def _get_optimization_history_plot(
studies: List[Study],
target: Optional[Callable[[FrozenTrial], float]],
target_name: str,
) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Optimization History Plot")
ax.set_xlabel("#Trials")
ax.set_ylabel(target_name)
if len(studies) == 0:
_logger.warning("There are no studies.")
return ax
# Prepare data for plotting.
all_trials = list(
itertools.chain.from_iterable(
(trial for trial in study.get_trials(deepcopy=False)
if trial.state == TrialState.COMPLETE) for study in studies))
if len(all_trials) == 0:
_logger.warning("Study instance does not contain trials.")
return ax
ax = _get_optimization_histories(studies, target, target_name, ax)
plt.legend(bbox_to_anchor=(1.05, 1.0), loc="upper left")
return ax
def _get_intermediate_plot(info: _IntermediatePlotInfo) -> "Axes":
# Set up the graph style.
plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots(tight_layout=True)
ax.set_title("Intermediate Values Plot")
ax.set_xlabel("Step")
ax.set_ylabel("Intermediate Value")
cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots.
trial_infos = info.trial_infos
for i, tinfo in enumerate(trial_infos):
ax.plot(
tuple((x for x, _ in tinfo.sorted_intermediate_values)),
tuple((y for _, y in tinfo.sorted_intermediate_values)),
color=cmap(i),
alpha=0.7,
label="Trial{}".format(tinfo.trial_number),
)
if len(trial_infos) >= 2:
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0)
return ax
def _get_edf_plot(
studies: List[Study],
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Empirical Distribution Function Plot")
ax.set_xlabel(target_name)
ax.set_ylabel("Cumulative Probability")
ax.set_ylim(0, 1)
cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots.
# Prepare data for plotting.
if len(studies) == 0:
_logger.warning("There are no studies.")
return ax
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
all_values: List[np.ndarray] = []
for study in studies:
trials = _filter_nonfinite(study.get_trials(
deepcopy=False, states=(TrialState.COMPLETE, )),
target=target)
values = np.array([target(trial) for trial in trials])
all_values.append(values)
if all(len(values) == 0 for values in all_values):
_logger.warning("There are no complete trials.")
return ax
min_x_value = np.min(np.concatenate(all_values))
max_x_value = np.max(np.concatenate(all_values))
x_values = np.linspace(min_x_value, max_x_value, 100)
# Draw multiple line plots.
for i, (values, study) in enumerate(zip(all_values, studies)):
y_values = np.sum(values[:, np.newaxis] <= x_values,
axis=0) / values.size
ax.plot(x_values,
y_values,
color=cmap(i),
alpha=0.7,
label=study.study_name)
if len(studies) >= 2:
ax.legend()
return ax
def _get_edf_plot(
studies: List[Study],
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Empirical Distribution Function Plot")
ax.set_xlabel(target_name)
ax.set_ylabel("Cumulative Probability")
ax.set_ylim(0, 1)
cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots.
# Prepare data for plotting.
if len(studies) == 0:
_logger.warning("There are no studies.")
return ax
all_trials = list(
itertools.chain.from_iterable(
(trial for trial in study.get_trials(deepcopy=False)
if trial.state == TrialState.COMPLETE) for study in studies))
if len(all_trials) == 0:
_logger.warning("There are no complete trials.")
return ax
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
min_x_value = min(target(trial) for trial in all_trials)
max_x_value = max(target(trial) for trial in all_trials)
x_values = np.linspace(min_x_value, max_x_value, 100)
# Draw multiple line plots.
for i, study in enumerate(studies):
values = np.asarray([
target(trial) for trial in study.get_trials(deepcopy=False)
if trial.state == TrialState.COMPLETE
])
y_values = np.sum(values[:, np.newaxis] <= x_values,
axis=0) / values.size
ax.plot(x_values,
y_values,
color=cmap(i),
alpha=0.7,
label=study.study_name)
return ax
def _get_pareto_front_2d(info: _ParetoFrontInfo) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Pareto-front Plot")
cmap = plt.get_cmap(
"tab10") # Use tab10 colormap for similar outputs to plotly.
