Gets the default values for the extra computation arguments
get_extra_comp_args_default(
internal,
paired_shap_sampling = isFALSE(internal$parameters$asymmetric),
semi_deterministic_sampling = FALSE,
kernelSHAP_reweighting = "on_all_cond",
compute_sd = isFALSE(internal$parameters$exact),
n_boot_samps = 100,
vS_batching_method = "future",
max_batch_size = 10,
min_n_batches = 10
)A list with the default values for the extra computation arguments.
List.
Not used directly, but passed through from explain().
Logical.
If TRUE paired versions of all sampled coalitions are also included in the computation.
That is, if there are 5 features and e.g. coalitions (1,3,5) are sampled, then also coalition (2,4) is used for
computing the Shapley values. This is done to reduce the variance of the Shapley value estimates.
TRUE is the default and is recommended for highest accuracy.
For asymmetric, FALSE is the default and the only legal value.
Logical.
If FALSE (default), then we sample from all coalitions.
If TRUE, the sampling of coalitions is semi-deterministic, i.e. the sampling is done in a way that ensures that
coalitions that are expected to be sampled based on the number of coalitions are deterministically included such
that we sample among fewer coalitions. This is done to reduce the variance of the Shapley value estimates,
and corresponds to the PySHAP* strategy in the paper Olsen & Jullum (2024).
String.
How to reweight the sampling frequency weights in the kernelSHAP solution after sampling.
The aim of this is to reduce the randomness and thereby the variance of the Shapley value estimates.
The options are one of 'none', 'on_N', 'on_all', 'on_all_cond' (default).
'none' means no reweighting, i.e. the sampling frequency weights are used as is.
'on_N' means the sampling frequencies are averaged over all coalitions with the same original sampling
probabilities.
'on_all' means the original sampling probabilities are used for all coalitions.
'on_all_cond' means the original sampling probabilities are used for all coalitions, while adjusting for the
probability that they are sampled at least once.
'on_all_cond' is preferred as it performs the best in simulation studies, see
Olsen & Jullum (2024).
Logical. Whether to estimate the standard deviations of the Shapley value estimates. This is TRUE whenever sampling based kernelSHAP is applied (either iteratively or with a fixed number of coalitions).
Integer. The number of bootstrapped samples (i.e. samples with replacement) from the set of all coalitions used to estimate the standard deviations of the Shapley value estimates.
String. The method used to perform batch computing of vS.
"future" (default), utilizes future.apply::future_apply (via the future::future package),
enabling parallelized computation and progress updates via progressr::progressr.
Alternatively, "forloop" can be used for straight forward sequential computation, which is mainly useful for
package development and debugging purposes.
Integer. The maximum number of coalitions to estimate simultaneously within each iteration. A larger numbers requires more memory, but may have a slight computational advantage.
Integer. The minimum number of batches to split the computation into within each iteration. Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller than the number of parallel workers.
Martin Jullum