.factor(y) ~ 1 + ., data = dat, family=binomial(link="logit"))
# } else{ glm(as.factor(y) ~ 0 + ., data = dat, family=binomial(link="logit")) }
AUCs=NA
}
if(grepl("NIMIWAE", method) | grepl("MICE",method)){
tab <- summary(pool(reg_fit), "all", conf.int = TRUE)
itab = tab[, c("estimate", "std.error", "2.5 %", "97.5 %")]
tab = tab[-1, c("estimate", "std.error", "2.5 %", "97.5 %")]
# if(!intercept){ tab = tab[-1, c("estimate", "std.error", "2.5 %", "97.5 %")]
# } else{ tab = tab[, c("estimate", "std.error", "2.5 %", "97.5 %")] }
} else{
tab <- summary(reg_fit)$coef
CIs = confint(reg_fit)
tab = cbind(tab[,1:2],CIs)
colnames(tab) = c("estimate", "std.error","2.5 %", "97.5 %")
itab = tab
colnames(itab) = c("estimate", "std.error","2.5 %", "97.5 %")
tab = tab[-1,]
# if(!intercept){ tab = tab[-1,] }
}
print(tab)
# print(AUCs)
print("Finished process_MI")
rhdf5::h5closeAll()
dat = cbind(datas$train, classess$train)
colnames(dat)[ncol(dat)]="y"
fit = glm(as.factor(y) ~ 1 + ., data=dat, family=binomial(link="logit"))
trues = fit$coef[-1]
itrues = fit$coef
print("Finished process_MI fx")
return(list(tab=tab, itab=itab, reg_fit=reg_fit, fit_data=fit_data, names=colnames(data), trues = trues, itrues=itrues))#, AUCs=AUCs))
}
processComparisons=function(dir_name="Results/SIM1/phi5",mechanisms=c("MCAR","MAR","MNAR"),miss_pct=c(15,25,35),
methods=c("IMIWAE","NIMIWAE","MIWAE","HIVAE","VAEAC","MEAN","MF","MICE"),
imputation_metric=c("MSE","NRMSE","L1","L2"), arch=NULL, rdeponz=NULL, outfile=NULL, init="default"){
library(ggplot2)
library(grid)
library(gridExtra)
library(reshape2)
if(init=="default"){NIM_pref = ""}else if(init=="alt"){NIM_pref = "/alt_init"}
dir_name2 = sprintf("%s%s", dir_name, NIM_pref)
ifelse(!dir.exists(dir_name2), dir.create(dir_name2,recursive=T), F)
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)}
mats_res = list(); mats_params=list()
print("Compiling results...")
list_res = list()
times=list()
for(ii in 1:length(miss_pct)){
params=list()
# input true probs, no learning R, Normal distrib, input_r="r", vary rdeponz and sample_r (4)
index=1
for(i in 1:length(mechanisms)){for(j in 1:length(methods)){
data.file.name=sprintf("%s/data_%s_%d.RData",dir_name,mechanisms[i],miss_pct[ii])
file.name = output_file.name(dir_name=dir_name,method=methods[j], mechanism=mechanisms[i],
miss_pct=miss_pct[ii], arch=arch, rdeponz=rdeponz, init=init)
list_res[[index]]=process_results(data.file.name,file.name,methods[j])
params[[index]]=c(methods[j],mechanisms[i],miss_pct[ii])
names(params[[index]])=c("method","mechanism","miss_pct")
times[[index]] = list_res[[index]]$time
index = index+1
}}
# flatten list to matrix
mat_res = matrix(unlist(lapply(list_res,function(x)x$results)),ncol=length(list_res))
rownames(mat_res)=names(list_res[[1]]$results) # MSE, NRMSE, ...
