import torch
from captum.attr import Lime, KernelShap, LimeBase
from captum.attr._core.lime import construct_feature_mask
from captum.attr._utils.batching import _batch_example_iterator
from captum.attr._utils.common import _format_input_baseline
from captum.log import log_usage
from captum._utils.models import Model
from captum._utils.common import (
_is_tuple,
_reduce_list,
_run_forward,
)
from captum._utils.typing import (
BaselineType,
TargetType,
TensorOrTupleOfTensorsGeneric,
)
from torch import Tensor
from typing import Any, Callable, Optional, Tuple, Union
import warnings
from .models import BLRRidge
class _BayesLime(Lime):
def __init__(
self,
forward_func: Callable,
interpretable_model: Model = None,
similarity_func: Optional[Callable] = None,
perturb_func: Optional[Callable] = None,
) -> None:
super().__init__(
forward_func=forward_func,
interpretable_model=interpretable_model,
similarity_func=similarity_func,
perturb_func=perturb_func,
)
def _attribute_kwargs( # type: ignore
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
n_samples: int = 25,
perturbations_per_eval: int = 1,
return_input_shape: bool = True,
show_progress: bool = False,
**kwargs,
) -> TensorOrTupleOfTensorsGeneric:
is_inputs_tuple = _is_tuple(inputs)
formatted_inputs, baselines = _format_input_baseline(inputs, baselines)
bsz = formatted_inputs[0].shape[0]
feature_mask, num_interp_features = construct_feature_mask(
feature_mask, formatted_inputs
)
if num_interp_features > 10000:
warnings.warn(
"Attempting to construct interpretable model with > 10000 features."
"This can be very slow or lead to OOM issues. Please provide a feature"
"mask which groups input features to reduce the number of interpretable"
"features. "
)
coefs: Tensor
if bsz > 1:
test_output = _run_forward(
self.forward_func, inputs, target, additional_forward_args
)
if (
isinstance(test_output, Tensor)
and torch.numel(test_output) > 1
):
if torch.numel(test_output) == bsz:
warnings.warn(
"You are providing multiple inputs for Lime / Kernel SHAP "
"attributions. This trains a separate interpretable model "
"for each example, which can be time consuming. It is "
"recommended to compute attributions for one example at a time."
)
output_list = []
creds_list = []
for (
curr_inps,
curr_target,
curr_additional_args,
curr_baselines,
curr_feature_mask,
) in _batch_example_iterator(
bsz,
formatted_inputs,
target,
additional_forward_args,
baselines,
feature_mask,
):
coefs, creds = LimeBase.attribute.__wrapped__(
self,
inputs=curr_inps
if is_inputs_tuple
else curr_inps[0],
target=curr_target,
additional_forward_args=curr_additional_args,
n_samples=n_samples,
perturbations_per_eval=perturbations_per_eval,
baselines=curr_baselines
if is_inputs_tuple
else curr_baselines[0],
feature_mask=curr_feature_mask
if is_inputs_tuple
else curr_feature_mask[0],
num_interp_features=num_interp_features,
show_progress=show_progress,
**kwargs,
)
if return_input_shape:
output_list.append(
self._convert_output_shape(
curr_inps,
curr_feature_mask,
coefs,
num_interp_features,
is_inputs_tuple,
)
)
creds_list.append(
self._convert_output_shape(
curr_inps,
curr_feature_mask,
creds,
num_interp_features,
is_inputs_tuple,
)
)
else:
output_list.append(coefs.reshape(1, -1)) # type: ignore
creds_list.append(creds.reshape(1, -1)) # type: ignore
return _reduce_list(output_list), _reduce_list(creds_list)
else:
raise AssertionError(
"Invalid number of outputs, forward function should return a"
"scalar per example or a scalar per input batch."
)
else:
assert perturbations_per_eval == 1, (
"Perturbations per eval must be 1 when forward function"
"returns single value per batch!"
