Profiling wether max vem imports : IT DOES
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code/analysis/investigating/impact_of_vem_max_steps.html
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code/analysis/investigating/impact_of_vem_max_steps.html
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||||
for (const annoteDlNode of annoteDls) {
|
||||
annoteDlNode.addEventListener('click', (event) => {
|
||||
const clickedEl = event.target;
|
||||
if (clickedEl !== selectedAnnoteEl) {
|
||||
unselectCodeLines();
|
||||
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||||
if (activeEl) {
|
||||
activeEl.classList.remove('code-annotation-active');
|
||||
}
|
||||
selectCodeLines(clickedEl);
|
||||
clickedEl.classList.add('code-annotation-active');
|
||||
} else {
|
||||
// Unselect the line
|
||||
unselectCodeLines();
|
||||
clickedEl.classList.remove('code-annotation-active');
|
||||
}
|
||||
});
|
||||
}
|
||||
const findCites = (el) => {
|
||||
const parentEl = el.parentElement;
|
||||
if (parentEl) {
|
||||
const cites = parentEl.dataset.cites;
|
||||
if (cites) {
|
||||
return {
|
||||
el,
|
||||
cites: cites.split(' ')
|
||||
};
|
||||
} else {
|
||||
return findCites(el.parentElement)
|
||||
}
|
||||
} else {
|
||||
return undefined;
|
||||
}
|
||||
};
|
||||
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
||||
for (var i=0; i<bibliorefs.length; i++) {
|
||||
const ref = bibliorefs[i];
|
||||
const citeInfo = findCites(ref);
|
||||
if (citeInfo) {
|
||||
tippyHover(citeInfo.el, function() {
|
||||
var popup = window.document.createElement('div');
|
||||
citeInfo.cites.forEach(function(cite) {
|
||||
var citeDiv = window.document.createElement('div');
|
||||
citeDiv.classList.add('hanging-indent');
|
||||
citeDiv.classList.add('csl-entry');
|
||||
var biblioDiv = window.document.getElementById('ref-' + cite);
|
||||
if (biblioDiv) {
|
||||
citeDiv.innerHTML = biblioDiv.innerHTML;
|
||||
}
|
||||
popup.appendChild(citeDiv);
|
||||
});
|
||||
return popup.innerHTML;
|
||||
});
|
||||
}
|
||||
}
|
||||
});
|
||||
</script>
|
||||
</div> <!-- /content -->
|
||||
|
||||
|
||||
|
||||
|
||||
</body></html>
|
||||
62
code/analysis/investigating/impact_of_vem_max_steps.qmd
Normal file
62
code/analysis/investigating/impact_of_vem_max_steps.qmd
Normal file
|
|
@ -0,0 +1,62 @@
|
|||
---
|
||||
title: Investigation sur l'impact de `max_vem_steps`
|
||||
format: html
|
||||
execute:
|
||||
echo: false
|
||||
warning: true
|
||||
---
|
||||
```{r libraries}
|
||||
library(colSBM)
|
||||
library(here)
|
||||
library(ggplot2)
|
||||
```
|
||||
|
||||
```{r constants}
|
||||
base_folder <- here("code", "results", "investigating", "vem_steps")
|
||||
|
||||
|
||||
if (!dir.exists(base_folder)) {
|
||||
dir.create(base_folder, recursive = TRUE)
|
||||
}
|
||||
|
||||
net_seed <- 0
|
||||
test_seeds <- c(12, 3)
|
||||
epsilons <- c(0.1, 0.4)
|
||||
vem_steps <- seq(10, 300, by = 40)
|
||||
|
||||
conditions <- expand.grid(
|
||||
