# ceml.torch¶

## ceml.torch.counterfactual¶

class ceml.torch.counterfactual.TorchCounterfactual(model, device=torch.device, **kwds)

Class for computing a counterfactual of a PyTorch model.

See parent class ceml.model.counterfactual.Counterfactual.

Parameters
• model (instance of torch.nn.Module and ceml.model.model.ModelWithLoss) – The PyTorch model that is used for computing counterfactuals. The model has to be wrapped inside a class that is derived from the classes torch.nn.Module and ceml.model.model.ModelWithLoss.

• device (torch.device) –

Specifies the hardware device (e.g. cpu or gpu) we are working on.

The default is torch.device(“cpu”).

Raises

TypeError – If model is not an instance of torch.nn.Module and ceml.model.model.ModelWithLoss.

compute_counterfactual(x, y_target, features_whitelist=None, regularization=None, C=1.0, optimizer='nelder-mead', optimizer_args=None, return_as_dict=True, done=None)

Computes a counterfactual of a given input x.

Parameters
• x (numpy.ndarray) – The input x whose prediction has to be explained.

• y_target (int or float) – The requested prediction of the counterfactual.

• feature_whitelist (list(int), optional) –

List of feature indices (dimensions of the input space) that can be used when computing the counterfactual.

If feature_whitelist is None, all features can be used.

The default is None.

• regularization (str or callable, optional) –

Regularizer of the counterfactual. Penalty for deviating from the original input x.

Supported values:

• l1: Penalizes the absolute deviation.

• l2: Penalizes the squared deviation.

You can use your own custom penalty function by setting regularization to a callable that can be called on a potential counterfactual and returns a scalar.

If regularization is None, no regularization is used.

The default is “l1”.

• C (float or list(float), optional) –

The regularization strength. If C is a list, all values in C are tried and as soon as a counterfactual is found, this counterfactual is returned and no other values of C are tried.

C is ignored if no regularization is used (regularization=None).

The default is 1.0

• optimizer (str or class that is derived from torch.optim.Optimizer, optional) –

Name/Identifier of the optimizer that is used for computing the counterfactual. See ceml.optim.optimizer.desc_to_optim() for details.

As an alternative, any optimizer from PyTorch can be used - optimizer must be class that is derived from torch.optim.Optimizer.

• optimizer_args (dict, optional) –

Dictionary containing additional parameters for the optimization algorithm.

Supported parameters (keys):

• args: Arguments of the optimization algorithm (e.g. learning rate, momentum, …)

• lr_scheduler: Learning rate scheduler (see torch.optim.lr_scheduler)

• lr_scheduler_args: Arguments of the learning rate scheduler

• tol: Tolerance for termination

• max_iter: Maximum number of iterations

If optimizer_args is None or if some parameters are missing, default values are used.

The default is None.

Note

The parameters tol and max_iter are passed to all optimization algorithms. Whereas the other parameters are only passed to PyTorch optimizers.

• return_as_dict (boolean, optional) –

If True, returns the counterfactual, its prediction and the needed changes to the input as dictionary. If False, the results are returned as a triple.

The default is True.

• done (callable, optional) –

A callable that returns True if a counterfactual with a given output/prediction is accepted and False otherwise.

If done is None, the output/prediction of the counterfactual must match y_target exactly.

The default is None.

Note

In case of a regression it might not always be possible to achieve a given output/prediction exactly.

Returns

A dictionary where the counterfactual is stored in ‘x_cf’, its prediction in ‘y_cf’ and the changes to the original input in ‘delta’.

(x_cf, y_cf, delta) : triple if return_as_dict is False

Return type

dict or triple

Raises

Exception – If no counterfactual was found.

ceml.torch.counterfactual.generate_counterfactual(model, x, y_target, device=torch.device, features_whitelist=None, regularization=None, C=1.0, optimizer='nelder-mead', optimizer_args=None, return_as_dict=True, done=None)

Computes a counterfactual of a given input x.

Parameters
• model (instance of torch.nn.Module and ceml.model.model.ModelWithLoss) – The PyTorch model that is used for computing the counterfactual.

• x (numpy.ndarray) – The input x whose prediction has to be explained.

• y_target (int or float) – The requested prediction of the counterfactual.

• device (torch.device) –

Specifies the hardware device (e.g. cpu or gpu) we are working on.

The default is torch.device(“cpu”).

• feature_whitelist (list(int), optional) –

List of feature indices (dimensions of the input space) that can be used when computing the counterfactual.

