from typing import Callable
from typing import cast
from typing import List
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 _is_log_scale
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
_logger = get_logger(__name__)
[文档]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.
Raises:
:exc:`ValueError`:
If ``target`` is :obj:`None` and ``study`` is being used for multi-objective
optimization.
"""
_imports.check()
_check_plot_args(study, target, target_name)
return _get_slice_plot(study, params, target, target_name)
def _get_slice_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="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], target)], layout=layout
)
figure.update_xaxes(title_text=sorted_params[0])
figure.update_yaxes(title_text=target_name)
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, target)
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=target_name, 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 _generate_slice_subplot(
study: Study,
trials: List[FrozenTrial],
param: str,
target: Optional[Callable[[FrozenTrial], float]],
) -> "Scatter":
if target is None:
def _target(t: FrozenTrial) -> float:
return cast(float, t.value)
target = _target
return go.Scatter(
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],
mode="markers",
marker={
"line": {"width": 0.5, "color": "Grey"},
"color": [t.number for t in trials if param in t.params],
"colorscale": "Blues",
"colorbar": {
"title": "#Trials",
"x": 1.0, # Offset the colorbar position with a fixed width `xpad`.
"xpad": 40,
},
},
showlegend=False,
)