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06b_plot_performance_items.R
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06b_plot_performance_items.R
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# Notes -------------------------------------------------------------------
# Plot performance at a given iteration (as a pointrange) for all severity items (x axis), models (colour) and dataset (facet)
# Plot learning curve and prediction horizon plot for all severity items
# Initialisation ----------------------------------------------------------
rm(list = ls()) # Clear Workspace (better to restart the session)
source(here::here("analysis", "00_init.R"))
#### OPTIONS
metric <- "lpd" # for comparing learning curves, not paired comparisons
t_horizon <- 4 # horizon that was used for forward chaining
max_horizon <- 14 # restric prediction horizon
pred_horizon <- 4 # prediction horizon
####
metric <- match.arg(metric, c("lpd", "RPS"))
stopifnot(t_horizon > 0,
max_horizon > 0)
datasets <- c("Derexyl", "PFDC")
list_models <- bind_rows(
data.frame(Item = "extent",
Model = c("uniform", "historical", "RW", "BinMC"), # "BinRW"
Component = "Extent") %>%
mutate(Label = case_when(Model == "BinMC" ~ "Main model",
TRUE ~ "Reference model")),
expand_grid(Item = c("itching", "sleep"),
Model = c("uniform", "historical", "RW", "BinRW"),
Component = "Subjective symptoms") %>%
mutate(Label = case_when(Model == "BinRW" ~ "Main model",
TRUE ~ "Reference model")),
expand_grid(Item = detail_POSCORAD("Intensity signs")$Name,
Model = c("uniform", "historical", "MC", "OrderedRW"),
Component = "Intensity signs") %>%
mutate(Label = case_when(Model == "OrderedRW" ~ "Main model",
TRUE ~ "Reference model"))
) %>%
expand_grid(.,
data.frame(Dataset = datasets))
list_models[["File"]] <- vapply(1:nrow(list_models),
function(i) {
get_results_files(outcome = list_models$Item[i],
model = list_models$Model[i],
dataset = list_models$Dataset[i],
val_horizon = t_horizon,
root_dir = here())$Val
},
character(1))
stopifnot(all(file.exists(list_models$File)))
# Processing --------------------------------------------------------------
perf <- lapply(1:nrow(list_models),
function(i) {
df <- load_dataset(as.character(list_models$Dataset[i]))
fc_it <- detail_fc_training(df %>% rename(Time = Day), t_horizon)
readRDS(list_models$File[i]) %>%
filter(Horizon <= max_horizon,
Iteration > 0 | list_models$Model[i] != "RW") %>%
estimate_performance(metric, ., fc_it, adjust_horizon = !(list_models$Model[i] %in% c("historical", "uniform"))) %>%
bind_cols(., list_models[i, ])
}) %>%
bind_rows()
# Performance at given iteration --------------------------------------------------------------------
### OPTIONS
it_Derexyl <- 19 # iteration to plot the performance for the Derexyl dataset
it_PFDC <- 16 # iteration to plot the performance for the PFDC dataset
###
brk <- c(.01, .1, .25, .5, 1)
tmp <- perf %>%
filter(Variable == "Fit",
(Iteration == it_Derexyl & Dataset == "Derexyl") | (Iteration == it_PFDC & Dataset == "PFDC"),
Horizon == pred_horizon) %>%
mutate(Label = factor(Label, levels = rev(c("Main model", "Reference model"))),
Model = factor(Model, levels = c("uniform", "historical", "RW", "MC",
"BinMC", "OrderedRW", "BinRW")),
Dataset = recode(Dataset, Derexyl = "Dataset 1", PFDC = "Dataset 2"))
pal <- cbbPalette[c(7, 6, 5, 4, 3, 1, 2)]
full_plot <- tmp %>%
ggplot(aes(x = Item, y = Mean, ymin = Mean - SE, ymax = Mean + SE, colour = Model, shape = Label)) +
facet_grid(cols = vars(Dataset), rows = vars(Component), space = "free", scale = "free") +
geom_pointrange(position = position_dodge(width = .66), size = 1, fill = "white") +
scale_colour_manual(values = pal) +
scale_y_continuous(breaks = log(brk), labels = paste0("log(", brk, ")")) +
scale_shape_manual(values = c(16, 21)) +
coord_flip() +
labs(x = "", y = metric, colour = "", shape = "") +
theme_bw(base_size = 15) +
theme(legend.position = "top",
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 30, vjust = .5, hjust = .5))
full_plot
# Custom legend
legend1 <- tmp %>%
filter(Label == "Main model") %>%
ggplot(aes(x = Item, y = Mean, ymin = Mean - SE, ymax = Mean + SE, colour = Model)) +