colnames(mat_res)=paste("case",c(1:length(list_res)),sep="")
mat_res=t(mat_res)
mat_params=matrix(unlist(params),ncol=length(params))
rownames(mat_params)=c("method","mechanism","miss_pct")
colnames(mat_params)=paste("case",c(1:ncol(mat_params)),sep="")
mat_params=t(mat_params)
df_bar = data.frame(cbind(mat_res,mat_params,rownames(mat_res)))
for(c in 1:ncol(mat_res)){df_bar[,c]=as.numeric(as.character(df_bar[,c]))}
colnames(df_bar)[ncol(df_bar)]="case"; df_bar$case = factor(df_bar$case,levels=paste("case",c(1:nrow(mat_params)),sep=""))
df_bar$mechanism = factor(df_bar$mechanism,levels=c("MCAR","MAR","MNAR"))
df_bar$method=as.character(df_bar$method)
df_bar$method[df_bar$method=="MEAN"]="Mean"; df_bar$method[df_bar$method=="MF"]="MissForest"
other_methods = methods[!grepl("NIMIWAE",methods)]; other_methods[other_methods=="MEAN"]="Mean"; other_methods[other_methods=="MF"]="MissForest"
df_bar$method0 = df_bar$method
NIMIWAE_methods = methods[grepl("NIMIWAE",methods)]; if(length(NIMIWAE_methods)==1){NIMIWAE_methods="NIMIWAE"; df_bar$method0[grepl("NIMIWAE",df_bar$method0)]="NIMIWAE"}
df_bar$method0 = factor(df_bar$method0,
levels=c(other_methods[order(other_methods)], NIMIWAE_methods))
# levels(df_bar$method)=methods[order(methods)]
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
colors = gg_color_hue(length(methods))
p=ggplot(df_bar,aes(x=method0,y=eval(parse(text=imputation_metric)),fill=mechanism,color=mechanism))+
geom_bar(stat="identity",position=position_dodge(.9),alpha=0.4)+#ylim(c(0,3))+#ylim(c(0,0.5))+
labs(title=sprintf("%s Imputation Performance, %d%% Missing",imputation_metric, miss_pct[ii]),
subtitle = "Imputation performance across missingness mechanisms",
y = imputation_metric, x="Method")+
theme(text=element_text(size = 20))
if(is.null(outfile)){
png(sprintf("%s/%s_competing_miss%d.png",dir_name2,imputation_metric,miss_pct[ii]),width=1200,height=500)
} else{ png(outfile, width=1200, height=500)}
#barplot(mat_res[rownames(mat_res)=="NRMSE",])
print(p)
dev.off()
# save in mats_res
mats_res[[ii]]=mat_res
mats_params[[ii]]=mat_params
}
names(mats_res)=miss_pct
return(list(res=mats_res, params=mats_params, times=times))
}
saveFigures = function(datasets=c("SIM"), sim_index=1:5, phi0=5, Ns=c(1e4, 1e5), Ps=c(25,100), Ds=c(2,8),
mechanisms=c("MCAR","MAR","MNAR"), miss_pct=c(15), methods=c("IMIWAE","NIMIWAE","MIWAE","HIVAE","VAEAC","MEAN","MF","MICE"),
outfile=NULL, init="default"){
list_res=list(); index=1
for(d in 1:length(datasets)){
for(a in 1:length(Ns)){for(b in 1:length(Ps)){for(c in 1:length(Ds)){
if(grepl("SIM",datasets[d])){ dataset=sprintf("%s_N%d_P%d_D%d", datasets[d], Ns[a], Ps[b], Ds[c])
}else{ dataset = datasets[d] }
for(s in 1:length(sim_index)){
dir_name=sprintf("Results/%s/phi%d/sim%d",dataset,phi0,sim_index[s])
data_dir_name=sprintf("Results/%s/phi%d/sim%d",dataset,phi0,sim_index[s])
imputation_metrics=c("MSE","L1","L2","NRMSE","RMSE")
for(i in 1:length(imputation_metrics)){
res = processComparisons(dir_name,data_dir_name,mechanisms,miss_pct,methods,imputation_metrics[i],
betaVAE=F, arch="IWAE", rdeponz=F, outfile=outfile, init=init)
list_res[[index]] = res
index = index+1
}
}
}
if(length(sim_index)>1){
if(init=="default"){NIM_pref = ""}else if(init=="alt"){NIM_pref="/alt_init"}
dir_name2 = sprintf("Results/%s/phi%d%s",dataset, phi0,NIM_pref)
params=list_res[[1]]$params
sims_list = list()
