def _get_slice_plot(study: Study,
params: Optional[List[str]] = None) -> "go.Figure":
layout = go.Layout(title="Slice 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 None:
sorted_params = sorted(list(all_params))
else:
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))
sorted_params = sorted(list(set(params)))
n_params = len(sorted_params)
if n_params == 1:
figure = go.Figure(
data=[_generate_slice_subplot(study, trials, sorted_params[0])],
layout=layout)
figure.update_xaxes(title_text=sorted_params[0])
figure.update_yaxes(title_text="Objective Value")
if _is_log_scale(trials, sorted_params[0]):
figure.update_xaxes(type="log")
else:
figure = make_subplots(rows=1,
cols=len(sorted_params),
shared_yaxes=True)
figure.update_layout(layout)
showscale = True # showscale option only needs to be specified once.
for i, param in enumerate(sorted_params):
trace = _generate_slice_subplot(study, trials, param)
trace.update(marker={"showscale":
showscale}) # showscale's default is True.
if showscale:
showscale = False
figure.add_trace(trace, row=1, col=i + 1)
figure.update_xaxes(title_text=param, row=1, col=i + 1)
if i == 0:
figure.update_yaxes(title_text="Objective Value", row=1, col=1)
if _is_log_scale(trials, param):
figure.update_xaxes(type="log", row=1, col=i + 1)
if n_params > 3:
# Ensure that each subplot has a minimum width without relying on autusizing.
figure.update_layout(width=300 * n_params)
return figure
def _get_slice_plot(info: _SlicePlotInfo) -> "go.Figure":
layout = go.Layout(title="Slice Plot")
if len(info.subplots) == 0:
return go.Figure(data=[], layout=layout)
elif len(info.subplots) == 1:
figure = go.Figure(data=[_generate_slice_subplot(info.subplots[0])],
layout=layout)
figure.update_xaxes(title_text=info.subplots[0].param_name)
figure.update_yaxes(title_text=info.target_name)
if info.subplots[0].is_log:
figure.update_xaxes(type="log")
else:
figure = make_subplots(rows=1,
cols=len(info.subplots),
shared_yaxes=True)
figure.update_layout(layout)
showscale = True # showscale option only needs to be specified once.
for column_index, subplot_info in enumerate(info.subplots, start=1):
trace = _generate_slice_subplot(subplot_info)
trace.update(marker={"showscale":
showscale}) # showscale's default is True.
if showscale:
showscale = False
figure.add_trace(trace, row=1, col=column_index)
figure.update_xaxes(title_text=subplot_info.param_name,
row=1,
col=column_index)
if column_index == 1:
figure.update_yaxes(title_text=info.target_name,
row=1,
col=column_index)
if subplot_info.is_log:
figure.update_xaxes(type="log", row=1, col=column_index)
if len(info.subplots) > 3:
# Ensure that each subplot has a minimum width without relying on autusizing.
figure.update_layout(width=300 * len(info.subplots))
return figure
def _get_contour_plot(info: _ContourInfo) -> "go.Figure":
layout = go.Layout(title="Contour Plot")
sorted_params = info.sorted_params
sub_plot_infos = info.sub_plot_infos
reverse_scale = info.reverse_scale
target_name = info.target_name
if len(sorted_params) <= 1:
return go.Figure(data=[], layout=layout)
if len(sorted_params) == 2:
x_param = sorted_params[0]
y_param = sorted_params[1]
sub_plot_info = sub_plot_infos[0][0]
sub_plots = _get_contour_subplot(sub_plot_info, reverse_scale, target_name)
figure = go.Figure(data=sub_plots, layout=layout)
figure.update_xaxes(title_text=x_param, range=sub_plot_info.xaxis.range)
figure.update_yaxes(title_text=y_param, range=sub_plot_info.yaxis.range)
if sub_plot_info.xaxis.is_cat:
figure.update_xaxes(type="category")
if sub_plot_info.yaxis.is_cat:
figure.update_yaxes(type="category")
if sub_plot_info.xaxis.is_log:
log_range = [math.log10(p) for p in sub_plot_info.xaxis.range]
figure.update_xaxes(range=log_range, type="log")
if sub_plot_info.yaxis.is_log:
log_range = [math.log10(p) for p in sub_plot_info.yaxis.range]
figure.update_yaxes(range=log_range, type="log")
else:
figure = make_subplots(
rows=len(sorted_params), cols=len(sorted_params), shared_xaxes=True, shared_yaxes=True
)
figure.update_layout(layout)
showscale = True # showscale option only needs to be specified once.
for x_i, x_param in enumerate(sorted_params):
for y_i, y_param in enumerate(sorted_params):
if x_param == y_param:
figure.add_trace(go.Scatter(), row=y_i + 1, col=x_i + 1)
else:
sub_plots = _get_contour_subplot(
sub_plot_infos[y_i][x_i], reverse_scale, target_name
)
contour = sub_plots[0]
scatter = sub_plots[1]
contour.update(showscale=showscale) # showscale's default is True.
