diff --git a/DESCRIPTION b/DESCRIPTION
index a95507b..765c7d6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: kernelshap
Title: Kernel SHAP
-Version: 0.9.0
+Version: 1.0.0
Authors@R: c(
person("Michael", "Mayer", , "mayermichael79@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0009-0007-2540-9629")),
@@ -19,18 +19,18 @@ Description: Efficient implementation of Kernel SHAP (Lundberg and Lee,
It supports multi-output models, case weights, and parallel
computations. Visualizations can be done using the R package
'shapviz'.
-License: GPL (>= 2)
+License: GPL (>= 3)
Depends:
R (>= 3.2.0)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
-Imports:
- doFuture,
- foreach,
+Imports:
+ future.apply,
stats,
utils
Suggests:
+ progressr,
testthat (>= 3.0.0)
Config/testthat/edition: 3
URL: https://github.com/ModelOriented/kernelshap
diff --git a/LICENSE.md b/LICENSE.md
index 0daa041..175443c 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -1,336 +1,595 @@
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+
+### 7. Additional Terms
+
+“Additional permissions” are terms that supplement the terms of this
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+permissions that are applicable to the entire Program shall be treated as though they
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+When you convey a copy of a covered work, you may at your option remove any
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+All other non-permissive additional terms are considered “further
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+license document contains a further restriction but permits relicensing or conveying
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+Additional terms, permissive or non-permissive, may be stated in the form of a
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+### 8. Termination
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+### 9. Acceptance Not Required for Having Copies
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+You are not required to accept this License in order to receive or run a copy of the
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+### 10. Automatic Licensing of Downstream Recipients
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+Each time you convey a covered work, the recipient automatically receives a license
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+### 11. Patents
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+A “contributor” is a copyright holder who authorizes use under this
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+A contributor's “essential patent claims” are all patent claims owned or
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+A patent license is “discriminatory” if it does not include within the
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+was granted, prior to 28 March 2007.
+
+Nothing in this License shall be construed as excluding or limiting any implied
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+under applicable patent law.
+
+### 12. No Surrender of Others' Freedom
+
+If conditions are imposed on you (whether by court order, agreement or otherwise)
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+conditions of this License. If you cannot convey a covered work so as to satisfy
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+agree to terms that obligate you to collect a royalty for further conveying from
+those to whom you convey the Program, the only way you could satisfy both those terms
+and this License would be to refrain entirely from conveying the Program.
+
+### 13. Use with the GNU Affero General Public License
+
+Notwithstanding any other provision of this License, you have permission to link or
+combine any covered work with a work licensed under version 3 of the GNU Affero
+General Public License into a single combined work, and to convey the resulting work.
+The terms of this License will continue to apply to the part which is the covered
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+13, concerning interaction through a network will apply to the combination as such.
+
+### 14. Revised Versions of this License
+
+The Free Software Foundation may publish revised and/or new versions of the GNU
+General Public License from time to time. Such new versions will be similar in spirit
+to the present version, but may differ in detail to address new problems or concerns.
+
+Each version is given a distinguishing version number. If the Program specifies that
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+conditions either of that numbered version or of any later version published by the
+Free Software Foundation. If the Program does not specify a version number of the GNU
+General Public License, you may choose any version ever published by the Free
+Software Foundation.
+
+If the Program specifies that a proxy can decide which future versions of the GNU
+General Public License can be used, that proxy's public statement of acceptance of a
+version permanently authorizes you to choose that version for the Program.
+
+Later license versions may give you additional or different permissions. However, no
+additional obligations are imposed on any author or copyright holder as a result of
+your choosing to follow a later version.
+
+### 15. Disclaimer of Warranty
+
+THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
+EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
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+QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
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+
+### 16. Limitation of Liability
+
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY
+COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS
+PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL,
+INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE
+PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE
+OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE
+WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
-END OF TERMS AND CONDITIONS
+### 17. Interpretation of Sections 15 and 16
+
+If the disclaimer of warranty and limitation of liability provided above cannot be
+given local legal effect according to their terms, reviewing courts shall apply local
+law that most closely approximates an absolute waiver of all civil liability in
+connection with the Program, unless a warranty or assumption of liability accompanies
+a copy of the Program in return for a fee.
-### How to Apply These Terms to Your New Programs
+_END OF TERMS AND CONDITIONS_
-If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
+## How to Apply These Terms to Your New Programs
-To do so, attach the following notices to the program. It is safest
-to attach them to the start of each source file to most effectively
-convey the exclusion of warranty; and each file should have at least
-the “copyright” line and a pointer to where the full notice is found.
+If you develop a new program, and you want it to be of the greatest possible use to
+the public, the best way to achieve this is to make it free software which everyone
+can redistribute and change under these terms.
+
+To do so, attach the following notices to the program. It is safest to attach them
+to the start of each source file to most effectively state the exclusion of warranty;
+and each file should have at least the “copyright” line and a pointer to
+where the full notice is found.
