Source code for optuna.visualization.contour

import math

from optuna.logging import get_logger
from optuna.structs import StudyDirection
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 typing import Tuple  # NOQA

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

logger = get_logger(__name__)


[docs]def plot_contour(study, params=None): # type: (Study, Optional[List[str]]) -> go.Figure """Plot the parameter relationship as contour 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 contour plot. .. code:: import optuna def objective(trial): ... study = optuna.create_study() study.optimize(objective, n_trials=100) optuna.visualization.plot_contour(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_contour_plot(study, params)
def _get_contour_plot(study, params=None): # type: (Study, Optional[List[str]]) -> go.Figure layout = go.Layout( title='Contour 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)) elif len(params) <= 1: logger.warning('The length of params must be greater than 1.') return go.Figure(data=[], layout=layout) 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))) param_values_range = {} for p_name in sorted_params: values = [t.params[p_name] for t in trials if p_name in t.params] param_values_range[p_name] = (min(values), max(values)) if len(sorted_params) == 2: x_param = sorted_params[0] y_param = sorted_params[1] sub_plots = _generate_contour_subplot( trials, x_param, y_param, study.direction) figure = go.Figure(data=sub_plots) figure.update_xaxes(title_text=x_param, range=param_values_range[x_param]) figure.update_yaxes(title_text=y_param, range=param_values_range[y_param]) if _is_log_scale(trials, x_param): log_range = [math.log10(p) for p in param_values_range[x_param]] figure.update_xaxes(range=log_range, type='log') if _is_log_scale(trials, y_param): log_range = [math.log10(p) for p in param_values_range[y_param]] figure.update_yaxes(range=log_range, type='log') else: figure = make_subplots(rows=len(sorted_params), cols=len(sorted_params), shared_xaxes=True, shared_yaxes=True) showscale = True # showscale option only needs to be specified once for x_i, x_param in enumerate(sorted_params): for y_i, y_param in enumerate(sorted_params): if x_param == y_param: figure.add_trace(go.Scatter(), row=y_i + 1, col=x_i + 1) else: sub_plots = _generate_contour_subplot( trials, x_param, y_param, study.direction) contour = sub_plots[0] scatter = sub_plots[1] contour.update(showscale=showscale) # showscale's default is True if showscale: showscale = False figure.add_trace(contour, row=y_i + 1, col=x_i + 1) figure.add_trace(scatter, row=y_i + 1, col=x_i + 1) figure.update_xaxes(range=param_values_range[x_param], row=y_i + 1, col=x_i + 1) figure.update_yaxes(range=param_values_range[y_param], row=y_i + 1, col=x_i + 1) if _is_log_scale(trials, x_param): log_range = [math.log10(p) for p in param_values_range[x_param]] figure.update_xaxes(range=log_range, type='log', row=y_i + 1, col=x_i + 1) if _is_log_scale(trials, y_param): log_range = [math.log10(p) for p in param_values_range[y_param]] figure.update_yaxes(range=log_range, type='log', row=y_i + 1, col=x_i + 1) if x_i == 0: figure.update_yaxes(title_text=y_param, row=y_i + 1, col=x_i + 1) if y_i == len(sorted_params) - 1: figure.update_xaxes(title_text=x_param, row=y_i + 1, col=x_i + 1) return figure def _generate_contour_subplot(trials, x_param, y_param, direction): # type: (List[FrozenTrial], str, str, StudyDirection) -> Tuple[Contour, Scatter] x_indices = sorted(list({t.params[x_param] for t in trials if x_param in t.params})) y_indices = sorted(list({t.params[y_param] for t in trials if y_param in t.params})) if len(x_indices) < 2: logger.warning('Param {} unique value length is less than 2.'.format(x_param)) return go.Contour(), go.Scatter() if len(y_indices) < 2: logger.warning('Param {} unique value length is less than 2.'.format(y_param)) return go.Contour(), go.Scatter() z = [[float('nan') for _ in range(len(x_indices))] for _ in range(len(y_indices))] x_values = [] y_values = [] for trial in trials: if x_param not in trial.params or y_param not in trial.params: continue x_values.append(trial.params[x_param]) y_values.append(trial.params[y_param]) x_i = x_indices.index(trial.params[x_param]) y_i = y_indices.index(trial.params[y_param]) if isinstance(trial.value, int): value = float(trial.value) elif isinstance(trial.value, float): value = trial.value else: raise ValueError( 'Trial{} has COMPLETE state, but its value is non-numeric.'.format(trial.number)) z[y_i][x_i] = value # TODO(Yanase): Use reversescale argument to reverse colorscale if Plotly's bug is fixed. # If contours_coloring='heatmap' is specified, reversesecale argument of go.Contour does not # work correctly. See https://github.com/pfnet/optuna/issues/606. colorscale = plotly.colors.PLOTLY_SCALES['Blues'] if direction == StudyDirection.MINIMIZE: colorscale = [[1 - t[0], t[1]] for t in colorscale] colorscale.reverse() contour = go.Contour( x=x_indices, y=y_indices, z=z, colorbar={'title': 'Objective Value'}, colorscale=colorscale, connectgaps=True, contours_coloring='heatmap', hoverinfo='none', line_smoothing=1.3, ) scatter = go.Scatter( x=x_values, y=y_values, marker={'color': 'black'}, mode='markers', showlegend=False ) return (contour, scatter)