ax.set_xlabel(info.target_names[info.axis_order[0]])
ax.set_ylabel(info.target_names[info.axis_order[1]])
trial_label: str = "Trial"
if len(info.infeasible_trials_with_values) > 0:
ax.scatter(
x=[
values[info.axis_order[0]]
for _, values in info.infeasible_trials_with_values
],
y=[
values[info.axis_order[1]]
for _, values in info.infeasible_trials_with_values
],
color="#cccccc",
label="Infeasible Trial",
)
trial_label = "Feasible Trial"
if len(info.non_best_trials_with_values) > 0:
ax.scatter(
x=[
values[info.axis_order[0]]
for _, values in info.non_best_trials_with_values
],
y=[
values[info.axis_order[1]]
for _, values in info.non_best_trials_with_values
],
color=cmap(0),
label=trial_label,
)
if len(info.best_trials_with_values) > 0:
ax.scatter(
x=[
values[info.axis_order[0]]
for _, values in info.best_trials_with_values
],
y=[
values[info.axis_order[1]]
for _, values in info.best_trials_with_values
],
color=cmap(3),
label="Best Trial",
)
if info.non_best_trials_with_values is not None and ax.has_data():
ax.legend()
return ax
def _get_optimization_history_plot(
study: Study,
target: Optional[Callable[[FrozenTrial], float]],
target_name: str,
) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Optimization History Plot")
ax.set_xlabel("#Trials")
ax.set_ylabel(target_name)
cmap = plt.get_cmap(
"tab10") # Use tab10 colormap for similar outputs to plotly.
# Prepare data for plotting.
trials = [t for t in study.trials if t.state == TrialState.COMPLETE]
if len(trials) == 0:
_logger.warning("Study instance does not contain trials.")
return ax
# Draw a scatter plot and a line plot.
if target is None:
if study.direction == StudyDirection.MINIMIZE:
best_values = np.minimum.accumulate(
[cast(float, t.value) for t in trials])
else:
best_values = np.maximum.accumulate(
[cast(float, t.value) for t in trials])
ax.scatter(
x=[t.number for t in trials],
y=[t.value for t in trials],
color=cmap(0),
alpha=1,
label=target_name,
)
ax.plot(
[t.number for t in trials],
best_values,
marker="o",
color=cmap(3),
alpha=0.5,
label="Best Value",
)
ax.legend()
else:
ax.scatter(
x=[t.number for t in trials],
y=[target(t) for t in trials],
color=cmap(0),
alpha=1,
label=target_name,
)
return ax
def _get_optimization_history_plot(
info_list: List[_OptimizationHistoryInfo],
target_name: str,
) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Optimization History Plot")
ax.set_xlabel("Trial")
ax.set_ylabel(target_name)
cmap = plt.get_cmap(
"tab10") # Use tab10 colormap for similar outputs to plotly.
for i, (trial_numbers, values_info,
best_values_info) in enumerate(info_list):
if values_info.stds is not None:
plt.errorbar(
x=trial_numbers,
y=values_info.values,
yerr=values_info.stds,
capsize=5,
fmt="o",
color="tab:blue",
)
ax.scatter(
x=trial_numbers,
y=values_info.values,
color=cmap(0) if len(info_list) == 1 else cmap(2 * i),
alpha=1,
label=values_info.label_name,
)
if best_values_info is not None:
ax.plot(
trial_numbers,
best_values_info.values,
marker="o",
color=cmap(3) if len(info_list) == 1 else cmap(2 * i + 1),
alpha=0.5,
label=best_values_info.label_name,
)
if best_values_info.stds is not None:
lower = np.array(best_values_info.values) - np.array(
best_values_info.stds)
upper = np.array(best_values_info.values) + np.array(
best_values_info.stds)
ax.fill_between(
x=trial_numbers,
y1=lower,
y2=upper,
color="tab:red",
alpha=0.4,
)
ax.legend()
plt.legend(bbox_to_anchor=(1.05, 1.0), loc="upper left")
return ax
def _get_optimization_history_plot(
study: Study,
target: Optional[Callable[[FrozenTrial], float]],
target_name: str,
) -> "Axes":
# Set up the graph style.
plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Optimization History Plot")
ax.set_xlabel("#Trials")
ax.set_ylabel(target_name)
cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly.
