def _get_parallel_coordinate_plot(
info: _ParallelCoordinateInfo) -> "go.Figure":
layout = go.Layout(title="Parallel Coordinate Plot")
if len(info.dims_params) == 0 or len(info.dim_objective.values) == 0:
return go.Figure(data=[], layout=layout)
dims = _get_dims_from_info(info)
reverse_scale = info.reverse_scale
target_name = info.target_name
traces = [
go.Parcoords(
dimensions=dims,
labelangle=30,
labelside="bottom",
line={
"color": dims[0]["values"],
"colorscale": COLOR_SCALE,
"colorbar": {
"title": target_name
},
"showscale": True,
"reversescale": reverse_scale,
},
)
]
figure = go.Figure(data=traces, layout=layout)
return figure
def _get_parallel_coordinate_plot(study: Study,
params: Optional[List[str]] = None
) -> "go.Figure":
layout = go.Layout(title="Parallel Coordinate Plot")
trials = [
trial for trial in study.trials if trial.state == TrialState.COMPLETE
]
if len(trials) == 0:
_logger.warning("Your study does not have any completed trials.")
return go.Figure(data=[], layout=layout)
all_params = {p_name for t in trials for p_name in t.params.keys()}
if params is not None:
for input_p_name in params:
if input_p_name not in all_params:
raise ValueError(
"Parameter {} does not exist in your study.".format(
input_p_name))
all_params = set(params)
sorted_params = sorted(list(all_params))
dims: List[Dict[str, Any]] = [{
"label":
"Objective Value",
"values":
tuple([t.value for t in trials]),
"range":
(min([t.value for t in trials]), max([t.value for t in trials])),
}]
for p_name in sorted_params:
values = []
for t in trials:
if p_name in t.params:
values.append(t.params[p_name])
is_categorical = False
try:
tuple(map(float, values))
except (TypeError, ValueError):
vocab: DefaultDict[str, int] = defaultdict(lambda: len(vocab))
values = [vocab[v] for v in values]
is_categorical = True
dim = {
"label":
p_name if len(p_name) < 20 else "{}...".format(p_name[:17]),
"values": tuple(values),
"range": (min(values), max(values)),
}
if is_categorical:
dim["tickvals"] = list(range(len(vocab)))
dim["ticktext"] = list(sorted(vocab.items(), key=lambda x: x[1]))
dims.append(dim)
traces = [
go.Parcoords(
dimensions=dims,
labelangle=30,
labelside="bottom",
line={
"color": dims[0]["values"],
"colorscale": "blues",
"colorbar": {
"title": "Objective Value"
},
"showscale": True,
"reversescale": study.direction == StudyDirection.MINIMIZE,
},
)
]
figure = go.Figure(data=traces, layout=layout)
return figure
def _get_parallel_coordinate_plot(
study: Study,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "go.Figure":
layout = go.Layout(title="Parallel Coordinate Plot")
reverse_scale = _is_reverse_scale(study, target)
trials = _filter_nonfinite(
study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target
)
if len(trials) == 0:
_logger.warning("Your study does not have any completed trials.")
return go.Figure(data=[], layout=layout)
all_params = {p_name for t in trials for p_name in t.params.keys()}
if params is not None:
for input_p_name in params:
if input_p_name not in all_params:
raise ValueError("Parameter {} does not exist in your study.".format(input_p_name))
all_params = set(params)
sorted_params = sorted(all_params)
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
skipped_trial_ids = _get_skipped_trial_numbers(trials, sorted_params)
objectives = tuple([target(t) for t in trials if t.number not in skipped_trial_ids])
if len(objectives) == 0:
_logger.warning("Your study has only completed trials with missing parameters.")
