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robustness.R
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version.name = "final" # "0529_120_t0_12"
rep = 100
ws = exp(seq(-2, 6, 2)) / 10
T_begin = 12
decision.t = 7
M = 100
T_end = 120
n_city = 6
lag = 9
t0 = 61
################################################################################################################
################################################################################################################
organize_data <- function(main_path, sub_path){
l_ws = 5
ws = exp(seq(-2, 6, 2)) / 10
save.path = paste(main_path, "_", sub_path, ".txt", sep = "")
r.ours = readRDS(save.path)
res = list()
for (i in 1:125){
seed = i %/% l_ws
w.index = i %% l_ws + 1
w = ws[w.index]
if(i <= 5){
res[[w.index]] = r.ours[[i]]$RC
}else{
res[[w.index]] = res[[w.index]] + r.ours[[i]]$RC
}
}
for (w in 1:5){
res[[w]] = res[[w]] / 25
res[[w]][1,] = log10(res[[w]][1,])
}
return(res)
}
# res = list()
# for (lag in 7:11){
# res[[lag]] = organize_data("/Users/wrunzhe/Google Drive/Confident/AAAI_COVID/robust_lag/0828_tuning_result", sub_path = lag)
# }
para = 1
res = list()
for (effect2 in seq(5, 20, 5)) {
for (effect3_effect2 in seq(5, 20, 5)) {
effect3 = effect3_effect2 + effect2
res[[para]] = organize_data("./robust_effect/0828_tuning_effect_result", sub_path = paste(effect3, effect2, sep = "_"))
para = para + 1
}
}
para.all = para - 1
################################################################################################################
################################################################################################################
## Paths
main_path = "./"
miceadds::source.all(paste(main_path, "codes", sep = ""))
out_path = paste(main_path, "output/", sep = "")
simu.res.path = paste(main_path, "output/", version.name, ".RDS", sep = "")
simu.plot.res.path = paste(main_path, "output/", version.name, "_plotres.RDS", sep = "")
plot.filename = paste(out_path, "output/", version.name, ".pdf", sep = "")
simu.monitor.path = paste(main_path, "output/", version.name, "__monitor.txt", sep = "")
simu.competing.monitor.path = paste(main_path,
"output/",
version.name,
"_competing__monitor.txt",
sep = "")
## Code
miceadds::source.all(paste(main_path, "codes", sep = ""))
## Reading Data
data = data_cn = readRDS(paste(main_path, "data.rds", sep = ""))
name = names(data_cn)
################################################################################################################
## Plotting: get results for each method ------------------------------------------------------------
simu.plot.res.path = "./output/0606_plotres.RDS"
# r_oracle = getParetoCities("oracle", oneSettingMultiSeeds, ws)
plot.res = readRDS(simu.plot.res.path)
r_thre_XN_R = plot.res$r_thre_XN
r_thre_T3_R = plot.res$r_thre_T3
r_thre_T2_R = plot.res$r_thre_T2
r_ours_R = plot.res$r_ours
inlog = T
origin_zero = F
point_size = 2
#######################################################
################################################################################################################
################################################################################################################
# lag, w, (2, cities)
plotParetoFrontierforSensitivityAnalysis <-function(ours, paras, point_size = 1, line_size = 1
, add_sd
, sd_ours
, sd_thre_XN
, sd_thre_T3
, sd_thre_T2
, r_thre_XN
, r_thre_T3
, r_thre_T2
, RC_fixed, behav, city_name, origin_zero = F, inlog = F, echo = F
, adjust_10 = T # at first, there is a 10 factor in our definition, and afterwards we removed it. to avoid re-running everything, we adjust it here.