# Prepare data for plotting.
trials = [t for t in study.trials if t.state == TrialState.COMPLETE]
if len(trials) == 0:
_logger.warning("Study instance does not contain trials.")
return ax
best_values = [float("inf")] if study.direction == StudyDirection.MINIMIZE else [-float("inf")]
comp = min if study.direction == StudyDirection.MINIMIZE else max
for trial in trials:
trial_value = trial.value
assert trial_value is not None # For mypy
best_values.append(comp(best_values[-1], trial_value))
best_values.pop(0)
# Draw a scatter plot and a line plot.
if target is None:
ax.scatter(
x=[t.number for t in trials],
y=[t.value for t in trials],
color=cmap(0),
alpha=1,
label=target_name,
)
ax.plot(
[t.number for t in trials],
best_values,
marker="o",
color=cmap(3),
alpha=0.5,
label="Best Value",
)
else:
ax.scatter(
x=[t.number for t in trials],
y=[target(t) for t in trials],
color=cmap(0),
alpha=1,
label=target_name,
)
ax.legend()
return ax
def _get_param_importance_plot(
study: Study,
evaluator: Optional[BaseImportanceEvaluator] = None,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
# Set up the graph style.
plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly.
fig, ax = plt.subplots()
ax.set_title("Hyperparameter Importances")
ax.set_xlabel(f"Importance for {target_name}")
ax.set_ylabel("Hyperparameter")
# Prepare data for plotting.
# Importances cannot be evaluated without completed trials.
# Return an empty figure for consistency with other visualization functions.
trials = [trial for trial in study.trials if trial.state == TrialState.COMPLETE]
if len(trials) == 0:
_logger.warning("Study instance does not contain completed trials.")
return ax
importances = optuna.importance.get_param_importances(
study, evaluator=evaluator, params=params, target=target
)
importances = OrderedDict(reversed(list(importances.items())))
importance_values = list(importances.values())
param_names = list(importances.keys())
pos = np.arange(len(param_names))
# Draw horizontal bars.
ax.barh(
pos,
importance_values,
align="center",
color=cm.get_cmap("tab20c")(0),
tick_label=param_names,
)
renderer = fig.canvas.get_renderer()
for idx, val in enumerate(importance_values):
label = f" {val:.2f}" if val >= 0.01 else " <0.01"
text = ax.text(val, idx, label, va="center")
# Sometimes horizontal axis needs to be re-scaled
# to avoid text going over plot area.
bbox = text.get_window_extent(renderer)
bbox = bbox.transformed(ax.transData.inverted())
_, plot_xmax = ax.get_xlim()
bbox_xmax = bbox.xmax
if bbox_xmax > plot_xmax:
ax.set_xlim(xmax=AXES_PADDING_RATIO * bbox_xmax)
return ax
def _get_contour_plot(info: _ContourInfo) -> "Axes":
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:
_, ax = plt.subplots()
return ax
n_params = len(sorted_params)
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
if n_params == 2:
# Set up the graph style.
fig, axs = plt.subplots()
axs.set_title("Contour Plot")
cmap = _set_cmap(reverse_scale)
cs = _generate_contour_subplot(sub_plot_infos[0][0], axs, cmap)
if isinstance(cs, ContourSet):
axcb = fig.colorbar(cs)
axcb.set_label(target_name)
else:
# Set up the graph style.
fig, axs = plt.subplots(n_params, n_params)
fig.suptitle("Contour Plot")
cmap = _set_cmap(reverse_scale)
# Prepare data and draw contour plots.
cs_list = []
for x_i in range(len(sorted_params)):
for y_i in range(len(sorted_params)):
ax = axs[y_i, x_i]
cs = _generate_contour_subplot(sub_plot_infos[y_i][x_i], ax,
cmap)
if isinstance(cs, ContourSet):
cs_list.append(cs)
if cs_list:
axcb = fig.colorbar(cs_list[0], ax=axs)
axcb.set_label(target_name)
return axs
def _get_slice_plot(info: _SlicePlotInfo) -> "Axes":
if len(info.subplots) == 0:
_, ax = plt.subplots()
return ax
# Set up the graph style.
cmap = plt.get_cmap("Blues")
padding_ratio = 0.05
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
if len(info.subplots) == 1:
# Set up the graph style.
fig, axs = plt.subplots()
axs.set_title("Slice Plot")
# Draw a scatter plot.
sc = _generate_slice_subplot(info.subplots[0], axs, cmap,
padding_ratio, info.target_name)
else:
# Set up the graph style.
min_figwidth = matplotlib.rcParams["figure.figsize"][0] / 2
fighight = matplotlib.rcParams["figure.figsize"][1]
# Ensure that each subplot has a minimum width without relying on auto-sizing.
fig, axs = plt.subplots(
1,
len(info.subplots),
sharey=True,
figsize=(min_figwidth * len(info.subplots), fighight),
)
fig.suptitle("Slice Plot")
# Draw scatter plots.
for i, subplot in enumerate(info.subplots):
ax = axs[i]
sc = _generate_slice_subplot(subplot, ax, cmap, padding_ratio,
info.target_name)
axcb = fig.colorbar(sc, ax=axs)
axcb.set_label("Trial")
return axs
def _get_param_importance_plot(
study: Study,
evaluator: Optional[BaseImportanceEvaluator] = None,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
