pybop.costs.base_cost

Classes

BaseCost	Base cost.
LogPrior	The log-prior as a cost, to be used with the LogPosterior class.

Module Contents

class pybop.costs.base_cost.BaseCost

Base cost.

_de

The gradient of the cost function to use if an error occurs during evaluation. Defaults to 1.0.

Type:

float

minimising

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

abstractmethod evaluate(solution: pybop.simulators.solution.Solution, inputs: pybop.parameters.parameter.Inputs | None = None, calculate_sensitivities: bool = False) → float | tuple[float, numpy.ndarray]

Computes the cost function for the given predictions.

Parameters:

• solution (pybop.Solution | pybamm.Solution) – The simulation result.

• inputs (Inputs, optional) – Input parameters (default: None).

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

Returns:

If the solution has sensitivities, returns a tuple containing the cost (float) and the gradient with dimension (len(parameters)), otherwise returns only the cost.

Return type:

np.float64 or tuple[np.float64, np.ndarray[np.float64]]

failure(parameter_names: list[str], calculate_sensitivities: bool = True)

set_fail_gradient(de: float = 1.0)

Set the fail gradient to a specified value.

The fail gradient is used if an error occurs during the calculation of the gradient. This method allows updating the default gradient value.

Parameters:

de (float) – The new fail gradient value to be used.

set_target(target: list[str] | str | None = None, dataset: pybop.processing.dataset.Dataset | None = None)

Set the target variable and target data from a pybop.Dataset.

stack_sensitivities(solution: pybop.simulators.solution.Solution) → dict[str, numpy.ndarray]

Stack the sensitivities for each output variable into a single array.

Parameters:

solution (pybop.Solution | pybamm.Solution) – A solution object containing a dictionary of sensitivities for each output variable.

Returns:

The sensitivities dy/dx(t) for each output variable y with respect to each parameter x over the domain t. The dictionary keys are the parameter names and the arrays are of dimensions (len(target), len(domain_data)).

Return type:

dict[str, np.ndarray[np.float64]]

_dataset = None

_de = 1.0

_domain = 'Time [s]'

_domain_data = None

_target = ['Voltage [V]']

_target_data = None

property domain

property domain_data

minimising = True

property n_parameters

property name

parameters

property target

property target_data

class pybop.costs.base_cost.LogPrior(parameters)

Bases: BaseCost

The log-prior as a cost, to be used with the LogPosterior class.

evaluate(solution: pybop.simulators.solution.Solution, inputs: pybop.parameters.parameter.Inputs | None = None, calculate_sensitivities: bool = False) → float | tuple[float, numpy.ndarray]

Computes the log-prior for the given inputs, and optionally the sensitivities.

minimising = False

parameters