Extend ARFSampler to store more arguments on construction, making it easier to “preconfigure” the sampler via arguments used in $sample().
Standardize on conditioning_set as the name for the character vector defining features to condition on in ConditionalSampler and RFI.
Streamline PerturbationImportance implementation.
Add batch_size argument for SAGE methods to control number of observations used at once per learner$predict_newdata() call (could lead to excessive RAM usage).
Add sim_dgp_ewald() to simulate data with a simple DGP as used for illustration in Ewald et al. (2024), which should make it easier to interpret the results of various importance methods.
Add $obs_loss and $predictions fields to FeatureImportanceMeasure, nosw used by LOCO and LOCI
Both get arugments obs_loss = FALSE and aggregation_fun, defaulting to median in case of obs_loss = TRUE, to allow for macro-averaged median of absolute differences calculcation as in original LOCO formulation, rather than the micro-averaged approach calculated by default.
Fix accidentally marginal ConditionalSAGE.
xplainfi 0.1.0
Initial prototype with
PFI
CFI and RFI (via arf-powered conditional sampling)
SAGE (marginal and conditional, the latter via arf)
LOCO and LOCI
Includes comparison to reference implementation in Python via fippy