sample_coalition_table function

Get table with sampled coalitions

sample_coalition_table(
  m,
  n_coalitions = 200,
  weight_zero_m = 10^6,
  paired_shap_sampling = TRUE,
  prev_coal_samples = NULL,
  prev_coal_samples_n_unique = NULL,
  kernelSHAP_reweighting,
  n_samps_scale = 10,
  dt_valid_causal_coalitions = NULL
)

Arguments

• m: Positive integer. Total number of features/groups.
• n_coalitions: Positive integer. Note that if exact = TRUE, n_coalitions is ignored.
• weight_zero_m: Numeric. The value to use as a replacement for infinite coalition weights when doing numerical operations.
• paired_shap_sampling: Logical. Whether to do paired sampling of coalitions.
• prev_coal_samples: Character vector. A vector of previously sampled coalitions as characters. Each string contains a coalition and the feature indices in the coalition is separated by a space. For example, "1 5 8" is a coalition with features 1, 5, and 8.
• prev_coal_samples_n_unique: Positive integer. The number of unique coalitions in prev_coal_samples. This is a separate argument to avoid recomputing the number unnecessarily.
• n_samps_scale: Positive integer. Integer that scales the number of coalitions n_coalitions to sample as sampling is cheap, while checking for n_coalitions unique coalitions is expensive, thus we over sample the number of coalitions by a factor of n_samps_scale and determine when we have n_coalitions unique coalitions and only use the coalitions up to this point and throw away the remaining coalitions.
• dt_valid_causal_coalitions: data.table. Only applicable for asymmetric Shapley values explanations, and is NULL for symmetric Shapley values. The data.table contains information about the coalitions that respects the causal ordering.