#!/usr/bin/env python3
import typing
from typing import Any, Callable, Tuple, Union
import numpy as np
import torch
from captum._utils.common import _is_tuple
from captum._utils.typing import (
BaselineType,
Literal,
TargetType,
Tensor,
TensorOrTupleOfTensorsGeneric,
)
from captum.attr._core.noise_tunnel import NoiseTunnel
from captum.attr._utils.attribution import GradientAttribution
from captum.attr._utils.common import (
_compute_conv_delta_and_format_attrs,
_format_callable_baseline,
_format_input_baseline,
)
from captum.log import log_usage
[docs]
class GradientShap(GradientAttribution):
r"""
Implements gradient SHAP based on the implementation from SHAP's primary
author. For reference, please view the original
`implementation
<https://github.com/slundberg/shap#deep-learning-example-with-gradientexplainer-tensorflowkeraspytorch-models>`_
and the paper: `A Unified Approach to Interpreting Model Predictions
<https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions>`_
GradientShap approximates SHAP values by computing the expectations of
gradients by randomly sampling from the distribution of baselines/references.
It adds white noise to each input sample `n_samples` times, selects a
random baseline from baselines' distribution and a random point along the
path between the baseline and the input, and computes the gradient of outputs
with respect to those selected random points. The final SHAP values represent
the expected values of gradients * (inputs - baselines).
GradientShap makes an assumption that the input features are independent
and that the explanation model is linear, meaning that the explanations
are modeled through the additive composition of feature effects.
Under those assumptions, SHAP value can be approximated as the expectation
of gradients that are computed for randomly generated `n_samples` input
samples after adding gaussian noise `n_samples` times to each input for
different baselines/references.
In some sense it can be viewed as an approximation of integrated gradients
by computing the expectations of gradients for different baselines.
Current implementation uses Smoothgrad from :class:`.NoiseTunnel` in order to
randomly draw samples from the distribution of baselines, add noise to input
samples and compute the expectation (smoothgrad).
"""
def __init__(self, forward_func: Callable, multiply_by_inputs: bool = True) -> None:
r"""
Args:
forward_func (Callable): The forward function of the model or
any modification of it.
multiply_by_inputs (bool, optional): Indicates whether to factor
model inputs' multiplier in the final attribution scores.
In the literature this is also known as local vs global
attribution. If inputs' multiplier isn't factored in
then this type of attribution method is also called local
attribution. If it is, then that type of attribution
method is called global.
More detailed can be found here:
https://arxiv.org/abs/1711.06104
In case of gradient shap, if `multiply_by_inputs`
is set to True, the sensitivity scores of scaled inputs
are being multiplied by (inputs - baselines).
"""
GradientAttribution.__init__(self, forward_func)
self._multiply_by_inputs = multiply_by_inputs
@typing.overload
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: Union[
TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric]
],
n_samples: int = 5,
stdevs: Union[float, Tuple[float, ...]] = 0.0,
target: TargetType = None,
additional_forward_args: Any = None,
*,
return_convergence_delta: Literal[True],
) -> Tuple[TensorOrTupleOfTensorsGeneric, Tensor]: ...
@typing.overload
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: Union[
TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric]
],
n_samples: int = 5,
stdevs: Union[float, Tuple[float, ...]] = 0.0,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: Literal[False] = False,
) -> TensorOrTupleOfTensorsGeneric: ...
[docs]
@log_usage()
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: Union[
TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric]
],
n_samples: int = 5,
stdevs: Union[float, Tuple[float, ...]] = 0.0,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
) -> Union[
TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor]
]:
r"""
Args:
inputs (Tensor or tuple[Tensor, ...]): Input for which SHAP attribution
values 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, and if multiple input tensors
are provided, the examples must be aligned appropriately.
baselines (Tensor, tuple[Tensor, ...], or Callable):
Baselines define the starting point from which expectation
is computed and can be provided as:
- a single tensor, if inputs is a single tensor, with
the first dimension equal to the number of examples
in the baselines' distribution. The remaining dimensions
must match with input tensor's dimension starting from
the second dimension.
- a tuple of tensors, if inputs is a tuple of tensors,
with the first dimension of any tensor inside the tuple
equal to the number of examples in the baseline's
distribution. The remaining dimensions must match
the dimensions of the corresponding input tensor
starting from the second dimension.
- callable function, optionally takes `inputs` as an
argument and either returns a single tensor
or a tuple of those.
It is recommended that the number of samples in the baselines'
tensors is larger than one.
n_samples (int, optional): The number of randomly generated examples
per sample in the input batch. Random examples are
generated by adding gaussian random noise to each sample.
Default: `5` if `n_samples` is not provided.
stdevs (float or tuple of float, optional): The standard deviation
of gaussian noise with zero mean that is added to each
input in the batch. If `stdevs` is a single float value
then that same value is used for all inputs. If it is
a tuple, then it must have the same length as the inputs
tuple. In this case, each stdev value in the stdevs tuple
corresponds to the input with the same index in the inputs
tuple.
Default: 0.0
target (int, tuple, Tensor, or list, optional): Output indices for
which gradients are 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 can contain a tuple of ND tensors or
any arbitrary python type of any shape.
In case of the ND tensor the first dimension of the
tensor must correspond to the batch size. It will be
repeated for each `n_steps` for each randomly generated
input sample.
Note that the gradients are not computed with respect
to these arguments.
Default: None
return_convergence_delta (bool, optional): Indicates whether to return
convergence delta or not. If `return_convergence_delta`
is set to True convergence delta will be returned in
a tuple following attributions.
