physbo.search.utility module

Utility helpers for the search module.

This module collects helpers used across the search policies:

• Simulator wraps a test function so it can be called by action index.

• make_grid() builds a grid of candidate points for a discrete search.

• plot_pareto_front() and plot_pareto_front_all() visualize the Pareto front stored in a multi-objective history.

• the remaining show_* / is_learning helpers format progress messages and drive the search loop.

class physbo.search.utility.Simulator(test_X, test_function, negate=False)

ベースクラス: object

Simulator class wrapping the test function for discrete search space.

パラメータ:

• test_X (numpy.ndarray) -- The test points. Each row is a candidate.

• test_function (physbo.test_functions.base.TestFunction) -- The test function.

• negate (bool, default=False) -- If True, the test function value is negated before returning.

physbo.search.utility.is_learning(n, interval)

Decide whether to (re)learn the hyperparameters at search step n.

パラメータ:

• n (int) -- Zero-based index of the current search step.

• interval (int) --

  Hyperparameter learning interval.

  • interval == 0: learn only at the first step (n == 0).

  • interval > 0: learn every interval steps.

  • interval < 0: never learn.

戻り値:

True if hyperparameters should be learned at this step.

戻り値の型:

bool

physbo.search.utility.length_vector(t)

Return the number of elements of t.

パラメータ:

t (array-like or scalar) -- Input object.

戻り値:

len(t) if t is sized, otherwise 1 (treating t as a scalar).

戻り値の型:

int

physbo.search.utility.make_grid(min_X: list[float] | ndarray, max_X: list[float] | ndarray, num_X: int | list[int] | ndarray, constraint=None) → ndarray

Make a grid of points in the search space.

パラメータ:

• min_X (np.ndarray | list[float] | float) -- Minimum value of search space for each dimension

• max_X (np.ndarray | list[float] | float) -- Maximum value of search space for each dimension

• num_X (int | list[int] | np.ndarray) -- Number of points in each dimension

戻り値:

The grid of points in the search space The output is a numpy array of shape (N, d), where N is the number of points and d is the dimension of the search space

戻り値の型:

np.ndarray

例外:

ValueError -- If min_X and max_X have different number of dimensions If num_X has different number of dimensions from min_X and max_X

physbo.search.utility.plot_pareto_front(history, x=0, y=1, steps_begin=0, steps_end=None, ax=None, xlim=None, ylim=None, grid=True, style_common: dict = {}, style_pareto_front: dict = {}, style_dominated: dict = {})

Plot the Pareto front of the history in the projection to the (x, y)-plane (objective x and y).

パラメータ:

• history (History) -- The history of the search.

• x (int, default=0) -- The index of the objective to plot on the x-axis.

• y (int, default=1) -- The index of the objective to plot on the y-axis.

• steps_begin (int, default=0) -- The index (inclusive) of the step to begin plotting.

• steps_end (int, optional) -- The index (exclusive) of the step to end plotting. If None, plot until the end.

• ax (matplotlib.axes.Axes, optional) -- The axes to plot on. If None, a new figure is created.

• xlim (tuple, optional) -- The x-axis limits. If None, the limits are determined automatically.

• ylim (tuple, optional) -- The y-axis limits. If None, the limits are determined automatically.

• grid (bool, default=True) -- Whether to plot the grid.

• style_common (dict, optional) -- The common setting for plotting the points.

• style_pareto_front (dict, optional) -- The setting for plotting the Pareto front.

• style_dominated (dict, optional) -- The setting for plotting the dominated points.

Note

• Items in style_* are passed to the scatter method of matplotlib.pyplot.

• For each item, style_pareto_front and style_dominated are higher priority than style_common.

• By default, the marker is "o".

• By default, the color is "blue" for dominated points and "red" for Pareto front.

physbo.search.utility.plot_pareto_front_all(history, steps_begin=0, steps_end=None, ax=None, xlim=None, ylim=None, grid=True, style_common: dict = {}, style_pareto_front: dict = {}, style_dominated: dict = {})

Plot the Pareto front of the history for all pairs of objectives.

パラメータ:

• history (History) -- The history of the search.

• steps_begin (int, optional) -- The index (inclusive) of the step to begin plotting. If None, plot from the beginning.

• steps_end (int, optional) -- The index (exclusive) of the step to end plotting. If None, plot until the end.

• ax (matplotlib.axes.Axes, optional) -- The axes to plot on. If None, a new figure is created.

• xlim (tuple, optional) -- The x-axis limits. If None, the limits are determined automatically.

• ylim (tuple, optional) -- The y-axis limits. If None, the limits are determined automatically.

• grid (bool, default=True) -- Whether to plot the grid.

• style_common (dict, optional) -- The common setting for plotting the points.

• style_pareto_front (dict, optional) -- The setting for plotting the Pareto front.

• style_dominated (dict, optional) -- The setting for plotting the dominated points.

Note

• Items in style_* are passed to the scatter method of matplotlib.pyplot.

• For each item, style_pareto_front and style_dominated are higher priority than style_common.

• By default, the marker is "o".

• By default, the color is "blue" for dominated points and "red" for Pareto front.

physbo.search.utility.show_interactive_mode(simulator, history)

Print a message when an interactive search session starts.

The message is shown only at the very first step (empty history) of an interactive run, i.e. when no simulator is provided.

パラメータ:

• simulator (callable or None) -- The simulator, or None for interactive mode.

• history (History) -- The search history.

physbo.search.utility.show_search_results(history, N)

Print a summary of the last N single-objective search steps.

パラメータ:

• history (History) -- The search history.

• N (int) -- Number of most recent steps to display. Capped at the total number of searches performed so far.

physbo.search.utility.show_search_results_mo(history, N, disp_pareto_set=False)

Print a summary of the last N multi-objective search steps.

パラメータ:

• history (History) -- The multi-objective search history.

• N (int) -- Number of most recent steps to display. Capped at the total number of searches performed so far.

• disp_pareto_set (bool, default=False) -- If True, also display the current Pareto set.

physbo.search.utility.show_start_message_multi_search(N, score=None)

Print the header announcing a multiple-probe search step.

パラメータ:

• N (int) -- Zero-based index of the current search round; the printed index is N + 1.

• score (str, optional) -- Name of the acquisition function. If None, "random" is shown.