Copyright (C)
-
- This program is free software; you can redistribute it and/or modify
+
+ This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
- the Free Software Foundation; either version 2 of the License, or
+ the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
-
+
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License along
- with this program; if not, write to the Free Software Foundation, Inc.,
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
-If the program is interactive, make it output a short notice like this
+If the program does terminal interaction, make it output a short notice like this
when it starts in an interactive mode:
- Gnomovision version 69, Copyright (C) year name of author
- Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ Copyright (C)
+ This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
This is free software, and you are welcome to redistribute it
- under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w` and `show c` should show the appropriate
-parts of the General Public License. Of course, the commands you use may
-be called something other than `show w` and `show c`; they could even be
-mouse-clicks or menu items--whatever suits your program.
-
-You should also get your employer (if you work as a programmer) or your
-school, if any, to sign a “copyright disclaimer” for the program, if
-necessary. Here is a sample; alter the names:
-
- Yoyodyne, Inc., hereby disclaims all copyright interest in the program
- `Gnomovision' (which makes passes at compilers) written by James Hacker.
-
- , 1 April 1989
- Ty Coon, President of Vice
-
-This General Public License does not permit incorporating your program into
-proprietary programs. If your program is a subroutine library, you may
-consider it more useful to permit linking proprietary applications with the
-library. If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.
+ under certain conditions; type 'show c' for details.
+
+The hypothetical commands `show w` and `show c` should show the appropriate parts of
+the General Public License. Of course, your program's commands might be different;
+for a GUI interface, you would use an “about box”.
+
+You should also get your employer (if you work as a programmer) or school, if any, to
+sign a “copyright disclaimer” for the program, if necessary. For more
+information on this, and how to apply and follow the GNU GPL, see
+<>.
+
+The GNU General Public License does not permit incorporating your program into
+proprietary programs. If your program is a subroutine library, you may consider it
+more useful to permit linking proprietary applications with the library. If this is
+what you want to do, use the GNU Lesser General Public License instead of this
+License. But first, please read
+<>.
diff --git a/NAMESPACE b/NAMESPACE
index 85b8c40..c41b10a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -10,4 +10,3 @@ export(additive_shap)
export(is.kernelshap)
export(kernelshap)
export(permshap)
-importFrom(doFuture,"%dofuture%")
diff --git a/NEWS.md b/NEWS.md
index d6c3903..2ab5062 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# kernelshap 0.9.0
+# kernelshap 1.0.0
### Bug fix
@@ -12,21 +12,29 @@ we have fixed a bug in how `kernelshap()` calculates Kernel weights.
Fixed in [#168](https://github.com/ModelOriented/kernelshap/pull/168), which also has received
unit tests against Python's "shap".
-### API
+### Major improvements for parallel computation
+We have switched from `%dopar%` to `future_lapply()`:
+
+- We have a progress bar! Activate via `progressr::handlers(global = TRUE).
+- No need for calling `registerDoFuture()` anymore.
+- No need to set `parallel = TRUE` anymore.
+- No need to declare missing packages anymore via `parallel_args`. Should you still encounter missing packages anyway,
+ use the new argument `future.packages`.
+- Random seeding is now properly handled, thanks [#163](https://github.com/ModelOriented/kernelshap/issues/163) for reporting.
+
+Implemented in PRs [#170](https://github.com/ModelOriented/kernelshap/pull/170) and [#173](https://github.com/ModelOriented/kernelshap/pull/173).
+
+### API changes and improvements
+
+- The traditional progress bar has been replaced by {progressr}. Activate e.g. via `progressr::handlers(global = TRUE).
+ It works in parallel mode as well, and you can choose your own style ([#173](https://github.com/ModelOriented/kernelshap/pull/173)).
- The argument `feature_names` can now also be used with matrix input ([#166](https://github.com/ModelOriented/kernelshap/pull/166)).
- `kernelshap()` and `permshap()` have received a `seed = NULL` argument ([#170](https://github.com/ModelOriented/kernelshap/pull/170)).
-- Parallel mode: If missing packages or globals have to be specified, this now has to be done through `parallel_args = list(packages = ..., globals = ...)`
-instead of `parallel_args = list(.packages = ..., .globals = ...)`, see section on parallelism below.
-The list is passed to `[foreach::foreach(.options.future = ...)]`.
-### Speed and memory improvements
+### Memory improvements
- `permshap()` and `kernelshap()` require about 10% less memory ([#166](https://github.com/ModelOriented/kernelshap/pull/166)).
-- `permshap()` and `kernelshap()` are faster for data.frame input,
- and slightly slower for matrix input ([#166](https://github.com/ModelOriented/kernelshap/pull/166)).
-- Additionally, `permshap(, exact = TRUE)` is faster by pre-calculating more
- elements used across rows ([#165](https://github.com/ModelOriented/kernelshap/pull/165)).
### Internal changes
@@ -34,19 +42,12 @@ The list is passed to `[foreach::foreach(.options.future = ...)]`.
- `kernelshap()` solver: Replacing the Moore-Penrose pseudo-inverse by two direct solves, a trick of [Ian Covert](https://github.com/iancovert/shapley-regression/blob/master/shapreg/shapley.py),
and ported to R in ([#171](https://github.com/ModelOriented/kernelshap/pull/171)).