return go.Figure(data=[], layout=layout)
dims: List[Dict[str, Any]] = [
{
"label": target_name,
"values": objectives,
"range": (min(objectives), max(objectives)),
}
]
numeric_cat_params_indices: List[int] = []
for dim_index, p_name in enumerate(sorted_params, start=1):
values = []
for t in trials:
if t.number in skipped_trial_ids:
continue
if p_name in t.params:
values.append(t.params[p_name])
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(range(math.ceil(min_value), math.ceil(max_value)))
if min_value not in tickvals:
tickvals = [min_value] + tickvals
if max_value not in tickvals:
tickvals = tickvals + [max_value]
dim = {
"label": p_name if len(p_name) < 20 else "{}...".format(p_name[:17]),
"values": tuple(values),
"range": (min_value, max_value),
"tickvals": tickvals,
"ticktext": ["{:.3g}".format(math.pow(10, x)) for x in tickvals],
}
elif _is_categorical(trials, p_name):
vocab: DefaultDict[str, int] = defaultdict(lambda: len(vocab))
if _is_numerical(trials, p_name):
_ = [vocab[v] for v in sorted(values)]
values = [vocab[v] for v in values]
ticktext = list(sorted(vocab.keys()))
numeric_cat_params_indices.append(dim_index)
else:
values = [vocab[v] for v in values]
ticktext = list(sorted(vocab.keys(), key=lambda x: vocab[x]))
dim = {
"label": p_name if len(p_name) < 20 else "{}...".format(p_name[:17]),
"values": tuple(values),
"range": (min(values), max(values)),
"tickvals": list(range(len(vocab))),
"ticktext": ticktext,
}
else:
dim = {
"label": p_name if len(p_name) < 20 else "{}...".format(p_name[:17]),
"values": tuple(values),
"range": (min(values), max(values)),
}
dims.append(dim)
if numeric_cat_params_indices:
# np.lexsort consumes the sort keys the order from back to front.
# So the values of parameters have to be reversed the order.
idx = np.lexsort([dims[index]["values"] for index in numeric_cat_params_indices][::-1])
for dim in dims:
# Since the values are mapped to other categories by the index,
# the index will be swapped according to the sorted index of numeric params.
dim.update({"values": tuple(np.array(dim["values"])[idx])})
traces = [
go.Parcoords(
dimensions=dims,
labelangle=30,
labelside="bottom",
line={
"color": dims[0]["values"],
"colorscale": COLOR_SCALE,
"colorbar": {"title": target_name},
"showscale": True,
"reversescale": reverse_scale,
},
)
]
figure = go.Figure(data=traces, layout=layout)
return figure
def _get_parallel_coordinate_plot(
study: Study,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "go.Figure":
layout = go.Layout(title="Parallel Coordinate Plot")
trials = [
trial for trial in study.trials if trial.state == TrialState.COMPLETE
]
if len(trials) == 0:
_logger.warning("Your study does not have any completed trials.")
return go.Figure(data=[], layout=layout)
all_params = {p_name for t in trials for p_name in t.params.keys()}
if params is not None:
for input_p_name in params:
if input_p_name not in all_params:
raise ValueError(
"Parameter {} does not exist in your study.".format(
input_p_name))
all_params = set(params)
sorted_params = sorted(list(all_params))
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
reversescale = study.direction == StudyDirection.MINIMIZE
else:
reversescale = True
dims: List[Dict[str, Any]] = [{
"label":
target_name,
"values":
tuple([target(t) for t in trials]),
"range":
(min([target(t) for t in trials]), max([target(t) for t in trials])),
}]
for p_name in sorted_params:
values = []
for t in trials:
if p_name in t.params:
values.append(t.params[p_name])
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(range(math.ceil(min_value), math.ceil(max_value)))
if min_value not in tickvals:
tickvals = [min_value] + tickvals
if max_value not in tickvals:
tickvals = tickvals + [max_value]
dim = {
"label":
p_name if len(p_name) < 20 else "{}...".format(p_name[:17]),
"values":
tuple(values),
"range": (min_value, max_value),
"tickvals":
tickvals,
"ticktext":
["{:.3g}".format(math.pow(10, x)) for x in tickvals],
}
elif _is_categorical(trials, p_name):
vocab: DefaultDict[str, int] = defaultdict(lambda: len(vocab))
values = [vocab[v] for v in values]
dim = {
"label":
p_name if len(p_name) < 20 else "{}...".format(p_name[:17]),
"values":
tuple(values),
"range": (min(values), max(values)),
"tickvals":
list(range(len(vocab))),
"ticktext":
list(sorted(vocab.keys(), key=lambda x: vocab[x])),
}
else:
dim = {
"label":
p_name if len(p_name) < 20 else "{}...".format(p_name[:17]),
"values":
tuple(values),
"range": (min(values), max(values)),
}
dims.append(dim)
traces = [
go.Parcoords(
dimensions=dims,
labelangle=30,
labelside="bottom",
line={
"color": dims[0]["values"],
"colorscale": "blues",
"colorbar": {
"title": target_name
},
"showscale": True,
"reversescale": reversescale,
},
)
]
figure = go.Figure(data=traces, layout=layout)
return figure