From 38316fdd7a4de8e3d9eac69b54a9fe46f47ed5be Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 7 Oct 2024 21:56:47 +0200 Subject: [PATCH 01/32] adaptive_arguments -> adaptive_args --- NAMESPACE | 2 +- R/approach_vaeac.R | 2 +- R/check_convergence.R | 6 +- R/cli.R | 2 +- R/compute_estimates.R | 2 +- R/explain.R | 16 ++-- R/prepare_next_iteration.R | 6 +- R/print_iter.R | 6 +- R/save_results.R | 2 +- R/setup.R | 94 +++++++++---------- inst/scripts/devel/devel_convergence_branch.R | 2 +- inst/scripts/devel/devel_verbose.R | 6 +- inst/scripts/devel/simtest_timing_to_Frida.R | 2 +- .../devel/testing_intermediate_saving.R | 14 +-- man/explain.Rd | 12 +-- man/explain_forecast.Rd | 2 +- man/finalize_explanation_forecast.Rd | 4 +- man/get_adaptive_arguments_default.Rd | 6 +- man/setup.Rd | 8 +- man/vaeac_get_extra_para_default.Rd | 2 +- tests/testthat/_snaps/adaptive-setup.md | 36 +++---- tests/testthat/_snaps/regular-setup.md | 4 +- tests/testthat/test-adaptive-output.R | 20 ++-- tests/testthat/test-adaptive-setup.R | 28 +++--- tests/testthat/test-regular-setup.R | 4 +- vignettes/understanding_shapr.Rmd | 18 ++-- vignettes/understanding_shapr.Rmd.orig | 18 ++-- 27 files changed, 162 insertions(+), 162 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index e377010ac..81ce1f3a9 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -67,7 +67,7 @@ export(explain) export(explain_forecast) export(finalize_explanation) export(finalize_explanation_forecast) -export(get_adaptive_arguments_default) +export(get_adaptive_args_default) export(get_cov_mat) export(get_data_specs) export(get_model_specs) diff --git a/R/approach_vaeac.R b/R/approach_vaeac.R index 0c7a812a5..858efb51f 100644 --- a/R/approach_vaeac.R +++ b/R/approach_vaeac.R @@ -1656,7 +1656,7 @@ vaeac_check_parameters <- function(x_train, #' each batch when generating the Monte Carlo samples. If `NULL`, then the function generates the Monte Carlo samples #' for the provided coalitions and all explicands sent to [shapr::explain()] at the time. #' The number of coalitions are determined by the `n_batches` used by [shapr::explain()]. We recommend to tweak -#' `adaptive_arguments$max_batch_size` and `adaptive_arguments$min_n_batches` +#' `adaptive_args$max_batch_size` and `adaptive_args$min_n_batches` #' rather than `vaeac.batch_size_sampling`. Larger batch sizes are often much faster provided sufficient memory. #' @param vaeac.running_avg_n_values Positive integer (default is `5`). The number of previous IWAE values to include #' when we compute the running means of the IWAE criterion. diff --git a/R/check_convergence.R b/R/check_convergence.R index 2f2ccc122..da94f8c1d 100644 --- a/R/check_convergence.R +++ b/R/check_convergence.R @@ -7,9 +7,9 @@ check_convergence <- function(internal) { iter <- length(internal$iter_list) - convergence_tolerance <- internal$parameters$adaptive_arguments$convergence_tolerance - max_iter <- internal$parameters$adaptive_arguments$max_iter - max_n_coalitions <- internal$parameters$adaptive_arguments$max_n_coalitions + convergence_tolerance <- internal$parameters$adaptive_args$convergence_tolerance + max_iter <- internal$parameters$adaptive_args$max_iter + max_n_coalitions <- internal$parameters$adaptive_args$max_n_coalitions paired_shap_sampling <- internal$parameters$paired_shap_sampling n_shapley_values <- internal$parameters$n_shapley_values diff --git a/R/cli.R b/R/cli.R index ba4d70c57..ae8e2da44 100644 --- a/R/cli.R +++ b/R/cli.R @@ -6,7 +6,7 @@ cli_startup <- function(internal, model, verbose) { adaptive <- internal$parameters$adaptive n_shapley_values <- internal$parameters$n_shapley_values n_explain <- internal$parameters$n_explain - saving_path <- internal$parameters$adaptive_arguments$saving_path + saving_path <- internal$parameters$adaptive_args$saving_path feat_group_txt <- ifelse(is_groupwise, "group-wise", "feature-wise") adaptive_txt <- ifelse(adaptive, "adaptive", "non-adaptive") diff --git a/R/compute_estimates.R b/R/compute_estimates.R index 1e548579e..b7e2044a7 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -16,7 +16,7 @@ compute_estimates <- function(internal, vS_list) { iter <- length(internal$iter_list) compute_sd <- internal$iter_list[[iter]]$compute_sd - n_boot_samps <- internal$parameters$adaptive_arguments$n_boot_samps + n_boot_samps <- internal$parameters$adaptive_args$n_boot_samps processed_vS_list <- postprocess_vS_list( vS_list = vS_list, diff --git a/R/explain.R b/R/explain.R index aeba4aed6..0331f11b8 100644 --- a/R/explain.R +++ b/R/explain.R @@ -115,11 +115,11 @@ #' If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more #' coalitions. #' The process is repeated until the variances are below the threshold. -#' Specifics related to the adaptive process and convergence criterion are set through `adaptive_arguments`. +#' Specifics related to the adaptive process and convergence criterion are set through `adaptive_args`. #' -#' @param adaptive_arguments Named list. +#' @param adaptive_args Named list. #' Specifices the arguments for the adaptive procedure. -#' See [shapr::get_adaptive_arguments_default()] for description of the arguments and their default values. +#' See [shapr::get_adaptive_args_default()] for description of the arguments and their default values. #' @param shapley_reweighting String. #' How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing #' the randomness and thereby the variance of the Shapley value estimates. @@ -174,7 +174,7 @@ #' \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. #' Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by #' definition 0 when all coalitions is used. -#' Only present when `adaptive = TRUE` and `adaptive_arguments$compute_sd=TRUE`.} +#' Only present when `adaptive = TRUE` and `adaptive_args$compute_sd=TRUE`.} #' \item{internal}{List with the different parameters, data, functions and other output used internally.} #' \item{pred_explain}{Numeric vector with the predictions for the explained observations} #' \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -332,7 +332,7 @@ #' prediction_zero = p, #' n_MC_samples = 1e2, #' adaptive = TRUE, -#' adaptive_arguments = list(initial_n_coalitions = 10) +#' adaptive_args = list(initial_n_coalitions = 10) #' ) #' #' @export @@ -358,7 +358,7 @@ explain <- function(model, get_model_specs = NULL, MSEv_uniform_comb_weights = TRUE, verbose = "basic", - adaptive_arguments = list(), + adaptive_args = list(), shapley_reweighting = "on_all_cond", prev_shapr_object = NULL, ...) { # ... is further arguments passed to specific approaches @@ -391,7 +391,7 @@ explain <- function(model, MSEv_uniform_comb_weights = MSEv_uniform_comb_weights, verbose = verbose, adaptive = adaptive, - adaptive_arguments = adaptive_arguments, + adaptive_args = adaptive_args, shapley_reweighting = shapley_reweighting, init_time = init_time, prev_shapr_object = prev_shapr_object, @@ -524,7 +524,7 @@ testing_cleanup <- function(output) { } # Delete the saving_path - output$internal$parameters$adaptive_arguments$saving_path <- NULL + output$internal$parameters$adaptive_args$saving_path <- NULL return(output) } diff --git a/R/prepare_next_iteration.R b/R/prepare_next_iteration.R index 7800db73f..4ce90680e 100644 --- a/R/prepare_next_iteration.R +++ b/R/prepare_next_iteration.R @@ -14,9 +14,9 @@ prepare_next_iteration <- function(internal) { next_iter_list <- list() n_shapley_values <- internal$parameters$n_shapley_values - reduction_factor_vec <- internal$parameters$adaptive_arguments$reduction_factor_vec - fixed_n_coalitions_per_iter <- internal$parameters$adaptive_arguments$fixed_n_coalitions_per_iter - max_n_coalitions <- internal$parameters$adaptive_arguments$max_n_coalitions + reduction_factor_vec <- internal$parameters$adaptive_args$reduction_factor_vec + fixed_n_coalitions_per_iter <- internal$parameters$adaptive_args$fixed_n_coalitions_per_iter + max_n_coalitions <- internal$parameters$adaptive_args$max_n_coalitions est_remaining_coalitions <- internal$iter_list[[iter]]$est_remaining_coalitions diff --git a/R/print_iter.R b/R/print_iter.R index 6c1a3491b..6fbef82de 100644 --- a/R/print_iter.R +++ b/R/print_iter.R @@ -16,12 +16,12 @@ print_iter <- function(internal) { overall_conv_measure <- internal$iter_list[[iter]]$overall_conv_measure reduction_factor <- internal$iter_list[[iter]]$reduction_factor - saving_path <- internal$parameters$adaptive_arguments$saving_path - convergence_tolerance <- internal$parameters$adaptive_arguments$convergence_tolerance + saving_path <- internal$parameters$adaptive_args$saving_path + convergence_tolerance <- internal$parameters$adaptive_args$convergence_tolerance testing <- internal$parameters$testing if ("convergence" %in% verbose) { - convergence_tolerance <- internal$parameters$adaptive_arguments$convergence_tolerance + convergence_tolerance <- internal$parameters$adaptive_args$convergence_tolerance current_n_coalitions <- internal$iter_list[[iter]]$n_coalitions est_remaining_coalitions <- internal$iter_list[[iter]]$est_remaining_coalitions diff --git a/R/save_results.R b/R/save_results.R index ca48c0098..142c0ea8e 100644 --- a/R/save_results.R +++ b/R/save_results.R @@ -5,7 +5,7 @@ #' @export #' @keywords internal save_results <- function(internal) { - saving_path <- internal$parameters$adaptive_arguments$saving_path + saving_path <- internal$parameters$adaptive_args$saving_path # Modify name for the new file filename <- basename(saving_path) diff --git a/R/setup.R b/R/setup.R index 96788227f..68edf75cd 100644 --- a/R/setup.R +++ b/R/setup.R @@ -47,7 +47,7 @@ setup <- function(x_train, group_lags = NULL, verbose, adaptive = NULL, - adaptive_arguments = list(), + adaptive_args = list(), shapley_reweighting = "none", is_python = FALSE, testing = FALSE, @@ -65,7 +65,7 @@ setup <- function(x_train, prev_iter_list <- prev_internal$iter_list # Overwrite the input arguments set in explain() with those from in prev_shapr_object - # except model, x_explain, x_train, max_n_coalitions, adaptive_arguments, seed + # except model, x_explain, x_train, max_n_coalitions, adaptive_args, seed list2env(prev_internal$parameters) } @@ -90,7 +90,7 @@ setup <- function(x_train, MSEv_uniform_comb_weights = MSEv_uniform_comb_weights, verbose = verbose, adaptive = adaptive, - adaptive_arguments = adaptive_arguments, + adaptive_args = adaptive_args, shapley_reweighting = shapley_reweighting, is_python = is_python, testing = testing, @@ -131,7 +131,7 @@ setup <- function(x_train, } get_prev_internal <- function(prev_shapr_object, - exclude_parameters = c("max_n_coalitions", "adaptive_arguments", "seed")) { + exclude_parameters = c("max_n_coalitions", "adaptive_args", "seed")) { cl <- class(prev_shapr_object)[1] if (cl == "character") { @@ -174,7 +174,7 @@ get_parameters <- function(approach, MSEv_uniform_comb_weights, verbose = "basic", adaptive = FALSE, - adaptive_arguments = list(), + adaptive_args = list(), shapley_reweighting = "none", testing, is_python, ...) { # Check input type for approach @@ -187,8 +187,8 @@ get_parameters <- function(approach, if (!is.logical(adaptive) && length(adaptive) == 1) { stop("`adaptive` must be a single logical.") } - if (!is.list(adaptive_arguments)) { - stop("`adaptive_arguments` must be a list.") + if (!is.list(adaptive_args)) { + stop("`adaptive_args` must be a list.") } @@ -316,7 +316,7 @@ get_parameters <- function(approach, verbose = verbose, shapley_reweighting = shapley_reweighting, adaptive = adaptive, - adaptive_arguments = adaptive_arguments, + adaptive_args = adaptive_args, testing = testing ) @@ -987,25 +987,25 @@ check_groups <- function(feature_names, group) { set_adaptive_parameters <- function(internal, prev_iter_list = NULL) { adaptive <- internal$parameters$adaptive - adaptive_arguments <- internal$parameters$adaptive_arguments + adaptive_args <- internal$parameters$adaptive_args - adaptive_arguments <- utils::modifyList(get_adaptive_arguments_default(internal), - adaptive_arguments, + adaptive_args <- utils::modifyList(get_adaptive_args_default(internal), + adaptive_args, keep.null = TRUE ) # Force setting the number of coalitions and iterations for non-adaptive method if (isFALSE(adaptive)) { - adaptive_arguments$max_iter <- 1 - adaptive_arguments$initial_n_coalitions <- adaptive_arguments$max_n_coalitions + adaptive_args$max_iter <- 1 + adaptive_args$initial_n_coalitions <- adaptive_args$max_n_coalitions } - check_adaptive_arguments(adaptive_arguments) + check_adaptive_args(adaptive_args) # Translate any null input - adaptive_arguments <- trans_null_adaptive_arguments(adaptive_arguments) + adaptive_args <- trans_null_adaptive_args(adaptive_args) - internal$parameters$adaptive_arguments <- adaptive_arguments + internal$parameters$adaptive_args <- adaptive_args if (!is.null(prev_iter_list)) { # Update internal with the iter_list from prev_shapr_object @@ -1013,8 +1013,8 @@ set_adaptive_parameters <- function(internal, prev_iter_list = NULL) { # Conveniently allow running non-adaptive estimation one step further if (isFALSE(internal$parameters$adaptive)) { - internal$parameters$adaptive_arguments$max_iter <- length(internal$iter_list) + 1 - internal$parameters$adaptive_arguments$reduction_factor_vec <- NULL + internal$parameters$adaptive_args$max_iter <- length(internal$iter_list) + 1 + internal$parameters$adaptive_args$reduction_factor_vec <- NULL } # Update convergence data with NEW adaptive arguments @@ -1028,20 +1028,20 @@ set_adaptive_parameters <- function(internal, prev_iter_list = NULL) { } else { internal$iter_list <- list() internal$iter_list[[1]] <- list( - n_coalitions = adaptive_arguments$initial_n_coalitions, - new_n_coalitions = adaptive_arguments$initial_n_coalitions, + n_coalitions = adaptive_args$initial_n_coalitions, + new_n_coalitions = adaptive_args$initial_n_coalitions, exact = internal$parameters$exact, - compute_sd = adaptive_arguments$compute_sd, - reduction_factor = adaptive_arguments$reduction_factor_vec[1], - n_batches = set_n_batches(adaptive_arguments$initial_n_coalitions, internal) + compute_sd = adaptive_args$compute_sd, + reduction_factor = adaptive_args$reduction_factor_vec[1], + n_batches = set_n_batches(adaptive_args$initial_n_coalitions, internal) ) } return(internal) } -check_adaptive_arguments <- function(adaptive_arguments) { - list2env(adaptive_arguments, envir = environment()) +check_adaptive_args <- function(adaptive_args) { + list2env(adaptive_args, envir = environment()) # initial_n_coalitions @@ -1050,7 +1050,7 @@ check_adaptive_arguments <- function(adaptive_arguments) { !is.na(initial_n_coalitions) && initial_n_coalitions <= max_n_coalitions && initial_n_coalitions > 2)) { - stop("`adaptive_arguments$initial_n_coalitions` must be a single integer between 2 and `max_n_coalitions`.") + stop("`adaptive_args$initial_n_coalitions` must be a single integer between 2 and `max_n_coalitions`.") } # fixed_n_coalitions @@ -1061,7 +1061,7 @@ check_adaptive_arguments <- function(adaptive_arguments) { fixed_n_coalitions_per_iter <= max_n_coalitions && fixed_n_coalitions_per_iter > 0)) { stop( - "`adaptive_arguments$fixed_n_coalitions_per_iter` must be NULL or a single positive integer no larger than", + "`adaptive_args$fixed_n_coalitions_per_iter` must be NULL or a single positive integer no larger than", "`max_n_coalitions`." ) } @@ -1072,7 +1072,7 @@ check_adaptive_arguments <- function(adaptive_arguments) { length(max_iter) == 1 && !is.na(max_iter) && max_iter > 0)) { - stop("`adaptive_arguments$max_iter` must be NULL, Inf or a single positive integer.") + stop("`adaptive_args$max_iter` must be NULL, Inf or a single positive integer.") } # convergence_tolerance @@ -1080,7 +1080,7 @@ check_adaptive_arguments <- function(adaptive_arguments) { !(length(convergence_tolerance) == 1 && !is.na(convergence_tolerance) && convergence_tolerance >= 0)) { - stop("`adaptive_arguments$convergence_tolerance` must be NULL, 0, or a positive numeric.") + stop("`adaptive_args$convergence_tolerance` must be NULL, 0, or a positive numeric.") } # reduction_factor_vec @@ -1088,7 +1088,7 @@ check_adaptive_arguments <- function(adaptive_arguments) { !(all(!is.na(reduction_factor_vec)) && all(reduction_factor_vec <= 1) && all(reduction_factor_vec >= 0))) { - stop("`adaptive_arguments$reduction_factor_vec` must be NULL or a vector or numerics between 0 and 1.") + stop("`adaptive_args$reduction_factor_vec` must be NULL or a vector or numerics between 0 and 1.") } # n_boot_samps @@ -1096,13 +1096,13 @@ check_adaptive_arguments <- function(adaptive_arguments) { length(n_boot_samps) == 1 && !is.na(n_boot_samps) && n_boot_samps > 0)) { - stop("`adaptive_arguments$n_boot_samps` must be a single positive integer.") + stop("`adaptive_args$n_boot_samps` must be a single positive integer.") } # compute_sd if (!(is.logical(compute_sd) && length(compute_sd) == 1)) { - stop("`adaptive_arguments$compute_sd` must be a single logical.") + stop("`adaptive_args$compute_sd` must be a single logical.") } @@ -1112,7 +1112,7 @@ check_adaptive_arguments <- function(adaptive_arguments) { length(min_n_batches) == 1 && !is.na(min_n_batches) && min_n_batches > 0)) { - stop("`adaptive_arguments$min_n_batches` must be NULL or a single positive integer.") + stop("`adaptive_args$min_n_batches` must be NULL or a single positive integer.") } # max_batch_size @@ -1121,41 +1121,41 @@ check_adaptive_arguments <- function(adaptive_arguments) { length(max_batch_size) == 1 && !is.na(max_batch_size) && max_batch_size > 0)) { - stop("`adaptive_arguments$max_batch_size` must be NULL, Inf or a single positive integer.") + stop("`adaptive_args$max_batch_size` must be NULL, Inf or a single positive integer.") } # saving_path if (!(is.character(saving_path) && length(saving_path) == 1)) { - stop("`adaptive_arguments$saving_path` must be a single character.") + stop("`adaptive_args$saving_path` must be a single character.") } # Check that the saving_path exists, and abort if not... if (!dir.exists(dirname(saving_path))) { stop( paste0( - "Directory ", dirname(saving_path), " in the adaptive_arguments$saving_path does not exists.\n", + "Directory ", dirname(saving_path), " in the adaptive_args$saving_path does not exists.\n", "Please create the directory with `dir.create('", dirname(saving_path), "')` or use another directory." ) ) } } -trans_null_adaptive_arguments <- function(adaptive_arguments) { - list2env(adaptive_arguments, envir = environment()) +trans_null_adaptive_args <- function(adaptive_args) { + list2env(adaptive_args, envir = environment()) # Translating NULL to always return n_batches = 1 (if just one approach) - adaptive_arguments$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) - adaptive_arguments$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) - adaptive_arguments$max_iter <- ifelse(is.null(max_iter), Inf, max_iter) + adaptive_args$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) + adaptive_args$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) + adaptive_args$max_iter <- ifelse(is.null(max_iter), Inf, max_iter) - return(adaptive_arguments) + return(adaptive_args) } set_n_batches <- function(n_coalitions, internal) { - min_n_batches <- internal$parameters$adaptive_arguments$min_n_batches - max_batch_size <- internal$parameters$adaptive_arguments$max_batch_size + min_n_batches <- internal$parameters$adaptive_args$min_n_batches + max_batch_size <- internal$parameters$adaptive_args$max_batch_size n_unique_approaches <- internal$parameters$n_unique_approaches @@ -1188,13 +1188,13 @@ check_vs_prev_shapr_object <- function(internal) { if (isTRUE(converged_sd)) { message0 <- c( message0, - "Convergence tolerance reached. Consider decreasing `adaptive_arguments$tolerance`.\n" + "Convergence tolerance reached. Consider decreasing `adaptive_args$tolerance`.\n" ) } if (isTRUE(converged_max_iter)) { message0 <- c( message0, - "Maximum number of iterations reached. Consider increasing `adaptive_arguments$max_iter`.\n" + "Maximum number of iterations reached. Consider increasing `adaptive_args$max_iter`.\n" ) } if (isTRUE(converged_max_n_coalitions)) { @@ -1242,7 +1242,7 @@ check_vs_prev_shapr_object <- function(internal) { #' #' @export #' @author Martin Jullum -get_adaptive_arguments_default <- function(internal, +get_adaptive_args_default <- function(internal, initial_n_coalitions = ceiling( min( 200, diff --git a/inst/scripts/devel/devel_convergence_branch.R b/inst/scripts/devel/devel_convergence_branch.R index 71ee609e3..dad9097fd 100644 --- a/inst/scripts/devel/devel_convergence_branch.R +++ b/inst/scripts/devel/devel_convergence_branch.R @@ -83,7 +83,7 @@ explanation_adaptive <- explain( n_coalitions = 500, prediction_zero = p0, adaptive = TRUE, - adaptive_arguments = list(initial_n_coalitions=10,convergence_tolerance=0.0001), + adaptive_args = list(initial_n_coalitions=10,convergence_tolerance=0.0001), print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp shapley_reweighting = "on_N" diff --git a/inst/scripts/devel/devel_verbose.R b/inst/scripts/devel/devel_verbose.R index f15bf15ec..51e9acbd1 100644 --- a/inst/scripts/devel/devel_verbose.R +++ b/inst/scripts/devel/devel_verbose.R @@ -6,7 +6,7 @@ ex <- explain( approach = "independence", prediction_zero = p0, max_n_coalitions = 30, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, reduction_factor_vec = rep(10^(-6), 10), @@ -111,7 +111,7 @@ ex <- explain( approach = "empirical", prediction_zero = p0, max_n_coalitions = 30, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, reduction_factor_vec = rep(10^(-6), 10), @@ -129,7 +129,7 @@ explain( approach = "independence", prediction_zero = p0, adaptive = TRUE, - adaptive_arguments <- list(n_initial_) + adaptive_args <- list(n_initial_) verbose = c("basic"), paired_shap_sampling = TRUE ) diff --git a/inst/scripts/devel/simtest_timing_to_Frida.R b/inst/scripts/devel/simtest_timing_to_Frida.R index d6366ce16..3f6177e57 100644 --- a/inst/scripts/devel/simtest_timing_to_Frida.R +++ b/inst/scripts/devel/simtest_timing_to_Frida.R @@ -87,7 +87,7 @@ expl <- explain( mu=mu, adaptive = TRUE, unique_sampling = FALSE, - adaptive_arguments = list(initial_n_coalitions = 50, + adaptive_args = list(initial_n_coalitions = 50, fixed_n_coalitions_per_iter = 50, max_iter = 10, convergence_tolerance = 10^(-10), diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index 81bb9149b..238b51a08 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -6,7 +6,7 @@ aa = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.01, reduction_factor_vec = rep(10^(-5), 10), @@ -23,7 +23,7 @@ bb = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -48,7 +48,7 @@ full = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -68,7 +68,7 @@ first = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -88,7 +88,7 @@ second = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -111,7 +111,7 @@ second_path = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -122,7 +122,7 @@ second_path = explain( print_iter_info = TRUE, shapley_reweighting = "on_N", seed=NULL, - prev_shapr_object = first$internal$parameters$adaptive_arguments$saving_path + prev_shapr_object = first$internal$parameters$adaptive_args$saving_path ) diff --git a/man/explain.Rd b/man/explain.Rd index dd0e18125..dea21808a 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -21,7 +21,7 @@ explain( get_model_specs = NULL, MSEv_uniform_comb_weights = TRUE, verbose = "basic", - adaptive_arguments = list(), + adaptive_args = list(), shapley_reweighting = "on_all_cond", prev_shapr_object = NULL, ... @@ -75,7 +75,7 @@ A convergence criterion is used to determine if the variances of the Shapley val If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through \code{adaptive_arguments}.} +Specifics related to the adaptive process and convergence criterion are set through \code{adaptive_args}.} \item{group}{List. If \code{NULL} regular feature wise Shapley values are computed. @@ -144,9 +144,9 @@ Note that any combination of four strings can be used. E.g. \code{verbose = c("basic", "vS_details")} will display basic information + details about the vS estimation process. }} -\item{adaptive_arguments}{Named list. +\item{adaptive_args}{Named list. Specifices the arguments for the adaptive procedure. -See \code{\link[=get_adaptive_arguments_default]{get_adaptive_arguments_default()}} for description of the arguments and their default values.} +See \code{\link[=get_adaptive_args_default]{get_adaptive_args_default()}} for description of the arguments and their default values.} \item{shapley_reweighting}{String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing @@ -297,7 +297,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{adaptive = TRUE} and \code{adaptive_arguments$compute_sd=TRUE}.} +Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -475,7 +475,7 @@ explain_adaptive <- explain( prediction_zero = p, n_MC_samples = 1e2, adaptive = TRUE, - adaptive_arguments = list(initial_n_coalitions = 10) + adaptive_args = list(initial_n_coalitions = 10) ) } diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index 45eca933b..24ffe895e 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -239,7 +239,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{adaptive = TRUE} and \code{adaptive_arguments$compute_sd=TRUE}.} +Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the diff --git a/man/finalize_explanation_forecast.Rd b/man/finalize_explanation_forecast.Rd index 924899fdd..45e452bc0 100644 --- a/man/finalize_explanation_forecast.Rd +++ b/man/finalize_explanation_forecast.Rd @@ -24,7 +24,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{adaptive = TRUE} and \code{adaptive_arguments$compute_sd=TRUE}.} +Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -202,7 +202,7 @@ explain_adaptive <- explain( prediction_zero = p, n_MC_samples = 1e2, adaptive = TRUE, - adaptive_arguments = list(initial_n_coalitions = 10) + adaptive_args = list(initial_n_coalitions = 10) ) } diff --git a/man/get_adaptive_arguments_default.Rd b/man/get_adaptive_arguments_default.Rd index 2b815cadf..a20401ac3 100644 --- a/man/get_adaptive_arguments_default.Rd +++ b/man/get_adaptive_arguments_default.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/setup.R -\name{get_adaptive_arguments_default} -\alias{get_adaptive_arguments_default} +\name{get_adaptive_args_default} +\alias{get_adaptive_args_default} \title{Function to specify arguments of the adaptive estimation procedure} \usage{ -get_adaptive_arguments_default( +get_adaptive_args_default( internal, initial_n_coalitions = ceiling(min(200, max(5, internal$parameters$n_features, (2^internal$parameters$n_features)/10))), diff --git a/man/setup.Rd b/man/setup.Rd index 602a92dcb..4574dcefe 100644 --- a/man/setup.Rd +++ b/man/setup.Rd @@ -29,7 +29,7 @@ setup( group_lags = NULL, verbose, adaptive = NULL, - adaptive_arguments = list(), + adaptive_args = list(), shapley_reweighting = "none", is_python = FALSE, testing = FALSE, @@ -172,11 +172,11 @@ A convergence criterion is used to determine if the variances of the Shapley val If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through \code{adaptive_arguments}.} +Specifics related to the adaptive process and convergence criterion are set through \code{adaptive_args}.} -\item{adaptive_arguments}{Named list. +\item{adaptive_args}{Named list. Specifices the arguments for the adaptive procedure. -See \code{\link[=get_adaptive_arguments_default]{get_adaptive_arguments_default()}} for description of the arguments and their default values.} +See \code{\link[=get_adaptive_args_default]{get_adaptive_args_default()}} for description of the arguments and their default values.} \item{shapley_reweighting}{String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing diff --git a/man/vaeac_get_extra_para_default.Rd b/man/vaeac_get_extra_para_default.Rd index 27968c11d..fb10e1d99 100644 --- a/man/vaeac_get_extra_para_default.Rd +++ b/man/vaeac_get_extra_para_default.Rd @@ -80,7 +80,7 @@ during the training of the vaeac model. Used in \code{\link[torch:dataloader]{to each batch when generating the Monte Carlo samples. If \code{NULL}, then the function generates the Monte Carlo samples for the provided coalitions and all explicands sent to \code{\link[=explain]{explain()}} at the time. The number of coalitions are determined by the \code{n_batches} used by \code{\link[=explain]{explain()}}. We recommend to tweak -\code{adaptive_arguments$max_batch_size} and \code{adaptive_arguments$min_n_batches} +\code{adaptive_args$max_batch_size} and \code{adaptive_args$min_n_batches} rather than \code{vaeac.batch_size_sampling}. Larger batch sizes are often much faster provided sufficient memory.