Feature Permutation

class captum.attr.FeaturePermutation(forward_func, perm_func=_permute_feature)[source]

A perturbation based approach to compute attribution, which takes each input feature, permutes the feature values within a batch, and computes the difference between original and shuffled outputs for the given batch. This difference signifies the feature importance for the permuted feature.

Example pseudocode for the algorithm is as follows:

perm_feature_importance(batch):
    importance = dict()
    baseline_error = error_metric(model(batch), batch_labels)
    for each feature:
        permute this feature across the batch
        error = error_metric(model(permuted_batch), batch_labels)
        importance[feature] = baseline_error - error
        "un-permute" the feature across the batch

    return importance

It should be noted that the error_metric must be called in the forward_func. You do not need to have an error metric, e.g. you could simply return the logits (the model output), but this may or may not provide a meaningful attribution.

This method, unlike other attribution methods, requires a batch of examples to compute attributions and cannot be performed on a single example.

By default, each scalar value within each input tensor is taken as a feature and shuffled independently. Passing a feature mask, allows grouping features to be shuffled together. Each input scalar in the group will be given the same attribution value equal to the change in target as a result of shuffling the entire feature group.

The forward function can either return a scalar per example, or a single scalar for the full batch. If a single scalar is returned for the batch, perturbations_per_eval must be 1, and the returned attributions will have first dimension 1, corresponding to feature importance across all examples in the batch.

More information can be found in the permutation feature importance algorithm description here: https://christophm.github.io/interpretable-ml-book/feature-importance.html

Parameters:

  • forward_func (Callable) – The forward function of the model or any modification of it.

  • perm_func (Callable, optional) – A function that accepts a batch of inputs and a feature mask, and “permutes” the feature using feature mask across the batch. This defaults to a function which applies a random permutation, this argument only needs to be provided if a custom permutation behavior is desired. Default: _permute_feature

attribute(inputs, target=None, additional_forward_args=None, feature_mask=None, perturbations_per_eval=1, show_progress=False, **kwargs)[source]

This function is almost equivalent to FeatureAblation.attribute. The main difference is the way ablated examples are generated. Specifically they are generated through the perm_func, as we set the baselines for FeatureAblation.attribute to None.

Parameters:

  • inputs (Tensor or tuple[Tensor, ...]) – Input for which permutation attributions are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples (aka batch size), and if multiple input tensors are provided, the examples must be aligned appropriately.

  • target (int, tuple, Tensor, or list, optional) –

    Output indices for which difference is computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, no target index is necessary. For general 2D outputs, targets can be either:

    • a single integer or a tensor containing a single integer, which is applied to all input examples

    • a list of integers or a 1D tensor, with length matching the number of examples in inputs (dim 0). Each integer is applied as the target for the corresponding example.

    For outputs with > 2 dimensions, targets can be either:

    • A single tuple, which contains #output_dims - 1 elements. This target index is applied to all examples.

    • A list of tuples with length equal to the number of examples in inputs (dim 0), and each tuple containing #output_dims - 1 elements. Each tuple is applied as the target for the corresponding example.

    Default: None

  • additional_forward_args (Any, optional) – If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (non-tuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order following the arguments in inputs. For a tensor, the first dimension of the tensor must correspond to the number of examples. For all other types, the given argument is used for all forward evaluations. Note that attributions are not computed with respect to these arguments. Default: None

  • feature_mask (Tensor or tuple[Tensor, ...], optional) –

    feature_mask defines a mask for the input, grouping features which should be ablated together. feature_mask should contain the same number of tensors as inputs. Each tensor should be the same size as the corresponding input or broadcastable to match the input tensor. Each tensor should contain integers in the range 0 to num_features - 1, and indices corresponding to the same feature should have the same value. Note that features within each input tensor are ablated independently (not across tensors).

    The first dimension of each mask must be 1, as we require to have the same group of features for each input sample.

    If None, then a feature mask is constructed which assigns each scalar within a tensor as a separate feature, which is permuted independently. Default: None

  • perturbations_per_eval (int, optional) – Allows permutations of multiple features to be processed simultaneously in one call to forward_fn. Each forward pass will contain a maximum of perturbations_per_eval * #examples samples. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain at most (perturbations_per_eval * #examples) / num_devices samples. If the forward function returns a single scalar per batch, perturbations_per_eval must be set to 1. Default: 1

  • show_progress (bool, optional) – Displays the progress of computation. It will try to use tqdm if available for advanced features (e.g. time estimation). Otherwise, it will fallback to a simple output of progress. Default: False

  • **kwargs (Any, optional) – Any additional arguments used by child classes of FeatureAblation (such as Occlusion) to construct ablations. These arguments are ignored when using FeatureAblation directly. Default: None

Returns:

  • attributions (Tensor or tuple[Tensor, …]):

    The attributions with respect to each input feature. If the forward function returns a scalar value per example, attributions will be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If the forward function returns a scalar per batch, then attribution tensor(s) will have first dimension 1 and the remaining dimensions will match the input. If a single tensor is provided as inputs, a single tensor is returned. If a tuple of tensors is provided for inputs, a tuple of corresponding sized tensors is returned.

Return type:

Tensor or tuple[Tensor, …] of attributions

Examples:

>>> # SimpleClassifier takes a single input tensor of size Nx4x4,
>>> # and returns an Nx3 tensor of class probabilities.
>>> net = SimpleClassifier()
>>> # Generating random input with size 10 x 4 x 4
>>> input = torch.randn(10, 4, 4)
>>> # Defining FeaturePermutation interpreter
>>> feature_perm = FeaturePermutation(net)
>>> # Computes permutation attribution, shuffling each of the 16
>>> # scalar input independently.
>>> attr = feature_perm.attribute(input, target=1)

>>> # Alternatively, we may want to permute features in groups, e.g.
>>> # grouping each 2x2 square of the inputs and shuffling them together.
>>> # This can be done by creating a feature mask as follows, which
>>> # defines the feature groups, e.g.:
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 0 | 0 | 1 | 1 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # | 2 | 2 | 3 | 3 |
>>> # +---+---+---+---+
>>> # With this mask, all inputs with the same value are shuffled
>>> # simultaneously, and the attribution for each input in the same
>>> # group (0, 1, 2, and 3) per example are the same.
>>> # The attributions can be calculated as follows:
>>> # feature mask has dimensions 1 x 4 x 4
>>> feature_mask = torch.tensor([[[0,0,1,1],[0,0,1,1],
>>>                             [2,2,3,3],[2,2,3,3]]])
>>> attr = feature_perm.attribute(input, target=1,
>>>                               feature_mask=feature_mask)
attribute_future(inputs, target=None, additional_forward_args=None, feature_mask=None, perturbations_per_eval=1, show_progress=False, **kwargs)[source]

Almost the same as the attribute function, except that it requires a forward function that returns a Future, and it returns a Future.

Return type:

Future[TypeVar(TensorOrTupleOfTensorsGeneric, Tensor, Tuple[Tensor, ...])]