Source code for optuna.visualization._slice

from __future__ import annotations

from typing import Any
from typing import Callable
from typing import cast
from typing import NamedTuple

from optuna.logging import get_logger
from 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: Callable[[FrozenTrial], float] | None,
    log_scale: bool,
    numerical: bool,
) -> _SliceSubplotInfo:
    if target is None:

        def _target(t: FrozenTrial) -> float:
            return cast(float, t.value)

        target = _target

    return _SliceSubplotInfo(
        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],

def _get_slice_plot_info(
    study: Study,
    params: list[str] | None,
    target: Callable[[FrozenTrial], float] | None,
    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)
        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(
                log_scale=_is_log_scale(trials, param),
                numerical=_is_numerical(trials, param),
            for param in sorted_params

[docs]def plot_slice( study: Study, params: list[str] | None = None, *, target: Callable[[FrozenTrial], float] | None = 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"]) Args: study: A :class:`` 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": x = [x if x is not None else "None" for x in subplot_info.x] y = [y if y is not None else "None" for y in subplot_info.y] return go.Scatter( x=x, y=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, )