-### Changes in parallelism
-
-We have switched from `%dopar%` to `doFuture` ([#170](https://github.com/ModelOriented/kernelshap/pull/170)) with the following impact:
-
-- No need for calling `registerDoFuture()` anymore.
-- Random seeding is properly handled, and respects `seed`, thanks [#163](https://github.com/ModelOriented/kernelshap/issues/163) for reporting.
-- If missing packages or globals have to be specified, this now has to be done through `parallel_args = list(packages = ..., globals = ...)`
-instead of `parallel_args = list(.packages = ..., .globals = ...)`. The list is passed to `[foreach::foreach(.options.future = ...)]`.
-
-### Dependencies
+### Change in dependencies
-- {MASS}: Dropped from imports
-- {doFuture}: suggests -> imports
+- {MASS}: Dropped from Imports
+- {foreach}: Dropped from Imports
+- {doFuture}: Dropped from suggests and replaced by {future.apply} in Imports
+- {progressr}: New in Suggests
# kernelshap 0.8.0
diff --git a/R/kernelshap.R b/R/kernelshap.R
index 4ddc690..f294c14 100644
--- a/R/kernelshap.R
+++ b/R/kernelshap.R
@@ -8,6 +8,11 @@
#' Otherwise, a partly exact hybrid algorithm combining exact calculations and
#' iterative paired sampling is used, see Details.
#'
+#' To activate the progress bar, e.g., run `progressr::handlers(global = TRUE)` first.
+#'
+#' To activate parallel processing, run `future::plan(multisession)` or similar.
+#' To deactivate later, run `plan("sequential")`.
+#'
#' @details
#' The pure iterative Kernel SHAP sampling as in Covert and Lee (2021) works like this:
#'
@@ -53,8 +58,6 @@
#' should not be higher than 10 for exact calculations.
#' For similar reasons, degree 2 hybrids should not use \eqn{p} larger than 40.
#'
-#' @importFrom doFuture %dofuture%
-#'
#' @param object Fitted model object.
#' @param X \eqn{(n \times p)} matrix or `data.frame` with rows to be explained.
#' The columns should only represent model features, not the response
@@ -104,16 +107,10 @@
#' For `permshap()`, the default is 0.01, while for `kernelshap()` it is set to 0.005.
#' @param max_iter If the stopping criterion (see `tol`) is not reached after
#' `max_iter` iterations, the algorithm stops. Ignored if `exact = TRUE`.
-#' @param parallel If `TRUE`, use [foreach::foreach()] and `%dofuture%` to loop over rows
-#' to be explained. Must register backend beforehand, e.g., `plan(multisession)`,
-#' see README for an example. Currently disables the progress bar.
-#' @param parallel_args Named list of arguments passed to
-#' `foreach::foreach(.options.future = ...)`, ideally `NULL` (default).
-#' Only relevant if `parallel = TRUE`.
-#' Example on Windows: if `object` is a GAM fitted with package 'mgcv',
-#' then one might need to set `parallel_args = list(packages = "mgcv")`.
-#' Similarly, if the model has been fitted with `ranger()`, then it might be necessary
-#' to pass `parallel_args = list(packages = "ranger")`.
+#' @param parallel Deprecated.
+#' @param parallel_args Deprecated (see `future.packages`).
+#' @param future.packages Character vector with packages to be attached in the
+#' R environment evaluating the future. Only if parallel computing.
#' @param verbose Set to `FALSE` to suppress messages and the progress bar.
#' @param seed Optional integer random seed. Note that it changes the global seed.
#' @param survival Should cumulative hazards ("chf", default) or survival
@@ -195,9 +192,19 @@ kernelshap.default <- function(
max_iter = 100L,
parallel = FALSE,
parallel_args = NULL,
+ future.packages = NULL,
verbose = TRUE,
seed = NULL,
...) {
+ if (parallel) {
+ warning("The 'parallel' argument has been deprecated. Simply set plan(...) to activate parallel computing.")
+ }
+ if (!is.null(parallel_args)) {
+ warning(
+ "The 'parallel_args' argument has been deprecated.
+ If your parallel sessions lack a package, use argument `future.packages`."
+ )
+ }
p <- length(feature_names)
basic_checks(X = X, feature_names = feature_names, pred_fun = pred_fun)
stopifnot(
@@ -258,54 +265,35 @@ kernelshap.default <- function(
warning_burden(max(m, m_exact), bg_n = bg_n)
}
- # Apply Kernel SHAP to each row of X
- if (isTRUE(parallel)) {
- future_args <- c(list(seed = TRUE), parallel_args)
- parallel_args <- c(list(i = seq_len(n)), list(.options.future = future_args))
- res <- do.call(foreach::foreach, parallel_args) %dofuture% kernelshap_one(
- x = X[i, , drop = FALSE],
- v1 = v1[i, , drop = FALSE],
- object = object,
- pred_fun = pred_fun,
- feature_names = feature_names,
- bg_w = bg_w,
- exact = exact,
- deg = hybrid_degree,
- m = m,
- tol = tol,
- max_iter = max_iter,
- 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]] <- kernelshap_one(
- x = X[i, , drop = FALSE],
- v1 = v1[i, , drop = FALSE],
- object = object,
- pred_fun = pred_fun,
- feature_names = feature_names,
- bg_w = bg_w,
- exact = exact,
- deg = hybrid_degree,
- m = m,
- tol = tol,
- max_iter = max_iter,
- v0 = v0,
- precalc = precalc,
- ...