# Set up the graph style.
_, ax = plt.subplots()
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
ax.set_title("Hyperparameter Importances")
ax.set_xlabel(f"Importance for {target_name}")
ax.set_ylabel("Hyperparameter")
# Prepare data for plotting.
# Importances cannot be evaluated without completed trials.
# Return an empty figure for consistency with other visualization functions.
trials = [
trial for trial in study.trials if trial.state == TrialState.COMPLETE
]
if len(trials) == 0:
_logger.warning("Study instance does not contain completed trials.")
return ax
importances = optuna.importance.get_param_importances(study,
evaluator=evaluator,
params=params,
target=target)
importances = OrderedDict(reversed(list(importances.items())))
importance_values = list(importances.values())
param_names = list(importances.keys())
pos = np.arange(len(param_names))
# Draw horizontal bars.
ax.barh(
pos,
importance_values,
align="center",
color=[_get_color(param_name, study) for param_name in param_names],
tick_label=param_names,
)
ax.legend(handles=_legend_elements,
title="Distributions",
loc="lower right")
return ax
def _get_intermediate_plot(study: Study) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots(tight_layout=True)
ax.set_title("Intermediate Values Plot")
ax.set_xlabel("Step")
ax.set_ylabel("Intermediate Value")
cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots.
# Prepare data for plotting.
target_state = [TrialState.PRUNED, TrialState.COMPLETE, TrialState.RUNNING]
trials = [trial for trial in study.trials if trial.state in target_state]
if len(trials) == 0:
_logger.warning("Study instance does not contain trials.")
return ax
# Draw multiple line plots.
traces = []
for i, trial in enumerate(trials):
if trial.intermediate_values:
sorted_intermediate_values = sorted(
trial.intermediate_values.items())
trace = ax.plot(
tuple((x for x, _ in sorted_intermediate_values)),
tuple((y for _, y in sorted_intermediate_values)),
color=cmap(i),
alpha=0.7,
label="Trial{}".format(trial.number),
)
traces.append(trace)
if not traces:
_logger.warning(
"You need to set up the pruning feature to utilize `plot_intermediate_values()`"
)
return ax
if len(trials) >= 2:
ax.legend(bbox_to_anchor=(1.05, 1),
loc="upper left",
borderaxespad=0.0)
return ax
def _get_importances_plot(info: _ImportancesInfo) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
fig, ax = plt.subplots()
ax.set_title("Hyperparameter Importances")
ax.set_xlabel(f"Importance for {info.target_name}")
ax.set_ylabel("Hyperparameter")
param_names = info.param_names
pos = np.arange(len(param_names))
importance_values = info.importance_values
if len(importance_values) == 0:
return ax
# Draw horizontal bars.
ax.barh(
pos,
importance_values,
align="center",
color=cm.get_cmap("tab20c")(0),
tick_label=param_names,
)
renderer = fig.canvas.get_renderer()
for idx, (val, label) in enumerate(
zip(importance_values, info.importance_labels)):
text = ax.text(val, idx, label, va="center")
# Sometimes horizontal axis needs to be re-scaled
# to avoid text going over plot area.
bbox = text.get_window_extent(renderer)
bbox = bbox.transformed(ax.transData.inverted())
_, plot_xmax = ax.get_xlim()
bbox_xmax = bbox.xmax
if bbox_xmax > plot_xmax:
ax.set_xlim(xmax=AXES_PADDING_RATIO * bbox_xmax)
return ax
def _get_pareto_front_2d(
study: Study,
target_names: Optional[List[str]],
include_dominated_trials: bool = False,
axis_order: Optional[List[int]] = None,
) -> "Axes":
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Pareto-front Plot")
cmap = plt.get_cmap(
"tab10") # Use tab10 colormap for similar outputs to plotly.
if target_names is None:
target_names = ["Objective 0", "Objective 1"]
elif len(target_names) != 2:
raise ValueError("The length of `target_names` is supposed to be 2.")