} \item{vaeac.running_avg_n_values}{Positive integer (default is \code{5}). The number of previous IWAE values to include diff --git a/tests/testthat/_snaps/adaptive-setup.md b/tests/testthat/_snaps/adaptive-setup.md index d8f0d0c82..ab3164e2b 100644 --- a/tests/testthat/_snaps/adaptive-setup.md +++ b/tests/testthat/_snaps/adaptive-setup.md @@ -4,15 +4,15 @@ n_batches_non_numeric_1 <- "bla" explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_numeric_1)) + adaptive_args = list(min_n_batches = n_batches_non_numeric_1)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_arguments()`: - ! `adaptive_arguments$min_n_batches` must be NULL or a single positive integer. + Error in `check_adaptive_args()`: + ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. --- @@ -20,15 +20,15 @@ n_batches_non_numeric_2 <- TRUE explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_numeric_2)) + adaptive_args = list(min_n_batches = n_batches_non_numeric_2)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_arguments()`: - ! `adaptive_arguments$min_n_batches` must be NULL or a single positive integer. + Error in `check_adaptive_args()`: + ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. --- @@ -36,15 +36,15 @@ n_batches_non_integer <- 10.5 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_integer)) + adaptive_args = list(min_n_batches = n_batches_non_integer)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_arguments()`: - ! `adaptive_arguments$min_n_batches` must be NULL or a single positive integer. + Error in `check_adaptive_args()`: + ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. --- @@ -52,15 +52,15 @@ n_batches_too_long <- c(1, 2) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_too_long)) + adaptive_args = list(min_n_batches = n_batches_too_long)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_arguments()`: - ! `adaptive_arguments$min_n_batches` must be NULL or a single positive integer. + Error in `check_adaptive_args()`: + ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. --- @@ -68,15 +68,15 @@ n_batches_is_NA <- as.numeric(NA) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_is_NA)) + adaptive_args = list(min_n_batches = n_batches_is_NA)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_arguments()`: - ! `adaptive_arguments$min_n_batches` must be NULL or a single positive integer. + Error in `check_adaptive_args()`: + ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. --- @@ -84,13 +84,13 @@ n_batches_non_positive <- 0 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_positive)) + adaptive_args = list(min_n_batches = n_batches_non_positive)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_arguments()`: - ! `adaptive_arguments$min_n_batches` must be NULL or a single positive integer. + Error in `check_adaptive_args()`: + ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. diff --git a/tests/testthat/_snaps/regular-setup.md b/tests/testthat/_snaps/regular-setup.md index dc2c191c0..91cfdcb1e 100644 --- a/tests/testthat/_snaps/regular-setup.md +++ b/tests/testthat/_snaps/regular-setup.md @@ -978,7 +978,7 @@ Code explanation_equal <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, adaptive_arguments = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, adaptive_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), adaptive = FALSE) Message @@ -997,7 +997,7 @@ Code explanation_larger <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, adaptive_arguments = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, adaptive_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, adaptive = FALSE) Message diff --git a/tests/testthat/test-adaptive-output.R b/tests/testthat/test-adaptive-output.R index c4e787f21..a8341f2d4 100644 --- a/tests/testthat/test-adaptive-output.R +++ b/tests/testthat/test-adaptive-output.R @@ -26,7 +26,7 @@ test_that("output_lm_numeric_independence_converges_tol", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.1 ), @@ -46,7 +46,7 @@ test_that("output_lm_numeric_independence_converges_maxit", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -93,7 +93,7 @@ test_that("output_lm_numeric_gaussian_group_converges_tol", { approach = "gaussian", group = groups, prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 5, convergence_tolerance = 0.1 ), @@ -113,7 +113,7 @@ test_that("output_lm_numeric_independence_converges_tol_paired", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.1 ), @@ -137,7 +137,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -159,7 +159,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -180,7 +180,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -208,7 +208,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -229,7 +229,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), @@ -237,7 +237,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { ), adaptive = TRUE, verbose = NULL, - prev_shapr_object = e_init_path$internal$parameters$adaptive_arguments$saving_path, + prev_shapr_object = e_init_path$internal$parameters$adaptive_args$saving_path, seed = NULL ), "output_lm_numeric_independence_cont_est_path" diff --git a/tests/testthat/test-adaptive-setup.R b/tests/testthat/test-adaptive-setup.R index 32e6236f9..bfe5a8127 100644 --- a/tests/testthat/test-adaptive-setup.R +++ b/tests/testthat/test-adaptive-setup.R @@ -1,4 +1,4 @@ -test_that("adaptive_arguments are respected", { +test_that("adaptive_args are respected", { ex <- explain( testing = TRUE, model = model_lm_numeric, @@ -7,7 +7,7 @@ test_that("adaptive_arguments are respected", { approach = "independence", prediction_zero = p0, max_n_coalitions = 30, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, reduction_factor_vec = rep(10^(-6), 10), @@ -41,7 +41,7 @@ test_that("adaptive feature wise and groupwise computations identical", { x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 5, convergence_tolerance = 0.1 ), @@ -57,7 +57,7 @@ test_that("adaptive feature wise and groupwise computations identical", { approach = "gaussian", group = groups, prediction_zero = p0, - adaptive_arguments = list( + adaptive_args = list( initial_n_coalitions = 5, convergence_tolerance = 0.1 ), @@ -83,7 +83,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_numeric_1) + adaptive_args = list(min_n_batches = n_batches_non_numeric_1) ) }, error = TRUE @@ -100,7 +100,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_numeric_2) + adaptive_args = list(min_n_batches = n_batches_non_numeric_2) ) }, error = TRUE @@ -117,7 +117,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_integer) + adaptive_args = list(min_n_batches = n_batches_non_integer) ) }, error = TRUE @@ -134,7 +134,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_too_long) + adaptive_args = list(min_n_batches = n_batches_too_long) ) }, error = TRUE @@ -151,7 +151,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_is_NA) + adaptive_args = list(min_n_batches = n_batches_is_NA) ) }, error = TRUE @@ -168,7 +168,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = n_batches_non_positive) + adaptive_args = list(min_n_batches = n_batches_non_positive) ) }, error = TRUE @@ -184,7 +184,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = 5, max_batch_size = 10) + adaptive_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.empirical_n_batches_10 <- explain( @@ -194,7 +194,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = 10, max_batch_size = 10) + adaptive_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) @@ -216,7 +216,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = 5, max_batch_size = 10) + adaptive_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.ctree_n_batches_10 <- explain( @@ -226,7 +226,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - adaptive_arguments = list(min_n_batches = 10, max_batch_size = 10) + adaptive_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index 50efaa18a..c932267bf 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -1225,7 +1225,7 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - adaptive_arguments = list(compute_sd = FALSE), + adaptive_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), adaptive = FALSE ) @@ -1243,7 +1243,7 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - adaptive_arguments = list(compute_sd = FALSE), + adaptive_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, adaptive = FALSE ) diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index fa3de1d01..77d29cc2a 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -111,7 +111,7 @@ The reason for this is that the number of possible coalitions grows exponentiall These defaults can be overridden by setting the `adaptive` argument to `TRUE` or `FALSE`. When using the `adaptive` argument, the estimation for an observation is stopped when all Shapley value standard deviations are below `t` times the range of the Shapley values. -The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `adaptive_arguments$convergence_tolerance` argument, see [Adaptive estimation](#adaptive) for more details. +The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `adaptive_args$convergence_tolerance` argument, see [Adaptive estimation](#adaptive) for more details. Since the adaptiveness default changes based on the number of features (or feature groups), the default is also to have no upper bound on the number of coalitions considered. @@ -1163,7 +1163,7 @@ A convergence criterion is used to determine if the variances of the Shapley val If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through `adaptive_arguments` argument. +Specifics related to the adaptive process and convergence criterion are set through `adaptive_args` argument. The convergence criterion we use is adopted from @covert2021improving, and slightly modified to work for multiple observations @@ -1215,7 +1215,7 @@ ex <- explain( approach = "gaussian", prediction_zero = p0, adaptive = TRUE, - adaptive_arguments = list(convergence_tolerance = 0.1) + adaptive_args = list(convergence_tolerance = 0.1) ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -1282,8 +1282,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`adaptive_arguments$max_batch_size` (defaults to 10) and -`adaptive_arguments$min_n_batches` (defaults to 10). +`adaptive_args$max_batch_size` (defaults to 10) and +`adaptive_args$min_n_batches` (defaults to 10). ## Parallelized computation @@ -1944,7 +1944,7 @@ In this section, we demonstrate how to continue to improve estimation accuracy w from a previous Shapley value computation based on `shapr::explain()` with the adaptive estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$adaptive_arguments$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$internal$parameters$adaptive_args$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. @@ -2017,7 +2017,7 @@ ex_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 25, - adaptive_arguments = list(convergence_tolerance = 0.005), # Decrease the convergence threshold + adaptive_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold prev_shapr_object = ex_init ) #> Note: Feature classes extracted from the model contains NA. @@ -2035,7 +2035,7 @@ ex_further <- explain( #> #> ℹ Using 26 of 32 coalitions. -print(ex_further$internal$parameters$adaptive_arguments$saving_path) +print(ex_further$internal$parameters$adaptive_args$saving_path) #> [1] "/tmp/RtmpPIciRY/shapr_obj_c1876481fd8a.rds" # Using the ex_init object to continue the computation for the remaining coalition samples @@ -2047,7 +2047,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$adaptive_arguments$saving_path + prev_shapr_object = ex_further$internal$parameters$adaptive_args$saving_path ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index 8730d9c55..787fbaf67 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -126,7 +126,7 @@ The reason for this is that the number of possible coalitions grows exponentiall These defaults can be overridden by setting the `adaptive` argument to `TRUE` or `FALSE`. When using the `adaptive` argument, the estimation for an observation is stopped when all Shapley value standard deviations are below `t` times the range of the Shapley values. -The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `adaptive_arguments$convergence_tolerance` argument, see [Adaptive estimation](#adaptive) for more details. +The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `adaptive_args$convergence_tolerance` argument, see [Adaptive estimation](#adaptive) for more details. Since the adaptiveness default changes based on the number of features (or feature groups), the default is also to have no upper bound on the number of coalitions considered. @@ -946,7 +946,7 @@ A convergence criterion is used to determine if the variances of the Shapley val If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through `adaptive_arguments` argument. +Specifics related to the adaptive process and convergence criterion are set through `adaptive_args` argument. The convergence criterion we use is adopted from @covert2021improving, and slightly modified to work for multiple observations @@ -997,7 +997,7 @@ ex <- explain( approach = "gaussian", prediction_zero = p0, adaptive = TRUE, - adaptive_arguments = list(convergence_tolerance = 0.1) + adaptive_args = list(convergence_tolerance = 0.1) ) ``` @@ -1026,8 +1026,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`adaptive_arguments$max_batch_size` (defaults to 10) and -`adaptive_arguments$min_n_batches` (defaults to 10). +`adaptive_args$max_batch_size` (defaults to 10) and +`adaptive_args$min_n_batches` (defaults to 10). ## Parallelized computation @@ -1522,7 +1522,7 @@ In this section, we demonstrate how to continue to improve estimation accuracy w from a previous Shapley value computation based on `shapr::explain()` with the adaptive estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$adaptive_arguments$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$internal$parameters$adaptive_args$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. @@ -1576,11 +1576,11 @@ ex_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 25, - adaptive_arguments = list(convergence_tolerance = 0.005), # Decrease the convergence threshold + adaptive_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold prev_shapr_object = ex_init ) -print(ex_further$internal$parameters$adaptive_arguments$saving_path) +print(ex_further$internal$parameters$adaptive_args$saving_path) # Using the ex_init object to continue the computation for the remaining coalition samples # but this time using the path to the saved intermediate estimation object @@ -1591,7 +1591,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$adaptive_arguments$saving_path + prev_shapr_object = ex_further$internal$parameters$adaptive_args$saving_path ) From 4f8f39144f5e8714041a9e8fbd38478c62b3703f Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 7 Oct 2024 22:17:34 +0200 Subject: [PATCH 02/32] move MSEv_uniform_comb_weights and keep_samp_for_vS to output_args --- R/explain.R | 18 ++++-------- R/setup.R | 80 +++++++++++++++++++++++++++++++++++++++++------------ 2 files changed, 68 insertions(+), 30 deletions(-) diff --git a/R/explain.R b/R/explain.R index 0331f11b8..68d9e95f6 100644 --- a/R/explain.R +++ b/R/explain.R @@ -53,10 +53,6 @@ #' Specifies the seed before any randomness based code is being run. #' If `NULL` no seed is set in the calling environment. #' -#' @param keep_samp_for_vS Logical. -#' Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in `internal$output`). -#' Not used for `approach="regression_separate"` or `approach="regression_surrogate"`. -#' #' @param predict_model Function. #' The prediction function used when `model` is not natively supported. #' (Run [get_supported_models()] for a list of natively supported models.) @@ -79,11 +75,6 @@ #' disabled for unsupported model classes. #' Can also be used to override the default function for natively supported model classes. #' -#' @param MSEv_uniform_comb_weights Logical. -#' If `TRUE` (default), then the function weights the coalitions uniformly when computing the MSEv criterion. -#' If `FALSE`, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv -#' criterion. -#' Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered. #' #' @param verbose String vector or NULL. #' Specifies the verbosity (printout detail level) through one or more of strings `"basic"`, `"progress"`, @@ -120,6 +111,9 @@ #' @param adaptive_args Named list. #' Specifices the arguments for the adaptive procedure. #' See [shapr::get_adaptive_args_default()] for description of the arguments and their default values. +#' @param output_args Named list. +#' Specifices the arguments for the adaptive procedure. +#' See [shapr::get_output_args_default()] for description of the arguments and their default values. #' @param shapley_reweighting String. #' How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing #' the randomness and thereby the variance of the Shapley value estimates. @@ -353,14 +347,13 @@ explain <- function(model, group = NULL, n_MC_samples = 1e3, seed = 1, - keep_samp_for_vS = FALSE, predict_model = NULL, get_model_specs = NULL, - MSEv_uniform_comb_weights = TRUE, verbose = "basic", adaptive_args = list(), shapley_reweighting = "on_all_cond", prev_shapr_object = NULL, + output_args = list(), ...) { # ... is further arguments passed to specific approaches @@ -386,15 +379,14 @@ explain <- function(model, group = group, n_MC_samples = n_MC_samples, seed = seed, - keep_samp_for_vS = keep_samp_for_vS, feature_specs = feature_specs, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights, verbose = verbose, adaptive = adaptive, adaptive_args = adaptive_args, shapley_reweighting = shapley_reweighting, init_time = init_time, prev_shapr_object = prev_shapr_object, + output_args = output_args, ... ) diff --git a/R/setup.R b/R/setup.R index 68edf75cd..e8a59da52 100644 --- a/R/setup.R +++ b/R/setup.R @@ -33,9 +33,7 @@ setup <- function(x_train, group, n_MC_samples, seed, - keep_samp_for_vS, feature_specs, - MSEv_uniform_comb_weights = TRUE, type = "normal", horizon = NULL, y = NULL, @@ -53,6 +51,7 @@ setup <- function(x_train, testing = FALSE, init_time = NULL, prev_shapr_object = NULL, + output_args = list(), ...) { internal <- list() @@ -79,7 +78,6 @@ setup <- function(x_train, group = group, n_MC_samples = n_MC_samples, seed = seed, - keep_samp_for_vS = keep_samp_for_vS, type = type, horizon = horizon, train_idx = train_idx, @@ -87,13 +85,13 @@ setup <- function(x_train, explain_y_lags = explain_y_lags, explain_xreg_lags = explain_xreg_lags, group_lags = group_lags, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights, verbose = verbose, adaptive = adaptive, adaptive_args = adaptive_args, shapley_reweighting = shapley_reweighting, is_python = is_python, testing = testing, + output_args = output_args, ... ) @@ -163,7 +161,6 @@ get_parameters <- function(approach, group, n_MC_samples, seed, - keep_samp_for_vS, type, horizon, train_idx, @@ -171,12 +168,14 @@ get_parameters <- function(approach, explain_y_lags, explain_xreg_lags, group_lags = NULL, - MSEv_uniform_comb_weights, verbose = "basic", adaptive = FALSE, adaptive_args = list(), shapley_reweighting = "none", - testing, is_python, ...) { + testing, + is_python, + output_args = list(), + ...) { # Check input type for approach # approach is checked more comprehensively later @@ -190,6 +189,9 @@ get_parameters <- function(approach, if (!is.list(adaptive_args)) { stop("`adaptive_args` must be a list.") } + if (!is.list(output_args)) { + stop("`output_args` must be a list.") + } # max_n_coalitions @@ -215,11 +217,6 @@ get_parameters <- function(approach, stop("`n_MC_samples` must be a single positive integer.") } - # keep_samp_for_vS - if (!(is.logical(keep_samp_for_vS) && - length(keep_samp_for_vS) == 1)) { - stop("`keep_samp_for_vS` must be single logical.") - } # type if (!(type %in% c("normal", "forecast"))) { @@ -270,10 +267,6 @@ get_parameters <- function(approach, } } - # Parameter used in the MSEv evaluation criterion - if (!(is.logical(MSEv_uniform_comb_weights) && length(MSEv_uniform_comb_weights) == 1)) { - stop("`MSEv_uniform_comb_weights` must be single logical.") - } #### Tests combining more than one parameter #### # prediction_zero vs output_size @@ -317,6 +310,7 @@ get_parameters <- function(approach, shapley_reweighting = shapley_reweighting, adaptive = adaptive, adaptive_args = adaptive_args, + output_args = output_args, testing = testing ) @@ -576,6 +570,8 @@ check_and_set_parameters <- function(internal) { check_max_n_coalitions_fc(internal) + internal <- set_output_parameters(internal) + internal <- check_and_set_adaptive(internal) # sets the adaptive parameter if it is NULL (default) internal <- set_exact(internal) @@ -714,6 +710,57 @@ check_max_n_coalitions_fc <- function(internal) { } } +#' @author Martin Jullum +#' @keywords internal +set_output_parameters <- function(internal) { + + output_args <- internal$parameters$output_args + + # Get defaults + output_args <- utils::modifyList(get_output_args_default(internal), + adaptive_args, + keep.null = TRUE + ) + + list2env(output_args, envir = environment()) # Make accessible in the environment + + # Check the output_args elements + + # keep_samp_for_vS + if (!(is.logical(keep_samp_for_vS) && + length(keep_samp_for_vS) == 1)) { + stop("`keep_samp_for_vS` must be single logical.") + } + + # Parameter used in the MSEv evaluation criterion + if (!(is.logical(MSEv_uniform_comb_weights) && length(MSEv_uniform_comb_weights) == 1)) { + stop("`MSEv_uniform_comb_weights` must be single logical.") + } + + internal$parameters$MSEv_uniform_comb_weights <- MSEv_uniform_comb_weights + internal$parameters$keep_samp_for_vS <- keep_samp_for_vS + + return(internal) +} + +#' Gets the default values for the output arguments +#' +#' @param keep_samp_for_vS Logical. +#' Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in `internal$output`). +#' Not used for `approach="regression_separate"` or `approach="regression_surrogate"`. +#' @param MSEv_uniform_comb_weights Logical. +#' If `TRUE` (default), then the function weights the coalitions uniformly when computing the MSEv criterion. +#' If `FALSE`, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv +#' criterion. +#' Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered. +#' @export +#' @author Martin Jullum +get_output_args_default <- function(keep_samp_for_vS = FALSE, + MSEv_uniform_comb_weights = TRUE){ + return(mget(methods::formalArgs(get_output_args_default))) +} + + check_and_set_adaptive <- function(internal) { adaptive <- internal$parameters$adaptive approach <- internal$parameters$approach @@ -982,7 +1029,6 @@ check_groups <- function(feature_names, group) { - #' @keywords internal set_adaptive_parameters <- function(internal, prev_iter_list = NULL) { adaptive <- internal$parameters$adaptive From 3ba0517e68ca34237d26c3b4bfbd1725d6e7e066 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 7 Oct 2024 22:20:20 +0200 Subject: [PATCH 03/32] adaptive -> iterative throughout (!) --- NAMESPACE | 2 +- NEWS.md | 2 +- R/approach_vaeac.R | 2 +- R/check_convergence.R | 6 +- R/cli.R | 18 +- R/compute_estimates.R | 2 +- R/explain.R | 56 +++--- R/explain_forecast.R | 2 +- R/prepare_next_iteration.R | 8 +- R/print_iter.R | 6 +- R/save_results.R | 2 +- R/setup.R | 176 +++++++++--------- README.Rmd | 2 +- README.md | 2 +- inst/scripts/devel/devel_batch_testing.R | 2 +- inst/scripts/devel/devel_convergence_branch.R | 62 +++--- inst/scripts/devel/devel_verbose.R | 24 +-- inst/scripts/devel/simtest_timing_to_Frida.R | 4 +- .../devel/testing_intermediate_saving.R | 26 +-- man/check_verbose.Rd | 4 +- man/explain.Rd | 46 ++--- man/explain_forecast.Rd | 16 +- man/finalize_explanation_forecast.Rd | 18 +- man/get_adaptive_arguments_default.Rd | 20 +- man/prepare_next_iteration.Rd | 4 +- man/regression.train_model.Rd | 4 +- man/setup.Rd | 28 +-- man/vaeac_check_parameters.Rd | 4 +- man/vaeac_get_extra_para_default.Rd | 2 +- man/vaeac_get_mask_generator_name.Rd | 4 +- man/vaeac_impute_missing_entries.Rd | 4 +- man/vaeac_train_model.Rd | 4 +- man/vaeac_train_model_auxiliary.Rd | 4 +- man/vaeac_train_model_continue.Rd | 4 +- tests/testthat/_snaps/adaptive-output.md | 20 +- tests/testthat/_snaps/adaptive-setup.md | 36 ++-- tests/testthat/_snaps/regression-output.md | 44 ++--- tests/testthat/_snaps/regression-setup.md | 20 +- tests/testthat/_snaps/regular-output.md | 60 +++--- tests/testthat/_snaps/regular-setup.md | 32 ++-- tests/testthat/test-adaptive-output.R | 50 ++--- tests/testthat/test-adaptive-setup.R | 36 ++-- tests/testthat/test-regression-output.R | 42 ++--- tests/testthat/test-regression-setup.R | 12 +- tests/testthat/test-regular-output.R | 62 +++--- tests/testthat/test-regular-setup.R | 20 +- vignettes/understanding_shapr.Rmd | 124 ++++++------ vignettes/understanding_shapr.Rmd.orig | 76 ++++---- vignettes/understanding_shapr_regression.Rmd | 110 +++++------ vignettes/understanding_shapr_vaeac.Rmd | 6 +- 50 files changed, 660 insertions(+), 660 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 81ce1f3a9..718f5249c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -67,7 +67,7 @@ export(explain) export(explain_forecast) export(finalize_explanation) export(finalize_explanation_forecast) -export(get_adaptive_args_default) +export(get_iterative_args_default) export(get_cov_mat) export(get_data_specs) export(get_model_specs) diff --git a/NEWS.md b/NEWS.md index 20c71de62..f84c87761 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,7 @@ # shapr 1.0.0 * (Just some notes so far) -* Adaptive estimatio/convergence detection +* iterative estimatio/convergence detection * Verbosity * Complete restructuring motivated by introducing the Python wrapper. The restructuring splits the explanation tasks into smaller pieces, which was necessary to allow the Python wrapper to move back and forth between R and Python. * As part of the restructuring, we also did a number of design changes, resulting in a series of breaking changes described below. diff --git a/R/approach_vaeac.R b/R/approach_vaeac.R index 858efb51f..63872648f 100644 --- a/R/approach_vaeac.R +++ b/R/approach_vaeac.R @@ -1656,7 +1656,7 @@ vaeac_check_parameters <- function(x_train, #' each batch when generating the Monte Carlo samples. If `NULL`, then the function generates the Monte Carlo samples #' for the provided coalitions and all explicands sent to [shapr::explain()] at the time. #' The number of coalitions are determined by the `n_batches` used by [shapr::explain()]. We recommend to tweak -#' `adaptive_args$max_batch_size` and `adaptive_args$min_n_batches` +#' `iterative_args$max_batch_size` and `iterative_args$min_n_batches` #' rather than `vaeac.batch_size_sampling`. Larger batch sizes are often much faster provided sufficient memory. #' @param vaeac.running_avg_n_values Positive integer (default is `5`). The number of previous IWAE values to include #' when we compute the running means of the IWAE criterion. diff --git a/R/check_convergence.R b/R/check_convergence.R index da94f8c1d..bcf455bd5 100644 --- a/R/check_convergence.R +++ b/R/check_convergence.R @@ -7,9 +7,9 @@ check_convergence <- function(internal) { iter <- length(internal$iter_list) - convergence_tolerance <- internal$parameters$adaptive_args$convergence_tolerance - max_iter <- internal$parameters$adaptive_args$max_iter - max_n_coalitions <- internal$parameters$adaptive_args$max_n_coalitions + convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance + max_iter <- internal$parameters$iterative_args$max_iter + max_n_coalitions <- internal$parameters$iterative_args$max_n_coalitions paired_shap_sampling <- internal$parameters$paired_shap_sampling n_shapley_values <- internal$parameters$n_shapley_values diff --git a/R/cli.R b/R/cli.R index ae8e2da44..7fee48960 100644 --- a/R/cli.R +++ b/R/cli.R @@ -3,20 +3,20 @@ cli_startup <- function(internal, model, verbose) { is_groupwise <- internal$parameters$is_groupwise approach <- internal$parameters$approach - adaptive <- internal$parameters$adaptive + iterative <- internal$parameters$iterative n_shapley_values <- internal$parameters$n_shapley_values n_explain <- internal$parameters$n_explain - saving_path <- internal$parameters$adaptive_args$saving_path + saving_path <- internal$parameters$iterative_args$saving_path feat_group_txt <- ifelse(is_groupwise, "group-wise", "feature-wise") - adaptive_txt <- ifelse(adaptive, "adaptive", "non-adaptive") + iterative_txt <- ifelse(iterative, "iterative", "non-iterative") testing <- internal$parameters$testing line_vec <- "Model class: {.cls {class(model)}}" line_vec <- c(line_vec, "Approach: {.emph {approach}}") - line_vec <- c(line_vec, "Adaptive estimation: {.emph {adaptive}}") + line_vec <- c(line_vec, "iterative estimation: {.emph {iterative}}") line_vec <- c(line_vec, "Number of {.emph {feat_group_txt}} Shapley values: {n_shapley_values}") line_vec <- c(line_vec, "Number of observations to explain: {n_explain}") if (isFALSE(testing)) { @@ -40,8 +40,8 @@ cli_startup <- function(internal, model, verbose) { } if ("basic" %in% verbose) { - if (isTRUE(adaptive)) { - msg <- "Adaptive computation started" + if (isTRUE(iterative)) { + msg <- "iterative computation started" } else { msg <- "Main computation started" } @@ -51,9 +51,9 @@ cli_startup <- function(internal, model, verbose) { cli_iter <- function(verbose, internal, iter) { - adaptive <- internal$parameters$adaptive + iterative <- internal$parameters$iterative - if (!is.null(verbose) && isTRUE(adaptive)) { + if (!is.null(verbose) && isTRUE(iterative)) { cli::cli_h1("Iteration {iter}") } @@ -62,7 +62,7 @@ cli_iter <- function(verbose, internal, iter) { tot_coal <- internal$iter_list[[iter]]$n_coalitions all_coal <- 2^internal$parameters$n_shapley_values - extra_msg <- ifelse(adaptive, ", {new_coal} new", "") + extra_msg <- ifelse(iterative, ", {new_coal} new", "") msg <- paste0("Using {tot_coal} of {all_coal} coalitions", extra_msg, ". ") diff --git a/R/compute_estimates.R b/R/compute_estimates.R index b7e2044a7..f0b2232b8 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -16,7 +16,7 @@ compute_estimates <- function(internal, vS_list) { iter <- length(internal$iter_list) compute_sd <- internal$iter_list[[iter]]$compute_sd - n_boot_samps <- internal$parameters$adaptive_args$n_boot_samps + n_boot_samps <- internal$parameters$iterative_args$n_boot_samps processed_vS_list <- postprocess_vS_list( vS_list = vS_list, diff --git a/R/explain.R b/R/explain.R index 68d9e95f6..102fc8c32 100644 --- a/R/explain.R +++ b/R/explain.R @@ -28,9 +28,9 @@ #' such as the mean of the predictions in the training data are also reasonable. #' #' @param max_n_coalitions Integer. -#' The upper limit on the number of unique feature/group coalitions to use in the adaptive procedure -#' (if `adaptive = TRUE`). -#' If `adaptive = FALSE` it represents the number of feature/group coalitions to use directly. +#' The upper limit on the number of unique feature/group coalitions to use in the iterative procedure +#' (if `iterative = TRUE`). +#' If `iterative = FALSE` it represents the number of feature/group coalitions to use directly. #' The quantity refers to the number of unique feature coalitions if `group = NULL`, #' and group coalitions if `group != NULL`. #' `max_n_coalitions = NULL` corresponds to `max_n_coalitions=2^n_features`. @@ -82,8 +82,8 @@ #' `"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 `adaptive = TRUE`) . -#' `"shapley"` displays intermediate Shapley value estimates and standard deviations (only when `adaptive = TRUE`) +#' (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"`). @@ -96,9 +96,9 @@ #' That is, if there are 5 features and e.g. coalitions (1,3,5) are sampled, then also coalition (2,4) is used for #' computing the Shapley values. This is done to reduce the variance of the Shapley value estimates. #' -#' @param adaptive Logical or NULL +#' @param iterative Logical or NULL #' If `NULL` (default), the argument is set to `TRUE` if there are more than 5 features/groups, and `FALSE` otherwise. -#' If eventually `TRUE`, the Shapley values are estimated adaptively in an iterative manner. +#' If eventually `TRUE`, the Shapley values are estimated iteratively in an iterative manner. #' This provides sufficiently accurate Shapley value estimates faster. #' First an initial number of coalitions is sampled, then bootsrapping is used to estimate the variance of the Shapley #' values. @@ -106,13 +106,13 @@ #' If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more #' coalitions. #' The process is repeated until the variances are below the threshold. -#' Specifics related to the adaptive process and convergence criterion are set through `adaptive_args`. +#' Specifics related to the iterative process and convergence criterion are set through `iterative_args`. #' -#' @param adaptive_args Named list. -#' Specifices the arguments for the adaptive procedure. -#' See [shapr::get_adaptive_args_default()] for description of the arguments and their default values. +#' @param iterative_args Named list. +#' Specifices the arguments for the iterative procedure. +#' See [shapr::get_iterative_args_default()] for description of the arguments and their default values. #' @param output_args Named list. -#' Specifices the arguments for the adaptive procedure. +#' Specifices the arguments for the iterative procedure. #' See [shapr::get_output_args_default()] for description of the arguments and their default values. #' @param shapley_reweighting String. #' How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing @@ -131,7 +131,7 @@ #' If an object of class `shapr` is provided or string with a path to where intermediate results are strored, #' then the function will use the previous object to continue the computation. #' This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore -#' want to continue the adaptive estimation. See the vignette for examples. +#' want to continue the iterative estimation. See the vignette for examples. #' @param ... Further arguments passed to specific approaches #' #' @inheritDotParams setup_approach.empirical @@ -153,10 +153,10 @@ #' #' The package allows for parallelized computation with progress updates through the tightly connected #' [future::future] and [progressr::progressr] packages. See the examples below. -#' For adaptive estimation (`adaptive=TRUE`), intermediate results may also be printed to the console +#' For iterative estimation (`iterative=TRUE`), intermediate results may also be printed to the console #' (according to the `verbose` argument). #' Moreover, the intermediate results are written to disk. -#' This combined with adaptive estimation with (optional) intermediate results printed to the console (and temporary +#' This combined with iterative estimation with (optional) intermediate results printed to the console (and temporary #' written to disk, and batch computing of the v(S) values, enables fast and accurate estimation of the Shapley values #' in a memory friendly manner. #' @@ -168,7 +168,7 @@ #' \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. #' Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by #' definition 0 when all coalitions is used. -#' Only present when `adaptive = TRUE` and `adaptive_args$compute_sd=TRUE`.} +#' Only present when `iterative = TRUE` and `iterative_args$compute_sd=TRUE`.} #' \item{internal}{List with the different parameters, data, functions and other output used internally.} #' \item{pred_explain}{Numeric vector with the predictions for the explained observations} #' \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -178,8 +178,8 @@ #' `init_time` and `end_time` gives the time stamps for the start and end of the computation. #' `total_time_secs` gives the total time in seconds for the complete execution of `explain()`. #' `main_timing_secs` gives the time in seconds for the main computations. -#' `iter_timing_secs` gives for each iteration of the adaptive estimation, the time spent on the different parts -#' adaptive estimation routine.} +#' `iter_timing_secs` gives for each iteration of the iterative estimation, the time spent on the different parts +#' iterative estimation routine.