model_remediation.counterfactual.losses.CounterfactualLoss

CounterfactualLoss abstract base class.

model_remediation.counterfactual.losses.CounterfactualLoss(
    name: Optional[str] = None
)

Inherits from: tf.keras.losses.Loss

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

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

Methods

`call`

View source

__call__(
    original: types.TensorType,
    counterfactual: types.TensorType,
    sample_weight: Optional[types.TensorType] = None
)

Computes Counterfactual loss.

Arguments
`original`	The predictions from the original example values. shape = `[batch_size, d0, .. dN]`. `Tensor` of type `float32` or `float64`. Required.
`counterfactual`	The predictions from the counterfactual examples. shape = `[batch_size, d0, .. dN]`. `Tensor` of the same type and shape as `original`. Required.
`sample_weight`	(Optional) `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector.

Returns
The computed counterfactual loss.

Raises
`ValueError`	If any of the input arguments are invalid.
`TypeError`	If any of the arguments are not of the expected type.
`InvalidArgumentError`	If `original`, `counterfactual` or `sample_weight` have incompatible shapes.