for(i in 1:length(list_res)){ sims_list[[i]] = list_res[[i]]$res }
dfs=data.frame()
for(i in 1:length(sims_list)){
df=data.frame(sims_list[[i]])
df=cbind(df,params)
names(df)=c("MSE","RMSE","NRMSE","L1","L2","method","mechanism","miss_pct")
df$sim = sim_index[i]
dfs = rbind(dfs,df)
}
bar_mean = aggregate(dfs[,1:length(list_res)], list(dfs$mechanism, dfs$method), mean)
bar_sd = aggregate(dfs[,1:length(list_res)], list(dfs$mechanism, dfs$method), sd)
bar_df = merge(bar_mean,bar_sd, by=c("Group.1","Group.2"))
names(bar_df) = c("mechanism","method",
paste("mean_",c("MSE","RMSE","NRMSE","L1","L2"),sep=""),
paste("sd_",c("MSE","RMSE","NRMSE","L1","L2"),sep=""))
library(ggplot2)
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
colors = gg_color_hue(length(methods))
imputation_metric="L1"
if(imputation_metric %in% c("L1","L2")){imputation_metric0=paste("Average ",imputation_metric,sep="")}
bar_df$method = as.character(bar_df$method)
bar_df$method[bar_df$method=="MEAN"]="Mean"; bar_df$method[bar_df$method=="MF"]="MissForest"
# other_methods = methods[!grepl("NIMIWAE",methods)]; other_methods[other_methods=="MEAN"]="Mean"; other_methods[other_methods=="MF"]="MissForest"
other_methods = methods[!grepl("IMIWAE",methods)]; other_methods[other_methods=="MEAN"]="Mean"; other_methods[other_methods=="MF"]="MissForest"
# other_methods[other_methods=="IMIWAE_unsup"]="IMIWAE"; other_methods[other_methods=="MICE_unsup"]="MICE"
other_methods[other_methods=="MICE_unsup"]="MICE"
# NIMIWAE_methods = methods[grepl("NIMIWAE",methods)]
NIMIWAE_methods = methods[grepl("IMIWAE",methods)]
bar_df$method0 = bar_df$method
# if(length(NIMIWAE_methods)==1){NIMIWAE_methods="NIMIWAE"; bar_df$method0[grepl("NIMIWAE",bar_df$method0)]="NIMIWAE"}
NIMIWAE_methods[NIMIWAE_methods=="NIMIWAE_unsup"] = "NIMIWAE"; NIMIWAE_methods[NIMIWAE_methods=="IMIWAE_unsup"] = "IMIWAE"; bar_df$method0[bar_df$method0=="MICE_unsup"] = "MICE"
bar_df$method0[bar_df$method0=="NIMIWAE_unsup"]="NIMIWAE"; bar_df$method0[bar_df$method0 == "IMIWAE_unsup"]="IMIWAE"
bar_df$method0 = factor(bar_df$method0,
levels=c(other_methods[order(other_methods)],NIMIWAE_methods[order(NIMIWAE_methods)]))
bar_df$method = bar_df$method0
bar_df$mechanism = factor(bar_df$mechanism, levels=mechanisms)
#eval(parse(text=paste("mean_",imputation_metric,sep="")))
##### Group by method, color by mechanism
# p=ggplot(bar_df,aes(x=method0, y=eval(parse(text=paste("mean_",imputation_metric,sep=""))), fill=mechanism, color=mechanism))+
# geom_bar(stat="identity",position=position_dodge(.9),alpha=0.4,color="black")+#ylim(c(0,3))+#ylim(c(0,0.5))+
# geom_errorbar(aes(ymin=eval(parse(text=paste("mean_",imputation_metric,sep="")))-eval(parse(text=paste("sd_",imputation_metric,sep=""))),
# ymax=eval(parse(text=paste("mean_",imputation_metric,sep="")))+eval(parse(text=paste("sd_",imputation_metric,sep="")))),
# width=.2, position=position_dodge(.9),color="black") +
# labs(title=bquote( list("n =" ~ .(N) ~ ", p =" ~ .(P) ~ ", d =" ~ .(D), ~ mu[phi]==.(phi0)) ), y = imputation_metric0, x="Method") +
# theme(legend.title = element_blank(), text=element_text(size = 20),axis.text.x = element_text(colour = c(rep("black",nlevels(bar_df$method0)-1),"red")))
##### Group by mechanism, color by method
p=ggplot(bar_df,aes(x=mechanism, y=eval(parse(text=paste("mean_",imputation_metric,sep=""))), fill=method, color=method))+
geom_bar(stat="identity",position=position_dodge(.9),alpha=0.4,color="black")+#ylim(c(0,3))+#ylim(c(0,0.5))+
