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plot_figure_tuh_model_comp.r
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library(tidyr)
library(reticulate)
library(ggbeeswarm)
source('config.r')
np <- import("numpy")
data <- np$load(
"./outputs/all_scores_models_tuh_mae_shuffle-split.npy",
# "./outputs/all_scores_mag_models_mnecommonsubjects_interval_rep-kfold.npy",
# "all_scores_mag_models_mnecommonsubjects.npy",
allow_pickle = T)[[1]] %>%
as.data.frame()
data_comp_scores <- read.csv(
"outputs/tuh_component_scores.csv",
stringsAsFactor = T
)
# data$spoc <- sample(data$spoc, size = 100, replace = F)
data_comp_scores_long <- data.frame(
score = c(data_comp_scores$spoc, data_comp_scores$riemann),
estimator = factor(rep(c("SPoC", "Riemann"),
each = nrow(data_comp_scores))),
n_components = rep(data_comp_scores$n_components, times = 2),
fold_idx = factor(rep(data_comp_scores$fold_idx, times = 2))
)
agg_scores <- aggregate(cbind(spoc, riemann) ~ n_components,
data = data_comp_scores, FUN = mean)
data_ <- data[, (!names(data) %in% c("dummy"))]
n_splits <- nrow(data_)
data_long <- data_ %>% gather(key = "estimator", value = "score")
# move to long format
data_long$estimator <- factor(data_long$estimator)
data_long <- subset(data_long, estimator != "spoc")
est_types <- c(
"naive",
"diag",
# "SPoC",
"Riemann",
"SPoC",
"Riemann"
)
est_names <- c(
"upper",
"diag",
# "SPoC",
"Riemann",
sprintf("SPoC[%d]", which.min(agg_scores$spoc)),
sprintf("Riemann[%d]", which.min(agg_scores$riemann))
)
est_labels <- setNames(
c("upper", est_types[c(-1, -5, -6)]),
est_types[c(-5, -6)]
)
# categorical colors based on: https://jfly.uni-koeln.de/color/
# beef up long data
data_long$est_type <- factor(rep(est_types, each = n_splits))
data_long$fold <- rep(1:n_splits, times = length(est_types))
# prepare properly sorted x labels
sort_idx <- order(apply(data_[,-c(3)], 2, mean))
levels_est <- est_names[sort_idx]
my_color_cats <- setNames(
with(
color_cats,
c(`sky blue`, `blueish green`, vermillon, orange)),
c("naive", "diag", "SPoC", "Riemann"))
ggplot(data = subset(data_long, estimator != "dummy"),
mapping = aes(y = score, x = reorder(estimator, I(score)))) +
coord_flip(ylim=c(5,15)) +
geom_beeswarm(
priority = 'none',
mapping = aes(color = est_type),
size = 2.5,
# dodge.width = 10,
# groupOnX = F,
alpha = 0.2,
show.legend = T, cex = 0.1) +
# scale_size_continuous(range = c(0.5, 2)) +
# scale_alpha_continuous(range = c(0.4, 0.7)) +
geom_boxplot(mapping = aes(fill = est_type, color = est_type),
alpha = 0.3,
outlier.fill = NA, outlier.colour = NA) +
stat_summary(geom = 'text',
mapping = aes(label = sprintf("%1.2f",
..y..)),
fun.y = mean, size = 3.2, show.legend = FALSE,
position = position_nudge(x = -0.49)) +
my_theme +
labs(y = "MAE", x = NULL, parse = T) +
guides(size = F, alpha = F) +
theme(legend.position = c(0.7, 0.25)) +
# legend.position = "top", legend.text = element_text(size = 18)) +
scale_fill_manual(values = my_color_cats,
breaks = names(my_color_cats),
labels = est_labels,
name = NULL) +
scale_color_manual(
values = my_color_cats,
breaks = names(my_color_cats),
labels = est_labels,
name = NULL) +
scale_x_discrete(labels = parse(text = levels_est)) +
geom_hline(yintercept = mean(data$dummy), linetype = 'dashed') +
annotate(geom = "text",
y = mean(data$dummy) + 0.2, x = 3, label = 'predicting~bar(age)',
size = annotate_text_size,
parse = T, angle = 270)
fname <- "./outputs/fig_tuh_model_comp"
ggsave(paste0(fname, ".png"),
width = 8, height = 5, dpi = 300)
ggsave(paste0(fname, ".pdf"),
useDingbats = F,
width = 8, height = 5, dpi = 300)
embedFonts(file = paste0(fname, ".pdf"), outfile = paste0(fname, ".pdf"))
data_comp_scores_long <- data.frame(
score = c(data_comp_scores$spoc, data_comp_scores$riemann),
estimator = factor(rep(c("SPoC", "Riemann"),
each = nrow(data_comp_scores))),
n_components = rep(data_comp_scores$n_components, times = 2),
fold_idx = factor(rep(data_comp_scores$fold_idx, times = 2))
)
agg_scores <- aggregate(cbind(spoc, riemann) ~ n_components,
data = data_comp_scores, FUN = mean)
data_ <- data[, (!names(data) %in% c("dummy"))]
n_splits <- nrow(data_)
data_long <- data_ %>% gather(key = "estimator", value = "score")
# move to long format
data_long$estimator <- factor(data_long$estimator)
est_types <- c(
"naive",
"diag",
"SPoC",
"Riemann",
"SPoC",
"Riemann"
)
est_names <- c(
"upper",
"diag",
"SPoC",
"Riemann",
sprintf("SPoC[%d]", which.min(agg_scores$spoc)),
sprintf("Riemann[%d]", which.min(agg_scores$riemann))
)
component_labels <- setNames(
rev(parse(text = est_names[-c(1:4, 7)])),
c("Riemann", "SPoC"))
fig_components <- ggplot(data = data_comp_scores_long,
mapping = aes(
x = n_components, y = score,
# group = interaction(estimator, fold_idx),
color = estimator, fill = estimator)) +
stat_summary(inherit.aes = F,
mapping = aes(fill = estimator, x = n_components,
y = score),
fun.ymin = function(x) mean(x) - sd(x),
fun.ymax = function(x) mean(x) + sd(x),
geom = 'ribbon', alpha = 0.2) +
stat_summary(fun.y = mean, geom = 'line', size = 1.5) +
my_theme +
theme(legend.position = c(0.8, 0.5)) +
scale_color_manual(
values = my_color_cats[c("Riemann", "SPoC")],
breaks = c("Riemann", "SPoC"),
labels = component_labels,
name = NULL) +
scale_fill_manual(
values = my_color_cats[c("Riemann", "SPoC")],
breaks = c("Riemann", "SPoC"),
labels = component_labels,
name = NULL) +
labs(x='#components', y = "MAE") +
geom_vline(
xintercept = which.min(agg_scores$spoc),
size = 0.6,
color = my_color_cats['SPoC'], linetype = 'dashed') +
geom_vline(
xintercept = which.min(agg_scores$riemann),
size = 0.6,
color = my_color_cats['Riemann'], linetype = 'dashed') +
scale_x_continuous(breaks = seq(0, 100, 10))
# scale_y_continuous(breaks = seq(0, .8, .1)) +
# coord_cartesian(ylim = c(0, 0.8))
fname <- "./outputs/fig_tuh_component_selection"
ggsave(paste0(fname, ".png"),
width = 8, height = 5, dpi = 300)
ggsave(paste0(fname, ".pdf"),
useDingbats = F,
width = 8, height = 5, dpi = 300)
embedFonts(file = paste0(fname, ".pdf"), outfile = paste0(fname, ".pdf"))