){
figure_size = point_size
citytype = c(rep(city_name, length(C)))
theme_set(theme_bw())
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# cbp1 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
# "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
colors <- c( "Pareto" = cbp1[4], "oracle" = cbp1[8], "threshold" = cbp1[2], "RC_fixed"=cbp1[3],
"mitigation" = cbp1[6], "suppresion" = cbp1[1], "behaviour" = cbp1[7])
shapes <- c("Pareto" = 16, "oracle" = 1
, "threshold" = 5,"mitigation"= 2, "suppresion" = 0
, "behaviour" = 13)
##########################################################################
# ours
##########################################################################
# scale_color_manual(name="Action",labels=c(1,2,3), values=colors, drop=FALSE) +
# scale_shape_manual(name="Action",labels=c(1,2,3), values=c(15, 16, 17), drop=FALSE) +
# , color=Action, shape=Action
gg = ggplot()
R_ours = c()
C_ours = c()
for (i in 1:length(paras)) {
R = ours[[paras[i]]][1,]
# if (inlog)R = log10(R) # already
C = ours[[paras[i]]][2,]
R_ours = c(R_ours, R)
C_ours = c(C_ours, C)
citytype = c(rep(city_name, length(C)))
dat = data.frame(R = R, C = C, citytype)
dat$policy = "Pareto"
gg = gg +
geom_point(data = dat, aes(x=R, y=C, colour=policy, shape=policy), size = figure_size * 1.3) + # 1.3
geom_line(data = dat, aes(x=R, y=C, colour=policy, shape=policy), show.legend = FALSE, alpha = 1, size = line_size) +
xlab("Count of cumulative infected (in log scale)") + ylab("Loss of GDP (billion Chinese Yuan)")+
# ggtitle(city_name) +
facet_wrap(~citytype, scale="free_y", dir="v") +
scale_colour_manual(name="Policy",values = colors, drop=F)+
scale_shape_manual(name="Policy",values = shapes, drop=F)+
scale_fill_manual(name="Policy",values = colors, drop=F)+
labs(colour = "Policy", shape = "Policy", fill="Policy", drop=F) +
# theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.caption = element_text(color = "#D6604D", face = "italic") )
if(add_sd){gg = gg + geom_rect(data=dat, aes(xmin=R-sd_ours[1,]*1.96, xmax=R+sd_ours[1,]*1.96, ymin=C-sd_ours[2,]*1.96,
ymax=C+sd_ours[2,]*1.96, fill=policy, colour=policy), alpha=0.1) }
if(echo){print(gg)}
}
##########################################################################
# competing
##########################################################################
if (!missing(r_thre_T2)) {
R_thre_T2 = r_thre_T2[1,]
if (inlog)R_thre_T2 = log10(R_thre_T2)
C_thre_T2 = r_thre_T2[2,]
if(adjust_10)C_thre_T2 = C_thre_T2 * 10
dat_thre_T2 = data.frame(R = R_thre_T2, C = C_thre_T2)
dat_thre_T2$policy = "mitigation"
if(add_sd){
dat_thre_T2$sdk_x = sd_thre_T2[1,]*1.96;
dat_thre_T2$sdk_y = sd_thre_T2[2,]*1.96;
}
gg = gg + geom_point(data = dat_thre_T2, aes(x = R, y = C, color= policy, shape=policy), size=figure_size) +
geom_line(data = dat_thre_T2, aes(x = R, y = C, color= policy, shape=policy)
, alpha = 1, show.legend = FALSE
, size = line_size)
if(add_sd){
gg = gg + geom_rect(data = dat_thre_T2, aes(xmin=R-sdk_x, xmax=R+sdk_x, ymin=C-sdk_y,
ymax=C+sdk_y, fill=policy, colour=policy), alpha=0.1)
}
if(echo){print(gg)}
}
if (!missing(r_thre_T3)) {
R_thre_T3 = r_thre_T3[1,]
if (inlog)R_thre_T3 = log10(R_thre_T3)
C_thre_T3 = r_thre_T3[2,]
if(adjust_10)C_thre_T3 = C_thre_T3 * 10
dat_thre_T3 = data.frame(R = R_thre_T3, C = C_thre_T3)
dat_thre_T3$policy = "suppresion"
gg = gg +
geom_point(data = dat_thre_T3, aes(x = R, y = C, color= policy, shape=policy), size=figure_size) +
geom_line(data = dat_thre_T3, aes(x = R, y = C, color= policy, shape=policy)
, alpha = 1, show.legend = FALSE
, size = line_size)
if(add_sd){
gg = gg + geom_rect(data = dat_thre_T3, aes(xmin=R-sd_thre_T3[1,]*1.96, xmax=R+sd_thre_T3[1,]*1.96, ymin=C-sd_thre_T3[2,]*1.96,
ymax=C+sd_thre_T3[2,]*1.96, fill=policy, colour=policy), alpha=0.1)
}
if(echo){print(gg)}
}
if (!missing(behav)) {
if(inlog) behav[1] = -log10(-behav[1])
dat_behav = data.frame(R=-behav[1], C=behav[2], policy="behaviour")
if(add_sd){