geom_errorbar(aes(ymin=eval(parse(text=paste("mean_",imputation_metric,sep="")))-eval(parse(text=paste("sd_",imputation_metric,sep=""))),
ymax=eval(parse(text=paste("mean_",imputation_metric,sep="")))+eval(parse(text=paste("sd_",imputation_metric,sep="")))),
width=.2, position=position_dodge(.9),color="black") +
labs(title=bquote( list("n =" ~ .(N) ~ ", p =" ~ .(P) ~ ", d =" ~ .(D), ~ mu[phi]==.(phi0)) ), y = imputation_metric0, x="Mechanism") +
theme(legend.title = element_blank(), text=element_text(size = 20))#,axis.text.x = element_text(colour = c(rep("black",nlevels(bar_df$mechanism)-1),"red")))
ggsave(filename=sprintf("%s/%s_5sims_miss%d.png",dir_name2,imputation_metric,miss_pct),
plot=p, width = 12, height=7, units="in")
}
}}}
}
save_MI=function(dataset, mechanism, miss_pct, phi0, sim_index, P, methods=c("MICE_sup","MICE_unsup","NIMIWAE_sup","NIMIWAE_unsup"), init="default"){
# P0 = if(!intercept){P}else{P+1}
iP = P+1
runs=length(sim_index)
ires <- array(NA, dim = c(length(methods), runs, iP, 4))
dimnames(ires) <- list(methods,
as.character(1:runs),
paste("x",1:iP,sep=""),
c("estimate", "std.error", "2.5 %","97.5 %"))
res <- array(NA, dim = c(length(methods), runs, P, 4))
dimnames(res) <- list(methods,
as.character(1:runs),
paste("x",1:P,sep=""),
c("estimate", "std.error", "2.5 %","97.5 %"))
m=50
# all_AUCs = array(NA, dim = c(length(methods),runs,m))
## Run each mice method and extract coef estimates
# trues = list(); fitted=list()
trues = matrix(nrow=length(sim_index), ncol=P); fitteds = matrix(nrow=length(sim_index), ncol=P)
itrues = matrix(nrow=length(sim_index), ncol=iP); ifitteds = matrix(nrow=length(sim_index), ncol=iP)
a=1;b=1;c=1;d=1;e=1
# if(!intercept){name1 = "MI"} else{ name1="MI0" }
# if(compare=="truth"){name1 = sprintf("%s",name1)} else if(compare=="est"){name1 = sprintf("e%s",name1)}
if(init=="default"){NIM_pref = ""} else if(init=="alt"){NIM_pref="/alt_init"}
res_file = sprintf("./Results/%s/phi%d%s/MI_res_%s_%d.RData",
dataset, NIM_pref, phi0, mechanism, miss_pct)
res_file2 = sprintf("./Results/%s/phi%d%s/MI_results_%s_%d.RData",
dataset, NIM_pref, phi0, mechanism, miss_pct)
# if(!file.exists(res_file)){
for(c in 1:length(sim_index)){
fits = list()
data.file.name=sprintf("Results/%s/phi%d/sim%d/data_%s_%d.RData",
dataset, phi0, sim_index[c], mechanism, miss_pct)
# rownames(all_AUCs) = methods
for(i in 1:length(methods)){
dir_name0 = if(grepl("^IMIWAE",methods[i])){
sprintf("Results/%s/phi%d/sim%d/Ignorable",dataset, phi0, sim_index[c], mechanism, miss_pct) # Ignorable NIMIWAE
} else if(grepl("^NIMIWAE",methods[i])){ sprintf("Results/%s/phi%d/sim%d%s",dataset, phi0, sim_index[c],NIM_pref)
} else { sprintf("Results/%s/phi%d/sim%d",dataset, phi0, sim_index[c]) }
if(grepl("^IMIWAE",methods[i])){ method0 = sprintf("N%s",methods[i]) } else{ method0 = methods[i] } # IMIWAE is labeled NIMIWAE too, except in Ignorable directory
if(grepl("IMIWAE",methods[i])){
file.name=sprintf("%s/res_%s_%s_%d_IWAE_rzF.RData",dir_name0, method0, mechanism, miss_pct)
}else{
file.name=sprintf("%s/res_%s_%s_%d.RData",dir_name0, method0, mechanism, miss_pct)
}
dir_name = if(grepl("_sup",method0)){
sprintf("%s/%s_sup/miss%d",dir_name0, mechanism, miss_pct)
} else{
sprintf("%s/%s_unsup/miss%d",dir_name0, mechanism, miss_pct)
}
if(!file.exists(file.name)){
next
} else{print(file.name)}
fit = process_results_MI(data.file.name, file.name, method0, dir_name)