seeds = test_seeds,
|
||||
epsilons = epsilons,
|
||||
vem_steps = vem_steps
|
||||
)
|
||||
|
||||
|
||||
base_alpha <- matrix(rep(0.3, 9L), nrow = 3L)
|
||||
pi <- c(0.3, 0.2, 0.5)
|
||||
rho <- c(0.55, 0.15, 0.3)
|
||||
M <- 10L
|
||||
nr <- c(rep(30L, M / 2L), rep(95L, M / 2L))
|
||||
nc <- c(rep(40L, M / 2L), rep(70L, M / 2L))
|
||||
```
|
||||
|
||||
```{r}
|
||||
df <- readRDS(file.path(base_folder, "impact_vem_1718196265.Rds"))
|
||||
|
||||
df$eps <- factor(df$eps)
|
||||
df$seed <- factor(df$seed)
|
||||
df$elapsed_time <- as.numeric(df$elapsed_time)
|
||||
```
|
||||
|
||||
```{r ari}
|
||||
ggplot(df) +
|
||||
aes(x = max_vem_steps, y = ari, color = eps) +
|
||||
geom_line(aes(linetype = seed)) +
|
||||
geom_point()
|
||||
```
|
||||
|
||||
```{r time}
|
||||
ggplot(df) +
|
||||
aes(x = max_vem_steps, y = elapsed_time, color = eps) +
|
||||
geom_line(aes(linetype = seed)) +
|
||||
geom_point()
|
||||
```
|
||||
170
code/analysis/investigating/profiling_clustering.R
Normal file
170
code/analysis/investigating/profiling_clustering.R
Normal file
|
|
@ -0,0 +1,170 @@
|
|||
library(colSBM)
|
||||
library(aricode)
|
||||
library(here)
|
||||
|
||||
|
||||
base_folder <- here("code", "results", "investigating", "profiling_clustering")
|
||||
|
||||
|
||||
if (!dir.exists(base_folder)) {
|
||||
dir.create(base_folder, recursive = TRUE)
|
||||
}
|
||||
|
||||
net_seed <- 0
|
||||
test_seeds <- c(12, 3)
|
||||
epsilons <- c(0.1, 0.4)
|
||||
vem_steps <- seq(10, 300, by = 40)
|
||||
|
||||
conditions <- expand.grid(
|
||||
seeds = test_seeds,
|
||||
epsilons = epsilons,
|
||||
vem_steps = vem_steps
|
||||
)
|
||||
|
||||
|
||||
base_alpha <- matrix(rep(0.3, 9L), nrow = 3L)
|
||||
pi <- c(0.3, 0.2, 0.5)
|
||||
rho <- c(0.55, 0.15, 0.3)
|
||||
M <- 10L
|
||||
nr <- c(rep(30L, M / 2L), rep(95L, M / 2L))
|
||||
nc <- c(rep(40L, M / 2L), rep(70L, M / 2L))
|
||||
|
||||
generate_net <- function(eps, net_seed = 0) {
|
||||
set.seed(net_seed)
|
||||
as_alpha <- base_alpha + matrix(
|
||||
c(
|
||||
eps, -eps / 2L, -eps / 2L,
|
||||
-eps / 2L, eps, -eps / 2L,
|
||||
-eps / 2L, -eps / 2L, eps
|
||||
),
|
||||
nrow = 3L
|
||||
)
|
||||
|
||||
cp_alpha <- base_alpha + matrix(
|
||||
c(
|
||||
3L * eps / 2L, eps, eps / 2L,
|
||||
eps, eps / 2L, 0L,
|
||||
eps / 2L, 0L, -eps / 2L
|
||||
),
|
||||
nrow = 3L
|
||||
)
|
||||
|
||||
dis_alpha <- base_alpha + matrix(
|
||||
c(
|
||||
-eps / 2L, eps, eps,
|
||||
eps, -eps / 2L, eps,
|
||||
eps, eps, -eps / 2L
|
||||
),
|
||||
nrow = 3L
|
||||
)
|
||||
|
||||
collection <- c(
|
||||
generate_bipartite_collection(
|
||||
nr = nr, nc = nc,
|
||||
pi = pi, rho = rho,
|
||||
alpha = as_alpha, M = M
|
||||
),
|
||||
generate_bipartite_collection(
|
||||
nr = nr, nc = nc,
|
||||
pi = pi, rho = rho,
|
||||
alpha = cp_alpha, M = M
|
||||
),
|
||||
generate_bipartite_collection(
|
||||
nr = nr, nc = nc,
|
||||
pi = pi, rho = rho,
|
||||
alpha = dis_alpha, M = M
|
||||
)
|
||||
)
|
||||
names(collection) <- c(
|
||||
