# Source code for captum.attr._core.feature_permutation

```
#!/usr/bin/env python3
from typing import Any, Callable, Tuple, Union
import torch
from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr._core.feature_ablation import FeatureAblation
from captum.log import log_usage
from torch import Tensor
def _permute_feature(x: Tensor, feature_mask: Tensor) -> Tensor:
n = x.size(0)
assert n > 1, "cannot permute features with batch_size = 1"
perm = torch.randperm(n)
no_perm = torch.arange(n)
while (perm == no_perm).all():
perm = torch.randperm(n)
return (x[perm] * feature_mask.to(dtype=x.dtype)) + (
x * feature_mask.bitwise_not().to(dtype=x.dtype)
)
[docs]
class FeaturePermutation(FeatureAblation):
r"""
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
"""
def __init__(
self, forward_func: Callable, perm_func: Callable = _permute_feature
) -> None:
r"""
Args:
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`
"""
FeatureAblation.__init__(self, forward_func=forward_func)
self.perm_func = perm_func
# suppressing error caused by the child class not having a matching
# signature to the parent
[docs]
@log_usage()
def attribute( # type: ignore
self,
inputs: TensorOrTupleOfTensorsGeneric,
target: TargetType = None,
additional_forward_args: Any = None,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
perturbations_per_eval: int = 1,
show_progress: bool = False,
**kwargs: Any,
) -> TensorOrTupleOfTensorsGeneric:
r"""
This function is almost equivalent to
:func:`FeatureAblation.attribute <captum.attr.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
:func:`FeatureAblation.attribute <captum.attr.FeatureAblation.attribute>` to
``None``.
Args:
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 :class:`.FeatureAblation` (such as
:class:`.Occlusion`) to construct ablations. These
arguments are ignored when using FeatureAblation directly.
Default: None
Returns:
*Tensor* or *tuple[Tensor, ...]* of **attributions**:
- **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.
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)
"""
# Remove baselines from kwargs if provided so we don't specify this field
# twice in the FeatureAblation.attribute call below.
if isinstance(kwargs, dict) and "baselines" in kwargs:
del kwargs["baselines"]
return FeatureAblation.attribute.__wrapped__(
self,
inputs,
baselines=None,
target=target,
additional_forward_args=additional_forward_args,
feature_mask=feature_mask,
perturbations_per_eval=perturbations_per_eval,
show_progress=show_progress,
**kwargs,
)
def _construct_ablated_input(
self,
expanded_input: Tensor,
input_mask: Tensor,
baseline: Union[int, float, Tensor],
start_feature: int,
end_feature: int,
**kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""
This function permutes the features of `expanded_input` with a given
feature mask and feature range. Permutation occurs via calling
`self.perm_func` across each batch within `expanded_input`. As with
`FeatureAblation._construct_ablated_input`:
- `expanded_input.shape = (num_features, num_examples, ...)`
- `num_features = end_feature - start_feature` (i.e. start and end is a
half-closed interval)
- `input_mask` is a tensor of the same shape as one input, which
describes the locations of each feature via their "index"
Since `baselines` is set to None for `FeatureAblation.attribute, this
will be the zero tensor, however, it is not used.
"""
assert input_mask.shape[0] == 1, (
"input_mask.shape[0] != 1: pass in one mask in order to permute"
"the same features for each input"
)
current_mask = torch.stack(
[input_mask == j for j in range(start_feature, end_feature)], dim=0
).bool()
current_mask = current_mask.to(expanded_input.device)
output = torch.stack(
[
self.perm_func(x, mask.squeeze(0))
for x, mask in zip(expanded_input, current_mask)
]
)
return output, current_mask
```