Source code for optuna.visualization.slice

from optuna.logging import get_logger
from optuna.structs import TrialState
from optuna import type_checking
from optuna.visualization.utils import _check_plotly_availability
from optuna.visualization.utils import _is_log_scale
from optuna.visualization.utils import is_available

if type_checking.TYPE_CHECKING:
    from typing import List  # NOQA
    from typing import Optional  # NOQA

    from optuna.structs import FrozenTrial  # NOQA
    from optuna.study import Study  # NOQA
    from optuna.visualization.plotly_imports import Scatter  # NOQA

if is_available():
    from optuna.visualization.plotly_imports import go
    from optuna.visualization.plotly_imports import make_subplots

logger = get_logger(__name__)


[docs]def plot_slice(study, params=None): # type: (Study, Optional[List[str]]) -> 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. .. code:: import optuna def objective(trial): ... study = optuna.create_study() study.optimize(objective, n_trials=100) optuna.visualization.plot_slice(study, params=['param_a', 'param_b']) Args: study: A :class:`~optuna.study.Study` object whose trials are plotted for their objective values. params: Parameter list to visualize. The default is all parameters. Returns: A :class:`plotly.graph_objs.Figure` object. """ _check_plotly_availability() return _get_slice_plot(study, params)
def _get_slice_plot(study, params=None): # type: (Study, Optional[List[str]]) -> 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 _generate_slice_subplot(study, trials, param): # type: (Study, List[FrozenTrial], str) -> Scatter return go.Scatter( x=[t.params[param] for t in trials if param in t.params], y=[t.value 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, )