gg = gg + geom_point(dat_behav, mapping = aes( x = R, y = C , color= policy, shape=policy, fill=policy), size= figure_size + 1) # , shape = 5
} else{
gg = gg + geom_point(dat_behav, mapping = aes( x = R, y = C , color= policy, shape=policy), size= figure_size + 1) # , shape = 5
}
if(echo){print(gg)}
}
# dat_behav = data.frame(x = -behav[1], y = behav[2])
if (!missing(r_thre_XN)) {
R_thre_XN = r_thre_XN[1,]
if (inlog)R_thre_XN = log10(R_thre_XN)
C_thre_XN = r_thre_XN[2,]
if(adjust_10)C_thre_XN = C_thre_XN * 10
dat_thre_XN= data.frame(R = R_thre_XN, C = C_thre_XN)
dat_thre_XN$policy = "threshold"
gg = gg +
geom_point(data = dat_thre_XN, aes(x = R, y = C
, color = policy, shape = policy) , size=figure_size) +
geom_line(data = dat_thre_XN, aes(x = R, y = C, color = policy, shape = policy)
, alpha = 1, show.legend = FALSE
, size = line_size)
if (add_sd){
gg = gg + geom_rect(data = dat_thre_XN, aes(xmin=R-sd_thre_XN[1,]*1.96, xmax=R+sd_thre_XN[1,]*1.96, ymin=C-sd_thre_XN[2,]*1.96,
ymax=C+sd_thre_XN[2,]*1.96, fill=policy, colour=policy), alpha=0.1)
}
if(echo){print(gg)}
}
##########################################################################
# limit
##########################################################################
if(echo){print(gg)}
xs = c(R_ours, -behav[1], R_thre_XN, R_thre_T3, R_thre_T2)
ys = c(C_ours, behav[2], C_thre_XN, C_thre_T3, C_thre_T2)
if (add_sd) {
sds = cbind(sd_ours, c(0,0), sd_thre_XN, sd_thre_T3, sd_thre_T2) *2
sdsx = sds[1,]; sdsy = sds[2,]
}else{
sdsx = 0
sdsy = 0
}
const = 1
xsmin = const * ( min(xs) - max(sdsx) )
xsmax = const * ( max(xs) + max(sdsx) )
ysmin = const * (min(ys) - max(sdsy))
ysmax = const * (max(ys) + max(sdsy))
if(origin_zero){
gg = gg + xlim(0, xsmax) +
ylim(0, ysmax)
}else{
gg= gg + xlim(xsmin, xsmax) + ylim(ysmin, ysmax)
}
gg = gg + theme(axis.title=element_blank()) + expand_limits(x=c(1,4))
return(gg)
}
################################################################################################################
################################################################################################################
# lag, w, (2, cities)
# range_tuning_para = seq(7, 11, 1)
range_tuning_para = c(1:para.all)
range_w = c(1:length(ws))
# FIGURE 3 in paper
ggs = list()
for (l in 1:n_city) {
ours = list()
for (para in range_tuning_para) {
ours[[para]] = matrix(NA, 2, length(range_w))
for (w in range_w){
ours[[para]][, w] = res[[para]][[w]][, l]
}
}
### we need to provide a list (len-range_tuning_para) of RC = [2, range_w]
ggs[[l]] = plotParetoFrontierforSensitivityAnalysis(
ours = ours,
paras = range_tuning_para,
point_size = point_size,
line_size = .8,
behav = evaluateBehavPolicySimu(data, l, T_begin, 120, lag),
add_sd = F,
#sd_ours = r_ours_ud$sd[[l]] / 10, # / sqrt{100}
#sd_thre_XN = r_thre_XN_ud$sd[[l]] / 10,
#sd_thre_T3 = r_thre_T3_ud$sd[[l]] / 10,
# sd_thre_T2 = r_thre_T2_ud$sd[[l]] / 10,
r_thre_XN = r_thre_XN_R[[l]],
r_thre_T3 = r_thre_T3_R[[l]],
r_thre_T2 = r_thre_T2_R[[l]],
city_name = name[l],
origin_zero = origin_zero,
inlog = inlog
)
print(ggs[[l]])
}
############################################################
combinePlots <-
function(ggs,
plot.filename,
width = 6,
height = 8) {
library(ggpubr)
g = ggarrange(
ggs[[1]],
ggs[[2]],
ggs[[3]],
ggs[[4]],
ggs[[5]],
ggs[[6]],
ncol = 2,
nrow = 3,
legend = "top",
label.x = 0,
label.y = 0,
font.label = list(size = 20, face = "bold"),
common.legend = T,
align = "v"
)
yvar = c("Loss of GDP (billion Chinese Yuan)")
xvar = c(expression(
paste("Count of cumulative infected (in ", log[10], " scale)", sep = "")
))
g1 = annotate_figure(
g,
left = text_grob(
yvar,
just = "centre",
size = 12,
rot = 90,
hjust = .5,
vjust = .5
),
bottom = text_grob(
xvar,
just = "centre",
size = 12,
rot = 0,
hjust = .48,
vjust = 0
)
)
print(g1)
ggsave(
filename = plot.filename,
plot = g1,
width = width,
height = height,
units = c("in"),
dpi = 500
)
}
combinePlots(ggs,
plot.filename = paste(out_path, "robust_effect.png", sep = "")
, 6 / 1.2, 8 / 1.2)