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#' Permutation SHAP
#'
#' Exact permutation SHAP algorithm with respect to a background dataset,
#' see Strumbelj and Kononenko. The function works for up to 14 features.
#'
#' @inheritParams kernelshap
#' @returns
#' An object of class "kernelshap" with the following components:
#' - `S`: \eqn{(n \times p)} matrix with SHAP values or, if the model output has
#' dimension \eqn{K > 1}, a list of \eqn{K} such matrices.
#' - `X`: Same as input argument `X`.
#' - `baseline`: Vector of length K representing the average prediction on the
#' background data.
#' - `m_exact`: Integer providing the effective number of exact on-off vectors used.
#' - `exact`: Logical flag indicating whether calculations are exact or not
#' (currently `TRUE`).
#' - `txt`: Summary text.
#' - `predictions`: \eqn{(n \times K)} matrix with predictions of `X`.
#' - `algorithm`: "permshap".
#' @references
#' 1. Erik Strumbelj and Igor Kononenko. Explaining prediction models and individual
#' predictions with feature contributions. Knowledge and Information Systems 41, 2014.
#' @export
#' @examples
#' # MODEL ONE: Linear regression
#' fit <- lm(Sepal.Length ~ ., data = iris)
#'
#' # Select rows to explain (only feature columns)
#' X_explain <- iris[1:2, -1]
#'
#' # Select small background dataset (could use all rows here because iris is small)
#' set.seed(1)
#' bg_X <- iris[sample(nrow(iris), 100), ]
#'
#' # Calculate SHAP values
#' s <- permshap(fit, X_explain, bg_X = bg_X)
#' s
#'
#' # MODEL TWO: Multi-response linear regression
#' fit <- lm(as.matrix(iris[, 1:2]) ~ Petal.Length + Petal.Width + Species, data = iris)
#' s <- permshap(fit, iris[1:4, 3:5], bg_X = bg_X)
#' s
#'
#' # Non-feature columns can be dropped via 'feature_names'
#' s <- permshap(
#' fit,
#' iris[1:4, ],
#' bg_X = bg_X,
#' feature_names = c("Petal.Length", "Petal.Width", "Species")
#' )
#' s
permshap <- function(object, ...) {
UseMethod("permshap")
}
#' @describeIn permshap Default permutation SHAP method.
#' @export
permshap.default <- function(object, X, bg_X, pred_fun = stats::predict,
feature_names = colnames(X), bg_w = NULL,
parallel = FALSE, parallel_args = NULL,
verbose = TRUE, ...) {
basic_checks(X = X, bg_X = bg_X, feature_names = feature_names, pred_fun = pred_fun)
p <- length(feature_names)
if (p <= 1L) {
stop("Case p = 1 not implemented. Use kernelshap() instead.")
}
if (p > 14L) {
stop("Permutation SHAP only supported for up to 14 features")
}
n <- nrow(X)
bg_n <- nrow(bg_X)
if (!is.null(bg_w)) {
bg_w <- prep_w(bg_w, bg_n = bg_n)
}
txt <- "Exact permutation SHAP"
if (verbose) {
message(txt)
}
# Baseline and predictions on explanation data
bg_preds <- align_pred(pred_fun(object, bg_X[, colnames(X), drop = FALSE], ...))
v0 <- wcolMeans(bg_preds, bg_w) # Average pred of bg data: 1 x K
v1 <- align_pred(pred_fun(object, X, ...)) # Predictions on X: n x K
# Drop unnecessary columns in bg_X. If X is matrix, also column order is relevant
# Predictions will never be applied directly to bg_X anymore
if (!identical(colnames(bg_X), feature_names)) {
bg_X <- bg_X[, feature_names, drop = FALSE]
}
# Precalculations that are identical for each row to be explained
Z <- exact_Z(p, feature_names = feature_names, keep_extremes = TRUE)
m_exact <- nrow(Z) - 2L # We won't evaluate vz for first and last row
precalc <- list(
Z = Z,
Z_code = rowpaste(Z),
bg_X_rep = rep_rows(bg_X, rep.int(seq_len(bg_n), m_exact))
)
if (m_exact * bg_n > 2e5) {
warning_burden(m_exact, bg_n = bg_n)
}
# Apply permutation SHAP to each row of X
if (isTRUE(parallel)) {
parallel_args <- c(list(i = seq_len(n)), parallel_args)
res <- do.call(foreach::foreach, parallel_args) %dopar% permshap_one(
x = X[i, , drop = FALSE],
v1 = v1[i, , drop = FALSE],
object = object,
pred_fun = pred_fun,
bg_w = bg_w,
v0 = v0,
precalc = precalc,
...
)
} else {
if (verbose && n >= 2L) {
pb <- utils::txtProgressBar(max = n, style = 3)
}
res <- vector("list", n)
for (i in seq_len(n)) {
res[[i]] <- permshap_one(
x = X[i, , drop = FALSE],
v1 = v1[i, , drop = FALSE],
object = object,
pred_fun = pred_fun,
bg_w = bg_w,
v0 = v0,
precalc = precalc,
...
)
if (verbose && n >= 2L) {
utils::setTxtProgressBar(pb, i)
}
}
}
out <- list(
S = reorganize_list(res),
X = X,
baseline = as.vector(v0),
m_exact = m_exact,
exact = TRUE,
txt = txt,
predictions = v1,
algorithm = "permshap"
)
class(out) <- "kernelshap"
out
}
#' @describeIn permshap Permutation SHAP method for "ranger" models, see Readme for an example.
#' @export
permshap.ranger <- function(object, X, bg_X,
pred_fun = function(m, X, ...) stats::predict(m, X, ...)$predictions,
feature_names = colnames(X),
bg_w = NULL, parallel = FALSE, parallel_args = NULL,
verbose = TRUE, ...) {
permshap.default(
object = object,
X = X,
bg_X = bg_X,
pred_fun = pred_fun,
feature_names = feature_names,
bg_w = bg_w,
parallel = parallel,
parallel_args = parallel_args,
verbose = verbose,
...
)
}
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