res[i, c, , ] <- as.matrix(fit$tab)
ires[i, c, , ] <- as.matrix(fit$itab)
# rownames(res[i,c,,]) = fit$names
print(res[i,c,,])
print(ires[i, c, , ])
names=fit$names
true_beta = fit$fit_data$params$beta
itrue_beta = c(fit$fit_data$params$beta0, fit$fit_data$params$beta)
fitted_beta = fit$trues
ifitted_beta = fit$itrues
# true = fit$betas
rm(fit); gc()
}
trues[c,] = if(!is.null(true_beta)){ true_beta }else{NA}
itrues[c,] = if(!is.null(itrue_beta)){ itrue_beta }else{NA}
fitteds[c,] = fitted_beta
ifitteds[c,] = ifitted_beta
print(c); print(trues); print(itrues); print(fitteds); print(ifitteds)
}
ests = t(apply(array(res[,,,1], dim=c(length(methods), runs, P)),c(1,3),function(x)mean(x,na.rm=T))); SEs = t(apply(array(res[,,,2],dim=c(length(methods), runs, iP)),c(1,3),function(x)mean(x,na.rm=T)))
iests = t(apply(array(ires[,,,1], dim=c(length(methods), runs, P)),c(1,3),function(x)mean(x,na.rm=T))); iSEs = t(apply(array(ires[,,,2], dim=c(length(methods), runs, iP)),c(1,3),function(x)mean(x,na.rm=T)))
# rownames(ests) = names; rownames(SEs) = names; rownames(iests) = c("Intercept",names); rownames(iSEs) = c("Intercept",names)
colnames(ests) = methods; colnames(SEs) = methods; colnames(iests) = methods; colnames(iSEs) = methods
save(list=c("res","ires","names","ests", "iests","SEs","iSEs","trues","fitteds", "itrues","ifitteds"), file=res_file)
print("saved res")
## Compute performance metrics
results = list(); all_results = list() # all_results when there are more than 1 rep --> save all rep and features
iresults = list(); iall_results = list() # all_results when there are more than 1 rep --> save all rep and features
# true = trues[[1]] # trues: runs x P (or P+1); res: methods x runs x P x metrics
for(j in 1:iP){
print("j:"); print(j)
if(j<iP){
true = matrix(trues[,j],byrow=T,nrow=length(methods),ncol=runs)
fitted = matrix(fitteds[,j],byrow=T,nrow=length(methods),ncol=runs)
}
itrue = matrix(itrues[,j],byrow=T,nrow=length(methods),ncol=runs)
ifitted = matrix(ifitteds[,j],byrow=T,nrow=length(methods),ncol=runs)
out_metrics = function(res,true,fitted,j){
RBs <- res[,,j, "estimate"] - true
ABs <- abs(RBs)
PBs <- 100 * abs((res[,,j, "estimate"] - true)/ true)
CRs <- (res[,,j, "2.5 %"] < true & true < res[,,j, "97.5 %"])^2
AWs <- res[,,j, "97.5 %"] - res[,,j, "2.5 %"]
RMSEs <- sqrt((res[,,j, "estimate"] - true)^2)
NRMSEs <- sqrt((res[,,j, "estimate"] - true)^2/abs(true))
eRBs <- res[,,j, "estimate"] - fitted
eABs <- abs(eRBs)
ePBs <- 100 * abs((res[,,j, "estimate"] - fitted)/ fitted)
eCRs <- (res[,,j, "2.5 %"] < fitted & fitted < res[,,j, "97.5 %"])^2
eAWs <- res[,,j, "97.5 %"] - res[,,j, "2.5 %"]
eRMSEs <- sqrt((res[,,j, "estimate"] - fitted)^2)
eNRMSEs <- sqrt((res[,,j, "estimate"] - fitted)^2/abs(fitted))
return(list(RB=RBs,AB=ABs,PB=PBs,CR=CRs,AW=AWs,RMSE=RMSEs,NRMSE=NRMSEs,
eRB=eRBs,eAB=eABs,ePB=ePBs,eCR=eCRs,eAW=eAWs,eRMSE=eRMSEs,eNRMSE=eNRMSEs))
}
if(length(sim_index)==1){
if(j<iP){
print(true)
print(res[,,j, "estimate"])
fit = out_metrics(res,true,fitted,j)
RB=fit$RB; AB=fit$AB; PB=fit$PB; CR=fit$CR; AW=fit$AW; RMSE=fit$RMSE; NRMSE=fit$NRMSE
eRB=fit$eRB; eAB=fit$eAB; ePB=fit$ePB; eCR=fit$eCR; eAW=fit$eAW; eRMSE=fit$eRMSE; fit$eNRMSE
all_results[[j]] = data.frame(RB=c(RB),AB=c(AB),PB=c(PB),CR=c(CR),AW=c(AW),RMSE=c(RMSE), #NRMSE=c(NRMSE),
eRB=c(eRB),eAB=c(eAB),ePB=c(ePB),eCR=c(eCR),eAW=c(eAW),eRMSE=c(eRMSE), #eNRMSE=c(eNRMSE),