# Prepare data for plotting.
trials = study.best_trials
if len(trials) == 0:
_logger.warning("Your study does not have any completed trials.")
if include_dominated_trials:
non_pareto_trials = _get_non_pareto_front_trials(study, trials)
trials += non_pareto_trials
if axis_order is None:
axis_order = list(range(2))
else:
if len(axis_order) != 2:
raise ValueError(
f"Size of `axis_order` {axis_order}. Expect: 2, Actual: {len(axis_order)}."
)
if len(set(axis_order)) != 2:
raise ValueError(
f"Elements of given `axis_order` {axis_order} are not unique!")
if max(axis_order) > 1:
raise ValueError(
f"Given `axis_order` {axis_order} contains invalid index {max(axis_order)} "
"higher than 1.")
if min(axis_order) < 0:
raise ValueError(
f"Given `axis_order` {axis_order} contains invalid index {min(axis_order)} "
"lower than 0.")
ax.set_xlabel(target_names[axis_order[0]])
ax.set_ylabel(target_names[axis_order[1]])
if len(trials) - len(study.best_trials) != 0:
ax.scatter(
x=[
t.values[axis_order[0]]
for t in trials[len(study.best_trials):]
],
y=[
t.values[axis_order[1]]
for t in trials[len(study.best_trials):]
],
color=cmap(0),
label="Trial",
)
if len(study.best_trials):
ax.scatter(
x=[
t.values[axis_order[0]]
for t in trials[:len(study.best_trials)]
],
y=[
t.values[axis_order[1]]
for t in trials[:len(study.best_trials)]
],
color=cmap(3),
label="Best Trial",
)
if include_dominated_trials and ax.has_data():
ax.legend()
return ax
def _get_contour_plot(
study: Study,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
# Calculate basic numbers for plotting.
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.")
_, ax = plt.subplots()
return ax
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.")
_, ax = plt.subplots()
return ax
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))
n_params = len(sorted_params)
plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly.
if n_params == 2:
# Set up the graph style.
fig, axs = plt.subplots()
axs.set_title("Contour Plot")
cmap = _set_cmap(study, target)
contour_point_num = 100
# Prepare data and draw contour plots.
if params:
x_param = params[0]
y_param = params[1]
else:
x_param = sorted_params[0]
y_param = sorted_params[1]
cs = _generate_contour_subplot(
trials, x_param, y_param, axs, cmap, contour_point_num, target
)
if isinstance(cs, ContourSet):
axcb = fig.colorbar(cs)
axcb.set_label(target_name)
else:
# Set up the graph style.
fig, axs = plt.subplots(n_params, n_params)
fig.suptitle("Contour Plot")
cmap = _set_cmap(study, target)
contour_point_num = 100
# Prepare data and draw contour plots.
cs_list = []
for x_i, x_param in enumerate(sorted_params):
for y_i, y_param in enumerate(sorted_params):
ax = axs[y_i, x_i]
cs = _generate_contour_subplot(
trials, x_param, y_param, ax, cmap, contour_point_num, target
)
if isinstance(cs, ContourSet):
cs_list.append(cs)
if cs_list:
axcb = fig.colorbar(cs_list[0], ax=axs)
axcb.set_label(target_name)
return axs
def _get_parallel_coordinate_plot(info: _ParallelCoordinateInfo) -> "Axes":
reversescale = info.reverse_scale
target_name = info.target_name
# Set up the graph style.
fig, ax = plt.subplots()
cmap = plt.get_cmap("Blues_r" if reversescale else "Blues")
ax.set_title("Parallel Coordinate Plot")
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
# Prepare data for plotting.
if len(info.dims_params) == 0 or len(info.dim_objective.values) == 0:
return ax
obj_min = info.dim_objective.range[0]
obj_max = info.dim_objective.range[1]
obj_w = obj_max - obj_min
dims_obj_base = [[o] for o in info.dim_objective.values]
for dim in info.dims_params:
p_min = dim.range[0]
p_max = dim.range[1]
p_w = p_max - p_min
if p_w == 0.0:
center = obj_w / 2 + obj_min
for i in range(len(dim.values)):
dims_obj_base[i].append(center)
else:
for i, v in enumerate(dim.values):
dims_obj_base[i].append((v - p_min) / p_w * obj_w + obj_min)
