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MinDiffLoss abstract base class.
model_remediation.min_diff.losses.MinDiffLoss(
membership_transform=None,
predictions_transform=None,
membership_kernel=None,
predictions_kernel=None,
name: Optional[Text] = None
)
Inherits from: tf.keras.losses.Loss
Arguments  

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. 
To be implemented by subclasses:
call()
: Contains the logic for loss calculation usingmembership
,predictions
and optionallysample_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.
Raises  

ValueError

If a *_transform parameter is passed in but is not callable.

ValueError

If a *_kernel parameter has an unrecognized type or value.

Methods
_apply_kernels
_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
_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
: hasself.membership_transform
applied to it, if it's present, and is cast totf.float32
.predictions
: hasself.predictions_transform
applied to it, if it's present, and is cast totf.float32
.sample_weight
: is validated, cast totf.float32
, and normalized. If it isNone
, 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.