method=methods, rep=sim_index)
}
print(ires[,,j, "estimate"])
fit = out_metrics(ires,itrue,ifitted,j)
iRB=fit$RB; iAB=fit$AB; iPB=fit$PB; iCR=fit$CR; iAW=fit$AW; iRMSE=fit$RMSE; iNRMSE=fit$NRMSE
ieRB=fit$eRB; ieAB=fit$eAB; iePB=fit$ePB; ieCR=fit$eCR; ieAW=fit$eAW; ieRMSE=fit$eRMSE; ieNRMSE=fit$eNRMSE
iall_results[[j]] = data.frame(RB=c(iRB),AB=c(iAB),PB=c(iPB),CR=c(iCR),AW=c(iAW),RMSE=c(iRMSE), NRMSE=c(iNRMSE),
eRB=c(ieRB),eAB=c(ieAB),ePB=c(iePB),eCR=c(ieCR),eAW=c(ieAW),eRMSE=c(ieRMSE), eNRMSE=c(ieNRMSE),
method=methods, rep=sim_index)
}else{
if(j<iP){
print(true)
print(res[,,j, "estimate"])
fit = out_metrics(res,true,fitted,j)
RBs=fit$RB; ABs=fit$AB; PBs=fit$PB; CRs=fit$CR; AWs=fit$AW; RMSEs=fit$RMSE; NRMSEs=fit$NRMSE
eRBs=fit$eRB; eABs=fit$eAB; ePBs=fit$ePB; eCRs=fit$eCR; eAWs=fit$eAW; eRMSEs=fit$eRMSE; eNRMSEs=fit$eNRMSE
print(RBs)
RB <- rowMeans(RBs,na.rm = T)
AB <- rowMeans(ABs,na.rm = T)
PB <- rowMeans(PBs,na.rm = T)
CR <- rowMeans(CRs,na.rm = T)
AW <- rowMeans(AWs,na.rm = T)
RMSE <- sqrt(rowMeans((res[,,j, "estimate"] - true)^2,na.rm = T))
NRMSE <- sqrt(rowMeans((res[,,j, "estimate"] - true)^2/abs(true),na.rm = T)) # normalized by the value of the truth
eRB <- rowMeans(eRBs,na.rm = T)
eAB <- rowMeans(eABs,na.rm = T)
ePB <- rowMeans(ePBs,na.rm = T)
eCR <- rowMeans(eCRs,na.rm = T)
eAW <- rowMeans(eAWs,na.rm = T)
eRMSE <- sqrt(rowMeans((res[,,j, "estimate"] - fitted)^2,na.rm = T))
eNRMSE <- sqrt(rowMeans((res[,,j, "estimate"] - fitted)^2/abs(fitted),na.rm = T)) # normalized by the value of the truth
all_results[[j]] = data.frame(RB=c(RBs),AB=c(ABs),PB=c(PBs),CR=c(CRs),AW=c(AWs),RMSE=c(RMSEs), NRMSE=c(NRMSEs),
eRB=c(eRBs),eAB=c(eABs),ePB=c(ePBs),eCR=c(eCRs),eAW=c(eAWs),eRMSE=c(eRMSEs), eNRMSE=c(eNRMSEs),
method=rep(methods, times=runs), rep=rep(sim_index, each=length(methods)))
}
print(ires[,,j, "estimate"])
fit = out_metrics(ires,itrue,ifitted,j)
iRBs=fit$RB; iABs=fit$AB; iPBs=fit$PB; iCRs=fit$CR; iAWs=fit$AW; iRMSEs=fit$RMSE; iNRMSEs=fit$NRMSE
ieRBs=fit$eRB; ieABs=fit$eAB; iePBs=fit$ePB; ieCRs=fit$eCR; ieAWs=fit$eAW; ieRMSEs=fit$eRMSE; ieNRMSEs=fit$eNRMSE
iRB <- rowMeans(iRBs,na.rm = T)
iAB <- rowMeans(iABs,na.rm = T)
iPB <- rowMeans(iPBs,na.rm = T)
iCR <- rowMeans(iCRs,na.rm = T)
iAW <- rowMeans(iAWs,na.rm = T)
iRMSE <- sqrt(rowMeans((ires[,,j, "estimate"] - itrue)^2,na.rm = T))
iNRMSE <- sqrt(rowMeans((ires[,,j, "estimate"] - itrue)^2/abs(itrue),na.rm = T)) # normalized by the value of the truth
ieRB <- rowMeans(ieRBs,na.rm = T)
ieAB <- rowMeans(ieABs,na.rm = T)
iePB <- rowMeans(iePBs,na.rm = T)
ieCR <- rowMeans(ieCRs,na.rm = T)
ieAW <- rowMeans(ieAWs,na.rm = T)
ieRMSE <- sqrt(rowMeans((ires[,,j, "estimate"] - ifitted)^2,na.rm = T))
ieNRMSE <- sqrt(rowMeans((ires[,,j, "estimate"] - ifitted)^2/abs(ifitted),na.rm = T)) # normalized by the value of the truth
iall_results[[j]] = data.frame(RB=c(iRBs),AB=c(iABs),PB=c(iPBs),CR=c(iCRs),AW=c(iAWs),RMSE=c(iRMSEs), NRMSE=c(iNRMSEs),
eRB=c(ieRBs),eAB=c(ieABs),ePB=c(iePBs),eCR=c(ieCRs),eAW=c(ieAWs),eRMSE=c(ieRMSEs), eNRMSE=c(ieNRMSEs),
method=rep(methods, times=runs), rep=rep(sim_index, each=length(methods)))
}
# results[[j]] = data.frame(RB, PB, CR, AW, RMSE)
if(j<iP){
results[[j]] = data.frame(RB=RB, AB=AB, PB=PB, CR=CR, AW=AW, RMSE=RMSE, NRMSE=NRMSE,
eRB=eRB, eAB=eAB, ePB=ePB, eCR=eCR, eAW=eAW, eRMSE=eRMSE, eNRMSE=eNRMSE)
}
iresults[[j]] = data.frame(RB=iRB, AB=iAB, PB=iPB, CR=iCR, AW=iAW, RMSE=iRMSE, NRMSE=iNRMSE,
