model_remediation.min_diff.keras.utils.build_min_diff_dataset

Build MinDiff dataset from sensitive and nonsensitive datasets.

model_remediation.min_diff.keras.utils.build_min_diff_dataset(
    sensitive_group_dataset, nonsensitive_group_dataset
) -> tf.data.Dataset

Arguments
sensitive_group_dataset	tf.data.Dataset or valid MinDiff structure (unnested dict) of tf.data.Datasets containing only examples that belong to the sensitive group.
nonsensitive_group_dataset	tf.data.Dataset or valid MinDiff structure (unnested dict) of tf.data.Datasets containing only examples that do not belong to the sensitive group.

This function builds a tf.data.Dataset containing examples that are meant to only be used when calculating a min_diff_loss. This resulting dataset will need to be packed with the original dataset used for the original task of the model which can be done by calling utils.pack_min_diff_data.

Warning: All input datasets should be batched before being passed in.

Each input dataset must output a tuple in the format used in tf.keras.Model.fit. Specifically the output must be a tuple of length 1, 2 or 3 in the form (x, y, sample_weight).

This output will be parsed internally in the following way:

batch = ...  # Batch from any of the input datasets.
x, y, sample_weight = tf.keras.utils.unpack_x_y_sample_weight(batch)

Note: the y component of input datasets will be ignored completely so it can be set to None or any other arbitrary value. If sample_weight is not included, it can be left out entirely.

Every batch from the returned tf.data.Dataset will contain one batch from each of the input datasets. Each returned batch will be a tuple or structure (matching the structure of the inputs) of (min_diff_x, min_diff_membership, min_diff_sample_weight) where, for each pair of input datasets:

• min_diff_x: is formed by concatenating the x components of the paired datasets. The structure of these must match. If they don't the dataset will raise an error at the first batch.
• min_diff_membership: is a tensor of size [min_diff_batch_size, 1] indicating which dataset each example comes from (1.0 for sensitive_group_dataset and 0.0 for nonsensitive_group_dataset).
• min_diff_sample_weight: is formed by concatenating the sample_weight components of the paired datasets. If both are None, then this will be set to None. If only one is None, it is replaced with a Tensor of ones of the appropriate shape.

Returns
A tf.data.Dataset whose output is a tuple or structure (matching the structure of the inputs) of (min_diff_x, min_diff_membership, min_diff_sample_weight).

Raises
ValueError	If either sensitive_group_dataset or nonsensitive_group_dataset is not a valid MinDiff structure (unnested dict).
ValueError	If sensitive_group_dataset and nonsensitive_group_dataset do not have the same structure.