)
coefs, creds = LimeBase.attribute.__wrapped__(
self,
inputs=inputs,
target=target,
additional_forward_args=additional_forward_args,
n_samples=n_samples,
perturbations_per_eval=perturbations_per_eval,
baselines=baselines if is_inputs_tuple else baselines[0],
feature_mask=feature_mask if is_inputs_tuple else feature_mask[0],
num_interp_features=num_interp_features,
show_progress=show_progress,
**kwargs,
)
if return_input_shape:
return self._convert_output_shape(
formatted_inputs,
feature_mask,
coefs,
num_interp_features,
is_inputs_tuple,
), self._convert_output_shape(
formatted_inputs,
feature_mask,
creds,
num_interp_features,
is_inputs_tuple,
)
else:
return coefs, creds
[docs]class BayesLime(_BayesLime):
"""
Bayesian version of Lime.
This method replace the linear regression of the original Lime with a
bayesian linear regression, allowing to model uncertainty in
explainability.
Args:
forward_func (callable): The forward function of the model or any
modification of it.
interpretable_model (Model): Model object to train interpretable model.
This argument is optional and defaults to SkLearnBayesianRidge(),
which is a wrapper around the Bayesian Ridge in SkLearn.
This requires having sklearn version >= 0.23 available.
Other predefined interpretable linear models are provided in
tint.attr.models.bayes_linear.
Alternatively, a custom model object must provide a `fit` method to
train the model, given a dataloader, with batches containing
three tensors:
- interpretable_inputs: Tensor
[2D num_samples x num_interp_features],
- expected_outputs: Tensor [1D num_samples],
- weights: Tensor [1D num_samples]
The model object must also provide a `representation` method to
access the appropriate coefficients or representation of the
interpretable model after fitting.
Note that calling fit multiple times should retrain the
interpretable model, each attribution call reuses
the same given interpretable model object.
Default: None
similarity_func (Callable, optional): Function which takes a single sample
along with its corresponding interpretable representation
and returns the weight of the interpretable sample for
training the interpretable model.
This is often referred to as a similarity kernel.
This argument is optional and defaults to a function which
applies an exponential kernel to the cosine distance between
the original input and perturbed input, with a kernel width
of 1.0.
A similarity function applying an exponential
kernel to cosine / euclidean distances can be constructed
using the provided get_exp_kernel_similarity_function in
captum.attr._core.lime.
Alternately, a custom callable can also be provided.
The expected signature of this callable is:
>>> def similarity_func(
>>> original_input: Tensor or tuple[Tensor, ...],
>>> perturbed_input: Tensor or tuple[Tensor, ...],
>>> perturbed_interpretable_input:
>>> Tensor [2D 1 x num_interp_features],
>>> **kwargs: Any
>>> ) -> float or Tensor containing float scalar
perturbed_input and original_input will be the same type and
contain tensors of the same shape, with original_input
being the same as the input provided when calling attribute.
kwargs includes baselines, feature_mask, num_interp_features
(integer, determined from feature mask).
perturb_func (Callable, optional): Function which returns a single
sampled input, which is a binary vector of length
num_interp_features, or a generator of such tensors.
This function is optional, the default function returns
a binary vector where each element is selected
independently and uniformly at random. Custom
logic for selecting sampled binary vectors can
be implemented by providing a function with the
following expected signature:
>>> perturb_func(
>>> original_input: Tensor or tuple[Tensor, ...],
>>> **kwargs: Any
>>> ) -> Tensor [Binary 2D Tensor 1 x num_interp_features]
>>> or generator yielding such tensors
kwargs includes baselines, feature_mask, num_interp_features
(integer, determined from feature mask).
percent (int): Percentage for the credible intervals. Must be between
0 and 100. Only used when no custom interpretable model is
passed. Otherwise, you must specify the percentage for
credible interval in the model definition:
>>> from tint.attr.models import BLRRegression
>>> model = BLRRegression(percent=90)
>>> explainer = BayesLime(mlp, interpretable_model=model)
Default: 95
References:
`Reliable Post hoc Explanations: Modeling Uncertainty in Explainability <https://arxiv.org/abs/2008.05030>`_
Examples:
>>> import torch as th
>>> from tint.attr import BayesLime
>>> from tint.models import MLP
<BLANKLINE>
>>> inputs = th.rand(8, 5)
>>> mlp = MLP([5, 3, 1])
<BLANKLINE>
>>> explainer = BayesLime(mlp)
>>> attr, credible_int = explainer.attribute(inputs)
"""
def __init__(
self,
forward_func: Callable,
interpretable_model: Model = None,
similarity_func: Callable = None,
perturb_func: Callable = None,
percent: int = 95,
) -> None:
super().__init__(
forward_func=forward_func,
interpretable_model=interpretable_model
or BLRRidge(percent=percent),
similarity_func=similarity_func,
perturb_func=perturb_func,
)
[docs] @log_usage()
def attribute( # type: ignore
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
n_samples: int = 25,
perturbations_per_eval: int = 1,
return_input_shape: bool = True,
show_progress: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
This method attributes the output of the model with given target index
(in case it is provided, otherwise it assumes that output is a
scalar) to the inputs of the model using the approach described above,
training an interpretable model and returning a representation of the
interpretable model.