} #' } #' #' @examples @@ -314,19 +314,19 @@ #' regression.model = parsnip::linear_reg() #' ) #' -#' ## Adaptive estimation +#' ## iterative estimation #' # For illustration purposes only. By default not used for such small dimensions as here #' #' # Gaussian approach -#' explain_adaptive <- explain( +#' explain_iterative <- explain( #' model = model, #' x_explain = x_explain, #' x_train = x_train, #' approach = "gaussian", #' prediction_zero = p, #' n_MC_samples = 1e2, -#' adaptive = TRUE, -#' adaptive_args = list(initial_n_coalitions = 10) +#' iterative = TRUE, +#' iterative_args = list(initial_n_coalitions = 10) #' ) #' #' @export @@ -343,14 +343,14 @@ explain <- function(model, paired_shap_sampling = TRUE, prediction_zero, max_n_coalitions = NULL, - adaptive = NULL, + iterative = NULL, group = NULL, n_MC_samples = 1e3, seed = 1, predict_model = NULL, get_model_specs = NULL, verbose = "basic", - adaptive_args = list(), + iterative_args = list(), shapley_reweighting = "on_all_cond", prev_shapr_object = NULL, output_args = list(), @@ -381,8 +381,8 @@ explain <- function(model, seed = seed, feature_specs = feature_specs, verbose = verbose, - adaptive = adaptive, - adaptive_args = adaptive_args, + iterative = iterative, + iterative_args = iterative_args, shapley_reweighting = shapley_reweighting, init_time = init_time, prev_shapr_object = prev_shapr_object, @@ -464,7 +464,7 @@ explain <- function(model, internal$main_timing_list$main_computation <- Sys.time() - # Rerun after convergence to get the same output format as for the non-adaptive approach + # Rerun after convergence to get the same output format as for the non-iterative approach output <- finalize_explanation(internal = internal) internal$main_timing_list$finalize_explanation <- Sys.time() @@ -516,7 +516,7 @@ testing_cleanup <- function(output) { } # Delete the saving_path - output$internal$parameters$adaptive_args$saving_path <- NULL + output$internal$parameters$iterative_args$saving_path <- NULL return(output) } diff --git a/R/explain_forecast.R b/R/explain_forecast.R index 2621b816d..08e03012a 100644 --- a/R/explain_forecast.R +++ b/R/explain_forecast.R @@ -128,7 +128,7 @@ explain_forecast <- function(model, output_size = horizon, max_n_coalitions = max_n_coalitions, n_MC_samples = n_MC_samples, - # n_batches = n_batches, # TODO: This is not used anymore, but the code does not use the adaptive version of it + # n_batches = n_batches, # TODO: This is not used anymore, but the code does not use the iterative version of it # either I think... I have now just set it to always 10 in the create_S_batches_forecast function. seed = seed, keep_samp_for_vS = keep_samp_for_vS, diff --git a/R/prepare_next_iteration.R b/R/prepare_next_iteration.R index 4ce90680e..4f0c0ba40 100644 --- a/R/prepare_next_iteration.R +++ b/R/prepare_next_iteration.R @@ -1,4 +1,4 @@ -#' Prepares the next iteration of the adaptive sampling algorithm +#' Prepares the next iteration of the iterative sampling algorithm #' #' @inheritParams default_doc_explain #' @@ -14,9 +14,9 @@ prepare_next_iteration <- function(internal) { next_iter_list <- list() n_shapley_values <- internal$parameters$n_shapley_values - reduction_factor_vec <- internal$parameters$adaptive_args$reduction_factor_vec - fixed_n_coalitions_per_iter <- internal$parameters$adaptive_args$fixed_n_coalitions_per_iter - max_n_coalitions <- internal$parameters$adaptive_args$max_n_coalitions + reduction_factor_vec <- internal$parameters$iterative_args$reduction_factor_vec + fixed_n_coalitions_per_iter <- internal$parameters$iterative_args$fixed_n_coalitions_per_iter + max_n_coalitions <- internal$parameters$iterative_args$max_n_coalitions est_remaining_coalitions <- internal$iter_list[[iter]]$est_remaining_coalitions diff --git a/R/print_iter.R b/R/print_iter.R index 6fbef82de..da915fc13 100644 --- a/R/print_iter.R +++ b/R/print_iter.R @@ -16,12 +16,12 @@ print_iter <- function(internal) { overall_conv_measure <- internal$iter_list[[iter]]$overall_conv_measure reduction_factor <- internal$iter_list[[iter]]$reduction_factor - saving_path <- internal$parameters$adaptive_args$saving_path - convergence_tolerance <- internal$parameters$adaptive_args$convergence_tolerance + saving_path <- internal$parameters$iterative_args$saving_path + convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance testing <- internal$parameters$testing if ("convergence" %in% verbose) { - convergence_tolerance <- internal$parameters$adaptive_args$convergence_tolerance + convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance current_n_coalitions <- internal$iter_list[[iter]]$n_coalitions est_remaining_coalitions <- internal$iter_list[[iter]]$est_remaining_coalitions diff --git a/R/save_results.R b/R/save_results.R index 142c0ea8e..722e30a88 100644 --- a/R/save_results.R +++ b/R/save_results.R @@ -5,7 +5,7 @@ #' @export #' @keywords internal save_results <- function(internal) { - saving_path <- internal$parameters$adaptive_args$saving_path + saving_path <- internal$parameters$iterative_args$saving_path # Modify name for the new file filename <- basename(saving_path) diff --git a/R/setup.R b/R/setup.R index e8a59da52..a683b9278 100644 --- a/R/setup.R +++ b/R/setup.R @@ -44,8 +44,8 @@ setup <- function(x_train, explain_xreg_lags = NULL, group_lags = NULL, verbose, - adaptive = NULL, - adaptive_args = list(), + iterative = NULL, + iterative_args = list(), shapley_reweighting = "none", is_python = FALSE, testing = FALSE, @@ -64,7 +64,7 @@ setup <- function(x_train, prev_iter_list <- prev_internal$iter_list # Overwrite the input arguments set in explain() with those from in prev_shapr_object - # except model, x_explain, x_train, max_n_coalitions, adaptive_args, seed + # except model, x_explain, x_train, max_n_coalitions, iterative_args, seed list2env(prev_internal$parameters) } @@ -86,8 +86,8 @@ setup <- function(x_train, explain_xreg_lags = explain_xreg_lags, group_lags = group_lags, verbose = verbose, - adaptive = adaptive, - adaptive_args = adaptive_args, + iterative = iterative, + iterative_args = iterative_args, shapley_reweighting = shapley_reweighting, is_python = is_python, testing = testing, @@ -118,7 +118,7 @@ setup <- function(x_train, internal <- check_and_set_parameters(internal) - internal <- set_adaptive_parameters(internal, prev_iter_list) + internal <- set_iterative_parameters(internal, prev_iter_list) internal$timing_list <- list( init_time = init_time, @@ -129,7 +129,7 @@ setup <- function(x_train, } get_prev_internal <- function(prev_shapr_object, - exclude_parameters = c("max_n_coalitions", "adaptive_args", "seed")) { + exclude_parameters = c("max_n_coalitions", "iterative_args", "seed")) { cl <- class(prev_shapr_object)[1] if (cl == "character") { @@ -169,8 +169,8 @@ get_parameters <- function(approach, explain_xreg_lags, group_lags = NULL, verbose = "basic", - adaptive = FALSE, - adaptive_args = list(), + iterative = FALSE, + iterative_args = list(), shapley_reweighting = "none", testing, is_python, @@ -183,11 +183,11 @@ get_parameters <- function(approach, stop("`paired_shap_sampling` must be a single logical.") } - if (!is.logical(adaptive) && length(adaptive) == 1) { - stop("`adaptive` must be a single logical.") + if (!is.logical(iterative) && length(iterative) == 1) { + stop("`iterative` must be a single logical.") } - if (!is.list(adaptive_args)) { - stop("`adaptive_args` must be a list.") + if (!is.list(iterative_args)) { + stop("`iterative_args` must be a list.") } if (!is.list(output_args)) { stop("`output_args` must be a list.") @@ -308,8 +308,8 @@ get_parameters <- function(approach, MSEv_uniform_comb_weights = MSEv_uniform_comb_weights, verbose = verbose, shapley_reweighting = shapley_reweighting, - adaptive = adaptive, - adaptive_args = adaptive_args, + iterative = iterative, + iterative_args = iterative_args, output_args = output_args, testing = testing ) @@ -572,7 +572,7 @@ check_and_set_parameters <- function(internal) { internal <- set_output_parameters(internal) - internal <- check_and_set_adaptive(internal) # sets the adaptive parameter if it is NULL (default) + internal <- check_and_set_iterative(internal) # sets the iterative parameter if it is NULL (default) internal <- set_exact(internal) @@ -718,7 +718,7 @@ set_output_parameters <- function(internal) { # Get defaults output_args <- utils::modifyList(get_output_args_default(internal), - adaptive_args, + iterative_args, keep.null = TRUE ) @@ -761,29 +761,29 @@ get_output_args_default <- function(keep_samp_for_vS = FALSE, } -check_and_set_adaptive <- function(internal) { - adaptive <- internal$parameters$adaptive +check_and_set_iterative <- function(internal) { + iterative <- internal$parameters$iterative approach <- internal$parameters$approach - # Always adaptive = FALSE for vaeac and regression_surrogate + # Always iterative = FALSE for vaeac and regression_surrogate if (any(approach %in% c("vaeac", "regression_surrogate"))) { unsupported <- approach[approach %in% c("vaeac", "regression_surrogate")] - if (isTRUE(adaptive)) { + if (isTRUE(iterative)) { warning( paste0( - "Adaptive estimation of Shapley values are not supported for approach = ", - paste0(unsupported, collapse = ", "), ". Setting adaptive = FALSE." + "iterative estimation of Shapley values are not supported for approach = ", + paste0(unsupported, collapse = ", "), ". Setting iterative = FALSE." ) ) } - internal$parameters$adaptive <- FALSE + internal$parameters$iterative <- FALSE } else { - # Sets the default value of adaptive to TRUE if computing more than 5 Shapley values for all other approaches - if (is.null(adaptive)) { + # Sets the default value of iterative to TRUE if computing more than 5 Shapley values for all other approaches + if (is.null(iterative)) { n_shapley_values <- internal$parameters$n_shapley_values # n_features if feature-wise and n_groups if group-wise - internal$parameters$adaptive <- isTRUE(n_shapley_values > 5) + internal$parameters$iterative <- isTRUE(n_shapley_values > 5) } } @@ -796,9 +796,9 @@ set_exact <- function(internal) { n_features <- internal$parameters$n_features n_groups <- internal$parameters$n_groups is_groupwise <- internal$parameters$is_groupwise - adaptive <- internal$parameters$adaptive + iterative <- internal$parameters$iterative - if (isFALSE(adaptive) && + if (isFALSE(iterative) && ( (isFALSE(is_groupwise) && max_n_coalitions == 2^n_features) || (isTRUE(is_groupwise) && max_n_coalitions == 2^n_groups) @@ -831,7 +831,7 @@ check_computability <- function(internal) { paste0( "Due to computation time, we recommend not computing Shapley values exactly \n", "with all 2^n_features (", 2^n_features, ") coalitions for n_features > 13.\n", - "Consider reducing max_n_coalitions and enabling adaptive estimation with adaptive = TRUE.\n" + "Consider reducing max_n_coalitions and enabling iterative estimation with iterative = TRUE.\n" ) ) } @@ -840,20 +840,20 @@ check_computability <- function(internal) { paste0( "Due to computation time, we recommend not computing Shapley values exactly \n", "with all 2^n_groups (", 2^n_groups, ") coalitions for n_groups > 13.\n", - "Consider reducing max_n_coalitions and enabling adaptive estimation with adaptive = TRUE.\n" + "Consider reducing max_n_coalitions and enabling iterative estimation with iterative = TRUE.\n" ) ) } } else { if (isFALSE(is_groupwise) && n_features > 30) { warning( - "Due to computation time, we strongly recommend enabling adaptive estimation with adaptive = TRUE", + "Due to computation time, we strongly recommend enabling iterative estimation with iterative = TRUE", " when n_features > 30.\n", ) } if (isTRUE(is_groupwise) && n_groups > 30) { warning( - "Due to computation time, we strongly recommend enabling adaptive estimation with adaptive = TRUE", + "Due to computation time, we strongly recommend enabling iterative estimation with iterative = TRUE", " when n_groups > 30.\n", ) } @@ -1030,43 +1030,43 @@ check_groups <- function(feature_names, group) { #' @keywords internal -set_adaptive_parameters <- function(internal, prev_iter_list = NULL) { - adaptive <- internal$parameters$adaptive +set_iterative_parameters <- function(internal, prev_iter_list = NULL) { + iterative <- internal$parameters$iterative - adaptive_args <- internal$parameters$adaptive_args + iterative_args <- internal$parameters$iterative_args - adaptive_args <- utils::modifyList(get_adaptive_args_default(internal), - adaptive_args, + iterative_args <- utils::modifyList(get_iterative_args_default(internal), + iterative_args, keep.null = TRUE ) - # Force setting the number of coalitions and iterations for non-adaptive method - if (isFALSE(adaptive)) { - adaptive_args$max_iter <- 1 - adaptive_args$initial_n_coalitions <- adaptive_args$max_n_coalitions + # Force setting the number of coalitions and iterations for non-iterative method + if (isFALSE(iterative)) { + iterative_args$max_iter <- 1 + iterative_args$initial_n_coalitions <- iterative_args$max_n_coalitions } - check_adaptive_args(adaptive_args) + check_iterative_args(iterative_args) # Translate any null input - adaptive_args <- trans_null_adaptive_args(adaptive_args) + iterative_args <- trans_null_iterative_args(iterative_args) - internal$parameters$adaptive_args <- adaptive_args + internal$parameters$iterative_args <- iterative_args if (!is.null(prev_iter_list)) { # Update internal with the iter_list from prev_shapr_object internal$iter_list <- prev_iter_list - # Conveniently allow running non-adaptive estimation one step further - if (isFALSE(internal$parameters$adaptive)) { - internal$parameters$adaptive_args$max_iter <- length(internal$iter_list) + 1 - internal$parameters$adaptive_args$reduction_factor_vec <- NULL + # Conveniently allow running non-iterative estimation one step further + if (isFALSE(internal$parameters$iterative)) { + internal$parameters$iterative_args$max_iter <- length(internal$iter_list) + 1 + internal$parameters$iterative_args$reduction_factor_vec <- NULL } - # Update convergence data with NEW adaptive arguments + # Update convergence data with NEW iterative arguments internal <- check_convergence(internal) - # Check for convergence based on last iter_list with new adaptive arguments + # Check for convergence based on last iter_list with new iterative arguments check_vs_prev_shapr_object(internal) # Prepare next iteration @@ -1074,20 +1074,20 @@ set_adaptive_parameters <- function(internal, prev_iter_list = NULL) { } else { internal$iter_list <- list() internal$iter_list[[1]] <- list( - n_coalitions = adaptive_args$initial_n_coalitions, - new_n_coalitions = adaptive_args$initial_n_coalitions, + n_coalitions = iterative_args$initial_n_coalitions, + new_n_coalitions = iterative_args$initial_n_coalitions, exact = internal$parameters$exact, - compute_sd = adaptive_args$compute_sd, - reduction_factor = adaptive_args$reduction_factor_vec[1], - n_batches = set_n_batches(adaptive_args$initial_n_coalitions, internal) + compute_sd = iterative_args$compute_sd, + reduction_factor = iterative_args$reduction_factor_vec[1], + n_batches = set_n_batches(iterative_args$initial_n_coalitions, internal) ) } return(internal) } -check_adaptive_args <- function(adaptive_args) { - list2env(adaptive_args, envir = environment()) +check_iterative_args <- function(iterative_args) { + list2env(iterative_args, envir = environment()) # initial_n_coalitions @@ -1096,7 +1096,7 @@ check_adaptive_args <- function(adaptive_args) { !is.na(initial_n_coalitions) && initial_n_coalitions <= max_n_coalitions && initial_n_coalitions > 2)) { - stop("`adaptive_args$initial_n_coalitions` must be a single integer between 2 and `max_n_coalitions`.") + stop("`iterative_args$initial_n_coalitions` must be a single integer between 2 and `max_n_coalitions`.") } # fixed_n_coalitions @@ -1107,7 +1107,7 @@ check_adaptive_args <- function(adaptive_args) { fixed_n_coalitions_per_iter <= max_n_coalitions && fixed_n_coalitions_per_iter > 0)) { stop( - "`adaptive_args$fixed_n_coalitions_per_iter` must be NULL or a single positive integer no larger than", + "`iterative_args$fixed_n_coalitions_per_iter` must be NULL or a single positive integer no larger than", "`max_n_coalitions`." ) } @@ -1118,7 +1118,7 @@ check_adaptive_args <- function(adaptive_args) { length(max_iter) == 1 && !is.na(max_iter) && max_iter > 0)) { - stop("`adaptive_args$max_iter` must be NULL, Inf or a single positive integer.") + stop("`iterative_args$max_iter` must be NULL, Inf or a single positive integer.") } # convergence_tolerance @@ -1126,7 +1126,7 @@ check_adaptive_args <- function(adaptive_args) { !(length(convergence_tolerance) == 1 && !is.na(convergence_tolerance) && convergence_tolerance >= 0)) { - stop("`adaptive_args$convergence_tolerance` must be NULL, 0, or a positive numeric.") + stop("`iterative_args$convergence_tolerance` must be NULL, 0, or a positive numeric.") } # reduction_factor_vec @@ -1134,7 +1134,7 @@ check_adaptive_args <- function(adaptive_args) { !(all(!is.na(reduction_factor_vec)) && all(reduction_factor_vec <= 1) && all(reduction_factor_vec >= 0))) { - stop("`adaptive_args$reduction_factor_vec` must be NULL or a vector or numerics between 0 and 1.") + stop("`iterative_args$reduction_factor_vec` must be NULL or a vector or numerics between 0 and 1.") } # n_boot_samps @@ -1142,13 +1142,13 @@ check_adaptive_args <- function(adaptive_args) { length(n_boot_samps) == 1 && !is.na(n_boot_samps) && n_boot_samps > 0)) { - stop("`adaptive_args$n_boot_samps` must be a single positive integer.") + stop("`iterative_args$n_boot_samps` must be a single positive integer.") } # compute_sd if (!(is.logical(compute_sd) && length(compute_sd) == 1)) { - stop("`adaptive_args$compute_sd` must be a single logical.") + stop("`iterative_args$compute_sd` must be a single logical.") } @@ -1158,7 +1158,7 @@ check_adaptive_args <- function(adaptive_args) { length(min_n_batches) == 1 && !is.na(min_n_batches) && min_n_batches > 0)) { - stop("`adaptive_args$min_n_batches` must be NULL or a single positive integer.") + stop("`iterative_args$min_n_batches` must be NULL or a single positive integer.") } # max_batch_size @@ -1167,41 +1167,41 @@ check_adaptive_args <- function(adaptive_args) { length(max_batch_size) == 1 && !is.na(max_batch_size) && max_batch_size > 0)) { - stop("`adaptive_args$max_batch_size` must be NULL, Inf or a single positive integer.") + stop("`iterative_args$max_batch_size` must be NULL, Inf or a single positive integer.") } # saving_path if (!(is.character(saving_path) && length(saving_path) == 1)) { - stop("`adaptive_args$saving_path` must be a single character.") + stop("`iterative_args$saving_path` must be a single character.") } # Check that the saving_path exists, and abort if not... if (!dir.exists(dirname(saving_path))) { stop( paste0( - "Directory ", dirname(saving_path), " in the adaptive_args$saving_path does not exists.\n", + "Directory ", dirname(saving_path), " in the iterative_args$saving_path does not exists.\n", "Please create the directory with `dir.create('", dirname(saving_path), "')` or use another directory." ) ) } } -trans_null_adaptive_args <- function(adaptive_args) { - list2env(adaptive_args, envir = environment()) +trans_null_iterative_args <- function(iterative_args) { + list2env(iterative_args, envir = environment()) # Translating NULL to always return n_batches = 1 (if just one approach) - adaptive_args$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) - adaptive_args$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) - adaptive_args$max_iter <- ifelse(is.null(max_iter), Inf, max_iter) + iterative_args$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) + iterative_args$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) + iterative_args$max_iter <- ifelse(is.null(max_iter), Inf, max_iter) - return(adaptive_args) + return(iterative_args) } set_n_batches <- function(n_coalitions, internal) { - min_n_batches <- internal$parameters$adaptive_args$min_n_batches - max_batch_size <- internal$parameters$adaptive_args$max_batch_size + min_n_batches <- internal$parameters$iterative_args$min_n_batches + max_batch_size <- internal$parameters$iterative_args$max_batch_size n_unique_approaches <- internal$parameters$n_unique_approaches @@ -1234,13 +1234,13 @@ check_vs_prev_shapr_object <- function(internal) { if (isTRUE(converged_sd)) { message0 <- c( message0, - "Convergence tolerance reached. Consider decreasing `adaptive_args$tolerance`.\n" + "Convergence tolerance reached. Consider decreasing `iterative_args$tolerance`.\n" ) } if (isTRUE(converged_max_iter)) { message0 <- c( message0, - "Maximum number of iterations reached. Consider increasing `adaptive_args$max_iter`.\n" + "Maximum number of iterations reached. Consider increasing `iterative_args$max_iter`.\n" ) } if (isTRUE(converged_max_n_coalitions)) { @@ -1254,11 +1254,11 @@ check_vs_prev_shapr_object <- function(internal) { } # Get functions ======================================================================================================== -#' Function to specify arguments of the adaptive estimation procedure +#' Function to specify arguments of the iterative estimation procedure #' -#' @details The functions sets default values for the adaptive estimation procedure, according to the function defaults. -#' If the argument `adaptive` of [shapr::explain()] is FALSE, it sets parameters corresponding to the use of a -#' non-adaptive estimation procedure +#' @details The functions sets default values for the iterative estimation procedure, according to the function defaults. +#' If the argument `iterative` of [shapr::explain()] is FALSE, it sets parameters corresponding to the use of a +#' non-iterative estimation procedure #' #' @param max_iter Integer. Maximum number of estimation iterations #' @param initial_n_coalitions Integer. Number of coalitions to use in the first estimation iteration. @@ -1282,13 +1282,13 @@ check_vs_prev_shapr_object <- function(internal) { #' Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller #' than the number of parallel workers. #' @param saving_path String. -#' The path to the directory where the results of the adaptive estimation procedure should be saved. +#' The path to the directory where the results of the iterative estimation procedure should be saved. #' Defaults to a temporary directory. #' @inheritParams default_doc_explain #' #' @export #' @author Martin Jullum -get_adaptive_args_default <- function(internal, +get_iterative_args_default <- function(internal, initial_n_coalitions = ceiling( min( 200, @@ -1304,16 +1304,16 @@ get_adaptive_args_default <- function(internal, convergence_tolerance = 0.02, reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)), n_boot_samps = 100, - compute_sd = isTRUE(internal$parameters$adaptive), + compute_sd = isTRUE(internal$parameters$iterative), max_batch_size = 10, min_n_batches = 10, saving_path = tempfile("shapr_obj_", fileext = ".rds")) { - adaptive <- internal$parameters$adaptive + iterative <- internal$parameters$iterative max_n_coalitions <- internal$parameters$max_n_coalitions exact <- internal$parameters$exact is_groupwise <- internal$parameters$is_groupwise - if (isTRUE(adaptive)) { + if (isTRUE(iterative)) { ret_list <- mget( c( "initial_n_coalitions", diff --git a/README.Rmd b/README.Rmd index aff32ad85..d645cceaa 100644 --- a/README.Rmd +++ b/README.Rmd @@ -84,7 +84,7 @@ Current methodological restrictions: Future releases will include: - Computational improvement of the AICc optimization approach, -- Adaptive selection of method to account for the feature dependence. +- iterative selection of method to account for the feature dependence. --> diff --git a/README.md b/README.md index eb18d9d99..7d22b1316 100644 --- a/README.md +++ b/README.md @@ -121,7 +121,7 @@ Current methodological restrictions: Future releases will include: - Computational improvement of the AICc optimization approach, -- Adaptive selection of method to account for the feature dependence. +- iterative selection of method to account for the feature dependence. --> Note the prediction outcome must be numeric. All approaches except diff --git a/inst/scripts/devel/devel_batch_testing.R b/inst/scripts/devel/devel_batch_testing.R index 1ad762b98..8fcb2c807 100644 --- a/inst/scripts/devel/devel_batch_testing.R +++ b/inst/scripts/devel/devel_batch_testing.R @@ -56,7 +56,7 @@ expl <- explain(model = model, prediction_zero = p0, n_batches = 100, n_samples = 1000, - adaptive = TRUE, + iterative = TRUE, print_iter_info = TRUE, print_shapleyres = TRUE) diff --git a/inst/scripts/devel/devel_convergence_branch.R b/inst/scripts/devel/devel_convergence_branch.R index dad9097fd..0e9677d4f 100644 --- a/inst/scripts/devel/devel_convergence_branch.R +++ b/inst/scripts/devel/devel_convergence_branch.R @@ -37,53 +37,53 @@ p0 <- mean(y_train) # Computing the actual Shapley values with kernelSHAP accounting for feature dependence using # the empirical (conditional) distribution approach with bandwidth parameter sigma = 0.1 (default) -explanation_adaptive <- explain( +explanation_iterative <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "gaussian", max_n_coalitions = 500, prediction_zero = p0, - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp shapley_reweighting = "on_N" ) -explanation_adaptive <- explain( +explanation_iterative <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "ctree", n_coalitions = 500, prediction_zero = p0, - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp shapley_reweighting = "on_N" ) -explanation_nonadaptive <- explain( +explanation_noniterative <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "gaussian", n_coalitions = 400, prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) -explanation_adaptive <- explain( +explanation_iterative <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "gaussian", n_coalitions = 500, prediction_zero = p0, - adaptive = TRUE, - adaptive_args = list(initial_n_coalitions=10,convergence_tolerance=0.0001), + iterative = TRUE, + iterative_args = list(initial_n_coalitions=10,convergence_tolerance=0.0001), print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp shapley_reweighting = "on_N" @@ -98,43 +98,43 @@ explanation_adaptive <- explain( -plot(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - explanation_adaptive$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1,Solar.R],type="l") -sd_full <- explanation_adaptive$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1][.N,Solar.R] -n_samples_full <- explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res[.N,n_current_samples] +plot(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, + explanation_iterative$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1,Solar.R],type="l") +sd_full <- explanation_iterative$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1][.N,Solar.R] +n_samples_full <- explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res[.N,n_current_samples] sd_full0 <- sd_full*sqrt(n_samples_full) -lines(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - sd_full0/sqrt(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples),type="l",col=2) +lines(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, + sd_full0/sqrt(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples),type="l",col=2) -plot(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$estimated_required_samples,type="l",ylim=c(0,4000),lwd=4) +plot(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, + explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$estimated_required_samples,type="l",ylim=c(0,4000),lwd=4) for(i in 1:20){ - lines(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res[[5+i]],type="l",col=1+i) + lines(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, + explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res[[5+i]],type="l",col=1+i) } -plot(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - explanation_adaptive$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1,Solar.R],type="l",ylim=c(0,2)) -sd_full <- explanation_adaptive$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1][.N,Solar.R] -n_samples_full <- explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res[.N,n_current_samples] +plot(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, + explanation_iterative$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1,Solar.R],type="l",ylim=c(0,2)) +sd_full <- explanation_iterative$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==1][.N,Solar.R] +n_samples_full <- explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res[.N,n_current_samples] sd_full0 <- sd_full*sqrt(n_samples_full) -lines(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - sd_full0/sqrt(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples),type="l",col=2,lwd=3) +lines(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, + sd_full0/sqrt(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples),type="l",col=2,lwd=3) for(i in 1:20){ - lines(explanation_adaptive$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - explanation_adaptive$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==i,Solar.R],type="l",col=1+i) + lines(explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, + explanation_iterative$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==i,Solar.R],type="l",col=1+i) } -lines(explanation_adaptive$internal$output$dt_iter_convergence_res$n_current_samples, - sd_full0/sqrt(explanation_adaptive$internal$output$dt_iter_convergence_res$n_current_samples),type="l",col=2) +lines(explanation_iterative$internal$output$dt_iter_convergence_res$n_current_samples, + sd_full0/sqrt(explanation_iterative$internal$output$dt_iter_convergence_res$n_current_samples),type="l",col=2) -plot(explanation_adaptive$internal$output$dt_iter_convergence_res$estimated_required_samples) +plot(explanation_iterative$internal$output$dt_iter_convergence_res$estimated_required_samples) explanation_regular <- explain( model = model, @@ -143,6 +143,6 @@ explanation_regular <- explain( approach = "gaussian", n_coalitions = NULL, prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) diff --git a/inst/scripts/devel/devel_verbose.R b/inst/scripts/devel/devel_verbose.R index 51e9acbd1..6f767ea17 100644 --- a/inst/scripts/devel/devel_verbose.R +++ b/inst/scripts/devel/devel_verbose.R @@ -6,13 +6,13 @@ ex <- explain( approach = "independence", prediction_zero = p0, max_n_coalitions = 30, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, reduction_factor_vec = rep(10^(-6), 10), max_iter = 8 ), - adaptive = TRUE,verbose=c("basic","progress") + iterative = TRUE,verbose=c("basic","progress") ) ex <- explain( @@ -23,7 +23,7 @@ ex <- explain( approach = "regression_separate", prediction_zero = p0, max_n_coalitions = 30, - adaptive = TRUE,verbose=c("vS_details") + iterative = TRUE,verbose=c("vS_details") ) ex <- explain( model = model_lm_numeric, @@ -32,7 +32,7 @@ ex <- explain( approach = "regression_separate", prediction_zero = p0, max_n_coalitions = 30, - adaptive = TRUE,verbose=c("basic","progress","vS_details"), + iterative = TRUE,verbose=c("basic","progress","vS_details"), regression.model = parsnip::decision_tree(tree_depth = hardhat::tune(), engine = "rpart", mode = "regression"), regression.tune_values = dials::grid_regular(dials::tree_depth(), levels = 4), regression.vfold_cv_para = list(v = 5) @@ -45,7 +45,7 @@ ex <- explain( approach = "regression_surrogate", prediction_zero = p0, max_n_coalitions = 30, - adaptive = FALSE,verbose=c("basic","vS_details"), + iterative = FALSE,verbose=c("basic","vS_details"), regression.model = parsnip::decision_tree(tree_depth = hardhat::tune(), engine = "rpart", mode = "regression"), regression.tune_values = dials::grid_regular(dials::tree_depth(), levels = 4), regression.vfold_cv_para = list(v = 5) @@ -64,7 +64,7 @@ ex <- explain( approach = "vaeac", prediction_zero = p0, max_n_coalitions = 30, - adaptive = FALSE,verbose=c("basic","progress","vS_details"), + iterative = FALSE,verbose=c("basic","progress","vS_details"), n_MC_samples = 100, vaeac.epochs = 3 ) @@ -77,7 +77,7 @@ ex2 <- explain( approach = "vaeac", prediction_zero = p0, max_n_coalitions = 30, - adaptive = FALSE,verbose=c("basic","progress","vS_details"), + iterative = FALSE,verbose=c("basic","progress","vS_details"), n_MC_samples = 100, vaeac.extra_parameters = list( vaeac.pretrained_vaeac_model = ex$internal$parameters$vaeac @@ -99,7 +99,7 @@ ex <- explain( approach = "regression_separate", prediction_zero = p0, max_n_coalitions = 30, - adaptive = FALSE,verbose=c("basic") + iterative = FALSE,verbose=c("basic") ) @@ -111,13 +111,13 @@ ex <- explain( approach = "empirical", prediction_zero = p0, max_n_coalitions = 30, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, reduction_factor_vec = rep(10^(-6), 10), max_iter = 8 ), - adaptive = TRUE,verbose=c("basic","convergence","shapley") + iterative = TRUE,verbose=c("basic","convergence","shapley") ) @@ -128,8 +128,8 @@ explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive = TRUE, - adaptive_args <- list(n_initial_) + iterative = TRUE, + iterative_args <- list(n_initial_) verbose = c("basic"), paired_shap_sampling = TRUE ) diff --git a/inst/scripts/devel/simtest_timing_to_Frida.R b/inst/scripts/devel/simtest_timing_to_Frida.R index 3f6177e57..0aafa2621 100644 --- a/inst/scripts/devel/simtest_timing_to_Frida.R +++ b/inst/scripts/devel/simtest_timing_to_Frida.R @@ -85,9 +85,9 @@ expl <- explain( n_coalitions = 100, Sigma=Sigma, mu=mu, - adaptive = TRUE, + iterative = TRUE, unique_sampling = FALSE, - adaptive_args = list(initial_n_coalitions = 50, + iterative_args = list(initial_n_coalitions = 50, fixed_n_coalitions_per_iter = 50, max_iter = 10, convergence_tolerance = 10^(-10), diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index 238b51a08..64c450793 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -6,13 +6,13 @@ aa = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.01, reduction_factor_vec = rep(10^(-5), 10), max_iter = 30 ), - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE,shapley_reweighting = "on_N" ) @@ -23,13 +23,13 @@ bb = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 30 ), - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE,shapley_reweighting = "on_N",prev_shapr_object = aa ) @@ -48,13 +48,13 @@ full = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 7 ), - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, shapley_reweighting = "on_N", @@ -68,13 +68,13 @@ first = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 4 ), - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, shapley_reweighting = "on_N", @@ -88,13 +88,13 @@ second = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 7 ), - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, shapley_reweighting = "on_N", @@ -111,18 +111,18 @@ second_path = explain( x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 5 ), - adaptive = TRUE, + iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, shapley_reweighting = "on_N", seed=NULL, - prev_shapr_object = first$internal$parameters$adaptive_args$saving_path + prev_shapr_object = first$internal$parameters$iterative_args$saving_path ) diff --git a/man/check_verbose.Rd b/man/check_verbose.Rd index 6300f3210..5af03b591 100644 --- a/man/check_verbose.Rd +++ b/man/check_verbose.Rd @@ -13,8 +13,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/man/explain.Rd b/man/explain.Rd index dea21808a..d618fcb31 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -12,7 +12,7 @@ explain( paired_shap_sampling = TRUE, prediction_zero, max_n_coalitions = NULL, - adaptive = NULL, + iterative = NULL, group = NULL, n_MC_samples = 1000, seed = 1, @@ -21,7 +21,7 @@ explain( get_model_specs = NULL, MSEv_uniform_comb_weights = TRUE, verbose = "basic", - adaptive_args = list(), + iterative_args = list(), shapley_reweighting = "on_all_cond", prev_shapr_object = NULL, ... @@ -58,16 +58,16 @@ Typically we set this value equal to the mean of the response variable in our tr such as the mean of the predictions in the training data are also reasonable.