from typing import Any
from typing import Callable
from typing import cast
from typing import List
from typing import NamedTuple
from typing import Optional
from optuna.logging import get_logger
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState
from optuna.visualization._plotly_imports import _imports
from optuna.visualization._utils import _check_plot_args
from optuna.visualization._utils import _filter_nonfinite
from optuna.visualization._utils import _is_log_scale
from optuna.visualization._utils import _is_numerical
if _imports.is_successful():
from optuna.visualization._plotly_imports import go
from optuna.visualization._plotly_imports import make_subplots
from optuna.visualization._plotly_imports import Scatter
from optuna.visualization._utils import COLOR_SCALE
_logger = get_logger(__name__)
class _SliceSubplotInfo(NamedTuple):
param_name: str
x: List[Any]
y: List[float]
trial_numbers: List[int]
is_log: bool
is_numerical: bool
class _SlicePlotInfo(NamedTuple):
target_name: str
subplots: List[_SliceSubplotInfo]
def _get_slice_subplot_info(
trials: List[FrozenTrial],
param: str,
target: Optional[Callable[[FrozenTrial], float]],
log_scale: bool,
numerical: bool,
) -> _SliceSubplotInfo:
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
return _SliceSubplotInfo(
param_name=param,
x=[t.params[param] for t in trials if param in t.params],
y=[target(t) for t in trials if param in t.params],
trial_numbers=[t.number for t in trials if param in t.params],
is_log=log_scale,
is_numerical=numerical,
)
def _get_slice_plot_info(
study: Study,
params: Optional[List[str]],
target: Optional[Callable[[FrozenTrial], float]],
target_name: str,
) -> _SlicePlotInfo:
_check_plot_args(study, target, target_name)
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 _SlicePlotInfo(target_name, [])
all_params = {p_name for t in trials for p_name in t.params.keys()}
if params is None:
sorted_params = sorted(all_params)
else:
for input_p_name in params:
if input_p_name not in all_params:
raise ValueError(f"Parameter {input_p_name} does not exist in your study.")
sorted_params = sorted(set(params))
return _SlicePlotInfo(
target_name=target_name,
subplots=[
_get_slice_subplot_info(
trials=trials,
param=param,
target=target,
log_scale=_is_log_scale(trials, param),
numerical=_is_numerical(trials, param),
)
for param in sorted_params
],
)
[docs]def plot_slice(
study: Study,
params: Optional[List[str]] = None,
*,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> "go.Figure":
"""Plot the parameter relationship as slice plot in a study.
Note that, if a parameter contains missing values, a trial with missing values is not plotted.
Example:
The following code snippet shows how to plot the parameter relationship as slice plot.
.. plotly::
import optuna
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y = trial.suggest_categorical("y", [-1, 0, 1])
return x ** 2 + y
sampler = optuna.samplers.TPESampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=10)
fig = optuna.visualization.plot_slice(study, params=["x", "y"])
fig.show()
Args:
study:
A :class:`~optuna.study.Study` object whose trials are plotted for their target values.
params:
Parameter list to visualize. The default is all parameters.
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:`plotly.graph_objs.Figure` object.
"""
_imports.check()
return _get_slice_plot(_get_slice_plot_info(study, params, target, target_name))
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 _generate_slice_subplot(subplot_info: _SliceSubplotInfo) -> "Scatter":
return go.Scatter(
x=subplot_info.x,
y=subplot_info.y,
mode="markers",
marker={
"line": {"width": 0.5, "color": "Grey"},
"color": subplot_info.trial_numbers,
"colorscale": COLOR_SCALE,
"colorbar": {
"title": "Trial",
"x": 1.0, # Offset the colorbar position with a fixed width `xpad`.
"xpad": 40,
},
},
showlegend=False,
)