# Draw multiple line plots and axes.
# Ref: https://stackoverflow.com/a/50029441
n_params = len(info.dims_params)
ax.set_xlim(0, n_params)
ax.set_ylim(info.dim_objective.range[0], info.dim_objective.range[1])
xs = [range(n_params + 1) for _ in range(len(dims_obj_base))]
segments = [np.column_stack([x, y]) for x, y in zip(xs, dims_obj_base)]
lc = LineCollection(segments, cmap=cmap)
lc.set_array(np.asarray(info.dim_objective.values))
axcb = fig.colorbar(lc, pad=0.1)
axcb.set_label(target_name)
var_names = [info.dim_objective.label
] + [dim.label for dim in info.dims_params]
plt.xticks(range(n_params + 1), var_names, rotation=330)
for i, dim in enumerate(info.dims_params):
ax2 = ax.twinx()
if dim.is_log:
ax2.set_ylim(np.power(10, dim.range[0]),
np.power(10, dim.range[1]))
ax2.set_yscale("log")
else:
ax2.set_ylim(dim.range[0], dim.range[1])
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.xaxis.set_visible(False)
ax2.spines["right"].set_position(("axes", (i + 1) / n_params))
if dim.is_cat:
ax2.set_yticks(dim.tickvals)
ax2.set_yticklabels(dim.ticktext)
ax.add_collection(lc)
return ax
def plot_edf(
study: Union[Study, Sequence[Study]],
*,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
"""Plot the objective value EDF (empirical distribution function) of a study with Matplotlib.
Note that only the complete trials are considered when plotting the EDF.
.. seealso::
Please refer to :func:`optuna.visualization.plot_edf` for an example,
where this function can be replaced with it.
.. note::
Please refer to `matplotlib.pyplot.legend
<https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html>`_
to adjust the style of the generated legend.
Example:
The following code snippet shows how to plot EDF.
.. plot::
import math
import optuna
def ackley(x, y):
a = 20 * math.exp(-0.2 * math.sqrt(0.5 * (x ** 2 + y ** 2)))
b = math.exp(0.5 * (math.cos(2 * math.pi * x) + math.cos(2 * math.pi * y)))
return -a - b + math.e + 20
def objective(trial, low, high):
x = trial.suggest_float("x", low, high)
y = trial.suggest_float("y", low, high)
return ackley(x, y)
sampler = optuna.samplers.RandomSampler(seed=10)
# Widest search space.
study0 = optuna.create_study(study_name="x=[0,5), y=[0,5)", sampler=sampler)
study0.optimize(lambda t: objective(t, 0, 5), n_trials=500)
# Narrower search space.
study1 = optuna.create_study(study_name="x=[0,4), y=[0,4)", sampler=sampler)
study1.optimize(lambda t: objective(t, 0, 4), n_trials=500)
# Narrowest search space but it doesn't include the global optimum point.
study2 = optuna.create_study(study_name="x=[1,3), y=[1,3)", sampler=sampler)
study2.optimize(lambda t: objective(t, 1, 3), n_trials=500)
optuna.visualization.matplotlib.plot_edf([study0, study1, study2])
Args:
study:
A target :class:`~optuna.study.Study` object.
You can pass multiple studies if you want to compare those EDFs.
target:
A function to specify the value to display. If it is :obj:`None` and ``study`` is being
used for single-objective optimization, the objective values are plotted.
.. note::
Specify this argument if ``study`` is being used for multi-objective optimization.
target_name:
Target's name to display on the axis label.
Returns:
A :class:`matplotlib.axes.Axes` object.
"""
_imports.check()
# Set up the graph style.
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
_, ax = plt.subplots()
ax.set_title("Empirical Distribution Function Plot")
ax.set_xlabel(target_name)
ax.set_ylabel("Cumulative Probability")
ax.set_ylim(0, 1)
cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots.