eRB=ieRB, eAB=ieAB, ePB=iePB, eCR=ieCR, eAW=ieAW, eRMSE=ieRMSE, eNRMSE=ieNRMSE)
}
# return(list(res=res, true=true, results=results, times=times))
print("Averaged results:")
print( Reduce("+",results)/length(results) )
print( Reduce("+",iresults)/length(iresults) )
df = data.frame(do.call(rbind, results))
idf = data.frame(do.call(rbind, iresults))
all_df = data.frame(do.call(rbind, all_results))
iall_df = data.frame(do.call(rbind, iall_results))
df$method=methods; idf$method=methods
df$feature=rep(c(1:P),each=length(methods)); idf$feature=rep(c(1:iP),each=length(methods))
df$feature = as.factor(df$feature); idf$feature = as.factor(idf$feature)
all_df$feature=rep(c(1:P),each=length(methods)*runs); iall_df$feature=rep(c(1:iP),each=length(methods)*runs)
all_df$feature = as.factor(all_df$feature); iall_df$feature = as.factor(iall_df$feature)
library(ggplot2)
library(grid)
library(gridExtra)
library(gtable)
save(list=c("results","all_results", "iresults","iall_results"), file=res_file2)
### MI table of results, Excluding intercept
p = tableGrob(round(Reduce("+",results)/length(results), 3))
title1 <- textGrob("Results MI (Mean Across Features), No Int",gp=gpar(fontsize=8))
padding <- unit(5,"mm")
p <- gtable_add_rows(
p,
heights = grobHeight(title1) + padding,
pos = 0)
p <- gtable_add_grob(
p,
title1,
1, 1, 1, ncol(p))
ggfname = sprintf("./Results/%s/phi%d%s/MI_results_%s_%d.png",
dataset,phi0,NIM_pref,mechanism,miss_pct)
ggsave(p,file=ggfname, width=16, height=10, units="in")
### MI table of results, Including intercept
p = tableGrob(round(Reduce("+",iresults)/length(iresults), 3))
title1 <- textGrob("Results MI (Mean Across Features), Int",gp=gpar(fontsize=8))
padding <- unit(5,"mm")
p <- gtable_add_rows(
p,
heights = grobHeight(title1) + padding,
pos = 0)
p <- gtable_add_grob(
p,
title1,
1, 1, 1, ncol(p))
ggfname = sprintf("./Results/%s/phi%d%s/iMI_results_%s_%d.png",
dataset, phi0,NIM_pref,mechanism,miss_pct)
ggsave(p,file=ggfname, width=16, height=10, units="in")
metrics = c("RB","AB","PB","CR","AW","RMSE","NRMSE",
"eRB","eAB","ePB","eCR","eAW","eRMSE","eNRMSE")
for(i in 1:length(metrics)){
print(paste("Plotting",metrics[i]))
p = ggplot(data=df, aes(x=feature, y=eval(parse(text=metrics[i])), fill=method)) +
geom_bar(stat="identity",position=position_dodge(), width=.5) +
labs(title=sprintf("P=%d, %s, %s vs Feature", P,mechanism, metrics[i]), y=metrics[i])
ggfname = sprintf("./Results/%s/phi%d%s/MI_%s_%s_%d.png",
dataset, phi0,NIM_pref,metrics[i], mechanism, miss_pct)
ggsave(filename=ggfname,
plot=p, height=6, width=9)
p = ggplot(data=idf, aes(x=feature, y=eval(parse(text=metrics[i])), fill=method)) +
geom_bar(stat="identity",position=position_dodge(), width=.5) +
labs(title=sprintf("P=%d, %s, %s vs Feature", iP,mechanism, metrics[i]), y=metrics[i])
ggfname = sprintf("./Results/%s/phi%d%s/iMI_%s_%s_%d.png",
dataset, phi0,NIM_pref,metrics[i], mechanism, miss_pct)
ggsave(filename=ggfname,
plot=p, height=6, width=9)
}
}
tabulate_UCI=function(datasets=c("RED","CONCRETE","BREAST","BANKNOTE","WHITE","YEAST","IRIS",
"HEPMASS","GAS","POWER","MINIBOONE"),
mechanisms=c("MCAR","MAR","MNAR"),miss_pct=c(25),
methods=c("IMIWAE","NIMIWAE","MIWAE","HIVAE","VAEAC","MEAN","MF"),phi0=5,sim_index=1,
arch=NULL, rdeponz=NULL){
# return df with columns: dataset, method, mechanism, and value (NRMSE, L1, L2, MSE, or RMSE. choose here)
nvals = length(datasets)*length(mechanisms)*length(miss_pct)*length(methods)*length(sim_index)