It is recommended to only provide a single example as input (tensors
with first dimension or batch size = 1). This is because LIME is generally
used for sample-based interpretability, training a separate interpretable
model to explain a model's prediction on each individual example.
A batch of inputs can also be provided as inputs, similar to
other perturbation-based attribution methods. In this case, if forward_fn
returns a scalar per example, attributions will be computed for each
example independently, with a separate interpretable model trained for each
example. Note that provided similarity and perturbation functions will be
provided each example separately (first dimension = 1) in this case.
If forward_fn returns a scalar per batch (e.g. loss), attributions will
still be computed using a single interpretable model for the full batch.
In this case, similarity and perturbation functions will be provided the
same original input containing the full batch.
The number of interpretable features is determined from the provided
feature mask, or if none is provided, from the default feature mask,
which considers each scalar input as a separate feature. It is
generally recommended to provide a feature mask which groups features
into a small number of interpretable features / components (e.g.
superpixels in images).
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which LIME
is 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, and if multiple input tensors
are provided, the examples must be aligned appropriately.
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
Baselines define reference value which replaces each
feature when the corresponding interpretable feature
is set to 0.
Baselines can be provided as:
- a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first
dimension is one and the remaining dimensions match
with inputs.
- a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in input tensor.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
target (int, tuple, Tensor, or list, optional): Output indices for
which surrogate model is trained
(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. It will be
repeated for each of `n_steps` along the integrated
path. 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 correspond to the same
interpretable feature. 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. Values across
all tensors should be integers in the range 0 to
num_interp_features - 1, and indices corresponding to the
same feature should have the same value.
Note that features are grouped across tensors
(unlike feature ablation and occlusion), so
if the same index is used in different tensors, those
features are still grouped and added simultaneously.
If None, then a feature mask is constructed which assigns
each scalar within a tensor as a separate feature.
Default: None
n_samples (int, optional): The number of samples of the original
model used to train the surrogate interpretable model.
Default: `50` if `n_samples` is not provided.
perturbations_per_eval (int, optional): Allows multiple samples
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
return_input_shape (bool, optional): Determines whether the returned
tensor(s) only contain the coefficients for each interp-
retable feature from the trained surrogate model, or
whether the returned attributions match the input shape.
When return_input_shape is True, the return type of attribute
matches the input shape, with each element containing the
coefficient of the corresponding interpretale feature.
All elements with the same value in the feature mask
will contain the same coefficient in the returned
attributions. If return_input_shape is False, a 1D
tensor is returned, containing only the coefficients
of the trained interpreatable models, with length
num_interp_features.
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
Returns:
2-element tuple of **attributions**, **credible_intervals**:
- **attributions** (*tensor* or tuple of *tensors*):
The attributions with respect to each input feature.
If return_input_shape = True, attributions will be
the same size as the provided inputs, with each value
providing the coefficient of the corresponding
interpretale feature.
If return_input_shape is False, a 1D
tensor is returned, containing only the coefficients
of the trained interpreatable models, with length
num_interp_features.
- **credible_intervals** (*tensor* or tuple of *tensors*):
The credible intervals associated with each attribution.
If return_input_shape = True, credible intervals will be
the same size as the provided inputs, with each value
providing the coefficient of the corresponding
interpretale feature.
If return_input_shape is False, a 1D
tensor is returned, containing only the credible intervals
of the trained interpreatable models, with length
num_interp_features.
