fairgrad.torch.fairness_loss

class fairgrad.torch.fairness_loss.FairnessLoss(base_loss_class, reduction='mean', fairness_measure=None, y_train=None, s_train=None, y_desirable=[1], epsilon=0.0, fairness_rate=0.01, **kwargs)

This is an extension of the losses provided by pytorch. Here, we augment the losses to enforce fairness. The exact algorithm can be found in the Fairgrad paper <https://arxiv.org/abs/2206.10923>.

Parameters

  • base_loss_class (nn.modules.loss._Loss) – Specifies the base loss as a proper base loss.

  • reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the weighted mean of the output is taken, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

  • fairness_measure (string, FairnessMeasure) – Currently supported are “equal_odds”, “equal_opportunity”, “demographic_parity”, and “accuracy_parity”.

  • y_train (np.asarray[int], Tensor, optional) – All train example’s corresponding label

  • s_train (np.asarray[int], Tensor, optional) – All train example’s corresponding sensitive attribute. This means if there are 2 sensitive attributes, with each of them being binary. For instance gender - (male and female) and age (above 45, below 45). Total unique sentive attributes are 4.

  • y_desirable (np.asarray[int], Tensor, optional) – All desirable labels, only used with equality of opportunity.

  • epsilon (float, optional) – The slack which is allowed for the final fairness level.

  • fairness_rate (float, optional) – Parameter which intertwines current fairness weights with sum of previous fairness rates.

  • **kwargs – Arbitrary keyword arguments passed to base_loss_class upon instantiation. Using is at your own risk as it might result in unexpected behaviours.

Examples:

>>> input = torch.randn(10, 5, requires_grad=True)
>>> target = torch.empty(10, dtype=torch.long).random_(2)
>>> s = torch.empty(10, dtype=torch.long).random_(2) # protected attribute
>>> loss = FairnessLoss(nn.CrossEntropyLoss,y_train = target, s_train = s, fairness_measure = 'equal_odds')
>>> output = loss(input, target, s, mode='train')
>>> output.backward()
forward(input, target, s=None, mode='train')

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters

  • input (Tensor) –

  • target (Tensor) –

  • s (Optional[Tensor]) –

  • mode (str) –

Return type

Tensor