Default: False
Returns:
**attributions** or 2-element tuple of **attributions**, **delta**:
- **attributions** (*Tensor* or *tuple[Tensor, ...]*):
Attribution score computed based on GradientSHAP with respect
to each input feature. Attributions will always be
the same size as the provided inputs, with each value
providing the attribution of the corresponding input index.
If a single tensor is provided as inputs, a single tensor is
returned. If a tuple is provided for inputs, a tuple of
corresponding sized tensors is returned.
- **delta** (*Tensor*, returned if return_convergence_delta=True):
This is computed using the property that the total
sum of forward_func(inputs) - forward_func(baselines)
must be very close to the total sum of the attributions
based on GradientSHAP.
Delta is calculated for each example in the input after adding
`n_samples` times gaussian noise to each of them. Therefore,
the dimensionality of the deltas tensor is equal to the
`number of examples in the input` * `n_samples`
The deltas are ordered by each input example and `n_samples`
noisy samples generated for it.
Examples::
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> gradient_shap = GradientShap(net)
>>> input = torch.randn(3, 3, 32, 32, requires_grad=True)
>>> # choosing baselines randomly
>>> baselines = torch.randn(20, 3, 32, 32)
>>> # Computes gradient shap for the input
>>> # Attribution size matches input size: 3x3x32x32
>>> attribution = gradient_shap.attribute(input, baselines,
target=5)
"""
# since `baselines` is a distribution, we can generate it using a function
# rather than passing it as an input argument
baselines = _format_callable_baseline(baselines, inputs)
assert isinstance(baselines[0], torch.Tensor), (
"Baselines distribution has to be provided in a form "
"of a torch.Tensor {}.".format(baselines[0])
)
input_min_baseline_x_grad = InputBaselineXGradient(
self.forward_func, self.multiplies_by_inputs
)
input_min_baseline_x_grad.gradient_func = self.gradient_func
nt = NoiseTunnel(input_min_baseline_x_grad)
# NOTE: using attribute.__wrapped__ to not log
attributions = nt.attribute.__wrapped__(
nt, # self
inputs,
nt_type="smoothgrad",
nt_samples=n_samples,
stdevs=stdevs,
draw_baseline_from_distrib=True,
baselines=baselines,
target=target,
additional_forward_args=additional_forward_args,
return_convergence_delta=return_convergence_delta,
)
return attributions
[docs]
def has_convergence_delta(self) -> bool:
return True
@property
def multiplies_by_inputs(self):
return self._multiply_by_inputs
class InputBaselineXGradient(GradientAttribution):
def __init__(self, forward_func: Callable, multiply_by_inputs=True) -> None:
r"""
Args:
forward_func (Callable): The forward function of the model or
any modification of it.
multiply_by_inputs (bool, optional): Indicates whether to factor
model inputs' multiplier in the final attribution scores.
In the literature this is also known as local vs global
attribution. If inputs' multiplier isn't factored in
then this type of attribution method is also called local
attribution. If it is, then that type of attribution
method is called global.
More detailed can be found here:
https://arxiv.org/abs/1711.06104
In case of gradient shap, if `multiply_by_inputs`
is set to True, the sensitivity scores of scaled inputs
are being multiplied by (inputs - baselines).
"""
GradientAttribution.__init__(self, forward_func)
self._multiply_by_inputs = multiply_by_inputs
@typing.overload
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
*,
return_convergence_delta: Literal[True],
) -> Tuple[TensorOrTupleOfTensorsGeneric, Tensor]: ...
@typing.overload
def attribute(
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: Literal[False] = False,
) -> TensorOrTupleOfTensorsGeneric: ...
@log_usage()
def attribute( # type: ignore
self,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: BaselineType = None,
target: TargetType = None,
additional_forward_args: Any = None,
return_convergence_delta: bool = False,
) -> Union[
TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor]
]:
# Keeps track whether original input is a tuple or not before
# converting it into a tuple.
is_inputs_tuple = _is_tuple(inputs)
inputs, baselines = _format_input_baseline(inputs, baselines)
rand_coefficient = torch.tensor(
np.random.uniform(0.0, 1.0, inputs[0].shape[0]),
device=inputs[0].device,
dtype=inputs[0].dtype,
)
input_baseline_scaled = tuple(
_scale_input(input, baseline, rand_coefficient)
for input, baseline in zip(inputs, baselines)
)
grads = self.gradient_func(
self.forward_func, input_baseline_scaled, target, additional_forward_args
)
if self.multiplies_by_inputs:
input_baseline_diffs = tuple(
input - baseline for input, baseline in zip(inputs, baselines)
)
attributions = tuple(
input_baseline_diff * grad
for input_baseline_diff, grad in zip(input_baseline_diffs, grads)
)
else:
attributions = grads
return _compute_conv_delta_and_format_attrs(
self,
return_convergence_delta,
attributions,
baselines,
inputs,
additional_forward_args,
target,
is_inputs_tuple,
)
def has_convergence_delta(self) -> bool:
return True
@property
def multiplies_by_inputs(self):
return self._multiply_by_inputs
def _scale_input(
input: Tensor, baseline: Union[Tensor, int, float], rand_coefficient: Tensor
) -> Tensor:
# batch size
bsz = input.shape[0]
inp_shape_wo_bsz = input.shape[1:]
inp_shape = (bsz,) + tuple([1] * len(inp_shape_wo_bsz))
# expand and reshape the indices
rand_coefficient = rand_coefficient.view(inp_shape)
input_baseline_scaled = (
rand_coefficient * input + (1.0 - rand_coefficient) * baseline
).requires_grad_()
return input_baseline_scaled