- )
- if (verbose && n >= 2L) {
- utils::setTxtProgressBar(pb, i)
- }
- }
+ pbar_step <- max(1L, n %/% 20L)
+ pbar <- if (verbose && n >= 2L && requireNamespace("progressr", quietly = TRUE)) {
+ progressr::progressor(ceiling(n / pbar_step))
}
+ # Apply Kernel SHAP to each row of X
+ res <- future.apply::future_lapply(
+ seq_len(n),
+ FUN = kernelshap_one,
+ future.packages = future.packages,
+ future.seed = TRUE,
+ x = X,
+ v1 = v1,
+ object = object,
+ pred_fun = pred_fun,
+ feature_names = feature_names,
+ bg_w = bg_w,
+ exact = exact,
+ deg = hybrid_degree,
+ m = m,
+ tol = tol,
+ max_iter = max_iter,
+ v0 = v0,
+ precalc = precalc,
+ pbar = pbar,
+ pbar_step = pbar_step,
+ ...
+ )
+
# Organize output
exact <- exact || trunc(p / 2) == hybrid_degree
diff --git a/R/permshap.R b/R/permshap.R
index 398bcf8..1226806 100644
--- a/R/permshap.R
+++ b/R/permshap.R
@@ -9,6 +9,11 @@
#' Otherwise, the sampling process iterates until the resulting values
#' are sufficiently precise, and standard errors are provided.
#'
+#' To activate the progress bar, e.g., run `progressr::handlers(global = TRUE)` first.
+#'
+#' To activate parallel processing, run `future::plan(multisession)` or similar.
+#' To deactivate later, run `plan("sequential")`.
+#'
#' @details
#' During each iteration, the algorithm cycles twice through a random permutation:
#' It starts with all feature components "turned on" (i.e., taking them
@@ -105,9 +110,19 @@ permshap.default <- function(
max_iter = 10L * length(feature_names),
parallel = FALSE,
parallel_args = NULL,
+ future.packages = NULL,
verbose = TRUE,
seed = NULL,
...) {
+ if (parallel) {
+ warning("The 'parallel' argument has been deprecated. Simply set plan(...) to activate parallel computing.")
+ }
+ if (!is.null(parallel_args)) {
+ warning(
+ "The 'parallel_args' argument has been deprecated.
+ If your parallel sessions lack a package, use argument `future.packages`."
+ )
+ }
p <- length(feature_names)
if (p <= 1L) {
stop("Case p = 1 not implemented. Use kernelshap() instead.")
@@ -167,52 +182,34 @@ permshap.default <- function(
warning_burden(max(m_eval, m_exact), bg_n = bg_n)
}
- # Apply permutation SHAP to each row of X
- if (isTRUE(parallel)) {
- future_args <- c(list(seed = TRUE), parallel_args)
- parallel_args <- c(list(i = seq_len(n)), list(.options.future = future_args))
- res <- do.call(foreach::foreach, parallel_args) %dofuture% 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,
- feature_names = feature_names,
- exact = exact,
- low_memory = low_memory,
- tol = tol,
- max_iter = max_iter,
- ...
- )
- } 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,
- feature_names = feature_names,
- exact = exact,
- low_memory = low_memory,
- tol = tol,
- max_iter = max_iter,
- ...
- )
- if (verbose && n >= 2L) {
- utils::setTxtProgressBar(pb, i)
- }
- }
+ pbar_step <- max(1L, n %/% 20L)
+ pbar <- if (verbose && n >= 2L && requireNamespace("progressr", quietly = TRUE)) {
+ progressr::progressor(ceiling(n / pbar_step))
}
+ # Apply permutation SHAP to each row of X
+ res <- future.apply::future_lapply(
+ seq_len(n),
+ FUN = permshap_one,
+ future.packages = future.packages,
+ future.seed = TRUE,
+ x = X,
+ v1 = v1,
+ object = object,
+ pred_fun = pred_fun,
+ bg_w = bg_w,
+ v0 = v0,
+ precalc = precalc,
+ feature_names = feature_names,
+ exact = exact,
+ low_memory = low_memory,
+ tol = tol,
+ max_iter = max_iter,
+ pbar = pbar,
+ pbar_step = pbar_step,
+ ...