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 1 x 4 x 4
>>> input = torch.randn(1, 4, 4)
>>> # Defining Lime interpreter
>>> lime = Lime(net)
>>> # Computes attribution, with each of the 4 x 4 = 16
>>> # features as a separate interpretable feature
>>> attr = lime.attribute(input, target=1, n_samples=200)
>>> # Alternatively, we can group each 2x2 square of the inputs
>>> # as one 'interpretable' feature and perturb 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 set to their
>>> # baseline value, when the corresponding binary interpretable
>>> # feature is set to 0.
>>> # 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]]])
>>> # Computes interpretable model and returning attributions
>>> # matching input shape.
>>> attr = lime.attribute(input, target=1, feature_mask=feature_mask)
"""
return super().attribute(
inputs=inputs,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
feature_mask=feature_mask,
n_samples=n_samples,
perturbations_per_eval=perturbations_per_eval,
return_input_shape=return_input_shape,
show_progress=show_progress,
)
[docs]class BayesKernelShap(KernelShap, _BayesLime):
"""
Bayesian version of KernelShap.
This method replace the linear regression of the original KernelShap with
a bayesian linear regression, allowing to model uncertainty in
explainability.
Args:
forward_func (callable): The forward function of the model or any
modification of it.
interpretable_model (Model): Model object to train interpretable model.
This argument is optional and defaults to SkLearnBayesianRidge(),
which is a wrapper around the Bayesian Ridge in SkLearn.
This requires having sklearn version >= 0.23 available.
Other predefined interpretable linear models are provided in
tint.attr.models.bayes_linear.
Alternatively, a custom model object must provide a `fit` method to
train the model, given a dataloader, with batches containing
three tensors:
- interpretable_inputs: Tensor
[2D num_samples x num_interp_features],
- expected_outputs: Tensor [1D num_samples],
- weights: Tensor [1D num_samples]
The model object must also provide a `representation` method to
access the appropriate coefficients or representation of the
interpretable model after fitting.
Note that calling fit multiple times should retrain the
interpretable model, each attribution call reuses
the same given interpretable model object.
Default: None
percent (int): Percentage for the credible intervals. Must be between
0 and 100. Only used when no custom interpretable model is
passed. Otherwise, you must specify the percentage for
credible interval in the model definition:
>>> from tint.attr.models import BLRRegression
>>> model = BLRRegression(percent=90)
>>> explainer = BayesKernelShap(mlp, interpretable_model=model)
Default: 95
References:
`Reliable Post hoc Explanations: Modeling Uncertainty in Explainability <https://arxiv.org/abs/2008.05030>`_
Examples:
>>> import torch as th
>>> from tint.attr import BayesKernelShap
>>> from tint.models import MLP
<BLANKLINE>
>>> inputs = th.rand(8, 5)
>>> mlp = MLP([5, 3, 1])
<BLANKLINE>
>>> explainer = BayesKernelShap(mlp)
>>> attr, credible_int = explainer.attribute(inputs)
"""
def __init__(
self,
forward_func: Callable,
interpretable_model: Model = None,
percent: int = 95,
) -> None:
super().__init__(forward_func=forward_func)
self.interpretable_model = interpretable_model or BLRRidge(
percent=percent
)
[docs] @log_usage()
def attribute( # type: ignore
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
n_samples: int = 25,
perturbations_per_eval: int = 1,
return_input_shape: bool = True,
show_progress: bool = False,
) -> TensorOrTupleOfTensorsGeneric:
r"""
This method attributes the output of the model with given target index
(in case it is provided, otherwise it assumes that output is a
scalar) to the inputs of the model using the approach described above,
training an interpretable model based on KernelSHAP and returning a
representation of the interpretable model.
It is recommended to only provide a single example as input (tensors
with first dimension or batch size = 1). This is because LIME / KernelShap
is generally used for sample-based interpretability, training a separate
interpretable model to explain a model's prediction on each individual example.
A batch of inputs can also be provided as inputs, similar to
other perturbation-based attribution methods. In this case, if forward_fn
returns a scalar per example, attributions will be computed for each
example independently, with a separate interpretable model trained for each
example. Note that provided similarity and perturbation functions will be
provided each example separately (first dimension = 1) in this case.
