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Merged
egenn merged 2 commits into from
Jul 3, 2026
Merged

Develop #12

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6ba1fca
move session to table to rtemis::session_timeline
egenn Jul 3, 2026
7534d1d
require rtemis 1.2.8
egenn Jul 3, 2026
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2 changes: 1 addition & 1 deletion r/DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@ Roxygen: list(markdown = TRUE)
Imports:
htmlwidgets,
jsonlite,
rtemis (>= 1.2.4),
rtemis (>= 1.2.8),
rtemis.core (>= 0.4.1),
S7,
signal,
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185 changes: 17 additions & 168 deletions r/R/plot.SupervisedSession.R
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Expand Up @@ -13,7 +13,12 @@ SupervisedSession <- utils::getFromNamespace("SupervisedSession", "rtemis")
#' Render the execution graph captured in a `SupervisedSession` (from rtemis
#' `train()`) as a timeline / Gantt chart: one bar per recorded step, ordered as
#' a depth-first walk of the execution tree, positioned by elapsed time and
#' colored by status (`ok`, `error`, `aborted`, `running`).
#' colored by node kind (failed/aborted steps are outlined in red).
#'
#' The timeline table and the kind-color map come from
#' `rtemis::session_timeline()` and `rtemis::session_kind_colors()`, the shared
#' helpers also used by rtemis.server for the rtemislive web UI, so both
#' renderers stay in sync.
#'
#' @param x `rtemis::SupervisedSession`: Session object, e.g. `model@session`.
#' @param title Optional Character: Chart title.
Expand All @@ -38,180 +43,24 @@ S7::method(plot, SupervisedSession) <- function(
filename = NULL,
...
) {
events <- x@events
if (length(events) == 0L) {
abort(
"This SupervisedSession has no recorded events to plot.",
class = c("rtemis_value_error", "rtemis_input_error")
)
}
started <- x@started

# Elapsed milliseconds from session start.
to_ms <- function(t) {
if (is.null(t) || length(t) == 0L || is.na(t)) {
return(NA_real_)
}
as.numeric(difftime(t, started, units = "secs")) * 1000
}

# End fallback for nodes that never closed (running / aborted, NA t_end):
# the session finish time, else the latest known start.
finished <- x@finished
starts_ms <- vapply(events, function(e) to_ms(e[["t_start"]]), numeric(1L))
end_fallback <- if (
!is.null(finished) && length(finished) > 0L && !is.na(finished)
) {
to_ms(finished)
} else if (all(is.na(starts_ms))) {
# Degenerate session (no finish time, no recorded starts): fall back to 0 so
# we never propagate -Inf (and its warning) into the bar geometry.
0
} else {
max(starts_ms, na.rm = TRUE)
}

# -- Rebuild the execution tree and order nodes depth-first ------------------
# Mirrors the console repr in rtemis: index by node_id, collect children per
# parent (preserving insertion order), then DFS from the roots.
ids <- vapply(events, function(e) e[["node_id"]], character(1L))
by_id <- stats::setNames(events, ids)
parent_of <- vapply(
events,
function(e) {
p <- e[["parent_id"]]
if (is.null(p) || length(p) == 0L) NA_character_ else as.character(p)
},
character(1L)
)
children <- list()
roots <- character(0L)
for (i in seq_along(events)) {
p <- parent_of[[i]]
if (is.na(p) || is.null(by_id[[p]])) {
roots <- c(roots, ids[[i]])
} else {
children[[p]] <- c(children[[p]], ids[[i]])
}
}
ordered_ids <- character(0L)
depths <- integer(0L)
walk <- function(id, depth) {
ordered_ids[[length(ordered_ids) + 1L]] <<- id
depths[[length(depths) + 1L]] <<- depth
for (k in children[[id]]) {
walk(k, depth + 1L)
}
}
for (r in roots) {
walk(r, 0L)
}

fmt_dur <- function(ms) {
if (is.na(ms)) {
return("running")
}
if (ms < 0.5) {
return("<0.5 ms")
}
if (ms < 1000) {
return(sprintf("%.0f ms", ms))
}
sprintf("%.1f s", ms / 1000)
}