info = _get_edf_info(study, target, target_name)
edf_lines = info.lines
if len(edf_lines) == 0:
return ax
for i, (study_name, y_values) in enumerate(edf_lines):
ax.plot(info.x_values,
y_values,
color=cmap(i),
alpha=0.7,
label=study_name)
if len(edf_lines) >= 2:
ax.legend()
return ax
def _get_parallel_coordinate_plot(
study: Study,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
reversescale = study.direction == StudyDirection.MINIMIZE
else:
reversescale = True
# Set up the graph style.
fig, ax = plt.subplots()
cmap = plt.get_cmap("Blues_r" if reversescale else "Blues")
ax.set_title("Parallel Coordinate Plot")
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
# Prepare data for plotting.
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 ax
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))
obj_org = [target(t) for t in trials]
obj_min = min(obj_org)
obj_max = max(obj_org)
obj_w = obj_max - obj_min
dims_obj_base = [[o] for o in obj_org]
cat_param_names = []
cat_param_values = []
cat_param_ticks = []
log_param_names = []
param_values = []
var_names = [target_name]
for p_name in sorted_params:
values = [
t.params[p_name] if p_name in t.params else np.nan for t in trials
]
if _is_log_scale(trials, p_name):
p_min = math.log10(min(values))
p_max = math.log10(max(values))
p_w = p_max - p_min
log_param_names.append(p_name)
for i, v in enumerate(values):
dims_obj_base[i].append((math.log10(v) - p_min) / p_w * obj_w +
obj_min)
elif _is_categorical(trials, p_name):
vocab = defaultdict(
lambda: len(vocab)) # type: DefaultDict[str, int]
values = [vocab[v] for v in values]
cat_param_names.append(p_name)
vocab_item_sorted = sorted(vocab.items(), key=lambda x: x[1])
cat_param_values.append([v[0] for v in vocab_item_sorted])
cat_param_ticks.append([v[1] for v in vocab_item_sorted])
p_min = min(values)
p_max = max(values)
p_w = p_max - p_min
for i, v in enumerate(values):
dims_obj_base[i].append((v - p_min) / p_w * obj_w + obj_min)
else:
p_min = min(values)
p_max = max(values)
p_w = p_max - p_min
for i, v in enumerate(values):
dims_obj_base[i].append((v - p_min) / p_w * obj_w + obj_min)
var_names.append(
p_name if len(p_name) < 20 else "{}...".format(p_name[:17]))
param_values.append(values)
# Draw multiple line plots and axes.
# Ref: https://stackoverflow.com/a/50029441
ax.set_xlim(0, len(sorted_params))
ax.set_ylim(obj_min, obj_max)
xs = [range(0, len(sorted_params) + 1) for i in range(len(dims_obj_base))]
segments = [np.column_stack([x, y]) for x, y in zip(xs, dims_obj_base)]
lc = LineCollection(segments, cmap=cmap)
lc.set_array(np.asarray([target(t) for t in trials] + [0]))
axcb = fig.colorbar(lc, pad=0.1)
axcb.set_label(target_name)
plt.xticks(range(0, len(sorted_params) + 1), var_names, rotation=330)
for i, p_name in enumerate(sorted_params):
ax2 = ax.twinx()
ax2.set_ylim(min(param_values[i]), max(param_values[i]))
if _is_log_scale(trials, p_name):
ax2.set_yscale("log")
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.get_xaxis().set_visible(False)
ax2.plot([1] * len(param_values[i]), param_values[i], visible=False)
ax2.spines["right"].set_position(
("axes", (i + 1) / len(sorted_params)))
if p_name in cat_param_names:
idx = cat_param_names.index(p_name)
tick_pos = cat_param_ticks[idx]
tick_labels = cat_param_values[idx]
ax2.set_yticks(tick_pos)
ax2.set_yticklabels(tick_labels)
ax.add_collection(lc)
return ax
def _get_slice_plot(study: Study,
params: Optional[List[str]] = None) -> "Axes":
# Calculate basic numbers for plotting.
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.")
_, ax = plt.subplots()
return ax
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)
# Set up the graph style.
cmap = plt.get_cmap("Blues")
padding_ratio = 0.05
plt.style.use(
"ggplot") # Use ggplot style sheet for similar outputs to plotly.
# Prepare data.
obj_values = [t.value for t in trials]
if n_params == 1:
# Set up the graph style.
fig, axs = plt.subplots()
axs.set_title("Slice Plot")
# Draw a scatter plot.
sc = _generate_slice_subplot(
trials,
sorted_params[0],
axs,
cmap,
padding_ratio,
obj_values # type: ignore
)
else:
# Set up the graph style.
min_figwidth = matplotlib.rcParams["figure.figsize"][0] / 2
fighight = matplotlib.rcParams["figure.figsize"][1]
# Ensure that each subplot has a minimum width without relying on auto-sizing.
fig, axs = plt.subplots(1,
n_params,
sharey=True,
figsize=(min_figwidth * n_params, fighight))
fig.suptitle("Slice Plot")