df=data.frame(matrix(nrow=nvals,ncol=10))
index=1
colnames(df) = c("dataset","mechanism","method","miss_pct","sim_index","MSE","RMSE","NRMSE","L1","L2")
for(i in 1:length(datasets)){for(j in 1:length(mechanisms)){for(k in 1:length(methods)){for(l in 1:length(miss_pct)){for(m in 1:length(sim_index)){
### LOAD Results/datasets[i]/phi5/sim(index)/res_(method)_(mechanism)_(miss_pct).RData
# if(methods[k]=="NIMIWAE2"){subdir="/Ignorable"; method0="NIMIWAE"}else{subdir=""; method0=methods[k]}
subdir=""; method0=methods[k]
dir_name0 = sprintf("Results/%s/phi%d/sim%d",datasets[i],phi0,sim_index[m])
dir_name = sprintf("%s%s",dir_name0,subdir)
data.file.name=sprintf("%s/data_%s_%d.RData",dir_name0,mechanisms[j],miss_pct[l])
file.name = output_file.name(dir_name=dir_name,method=method0, mechanism=mechanisms[j],
miss_pct=miss_pct[l], arch=arch, rdeponz=rdeponz)
df[index,1:5] = c(datasets[i],mechanisms[j],methods[k],miss_pct[l],sim_index[m])
print(data.file.name)
print(file.name)
if(file.exists(file.name)){
res = process_results(data.file.name,file.name,method0)$results # returns unlist(list(MSE=MSE,RMSE=RMSE,NRMSE=NRMSE,L1=L1,L2=L2))
df[index,6:10] = res
} else{ df[index,6:10] = NA }
index = index + 1
}}}}}
df$dataset = as.factor(df$dataset); df$mechanism = as.factor(df$mechanism); df$method = as.factor(df$method)
df$miss_pct = as.factor(df$miss_pct); df$sim_index = as.factor(df$sim_index)
df$MSE = as.numeric(df$MSE); df$NRMSE = as.numeric(df$NRMSE); df$RMSE = as.numeric(df$RMSE);
df$L1 = as.numeric(df$L1); df$L2 = as.numeric(df$L2)
return(df)
}
# DONE ######### NEED TO ADD MI FUNCTIONS FOR SIM AND FOR PHYSIONET
######### NEED TO ADD FIGURES FOR MI FOR SIM
######### NEED TO SAVE MI RESULTS FOR PHYSIONET TO Results/Physionet/phiNA/MI_res_MNAR_NA.RData
######### NEED TO REDO THIS FUNCTION FOR PHYSIONET
summarize_Physionet = function(prefix="",mechanism="MNAR",miss_pct=NA,sim_index=1,dataset="Physionet", init="alt"){
phi0 = NA; D=NA
NIM_pref = if(init=="default"){""}else if(init=="alt"){"alt_init/"}
# df = data.frame()
dir_name = sprintf("Results/%s/phi%d/Z%d",dataset,phi0,D)
load(sprintf("%s/sim%d/data_MNAR_%d.RData", dir_name,sim_index, miss_pct))
load(sprintf("%s/%sMI_res_%s_%d.RData",dir_name,NIM_pref,mechanism,miss_pct)) # 100% train --> just inference (no predictions)
rownames(ests) = colnames(data)
df = ests[order(-abs(ests[,1])),]; df2 = SEs[order(-abs(ests[,1])),]
pheatmap::pheatmap(df,cluster_rows=F,scale="row",cluster_cols=F)
library(grid); library(gridExtra)
n=20
tab = matrix(paste( round(df[1:n,],3), " (",round(df2[1:n,],3), ")", sep=""), nrow=n)
rownames(tab) = rownames(df)[1:n]; colnames(tab)=colnames(df)
grid.table(tab)
ratios=c(train = .8, valid = .2)
set.seed(333)
g = sample(cut(
seq(nrow(data)),
nrow(data)*cumsum(c(0,ratios)),
labels = names(ratios)
))
classes = fit_data$classes
P=ncol(data); N=nrow(data)
# if(grepl("_sup",method)){
# ## supervised methods
# data = cbind(data, classes)
# Missing = cbind(Missing, rep(1, nrow(Missing))) # include 1's for classes (fully observed)
# }
datas = split(data.frame(data), g) # split by $train, $test, and $valid
Missings = split(data.frame(Missing), g)
classess = split(classes,g)
datas$test = datas$train
Missings$test = Missings$train
classess$test = classess$train
# id = 95 # FiO2_last
# id = 27 # RespRate_last
for(id in 1:ncol(data)){