Function that determines which mask generator to use
vaeac_get_mask_generator_name(
mask_gen_coalitions,
mask_gen_coalitions_prob,
masking_ratio,
verbose
)The function does not return anything.
Matrix (default is NULL). Matrix containing the coalitions that the
vaeac model will be trained on, see specified_masks_mask_generator(). This parameter is used internally
in shapr when we only consider a subset of coalitions, i.e., when
n_coalitions \(< 2^{n_{\text{features}}}\), and for group Shapley, i.e.,
when group is specified in explain().
Numeric array (default is NULL). Array of length equal to the height
of mask_gen_coalitions containing the probabilities of sampling the corresponding coalitions in
mask_gen_coalitions.
Numeric (default is 0.5). Probability of masking a feature in the
mcar_mask_generator() (MCAR = Missing Completely At Random). The MCAR masking scheme ensures that vaeac
model can do arbitrary conditioning as all coalitions will be trained. masking_ratio will be overruled if
mask_gen_coalitions is specified.
String vector or NULL.
Specifies the verbosity (printout detail level) through one or more of strings "basic", "progress",
"convergence", "shapley" and "vS_details".
"basic" (default) displays basic information about the computation which is being performed,
in addition to some messages about parameters being sets or checks being unavailable due to specific input.
"progress displays information about where in the calculation process the function currently is.
#' "convergence" displays information on how close to convergence the Shapley value estimates are
(only when iterative = TRUE) .
"shapley" displays intermediate Shapley value estimates and standard deviations (only when iterative = TRUE)
and the final estimates.
"vS_details" displays information about the v_S estimates.
This is most relevant for approach %in% c("regression_separate", "regression_surrogate", "vaeac").
NULL means no printout.
Note that any combination of four strings can be used.
E.g. verbose = c("basic", "vS_details") will display basic information + details about the v(S)-estimation process.
Lars Henry Berge Olsen