# -- Build the tasks frame in DFS order --------------------------------------
n <- length(ordered_ids)
label <- character(n)
start <- numeric(n)
end <- numeric(n)
status <- character(n)
kinds <- character(n)
tip <- character(n)
for (i in seq_len(n)) {
e <- by_id[[ordered_ids[[i]]]]
kind <- e[["kind"]]
kinds[[i]] <- kind
lbl <- e[["label"]]
# Non-redundant name: append the label only when it adds to `kind`.
nm <- if (!is.null(lbl) && nzchar(lbl) && !identical(lbl, kind)) {
paste(kind, lbl)
} else {
kind
}
# Indent by depth to convey hierarchy on the category axis.
label[[i]] <- paste0(strrep(" ", depths[[i]]), nm)
st <- to_ms(e[["t_start"]])
en <- to_ms(e[["t_end"]])
if (is.na(st)) {
st <- 0
}
if (is.na(en)) {
en <- end_fallback
}
if (en < st) {
en <- st
}
start[[i]] <- st
end[[i]] <- en
status[[i]] <- e[["status"]] %||% "ok"
tip[[i]] <- paste0(
trimws(nm),
" \u2014 ",
fmt_dur(en - st),
" [",
status[[i]],
"]"
)
}
# One row per node, DFS order, ms offsets, unique indented labels, tooltip
# text, and a `failed` flag -- all computed by the shared rtemis helper.
tasks <- rtemis::session_timeline(x)

# Duplicate display labels would collapse onto one category row; keep one row
# per node so the timeline matches the execution graph one-to-one.
label <- make.unique(label, sep = " #")

tasks <- data.frame(
label = label,
start = start,
end = end,
kind = kinds,
# Outline failed/aborted nodes (fill still encodes the kind, so a parallel
# failed cell keeps its siblings' color -> the same-color = parallel reading
# holds, while failures still pop via the red border).
failed = status %in% c("error", "aborted"),
tip = tip,
stringsAsFactors = FALSE
)

# Color by event KIND so the legend filters by type and -- critically -- the
# fill is identical for like events, making same-color overlap read as a
# parallel process (only grid cells run concurrently; containers and
# sequential steps never share a fill *and* a time span). Fixed map keeps each
# kind's color stable across runs; draw_gantt() zips groups in first-seen (DFS)
# order, so index the map by that order.
kind_colors <- c(
train = "#808080",
outer_fold = getFromNamespace("col_outer", "rtemis"),
tune = getFromNamespace("col_tuner", "rtemis"),
grid_cell = lighten(getFromNamespace("col_tuner", "rtemis"), 0.5),
preprocess = getFromNamespace("col_preprocessor", "rtemis"),
decompose = getFromNamespace("col_decom", "rtemis"),
train_alg = getFromNamespace("highlight_col", "rtemis"),
predict = rtemis.core::rtemis_colors[["blue"]],
varimp = rtemis.core::rtemis_colors[["light_blue"]],
metrics = rtemis.core::rtemis_colors[["orange"]]
)
present <- unique(tasks[["kind"]])
# Fall back to the default palette for any unmapped kind.
cols <- kind_colors[present]
# rep_len recycles the palette so more unmapped kinds than palette colors still
# get a (repeated) color rather than NA, which would break ECharts rendering.
cols[is.na(cols)] <- rep_len(rtemis_colors, sum(is.na(cols)))
# Color by event KIND so the legend filters by type and the fill is identical
# for like events, making same-color overlap read as a parallel process.
# draw_gantt() zips groups in first-seen (DFS) order, so index the map by
# that order.
cols <- rtemis::session_kind_colors(unique(tasks[["kind"]]))

draw_gantt(
tasks,
group = "kind",
axis_type = "value",
tooltip = "tip",
# Outline failed/aborted nodes (fill still encodes the kind, so a parallel
# failed cell keeps its siblings' color -> the same-color = parallel
# reading holds, while failures still pop via the red border).
border = "failed",
xlab = "Elapsed (ms)",
title = title,
Expand Down