pybop.costs.base_cost#

Classes#

BaseCost

Base class for defining cost functions.

Module Contents#

class pybop.costs.base_cost.BaseCost(problem: pybop.BaseProblem | None = None)[source]#

Base class for defining cost functions.

This class is intended to be subclassed to create specific cost functions for evaluating model predictions against a set of data. The cost function quantifies the goodness-of-fit between the model predictions and the observed data, with a lower cost value indicating a better fit.

Parameters:

problem (object) – A problem instance containing the data and functions necessary for evaluating the cost function.
target (array-like) – An array containing the target data to fit.
n_outputs (int) – The number of outputs in the model.
has_separable_problem (bool) – If True, the problem is separable from the cost function and will be evaluated in advance of the call to self.compute() (default: False).
_de (float) – The gradient of the cost function to use if an error occurs during evaluation. Defaults to 1.0.

__call__(inputs: pybop.parameters.parameter.Inputs | list, calculate_grad: bool = False, apply_transform: bool = False)[source]#

This method calls the forward model via problem.evaluate(inputs), and computes the cost for the given output by calling self.compute().

Parameters:

inputs (Inputs or array-like) – The parameters for which to compute the cost and gradient.
calculate_grad (bool, optional) – A bool condition designating whether to calculate the gradient.

Returns:

The calculated cost function value.

Return type:

float

Raises:

ValueError – If an error occurs during the calculation of the cost.

abstract compute(y: dict, dy: numpy.ndarray, calculate_grad: bool = False)[source]#

Compute the cost and if calculate_grad is True, its gradient with respect to the predictions.

This method only computes the cost, without calling the problem.evaluate(). This method must be implemented by subclasses.

Parameters:

y (dict) – The dictionary of predictions with keys designating the signals for fitting.
dy (np.ndarray, optional) – The corresponding gradient with respect to the parameters for each signal.
calculate_grad (bool, optional) – A bool condition designating whether to calculate the gradient.

Returns:

The calculated cost function value.

Return type:

float

Raises:

NotImplementedError – If the method has not been implemented by the subclass.

join_parameters(parameters)[source]#: Setter for joining parameters. This method sets the fail gradient if the join adds parameters.

set_fail_gradient(de: float = 1.0)[source]#

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.

verify_args(dy: numpy.ndarray, calculate_grad: bool)[source]#

verify_prediction(y)[source]#

Verify that the prediction matches the target data.

Parameters:: y (dict) – The model predictions.
Returns:: True if the prediction matches the target data, otherwise False.
Return type:: bool

_de = 1.0[source]#

_has_separable_problem = False[source]#

_parameters[source]#

dy = None[source]#

property has_separable_problem[source]#

property n_parameters[source]#

property parameters[source]#

problem[source]#

property target[source]#

transformation = None[source]#

verbose = False[source]#

y = None[source]#