Source code for optuna.visualization.parallel_coordinate

from collections import defaultdict
from typing import Any
from typing import DefaultDict
from typing import Dict
from typing import List
from typing import Optional

from optuna.logging import get_logger
from import Study
from import StudyDirection
from optuna.trial import TrialState
from optuna.visualization.utils import _check_plotly_availability
from optuna.visualization.utils import is_available

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

logger = get_logger(__name__)

[docs]def plot_parallel_coordinate(study: Study, params: Optional[List[str]] = None) -> "go.Figure": """Plot the high-dimentional parameter relationships 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 high-dimentional parameter relationships. .. testcode:: import optuna def objective(trial): x = trial.suggest_uniform('x', -100, 100) y = trial.suggest_categorical('y', [-1, 0, 1]) return x ** 2 + y study = optuna.create_study() study.optimize(objective, n_trials=10) optuna.visualization.plot_parallel_coordinate(study, params=['x', 'y']) .. raw:: html <iframe src="../_static/plot_parallel_coordinate.html" width="100%" height="500px" frameborder="0"> </iframe> Args: study: A :class:`` 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_parallel_coordinate_plot(study, params)
def _get_parallel_coordinate_plot(study: Study, params: Optional[List[str]] = None) -> "go.Figure": layout = go.Layout(title="Parallel Coordinate 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 not None: 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)) all_params = set(params) sorted_params = sorted(list(all_params)) dims = [ { "label": "Objective Value", "values": tuple([t.value for t in trials]), "range": (min([t.value for t in trials]), max([t.value for t in trials])), } ] # type: List[Dict[str, Any]] for p_name in sorted_params: values = [] for t in trials: if p_name in t.params: values.append(t.params[p_name]) is_categorical = False try: tuple(map(float, values)) except (TypeError, ValueError): vocab = defaultdict(lambda: len(vocab)) # type: DefaultDict[str, int] values = [vocab[v] for v in values] is_categorical = True dim = { "label": p_name if len(p_name) < 20 else "{}...".format(p_name[:17]), "values": tuple(values), "range": (min(values), max(values)), } if is_categorical: dim["tickvals"] = list(range(len(vocab))) dim["ticktext"] = list(sorted(vocab.items(), key=lambda x: x[1])) dims.append(dim) traces = [ go.Parcoords( dimensions=dims, labelangle=30, labelside="bottom", line={ "color": dims[0]["values"], "colorscale": "blues", "colorbar": {"title": "Objective Value"}, "showscale": True, "reversescale": study.direction == StudyDirection.MINIMIZE, }, ) ] figure = go.Figure(data=traces, layout=layout) return figure