} \item{max_n_coalitions}{Integer. -The upper limit on the number of unique feature/group coalitions to use in the adaptive procedure -(if \code{adaptive = TRUE}). -If \code{adaptive = FALSE} it represents the number of feature/group coalitions to use directly. +The upper limit on the number of unique feature/group coalitions to use in the iterative procedure +(if \code{iterative = TRUE}). +If \code{iterative = FALSE} it represents the number of feature/group coalitions to use directly. The quantity refers to the number of unique feature coalitions if \code{group = NULL}, and group coalitions if \code{group != NULL}. \code{max_n_coalitions = NULL} corresponds to \code{max_n_coalitions=2^n_features}.} -\item{adaptive}{Logical or NULL +\item{iterative}{Logical or NULL If \code{NULL} (default), the argument is set to \code{TRUE} if there are more than 5 features/groups, and \code{FALSE} otherwise. -If eventually \code{TRUE}, the Shapley values are estimated adaptively in an iterative manner. +If eventually \code{TRUE}, the Shapley values are estimated iteratively in an iterative manner. This provides sufficiently accurate Shapley value estimates faster. First an initial number of coalitions is sampled, then bootsrapping is used to estimate the variance of the Shapley values. @@ -75,7 +75,7 @@ A convergence criterion is used to determine if the variances of the Shapley val If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through \code{adaptive_args}.} +Specifics related to the iterative process and convergence criterion are set through \code{iterative_args}.} \item{group}{List. If \code{NULL} regular feature wise Shapley values are computed. @@ -133,8 +133,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. @@ -144,9 +144,9 @@ Note that any combination of four strings can be used. E.g. \code{verbose = c("basic", "vS_details")} will display basic information + details about the vS estimation process. }} -\item{adaptive_args}{Named list. -Specifices the arguments for the adaptive procedure. -See \code{\link[=get_adaptive_args_default]{get_adaptive_args_default()}} for description of the arguments and their default values.} +\item{iterative_args}{Named list. +Specifices the arguments for the iterative procedure. +See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} \item{shapley_reweighting}{String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing @@ -165,7 +165,7 @@ This method is preferred as it has performed the best in simulation studies.} If an object of class \code{shapr} is provided or string with a path to where intermediate results are strored, then the function will use the previous object to continue the computation. This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore -want to continue the adaptive estimation. See the vignette for examples.} +want to continue the iterative estimation. See the vignette for examples.} \item{...}{ Arguments passed on to \code{\link[=setup_approach.empirical]{setup_approach.empirical}}, \code{\link[=setup_approach.independence]{setup_approach.independence}}, \code{\link[=setup_approach.gaussian]{setup_approach.gaussian}}, \code{\link[=setup_approach.copula]{setup_approach.copula}}, \code{\link[=setup_approach.ctree]{setup_approach.ctree}}, \code{\link[=setup_approach.vaeac]{setup_approach.vaeac}}, \code{\link[=setup_approach.categorical]{setup_approach.categorical}}, \code{\link[=setup_approach.regression_separate]{setup_approach.regression_separate}}, \code{\link[=setup_approach.regression_surrogate]{setup_approach.regression_surrogate}}, \code{\link[=setup_approach.timeseries]{setup_approach.timeseries}} @@ -297,7 +297,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE}.} +Only present when \code{iterative = TRUE} and \code{iterative_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -307,8 +307,8 @@ Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE \code{init_time} and \code{end_time} gives the time stamps for the start and end of the computation. \code{total_time_secs} gives the total time in seconds for the complete execution of \code{explain()}. \code{main_timing_secs} gives the time in seconds for the main computations. -\code{iter_timing_secs} gives for each iteration of the adaptive estimation, the time spent on the different parts -adaptive estimation routine.} +\code{iter_timing_secs} gives for each iteration of the iterative estimation, the time spent on the different parts +iterative estimation routine.} } } \description{ @@ -324,10 +324,10 @@ It is also possible to combine the different approaches, see the vignettes for m The package allows for parallelized computation with progress updates through the tightly connected \link[future:future]{future::future} and \link[progressr:progressr]{progressr::progressr} packages. See the examples below. -For adaptive estimation (\code{adaptive=TRUE}), intermediate results may also be printed to the console +For iterative estimation (\code{iterative=TRUE}), intermediate results may also be printed to the console (according to the \code{verbose} argument). Moreover, the intermediate results are written to disk. -This combined with adaptive estimation with (optional) intermediate results printed to the console (and temporary +This combined with iterative estimation with (optional) intermediate results printed to the console (and temporary written to disk, and batch computing of the v(S) values, enables fast and accurate estimation of the Shapley values in a memory friendly manner. } @@ -463,19 +463,19 @@ explain_surrogate_lm <- explain( regression.model = parsnip::linear_reg() ) -## Adaptive estimation +## iterative estimation # For illustration purposes only. By default not used for such small dimensions as here # Gaussian approach -explain_adaptive <- explain( +explain_iterative <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "gaussian", prediction_zero = p, n_MC_samples = 1e2, - adaptive = TRUE, - adaptive_args = list(initial_n_coalitions = 10) + iterative = TRUE, + iterative_args = list(initial_n_coalitions = 10) ) } diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index 24ffe895e..0fde90c39 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -75,9 +75,9 @@ Typically we set this value equal to the mean of the response variable in our tr such as the mean of the predictions in the training data are also reasonable.} \item{max_n_coalitions}{Integer. -The upper limit on the number of unique feature/group coalitions to use in the adaptive procedure -(if \code{adaptive = TRUE}). -If \code{adaptive = FALSE} it represents the number of feature/group coalitions to use directly. +The upper limit on the number of unique feature/group coalitions to use in the iterative procedure +(if \code{iterative = TRUE}). +If \code{iterative = FALSE} it represents the number of feature/group coalitions to use directly. The quantity refers to the number of unique feature coalitions if \code{group = NULL}, and group coalitions if \code{group != NULL}. \code{max_n_coalitions = NULL} corresponds to \code{max_n_coalitions=2^n_features}.} @@ -136,8 +136,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. @@ -239,7 +239,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE}.} +Only present when \code{iterative = TRUE} and \code{iterative_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -249,8 +249,8 @@ Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE \code{init_time} and \code{end_time} gives the time stamps for the start and end of the computation. \code{total_time_secs} gives the total time in seconds for the complete execution of \code{explain()}. \code{main_timing_secs} gives the time in seconds for the main computations. -\code{iter_timing_secs} gives for each iteration of the adaptive estimation, the time spent on the different parts -adaptive estimation routine.} +\code{iter_timing_secs} gives for each iteration of the iterative estimation, the time spent on the different parts +iterative estimation routine.} } } \description{ diff --git a/man/finalize_explanation_forecast.Rd b/man/finalize_explanation_forecast.Rd index 45e452bc0..d70fa3ca4 100644 --- a/man/finalize_explanation_forecast.Rd +++ b/man/finalize_explanation_forecast.Rd @@ -24,7 +24,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE}.} +Only present when \code{iterative = TRUE} and \code{iterative_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -34,8 +34,8 @@ Only present when \code{adaptive = TRUE} and \code{adaptive_args$compute_sd=TRUE \code{init_time} and \code{end_time} gives the time stamps for the start and end of the computation. \code{total_time_secs} gives the total time in seconds for the complete execution of \code{explain()}. \code{main_timing_secs} gives the time in seconds for the main computations. -\code{iter_timing_secs} gives for each iteration of the adaptive estimation, the time spent on the different parts -adaptive estimation routine.} +\code{iter_timing_secs} gives for each iteration of the iterative estimation, the time spent on the different parts +iterative estimation routine.} } } \description{ @@ -51,10 +51,10 @@ It is also possible to combine the different approaches, see the vignettes for m The package allows for parallelized computation with progress updates through the tightly connected \link[future:future]{future::future} and \link[progressr:progressr]{progressr::progressr} packages. See the examples below. -For adaptive estimation (\code{adaptive=TRUE}), intermediate results may also be printed to the console +For iterative estimation (\code{iterative=TRUE}), intermediate results may also be printed to the console (according to the \code{verbose} argument). Moreover, the intermediate results are written to disk. -This combined with adaptive estimation with (optional) intermediate results printed to the console (and temporary +This combined with iterative estimation with (optional) intermediate results printed to the console (and temporary written to disk, and batch computing of the v(S) values, enables fast and accurate estimation of the Shapley values in a memory friendly manner. } @@ -190,19 +190,19 @@ explain_surrogate_lm <- explain( regression.model = parsnip::linear_reg() ) -## Adaptive estimation +## iterative estimation # For illustration purposes only. By default not used for such small dimensions as here # Gaussian approach -explain_adaptive <- explain( +explain_iterative <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "gaussian", prediction_zero = p, n_MC_samples = 1e2, - adaptive = TRUE, - adaptive_args = list(initial_n_coalitions = 10) + iterative = TRUE, + iterative_args = list(initial_n_coalitions = 10) ) } diff --git a/man/get_adaptive_arguments_default.Rd b/man/get_adaptive_arguments_default.Rd index a20401ac3..a60d0f5a7 100644 --- a/man/get_adaptive_arguments_default.Rd +++ b/man/get_adaptive_arguments_default.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/setup.R -\name{get_adaptive_args_default} -\alias{get_adaptive_args_default} -\title{Function to specify arguments of the adaptive estimation procedure} +\name{get_iterative_args_default} +\alias{get_iterative_args_default} +\title{Function to specify arguments of the iterative estimation procedure} \usage{ -get_adaptive_args_default( +get_iterative_args_default( internal, initial_n_coalitions = ceiling(min(200, max(5, internal$parameters$n_features, (2^internal$parameters$n_features)/10))), @@ -13,7 +13,7 @@ get_adaptive_args_default( convergence_tolerance = 0.02, reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)), n_boot_samps = 100, - compute_sd = isTRUE(internal$parameters$adaptive), + compute_sd = isTRUE(internal$parameters$iterative), max_batch_size = 10, min_n_batches = 10, saving_path = tempfile("shapr_obj_", fileext = ".rds") @@ -54,16 +54,16 @@ Larger numbers gives more frequent progress updates. If parallelization is appli than the number of parallel workers.} \item{saving_path}{String. -The path to the directory where the results of the adaptive estimation procedure should be saved. +The path to the directory where the results of the iterative estimation procedure should be saved. Defaults to a temporary directory.} } \description{ -Function to specify arguments of the adaptive estimation procedure +Function to specify arguments of the iterative estimation procedure } \details{ -The functions sets default values for the adaptive estimation procedure, according to the function defaults. -If the argument \code{adaptive} of \code{\link[=explain]{explain()}} is FALSE, it sets parameters corresponding to the use of a -non-adaptive estimation procedure +The functions sets default values for the iterative estimation procedure, according to the function defaults. +If the argument \code{iterative} of \code{\link[=explain]{explain()}} is FALSE, it sets parameters corresponding to the use of a +non-iterative estimation procedure } \author{ Martin Jullum diff --git a/man/prepare_next_iteration.Rd b/man/prepare_next_iteration.Rd index 9a23bd2d3..996a7330d 100644 --- a/man/prepare_next_iteration.Rd +++ b/man/prepare_next_iteration.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/prepare_next_iteration.R \name{prepare_next_iteration} \alias{prepare_next_iteration} -\title{Prepares the next iteration of the adaptive sampling algorithm} +\title{Prepares the next iteration of the iterative sampling algorithm} \usage{ prepare_next_iteration(internal) } @@ -11,6 +11,6 @@ prepare_next_iteration(internal) Not used directly, but passed through from \code{\link[=explain]{explain()}}.} } \description{ -Prepares the next iteration of the adaptive sampling algorithm +Prepares the next iteration of the iterative sampling algorithm } \keyword{internal} diff --git a/man/regression.train_model.Rd b/man/regression.train_model.Rd index 60f227175..6d5c0807e 100644 --- a/man/regression.train_model.Rd +++ b/man/regression.train_model.Rd @@ -32,8 +32,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/man/setup.Rd b/man/setup.Rd index 4574dcefe..9f9170d1b 100644 --- a/man/setup.Rd +++ b/man/setup.Rd @@ -28,8 +28,8 @@ setup( explain_xreg_lags = NULL, group_lags = NULL, verbose, - adaptive = NULL, - adaptive_args = list(), + iterative = NULL, + iterative_args = list(), shapley_reweighting = "none", is_python = FALSE, testing = FALSE, @@ -65,9 +65,9 @@ such as the mean of the predictions in the training data are also reasonable.} \item{output_size}{TODO: Document} \item{max_n_coalitions}{Integer. -The upper limit on the number of unique feature/group coalitions to use in the adaptive procedure -(if \code{adaptive = TRUE}). -If \code{adaptive = FALSE} it represents the number of feature/group coalitions to use directly. +The upper limit on the number of unique feature/group coalitions to use in the iterative procedure +(if \code{iterative = TRUE}). +If \code{iterative = FALSE} it represents the number of feature/group coalitions to use directly. The quantity refers to the number of unique feature coalitions if \code{group = NULL}, and group coalitions if \code{group != NULL}. \code{max_n_coalitions = NULL} corresponds to \code{max_n_coalitions=2^n_features}.} @@ -151,8 +151,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. @@ -162,9 +162,9 @@ Note that any combination of four strings can be used. E.g. \code{verbose = c("basic", "vS_details")} will display basic information + details about the vS estimation process. }} -\item{adaptive}{Logical or NULL +\item{iterative}{Logical or NULL If \code{NULL} (default), the argument is set to \code{TRUE} if there are more than 5 features/groups, and \code{FALSE} otherwise. -If eventually \code{TRUE}, the Shapley values are estimated adaptively in an iterative manner. +If eventually \code{TRUE}, the Shapley values are estimated iteratively in an iterative manner. This provides sufficiently accurate Shapley value estimates faster. First an initial number of coalitions is sampled, then bootsrapping is used to estimate the variance of the Shapley values. @@ -172,11 +172,11 @@ A convergence criterion is used to determine if the variances of the Shapley val If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through \code{adaptive_args}.} +Specifics related to the iterative process and convergence criterion are set through \code{iterative_args}.} -\item{adaptive_args}{Named list. -Specifices the arguments for the adaptive procedure. -See \code{\link[=get_adaptive_args_default]{get_adaptive_args_default()}} for description of the arguments and their default values.} +\item{iterative_args}{Named list. +Specifices the arguments for the iterative procedure. +See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} \item{shapley_reweighting}{String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing @@ -207,7 +207,7 @@ Used to calculate the time it took to run the full \code{explain} call.} If an object of class \code{shapr} is provided or string with a path to where intermediate results are strored, then the function will use the previous object to continue the computation. This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore -want to continue the adaptive estimation. See the vignette for examples.} +want to continue the iterative estimation. See the vignette for examples.} \item{...}{Further arguments passed to specific approaches} } diff --git a/man/vaeac_check_parameters.Rd b/man/vaeac_check_parameters.Rd index d49550d7c..70b539ee6 100644 --- a/man/vaeac_check_parameters.Rd +++ b/man/vaeac_check_parameters.Rd @@ -169,8 +169,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/man/vaeac_get_extra_para_default.Rd b/man/vaeac_get_extra_para_default.Rd index fb10e1d99..52f385750 100644 --- a/man/vaeac_get_extra_para_default.Rd +++ b/man/vaeac_get_extra_para_default.Rd @@ -80,7 +80,7 @@ during the training of the vaeac model. Used in \code{\link[torch:dataloader]{to each batch when generating the Monte Carlo samples. If \code{NULL}, then the function generates the Monte Carlo samples for the provided coalitions and all explicands sent to \code{\link[=explain]{explain()}} at the time. The number of coalitions are determined by the \code{n_batches} used by \code{\link[=explain]{explain()}}. We recommend to tweak -\code{adaptive_args$max_batch_size} and \code{adaptive_args$min_n_batches} +\code{iterative_args$max_batch_size} and \code{iterative_args$min_n_batches} rather than \code{vaeac.batch_size_sampling}. Larger batch sizes are often much faster provided sufficient memory.} \item{vaeac.running_avg_n_values}{Positive integer (default is \code{5}). The number of previous IWAE values to include diff --git a/man/vaeac_get_mask_generator_name.Rd b/man/vaeac_get_mask_generator_name.Rd index 4fdfeae2b..00601f6d7 100644 --- a/man/vaeac_get_mask_generator_name.Rd +++ b/man/vaeac_get_mask_generator_name.Rd @@ -33,8 +33,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/man/vaeac_impute_missing_entries.Rd b/man/vaeac_impute_missing_entries.Rd index 2cb567060..e1f36ce83 100644 --- a/man/vaeac_impute_missing_entries.Rd +++ b/man/vaeac_impute_missing_entries.Rd @@ -37,8 +37,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/man/vaeac_train_model.Rd b/man/vaeac_train_model.Rd index cfa8e88b6..4d1314f5e 100644 --- a/man/vaeac_train_model.Rd +++ b/man/vaeac_train_model.Rd @@ -169,8 +169,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/man/vaeac_train_model_auxiliary.Rd b/man/vaeac_train_model_auxiliary.Rd index 889b5d5d5..65f1fb617 100644 --- a/man/vaeac_train_model_auxiliary.Rd +++ b/man/vaeac_train_model_auxiliary.Rd @@ -49,8 +49,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/man/vaeac_train_model_continue.Rd b/man/vaeac_train_model_continue.Rd index 645025ea0..552c561c7 100644 --- a/man/vaeac_train_model_continue.Rd +++ b/man/vaeac_train_model_continue.Rd @@ -32,8 +32,8 @@ Specifies the verbosity (printout detail level) through one or more of strings \ \code{"basic"} (default) displays basic information about the computation which is being performed. \verb{"progress} displays information about where in the calculation process the function currently is. #' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{adaptive = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{adaptive = TRUE}) +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) \itemize{ \item the final estimates. \code{"vS_details"} displays information about the v_S estimates. diff --git a/tests/testthat/_snaps/adaptive-output.md b/tests/testthat/_snaps/adaptive-output.md index 7e483da59..86175b4d2 100644 --- a/tests/testthat/_snaps/adaptive-output.md +++ b/tests/testthat/_snaps/adaptive-output.md @@ -9,11 +9,11 @@ * Model class: * Approach: independence - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 32 coalitions, 5 new. @@ -621,11 +621,11 @@ * Model class: * Approach: gaussian - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 32 coalitions, 5 new. @@ -659,11 +659,11 @@ * Model class: * Approach: gaussian - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 32 coalitions, 5 new. @@ -725,11 +725,11 @@ * Model class: * Approach: gaussian - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 32 coalitions, 5 new. @@ -859,11 +859,11 @@ * Model class: * Approach: gaussian - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 32 coalitions, 5 new. diff --git a/tests/testthat/_snaps/adaptive-setup.md b/tests/testthat/_snaps/adaptive-setup.md index ab3164e2b..23bde8383 100644 --- a/tests/testthat/_snaps/adaptive-setup.md +++ b/tests/testthat/_snaps/adaptive-setup.md @@ -4,15 +4,15 @@ n_batches_non_numeric_1 <- "bla" explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_numeric_1)) + iterative_args = list(min_n_batches = n_batches_non_numeric_1)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_args()`: - ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_iterative_args()`: + ! `iterative_args$min_n_batches` must be NULL or a single positive integer. --- @@ -20,15 +20,15 @@ n_batches_non_numeric_2 <- TRUE explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_numeric_2)) + iterative_args = list(min_n_batches = n_batches_non_numeric_2)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_args()`: - ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_iterative_args()`: + ! `iterative_args$min_n_batches` must be NULL or a single positive integer. --- @@ -36,15 +36,15 @@ n_batches_non_integer <- 10.5 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_integer)) + iterative_args = list(min_n_batches = n_batches_non_integer)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_args()`: - ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_iterative_args()`: + ! `iterative_args$min_n_batches` must be NULL or a single positive integer. --- @@ -52,15 +52,15 @@ n_batches_too_long <- c(1, 2) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_too_long)) + iterative_args = list(min_n_batches = n_batches_too_long)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_args()`: - ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_iterative_args()`: + ! `iterative_args$min_n_batches` must be NULL or a single positive integer. --- @@ -68,15 +68,15 @@ n_batches_is_NA <- as.numeric(NA) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_is_NA)) + iterative_args = list(min_n_batches = n_batches_is_NA)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_args()`: - ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_iterative_args()`: + ! `iterative_args$min_n_batches` must be NULL or a single positive integer. --- @@ -84,13 +84,13 @@ n_batches_non_positive <- 0 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_positive)) + iterative_args = list(min_n_batches = n_batches_non_positive)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_adaptive_args()`: - ! `adaptive_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_iterative_args()`: + ! `iterative_args$min_n_batches` must be NULL or a single positive integer. diff --git a/tests/testthat/_snaps/regression-output.md b/tests/testthat/_snaps/regression-output.md index f1ced1801..4f1db6b3f 100644 --- a/tests/testthat/_snaps/regression-output.md +++ b/tests/testthat/_snaps/regression-output.md @@ -1,4 +1,4 @@ -# output_lm_numeric_lm_separate_adaptive +# output_lm_numeric_lm_separate_iterative Code (out <- code) @@ -9,11 +9,11 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 32 coalitions, 5 new. @@ -47,7 +47,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -68,7 +68,7 @@ Message * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -93,7 +93,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -118,7 +118,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -143,7 +143,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -168,7 +168,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -193,7 +193,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -218,7 +218,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -232,7 +232,7 @@ 2: 2 42.44 8.183 -1.463 -16.499 3.63 -9.233 3: 3 42.44 3.364 -14.946 0.401 -11.32 11.905 -# output_lm_numeric_lm_surrogate_adaptive +# output_lm_numeric_lm_surrogate_iterative Code (out <- code) @@ -242,12 +242,12 @@ and is therefore set to 2^n_features = 32. Condition - Warning in `check_and_set_adaptive()`: - Adaptive estimation of Shapley values are not supported for approach = regression_surrogate. Setting adaptive = FALSE. + Warning in `check_and_set_iterative()`: + iterative estimation of Shapley values are not supported for approach = regression_surrogate. Setting iterative = FALSE. Message * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -272,7 +272,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -293,7 +293,7 @@ Message * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -314,7 +314,7 @@ Message * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -339,7 +339,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -364,7 +364,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -389,7 +389,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -414,7 +414,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 diff --git a/tests/testthat/_snaps/regression-setup.md b/tests/testthat/_snaps/regression-setup.md index 224bafd8e..d0881e994 100644 --- a/tests/testthat/_snaps/regression-setup.md +++ b/tests/testthat/_snaps/regression-setup.md @@ -3,7 +3,7 @@ Code explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, prediction_zero = p0, approach = c( - "regression_surrogate", "gaussian", "independence", "empirical"), adaptive = FALSE) + "regression_surrogate", "gaussian", "independence", "empirical"), iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -18,7 +18,7 @@ Code explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, prediction_zero = p0, approach = c( - "regression_separate", "gaussian", "independence", "empirical"), adaptive = FALSE) + "regression_separate", "gaussian", "independence", "empirical"), iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -130,7 +130,7 @@ Code explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, prediction_zero = p0, approach = "regression_surrogate", - regression.tune_values = data.frame(tree_depth = c(1, 2, 3)), adaptive = FALSE) + regression.tune_values = data.frame(tree_depth = c(1, 2, 3)), iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -138,7 +138,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -272,7 +272,7 @@ x_train = x_train_numeric, prediction_zero = p0, approach = "regression_surrogate", regression.recipe_func = function(x) { return(2) - }, adaptive = FALSE) + }, iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -280,7 +280,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -296,7 +296,7 @@ Code explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, prediction_zero = p0, approach = "regression_surrogate", - regression.surrogate_n_comb = 2^ncol(x_explain_numeric) - 1, adaptive = FALSE) + regression.surrogate_n_comb = 2^ncol(x_explain_numeric) - 1, iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -304,7 +304,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -320,7 +320,7 @@ Code explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, prediction_zero = p0, approach = "regression_surrogate", - regression.surrogate_n_comb = 0, adaptive = FALSE) + regression.surrogate_n_comb = 0, iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -328,7 +328,7 @@ * Model class: * Approach: regression_surrogate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 diff --git a/tests/testthat/_snaps/regular-output.md b/tests/testthat/_snaps/regular-output.md index f2f7a2652..2f03e489a 100644 --- a/tests/testthat/_snaps/regular-output.md +++ b/tests/testthat/_snaps/regular-output.md @@ -9,7 +9,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -34,7 +34,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -59,7 +59,7 @@ * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -80,7 +80,7 @@ Message * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -110,7 +110,7 @@ Message * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -161,7 +161,7 @@ Message * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -182,7 +182,7 @@ Message * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -207,7 +207,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -232,7 +232,7 @@ * Model class: * Approach: copula - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -257,7 +257,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -282,7 +282,7 @@ * Model class: * Approach: vaeac - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -307,7 +307,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -332,7 +332,7 @@ * Model class: * Approach: vaeac - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -357,7 +357,7 @@ * Model class: * Approach: categorical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -382,7 +382,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -407,7 +407,7 @@ * Model class: * Approach: timeseries - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of group-wise Shapley values: 4 * Number of observations to explain: 2 @@ -431,7 +431,7 @@ * Model class: * Approach: gaussian, empirical, ctree, and independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -456,7 +456,7 @@ * Model class: * Approach: ctree, copula, independence, and copula - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -481,7 +481,7 @@ * Model class: * Approach: independence, empirical, gaussian, and empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -506,7 +506,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -531,7 +531,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -556,7 +556,7 @@ * Model class: * Approach: vaeac - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -581,7 +581,7 @@ * Model class: * Approach: ctree, independence, ctree, and independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -606,7 +606,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -634,7 +634,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -662,7 +662,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -687,7 +687,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 2 * Number of observations to explain: 3 @@ -712,7 +712,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -737,7 +737,7 @@ * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -762,7 +762,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 diff --git a/tests/testthat/_snaps/regular-setup.md b/tests/testthat/_snaps/regular-setup.md index 91cfdcb1e..13e3baac1 100644 --- a/tests/testthat/_snaps/regular-setup.md +++ b/tests/testthat/_snaps/regular-setup.md @@ -34,7 +34,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -67,7 +67,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -100,7 +100,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -134,7 +134,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -396,7 +396,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -425,7 +425,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 @@ -910,7 +910,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -937,7 +937,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 @@ -957,7 +957,7 @@ explanation_exact <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, n_MC_samples = 2, seed = 123, max_n_coalitions = NULL, - adaptive = FALSE) + iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -965,7 +965,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -978,13 +978,13 @@ Code explanation_equal <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, adaptive_args = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, iterative_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), - adaptive = FALSE) + iterative = FALSE) Message * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -997,9 +997,9 @@ Code explanation_larger <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, adaptive_args = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, iterative_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, - adaptive = FALSE) + iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, @@ -1007,7 +1007,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 diff --git a/tests/testthat/test-adaptive-output.R b/tests/testthat/test-adaptive-output.R index a8341f2d4..da63d3956 100644 --- a/tests/testthat/test-adaptive-output.R +++ b/tests/testthat/test-adaptive-output.R @@ -9,7 +9,7 @@ test_that("output_lm_numeric_independence_reach_exact", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive = TRUE, + iterative = TRUE, verbose = c("basic", "convergence", "shapley"), paired_shap_sampling = TRUE ), @@ -26,11 +26,11 @@ test_that("output_lm_numeric_independence_converges_tol", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.1 ), - adaptive = TRUE, + iterative = TRUE, verbose = c("convergence", "shapley") ), "output_lm_numeric_independence_converges_tol" @@ -46,13 +46,13 @@ test_that("output_lm_numeric_independence_converges_maxit", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 8 ), - adaptive = TRUE, + iterative = TRUE, verbose = c("convergence", "shapley") ), "output_lm_numeric_independence_converges_maxit" @@ -69,7 +69,7 @@ test_that("output_lm_numeric_indep_conv_max_n_coalitions", { approach = "independence", prediction_zero = p0, max_n_coalitions = 20, - adaptive = TRUE, + iterative = TRUE, verbose = c("convergence", "shapley") ), "output_lm_numeric_indep_conv_max_n_coalitions" @@ -93,11 +93,11 @@ test_that("output_lm_numeric_gaussian_group_converges_tol", { approach = "gaussian", group = groups, prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 5, convergence_tolerance = 0.1 ), - adaptive = TRUE, + iterative = TRUE, verbose = c("convergence", "shapley") ), "output_lm_numeric_gaussian_group_converges_tol" @@ -113,11 +113,11 @@ test_that("output_lm_numeric_independence_converges_tol_paired", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.1 ), - adaptive = TRUE, + iterative = TRUE, verbose = c("convergence", "shapley"), paired_shap_sampling = TRUE ), @@ -137,13 +137,13 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 8 ), - adaptive = TRUE, + iterative = TRUE, seed = NULL, verbose = NULL ) @@ -159,13 +159,13 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 5 ), - adaptive = TRUE, + iterative = TRUE, seed = NULL, verbose = NULL ) @@ -180,13 +180,13 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 8 ), - adaptive = TRUE, + iterative = TRUE, verbose = NULL, prev_shapr_object = e_init_object, seed = NULL, @@ -208,13 +208,13 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 5 ), - adaptive = TRUE, + iterative = TRUE, seed = NULL, verbose = NULL ) @@ -229,15 +229,15 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { approach = "independence", prediction_zero = p0, paired_shap_sampling = FALSE, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, reduction_factor_vec = rep(10^(-5), 10), max_iter = 8 ), - adaptive = TRUE, + iterative = TRUE, verbose = NULL, - prev_shapr_object = e_init_path$internal$parameters$adaptive_args$saving_path, + prev_shapr_object = e_init_path$internal$parameters$iterative_args$saving_path, seed = NULL ), "output_lm_numeric_independence_cont_est_path" @@ -256,7 +256,7 @@ test_that("output_verbose_1", { x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, - adaptive = TRUE, + iterative = TRUE, verbose = c("basic") ), "output_verbose_1" @@ -272,7 +272,7 @@ test_that("output_verbose_1_3", { x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, - adaptive = TRUE, + iterative = TRUE, verbose = c("basic", "convergence") ), "output_verbose_1_3" @@ -288,7 +288,7 @@ test_that("output_verbose_1_3_4", { x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, - adaptive = TRUE, + iterative = TRUE, verbose = c("basic", "convergence", "shapley") ), "output_verbose_1_3_4" @@ -304,7 +304,7 @@ test_that("output_verbose_1_3_4_5", { x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, - adaptive = TRUE, + iterative = TRUE, verbose = c("basic", "convergence", "shapley", "vS_details") ), "output_verbose_1_3_4_5" diff --git a/tests/testthat/test-adaptive-setup.R b/tests/testthat/test-adaptive-setup.R index bfe5a8127..ddd9b88bd 100644 --- a/tests/testthat/test-adaptive-setup.R +++ b/tests/testthat/test-adaptive-setup.R @@ -1,4 +1,4 @@ -test_that("adaptive_args are respected", { +test_that("iterative_args are respected", { ex <- explain( testing = TRUE, model = model_lm_numeric, @@ -7,13 +7,13 @@ test_that("adaptive_args are respected", { approach = "independence", prediction_zero = p0, max_n_coalitions = 30, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, reduction_factor_vec = rep(10^(-6), 10), max_iter = 8 ), - adaptive = TRUE + iterative = TRUE ) # Check that initial_n_coalitions is respected @@ -25,7 +25,7 @@ test_that("adaptive_args are respected", { }) -test_that("adaptive feature wise and groupwise computations identical", { +test_that("iterative feature wise and groupwise computations identical", { groups <- list( Solar.R = "Solar.R", Wind = "Wind", @@ -41,11 +41,11 @@ test_that("adaptive feature wise and groupwise computations identical", { x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 5, convergence_tolerance = 0.1 ), - adaptive = TRUE + iterative = TRUE ) @@ -57,11 +57,11 @@ test_that("adaptive feature wise and groupwise computations identical", { approach = "gaussian", group = groups, prediction_zero = p0, - adaptive_args = list( + iterative_args = list( initial_n_coalitions = 5, convergence_tolerance = 0.1 ), - adaptive = TRUE + iterative = TRUE ) @@ -83,7 +83,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_numeric_1) + iterative_args = list(min_n_batches = n_batches_non_numeric_1) ) }, error = TRUE @@ -100,7 +100,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_numeric_2) + iterative_args = list(min_n_batches = n_batches_non_numeric_2) ) }, error = TRUE @@ -117,7 +117,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_integer) + iterative_args = list(min_n_batches = n_batches_non_integer) ) }, error = TRUE @@ -134,7 +134,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_too_long) + iterative_args = list(min_n_batches = n_batches_too_long) ) }, error = TRUE @@ -151,7 +151,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_is_NA) + iterative_args = list(min_n_batches = n_batches_is_NA) ) }, error = TRUE @@ -168,7 +168,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive_args = list(min_n_batches = n_batches_non_positive) + iterative_args = list(min_n_batches = n_batches_non_positive) ) }, error = TRUE @@ -184,7 +184,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - adaptive_args = list(min_n_batches = 5, max_batch_size = 10) + iterative_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.empirical_n_batches_10 <- explain( @@ -194,7 +194,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - adaptive_args = list(min_n_batches = 10, max_batch_size = 10) + iterative_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) @@ -216,7 +216,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - adaptive_args = list(min_n_batches = 5, max_batch_size = 10) + iterative_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.ctree_n_batches_10 <- explain( @@ -226,7 +226,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - adaptive_args = list(min_n_batches = 10, max_batch_size = 10) + iterative_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) diff --git a/tests/testthat/test-regression-output.R b/tests/testthat/test-regression-output.R index d2418abb0..d730fdb60 100644 --- a/tests/testthat/test-regression-output.R +++ b/tests/testthat/test-regression-output.R @@ -1,5 +1,5 @@ # Separate regression ================================================================================================== -test_that("output_lm_numeric_lm_separate_adaptive", { +test_that("output_lm_numeric_lm_separate_iterative", { expect_snapshot_rds( explain( testing = TRUE, @@ -9,9 +9,9 @@ test_that("output_lm_numeric_lm_separate_adaptive", { approach = "regression_separate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = TRUE + iterative = TRUE ), - "output_lm_numeric_lm_separate_adaptive" + "output_lm_numeric_lm_separate_iterative" ) }) @@ -26,7 +26,7 @@ test_that("output_lm_numeric_lm_separate", { approach = "regression_separate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_lm_separate" ) @@ -43,7 +43,7 @@ test_that("output_lm_numeric_lm_separate_n_comb", { prediction_zero = p0, max_n_coalitions = 10, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_lm_separate_n_comb" ) @@ -59,7 +59,7 @@ test_that("output_lm_categorical_lm_separate", { approach = "regression_separate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_categorical_lm_separate" ) @@ -75,7 +75,7 @@ test_that("output_lm_mixed_lm_separate", { approach = "regression_separate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_lm_separate" ) @@ -94,7 +94,7 @@ test_that("output_lm_mixed_splines_separate", { regression.recipe_func = function(regression.recipe) { recipes::step_ns(regression.recipe, recipes::all_numeric_predictors(), deg_free = 2) }, - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_splines_separate" ) @@ -112,7 +112,7 @@ test_that("output_lm_mixed_decision_tree_cv_separate", { regression.model = parsnip::decision_tree(tree_depth = hardhat::tune(), engine = "rpart", mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1, 2)), regression.vfold_cv_para = list(v = 2), - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_decision_tree_cv_separate" ) @@ -131,7 +131,7 @@ test_that("output_lm_mixed_decision_tree_cv_separate_parallel", { regression.model = parsnip::decision_tree(tree_depth = hardhat::tune(), engine = "rpart", mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1, 2)), regression.vfold_cv_para = list(v = 2), - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_decision_tree_cv_separate_parallel" ) @@ -151,14 +151,14 @@ test_that("output_lm_mixed_xgboost_separate", { regression.recipe_func = function(regression.recipe) { return(recipes::step_dummy(regression.recipe, recipes::all_factor_predictors())) }, - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_xgboost_separate" ) }) # Surrogate regression ================================================================================================= -test_that("output_lm_numeric_lm_surrogate_adaptive", { +test_that("output_lm_numeric_lm_surrogate_iterative", { expect_snapshot_rds( explain( testing = TRUE, @@ -168,9 +168,9 @@ test_that("output_lm_numeric_lm_surrogate_adaptive", { approach = "regression_surrogate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = TRUE + iterative = TRUE ), - "output_lm_numeric_lm_surrogate_adaptive" + "output_lm_numeric_lm_surrogate_iterative" ) }) @@ -185,7 +185,7 @@ test_that("output_lm_numeric_lm_surrogate", { approach = "regression_surrogate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_lm_surrogate" ) @@ -202,7 +202,7 @@ test_that("output_lm_numeric_lm_surrogate_n_comb", { prediction_zero = p0, max_n_coalitions = 10, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_lm_surrogate_n_comb" ) @@ -220,7 +220,7 @@ test_that("output_lm_numeric_lm_surrogate_reg_surr_n_comb", { max_n_coalitions = 10, regression.model = parsnip::linear_reg(), regression.surrogate_n_comb = 8, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_lm_surrogate_reg_surr_n_comb" ) @@ -236,7 +236,7 @@ test_that("output_lm_categorical_lm_surrogate", { approach = "regression_surrogate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_categorical_lm_surrogate" ) @@ -252,7 +252,7 @@ test_that("output_lm_mixed_lm_surrogate", { approach = "regression_surrogate", prediction_zero = p0, regression.model = parsnip::linear_reg(), - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_lm_surrogate" ) @@ -270,7 +270,7 @@ test_that("output_lm_mixed_decision_tree_cv_surrogate", { regression.model = parsnip::decision_tree(tree_depth = hardhat::tune(), engine = "rpart", mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1, 2)), regression.vfold_cv_para = list(v = 2), - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_decision_tree_cv_surrogate" ) @@ -289,7 +289,7 @@ test_that("output_lm_mixed_xgboost_surrogate", { regression.recipe_func = function(regression.recipe) { recipes::step_dummy(regression.recipe, recipes::all_factor_predictors()) }, - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_xgboost_surrogate" ) diff --git a/tests/testthat/test-regression-setup.R b/tests/testthat/test-regression-setup.R index 03feedd04..9a8998ae0 100644 --- a/tests/testthat/test-regression-setup.R +++ b/tests/testthat/test-regression-setup.R @@ -11,7 +11,7 @@ test_that("regression erroneous input: `approach`", { x_train = x_train_numeric, prediction_zero = p0, approach = c("regression_surrogate", "gaussian", "independence", "empirical"), - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -27,7 +27,7 @@ test_that("regression erroneous input: `approach`", { x_train = x_train_numeric, prediction_zero = p0, approach = c("regression_separate", "gaussian", "independence", "empirical"), - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -147,7 +147,7 @@ test_that("regression erroneous input: `regression.model`", { prediction_zero = p0, approach = "regression_surrogate", regression.tune_values = data.frame(tree_depth = c(1, 2, 3)), - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -301,7 +301,7 @@ test_that("regression erroneous input: `regression.recipe_func`", { regression.recipe_func = function(x) { return(2) }, - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -322,7 +322,7 @@ test_that("regression erroneous input: `regression.surrogate_n_comb`", { prediction_zero = p0, approach = "regression_surrogate", regression.surrogate_n_comb = 2^ncol(x_explain_numeric) - 1, - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -339,7 +339,7 @@ test_that("regression erroneous input: `regression.surrogate_n_comb`", { prediction_zero = p0, approach = "regression_surrogate", regression.surrogate_n_comb = 0, - adaptive = FALSE + iterative = FALSE ) }, error = TRUE diff --git a/tests/testthat/test-regular-output.R b/tests/testthat/test-regular-output.R index a5bfc02d6..6591b8368 100644 --- a/tests/testthat/test-regular-output.R +++ b/tests/testthat/test-regular-output.R @@ -9,7 +9,7 @@ test_that("output_lm_numeric_independence", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_independence" ) @@ -25,7 +25,7 @@ test_that("output_lm_numeric_independence_MSEv_Shapley_weights", { approach = "independence", prediction_zero = p0, MSEv_uniform_comb_weights = FALSE, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_independence_MSEv_Shapley_weights" ) @@ -40,7 +40,7 @@ test_that("output_lm_numeric_empirical", { x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_empirical" ) @@ -56,7 +56,7 @@ test_that("output_lm_numeric_empirical_n_coalitions", { approach = "empirical", prediction_zero = p0, max_n_coalitions = 20, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_empirical_n_coalitions" ) @@ -73,7 +73,7 @@ test_that("output_lm_numeric_empirical_independence", { approach = "empirical", prediction_zero = p0, empirical.type = "independence", - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_empirical_independence" ) @@ -91,7 +91,7 @@ test_that("output_lm_numeric_empirical_AICc_each", { prediction_zero = p0, max_n_coalitions = 8, empirical.type = "AICc_each_k", - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_empirical_AICc_each" ) @@ -109,7 +109,7 @@ test_that("output_lm_numeric_empirical_AICc_full", { prediction_zero = p0, max_n_coalitions = 8, empirical.type = "AICc_full", - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_empirical_AICc_full" ) @@ -124,7 +124,7 @@ test_that("output_lm_numeric_gaussian", { x_train = x_train_numeric, approach = "gaussian", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_gaussian" ) @@ -139,7 +139,7 @@ test_that("output_lm_numeric_copula", { x_train = x_train_numeric, approach = "copula", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_copula" ) @@ -154,7 +154,7 @@ test_that("output_lm_numeric_ctree", { x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_ctree" ) @@ -177,7 +177,7 @@ test_that("output_lm_numeric_vaeac", { vaeac.epochs_initiation_phase = 2, # Low value here to speed up the time vaeac.save_model = FALSE # Removes names and objects such as tmpdir and tmpfile ), - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_vaeac" ) @@ -192,7 +192,7 @@ test_that("output_lm_categorical_ctree", { x_train = x_train_categorical, approach = "ctree", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_categorical_ctree" ) @@ -215,7 +215,7 @@ test_that("output_lm_categorical_vaeac", { vaeac.epochs_initiation_phase = 2, # Low value here to speed up the time vaeac.save_model = FALSE # Removes tmpdir and tmpfiles ), - adaptive = FALSE + iterative = FALSE ), "output_lm_categorical_vaeac" ) @@ -230,7 +230,7 @@ test_that("output_lm_categorical_categorical", { x_train = x_train_categorical, approach = "categorical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_categorical_method" ) @@ -245,7 +245,7 @@ test_that("output_lm_categorical_independence", { x_train = x_train_categorical, approach = "independence", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_categorical_independence" ) @@ -261,7 +261,7 @@ test_that("output_lm_ts_timeseries", { approach = "timeseries", prediction_zero = p0_ts, group = group_ts, - adaptive = FALSE + iterative = FALSE ), "output_lm_timeseries_method" ) @@ -276,7 +276,7 @@ test_that("output_lm_numeric_comb1", { x_train = x_train_numeric, approach = c("gaussian", "empirical", "ctree", "independence"), prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_comb1" ) @@ -291,7 +291,7 @@ test_that("output_lm_numeric_comb2", { x_train = x_train_numeric, approach = c("ctree", "copula", "independence", "copula"), prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_comb2" ) @@ -306,7 +306,7 @@ test_that("output_lm_numeric_comb3", { x_train = x_train_numeric, approach = c("independence", "empirical", "gaussian", "empirical"), prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_comb3" ) @@ -324,7 +324,7 @@ test_that("output_lm_mixed_independence", { x_train = x_train_mixed, approach = "independence", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_independence" ) @@ -339,7 +339,7 @@ test_that("output_lm_mixed_ctree", { x_train = x_train_mixed, approach = "ctree", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_ctree" ) @@ -362,7 +362,7 @@ test_that("output_lm_mixed_vaeac", { vaeac.epochs_initiation_phase = 2, # Low value here to speed up the time vaeac.save_model = FALSE # Removes tmpdir and tmpfiles ), - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_vaeac" ) @@ -378,7 +378,7 @@ test_that("output_lm_mixed_comb", { x_train = x_train_mixed, approach = c("ctree", "independence", "ctree", "independence"), prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_mixed_comb" ) @@ -406,7 +406,7 @@ test_that("output_custom_lm_numeric_independence_1", { approach = "independence", prediction_zero = p0, predict_model = custom_pred_func, - adaptive = FALSE + iterative = FALSE ), "output_custom_lm_numeric_independence_1" ) @@ -433,7 +433,7 @@ test_that("output_custom_lm_numeric_independence_2", { approach = "independence", prediction_zero = p0, predict_model = custom_pred_func, - adaptive = FALSE + iterative = FALSE )), "output_custom_lm_numeric_independence_2" ) @@ -445,7 +445,7 @@ test_that("output_custom_lm_numeric_independence_2", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) # Check that the printed Shapley values are identical @@ -497,7 +497,7 @@ test_that("output_custom_xgboost_mixed_dummy_ctree", { prediction_zero = p0, predict_model = predict_model.xgboost_dummy, get_model_specs = NA, - adaptive = FALSE + iterative = FALSE ) # custom$internal$objects$predict_model <- "Del on purpose" # Avoids issues with xgboost package updates custom @@ -518,7 +518,7 @@ test_that("output_lm_numeric_interaction", { x_train = x_train_interaction, approach = "independence", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ), "output_lm_numeric_interaction" ) @@ -535,7 +535,7 @@ test_that("output_lm_numeric_ctree_parallelized", { x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) }, "output_lm_numeric_ctree_parallelized" @@ -557,7 +557,7 @@ test_that("output_lm_numeric_empirical_progress", { x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) }) }, @@ -577,7 +577,7 @@ test_that("output_lm_numeric_independence_keep_samp_for_vS", { approach = "independence", prediction_zero = p0, keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE )), "output_lm_numeric_independence_keep_samp_for_vS" ) diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index c932267bf..a64dc8a76 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -1211,7 +1211,7 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight n_MC_samples = 2, # Low value for fast computations seed = 123, max_n_coalitions = NULL, - adaptive = FALSE + iterative = FALSE ) ) @@ -1225,9 +1225,9 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - adaptive_args = list(compute_sd = FALSE), + iterative_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), - adaptive = FALSE + iterative = FALSE ) ) @@ -1243,9 +1243,9 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - adaptive_args = list(compute_sd = FALSE), + iterative_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, - adaptive = FALSE + iterative = FALSE ) ) @@ -1558,7 +1558,7 @@ test_that("vaeac_set_seed_works", { vaeac.extra_parameters = list( vaeac.epochs_initiation_phase = 2 ), - adaptive = FALSE + iterative = FALSE ) explanation_vaeac_2 <- explain( @@ -1575,7 +1575,7 @@ test_that("vaeac_set_seed_works", { vaeac.extra_parameters = list( vaeac.epochs_initiation_phase = 2 ), - adaptive = FALSE + iterative = FALSE ) # Check for equal Shapley values @@ -1600,7 +1600,7 @@ test_that("vaeac_pretreained_vaeac_model", { vaeac.extra_parameters = list( vaeac.epochs_initiation_phase = 2 ), - adaptive = FALSE + iterative = FALSE ) #### We can do this by reusing the vaeac model OBJECT @@ -1620,7 +1620,7 @@ test_that("vaeac_pretreained_vaeac_model", { vaeac.extra_parameters = list( vaeac.pretrained_vaeac_model = vaeac.pretrained_vaeac_model ), - adaptive = FALSE + iterative = FALSE ) # Check for equal Shapley values @@ -1643,7 +1643,7 @@ test_that("vaeac_pretreained_vaeac_model", { vaeac.extra_parameters = list( vaeac.pretrained_vaeac_model = vaeac.pretrained_vaeac_path ), - adaptive = FALSE + iterative = FALSE ) # Check for equal Shapley values diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index 77d29cc2a..4371b0ef3 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -24,7 +24,7 @@ editor_options: > [Estimation approaches and plotting functionality](#ex) -> [Adaptive estimation](#adaptive) +> [iterative estimation](#iterative) > [Parallelization](#para) @@ -84,10 +84,10 @@ on the process of estimating the `v(S)`'s (and training the `"vaeac"` model) is `progressr` package, supporting progress updates also for parallelized computation. See [Verbosity and progress updates](#verbose) for details. -Moreover, the default behavior is to estimate the Shapley values adaptively/iteratively, with increasing number of +Moreover, the default behavior is to estimate the Shapley values iteratively/iteratively, with increasing number of feature coalitions being added, and to stop estimation as the estimated Shapley values has achieved a certain level of stability. -More information about this is provided in [Adaptive estimation](#adaptive) +More information about this is provided in [iterative estimation](#iterative) The above, combined with batch computation of the `v(S)` values, enables fast and accurate estimation of the Shapley values in a memory friendly manner. @@ -102,18 +102,18 @@ Below we provide brief descriptions of the most important parts of the default b By default `explain` always compute feature-wise Shapley values. Groups of features can be explained by providing the feature groups through the `group` argument. -When there are five or less features (or feature groups), adaptive estimation is by default disabled. +When there are five or less features (or feature groups), iterative estimation is by default disabled. The reason for this is that it is usually faster to estimate the Shapley values for all possible coalitions (`v(S)`), than to estimate the uncertainty of the Shapley values, and potentially stop estimation earlier. -While adaptive estimation is the default starting from six features, it is mainly when there are more than ten features, +While iterative estimation is the default starting from six features, it is mainly when there are more than ten features, that it is most beneficial, and can save a lot of computation time. The reason for this is that the number of possible coalitions grows exponentially. -These defaults can be overridden by setting the `adaptive` argument to `TRUE` or `FALSE`. -When using the `adaptive` argument, the estimation for an observation is stopped when all Shapley value +These defaults can be overridden by setting the `iterative` argument to `TRUE` or `FALSE`. +When using the `iterative` argument, the estimation for an observation is stopped when all Shapley value standard deviations are below `t` times the range of the Shapley values. -The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `adaptive_args$convergence_tolerance` argument, see [Adaptive estimation](#adaptive) for more details. +The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `iterative_args$convergence_tolerance` argument, see [iterative estimation](#iterative) for more details. -Since the adaptiveness default changes based on the number of features (or feature groups), the default is also to have +Since the iterativeness default changes based on the number of features (or feature groups), the default is also to have no upper bound on the number of coalitions considered. This can be controlled through the `max_n_coalitions` argument. @@ -472,7 +472,7 @@ explanation <- explain( x_train = x_train, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -483,7 +483,7 @@ explanation <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:03:47 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876451ac64e.rds' @@ -542,7 +542,7 @@ explanation_plot <- explain( x_train = x_train, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -553,7 +553,7 @@ explanation_plot <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:03:51 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 111 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18766d271f51.rds' @@ -606,7 +606,7 @@ explanation_lm_cat <- explain( x_train = x_train_cat, approach = "ctree", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) #> Success with message: #> max_n_coalitions is NULL or larger than or 2^n_features = 16, @@ -615,7 +615,7 @@ explanation_lm_cat <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:03:59 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: ctree -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876d108055.rds' @@ -649,7 +649,7 @@ explanation_ctree <- explain( ctree.mincriterion = 0.80, ctree.minsplit = 20, ctree.minbucket = 20, - adaptive = FALSE + iterative = FALSE ) #> Success with message: #> max_n_coalitions is NULL or larger than or 2^n_features = 16, @@ -658,7 +658,7 @@ explanation_ctree <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:00 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: ctree -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18761fdca59c.rds' @@ -703,7 +703,7 @@ explanation_cat_method <- explain( x_train = x_train_all_cat, approach = "categorical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) #> Success with message: #> max_n_coalitions is NULL or larger than or 2^n_features = 16, @@ -712,7 +712,7 @@ explanation_cat_method <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:01 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: categorical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18766a1742b.rds' @@ -781,7 +781,7 @@ explanation_timeseries <- explain( approach = "timeseries", prediction_zero = p0_ts, group = group_ts, - adaptive = FALSE + iterative = FALSE ) #> Success with message: #> max_n_coalitions is NULL or larger than or 2^n_groups = 16, @@ -790,7 +790,7 @@ explanation_timeseries <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:02 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: timeseries -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of group-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18765c67f37.rds' @@ -918,7 +918,7 @@ explanation_independence <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:04 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: independence -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 25 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187615909571.rds' @@ -946,7 +946,7 @@ explanation_empirical <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:05 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 25 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187669394043.rds' @@ -974,7 +974,7 @@ explanation_gaussian_1e1 <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:09 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 25 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876196cf612.rds' @@ -1002,7 +1002,7 @@ explanation_gaussian_1e2 <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:09 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 25 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187610b52b5a.rds' @@ -1030,7 +1030,7 @@ explanation_combined <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:09 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian, empirical, and independence -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 25 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187636ab1203.rds' @@ -1150,20 +1150,20 @@ plot_MSEv_eval_crit(explanation_list_named) + ![](figure_main/unnamed-chunk-14-1.png) - + -# Adaptive estimation +# iterative estimation -Adaptive estimation is the default when computing Shapley values with six or more features (or feature groups), and -can always be manually overridden by setting `adaptive = FALSE` in the `explain()` function. -The idea behind adaptive estimation is to estimate sufficiently accurate Shapley value estimates faster. +iterative estimation is the default when computing Shapley values with six or more features (or feature groups), and +can always be manually overridden by setting `iterative = FALSE` in the `explain()` function. +The idea behind iterative estimation is to estimate sufficiently accurate Shapley value estimates faster. First, an initial number of coalitions is sampled, then, bootsrapping is used to estimate the variance of the Shapley values. A convergence criterion is used to determine if the variances of the Shapley values are sufficently small. If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through `adaptive_args` argument. +Specifics related to the iterative process and convergence criterion are set through `iterative_args` argument. The convergence criterion we use is adopted from @covert2021improving, and slightly modified to work for multiple observations @@ -1172,7 +1172,7 @@ observations where $\hat{\phi}_{ij}$ is the Shapley value of feature $j$ for observation $i$, and $\text{sd}(\phi_{ij})$ is the its (bootstrap) estimated standard deviation. The default value of $t$ is 0.02. -Below we provide some examples of how to use the adaptive estimation procedure +Below we provide some examples of how to use the iterative estimation procedure @@ -1214,8 +1214,8 @@ ex <- explain( x_train = x_train, approach = "gaussian", prediction_zero = p0, - adaptive = TRUE, - adaptive_args = list(convergence_tolerance = 0.1) + iterative = TRUE, + iterative_args = list(convergence_tolerance = 0.1) ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -1226,12 +1226,12 @@ ex <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:13 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian -#> • Adaptive estimation: TRUE +#> • iterative estimation: TRUE #> • Number of feature-wise Shapley values: 5 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18761c624bbf.rds' #> -#> ── Adaptive computation started ── +#> ── iterative computation started ── #> #> ── Iteration 1 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── #> ℹ Using 5 of 32 coalitions, 5 new. @@ -1282,8 +1282,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`adaptive_args$max_batch_size` (defaults to 10) and -`adaptive_args$min_n_batches` (defaults to 10). +`iterative_args$max_batch_size` (defaults to 10) and +`iterative_args$min_n_batches` (defaults to 10). ## Parallelized computation @@ -1325,7 +1325,7 @@ explanation_par <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:21 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 5 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187625c75286.rds' @@ -1346,7 +1346,7 @@ and allows one or more of the strings `"basic"`, `"progress"`, `"convergence"`, `"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 -(for adaptive estimation), +(for iterative estimation), `"shapley"` displays (intermediate) Shapley value estimates and standard deviations + the final estimates, while `"vS_details"` displays information about the `v(S)` estimates for some of the approaches. If the user wants no printout, the argument can be set to `NULL`. @@ -1489,7 +1489,7 @@ explanation_combined <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:24 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical, copula, and gaussian -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876ed292f2.rds' @@ -1526,7 +1526,7 @@ explanation_combined <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:26 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: ctree, ctree, and empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187631b56c11.rds' @@ -1560,7 +1560,7 @@ explanation_group <- explain( approach = "empirical", prediction_zero = p0, group = group_list, - adaptive = FALSE + iterative = FALSE ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -1571,7 +1571,7 @@ explanation_group <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:27 