0 + seq(0, M %/% 2), 0 + seq(M %/% 2 + 1, M - 1),
|
||||
10 + seq(0, M %/% 2), 10 + seq(M %/% 2 + 1, M - 1),
|
||||
20 + seq(0, M %/% 2), 20 + seq(M %/% 2 + 1, M - 1)
|
||||
)
|
||||
|
||||
return(collection)
|
||||
}
|
||||
|
||||
list_collections <- lapply(epsilons, function(eps) {
|
||||
generate_net(eps = eps, net_seed = net_seed)
|
||||
})
|
||||
names(list_collections) <- epsilons
|
||||
|
||||
true_clustering <- c(rep(1, M), rep(2, M), rep(3, M))
|
||||
|
||||
begin_time <- format(Sys.time(), "%s")
|
||||
tmp_folder <- file.path(base_folder, paste0("tmp", begin_time))
|
||||
|
||||
if (!dir.exists(tmp_folder)) {
|
||||
dir.create(tmp_folder, recursive = TRUE)
|
||||
}
|
||||
|
||||
results <- parallel::mclapply(seq_len(nrow(conditions)), function(idx) {
|
||||
current_seed <- conditions[["seeds"]][idx]
|
||||
eps <- conditions[["epsilons"]][idx]
|
||||
max_vem_steps <- conditions[["vem_steps"]][idx]
|
||||
|
||||
message("Condition ", idx, " on ", nrow(conditions))
|
||||
|
||||
collection <- list_collections[[as.character(eps)]]
|
||||
|
||||
set.seed(current_seed)
|
||||
start_time <- Sys.time()
|
||||
clust <- clusterize_bipartite_networks(
|
||||
netlist = collection, net_id = names(collection),
|
||||
colsbm_model = "iid", fit_opts = list(max_vem_steps = max_vem_steps),
|
||||
global_opts = list(
|
||||
verbosity = 0L,
|
||||
nb_cores = parallelly::availableCores(omit = 1L)
|
||||
)
|
||||
)
|
||||
stop_time <- Sys.time()
|
||||
|
||||
elapsed_time <- stop_time - start_time
|
||||
|
||||
unlisted_best_partition <- extract_best_bipartite_partition(clust)
|
||||
|
||||
if (!is.list(unlisted_best_partition)) {
|
||||
unlisted_best_partition <- list(unlisted_best_partition)
|
||||
}
|
||||
|
||||
clustering_vec <- sort(unlist(lapply(seq_len(length(unlisted_best_partition)), function(idx) {
|
||||
ids_nets <- as.numeric(unlisted_best_partition[[idx]]$net_id)
|
||||
names(ids_nets) <- rep(idx, length(ids_nets))
|
||||
ids_nets
|
||||
})))
|
||||
|
||||
cluster_membership <- as.numeric(names(clustering_vec))
|
||||
|
||||
ari <- try(ARI(cluster_membership, true_clustering))
|
||||
|
||||
if (inherits(ari, "try-error")) {
|
||||
ari <- NA
|
||||
}
|
||||
|
||||
out <- data.frame(
|
||||
eps = eps, seed = current_seed,
|
||||
max_vem_steps = max_vem_steps,
|
||||
ari = ari,
|
||||
elapsed_time = elapsed_time,
|
||||
start_time = start_time,
|
||||
stop_time = stop_time,
|
||||
clustering = matrix(
|
||||
cluster_membership,
|
||||
nrow = 1L
|
||||
)
|
||||
)
|
||||
saveRDS(out,
|
||||
file = file.path(
|
||||
tmp_folder,
|
||||
paste0("c_", idx, "_on_", nrow(conditions), ".Rds")
|
||||
)
|
||||
)
|
||||
message("Finished condition ", idx)
|
||||
out
|
||||
},
|
||||
mc.cores = parallelly::availableCores(omit = 1L)
|
||||
)
|
||||
|
||||
to_save <- do.call(rbind, results)
|
||||
filename_to_save <- paste0("impact_vem_", begin_time, ".Rds")
|
||||
saveRDS(to_save, file = file.path(base_folder, filename_to_save))
|
||||
Loading…
Add table
Reference in a new issue