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 test_is_log_scale():
# type: () -> None
study = create_study()
study._append_trial(value=0.0,
params={
'param_linear': 1.0,
},
distributions={
'param_linear': UniformDistribution(0.0, 3.0),
})
study._append_trial(value=2.0,
params={
'param_linear': 2.0,
'param_log': 1e-3,
},
distributions={
'param_linear': UniformDistribution(0.0, 3.0),
'param_log': LogUniformDistribution(1e-5, 1.),
})
assert _is_log_scale(study.trials, 'param_log')
assert not _is_log_scale(study.trials, 'param_linear')
def _get_contour_plot(study, params=None):
# type: (Study, Optional[List[str]]) -> 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)))
param_values_range = {}
for p_name in sorted_params:
values = [t.params[p_name] for t in trials if p_name in t.params]
param_values_range[p_name] = (min(values), max(values))
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)
figure = go.Figure(data=sub_plots)
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 _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)
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)
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 _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
