pybop.plot.predictive#

Functions#

predictive(result[, number_of_traces, ...])

Plot the predictive posterior of a Bayesian optimisation result.

Module Contents#

pybop.plot.predictive.predictive(result: BayesianOptimisationResult | SamplingResult, number_of_traces: int = 8, data_legend_entry=None, rvs_legend_entry=None, pdf_plot=None, pdf_label: str = 'PDF', colour_scale='viridis', show: bool = True, backend: str | None = None, figures=None, axes=None)[source]#

Plot the predictive posterior of a Bayesian optimisation result.

Parameters:

  • result (pybop.BayesianOptimisationResult or pybop.SamplingResult) – The result of the Bayesian optimisation or sampling process.

  • number_of_traces (int, optional) – The number of posterior predictive traces to plot (default: 8).

  • data_legend_entry (str, optional) – The legend entry for the observed data (default: None).

  • rvs_legend_entry (str, optional) – The legend entry for the random variable samples (default: None).

  • pdf_plot (tuple, optional) – A tuple containing the x and y values for a PDF plot to overlay on the predictive plot (default: None).

  • pdf_label (str, optional) – The label for the PDF plot (default: “PDF”).

  • colour_scale (str, optional) – The colour scale to use for the predictive traces (default: “viridis”).

  • show (bool, optional) – If True, the figure is shown upon creation (default: True).

  • backend (str or pybop.plot.backends.PlotBackend, optional) – Select a plotting backend. If None, the current default backend is used.

  • figures (figure object, optional) – Figure for plotting. If not provided a new figure is created for each problem. Can be a single figure or one figure per problem.

  • axes (axis, optional) – The axes to be used for plotting. One axis per problem is expected. plotly: axes expected to be of the form list of tuple(row, col)

Returns:

  • None (if show is True)

  • Figure or list of figures (if show is False) – If show is False, returns a single figure or a list of figures containing the predictive posterior for each problem.