sample_coalition_table: Get table with sampled coalitions using the...

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

Get table with sampled coalitions using the semi-deterministic sampling approach

Description

Get table with sampled coalitions using the semi-deterministic sampling approach

Usage

sample_coalition_table(
  m,
  n_coalitions = 200,
  n_coal_each_size = choose(m, seq(m - 1)),
  weight_zero_m = 10^6,
  paired_shap_sampling = TRUE,
  prev_X = NULL,
  kernelSHAP_reweighting = "on_all_cond",
  semi_deterministic_sampling = FALSE,
  dt_coal_samp_info = NULL,
  dt_valid_causal_coalitions = NULL,
  n_samps_scale = 10
)

Arguments

m	Positive integer. Total number of features/groups.
n_coalitions	Positive integer. Note that if exact = TRUE, n_coalitions is ignored.
n_coal_each_size	Vector of integers of length m-1. The number of valid coalitions of each coalition size 1, 2,..., m-1. For symmetric Shapley values, this is choose(m, seq(m-1)) (default). While for asymmetric Shapley values, this is the number of valid coalitions of each size in the causal ordering. Used to correctly normalize the Shapley weights.
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_X	data.table. The X data.table from the previous iteration.
kernelSHAP_reweighting	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).
semi_deterministic_sampling	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).
dt_coal_samp_info	data.table. The data.table contains information about the which coalitions should be deterministically included and which can be sampled, in addition to the sampling probabilities of each available coalition size, and the weight given to the sampled and deterministically included coalitions (excluding empty and grand coalitions which are given the weight_zero_m weight).
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.
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.

shapr documentation built on June 8, 2025, 10:22 a.m.