if showscale:
showscale = False
figure.add_trace(contour, row=y_i + 1, col=x_i + 1)
figure.add_trace(scatter, row=y_i + 1, col=x_i + 1)
xaxis = sub_plot_infos[y_i][x_i].xaxis
yaxis = sub_plot_infos[y_i][x_i].yaxis
figure.update_xaxes(range=xaxis.range, row=y_i + 1, col=x_i + 1)
figure.update_yaxes(range=yaxis.range, row=y_i + 1, col=x_i + 1)
if xaxis.is_cat:
figure.update_xaxes(type="category", row=y_i + 1, col=x_i + 1)
if yaxis.is_cat:
figure.update_yaxes(type="category", row=y_i + 1, col=x_i + 1)
if xaxis.is_log:
log_range = [math.log10(p) for p in xaxis.range]
figure.update_xaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1)
if yaxis.is_log:
log_range = [math.log10(p) for p in yaxis.range]
figure.update_yaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1)
if x_i == 0:
figure.update_yaxes(title_text=y_param, row=y_i + 1, col=x_i + 1)
if y_i == len(sorted_params) - 1:
figure.update_xaxes(title_text=x_param, row=y_i + 1, col=x_i + 1)
return figure
def _get_contour_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="Contour Plot")
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 None:
sorted_params = sorted(all_params)
elif len(params) <= 1:
_logger.warning("The length of params must be greater than 1.")
return go.Figure(data=[], layout=layout)
else:
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))
sorted_params = sorted(set(params))
padding_ratio = 0.05
param_values_range = {}
for p_name in sorted_params:
values = _get_param_values(trials, p_name)
min_value = min(values)
max_value = max(values)
if _is_log_scale(trials, p_name):
padding = (math.log10(max_value) -
math.log10(min_value)) * padding_ratio
min_value = math.pow(10, math.log10(min_value) - padding)
max_value = math.pow(10, math.log10(max_value) + padding)
elif _is_numerical(trials, p_name):
padding = (max_value - min_value) * padding_ratio
min_value = min_value - padding
max_value = max_value + padding
else:
# Plotly>=4.12.0 draws contours using the indices of categorical variables instead of
# raw values and the range should be updated based on the cardinality of categorical
# variables. See https://github.com/optuna/optuna/issues/1967.
if version.parse(plotly.__version__) >= version.parse("4.12.0"):
span = len(set(values)) - 1
padding = span * padding_ratio
min_value = -padding
max_value = span + padding
param_values_range[p_name] = (min_value, max_value)
reverse_scale = _is_reverse_scale(study, target)
if len(sorted_params) == 2:
x_param = sorted_params[0]
y_param = sorted_params[1]
sub_plots = _generate_contour_subplot(trials, x_param, y_param,
reverse_scale,
param_values_range, target,
target_name)
figure = go.Figure(data=sub_plots, layout=layout)
figure.update_xaxes(title_text=x_param,
range=param_values_range[x_param])
figure.update_yaxes(title_text=y_param,
range=param_values_range[y_param])
if not _is_numerical(trials, x_param):
figure.update_xaxes(type="category")
if not _is_numerical(trials, y_param):
figure.update_yaxes(type="category")
if _is_log_scale(trials, x_param):
log_range = [math.log10(p) for p in param_values_range[x_param]]
figure.update_xaxes(range=log_range, type="log")
if _is_log_scale(trials, y_param):
log_range = [math.log10(p) for p in param_values_range[y_param]]
figure.update_yaxes(range=log_range, type="log")
else:
figure = make_subplots(rows=len(sorted_params),
cols=len(sorted_params),
shared_xaxes=True,
shared_yaxes=True)
figure.update_layout(layout)
showscale = True # showscale option only needs to be specified once
for x_i, x_param in enumerate(sorted_params):
for y_i, y_param in enumerate(sorted_params):
if x_param == y_param:
figure.add_trace(go.Scatter(), row=y_i + 1, col=x_i + 1)
else:
sub_plots = _generate_contour_subplot(
trials,
x_param,
y_param,
reverse_scale,
param_values_range,
target,
target_name,
)
contour = sub_plots[0]
scatter = sub_plots[1]
contour.update(
showscale=showscale) # showscale's default is True
if showscale:
showscale = False
figure.add_trace(contour, row=y_i + 1, col=x_i + 1)
figure.add_trace(scatter, row=y_i + 1, col=x_i + 1)
figure.update_xaxes(range=param_values_range[x_param],
row=y_i + 1,
col=x_i + 1)
figure.update_yaxes(range=param_values_range[y_param],
row=y_i + 1,
col=x_i + 1)
if not _is_numerical(trials, x_param):
figure.update_xaxes(type="category",
row=y_i + 1,
col=x_i + 1)
if not _is_numerical(trials, y_param):
figure.update_yaxes(type="category",
row=y_i + 1,
col=x_i + 1)
if _is_log_scale(trials, x_param):
log_range = [
math.log10(p) for p in param_values_range[x_param]
]
figure.update_xaxes(range=log_range,
type="log",
row=y_i + 1,
col=x_i + 1)
if _is_log_scale(trials, y_param):
log_range = [
math.log10(p) for p in param_values_range[y_param]
]
figure.update_yaxes(range=log_range,
type="log",
row=y_i + 1,
col=x_i + 1)
if x_i == 0:
figure.update_yaxes(title_text=y_param,
row=y_i + 1,
col=x_i + 1)
if y_i == len(sorted_params) - 1:
figure.update_xaxes(title_text=x_param,
row=y_i + 1,
col=x_i + 1)
return figure
def _get_contour_plot(study: Study, params: Optional[List[str]] = None) -> "go.Figure":
layout = go.Layout(title="Contour 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 None:
sorted_params = sorted(list(all_params))
elif len(params) <= 1:
_logger.warning("The length of params must be greater than 1.")
