pybop.problems.problem#

Classes#

Problem

Base class for defining a problem within the PyBOP framework, compatible with PINTS.

Module Contents#

class pybop.problems.problem.Problem(simulator: pybop.simulators.base_simulator.BaseSimulator = None, cost: pybop.costs.base_cost.BaseCost = None)[source]#

Base class for defining a problem within the PyBOP framework, compatible with PINTS.

Parameters:

  • simulator (pybop.BaseSimulator) – The model, protocol and optional dataset combined into a simulator object.

  • parameters (list[pybop.Parameter] or pybop.Parameters) – An object or list of the parameters for the problem.

  • cost (pybop.BaseCost, optional) – An cost e.g. an error measure, a log-likelihood or a design cost.

target_data[source]#

An array containing the target data to fit.

Type:

array-like

n_outputs[source]#

The number of outputs in the model.

Type:

int

minimising[source]#

If False, tells the optimiser to switch the sign of the cost and gradient to maximise by default rather than minimise (default: True).

Type:

bool, optional

__call__(inputs: pybop.parameters.parameter.Inputs | list[pybop.parameters.parameter.Inputs]) → float | list[float][source]#

Evaluate the cost for one or more sets of inputs and return the cost value(s).

Parameters:

inputs (Inputs | list[Inputs]) – Input parameters for cost evaluation. Supports a list of inputs.

Returns:

The cost value(s).

Return type:

float | list[float]

evaluate(inputs: pybop.parameters.parameter.Inputs | list[pybop.parameters.parameter.Inputs], calculate_sensitivities: bool = False) → pybop.costs.evaluation.Evaluation[source]#

Evaluate the cost for one or more sets of inputs and return the cost value(s) and (optionally) the sensitivities.

Parameters:

  • inputs (Inputs | list[Inputs]) – Input parameters for cost evaluation. Supports a list of inputs.

  • calculate_sensitivities (bool) – Whether to also return the sensitivities (default: False).

Returns:

The cost value(s) and (optionally) the gradient of the cost with respect to each input parameter.

Return type:

Evaluation

evaluate_batch(inputs: list[pybop.parameters.parameter.Inputs], calculate_sensitivities: bool = False) → pybop.costs.evaluation.Evaluation[source]#

Evaluate the cost for each set of inputs and return the cost value(s) and (optionally) the sensitivities.

Parameters:

  • inputs (list[Inputs]) – A list of input parameters.

  • calculate_sensitivities (bool) – Whether to also return the sensitivities (default: False).

Returns:

Cost values of len(inputs) and (optionally) the gradient of the cost with respect to each input parameter with shape (len(inputs), len(parameters)).

Return type:

Evaluation

get_finite_initial_cost()[source]#

Compute the absolute initial cost, resampling the initial parameters if needed.

get_model_inputs(inputs)[source]#
join_parameters(parameters)[source]#

Setter for joining parameters. This method sets the fail gradient if the join adds parameters.

sensitivity_analysis(n_samples: int = 256, calc_second_order: bool = False) → dict[source]#

Computes the parameter sensitivities on the combined cost function using SOBOL analysis. See pybop.analysis.sensitivity_analysis for more details.

Parameters:

  • n_samples (int, optional) – Number of samples for SOBOL sensitivity analysis, performs best as a power of 2, i.e. 128, 256, etc.

  • calc_second_order (bool, optional) – Whether to calculate second-order sensitivities.

simulate(inputs: pybop.parameters.parameter.Inputs | list[pybop.parameters.parameter.Inputs], calculate_sensitivities: bool = False) → pybop.simulators.base_simulator.Solution | list[pybop.simulators.base_simulator.Solution][source]#

Simulate the model for one or more sets of inputs and return the solution and (optionally) the sensitivities.

Parameters:

  • inputs (Inputs | list[Inputs]) – Input parameters. Support a list of inputs.

  • calculate_sensitivities (bool) – Whether to also return the sensitivities (default: False).

Returns:

The simulated model output y(t) and (optionally) the sensitivities dy/dx(t)

for output variable(s) y, domain t and parameter(s) x.

Return type:

Solution | list[Solution]

simulate_batch(inputs: list[pybop.parameters.parameter.Inputs], calculate_sensitivities: bool = False) → list[pybop.simulators.base_simulator.Solution | pybop._utils.FailedSolution][source]#

Simulate the model for each set of inputs and return the solution and (optionally) the sensitivities.

Parameters:

inputs (list[Inputs]) – A list of input parameters.

Returns:

A list of length(inputs) containing the simulated model output y(t) and (optionally) the sensitivities dy/dx(t) for output variable(s) y, domain t and parameter(s) x.

Return type:

list[Solution]

_cost[source]#
_has_sensitivities[source]#
_minimising = True[source]#
_simulator[source]#
property cost[source]#
domain = 'Time [s]'[source]#
property domain_data[source]#
property eis[source]#
property has_sensitivities[source]#
property minimising[source]#
property n_outputs[source]#
property n_parameters[source]#
property parameters[source]#
property simulator[source]#
property target[source]#
property target_data[source]#