Enhance plot_parallel_coordinate() by eliminating redundant for-loops

[c-bata]

Motivation

In parallel coordinate plot, _is_log_scale(), _is_categorical(), and _is_numerical() functions are invoked.

optuna/optuna/visualization/_parallel_coordinate.py

Lines 213 to 237 in fcffbea

	if _is_log_scale(trials, p_name):
	values = [math.log10(v) for v in values]
	min_value = min(values)
	max_value = max(values)
	tickvals: list[int | float] = list(
	range(math.ceil(min_value), math.floor(max_value) + 1)
	)
	if min_value not in tickvals:
	tickvals = [min_value] + tickvals
	if max_value not in tickvals:
	tickvals = tickvals + [max_value]
	dim = _DimensionInfo(
	label=_truncate_label(p_name),
	values=tuple(values),
	range=(min_value, max_value),
	is_log=True,
	is_cat=False,
	tickvals=tickvals,
	ticktext=["{:.3g}".format(math.pow(10, x)) for x in tickvals],
	)
	elif _is_categorical(trials, p_name):
	vocab: defaultdict[int | str, int] = defaultdict(lambda: len(vocab))
	ticktext: list[str]
	if _is_numerical(trials, p_name):

Each one of these functions iterates over the trials, which is clearly redundant.

optuna/optuna/visualization/_utils.py

Lines 72 to 98 in fcffbea

	def _is_log_scale(trials: list[FrozenTrial], param: str) -> bool:
	for trial in trials:
	if param in trial.params:
	dist = trial.distributions[param]
	if isinstance(dist, (FloatDistribution, IntDistribution)):
	if dist.log:
	return True
	return False
	def _is_categorical(trials: list[FrozenTrial], param: str) -> bool:
	return any(
	isinstance(t.distributions[param], CategoricalDistribution)
	for t in trials
	if param in t.params
	)
	def _is_numerical(trials: list[FrozenTrial], param: str) -> bool:
	return all(
	(isinstance(t.params[param], int) or isinstance(t.params[param], float))
	and not isinstance(t.params[param], bool)
	for t in trials
	if param in t.params
	)

Suggestion

Making plot_parallel_coorinate() faster by removing wasted for-loop.

Additional context (optional)

No response