If forward_fn returns a scalar per batch (e.g. loss), attributions will
still be computed using a single interpretable model for the full batch.
In this case, similarity and perturbation functions will be provided the
same original input containing the full batch.
The number of interpretable features is determined from the provided
feature mask, or if none is provided, from the default feature mask,
which considers each scalar input as a separate feature. It is
generally recommended to provide a feature mask which groups features
into a small number of interpretable features / components (e.g.
superpixels in images).
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which KernelShap
is 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, and if multiple input tensors
are provided, the examples must be aligned appropriately.
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
Baselines define the reference value which replaces each
feature when the corresponding interpretable feature
is set to 0.
Baselines can be provided as:
- a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first
dimension is one and the remaining dimensions match
with inputs.
- a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in input tensor.
- a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs' tuple can be:
- either a tensor with matching dimensions to
corresponding tensor in the inputs' tuple
or the first dimension is one and the remaining
dimensions match with the corresponding
input tensor.
- or a scalar, corresponding to a tensor in the
inputs' tuple. This scalar value is broadcasted
for corresponding input tensor.
In the cases when `baselines` is not provided, we internally
use zero scalar corresponding to each input tensor.
Default: None
target (int, tuple, Tensor, or list, optional): Output indices for
which surrogate model is trained
(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. It will be
repeated for each of `n_steps` along the integrated
path. 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 correspond to the same
interpretable feature. 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. Values across
all tensors should be integers in the range 0 to
num_interp_features - 1, and indices corresponding to the
same feature should have the same value.
Note that features are grouped across tensors
(unlike feature ablation and occlusion), so
if the same index is used in different tensors, those
features are still grouped and added simultaneously.
If None, then a feature mask is constructed which assigns
each scalar within a tensor as a separate feature.
Default: None
n_samples (int, optional): The number of samples of the original
model used to train the surrogate interpretable model.
Default: `50` if `n_samples` is not provided.
perturbations_per_eval (int, optional): Allows multiple samples
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
return_input_shape (bool, optional): Determines whether the returned
tensor(s) only contain the coefficients for each interp-
retable feature from the trained surrogate model, or
whether the returned attributions match the input shape.
When return_input_shape is True, the return type of attribute
matches the input shape, with each element containing the
coefficient of the corresponding interpretable feature.
All elements with the same value in the feature mask
will contain the same coefficient in the returned
attributions. If return_input_shape is False, a 1D
tensor is returned, containing only the coefficients
of the trained interpretable model, with length
num_interp_features.
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
Returns:
2-element tuple of **attributions**, **credible_intervals**:
- **attributions** (*tensor* or tuple of *tensors*):
The attributions with respect to each input feature.
If return_input_shape = True, attributions will be
the same size as the provided inputs, with each value
providing the coefficient of the corresponding
interpretale feature.
If return_input_shape is False, a 1D
tensor is returned, containing only the coefficients
of the trained interpreatable models, with length
num_interp_features.
- **credible_intervals** (*tensor* or tuple of *tensors*):
The credible intervals associated with each attribution.
If return_input_shape = True, credible intervals will be
the same size as the provided inputs, with each value
providing the coefficient of the corresponding
interpretale feature.
If return_input_shape is False, a 1D
tensor is returned, containing only the credible intervals
of the trained interpreatable models, with length
num_interp_features.
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 1 x 4 x 4
>>> input = torch.randn(1, 4, 4)
>>> # Defining KernelShap interpreter
>>> ks = KernelShap(net)
>>> # Computes attribution, with each of the 4 x 4 = 16
>>> # features as a separate interpretable feature
>>> attr = ks.attribute(input, target=1, n_samples=200)
>>> # Alternatively, we can group each 2x2 square of the inputs
>>> # as one 'interpretable' feature and perturb 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 set to their
>>> # baseline value, when the corresponding binary interpretable
>>> # feature is set to 0.
>>> # 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]]])
>>> # Computes KernelSHAP attributions with feature mask.
>>> attr = ks.attribute(input, target=1, feature_mask=feature_mask)
"""
return super().attribute(
inputs=inputs,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
feature_mask=feature_mask,
n_samples=n_samples,
perturbations_per_eval=perturbations_per_eval,
return_input_shape=return_input_shape,
show_progress=show_progress,
)