+ )
+
# Organize output
out <- list(
S = reorganize_list(lapply(res, `[[`, "beta")),
diff --git a/R/utils_kernelshap.R b/R/utils_kernelshap.R
index ed33654..d41c678 100644
--- a/R/utils_kernelshap.R
+++ b/R/utils_kernelshap.R
@@ -2,6 +2,7 @@
# If exact, a single call to predict() is necessary.
# If sampling is involved, we need at least two additional calls to predict().
kernelshap_one <- function(
+ i,
x,
v1,
object,
@@ -15,7 +16,11 @@ kernelshap_one <- function(
max_iter,
v0,
precalc,
+ pbar,
+ pbar_step,
...) {
+ x <- x[i, , drop = FALSE]
+ v1 <- v1[i, , drop = FALSE]
p <- length(feature_names)
K <- ncol(v1)
K_names <- colnames(v1)
@@ -38,6 +43,11 @@ kernelshap_one <- function(
# Some of the hybrid cases are exact as well
if (exact || trunc(p / 2) == deg) {
beta <- solver(A_exact, b_exact, constraint = v1 - v0) # (p x K)
+
+ if (!is.null(pbar) && (i %% pbar_step == 0L)) {
+ pbar()
+ }
+
return(list(beta = beta))
}
}
@@ -90,7 +100,13 @@ kernelshap_one <- function(
sigma_n <- NA * beta_n # (p x K)
}
}
- list(beta = beta_n, sigma = sigma_n, n_iter = n_iter, converged = converged)
+ out <- list(beta = beta_n, sigma = sigma_n, n_iter = n_iter, converged = converged)
+
+ if (!is.null(pbar) && (i %% pbar_step == 0L)) {
+ pbar()
+ }
+
+ return(out)
}
# Regression coefficients given sum(beta) = constraint
diff --git a/R/utils_permshap.R b/R/utils_permshap.R
index 1f7ed88..4769f1f 100644
--- a/R/utils_permshap.R
+++ b/R/utils_permshap.R
@@ -6,6 +6,7 @@
#' @keywords internal
#'
#' @inheritParams permshap
+#' @param i Row ID to be explained.
#' @param v1 Prediction of `x`.
#' @param v0 Average prediction on background data.
#' @param x A single row to be explained.
@@ -14,6 +15,7 @@
#' a (p x K) matrix of standard errors, number of iterations,
#' and convergence status.
permshap_one <- function(
+ i,
x,
v1,
object,
@@ -26,9 +28,12 @@ permshap_one <- function(
low_memory,
tol,
max_iter,
+ pbar,
+ pbar_step,
...) {
+ x <- x[i, , drop = FALSE]
+ v1 <- v1[i, , drop = FALSE]
bg <- precalc$bg_X_rep
-
p <- length(feature_names)
K <- ncol(v1)
K_names <- colnames(v1)
@@ -54,6 +59,11 @@ permshap_one <- function(
w = precalc$shapley_w[pos$on]
)
}
+
+ if (!is.null(pbar) && (i %% pbar_step == 0L)) {
+ pbar()
+ }
+
return(list(beta = beta_n))
}
@@ -128,6 +138,11 @@ permshap_one <- function(
out <- list(
beta = beta_n / n_iter, sigma = sigma_n, n_iter = n_iter, converged = converged
)
+
+ if (!is.null(pbar) && (i %% pbar_step == 0L)) {
+ pbar()
+ }
+
return(out)
}
diff --git a/R/zzz.R b/R/zzz.R
new file mode 100644
index 0000000..764ce86
--- /dev/null
+++ b/R/zzz.R
@@ -0,0 +1 @@
+globalVariables("i")
diff --git a/README.md b/README.md
index b0b9858..0ccb86d 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@ To explain your model, select an explanation dataset `X` (up to 1000 rows from t
**Remarks to `permshap()` and `kernelshap()`**
- Both algorithms need a representative background data `bg_X` to calculate marginal means (up to 500 rows from the training data). In cases with a natural "off" value (like MNIST digits), this can also be a single row with all values set to the off value. If unspecified, 200 rows are randomly sampled from `X`.
-- Exact Kernel SHAP gives identical results as exact permutation SHAP. Both algorithms are fast up to 8 features.
+- Exact Kernel SHAP gives identical results as exact permutation SHAP. The algorithms are fast for up to 8 features.
With more features, `kernelshap()` switches to a partly exact algorithm with faster convergence than the sampling version of permutation SHAP.
- For models with interactions of order up to two, the sampling versions provide the same results as the exact versions.
- Sampling versions iterate until standard errors of SHAP values are sufficiently small.
@@ -51,6 +51,8 @@ library(ggplot2)
library(ranger)
library(shapviz)
+progressr::handlers(global = TRUE) # activates progress bar
+
options(ranger.num.threads = 8)
diamonds <- transform(
@@ -111,27 +113,19 @@ The {kernelshap} package can deal with almost any situation. We will show some o
### Parallel computing
-Parallel computing for `permshap()` and `kernelshap()` is supported via {doFuture} and {foreach}.
-Note that this does not work for all models.
-
-On Windows, sometimes not all packages or global objects are passed to the parallel sessions.
-Often, this can be fixed via `parallel_args` using the arguments "packages" and "globals" passed
-to `foreach(.options.future = ...)`, see this example:
+Parallel computing for `permshap()` and `kernelshap()` is supported via {future.apply}. It works for most, but not all models (e.g., keras).
