vaeac_impute_missing_entries: Impute Missing Values Using Vaeac

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

Impute Missing Values Using Vaeac

Description

Impute Missing Values Using Vaeac

Usage

vaeac_impute_missing_entries(
  x_explain_with_NaNs,
  n_MC_samples,
  vaeac_model,
  checkpoint,
  sampler,
  batch_size,
  verbose = NULL,
  seed = NULL,
  n_explain = NULL,
  index_features = NULL
)

Arguments

x_explain_with_NaNs	A 2D matrix, where the missing entries to impute are represented by NaN.
n_MC_samples	Integer. The number of imputed versions we create for each row in x_explain_with_NaNs.
vaeac_model	An initialized vaeac model that we are going to use to generate the MC samples.
checkpoint	List containing the parameters of the vaeac model.
sampler	A sampler object used to sample the MC samples.
batch_size	Positive integer (default is 64). The number of samples to include in each batch during the training of the vaeac model. Used in torch::dataloader().
verbose	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. ⁠"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) 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.
seed	Positive integer (default is 1). Seed for reproducibility. Specifies the seed before any randomness based code is being run.
n_explain	Positive integer. The number of explicands.
index_features	Optional integer vector. Used internally in shapr package to index the coalitions.

Details

Function that imputes the missing values in 2D matrix where each row constitute an individual. The values are sampled from the conditional distribution estimated by a vaeac model.

Value

A data.table where the missing values (NaN) in x_explain_with_NaNs have been imputed n_MC_samples times. The data table will contain extra id columns if index_features and n_explain are provided.

Author(s)

Lars Henry Berge Olsen

shapr documentation built on April 4, 2025, 12:18 a.m.