ceml.torch
ceml.torch.counterfactual
- class ceml.torch.counterfactual.TorchCounterfactual(model, device=torch.device, **kwds)
Bases:
ceml.model.counterfactual.Counterfactual
Class for computing a counterfactual of a PyTorch model.
See parent class
ceml.model.counterfactual.Counterfactual
.- Parameters
model (instance of
torch.nn.Module
andceml.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 classestorch.nn.Module
andceml.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
andceml.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
.The default is “nelder-mead”.
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
andceml.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
.The default is “nelder-mead”.
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
orceml.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.