```r
-library(doFuture)
library(mgcv)
-plan(multisession, workers = 4) # Windows
-# plan(multicore, workers = 4) # Linux, macOS, Solaris
+future::plan(multisession, workers = 4) # Windows
+# future::plan(multicore, workers = 4) # Linux, macOS, Solaris
# GAM with interactions - we cannot use additive_shap()
fit <- gam(log_price ~ s(log_carat) + clarity * color + cut, data = diamonds)
system.time( # 4 seconds in parallel
- ps <- permshap(
- fit, X, bg_X = bg_X, parallel = TRUE, parallel_args = list(packages = "mgcv")
- )
+ ps <- permshap(fit, X, bg_X = bg_X)
)
ps
@@ -141,6 +135,7 @@ ps
# [2,] -0.51546 -0.1174766 0.11122775 0.030243973
# Because there are no interactions of order above 2, Kernel SHAP gives the same:
+future::plan("sequential")
system.time( # 12 s non-parallel
ks <- kernelshap(fit, X, bg_X = bg_X)
)
diff --git a/backlog/compare_with_python.R b/backlog/compare_with_python.R
index 936464f..ad3c8c8 100644
--- a/backlog/compare_with_python.R
+++ b/backlog/compare_with_python.R
@@ -1,6 +1,8 @@
library(ggplot2)
library(kernelshap)
+progressr::handlers(global = TRUE)
+
# Turn ordinal factors into unordered
ord <- c("clarity", "color", "cut")
diamonds[, ord] <- lapply(diamonds[ord], factor, ordered = FALSE)
diff --git a/backlog/compare_with_python2.R b/backlog/compare_with_python2.R
index 8e23801..565bcf7 100644
--- a/backlog/compare_with_python2.R
+++ b/backlog/compare_with_python2.R
@@ -33,7 +33,7 @@ kss <- kernelshap(
max_iter = 100,
tol = 0.0005
)
-kss # -1.198078 -1.246508 -0.9580638 3.877532 -0.3241824 0.541247
+kss # -1.203484 -1.241578 -0.9653531 3.889111 -0.3360786 0.5493293
set.seed(2)
ksh <- kernelshap(
@@ -46,7 +46,7 @@ ksh <- kernelshap(
max_iter = 10000,
tol = 0.0005
)
-ksh # -1.191981 -1.240656 -0.9516264 3.86776 -0.3342143 0.5426642
+ksh # -1.195273 -1.240459 -0.9546994 3.884469 -0.3382316 0.5361404
set.seed(1)
ksh2 <- kernelshap(
@@ -60,4 +60,17 @@ ksh2 <- kernelshap(
max_iter = 10000,
tol = 0.0001
)
-ksh2 # 1.195976 -1.241107 -0.9565121 3.878891 -0.3384621 0.5451118
+ksh2 # -1.195725 -1.241576 -0.9567066 3.87909 -0.3385894 0.5454539
+
+set.seed(1)
+ps0 <- permshap(
+ pf,
+ head(X, 1),
+ bg_X = X,
+ pred_fun = pf,
+ exact = FALSE,
+ low_memory = FALSE,
+ max_iter = 20000,
+ tol = 0.0001
+)
+ps0 # -1.20929 -1.225766 -0.9602608 3.879889 -0.33825 0.5456252
diff --git a/cran-comments.md b/cran-comments.md
index 3ba42fb..460cde2 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,11 +1,10 @@
-# kernelshap 0.9.0
+# kernelshap 1.0.0
-We have figured out a bug in the weighting logic of Kernel SHAP.
-
-This update comes with a fix which has been tested against two other implementations.
+- We have fixed a problematic bug in the weighting logic of Kernel SHAP.
+- Better parallel processing via {future.apply}.
I am aware that the last release of {kernelshap} is not too long ago, but I still would love to see
-this fixed before the (well-deserved) summer break.
+this fixed before your (well-deserved) summer break.
Thanks a lot!
@@ -21,8 +20,8 @@ Status: OK
### Revdep OK
-✔ survex 1.2.0 ── E: 0 | W: 0 | N: 0
-✔ XAItest 1.0.1 ── E: 1 | W: 0 | N: 0
-✔ SEMdeep 1.0.0 ── E: 1 | W: 1 | N: 0
+✔ SEMdeep 1.0.0 ── E: 0 | W: 0 | N: 0
+✔ XAItest 1.0.1 ── E: 0 | W: 1 | N: 1
+✔ survex 1.2.0 ── E: 0 | W: 0 | N: 0
OK: 3
diff --git a/man/kernelshap.Rd b/man/kernelshap.Rd
index f274067..ee17f92 100644
--- a/man/kernelshap.Rd
+++ b/man/kernelshap.Rd
@@ -23,6 +23,7 @@ kernelshap(object, ...)
max_iter = 100L,
parallel = FALSE,
parallel_args = NULL,
+ future.packages = NULL,
verbose = TRUE,
seed = NULL,
...