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of group-wise Shapley values: 2 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187646237fdf.rds' @@ -1706,7 +1706,7 @@ explanation_custom <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:29 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187675d1a955.rds' @@ -1751,7 +1751,7 @@ explanation_custom_minimal <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:32 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18764d3e2d93.rds' @@ -1810,7 +1810,7 @@ explanation_tidymodels <- explain( x_train = x_train, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) #> Success with message: #> max_n_coalitions is NULL or larger than or 2^n_features = 16, @@ -1819,7 +1819,7 @@ explanation_tidymodels <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:36 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876330edd61.rds' @@ -1863,7 +1863,7 @@ explanation_vaeac <- explain( vaeac.depth = 2, vaeac.epochs = 3, vaeac.n_vaeacs_initialize = 2, - adaptive = FALSE + iterative = FALSE ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -1909,7 +1909,7 @@ explanation_vaeac_early_stop <- explain( vaeac.epochs = 1000, # Set it to a large number vaeac.n_vaeacs_initialize = 2, vaeac.extra_parameters = list(vaeac.epochs_early_stopping = 2), - adaptive = FALSE + iterative = FALSE ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -1941,16 +1941,16 @@ plot_MSEv_eval_crit(list("Vaeac 3 epochs" = explanation_vaeac, "Vaeac early stop ## Continued computation {#cont_computation} In this section, we demonstrate how to continue to improve estimation accuracy with additional coalition samples, -from a previous Shapley value computation based on `shapr::explain()` with the adaptive estimation procedure. +from a previous Shapley value computation based on `shapr::explain()` with the iterative estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$adaptive_args$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$internal$parameters$iterative_args$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. ``` r -# First we run the computation with the adaptive estimation procedure for a limited number of coalition samples +# First we run the computation with the iterative estimation procedure for a limited number of coalition samples library(xgboost) library(data.table) @@ -1988,7 +1988,7 @@ ex_init <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 20, - adaptive = TRUE + iterative = TRUE ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -1996,12 +1996,12 @@ ex_init <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:39 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian -#> • Adaptive estimation: TRUE +#> • iterative estimation: TRUE #> • Number of feature-wise Shapley values: 5 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18767c70b709.rds' #> -#> ── Adaptive computation started ── +#> ── iterative computation started ── #> #> ── Iteration 1 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── #> ℹ Using 5 of 32 coalitions, 5 new. @@ -2017,7 +2017,7 @@ ex_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 25, - adaptive_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold + iterative_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold prev_shapr_object = ex_init ) #> Note: Feature classes extracted from the model contains NA. @@ -2026,7 +2026,7 @@ ex_further <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:41 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 5 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876481fd8a.rds' @@ -2035,7 +2035,7 @@ ex_further <- explain( #> #> ℹ Using 26 of 32 coalitions. -print(ex_further$internal$parameters$adaptive_args$saving_path) +print(ex_further$internal$parameters$iterative_args$saving_path) #> [1] "/tmp/RtmpPIciRY/shapr_obj_c1876481fd8a.rds" # Using the ex_init object to continue the computation for the remaining coalition samples @@ -2047,7 +2047,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$adaptive_args$saving_path + prev_shapr_object = ex_further$internal$parameters$iterative_args$saving_path ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -2058,7 +2058,7 @@ ex_even_further <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:04:42 ───────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 5 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187699a1121.rds' diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index 787fbaf67..890119f18 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -39,7 +39,7 @@ library(shapr) > [Estimation approaches and plotting functionality](#ex) -> [Adaptive estimation](#adaptive) +> [iterative estimation](#iterative) > [Parallelization](#para) @@ -99,10 +99,10 @@ on the process of estimating the `v(S)`'s (and training the `"vaeac"` model) is `progressr` package, supporting progress updates also for parallelized computation. See [Verbosity and progress updates](#verbose) for details. -Moreover, the default behavior is to estimate the Shapley values adaptively/iteratively, with increasing number of +Moreover, the default behavior is to estimate the Shapley values iteratively/iteratively, with increasing number of feature coalitions being added, and to stop estimation as the estimated Shapley values has achieved a certain level of stability. -More information about this is provided in [Adaptive estimation](#adaptive) +More information about this is provided in [iterative estimation](#iterative) The above, combined with batch computation of the `v(S)` values, enables fast and accurate estimation of the Shapley values in a memory friendly manner. @@ -117,18 +117,18 @@ Below we provide brief descriptions of the most important parts of the default b By default `explain` always compute feature-wise Shapley values. Groups of features can be explained by providing the feature groups through the `group` argument. -When there are five or less features (or feature groups), adaptive estimation is by default disabled. +When there are five or less features (or feature groups), iterative estimation is by default disabled. The reason for this is that it is usually faster to estimate the Shapley values for all possible coalitions (`v(S)`), than to estimate the uncertainty of the Shapley values, and potentially stop estimation earlier. -While adaptive estimation is the default starting from six features, it is mainly when there are more than ten features, +While iterative estimation is the default starting from six features, it is mainly when there are more than ten features, that it is most beneficial, and can save a lot of computation time. The reason for this is that the number of possible coalitions grows exponentially. -These defaults can be overridden by setting the `adaptive` argument to `TRUE` or `FALSE`. -When using the `adaptive` argument, the estimation for an observation is stopped when all Shapley value +These defaults can be overridden by setting the `iterative` argument to `TRUE` or `FALSE`. +When using the `iterative` argument, the estimation for an observation is stopped when all Shapley value standard deviations are below `t` times the range of the Shapley values. -The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `adaptive_args$convergence_tolerance` argument, see [Adaptive estimation](#adaptive) for more details. +The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `iterative_args$convergence_tolerance` argument, see [iterative estimation](#iterative) for more details. -Since the adaptiveness default changes based on the number of features (or feature groups), the default is also to have +Since the iterativeness default changes based on the number of features (or feature groups), the default is also to have no upper bound on the number of coalitions considered. This can be controlled through the `max_n_coalitions` argument. @@ -486,7 +486,7 @@ explanation <- explain( x_train = x_train, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) # Printing the Shapley values for the test data. @@ -525,7 +525,7 @@ explanation_plot <- explain( x_train = x_train, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) plot(explanation_plot, plot_type = "beeswarm") ``` @@ -566,7 +566,7 @@ explanation_lm_cat <- explain( x_train = x_train_cat, approach = "ctree", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) # Plot the resulting explanations for observations 1 and 6, excluding @@ -591,7 +591,7 @@ explanation_ctree <- explain( ctree.mincriterion = 0.80, ctree.minsplit = 20, ctree.minbucket = 20, - adaptive = FALSE + iterative = FALSE ) # Default parameters (based on (Hothorn, 2006)) are: # mincriterion = 0.95 @@ -629,7 +629,7 @@ explanation_cat_method <- explain( x_train = x_train_all_cat, approach = "categorical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) ``` @@ -691,7 +691,7 @@ explanation_timeseries <- explain( approach = "timeseries", prediction_zero = p0_ts, group = group_ts, - adaptive = FALSE + iterative = FALSE ) ``` @@ -933,20 +933,20 @@ plot_MSEv_eval_crit(explanation_list_named) + ) ``` - + -# Adaptive estimation +# iterative estimation -Adaptive estimation is the default when computing Shapley values with six or more features (or feature groups), and -can always be manually overridden by setting `adaptive = FALSE` in the `explain()` function. -The idea behind adaptive estimation is to estimate sufficiently accurate Shapley value estimates faster. +iterative estimation is the default when computing Shapley values with six or more features (or feature groups), and +can always be manually overridden by setting `iterative = FALSE` in the `explain()` function. +The idea behind iterative estimation is to estimate sufficiently accurate Shapley value estimates faster. First, an initial number of coalitions is sampled, then, bootsrapping is used to estimate the variance of the Shapley values. A convergence criterion is used to determine if the variances of the Shapley values are sufficently small. If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more coalitions. The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through `adaptive_args` argument. +Specifics related to the iterative process and convergence criterion are set through `iterative_args` argument. The convergence criterion we use is adopted from @covert2021improving, and slightly modified to work for multiple observations @@ -955,7 +955,7 @@ observations where $\hat{\phi}_{ij}$ is the Shapley value of feature $j$ for observation $i$, and $\text{sd}(\phi_{ij})$ is the its (bootstrap) estimated standard deviation. The default value of $t$ is 0.02. -Below we provide some examples of how to use the adaptive estimation procedure +Below we provide some examples of how to use the iterative estimation procedure @@ -996,8 +996,8 @@ ex <- explain( x_train = x_train, approach = "gaussian", prediction_zero = p0, - adaptive = TRUE, - adaptive_args = list(convergence_tolerance = 0.1) + iterative = TRUE, + iterative_args = list(convergence_tolerance = 0.1) ) ``` @@ -1026,8 +1026,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`adaptive_args$max_batch_size` (defaults to 10) and -`adaptive_args$min_n_batches` (defaults to 10). +`iterative_args$max_batch_size` (defaults to 10) and +`iterative_args$min_n_batches` (defaults to 10). ## Parallelized computation @@ -1072,7 +1072,7 @@ and allows one or more of the strings `"basic"`, `"progress"`, `"convergence"`, `"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 -(for adaptive estimation), +(for iterative estimation), `"shapley"` displays (intermediate) Shapley value estimates and standard deviations + the final estimates, while `"vS_details"` displays information about the `v(S)` estimates for some of the approaches. If the user wants no printout, the argument can be set to `NULL`. @@ -1246,7 +1246,7 @@ explanation_group <- explain( approach = "empirical", prediction_zero = p0, group = group_list, - adaptive = FALSE + iterative = FALSE ) # Prints the group-wise explanations explanation_group @@ -1425,7 +1425,7 @@ explanation_tidymodels <- explain( x_train = x_train, approach = "empirical", prediction_zero = p0, - adaptive = FALSE + iterative = FALSE ) # See that the Shapley value explanations are identical too @@ -1461,7 +1461,7 @@ explanation_vaeac <- explain( vaeac.depth = 2, vaeac.epochs = 3, vaeac.n_vaeacs_initialize = 2, - adaptive = FALSE + iterative = FALSE ) ``` @@ -1498,7 +1498,7 @@ explanation_vaeac_early_stop <- explain( vaeac.epochs = 1000, # Set it to a large number vaeac.n_vaeacs_initialize = 2, vaeac.extra_parameters = list(vaeac.epochs_early_stopping = 2), - adaptive = FALSE + iterative = FALSE ) ``` @@ -1519,15 +1519,15 @@ plot_MSEv_eval_crit(list("Vaeac 3 epochs" = explanation_vaeac, "Vaeac early stop ## Continued computation {#cont_computation} In this section, we demonstrate how to continue to improve estimation accuracy with additional coalition samples, -from a previous Shapley value computation based on `shapr::explain()` with the adaptive estimation procedure. +from a previous Shapley value computation based on `shapr::explain()` with the iterative estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$adaptive_args$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$internal$parameters$iterative_args$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. ```{r} -# First we run the computation with the adaptive estimation procedure for a limited number of coalition samples +# First we run the computation with the iterative estimation procedure for a limited number of coalition samples library(xgboost) library(data.table) @@ -1565,7 +1565,7 @@ ex_init <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 20, - adaptive = TRUE + iterative = TRUE ) # Using the ex_init object to continue the computation with 5 more coalition samples @@ -1576,11 +1576,11 @@ ex_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 25, - adaptive_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold + iterative_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold prev_shapr_object = ex_init ) -print(ex_further$internal$parameters$adaptive_args$saving_path) +print(ex_further$internal$parameters$iterative_args$saving_path) # Using the ex_init object to continue the computation for the remaining coalition samples # but this time using the path to the saved intermediate estimation object @@ -1591,7 +1591,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$adaptive_args$saving_path + prev_shapr_object = ex_further$internal$parameters$iterative_args$saving_path ) diff --git a/vignettes/understanding_shapr_regression.Rmd b/vignettes/understanding_shapr_regression.Rmd index f5fc19534..4f9108d68 100644 --- a/vignettes/understanding_shapr_regression.Rmd +++ b/vignettes/understanding_shapr_regression.Rmd @@ -274,7 +274,7 @@ explanation_list$MC_empirical <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:21 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: empirical -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18761ea04792.rds' @@ -308,7 +308,7 @@ explanation_list$sep_lm <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:25 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187667f08ebc.rds' @@ -384,7 +384,7 @@ explanation_list$sep_pcr <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:26 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187663c08a23.rds' @@ -420,7 +420,7 @@ explanation_list$sep_splines <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:27 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187623bd0bdd.rds' @@ -489,7 +489,7 @@ explanation_list$sep_reicpe_example <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:28 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18763690cbf5.rds' @@ -567,7 +567,7 @@ explanation_list$sep_tree_stump <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:29 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187674517ff7.rds' @@ -594,7 +594,7 @@ explanation_list$sep_tree_default <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:30 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876712b369f.rds' @@ -715,7 +715,7 @@ explanation_list$sep_tree_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:31 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18766fce32eb.rds' @@ -750,7 +750,7 @@ explanation_list$sep_tree_cv_2 <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:13:44 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18764668cc32.rds' @@ -790,7 +790,7 @@ explanation_list$sep_rf <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:14:11 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187644d0257b.rds' @@ -825,7 +825,7 @@ explanation_list$sep_rf_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:14:12 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18764f66dc0d.rds' @@ -1046,7 +1046,7 @@ explanation_list$sep_xgboost <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:14:44 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187615b17860.rds' @@ -1076,7 +1076,7 @@ explanation_list$sep_xgboost_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:14:45 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876235ef29a.rds' @@ -1107,7 +1107,7 @@ explanation_list$sep_xgboost_cv_par <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:01 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18765c4ad0e3.rds' @@ -1142,7 +1142,7 @@ explanation_list$sep_xgboost_cv_2_par <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:14 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18765f8e54c.rds' @@ -1242,7 +1242,7 @@ explanation_list$sur_lm <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:28 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876734af388.rds' @@ -1269,7 +1269,7 @@ explanation_list$sur_xgboost <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:29 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187662e749f7.rds' @@ -1303,7 +1303,7 @@ explanation_list$sur_xgboost_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:29 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18764162f6f8.rds' @@ -1330,7 +1330,7 @@ explanation_list$sur_rf <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:31 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18767b67ab19.rds' @@ -1365,7 +1365,7 @@ explanation_list$sur_rf_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:32 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876c4ce446.rds' @@ -1422,7 +1422,7 @@ explanation_list$sur_rf_cv_par <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:15:56 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18763a6d5131.rds' @@ -1638,7 +1638,7 @@ explanation_list$sep_ppr <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:17 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876877ce97.rds' @@ -1667,7 +1667,7 @@ explanation_list$sep_ppr_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:17 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876785fc43a.rds' @@ -1694,7 +1694,7 @@ explanation_list$sur_ppr <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:28 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187667ef9ee4.rds' @@ -1723,7 +1723,7 @@ explanation_list$sur_ppr_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:29 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18767a5af0d2.rds' @@ -1807,7 +1807,7 @@ explanation_list_MC$MC_independence <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:31 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: independence -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187642229bc6.rds' @@ -1836,7 +1836,7 @@ explanation_list_MC$MC_gaussian <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:32 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: gaussian -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18765d1c7e24.rds' @@ -1862,7 +1862,7 @@ explanation_list_MC$MC_copula <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:32 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: copula -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18767e5194b5.rds' @@ -1888,7 +1888,7 @@ explanation_list_MC$MC_ctree <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:33 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: ctree -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187671d33cfd.rds' @@ -1915,7 +1915,7 @@ explanation_list_MC$MC_vaeac <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:16:34 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: vaeac -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18764a74529.rds' @@ -2099,7 +2099,7 @@ explanation_list_mixed$MC_independence <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:18:45 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: independence -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187649548cac.rds' @@ -2122,7 +2122,7 @@ explanation_list_mixed$MC_ctree <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:18:46 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: ctree -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18763bd63adf.rds' @@ -2145,7 +2145,7 @@ explanation_list_mixed$MC_vaeac <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:18:48 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: vaeac -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187658b05f43.rds' @@ -2180,7 +2180,7 @@ explanation_list_mixed$sep_lm <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:22:11 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18763c8f338e.rds' @@ -2208,7 +2208,7 @@ explanation_list_mixed$sep_splines <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:22:12 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18764ac0dcfe.rds' @@ -2233,7 +2233,7 @@ explanation_list_mixed$sep_tree <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:22:13 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187611313f7d.rds' @@ -2266,7 +2266,7 @@ explanation_list_mixed$sep_tree_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:22:14 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187664fb36e5.rds' @@ -2291,7 +2291,7 @@ explanation_list_mixed$sep_rf <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:22:44 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18761500adf5.rds' @@ -2323,7 +2323,7 @@ explanation_list_mixed$sep_rf_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:22:46 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18766af223df.rds' @@ -2351,7 +2351,7 @@ explanation_list_mixed$sep_xgboost <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:26 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187695249ed.rds' @@ -2386,7 +2386,7 @@ explanation_list_mixed$sep_xgboost_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:27 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18764174cab8.rds' @@ -2421,7 +2421,7 @@ explanation_list_mixed$sur_lm <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:47 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18767adcc2a7.rds' @@ -2450,7 +2450,7 @@ explanation_list_mixed$sur_splines <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:47 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18766add98f1.rds' @@ -2475,7 +2475,7 @@ explanation_list_mixed$sur_tree <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:48 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18767848027.rds' @@ -2508,7 +2508,7 @@ explanation_list_mixed$sur_tree_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:48 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187674dc3671.rds' @@ -2533,7 +2533,7 @@ explanation_list_mixed$sur_rf <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:51 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187631bd7b76.rds' @@ -2562,7 +2562,7 @@ explanation_list_mixed$sur_rf_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:23:51 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18763a596175.rds' @@ -2590,7 +2590,7 @@ explanation_list_mixed$sur_xgboost <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:04 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187654cedcd1.rds' @@ -2625,7 +2625,7 @@ explanation_list_mixed$sur_xgboost_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:04 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876213e5a19.rds' @@ -2749,7 +2749,7 @@ explanation_list_str$sep_lm <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:09 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876235f515f.rds' @@ -2778,7 +2778,7 @@ explanation_list_str$sep_pcr <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:10 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876437d5b94.rds' @@ -2807,7 +2807,7 @@ explanation_list_str$sep_splines <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:11 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187632223395.rds' @@ -2837,7 +2837,7 @@ explanation_list_str$sep_tree_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:12 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c18766919e2a5.rds' @@ -2871,7 +2871,7 @@ explanation_list_str$sep_rf_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:25 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_separate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c1876616a45e6.rds' @@ -2906,7 +2906,7 @@ explanation_list_str$sur_rf_cv <- explain( #> ── Starting `shapr::explain()` at 2024-10-05 23:24:56 ─────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: regression_surrogate -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 20 #> • Computations (temporary) saved at: '/tmp/RtmpPIciRY/shapr_obj_c187639202932.rds' diff --git a/vignettes/understanding_shapr_vaeac.Rmd b/vignettes/understanding_shapr_vaeac.Rmd index 2f3a79377..76464eada 100644 --- a/vignettes/understanding_shapr_vaeac.Rmd +++ b/vignettes/understanding_shapr_vaeac.Rmd @@ -442,7 +442,7 @@ rbind( "Paired" = expl_paired_sampling_TRUE$timing$main_timing_secs, "Regular" = expl_paired_sampling_FALSE$timing$main_timing_secs ) -#> setup test_prediction adaptive_estimation finalize_explanation +#> setup test_prediction iterative_estimation finalize_explanation #> Paired 0.048088 0.036740 11.721 0.0049973 #> Regular 0.047131 0.036345 11.517 0.0049357 ``` @@ -482,7 +482,7 @@ expl_with_messages <- explain( #> ── Starting `shapr::explain()` at 2024-10-04 14:57:22 ───────────────────────────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: vaeac -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpIQRVZ2/shapr_obj_acefb1be76dcf.rds' @@ -741,7 +741,7 @@ expl_early_stopping <- explain( #> ── Starting `shapr::explain()` at 2024-10-04 14:57:44 ───────────────────────────────────────────────────────────────────────────────────────────────────────── #> • Model class: #> • Approach: vaeac -#> • Adaptive estimation: FALSE +#> • iterative estimation: FALSE #> • Number of feature-wise Shapley values: 4 #> • Number of observations to explain: 6 #> • Computations (temporary) saved at: '/tmp/RtmpIQRVZ2/shapr_obj_acefb6c654eee.rds' From 06ff7868b9ca3a98eadf1aa1c46fb431ef105a08 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 7 Oct 2024 22:22:23 +0200 Subject: [PATCH 04/32] shapley_reweighting -> kernelSHAP_reweighting --- R/compute_estimates.R | 8 ++--- R/explain.R | 6 ++-- R/setup.R | 12 +++---- R/shapley_setup.R | 16 ++++----- inst/scripts/devel/devel_convergence_branch.R | 6 ++-- .../devel/real_data_iterative_kernelshap.R | 12 +++---- ...ata_iterative_kernelshap_analyze_results.R | 10 +++--- ...tive_kernelshap_lingauss_analyze_results.R | 10 +++--- ...simtest_iterative_kernelshap_lingauss_v2.R | 12 +++---- ...e_kernelshap_nonlingauss_analyze_results.R | 10 +++--- .../devel/simtest_reweighting_strategies.R | 26 +++++++------- ...simtest_reweighting_strategies_nonlinear.R | 20 +++++------ ..._strategies_nonlinear_nonunique_sampling.R | 34 +++++++++---------- inst/scripts/devel/simtest_timing_to_Frida.R | 2 +- .../devel/testing_intermediate_saving.R | 12 +++---- man/create_coalition_table.Rd | 4 +-- man/explain.Rd | 4 +-- man/setup.Rd | 4 +-- 18 files changed, 104 insertions(+), 104 deletions(-) diff --git a/R/compute_estimates.R b/R/compute_estimates.R index f0b2232b8..8a4c9cd5a 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -182,7 +182,7 @@ bootstrap_shapley <- function(internal, dt_vS, n_boot_samps = 100, seed = 123) { n_features <- internal$parameters$n_features shap_names <- internal$parameters$shap_names paired_shap_sampling <- internal$parameters$paired_shap_sampling - shapley_reweight <- internal$parameters$shapley_reweighting + shapley_reweight <- internal$parameters$kernelSHAP_reweighting boot_sd_array <- array(NA, dim = c(n_explain, n_features + 1, n_boot_samps)) @@ -239,7 +239,7 @@ bootstrap_shapley <- function(internal, dt_vS, n_boot_samps = 100, seed = 123) { X_boot <- rbind(X_keep, X_boot0) data.table::setorder(X_boot, id_coalition) - shapley_reweighting(X_boot, reweight = shapley_reweight) # reweights the shapley weights by reference + kernelSHAP_reweighting(X_boot, reweight = shapley_reweight) # reweights the shapley weights by reference W_boot <- shapr::weight_matrix( X = X_boot, @@ -271,7 +271,7 @@ bootstrap_shapley_new <- function(internal, dt_vS, n_boot_samps = 100, seed = 12 n_explain <- internal$parameters$n_explain paired_shap_sampling <- internal$parameters$paired_shap_sampling - shapley_reweight <- internal$parameters$shapley_reweighting + shapley_reweight <- internal$parameters$kernelSHAP_reweighting shap_names <- internal$parameters$shap_names n_shapley_values <- internal$parameters$n_shapley_values @@ -343,7 +343,7 @@ bootstrap_shapley_new <- function(internal, dt_vS, n_boot_samps = 100, seed = 12 for (i in seq_len(n_boot_samps)) { this_X <- X_boot[boot_id == i] # This is highly inefficient, but the best way to deal with the reweighting for now - shapley_reweighting(this_X, reweight = shapley_reweight) + kernelSHAP_reweighting(this_X, reweight = shapley_reweight) W_boot <- weight_matrix( X = this_X, diff --git a/R/explain.R b/R/explain.R index 102fc8c32..2ab92caac 100644 --- a/R/explain.R +++ b/R/explain.R @@ -114,7 +114,7 @@ #' @param output_args Named list. #' Specifices the arguments for the iterative procedure. #' See [shapr::get_output_args_default()] for description of the arguments and their default values. -#' @param shapley_reweighting String. +#' @param kernelSHAP_reweighting String. #' How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing #' the randomness and thereby the variance of the Shapley value estimates. #' One of `'none'`, `'on_N'`, `'on_all'`, `'on_all_cond'` (default). @@ -351,7 +351,7 @@ explain <- function(model, get_model_specs = NULL, verbose = "basic", iterative_args = list(), - shapley_reweighting = "on_all_cond", + kernelSHAP_reweighting = "on_all_cond", prev_shapr_object = NULL, output_args = list(), ...) { # ... is further arguments passed to specific approaches @@ -383,7 +383,7 @@ explain <- function(model, verbose = verbose, iterative = iterative, iterative_args = iterative_args, - shapley_reweighting = shapley_reweighting, + kernelSHAP_reweighting = kernelSHAP_reweighting, init_time = init_time, prev_shapr_object = prev_shapr_object, output_args = output_args, diff --git a/R/setup.R b/R/setup.R index a683b9278..8e846d057 100644 --- a/R/setup.R +++ b/R/setup.R @@ -46,7 +46,7 @@ setup <- function(x_train, verbose, iterative = NULL, iterative_args = list(), - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", is_python = FALSE, testing = FALSE, init_time = NULL, @@ -88,7 +88,7 @@ setup <- function(x_train, verbose = verbose, iterative = iterative, iterative_args = iterative_args, - shapley_reweighting = shapley_reweighting, + kernelSHAP_reweighting = kernelSHAP_reweighting, is_python = is_python, testing = testing, output_args = output_args, @@ -171,7 +171,7 @@ get_parameters <- function(approach, verbose = "basic", iterative = FALSE, iterative_args = list(), - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", testing, is_python, output_args = list(), @@ -281,10 +281,10 @@ get_parameters <- function(approach, } # type - if (!(length(shapley_reweighting) == 1 && shapley_reweighting %in% + if (!(length(kernelSHAP_reweighting) == 1 && kernelSHAP_reweighting %in% c("none", "on_N", "on_coal_size", "on_all", "on_N_sum", "on_all_cond", "on_all_cond_paired", "comb"))) { stop( - "`shapley_reweighting` must be one of `none`, `on_N`, `on_coal_size`, `on_N_sum`, ", + "`kernelSHAP_reweighting` must be one of `none`, `on_N`, `on_coal_size`, `on_N_sum`, ", "`on_all`, `on_all_cond`, `on_all_cond_paired` or `comb`.