pybop._result

Classes

Result

Stores the result produced by an optimiser or sampler.

Module Contents

class pybop._result.Result(problem: pybop.problems.problem.Problem, logger: pybop._logging.Logger, time: float, method_name: str | None = None, message: str | None = None, scipy_result=None)

Stores the result produced by an optimiser or sampler.

problem

The optimisation problem used to generate the results.

Type:

pybop.Problem

logger

The log of the optimisation or sampling process.

Type:

pybop.Logger

time

The time taken.

Type:

float

method_name

The name of the optimiser or sampler.

Type:

str

message

The reason for stopping given by the optimiser or sampler.

Type:

str

__str__() → str

A string representation of the Result object.

Returns:

A formatted string containing optimisation result information.

Return type:

str

_check_for_finite_cost() → None

Validate the optimised parameters and ensure they produce a finite cost value.

Raises:

ValueError – If the optimised parameters do not produce a finite cost value.

_get_single_or_all(attr)

_validate()

Check that there is a finite cost and update best run.

static combine(results: list[Result]) → Result

Combine multiple Result objects into a single one.

Parameters:

results (list[Result]) – List of Result objects to combine.

Returns:

Combined Result object.

Return type:

Result

data_dict() → dict

return result data as dictionary for saving to file

static load(filename: str) → Result

Load a saved Result.

static load_data(filename: str, file_format: str = 'pickle') → dict

Load results data as dictionary from a given file. Restores data saved with save_data.

Calls load_data_dict defined in _utils.py and provides the keys of data that is 0-d and 1-d to ensure consistent data dimensions.

Parameters:

• filename (str) – The name of the file containing the data.

• file_format (str, optional) – The format the data was save to. Options are: - ‘pickle’ (default) - ‘matlab’ - ‘csv’ - ‘json’

Returns:

python dictionary containing the data in the file.

Return type:

data_dict

plot_contour(**kwargs)

Generate and plot a 2D visualisation of the cost landscape with the optimisation trace.

Parameters:

• gradient (bool, optional) – If True, gradient plots are also generated (default: False).

• bounds (numpy.ndarray | list[list[float]], optional) – A 2x2 array specifying the [min, max] bounds for each parameter.

• transformed (bool, optional) – Uses the transformed parameter values, as seen by the optimiser (default: False).

• steps (int, optional) – The number of grid points to divide the parameter space into along each dimension (default: 10).

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

• **layout_kwargs (optional) – Valid Plotly layout keys and their values.

plot_convergence(**kwargs)

Plot the evolution of the best cost during the optimisation.

Parameters:

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

• **layout_kwargs (optional) – Valid Plotly layout keys and their values.

plot_parameters(**kwargs)

Plot the evolution of parameter values during the optimisation.

Parameters:

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

• **layout_kwargs (optional) – Valid Plotly layout keys and their values.

plot_surface(**kwargs)

Plot a 2D representation of the Voronoi diagram with color-coded regions.

Parameters:

• bounds (numpy.ndarray, optional) – A 2x2 array specifying the [min, max] bounds for each parameter.

• normalise (bool, optional) – If True, the voronoi regions are computed using the Euclidean distance between points normalised with respect to the bounds (default: True).

• resolution (int, optional) – Resolution of the plot (default: 500).

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

• **layout_kwargs (optional) – Valid Plotly layout keys and their values.

save(filename) → None

Save the whole result using pickle

save_data(filename=None, to_format='pickle') → str | None

Save result data

Based on pybamm.Solution.save_data

Parameters:

• filename (str, optional) – The name of the file to save data to. If None, then a str is returned for json format or an error is thrown for pickle/matlab.

• to_format (str, optional) –

  The format to save to. Options are:

  • ’pickle’ (default): creates a pickle file with the data dictionary

  • ’matlab’: creates a .mat file, for loading in matlab

  • ’json’: creates a json file

Returns:

data – str if ‘json’ is chosen and filename is None, otherwise None

Return type:

str, optional

total_evaluations() → numpy.floating | None

Calculates the total number of evaluations across all runs.

total_iterations() → numpy.floating | None

Calculates the total number of iterations across all runs.

total_runtime() → numpy.floating | None

Calculates the total runtime across all runs.

_best_cost

_best_run = None

_cost

_initial_cost

_iteration_number

_message

_minimising

_n_evaluations

_n_iterations

_problem

_scipy_result

_time

_x

_x0

_x_model

property best_cost: float

The best cost value(s).

property best_inputs: dict[str, numpy.ndarray]

The best parameters as a dictionary.

property cost: numpy.ndarray

The log of the cost values.

property initial_cost: float

The initial cost value(s).

property iteration_number: numpy.ndarray | None

The number of iterations.

property message: str | None

The optimisation termination message(s).

method_name = None

property minimising: bool

Whether the cost was minimised (or maximised).

property n_evaluations: int

The number of evaluations.

property n_iterations: int

The number of iterations.

n_runs = 1

property problem: pybop.problems.problem.Problem

The optimisation problem.

property scipy_result

The SciPy result.

property time: float | None

The optimisation time(s).

property x: numpy.ndarray

The best parameter values (in model space).

property x0: numpy.ndarray

The initial parameter values.

property x_model: numpy.ndarray

The log of the evaluated parameters (in model space).