geom_pointrange(position = position_dodge(width = .66), size = 1, fill = "white", shape = 21) +
scale_colour_manual(values = pal[-(1:4)], name = "EczemaPred models") +
labs(x = "", y = metric, colour = "") +
theme_bw(base_size = 15) +
theme(legend.position = "top") +
guides(colour = guide_legend(title.position = "top"))
legend1 <- get_legend(legend1)
legend2 <- tmp %>%
filter(Label == "Reference model") %>%
ggplot(aes(x = Item, y = Mean, ymin = Mean - SE, ymax = Mean + SE, colour = Model)) +
geom_pointrange(position = position_dodge(width = .66), size = 1, shape = 16) +
scale_colour_manual(values = pal[1:4], name = "Reference models") +
labs(x = "", y = metric, colour = "") +
theme_bw(base_size = 15) +
theme(legend.position = "top") +
guides(colour = guide_legend(title.position = "top"))
legend2 <- get_legend(legend2)
plot_grid(plot_grid(NULL, legend1, NULL, legend2, NULL, nrow = 1),
full_plot + theme(legend.position = "none"),
ncol = 1, rel_heights = c(.1, .9))
# ggsave(here("results", "performance_items.jpg"), width = 13, height = 13, units = "cm", dpi = 300, scale = 2)
# Learning curves and prediction horizon ---------------------------------------------------------
brk <- c(.01, .05, .1, .25, .5, .75, 1)
for (severity_item in unique(list_models[["Item"]])) {
tmp <- perf %>%
filter(Item == severity_item) %>%
mutate(Model = factor(Model, levels = rev(c("uniform", "historical", "RW", "MC", "BinMC", "BinRW", "OrderedRW"))))
# Learning curves
pl1 <- lapply(datasets,
function(dataset) {
df <- load_dataset(dataset)
fc_it <- detail_fc_training(df %>% rename(Time = Day), t_horizon)
id_xbrk2 <- vapply(seq(0, 1, length.out = 10),
function(x) {which.min((x - fc_it$Proportion)^2)},
numeric(1))
title <- case_when(dataset == "Derexyl" ~ "Dataset 1",
dataset == "PFDC" ~ "Dataset 2")
# Learning curve
p1 <- tmp %>%
filter(Dataset == dataset,
Variable == "Fit",
Horizon == pred_horizon) %>%
ggplot(aes(x = N, y = Mean, ymin = Mean - SE, ymax = Mean + SE, colour = Model, fill = Model)) +
geom_point() +
geom_line() +
geom_ribbon(alpha = 0.5) +
scale_colour_manual(values = cbbPalette) +
scale_fill_manual(values = cbbPalette) +
scale_x_continuous(sec.axis = dup_axis(breaks = fc_it$N[id_xbrk2],
labels = fc_it$LastTime[id_xbrk2],
name = "Training days")) +
labs(x = "Number of training observations", y = metric, colour = "", fill = "", title = title) +
theme_bw(base_size = 15) +
theme(panel.grid.minor.y = element_blank(),
legend.position = "top")
if (metric == "lpd") {
p1 <- p1 +
scale_y_continuous(limits = c(NA, 0), breaks = log(brk), labels = paste0("log(", brk, ")"))
} else if (metric == "RPS") {
p1 <- p1 +
coord_cartesian(ylim = c(0, .3))
}
p1
})
# Prediction horizon
pl2 <- lapply(datasets,
function(dataset) {
title <- case_when(dataset == "Derexyl" ~ "Dataset 1",
dataset == "PFDC" ~ "Dataset 2")
p2 <- tmp %>%
filter(Dataset == dataset,
Variable == "Horizon") %>%
ggplot(aes(x = Model, y = Mean, ymin = Mean - SE, ymax = Mean + SE, colour = Model)) +
geom_pointrange(size = 1.5) +
scale_colour_manual(values = cbbPalette) +
labs(x = "", y = paste0("Change in ", metric, " with\nincreasing prediction horizon"), colour = "", title = title) +
theme_bw(base_size = 15) +
theme(legend.position = "bottom")
if (metric == "lpd") {
ymin <- tmp %>% filter(Variable == "Horizon") %>% summarise(min(Mean)) %>% as.numeric()
p2 <- p2 + coord_flip(ylim = c(floor(ymin * 10) / 10, 0))
}
if (metric == "RPS") {
p2 <- p2 + coord_flip(ylim = c(0, NA))
}
p2
})
pl <- c(pl1, pl2)
# Capitalise first letter of severity_item
Item <- gsub("(^|[[:space:]])([[:alpha:]])", "\\1\\U\\2",
severity_item,
perl = TRUE)
# Shared title
plot_title <- ggdraw() +
draw_label(Item,
fontface = "bold",
size = 20,
x = .5,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
# Combine plots
plot_grid(
plot_title,
get_legend(pl[[1]]),
plot_grid(
plotlist = lapply(pl,
function(x) {
x + theme(legend.position = "none",
plot.title = element_text(face = "bold"))
}),
ncol = 2, rel_heights = c(.6, .4), align = "v"),
ncol = 1,
rel_heights = c(.05, .05, .9))
ggsave(here("results", paste0("performance_", severity_item, ".jpg")), width = 13, height = 8, units = "cm", dpi = 300, scale = 2.5)
}