\n" ) } @@ -307,7 +307,7 @@ get_parameters <- function(approach, group_lags = group_lags, MSEv_uniform_comb_weights = MSEv_uniform_comb_weights, verbose = verbose, - shapley_reweighting = shapley_reweighting, + kernelSHAP_reweighting = kernelSHAP_reweighting, iterative = iterative, iterative_args = iterative_args, output_args = output_args, diff --git a/R/shapley_setup.R b/R/shapley_setup.R index 29fdb08a4..5caa91c5a 100644 --- a/R/shapley_setup.R +++ b/R/shapley_setup.R @@ -11,7 +11,7 @@ shapley_setup <- function(internal) { approach <- internal$parameters$approach is_groupwise <- internal$parameters$is_groupwise paired_shap_sampling <- internal$parameters$paired_shap_sampling - shapley_reweighting <- internal$parameters$shapley_reweighting + kernelSHAP_reweighting <- internal$parameters$kernelSHAP_reweighting coal_feature_list <- internal$objects$coal_feature_list @@ -35,7 +35,7 @@ shapley_setup <- function(internal) { prev_coal_samples = prev_coal_samples, coal_feature_list = coal_feature_list, approach0 = approach, - shapley_reweighting = shapley_reweighting + kernelSHAP_reweighting = kernelSHAP_reweighting ) @@ -143,7 +143,7 @@ create_coalition_table <- function(m, exact = TRUE, n_coalitions = 200, weight_z paired_shap_sampling = TRUE, prev_coal_samples = NULL, coal_feature_list = as.list(seq_len(m)), approach0 = "gaussian", - shapley_reweighting = "none") { + kernelSHAP_reweighting = "none") { if (exact) { dt <- exact_coalition_table(m, weight_zero_m) } else { @@ -152,7 +152,7 @@ create_coalition_table <- function(m, exact = TRUE, n_coalitions = 200, weight_z weight_zero_m, paired_shap_sampling = paired_shap_sampling, prev_coal_samples = prev_coal_samples, - shapley_reweighting = shapley_reweighting + kernelSHAP_reweighting = kernelSHAP_reweighting ) stopifnot( data.table::is.data.table(dt), @@ -176,7 +176,7 @@ create_coalition_table <- function(m, exact = TRUE, n_coalitions = 200, weight_z } #' @keywords internal -shapley_reweighting <- function(X, reweight = "on_N") { +kernelSHAP_reweighting <- function(X, reweight = "on_N") { # Updates the shapley weights in X based on the reweighting strategy BY REFERENCE @@ -240,7 +240,7 @@ sample_coalition_table <- function(m, weight_zero_m = 10^6, paired_shap_sampling = TRUE, prev_coal_samples = NULL, - shapley_reweighting) { + kernelSHAP_reweighting) { # Setup coal_samp_vec <- seq(m - 1) n <- sapply(coal_samp_vec, choose, n = m) @@ -340,7 +340,7 @@ sample_coalition_table <- function(m, nms <- c("id_coalition", "coalitions", "coalition_size", "N", "shapley_weight", "p", "sample_freq") data.table::setcolorder(X, nms) - shapley_reweighting(X, reweight = shapley_reweighting) # Reweights the shapley weights in X by reference + kernelSHAP_reweighting(X, reweight = kernelSHAP_reweighting) # Reweights the shapley weights in X by reference return(X) } @@ -641,7 +641,7 @@ shapley_setup_forecast <- function(internal) { approach <- internal$parameters$approach is_groupwise <- internal$parameters$is_groupwise paired_shap_sampling <- internal$parameters$paired_shap_sampling - shapley_reweighting <- internal$parameters$shapley_reweighting + kernelSHAP_reweighting <- internal$parameters$kernelSHAP_reweighting coal_feature_list <- internal$objects$coal_feature_list horizon <- internal$parameters$horizon diff --git a/inst/scripts/devel/devel_convergence_branch.R b/inst/scripts/devel/devel_convergence_branch.R index 0e9677d4f..ea1d33b3c 100644 --- a/inst/scripts/devel/devel_convergence_branch.R +++ b/inst/scripts/devel/devel_convergence_branch.R @@ -47,7 +47,7 @@ explanation_iterative <- explain( iterative = TRUE, print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp - shapley_reweighting = "on_N" + kernelSHAP_reweighting = "on_N" ) explanation_iterative <- explain( @@ -60,7 +60,7 @@ explanation_iterative <- explain( iterative = TRUE, print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp - shapley_reweighting = "on_N" + kernelSHAP_reweighting = "on_N" ) @@ -86,7 +86,7 @@ explanation_iterative <- explain( iterative_args = list(initial_n_coalitions=10,convergence_tolerance=0.0001), print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp - shapley_reweighting = "on_N" + kernelSHAP_reweighting = "on_N" ) diff --git a/inst/scripts/devel/real_data_iterative_kernelshap.R b/inst/scripts/devel/real_data_iterative_kernelshap.R index 00447ee0b..6cb2d9973 100644 --- a/inst/scripts/devel/real_data_iterative_kernelshap.R +++ b/inst/scripts/devel/real_data_iterative_kernelshap.R @@ -88,7 +88,7 @@ Sigma = cov(x_train) ### First run proper shapr call on this sim_results_saving_folder = "/nr/project/stat/BigInsight/Projects/Explanations/EffektivShapley/Frida/simuleringsresultater/gmc_data_v3/"#"../effektiv_shapley_output/" -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" predict_model_xgb <- function(object,newdata){ xgboost:::predict.xgb.Booster(object,as.matrix(newdata)) @@ -109,7 +109,7 @@ expl <- shapr::explain(model = model, approach = "ctree", prediction_zero = p0 ) -fwrite(expl$shapley_values,paste0(sim_results_saving_folder,"exact_shapley_values_", shapley_reweighting_strategy, ".csv")) +fwrite(expl$shapley_values,paste0(sim_results_saving_folder,"exact_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) print(Sys.time()) # These are the parameters for for iterative_kshap_func @@ -148,7 +148,7 @@ for(kk in seq_along(testObs_computed_vec)){ shapley_threshold_val = shapley_threshold_val, shapley_threshold_prob = shapley_threshold_prob, approach = approach, - shapley_reweighting_strategy = shapley_reweighting_strategy) + kernelSHAP_reweighting_strategy = kernelSHAP_reweighting_strategy) runres_list[[kk]] <- run$kshap_final runcomps_list[[kk]] <- sum(sapply(run$keep_list,"[[","no_computed_combinations")) @@ -159,7 +159,7 @@ for(kk in seq_along(testObs_computed_vec)){ est <- rbindlist(runres_list) est[,other_features:=NULL] -fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_", shapley_reweighting_strategy, ".csv")) +fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) expl_approx <- matrix(0, nrow = length(inds), ncol = m+1) expl_approx_obj_list <- list() @@ -177,7 +177,7 @@ expl_approx <- as.data.table(expl_approx) truth <- expl$shapley_values colnames(expl_approx) <- colnames(truth) -fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_", shapley_reweighting_strategy, ".csv")) +fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) bias_vec <- colMeans(est-truth) rmse_vec <- sqrt(colMeans((est-truth)^2)) @@ -187,7 +187,7 @@ bias_vec_approx <- colMeans(expl_approx-truth) rmse_vec_approx <- sqrt(colMeans((expl_approx-truth)^2)) mae_vec_approx <- colMeans(abs(expl_approx-truth)) -save.image(paste0(sim_results_saving_folder, "iterative_kernelshap_lingauss_p12_", shapley_reweighting_strategy, ".RData")) +save.image(paste0(sim_results_saving_folder, "iterative_kernelshap_lingauss_p12_", kernelSHAP_reweighting_strategy, ".RData")) hist(unlist(runcomps_list),breaks = 20) diff --git a/inst/scripts/devel/real_data_iterative_kernelshap_analyze_results.R b/inst/scripts/devel/real_data_iterative_kernelshap_analyze_results.R index bbacbef75..866d28bf9 100644 --- a/inst/scripts/devel/real_data_iterative_kernelshap_analyze_results.R +++ b/inst/scripts/devel/real_data_iterative_kernelshap_analyze_results.R @@ -1,19 +1,19 @@ library(data.table) -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" shapley_threshold_val <- 0.2 sim_results_folder = "/nr/project/stat/BigInsight/Projects/Explanations/EffektivShapley/Frida/simuleringsresultater/gmc_data_v3/" -load(paste0("/nr/project/stat/BigInsight/Projects/Explanations/EffektivShapley/Frida/simuleringsresultater/gmc_data_v3/iterative_kernelshap_lingauss_p12_", shapley_reweighting_strategy, ".RData")) +load(paste0("/nr/project/stat/BigInsight/Projects/Explanations/EffektivShapley/Frida/simuleringsresultater/gmc_data_v3/iterative_kernelshap_lingauss_p12_", kernelSHAP_reweighting_strategy, ".RData")) -exact_vals = fread(paste0(sim_results_folder,"exact_shapley_values_", shapley_reweighting_strategy, ".csv")) +exact_vals = fread(paste0(sim_results_folder,"exact_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) # names(exact_vals) <- c("phi0", paste0("VV",1:12)) -iterative_vals = fread(paste0(sim_results_folder,"iterative_shapley_values_", shapley_reweighting_strategy, ".csv")) -approx_vals = fread(paste0(sim_results_folder,"approx_shapley_values_", shapley_reweighting_strategy, ".csv")) +iterative_vals = fread(paste0(sim_results_folder,"iterative_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) +approx_vals = fread(paste0(sim_results_folder,"approx_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) bias_vec <- colMeans(exact_vals - iterative_vals) rmse_vec <- sqrt(colMeans((exact_vals - iterative_vals)^2)) diff --git a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_analyze_results.R b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_analyze_results.R index 51feb23d2..ac35df40c 100644 --- a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_analyze_results.R +++ b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_analyze_results.R @@ -1,18 +1,18 @@ library(data.table) -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" shapley_threshold_val <- 0.2 sim_results_folder = "/nr/project/stat/BigInsight/Projects/Explanations/EffektivShapley/Frida/simuleringsresultater/sim_lingauss_v2/" -load(paste0(sim_results_folder,"iterative_kernelshap_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".RData")) +load(paste0(sim_results_folder,"iterative_kernelshap_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".RData")) -exact_vals = fread(paste0(sim_results_folder,"exact_shapley_values_", shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +exact_vals = fread(paste0(sim_results_folder,"exact_shapley_values_", shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) names(exact_vals) <- c("phi0", paste0("VV",1:12)) -iterative_vals = fread(paste0(sim_results_folder,"iterative_shapley_values_", shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) -approx_vals = fread(paste0(sim_results_folder,"approx_shapley_values_", shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +iterative_vals = fread(paste0(sim_results_folder,"iterative_shapley_values_", shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) +approx_vals = fread(paste0(sim_results_folder,"approx_shapley_values_", shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) bias_vec <- colMeans(exact_vals - iterative_vals) rmse_vec <- sqrt(colMeans((exact_vals - iterative_vals)^2)) diff --git a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R index bedcbfcd0..852175d8f 100644 --- a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R +++ b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R @@ -71,7 +71,7 @@ p0 <- mean(y_train) ### First run proper shapr call on this sim_results_saving_folder = "/nr/project/stat/BigInsight/Projects/Explanations/EffektivShapley/Frida/simuleringsresultater/sim_lingauss_v2/"#"../effektiv_shapley_output/" -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" set.seed(465132) inds = 1:n_explain @@ -82,7 +82,7 @@ expl <- shapr::explain(model = model, approach = "gaussian", prediction_zero = p0,Sigma=Sigma,mu=mu) -fwrite(expl$shapley_values,paste0(sim_results_saving_folder,"exact_shapley_values_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +fwrite(expl$shapley_values,paste0(sim_results_saving_folder,"exact_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) cutoff_feats <- paste0("VV",1:12) @@ -127,7 +127,7 @@ for(kk in testObs_computed_vec){ n_samples = n_samples, gaussian.mu = mu, gaussian.cov_mat = Sigma, - shapley_reweighting_strategy = shapley_reweighting_strategy) + kernelSHAP_reweighting_strategy = kernelSHAP_reweighting_strategy) runres_list[[kk]] <- run$kshap_final runcomps_list[[kk]] <- sum(sapply(run$keep_list,"[[","no_computed_combinations")) print(kk) @@ -135,7 +135,7 @@ for(kk in testObs_computed_vec){ est <- rbindlist(runres_list) est[,other_features:=NULL] -fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) @@ -157,7 +157,7 @@ for (i in testObs_computed_vec){ } expl_approx <- as.data.table(expl_approx) colnames(expl_approx) <- colnames(truth) -fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) bias_vec <- colMeans(est-truth) rmse_vec <- sqrt(colMeans((est-truth)^2)) @@ -167,7 +167,7 @@ bias_vec_approx <- colMeans(expl_approx-truth) rmse_vec_approx <- sqrt(colMeans((expl_approx-truth)^2)) mae_vec_approx <- colMeans(abs(expl_approx-truth)) -save.image(paste0(sim_results_saving_folder, "iterative_kernelshap_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".RData")) +save.image(paste0(sim_results_saving_folder, "iterative_kernelshap_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".RData")) hist(unlist(runcomps_list),breaks = 20) diff --git a/inst/scripts/devel/simtest_iterative_kernelshap_nonlingauss_analyze_results.R b/inst/scripts/devel/simtest_iterative_kernelshap_nonlingauss_analyze_results.R index cc994057e..9888f57f1 100644 --- a/inst/scripts/devel/simtest_iterative_kernelshap_nonlingauss_analyze_results.R +++ b/inst/scripts/devel/simtest_iterative_kernelshap_nonlingauss_analyze_results.R @@ -1,18 +1,18 @@ library(data.table) -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" shapley_threshold_val <- 0.2 sim_results_folder = "/nr/project/stat/BigInsight/Projects/Explanations/EffektivShapley/Frida/simuleringsresultater/sim_nonlingauss_v2/" -load(paste0(sim_results_folder,"iterative_kernelshap_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".RData")) +load(paste0(sim_results_folder,"iterative_kernelshap_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".RData")) -exact_vals = fread(paste0(sim_results_folder,"exact_shapley_values_", shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +exact_vals = fread(paste0(sim_results_folder,"exact_shapley_values_", shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) names(exact_vals) <- c("phi0", paste0("VV",1:12)) -iterative_vals = fread(paste0(sim_results_folder,"iterative_shapley_values_", shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) -approx_vals = fread(paste0(sim_results_folder,"approx_shapley_values_", shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +iterative_vals = fread(paste0(sim_results_folder,"iterative_shapley_values_", shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) +approx_vals = fread(paste0(sim_results_folder,"approx_shapley_values_", shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) bias_vec <- colMeans(exact_vals - iterative_vals) rmse_vec <- sqrt(colMeans((exact_vals - iterative_vals)^2)) diff --git a/inst/scripts/devel/simtest_reweighting_strategies.R b/inst/scripts/devel/simtest_reweighting_strategies.R index b1356faa6..950315ac4 100644 --- a/inst/scripts/devel/simtest_reweighting_strategies.R +++ b/inst/scripts/devel/simtest_reweighting_strategies.R @@ -52,7 +52,7 @@ p0 <- mean(y_train) ### First run proper shapr call on this -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" set.seed(465132) progressr::handlers(global = TRUE) @@ -66,7 +66,7 @@ expl <- shapr::explain(model = model, dt_vS_map <- merge(expl$internal$iter_list[[1]]$coalition_map,expl$internal$output$dt_vS,by="id_coalition")[,-"id_coalition"] -shapley_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond") +kernelSHAP_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond") n_coalitions_vec <- c(50,100,200,400,800,1200,1600,2000,2400,2800,3200,3600,4000) @@ -97,7 +97,7 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ mu=mu, seed = this_seed, max_n_coalitions = this_n_coalitions, - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", paired_shap_sampling = this_paired_shap_sampling) this0_X <- this$internal$objects$X @@ -107,12 +107,12 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ setorder(exact_dt_vS,id_coalition) - for(iii in seq_along(shapley_reweighting_strategy_vec)){ - this_shapley_reweighting_strategy <- shapley_reweighting_strategy_vec[iii] + for(iii in seq_along(kernelSHAP_reweighting_strategy_vec)){ + this_kernelSHAP_reweighting_strategy <- kernelSHAP_reweighting_strategy_vec[iii] this_X <- copy(this0_X) - shapr:::shapley_reweighting(this_X,reweight=this_shapley_reweighting_strategy) + shapr:::kernelSHAP_reweighting(this_X,reweight=this_kernelSHAP_reweighting_strategy) this_W <- weight_matrix( X = this_X, @@ -130,7 +130,7 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ res_vec <- data.table(n_coalitions = this_n_coalitions, paired_shap_sampling = this_paired_shap_sampling, - shapley_reweighting_strategy = this_shapley_reweighting_strategy, + kernelSHAP_reweighting_strategy = this_kernelSHAP_reweighting_strategy, seed = this_seed, bias=this_bias, var = this_var, @@ -154,11 +154,11 @@ res_dt <- rbindlist(res_list) fwrite(res_dt,file = "../../Div/extra_shapr_scripts_etc/res_dt_reweighting_sims_lingaus.csv") -resres <- res_dt[,lapply(.SD,mean),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,shapley_reweighting_strategy)] +resres <- res_dt[,lapply(.SD,mean),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,kernelSHAP_reweighting_strategy)] library(ggplot2) -ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=shapley_reweighting_strategy,linetype= paired_shap_sampling))+ +ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ geom_line() @@ -204,7 +204,7 @@ for(kk in testObs_computed_vec){ n_samples = n_samples, gaussian.mu = mu, gaussian.cov_mat = Sigma, - shapley_reweighting_strategy = shapley_reweighting_strategy) + kernelSHAP_reweighting_strategy = kernelSHAP_reweighting_strategy) runres_list[[kk]] <- run$kshap_final runcomps_list[[kk]] <- sum(sapply(run$keep_list,"[[","no_computed_combinations")) print(kk) @@ -212,7 +212,7 @@ for(kk in testObs_computed_vec){ est <- rbindlist(runres_list) est[,other_features:=NULL] -fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) @@ -234,7 +234,7 @@ for (i in testObs_computed_vec){ } expl_approx <- as.data.table(expl_approx) colnames(expl_approx) <- colnames(truth) -fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".csv")) +fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) bias_vec <- colMeans(est-truth) rmse_vec <- sqrt(colMeans((est-truth)^2)) @@ -244,7 +244,7 @@ bias_vec_approx <- colMeans(expl_approx-truth) rmse_vec_approx <- sqrt(colMeans((expl_approx-truth)^2)) mae_vec_approx <- colMeans(abs(expl_approx-truth)) -save.image(paste0(sim_results_saving_folder, "iterative_kernelshap_",shapley_threshold_val,"_",shapley_reweighting_strategy, ".RData")) +save.image(paste0(sim_results_saving_folder, "iterative_kernelshap_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".RData")) hist(unlist(runcomps_list),breaks = 20) diff --git a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R index 5e4d09522..d04203af4 100644 --- a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R +++ b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R @@ -69,7 +69,7 @@ p0 <- mean(y_train) ### First run proper shapr call on this -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" set.seed(465132) progressr::handlers(global = TRUE) @@ -83,7 +83,7 @@ expl <- shapr::explain(model = model, dt_vS_map <- merge(expl$internal$iter_list[[1]]$coalition_map,expl$internal$output$dt_vS,by="id_coalition")[,-"id_coalition"] -shapley_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond") +kernelSHAP_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond") n_coalitions_vec <- c(50,100,200,400,800,1200,1600,2000,2400,2800,3200,3600,4000) @@ -114,7 +114,7 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ mu=mu, seed = this_seed, max_n_coalitions = this_n_coalitions, - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", paired_shap_sampling = this_paired_shap_sampling) this0_X <- this$internal$objects$X @@ -124,12 +124,12 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ setorder(exact_dt_vS,id_coalition) - for(iii in seq_along(shapley_reweighting_strategy_vec)){ - this_shapley_reweighting_strategy <- shapley_reweighting_strategy_vec[iii] + for(iii in seq_along(kernelSHAP_reweighting_strategy_vec)){ + this_kernelSHAP_reweighting_strategy <- kernelSHAP_reweighting_strategy_vec[iii] this_X <- copy(this0_X) - shapr:::shapley_reweighting(this_X,reweight=this_shapley_reweighting_strategy) + shapr:::kernelSHAP_reweighting(this_X,reweight=this_kernelSHAP_reweighting_strategy) this_W <- weight_matrix( X = this_X, @@ -147,7 +147,7 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ res_vec <- data.table(n_coalitions = this_n_coalitions, paired_shap_sampling = this_paired_shap_sampling, - shapley_reweighting_strategy = this_shapley_reweighting_strategy, + kernelSHAP_reweighting_strategy = this_kernelSHAP_reweighting_strategy, seed = this_seed, bias=this_bias, var = this_var, @@ -171,12 +171,12 @@ res_dt <- rbindlist(res_list) fwrite(res_dt,file = "../../Div/extra_shapr_scripts_etc/res_dt_reweighting_sims_nonlingaus.csv") -resres <- res_dt[,lapply(.SD,mean),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,shapley_reweighting_strategy)] +resres <- res_dt[,lapply(.SD,mean),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,kernelSHAP_reweighting_strategy)] library(ggplot2) -ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=shapley_reweighting_strategy,linetype= paired_shap_sampling))+ +ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ geom_line() -ggplot(resres[paired_shap_sampling==FALSE],aes(x=n_coalitions,y=MAE,col=shapley_reweighting_strategy,linetype= paired_shap_sampling))+ +ggplot(resres[paired_shap_sampling==FALSE],aes(x=n_coalitions,y=MAE,col=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ geom_line() diff --git a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R index 82c573e90..4739b5429 100644 --- a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R +++ b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R @@ -69,7 +69,7 @@ p0 <- mean(y_train) ### First run proper shapr call on this -shapley_reweighting_strategy = "none" +kernelSHAP_reweighting_strategy = "none" set.seed(465132) progressr::handlers(global = TRUE) @@ -83,7 +83,7 @@ expl <- shapr::explain(model = model, dt_vS_map <- merge(expl$internal$iter_list[[1]]$coalition_map,expl$internal$output$dt_vS,by="id_coalition")[,-"id_coalition"] -shapley_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond","on_all_cond_paired","comb") +kernelSHAP_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond","on_all_cond_paired","comb") n_coalitions_vec <- c(50,100,200,400,800,1200,1600,2000,2400,2800,3200,3600,4000) @@ -114,7 +114,7 @@ for(ii in seq_along(n_coalitions_vec)){ mu=mu, seed = this_seed, max_n_coalitions = this_n_coalitions, - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", unique_sampling = TRUE, paired_shap_sampling = this_paired_shap_sampling) @@ -125,12 +125,12 @@ for(ii in seq_along(n_coalitions_vec)){ setorder(exact_dt_vS,id_coalition) - for(iii in seq_along(shapley_reweighting_strategy_vec)){ - this_shapley_reweighting_strategy <- shapley_reweighting_strategy_vec[iii] + for(iii in seq_along(kernelSHAP_reweighting_strategy_vec)){ + this_kernelSHAP_reweighting_strategy <- kernelSHAP_reweighting_strategy_vec[iii] this_X <- copy(this0_X) - shapr:::shapley_reweighting(this_X,reweight=this_shapley_reweighting_strategy) + shapr:::kernelSHAP_reweighting(this_X,reweight=this_kernelSHAP_reweighting_strategy) this_W <- weight_matrix( X = this_X, @@ -148,7 +148,7 @@ for(ii in seq_along(n_coalitions_vec)){ res_vec <- data.table(n_coalitions = this_n_coalitions, paired_shap_sampling = this_paired_shap_sampling, - shapley_reweighting_strategy = this_shapley_reweighting_strategy, + kernelSHAP_reweighting_strategy = this_kernelSHAP_reweighting_strategy, seed = this_seed, bias=this_bias, var = this_var, @@ -162,7 +162,7 @@ for(ii in seq_along(n_coalitions_vec)){ weight_dt[,n_coalitions:=this_n_coalitions] weight_dt[,paired_shap_sampling:=this_paired_shap_sampling] - weight_dt[,shapley_reweighting_strategy:=this_shapley_reweighting_strategy] + weight_dt[,kernelSHAP_reweighting_strategy:=this_kernelSHAP_reweighting_strategy] weight_dt[,seed:=this_seed] weight_list[[length(weight_list)+1]] <- copy(weight_dt) @@ -184,19 +184,19 @@ res_dt <- rbindlist(res_list) fwrite(res_dt,file = "../../Div/extra_shapr_scripts_etc/res_dt_reweighting_sims_nonlingaus_nonunique_sampling_new.csv") -resres <- res_dt[,lapply(.SD,mean),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,shapley_reweighting_strategy)] -resres_sd <- res_dt[,lapply(.SD,sd),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,shapley_reweighting_strategy)] +resres <- res_dt[,lapply(.SD,mean),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,kernelSHAP_reweighting_strategy)] +resres_sd <- res_dt[,lapply(.SD,sd),.SDcols=c("bias","var","MAE","RMSE"),by=.(paired_shap_sampling,n_coalitions,kernelSHAP_reweighting_strategy)] library(ggplot2) -ggplot(resres,aes(x=n_coalitions,y=MAE,col=shapley_reweighting_strategy,linetype= paired_shap_sampling))+ +ggplot(resres,aes(x=n_coalitions,y=MAE,col=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ geom_line() -ggplot(resres[paired_shap_sampling==FALSE],aes(x=n_coalitions,y=MAE,col=shapley_reweighting_strategy,linetype= paired_shap_sampling))+ +ggplot(resres[paired_shap_sampling==FALSE],aes(x=n_coalitions,y=MAE,col=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ geom_line()+scale_y_log10() -ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=shapley_reweighting_strategy,linetype= paired_shap_sampling))+ +ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ geom_line()+scale_y_log10() @@ -205,13 +205,13 @@ ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=shapley_r weight_dt <- rbindlist(weight_list) -weight_dt[!(coalition_size%in%c(0,12)),sum_shapley_weight:=sum(shapley_weight),by=.(seed,paired_shap_sampling,n_coalitions,shapley_reweighting_strategy)] +weight_dt[!(coalition_size%in%c(0,12)),sum_shapley_weight:=sum(shapley_weight),by=.(seed,paired_shap_sampling,n_coalitions,kernelSHAP_reweighting_strategy)] weight_dt[!(coalition_size%in%c(0,12)),shapley_weight:=shapley_weight/sum_shapley_weight] -weight_dt[!(coalition_size%in%c(0,12)),mean(shapley_weight),by=.(seed,paired_shap_sampling,n_coalitions,shapley_reweighting_strategy)] +weight_dt[!(coalition_size%in%c(0,12)),mean(shapley_weight),by=.(seed,paired_shap_sampling,n_coalitions,kernelSHAP_reweighting_strategy)] -ww_dt <- weight_dt[!(coalition_size%in%c(0,12)),list(mean_weight=mean(shapley_weight)),by=.(coalition_size,paired_shap_sampling,n_coalitions,shapley_reweighting_strategy)] +ww_dt <- weight_dt[!(coalition_size%in%c(0,12)),list(mean_weight=mean(shapley_weight)),by=.(coalition_size,paired_shap_sampling,n_coalitions,kernelSHAP_reweighting_strategy)] -ggplot(ww_dt[paired_shap_sampling==TRUE & shapley_reweighting_strategy %in% c("none","on_all_cond_paired","on_N")],aes(x=coalition_size,y=mean_weight,col=shapley_reweighting_strategy))+ +ggplot(ww_dt[paired_shap_sampling==TRUE & kernelSHAP_reweighting_strategy %in% c("none","on_all_cond_paired","on_N")],aes(x=coalition_size,y=mean_weight,col=kernelSHAP_reweighting_strategy))+ geom_point()+facet_grid(~n_coalitions) diff --git a/inst/scripts/devel/simtest_timing_to_Frida.R b/inst/scripts/devel/simtest_timing_to_Frida.R index 0aafa2621..e221358a4 100644 --- a/inst/scripts/devel/simtest_timing_to_Frida.R +++ b/inst/scripts/devel/simtest_timing_to_Frida.R @@ -92,7 +92,7 @@ expl <- explain( max_iter = 10, convergence_tolerance = 10^(-10), compute_sd = TRUE), - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", print_iter_info = TRUE ) diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index 64c450793..27d73c0ca 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -14,7 +14,7 @@ aa = explain( ), iterative = TRUE, print_shapleyres = TRUE, - print_iter_info = TRUE,shapley_reweighting = "on_N" + print_iter_info = TRUE,kernelSHAP_reweighting = "on_N" ) bb = explain( @@ -31,7 +31,7 @@ bb = explain( ), iterative = TRUE, print_shapleyres = TRUE, - print_iter_info = TRUE,shapley_reweighting = "on_N",prev_shapr_object = aa + print_iter_info = TRUE,kernelSHAP_reweighting = "on_N",prev_shapr_object = aa ) @@ -57,7 +57,7 @@ full = explain( iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, - shapley_reweighting = "on_N", + kernelSHAP_reweighting = "on_N", seed=NULL ) @@ -77,7 +77,7 @@ first = explain( iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, - shapley_reweighting = "on_N", + kernelSHAP_reweighting = "on_N", seed=NULL ) @@ -97,7 +97,7 @@ second = explain( iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, - shapley_reweighting = "on_N", + kernelSHAP_reweighting = "on_N", seed=NULL, prev_shapr_object = first ) @@ -120,7 +120,7 @@ second_path = explain( iterative = TRUE, print_shapleyres = TRUE, print_iter_info = TRUE, - shapley_reweighting = "on_N", + kernelSHAP_reweighting = "on_N", seed=NULL, prev_shapr_object = first$internal$parameters$iterative_args$saving_path ) diff --git a/man/create_coalition_table.Rd b/man/create_coalition_table.Rd index dd0b12723..cd6d25fd4 100644 --- a/man/create_coalition_table.Rd +++ b/man/create_coalition_table.Rd @@ -13,7 +13,7 @@ create_coalition_table( prev_coal_samples = NULL, coal_feature_list = as.list(seq_len(m)), approach0 = "gaussian", - shapley_reweighting = "none" + kernelSHAP_reweighting = "none" ) } \arguments{ @@ -41,7 +41,7 @@ A list mapping each coalition to the features it contains.} \item{approach0}{Character vector. Contains the approach to be used for eastimation of each coalition size. Same as \code{approach} in \code{explain()}.} -\item{shapley_reweighting}{String. +\item{kernelSHAP_reweighting}{String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing the randomness and thereby the variance of the Shapley value estimates. One of \code{'none'}, \code{'on_N'}, \code{'on_all'}, \code{'on_all_cond'} (default). diff --git a/man/explain.Rd b/man/explain.Rd index d618fcb31..02cc4ab95 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -22,7 +22,7 @@ explain( MSEv_uniform_comb_weights = TRUE, verbose = "basic", iterative_args = list(), - shapley_reweighting = "on_all_cond", + kernelSHAP_reweighting = "on_all_cond", prev_shapr_object = NULL, ... ) @@ -148,7 +148,7 @@ E.g. \code{verbose = c("basic", "vS_details")} will display basic information + Specifices the arguments for the iterative procedure. See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} -\item{shapley_reweighting}{String. +\item{kernelSHAP_reweighting}{String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing the randomness and thereby the variance of the Shapley value estimates. One of \code{'none'}, \code{'on_N'}, \code{'on_all'}, \code{'on_all_cond'} (default). diff --git a/man/setup.Rd b/man/setup.Rd index 9f9170d1b..1c4333fc8 100644 --- a/man/setup.Rd +++ b/man/setup.Rd @@ -30,7 +30,7 @@ setup( verbose, iterative = NULL, iterative_args = list(), - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", is_python = FALSE, testing = FALSE, init_time = NULL, @@ -178,7 +178,7 @@ Specifics related to the iterative process and convergence criterion are set thr Specifices the arguments for the iterative procedure. See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} -\item{shapley_reweighting}{String. +\item{kernelSHAP_reweighting}{String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing the randomness and thereby the variance of the Shapley value estimates. One of \code{'none'}, \code{'on_N'}, \code{'on_all'}, \code{'on_all_cond'} (default). From d06d54401c5e709811aa240ae4cfd02dabab03e4 Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 16:38:54 +0200 Subject: [PATCH 05/32] move saving_path to output_args --- R/cli.R | 2 +- R/explain.R | 2 +- R/print_iter.R | 2 +- R/save_results.R | 2 +- R/setup.R | 55 ++++++++++--------- .../devel/testing_intermediate_saving.R | 2 +- tests/testthat/test-adaptive-output.R | 2 +- vignettes/understanding_shapr.Rmd | 6 +- vignettes/understanding_shapr.Rmd.orig | 6 +- 9 files changed, 41 insertions(+), 38 deletions(-) diff --git a/R/cli.R b/R/cli.R index 7fee48960..86466e25d 100644 --- a/R/cli.R +++ b/R/cli.R @@ -6,7 +6,7 @@ cli_startup <- function(internal, model, verbose) { iterative <- internal$parameters$iterative n_shapley_values <- internal$parameters$n_shapley_values n_explain <- internal$parameters$n_explain - saving_path <- internal$parameters$iterative_args$saving_path + saving_path <- internal$parameters$saving_path feat_group_txt <- ifelse(is_groupwise, "group-wise", "feature-wise") iterative_txt <- ifelse(iterative, "iterative", "non-iterative") diff --git a/R/explain.R b/R/explain.R index 2ab92caac..8aeed87d1 100644 --- a/R/explain.R +++ b/R/explain.R @@ -516,7 +516,7 @@ testing_cleanup <- function(output) { } # Delete the saving_path - output$internal$parameters$iterative_args$saving_path <- NULL + output$internal$parameters$saving_path <- NULL return(output) } diff --git a/R/print_iter.R b/R/print_iter.R index da915fc13..090facdca 100644 --- a/R/print_iter.R +++ b/R/print_iter.R @@ -16,7 +16,7 @@ print_iter <- function(internal) { overall_conv_measure <- internal$iter_list[[iter]]$overall_conv_measure reduction_factor <- internal$iter_list[[iter]]$reduction_factor - saving_path <- internal$parameters$iterative_args$saving_path + saving_path <- internal$parameters$saving_path convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance testing <- internal$parameters$testing diff --git a/R/save_results.R b/R/save_results.R index 722e30a88..2f68b5071 100644 --- a/R/save_results.R +++ b/R/save_results.R @@ -5,7 +5,7 @@ #' @export #' @keywords internal save_results <- function(internal) { - saving_path <- internal$parameters$iterative_args$saving_path + saving_path <- internal$parameters$saving_path # Modify name for the new file filename <- basename(saving_path) diff --git a/R/setup.R b/R/setup.R index 8e846d057..6b8013912 100644 --- a/R/setup.R +++ b/R/setup.R @@ -729,16 +729,35 @@ set_output_parameters <- function(internal) { # keep_samp_for_vS if (!(is.logical(keep_samp_for_vS) && length(keep_samp_for_vS) == 1)) { - stop("`keep_samp_for_vS` must be single logical.") + stop("`output_args$keep_samp_for_vS` must be single logical.") } # Parameter used in the MSEv evaluation criterion if (!(is.logical(MSEv_uniform_comb_weights) && length(MSEv_uniform_comb_weights) == 1)) { - stop("`MSEv_uniform_comb_weights` must be single logical.") + stop("`output_args$MSEv_uniform_comb_weights` must be single logical.") } + # saving_path + if (!(is.character(saving_path) && + length(saving_path) == 1)) { + stop("`output_args$saving_path` must be a single character.") + } + + # Also check that saving_path exists, and abort if not... + if (!dir.exists(dirname(saving_path))) { + stop( + paste0( + "Directory ", dirname(saving_path), " in the output_args$saving_path does not exists.