@@ -115,17 +116,12 @@ For \code{permshap()}, the default is 0.01, while for \code{kernelshap()} it is
\item{max_iter}{If the stopping criterion (see \code{tol}) is not reached after
\code{max_iter} iterations, the algorithm stops. Ignored if \code{exact = TRUE}.}
-\item{parallel}{If \code{TRUE}, use \code{\link[foreach:foreach]{foreach::foreach()}} and \verb{\%dofuture\%} to loop over rows
-to be explained. Must register backend beforehand, e.g., \code{plan(multisession)},
-see README for an example. Currently disables the progress bar.}
+\item{parallel}{Deprecated.}
-\item{parallel_args}{Named list of arguments passed to
-\code{foreach::foreach(.options.future = ...)}, ideally \code{NULL} (default).
-Only relevant if \code{parallel = TRUE}.
-Example on Windows: if \code{object} is a GAM fitted with package 'mgcv',
-then one might need to set \code{parallel_args = list(packages = "mgcv")}.
-Similarly, if the model has been fitted with \code{ranger()}, then it might be necessary
-to pass \code{parallel_args = list(packages = "ranger")}.}
+\item{parallel_args}{Deprecated (see \code{future.packages}).}
+
+\item{future.packages}{Character vector with packages to be attached in the
+R environment evaluating the future. Only if parallel computing.}
\item{verbose}{Set to \code{FALSE} to suppress messages and the progress bar.}
@@ -166,6 +162,11 @@ By default, for up to p=8 features, exact SHAP values are returned
(with respect to the selected background data).
Otherwise, a partly exact hybrid algorithm combining exact calculations and
iterative paired sampling is used, see Details.
+
+To activate the progress bar, e.g., run \code{progressr::handlers(global = TRUE)} first.
+
+To activate parallel processing, run \code{future::plan(multisession)} or similar.
+To deactivate later, run \code{plan("sequential")}.
}
\details{
The pure iterative Kernel SHAP sampling as in Covert and Lee (2021) works like this:
diff --git a/man/permshap.Rd b/man/permshap.Rd
index 6abcb34..9ecfe8e 100644
--- a/man/permshap.Rd
+++ b/man/permshap.Rd
@@ -22,6 +22,7 @@ permshap(object, ...)
max_iter = 10L * length(feature_names),
parallel = FALSE,
parallel_args = NULL,
+ future.packages = NULL,
verbose = TRUE,
seed = NULL,
...
@@ -98,17 +99,12 @@ For \code{permshap()}, the default is 0.01, while for \code{kernelshap()} it is
\item{max_iter}{If the stopping criterion (see \code{tol}) is not reached after
\code{max_iter} iterations, the algorithm stops. Ignored if \code{exact = TRUE}.}
-\item{parallel}{If \code{TRUE}, use \code{\link[foreach:foreach]{foreach::foreach()}} and \verb{\%dofuture\%} to loop over rows
-to be explained. Must register backend beforehand, e.g., \code{plan(multisession)},
-see README for an example. Currently disables the progress bar.}
+\item{parallel}{Deprecated.}
-\item{parallel_args}{Named list of arguments passed to
-\code{foreach::foreach(.options.future = ...)}, ideally \code{NULL} (default).
-Only relevant if \code{parallel = TRUE}.
-Example on Windows: if \code{object} is a GAM fitted with package 'mgcv',
-then one might need to set \code{parallel_args = list(packages = "mgcv")}.
-Similarly, if the model has been fitted with \code{ranger()}, then it might be necessary
-to pass \code{parallel_args = list(packages = "ranger")}.}
+\item{parallel_args}{Deprecated (see \code{future.packages}).}
+
+\item{future.packages}{Character vector with packages to be attached in the
+R environment evaluating the future. Only if parallel computing.}
\item{verbose}{Set to \code{FALSE} to suppress messages and the progress bar.}
@@ -148,6 +144,11 @@ By default, for up to p=8 features, exact SHAP values are returned
(exact with respect to the selected background data).
Otherwise, the sampling process iterates until the resulting values
are sufficiently precise, and standard errors are provided.
+
+To activate the progress bar, e.g., run \code{progressr::handlers(global = TRUE)} first.
+
+To activate parallel processing, run \code{future::plan(multisession)} or similar.
+To deactivate later, run \code{plan("sequential")}.
}
\details{
During each iteration, the algorithm cycles twice through a random permutation:
diff --git a/packaging.R b/packaging.R
index cc9ab08..e2dcead 100644
--- a/packaging.R
+++ b/packaging.R
@@ -15,7 +15,7 @@ library(usethis)
use_description(
fields = list(
Title = "Kernel SHAP",
- Version = "0.9.0",
+ Version = "1.0.0",
Description = "Efficient implementation of Kernel SHAP
(Lundberg and Lee, 2017, )
permutation SHAP, and additive SHAP for model interpretability.