# Draw scatter plots.
for i, param in enumerate(sorted_params):
ax = axs[i]
sc = _generate_slice_subplot(
trials,
param,
ax,
cmap,
padding_ratio,
obj_values # type: ignore
)
axcb = fig.colorbar(sc, ax=axs)
axcb.set_label("#Trials")
return axs
def _get_parallel_coordinate_plot(
study: Study,
params: Optional[List[str]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "Axes":
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
reversescale = study.direction == StudyDirection.MINIMIZE
else:
reversescale = True
# Set up the graph style.
fig, ax = plt.subplots()
cmap = plt.get_cmap("Blues_r" if reversescale else "Blues")
ax.set_title("Parallel Coordinate Plot")
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
# Prepare data for plotting.
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 ax
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)
skipped_trial_numbers = _get_skipped_trial_numbers(trials, sorted_params)
obj_org = [
target(t) for t in trials if t.number not in skipped_trial_numbers
]
if len(obj_org) == 0:
_logger.warning(
"Your study has only completed trials with missing parameters.")
return ax
obj_min = min(obj_org)
obj_max = max(obj_org)
obj_w = obj_max - obj_min
dims_obj_base = [[o] for o in obj_org]
cat_param_names = []
cat_param_values = []
cat_param_ticks = []
param_values = []
var_names = [target_name]
numeric_cat_params_indices: List[int] = []
for param_index, p_name in enumerate(sorted_params):
values = [
t.params[p_name] for t in trials
if t.number not in skipped_trial_numbers
]
if _is_categorical(trials, p_name):
vocab = defaultdict(
lambda: len(vocab)) # type: DefaultDict[str, int]
if _is_numerical(trials, p_name):
_ = [vocab[v] for v in sorted(values)]
numeric_cat_params_indices.append(param_index)
values = [vocab[v] for v in values]
cat_param_names.append(p_name)
vocab_item_sorted = sorted(vocab.items(), key=lambda x: x[1])
cat_param_values.append([v[0] for v in vocab_item_sorted])
cat_param_ticks.append([v[1] for v in vocab_item_sorted])
if _is_log_scale(trials, p_name):
values_for_lc = [np.log10(v) for v in values]
else:
values_for_lc = values
p_min = min(values_for_lc)
p_max = max(values_for_lc)
p_w = p_max - p_min
if p_w == 0.0:
center = obj_w / 2 + obj_min
for i in range(len(values)):
dims_obj_base[i].append(center)
else:
for i, v in enumerate(values_for_lc):
dims_obj_base[i].append((v - p_min) / p_w * obj_w + obj_min)
var_names.append(
p_name if len(p_name) < 20 else "{}...".format(p_name[:17]))
param_values.append(values)
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.
sorted_idx = np.lexsort([
param_values[index] for index in numeric_cat_params_indices
][::-1])
# Since the values are mapped to other categories by the index,
# the index will be swapped according to the sorted index of numeric params.
param_values = [list(np.array(v)[sorted_idx]) for v in param_values]
# Draw multiple line plots and axes.
# Ref: https://stackoverflow.com/a/50029441
ax.set_xlim(0, len(sorted_params))
ax.set_ylim(obj_min, obj_max)
xs = [range(len(sorted_params) + 1) for _ in range(len(dims_obj_base))]
segments = [np.column_stack([x, y]) for x, y in zip(xs, dims_obj_base)]
lc = LineCollection(segments, cmap=cmap)
lc.set_array(np.asarray(obj_org))
axcb = fig.colorbar(lc, pad=0.1)
axcb.set_label(target_name)
plt.xticks(range(len(sorted_params) + 1), var_names, rotation=330)
for i, p_name in enumerate(sorted_params):
ax2 = ax.twinx()
ax2.set_ylim(min(param_values[i]), max(param_values[i]))
if _is_log_scale(trials, p_name):
ax2.set_yscale("log")
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.xaxis.set_visible(False)
ax2.plot([1] * len(param_values[i]), param_values[i], visible=False)
ax2.spines["right"].set_position(
("axes", (i + 1) / len(sorted_params)))
if p_name in cat_param_names:
idx = cat_param_names.index(p_name)
tick_pos = cat_param_ticks[idx]
tick_labels = cat_param_values[idx]
ax2.set_yticks(tick_pos)
ax2.set_yticklabels(tick_labels)
ax.add_collection(lc)
return ax