model_remediation.min_diff.losses.MinDiffLoss

MinDiffLoss abstract base class.

model_remediation.min_diff.losses.MinDiffLoss(
    membership_transform=None,
    predictions_transform=None,
    membership_kernel=None,
    predictions_kernel=None,
    name: Optional[str] = None,
    enable_summary_histogram: Optional[bool] = True
)

Inherits from: tf.keras.losses.Loss

membership_transform Transform function used on membership. If None is passed in then membership is left as is. The function must return a tf.Tensor.
predictions_transform Transform function used on predictions. If None is passed in then predictions is left as is. The function must return a tf.Tensor.
membership_kernel String (name of kernel) or min_diff.losses.MinDiffKernel to be applied on membership. If None is passed in, then membership is left untouched when applying kernels.
predictions_kernel String (name of kernel) or min_diff.losses.MinDiffKernel to be applied on predictions. If None is passed in, then predictions is left untouched when applying kernels.
name Name used for logging and tracking.
enable_summary_histogram Boolean indicating if histogram summary should be written. Defaults to True.

To be implemented by subclasses:

  • call(): Contains the logic for loss calculation using membership, predictions and optionally sample_weight.

Example subclass implementation:

class MyMinDiffLoss(MinDiffLoss):

  def call(membership, predictions, sample_weight=None):
    loss = ...  # Internal logic to calculate loss.
    return loss

A MinDiffLoss instance measures the difference in prediction scores (typically score distributions) between two groups of examples identified by the value in the membership column.

If the predictions between the two groups are indistinguishable, the loss should be 0. The more different the two scores are, the higher the loss.

ValueError If a *_transform parameter is passed in but is not callable.
ValueError If a *_kernel parameter has an unrecognized type or value.

Methods

__call__

View source

__call__(
    membership: types.TensorType,
    predictions: types.TensorType,
    sample_weight: Optional[types.TensorType] = None
)

Invokes the MinDiffLoss instance.

Args
membership Labels indicating whether examples are part of the sensitive group. Shape must be [batch_size, d0, .. dN].
predictions Predicted values. Must be the same shape as membership.
sample_weight (Optional) acts as a coefficient for the loss. Must be of shape [batch_size] or [batch_size, 1]. If None then a tensor of ones with the appropriate shape is used.

Returns
Scalar min_diff_loss.

_apply_kernels

View source

_apply_kernels(
    membership: types.TensorType, predictions: types.TensorType
) -> Tuple[types.TensorType, types.TensorType]

Applies losses.MinDiffKernel attributes to corresponding inputs.

Arguments
membership membership column as described in MinDiffLoss.call.
predictions predictions tensor as described in MinDiffLoss.call.

In particular, self.membership_kernel, if not None, will be applied to membership and self.predictions_kernel, if not None, will be applied to predictions.

loss = ...  # MinDiffLoss subclass instance
loss.membership_kernel = min_diff.losses.GaussKernel()
loss.predictions_kernel = min_diff.losses.LaplaceKernel()

# Call with test inputs.
loss._apply_kernels([1, 2, 3], [4, 5, 6])  # (GaussKernel([1, 2, 3]),
                                           #  LaplaceKernel([4, 5, 6]))

If self.*_kernel is None, then the corresponding input is returned unchanged.

loss = ...  # MinDiffLoss subclass instance
loss.membership_kernel = None
loss.predictions_kernel = min_diff.losses.GaussKernel
# Call with test inputs.
loss._apply_kernels([1, 2, 3], [4, 5, 6])  # ([1, 2, 3], GaussKernel([4, 5, 6]))

# With both kernels set to None, _apply_kernels is the identity.
loss.predictions_kernel = None
# Call with test inputs.
loss._apply_kernels([1, 2, 3], [4, 5, 6])  # ([1, 2, 3], [4, 5, 6])

This function is meant for internal use by subclasses when the instance is invoked.

Returns
Tuple of (membership_kernel_output, predictions_kernel_output).

_preprocess_inputs

View source

_preprocess_inputs(
    membership: types.TensorType,
    predictions: types.TensorType,
    sample_weight: Optional[types.TensorType] = None
) -> Tuple[types.TensorType, types.TensorType, types.TensorType]

Preprocesses inputs by applying transforms and normalizing weights.

Arguments
membership membership column as described in MinDiffLoss.call.
predictions predictions tensor as described in MinDiffLoss.call.
sample_weight sample_weight tensor as described in MinDiffLoss.call.

The three inputs are processed in the following way:

  • membership: has self.membership_transform applied to it, if it's present, and is cast to tf.float32.
  • predictions: has self.predictions_transform applied to it, if it's present, and is cast to tf.float32.
  • sample_weight: is validated, cast to tf.float32, and normalized. If it is None, it is set to a normalized tensor of equal weights.

This method is meant for internal use by subclasses when the instance is invoked.

Returns
Tuple of (membership, predictions, normed_weights).

Raises
tf.errors.InvalidArgumentError if sample_weight has negative entries.