return go.Figure(data=[], layout=layout)
else:
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))
sorted_params = sorted(list(set(params)))
padding_ratio = 0.05
param_values_range = {}
update_category_axes = {}
for p_name in sorted_params:
values = _get_param_values(trials, p_name)
min_value = min(values)
max_value = max(values)
if _is_log_scale(trials, p_name):
padding = (math.log10(max_value) - math.log10(min_value)) * padding_ratio
min_value = math.pow(10, math.log10(min_value) - padding)
max_value = math.pow(10, math.log10(max_value) + padding)
elif _is_categorical(trials, p_name):
# For numeric values, plotly does not automatically plot as "category" type.
update_category_axes[p_name] = any([str(v).isnumeric() for v in set(values)])
else:
padding = (max_value - min_value) * padding_ratio
min_value = min_value - padding
max_value = max_value + padding
param_values_range[p_name] = (min_value, max_value)
if len(sorted_params) == 2:
x_param = sorted_params[0]
y_param = sorted_params[1]
sub_plots = _generate_contour_subplot(
trials, x_param, y_param, study.direction, param_values_range
)
figure = go.Figure(data=sub_plots, layout=layout)
figure.update_xaxes(title_text=x_param, range=param_values_range[x_param])
figure.update_yaxes(title_text=y_param, range=param_values_range[y_param])
if update_category_axes.get(x_param, False):
figure.update_xaxes(type="category")
if update_category_axes.get(y_param, False):
figure.update_yaxes(type="category")
if _is_log_scale(trials, x_param):
log_range = [math.log10(p) for p in param_values_range[x_param]]
figure.update_xaxes(range=log_range, type="log")
if _is_log_scale(trials, y_param):
log_range = [math.log10(p) for p in param_values_range[y_param]]
figure.update_yaxes(range=log_range, type="log")
else:
figure = make_subplots(
rows=len(sorted_params), cols=len(sorted_params), shared_xaxes=True, shared_yaxes=True
)
figure.update_layout(layout)
showscale = True # showscale option only needs to be specified once
for x_i, x_param in enumerate(sorted_params):
for y_i, y_param in enumerate(sorted_params):
if x_param == y_param:
figure.add_trace(go.Scatter(), row=y_i + 1, col=x_i + 1)
else:
sub_plots = _generate_contour_subplot(
trials, x_param, y_param, study.direction, param_values_range
)
contour = sub_plots[0]
scatter = sub_plots[1]
contour.update(showscale=showscale) # showscale's default is True
if showscale:
showscale = False
figure.add_trace(contour, row=y_i + 1, col=x_i + 1)
figure.add_trace(scatter, row=y_i + 1, col=x_i + 1)
figure.update_xaxes(range=param_values_range[x_param], row=y_i + 1, col=x_i + 1)
figure.update_yaxes(range=param_values_range[y_param], row=y_i + 1, col=x_i + 1)
if update_category_axes.get(x_param, False):
figure.update_xaxes(type="category", row=y_i + 1, col=x_i + 1)
if update_category_axes.get(y_param, False):
figure.update_yaxes(type="category", row=y_i + 1, col=x_i + 1)
if _is_log_scale(trials, x_param):
log_range = [math.log10(p) for p in param_values_range[x_param]]
figure.update_xaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1)
if _is_log_scale(trials, y_param):
log_range = [math.log10(p) for p in param_values_range[y_param]]
figure.update_yaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1)
if x_i == 0:
figure.update_yaxes(title_text=y_param, row=y_i + 1, col=x_i + 1)
if y_i == len(sorted_params) - 1:
figure.update_xaxes(title_text=x_param, row=y_i + 1, col=x_i + 1)
return figure