@@ -37,12 +37,13 @@ use_description(
roxygen = TRUE
)
-use_package("doFuture", "Imports")
-use_package("foreach", "Imports")
+use_package("future.apply", "Imports")
use_package("stats", "Imports")
use_package("utils", "Imports")
-use_gpl_license(2)
+use_package("progressr", "Suggests")
+
+use_gpl_license(3)
# Your files that do not belong to the package itself (others are added by "use_* function")
use_build_ignore(c(
diff --git a/revdep/README.md b/revdep/README.md
index 5e3d79c..56ba958 100644
--- a/revdep/README.md
+++ b/revdep/README.md
@@ -1,31 +1,31 @@
# Platform
-|field |value |
-|:--------|:---------------------------------------|
-|version |R version 4.4.1 (2024-06-14 ucrt) |
-|os |Windows 11 x64 (build 22631) |
-|system |x86_64, mingw32 |
-|ui |RStudio |
-|language |(EN) |
-|collate |English_Switzerland.utf8 |
-|ctype |English_Switzerland.utf8 |
-|tz |Europe/Zurich |
-|date |2025-07-21 |
-|rstudio |2025.05.0+496 Mariposa Orchid (desktop) |
-|pandoc |NA |
+|field |value |
+|:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+|version |R version 4.5.1 (2025-06-13 ucrt) |
+|os |Windows 11 x64 (build 22631) |
+|system |x86_64, mingw32 |
+|ui |RStudio |
+|language |(EN) |
+|collate |English_Switzerland.utf8 |
+|ctype |English_Switzerland.utf8 |
+|tz |Europe/Zurich |
+|date |2025-07-23 |
+|rstudio |2025.05.0+496 Mariposa Orchid (desktop) |
+|pandoc |NA |
+|quarto |ERROR: Unknown command "TMPDIR=C:/Users/mayer/AppData/Local/Temp/RtmpQX07M4/file57e03fb40a5". Did you mean command "create"? @ C:\Users\mayer\AppData\Local\Programs\Quarto\bin\quarto.exe |
# Dependencies
|package |old |new |Δ |
|:------------|:------|:------|:--|
-|kernelshap |0.8.0 |0.9.0 |* |
+|kernelshap |0.8.0 |1.0.0 |* |
|digest |NA |0.6.37 |* |
-|doFuture |NA |1.1.2 |* |
-|foreach |1.5.2 |1.5.2 | |
+|foreach |1.5.2 |NA |* |
|future |NA |1.58.0 |* |
|future.apply |NA |1.20.0 |* |
|globals |NA |0.18.0 |* |
-|iterators |1.0.14 |1.0.14 | |
+|iterators |1.0.14 |NA |* |
|listenv |NA |0.9.1 |* |
|parallelly |NA |1.45.0 |* |
diff --git a/tests/testthat/test-basic.R b/tests/testthat/test-basic.R
index eea4634..5e44853 100644
--- a/tests/testthat/test-basic.R
+++ b/tests/testthat/test-basic.R
@@ -280,7 +280,7 @@ test_that("kernelshap and permshap work for models with high-order interactions"
)
expect_equal(
c(ksh2$S),
- c(-1.194878, -1.24747, -0.9596389, 3.883523, -0.3349787, 0.5453894),
+ c(-1.202621, -1.245655, -0.9510531, 3.880391, -0.3384063, 0.5492909),
tolerance = 1e-4
)
@@ -299,7 +299,7 @@ test_that("kernelshap and permshap work for models with high-order interactions"
)
expect_equal(
c(ksh1$S),
- c(-1.196958, -1.256924, -0.9603291, 3.886163, -0.3277153, 0.5477104),
+ c(-1.203028, -1.240056, -0.9574796, 3.89151, -0.3360694, 0.5370693),
tolerance = 1e-3
)
@@ -314,13 +314,13 @@ test_that("kernelshap and permshap work for models with high-order interactions"
exact = FALSE,
m = 10000,
max_iter = 10000,
- tol = 0.003,
+ tol = 0.005,
verbose = FALSE
)
)
expect_equal(
c(ksh0$S),
- c(-1.18917, -1.2298, -0.9247995, 3.80673, -0.3144175, 0.5434034),
+ c(-1.212142, -1.238348, -0.9795299, 3.937577, -0.3726428, 0.5570322),
tolerance = 1e-3
)
})
@@ -351,13 +351,3 @@ test_that("Random seed works", {
expect_false(identical(s1a$S, s2$S))
}
})
-
-test_that("using foreach (non-parallel) gives the same as normal mode", {
- for (algo in c(kernelshap, permshap)) {
- s <- algo(fit, iris[J, x], bg_X = iris, verbose = FALSE)
- s2 <- suppressWarnings(
- algo(fit, iris[J, x], bg_X = iris, verbose = FALSE, parallel = TRUE)
- )
- expect_equal(s, s2)
- }
-})
diff --git a/tests/testthat/test-deprecation.R b/tests/testthat/test-deprecation.R
new file mode 100644
index 0000000..380a09e
--- /dev/null
+++ b/tests/testthat/test-deprecation.R
@@ -0,0 +1,18 @@
+test_that("Deprecation warnings work", {
+ fit <- lm(Sepal.Length ~ ., data = iris)
+
+ for (algo in c(kernelshap, permshap)) {
+ expect_warning(
+ algo(fit, X = iris[1:2, -1], bg_X = iris, parallel = TRUE, verbose = FALSE)
+ )
+ expect_warning(
+ algo(
+ fit,
+ X = iris[1:2, -1],
+ bg_X = iris,
+ parallel_args = list(a = 1),
+ verbose = FALSE
+ )
+ )
+ }
+})