If feature_whitelist is None, all features can be used.

The default is None.

• regularization (str or callable, optional) –

Regularizer of the counterfactual. Penalty for deviating from the original input x.

Supported values:

• l1: Penalizes the absolute deviation.

• l2: Penalizes the squared deviation.

You can use your own custom penalty function by setting regularization to a callable that can be called on a potential counterfactual and returns a scalar.

If regularization is None, no regularization is used.

The default is “l1”.

• C (float or list(float), optional) –

The regularization strength. If C is a list, all values in C are tried and as soon as a counterfactual is found, this counterfactual is returned and no other values of C are tried.

If no regularization is used (regularization=None), C is ignored.

The default is 1.0

• optimizer (str or class that is derived from torch.optim.Optimizer, optional) –

Name/Identifier of the optimizer that is used for computing the counterfactual. See ceml.optim.optimizer.desc_to_optim() for details.

As an alternative, any optimizer from PyTorch can be used - optimizer must be class that is derived from torch.optim.Optimizer.

• optimizer_args (dict, optional) –

Dictionary containing additional parameters for the optimization algorithm.

Supported parameters (keys):

• args: Arguments of the optimization algorithm (e.g. learning rate, momentum, …)

• lr_scheduler: Learning rate scheduler (see torch.optim.lr_scheduler)

• lr_scheduler_args: Arguments of the learning rate scheduler

• tol: Tolerance for termination

• max_iter: Maximum number of iterations

If optimizer_args is None or if some parameters are missing, default values are used.

The default is None.

Note

The parameters tol and max_iter are passed to all optimization algorithms. Whereas the other parameters are only passed to PyTorch optimizers.

• return_as_dict (boolean, optional) –

If True, returns the counterfactual, its prediction and the needed changes to the input as dictionary. If False, the results are returned as a triple.

The default is True.

• done (callable, optional) –

A callable that returns True if a counterfactual with a given output/prediction is accepted and False otherwise.

If done is None, the output/prediction of the counterfactual must match y_target exactly.

The default is None.

Note

In case of a regression it might not always be possible to achieve a given output/prediction exactly.

Returns

A dictionary where the counterfactual is stored in ‘x_cf’, its prediction in ‘y_cf’ and the changes to the original input in ‘delta’.

(x_cf, y_cf, delta) : triple if return_as_dict is False

Return type

dict or triple

## ceml.torch.utils¶

ceml.torch.utils.build_regularization_loss(regularization, x, input_wrapper=None)

Builds a regularization loss.

Parameters
• desc (str, callable or None) –

Description of the regularization, a callable regularization (not mandatory but we recommend to put your custom regularization into a class and make it a child of ceml.costfunctions.costfunctions.CostFunction or ceml.costfunctions.costfunctions.DifferentiableCostFunction if your cost function is differentiable) or None if no regularization is desired.

See ceml.torch.utils.desc_to_regcost() for a list of supported descriptions.

If no regularization is requested, an instance of ceml.backend.torch.costfunctions.costfunctions.DummyCost is returned. This cost function always outputs zero, no matter what the input is.

• x (numpy.array) – The original input from which we do not want to deviate much.

• input_wrapper (callable, optional) –

Converts the input (e.g. if we want to exclude some features/dimensions, we might have to include these missing features before applying any function to it).

If input_wrapper is None, input is passed without any modifications.

The default is None.

Returns

An instance of ceml.costfunctions.costfunctions.CostFunction or the user defined, callable, regularization.

Return type

callable

Raises

TypeError – If regularization has an invalid type.

ceml.torch.utils.desc_to_dist(desc)

Converts a description of a distance metric into a torch function.

Supported descriptions:

• l1: l1-norm

• l2: l2-norm

Parameters

desc (str) – Description of the distance metric.

Returns

The distance function implemented as a torch function.

Return type

callable

Raises

ValueError – If desc contains an invalid description.

ceml.torch.utils.desc_to_regcost(desc, x, input_wrapper)

Converts a description of a regularization into a torch function.

Supported descriptions:

• l1: l1-regularization

• l2: l2-regularization

Parameters
• desc (str) – Description of the distance metric.

• x (numpy.array) – The original input from which we do not want to deviate much.

• input_wrapper (callable) –

Converts the input (e.g. if we want to exclude some features/dimensions, we might have to include these missing features before applying any function to it).

Is ignored!

Returns

The regularization function implemented as a torch function.

Return type

callable

Raises

ValueError – If desc contains an invalid description.