\n", + "Please create the directory with `dir.create('", dirname(saving_path), "')` or use another directory." + ) + ) + } + + + internal$parameters$MSEv_uniform_comb_weights <- MSEv_uniform_comb_weights internal$parameters$keep_samp_for_vS <- keep_samp_for_vS + internal$parameters$saving_path <- saving_path return(internal) } @@ -753,10 +772,14 @@ set_output_parameters <- function(internal) { #' If `FALSE`, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv #' criterion. #' Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered. +#' @param saving_path String. +#' The path to the directory where the results of the iterative estimation procedure should be saved. +#' Defaults to a temporary directory. #' @export #' @author Martin Jullum get_output_args_default <- function(keep_samp_for_vS = FALSE, - MSEv_uniform_comb_weights = TRUE){ + MSEv_uniform_comb_weights = TRUE, + saving_path = tempfile("shapr_obj_", fileext = ".rds")){ return(mget(methods::formalArgs(get_output_args_default))) } @@ -1170,21 +1193,7 @@ check_iterative_args <- function(iterative_args) { stop("`iterative_args$max_batch_size` must be NULL, Inf or a single positive integer.") } - # saving_path - if (!(is.character(saving_path) && - length(saving_path) == 1)) { - stop("`iterative_args$saving_path` must be a single character.") - } - # Check that the saving_path exists, and abort if not... - if (!dir.exists(dirname(saving_path))) { - stop( - paste0( - "Directory ", dirname(saving_path), " in the iterative_args$saving_path does not exists.\n", - "Please create the directory with `dir.create('", dirname(saving_path), "')` or use another directory." - ) - ) - } } trans_null_iterative_args <- function(iterative_args) { @@ -1281,9 +1290,6 @@ check_vs_prev_shapr_object <- function(internal) { #' @param min_n_batches Integer. The minimum number of batches to split the computation into within each iteration. #' Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller #' than the number of parallel workers. -#' @param saving_path String. -#' The path to the directory where the results of the iterative estimation procedure should be saved. -#' Defaults to a temporary directory. #' @inheritParams default_doc_explain #' #' @export @@ -1306,8 +1312,7 @@ get_iterative_args_default <- function(internal, n_boot_samps = 100, compute_sd = isTRUE(internal$parameters$iterative), max_batch_size = 10, - min_n_batches = 10, - saving_path = tempfile("shapr_obj_", fileext = ".rds")) { + min_n_batches = 10) { iterative <- internal$parameters$iterative max_n_coalitions <- internal$parameters$max_n_coalitions exact <- internal$parameters$exact @@ -1325,8 +1330,7 @@ get_iterative_args_default <- function(internal, "n_boot_samps", "compute_sd", "max_batch_size", - "min_n_batches", - "saving_path" + "min_n_batches" ) ) } else { @@ -1340,8 +1344,7 @@ get_iterative_args_default <- function(internal, n_boot_samps = n_boot_samps, compute_sd = isFALSE(exact) && isFALSE(is_groupwise), max_batch_size = max_batch_size, - min_n_batches = min_n_batches, - saving_path = saving_path + min_n_batches = min_n_batches ) } return(ret_list) diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index 27d73c0ca..17496e6c2 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -122,7 +122,7 @@ second_path = explain( print_iter_info = TRUE, kernelSHAP_reweighting = "on_N", seed=NULL, - prev_shapr_object = first$internal$parameters$iterative_args$saving_path + prev_shapr_object = first$internal$parameters$saving_path ) diff --git a/tests/testthat/test-adaptive-output.R b/tests/testthat/test-adaptive-output.R index da63d3956..11437e256 100644 --- a/tests/testthat/test-adaptive-output.R +++ b/tests/testthat/test-adaptive-output.R @@ -237,7 +237,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { ), iterative = TRUE, verbose = NULL, - prev_shapr_object = e_init_path$internal$parameters$iterative_args$saving_path, + prev_shapr_object = e_init_path$internal$parameters$saving_path, seed = NULL ), "output_lm_numeric_independence_cont_est_path" diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index 4371b0ef3..49113bc35 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -1944,7 +1944,7 @@ In this section, we demonstrate how to continue to improve estimation accuracy w from a previous Shapley value computation based on `shapr::explain()` with the iterative estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$iterative_args$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$internal$parameters$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. @@ -2035,7 +2035,7 @@ ex_further <- explain( #> #> ℹ Using 26 of 32 coalitions. -print(ex_further$internal$parameters$iterative_args$saving_path) +print(ex_further$internal$parameters$saving_path) #> [1] "/tmp/RtmpPIciRY/shapr_obj_c1876481fd8a.rds" # Using the ex_init object to continue the computation for the remaining coalition samples @@ -2047,7 +2047,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$iterative_args$saving_path + prev_shapr_object = ex_further$internal$parameters$saving_path ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index 890119f18..f3a142201 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -1522,7 +1522,7 @@ In this section, we demonstrate how to continue to improve estimation accuracy w from a previous Shapley value computation based on `shapr::explain()` with the iterative estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$iterative_args$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$internal$parameters$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. @@ -1580,7 +1580,7 @@ ex_further <- explain( prev_shapr_object = ex_init ) -print(ex_further$internal$parameters$iterative_args$saving_path) +print(ex_further$internal$parameters$saving_path) # Using the ex_init object to continue the computation for the remaining coalition samples # but this time using the path to the saved intermediate estimation object @@ -1591,7 +1591,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$iterative_args$saving_path + prev_shapr_object = ex_further$internal$parameters$saving_path ) From 4f4e56712380fd1f3fd6afcf5d86f33faebddbf0 Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 20:31:13 +0200 Subject: [PATCH 06/32] [skip actions] make saving_path and iterative_results easier to access --- R/documentation.R | 2 +- R/explain.R | 1 + R/finalize_explanation.R | 8 +++++--- tests/testthat/test-adaptive-output.R | 2 +- tests/testthat/test-adaptive-setup.R | 4 ++-- vignettes/understanding_shapr.Rmd | 6 +++--- vignettes/understanding_shapr.Rmd.orig | 6 +++--- 7 files changed, 16 insertions(+), 13 deletions(-) diff --git a/R/documentation.R b/R/documentation.R index 734fe20cd..51e0eb7d2 100644 --- a/R/documentation.R +++ b/R/documentation.R @@ -3,7 +3,7 @@ #' @param internal List. #' Holds all parameters, data, functions and computed objects used within [explain()] #' The list contains one or more of the elements `parameters`, `data`, `objects`, `iter_list`, `timing_list`, -#' `main_timing_list`, `output`, `iter_timing_list` and `iter_results`. +#' `main_timing_list`, `output`, and `iter_timing_list`. #' #' @param model Objects. #' The model object that ought to be explained. diff --git a/R/explain.R b/R/explain.R index 8aeed87d1..fc637988f 100644 --- a/R/explain.R +++ b/R/explain.R @@ -517,6 +517,7 @@ testing_cleanup <- function(output) { # Delete the saving_path output$internal$parameters$saving_path <- NULL + output$saving_path <- NULL return(output) } diff --git a/R/finalize_explanation.R b/R/finalize_explanation.R index a4c5d9bf5..c5733e397 100644 --- a/R/finalize_explanation.R +++ b/R/finalize_explanation.R @@ -45,14 +45,16 @@ finalize_explanation <- function(internal) { } # Extract iterative results in a simplified format - internal$iter_results <- get_iter_results(internal$iter_list) + iterative_results <- get_iter_results(internal$iter_list) output <- list( shapley_values = dt_shapley_est, shapley_values_sd = dt_shapley_sd, - internal = internal, pred_explain = p, - MSEv = MSEv + MSEv = MSEv, + iterative_results = iterative_results, + saving_path = internal$parameters$saving_path, + internal = internal ) attr(output, "class") <- c("shapr", "list") diff --git a/tests/testthat/test-adaptive-output.R b/tests/testthat/test-adaptive-output.R index 11437e256..e0e9887f0 100644 --- a/tests/testthat/test-adaptive-output.R +++ b/tests/testthat/test-adaptive-output.R @@ -237,7 +237,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { ), iterative = TRUE, verbose = NULL, - prev_shapr_object = e_init_path$internal$parameters$saving_path, + prev_shapr_object = e_init_path$saving_path, seed = NULL ), "output_lm_numeric_independence_cont_est_path" diff --git a/tests/testthat/test-adaptive-setup.R b/tests/testthat/test-adaptive-setup.R index ddd9b88bd..3f8ebc3c8 100644 --- a/tests/testthat/test-adaptive-setup.R +++ b/tests/testthat/test-adaptive-setup.R @@ -21,7 +21,7 @@ test_that("iterative_args are respected", { # Check that max_iter is respected expect_equal(length(ex$internal$iter_list), 8) - expect_true(ex$internal$iter_results$iter_info_dt[.N, converged_max_iter]) + expect_true(ex$iterative_results$iter_info_dt[.N, converged_max_iter]) }) @@ -66,7 +66,7 @@ test_that("iterative feature wise and groupwise computations identical", { # Checking equality in the list with all final and intermediate results - expect_equal(expl_feat$internal$iter_results, expl_group$internal$iter_results) + expect_equal(expl_feat$iter_results, expl_group$iter_results) }) test_that("erroneous input: `min_n_batches`", { diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index 49113bc35..75fed4089 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -1944,7 +1944,7 @@ In this section, we demonstrate how to continue to improve estimation accuracy w from a previous Shapley value computation based on `shapr::explain()` with the iterative estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. @@ -2035,7 +2035,7 @@ ex_further <- explain( #> #> ℹ Using 26 of 32 coalitions. -print(ex_further$internal$parameters$saving_path) +print(ex_further$saving_path) #> [1] "/tmp/RtmpPIciRY/shapr_obj_c1876481fd8a.rds" # Using the ex_init object to continue the computation for the remaining coalition samples @@ -2047,7 +2047,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$saving_path + prev_shapr_object = ex_further$saving_path ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index f3a142201..980018e64 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -1522,7 +1522,7 @@ In this section, we demonstrate how to continue to improve estimation accuracy w from a previous Shapley value computation based on `shapr::explain()` with the iterative estimation procedure. This can be done either by passing an existing object of class `shapr`, or by passing a string with the path to the intermediately saved results. -The latter is found at `SHAPR_OBJ$internal$parameters$saving_path`, defaults to a temporary folder, +The latter is found at `SHAPR_OBJ$saving_path`, defaults to a temporary folder, and is updated after each iteration. This can be particularly handy for long-running computations. @@ -1580,7 +1580,7 @@ ex_further <- explain( prev_shapr_object = ex_init ) -print(ex_further$internal$parameters$saving_path) +print(ex_further$saving_path) # Using the ex_init object to continue the computation for the remaining coalition samples # but this time using the path to the saved intermediate estimation object @@ -1591,7 +1591,7 @@ ex_even_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = NULL, - prev_shapr_object = ex_further$internal$parameters$saving_path + prev_shapr_object = ex_further$saving_path ) From ee0d6abe6eecfd329d09fcd95ad6fb1dbc42dc8f Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 20:41:36 +0200 Subject: [PATCH 07/32] new input order for explain --- R/explain.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R/explain.R b/R/explain.R index fc637988f..c81b4b858 100644 --- a/R/explain.R +++ b/R/explain.R @@ -340,19 +340,19 @@ explain <- function(model, x_explain, x_train, approach, - paired_shap_sampling = TRUE, prediction_zero, - max_n_coalitions = NULL, iterative = NULL, + max_n_coalitions = NULL, group = NULL, + paired_shap_sampling = TRUE, n_MC_samples = 1e3, + kernelSHAP_reweighting = "on_all_cond", seed = 1, + verbose = "basic", predict_model = NULL, get_model_specs = NULL, - verbose = "basic", - iterative_args = list(), - kernelSHAP_reweighting = "on_all_cond", prev_shapr_object = NULL, + iterative_args = list(), output_args = list(), ...) { # ... is further arguments passed to specific approaches From f2ee13cab8cd0477761035170fe5f5212a99d682 Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 22:01:28 +0200 Subject: [PATCH 08/32] move compute_sd, min_n_batches, max_batch_size, n_boot_samples to newarg + doc --- NAMESPACE | 3 +- R/approach_vaeac.R | 2 +- R/cli.R | 2 +- R/compute_estimates.R | 4 +- R/compute_vS.R | 4 +- R/explain.R | 6 +- R/finalize_explanation.R | 6 +- R/print_iter.R | 2 +- R/save_results.R | 2 +- R/setup.R | 215 +++++++++++------- .../devel/testing_intermediate_saving.R | 2 +- man/compute_MSEv_eval_crit.Rd | 8 +- man/compute_shapley_new.Rd | 2 +- man/compute_vS_forecast.Rd | 2 +- man/default_doc.Rd | 2 +- man/explain.Rd | 119 +++++----- man/explain_forecast.Rd | 4 - man/finalize_explanation_forecast.Rd | 2 +- ...fault.Rd => get_iterative_args_default.Rd} | 7 +- man/get_output_args_default.Rd | 33 +++ man/regression.check_parameters.Rd | 2 +- man/regression.get_y_hat.Rd | 2 +- man/regression.surrogate_aug_data.Rd | 2 +- man/setup.Rd | 17 +- man/setup_computation.Rd | 2 +- man/test_predict_model.Rd | 2 +- python/shaprpy/explain.py | 4 +- tests/testthat/test-adaptive-setup.R | 20 +- tests/testthat/test-regular-setup.R | 4 +- vignettes/understanding_shapr.Rmd | 4 +- vignettes/understanding_shapr.Rmd.orig | 4 +- 31 files changed, 271 insertions(+), 219 deletions(-) rename man/{get_adaptive_arguments_default.Rd => get_iterative_args_default.Rd} (92%) create mode 100644 man/get_output_args_default.Rd diff --git a/NAMESPACE b/NAMESPACE index 718f5249c..7a35bfa90 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -67,11 +67,12 @@ export(explain) export(explain_forecast) export(finalize_explanation) export(finalize_explanation_forecast) -export(get_iterative_args_default) export(get_cov_mat) export(get_data_specs) +export(get_iterative_args_default) export(get_model_specs) export(get_mu_vec) +export(get_output_args_default) export(get_supported_approaches) export(hat_matrix_cpp) export(mahalanobis_distance_cpp) diff --git a/R/approach_vaeac.R b/R/approach_vaeac.R index 63872648f..9a6e9e9fb 100644 --- a/R/approach_vaeac.R +++ b/R/approach_vaeac.R @@ -1656,7 +1656,7 @@ vaeac_check_parameters <- function(x_train, #' each batch when generating the Monte Carlo samples. If `NULL`, then the function generates the Monte Carlo samples #' for the provided coalitions and all explicands sent to [shapr::explain()] at the time. #' The number of coalitions are determined by the `n_batches` used by [shapr::explain()]. We recommend to tweak -#' `iterative_args$max_batch_size` and `iterative_args$min_n_batches` +#' `extra_estimation_args$max_batch_size` and `extra_estimation_args$min_n_batches` #' rather than `vaeac.batch_size_sampling`. Larger batch sizes are often much faster provided sufficient memory. #' @param vaeac.running_avg_n_values Positive integer (default is `5`). The number of previous IWAE values to include #' when we compute the running means of the IWAE criterion. diff --git a/R/cli.R b/R/cli.R index 86466e25d..4537d2473 100644 --- a/R/cli.R +++ b/R/cli.R @@ -6,7 +6,7 @@ cli_startup <- function(internal, model, verbose) { iterative <- internal$parameters$iterative n_shapley_values <- internal$parameters$n_shapley_values n_explain <- internal$parameters$n_explain - saving_path <- internal$parameters$saving_path + saving_path <- internal$parameters$output_args$saving_path feat_group_txt <- ifelse(is_groupwise, "group-wise", "feature-wise") iterative_txt <- ifelse(iterative, "iterative", "non-iterative") diff --git a/R/compute_estimates.R b/R/compute_estimates.R index 8a4c9cd5a..4f2c747c3 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -16,7 +16,7 @@ compute_estimates <- function(internal, vS_list) { iter <- length(internal$iter_list) compute_sd <- internal$iter_list[[iter]]$compute_sd - n_boot_samps <- internal$parameters$iterative_args$n_boot_samps + n_boot_samps <- internal$parameters$extra_estimation_args$n_boot_samps processed_vS_list <- postprocess_vS_list( vS_list = vS_list, @@ -73,7 +73,7 @@ compute_estimates <- function(internal, vS_list) { #' @keywords internal postprocess_vS_list <- function(vS_list, internal) { - keep_samp_for_vS <- internal$parameters$keep_samp_for_vS + keep_samp_for_vS <- internal$parameters$output_args$keep_samp_for_vS prediction_zero <- internal$parameters$prediction_zero n_explain <- internal$parameters$n_explain diff --git a/R/compute_vS.R b/R/compute_vS.R index e235810bf..ead0aaf94 100644 --- a/R/compute_vS.R +++ b/R/compute_vS.R @@ -115,7 +115,7 @@ batch_compute_vS <- function(S, internal, model, predict_model, p = NULL) { if (regression) { dt_vS <- batch_prepare_vS_regression(S = S, internal = internal) } else { - # Here dt_vS is either only dt_vS or a list containing dt_vS and dt if internal$parameters$keep_samp_for_vS = TRUE + # Here dt_vS is either only dt_vS or a list containing dt_vS and dt if internal$parameters$output_args$keep_samp_for_vS = TRUE dt_vS <- batch_prepare_vS_MC(S = S, internal = internal, model = model, predict_model = predict_model) } @@ -168,7 +168,7 @@ batch_prepare_vS_MC <- function(S, internal, model, predict_model) { explain_lags <- internal$parameters$explain_lags y <- internal$data$y xreg <- internal$data$xreg - keep_samp_for_vS <- internal$parameters$keep_samp_for_vS + keep_samp_for_vS <- internal$parameters$output_args$keep_samp_for_vS dt <- batch_prepare_vS_MC_auxiliary(S = S, internal = internal) # Make it optional to store and return the dt_list diff --git a/R/explain.R b/R/explain.R index c81b4b858..58cb1a9f1 100644 --- a/R/explain.R +++ b/R/explain.R @@ -168,7 +168,7 @@ #' \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. #' Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by #' definition 0 when all coalitions is used. -#' Only present when `iterative = TRUE` and `iterative_args$compute_sd=TRUE`.} +#' Only present when `extra_estimation_args$compute_sd=TRUE`.} #' \item{internal}{List with the different parameters, data, functions and other output used internally.} #' \item{pred_explain}{Numeric vector with the predictions for the explained observations} #' \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -352,6 +352,7 @@ explain <- function(model, predict_model = NULL, get_model_specs = NULL, prev_shapr_object = NULL, + extra_estimation_args = list(), iterative_args = list(), output_args = list(), ...) { # ... is further arguments passed to specific approaches @@ -387,6 +388,7 @@ explain <- function(model, init_time = init_time, prev_shapr_object = prev_shapr_object, output_args = output_args, + extra_estimation_args = extra_estimation_args, ... ) @@ -516,7 +518,7 @@ testing_cleanup <- function(output) { } # Delete the saving_path - output$internal$parameters$saving_path <- NULL + output$internal$parameters$output_args$saving_path <- NULL output$saving_path <- NULL return(output) diff --git a/R/finalize_explanation.R b/R/finalize_explanation.R index c5733e397..5596a432b 100644 --- a/R/finalize_explanation.R +++ b/R/finalize_explanation.R @@ -4,7 +4,7 @@ #' #' @export finalize_explanation <- function(internal) { - MSEv_uniform_comb_weights <- internal$parameters$MSEv_uniform_comb_weights + MSEv_uniform_comb_weights <- internal$parameters$output_args$MSEv_uniform_comb_weights output_size <- internal$parameters$output_size dt_vS <- internal$output$dt_vS @@ -53,7 +53,7 @@ finalize_explanation <- function(internal) { pred_explain = p, MSEv = MSEv, iterative_results = iterative_results, - saving_path = internal$parameters$saving_path, + saving_path = internal$parameters$output_args$saving_path, internal = internal ) attr(output, "class") <- c("shapr", "list") @@ -233,7 +233,7 @@ compute_MSEv_eval_crit <- function(internal, #' #' @export finalize_explanation_forecast <- function(vS_list, internal) { # Temporary used for forecast only (the old function) - MSEv_uniform_comb_weights <- internal$parameters$MSEv_uniform_comb_weights + MSEv_uniform_comb_weights <- internal$parameters$output_args$MSEv_uniform_comb_weights processed_vS_list <- postprocess_vS_list( vS_list = vS_list, diff --git a/R/print_iter.R b/R/print_iter.R index 090facdca..f491743f3 100644 --- a/R/print_iter.R +++ b/R/print_iter.R @@ -16,7 +16,7 @@ print_iter <- function(internal) { overall_conv_measure <- internal$iter_list[[iter]]$overall_conv_measure reduction_factor <- internal$iter_list[[iter]]$reduction_factor - saving_path <- internal$parameters$saving_path + saving_path <- internal$parameters$output_args$saving_path convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance testing <- internal$parameters$testing diff --git a/R/save_results.R b/R/save_results.R index 2f68b5071..cef0e97b9 100644 --- a/R/save_results.R +++ b/R/save_results.R @@ -5,7 +5,7 @@ #' @export #' @keywords internal save_results <- function(internal) { - saving_path <- internal$parameters$saving_path + saving_path <- internal$parameters$output_args$saving_path # Modify name for the new file filename <- basename(saving_path) diff --git a/R/setup.R b/R/setup.R index 6b8013912..c234fa953 100644 --- a/R/setup.R +++ b/R/setup.R @@ -52,6 +52,7 @@ setup <- function(x_train, init_time = NULL, prev_shapr_object = NULL, output_args = list(), + extra_estimation_args = list(), ...) { internal <- list() @@ -92,6 +93,7 @@ setup <- function(x_train, is_python = is_python, testing = testing, output_args = output_args, + extra_estimation_args = extra_estimation_args, ... ) @@ -175,6 +177,7 @@ get_parameters <- function(approach, testing, is_python, output_args = list(), + extra_estimation_args = list(), ...) { # Check input type for approach @@ -192,6 +195,10 @@ get_parameters <- function(approach, if (!is.list(output_args)) { stop("`output_args` must be a list.") } + if (!is.list(extra_estimation_args)) { + stop("`extra_estimation_args` must be a list.") + } + # max_n_coalitions @@ -311,6 +318,7 @@ get_parameters <- function(approach, iterative = iterative, iterative_args = iterative_args, output_args = output_args, + extra_estimation_args = extra_estimation_args, testing = testing ) @@ -576,6 +584,8 @@ check_and_set_parameters <- function(internal) { internal <- set_exact(internal) + internal <- set_extra_estimation_params(internal) + check_computability(internal) # Check approach @@ -718,10 +728,39 @@ set_output_parameters <- function(internal) { # Get defaults output_args <- utils::modifyList(get_output_args_default(internal), - iterative_args, + output_args, keep.null = TRUE ) + check_output_args(output_args) + + internal$parameters$output_args <- output_args + + return(internal) +} + +#' Gets the default values for the output arguments +#' +#' @param keep_samp_for_vS Logical. +#' Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in `internal$output`). +#' Not used for `approach="regression_separate"` or `approach="regression_surrogate"`. +#' @param MSEv_uniform_comb_weights Logical. +#' If `TRUE` (default), then the function weights the coalitions uniformly when computing the MSEv criterion. +#' If `FALSE`, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv +#' criterion. +#' Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered. +#' @param saving_path String. +#' The path to the directory where the results of the iterative estimation procedure should be saved. +#' Defaults to a temporary directory. +#' @export +#' @author Martin Jullum +get_output_args_default <- function(keep_samp_for_vS = FALSE, + MSEv_uniform_comb_weights = TRUE, + saving_path = tempfile("shapr_obj_", fileext = ".rds")){ + return(mget(methods::formalArgs(get_output_args_default))) +} + +check_output_args <- function(output_args){ list2env(output_args, envir = environment()) # Make accessible in the environment # Check the output_args elements @@ -753,34 +792,98 @@ set_output_parameters <- function(internal) { ) } +} + + +#' @author Martin Jullum +#' @keywords internal +set_extra_estimation_params <- function(internal) { + extra_estimation_args <- internal$parameters$extra_estimation_args - internal$parameters$MSEv_uniform_comb_weights <- MSEv_uniform_comb_weights - internal$parameters$keep_samp_for_vS <- keep_samp_for_vS - internal$parameters$saving_path <- saving_path + # Get defaults + extra_estimation_args <- utils::modifyList(get_extra_est_args_default(internal), + extra_estimation_args, + keep.null = TRUE + ) + + # Check the output_args elements + check_extra_estimation_args(extra_estimation_args) + + extra_estimation_args <- trans_null_extra_est_args(extra_estimation_args) + + internal$parameters$extra_estimation_args <- extra_estimation_args return(internal) } -#' Gets the default values for the output arguments +#' Gets the default values for the extra estimation arguments #' -#' @param keep_samp_for_vS Logical. -#' Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in `internal$output`). -#' Not used for `approach="regression_separate"` or `approach="regression_surrogate"`. -#' @param MSEv_uniform_comb_weights Logical. -#' If `TRUE` (default), then the function weights the coalitions uniformly when computing the MSEv criterion. -#' If `FALSE`, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv -#' criterion. -#' Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered. -#' @param saving_path String. -#' The path to the directory where the results of the iterative estimation procedure should be saved. -#' Defaults to a temporary directory. +#' @param compute_sd Logical. Whether to estimate the standard deviations of the Shapley value estimates. This is TRUE +#' whenever sampling based kernelSHAP is applied (either iteratively or with a fixed number of coalitions). +#' @param n_boot_samps Integer. The number of bootstrapped samples (i.e. samples with replacement) from the set of all +#' coalitions used to estimate the standard deviations of the Shapley value estimates. +#' @param max_batch_size Integer. The maximum number of coalitions to estimate simultaneously within each iteration. +#' A larger numbers requires more memory, but may have a slight computational advantage. +#' @param min_n_batches Integer. The minimum number of batches to split the computation into within each iteration. +#' Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller +#' than the number of parallel workers. +#' @inheritParams default_doc_explain #' @export #' @author Martin Jullum -get_output_args_default <- function(keep_samp_for_vS = FALSE, - MSEv_uniform_comb_weights = TRUE, - saving_path = tempfile("shapr_obj_", fileext = ".rds")){ - return(mget(methods::formalArgs(get_output_args_default))) +get_extra_est_args_default <- function(internal, # Only used to get the default value of compute_sd + compute_sd = isFALSE(internal$parameters$exact), + n_boot_samps = 100, + max_batch_size = 10, + min_n_batches = 10){ + return(mget(methods::formalArgs(get_extra_est_args_default)[-1])) # [-1] to exclude internal +} + +check_extra_estimation_args <- function(extra_estimation_args){ + list2env(extra_estimation_args, envir = environment()) # Make accessible in the environment + + # compute_sd + if (!(is.logical(compute_sd) && + length(compute_sd) == 1)) { + stop("`extra_estimation_args$compute_sd` must be single logical.") + } + + # n_boot_samps + if (!(is.wholenumber(n_boot_samps) && + length(n_boot_samps) == 1 && + !is.na(n_boot_samps) && + n_boot_samps > 0)) { + stop("`extra_estimation_args$n_boot_samps` must be a single positive integer.") + } + + # max_batch_size + if (!is.null(max_batch_size) && + !((is.wholenumber(max_batch_size) || is.infinite(max_batch_size)) && + length(max_batch_size) == 1 && + !is.na(max_batch_size) && + max_batch_size > 0)) { + stop("`extra_estimation_args$max_batch_size` must be NULL, Inf or a single positive integer.") + } + + # min_n_batches + if (!is.null(min_n_batches) && + !(is.wholenumber(min_n_batches) && + length(min_n_batches) == 1 && + !is.na(min_n_batches) && + min_n_batches > 0)) { + stop("`extra_estimation_args$min_n_batches` must be NULL or a single positive integer.") + } + +} + +trans_null_extra_est_args <- function(extra_estimation_args) { + list2env(extra_estimation_args, envir = environment()) + + # Translating NULL to always return n_batches = 1 (if just one approach) + extra_estimation_args$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) + extra_estimation_args$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) + + return(extra_estimation_args) } @@ -939,7 +1042,7 @@ check_regression <- function(internal) { } # Check that we are not to keep the Monte Carlo samples - if (internal$parameters$keep_samp_for_vS) { + if (internal$parameters$output_args$keep_samp_for_vS) { stop(paste( "`keep_samp_for_vS` must be `FALSE` for the `regression_separate` and `regression_surrogate`", "approaches as there are no Monte Carlo samples to keep for these approaches." @@ -1100,7 +1203,7 @@ set_iterative_parameters <- function(internal, prev_iter_list = NULL) { n_coalitions = iterative_args$initial_n_coalitions, new_n_coalitions = iterative_args$initial_n_coalitions, exact = internal$parameters$exact, - compute_sd = iterative_args$compute_sd, + compute_sd = internal$parameters$compute_sd, reduction_factor = iterative_args$reduction_factor_vec[1], n_batches = set_n_batches(iterative_args$initial_n_coalitions, internal) ) @@ -1160,48 +1263,12 @@ check_iterative_args <- function(iterative_args) { stop("`iterative_args$reduction_factor_vec` must be NULL or a vector or numerics between 0 and 1.") } - # n_boot_samps - if (!(is.wholenumber(n_boot_samps) && - length(n_boot_samps) == 1 && - !is.na(n_boot_samps) && - n_boot_samps > 0)) { - stop("`iterative_args$n_boot_samps` must be a single positive integer.") - } - - # compute_sd - if (!(is.logical(compute_sd) && - length(compute_sd) == 1)) { - stop("`iterative_args$compute_sd` must be a single logical.") - } - - - # min_n_batches - if (!is.null(min_n_batches) && - !(is.wholenumber(min_n_batches) && - length(min_n_batches) == 1 && - !is.na(min_n_batches) && - min_n_batches > 0)) { - stop("`iterative_args$min_n_batches` must be NULL or a single positive integer.") - } - - # max_batch_size - if (!is.null(max_batch_size) && - !((is.wholenumber(max_batch_size) || is.infinite(max_batch_size)) && - length(max_batch_size) == 1 && - !is.na(max_batch_size) && - max_batch_size > 0)) { - stop("`iterative_args$max_batch_size` must be NULL, Inf or a single positive integer.") - } - - } trans_null_iterative_args <- function(iterative_args) { list2env(iterative_args, envir = environment()) # Translating NULL to always return n_batches = 1 (if just one approach) - iterative_args$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) - iterative_args$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) iterative_args$max_iter <- ifelse(is.null(max_iter), Inf, max_iter) return(iterative_args) @@ -1209,8 +1276,8 @@ trans_null_iterative_args <- function(iterative_args) { set_n_batches <- function(n_coalitions, internal) { - min_n_batches <- internal$parameters$iterative_args$min_n_batches - max_batch_size <- internal$parameters$iterative_args$max_batch_size + min_n_batches <- internal$parameters$extra_estimation_args$min_n_batches + max_batch_size <- internal$parameters$extra_estimation_args$max_batch_size n_unique_approaches <- internal$parameters$n_unique_approaches @@ -1282,14 +1349,6 @@ check_vs_prev_shapr_object <- function(internal) { #' `reduction_factor_vec[i]` for iteration `i`. #' It is wise to start with smaller numbers to avoid using too many `n_coalitions` due to uncertain estimates in #' the first iterations. -#' @param n_boot_samps Integer. The number of bootstrapped samples (i.e. samples with replacement) from the set of all -#' coalitions used to estimate the standard deviations of the Shapley value estimates. -#' @param compute_sd Logical. Whether to estimate the standard deviations of the Shapley value estimates. -#' @param max_batch_size Integer. The maximum number of coalitions to estimate simultaneously within each iteration. -#' A larger numbers requires more memory, but may have a slight computational advantage. -#' @param min_n_batches Integer. The minimum number of batches to split the computation into within each iteration. -#' Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller -#' than the number of parallel workers. #' @inheritParams default_doc_explain #' #' @export @@ -1308,15 +1367,9 @@ get_iterative_args_default <- function(internal, fixed_n_coalitions_per_iter = NULL, max_iter = 20, convergence_tolerance = 0.02, - reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)), - n_boot_samps = 100, - compute_sd = isTRUE(internal$parameters$iterative), - max_batch_size = 10, - min_n_batches = 10) { + reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10))) { iterative <- internal$parameters$iterative max_n_coalitions <- internal$parameters$max_n_coalitions - exact <- internal$parameters$exact - is_groupwise <- internal$parameters$is_groupwise if (isTRUE(iterative)) { ret_list <- mget( @@ -1326,11 +1379,7 @@ get_iterative_args_default <- function(internal, "max_n_coalitions", "max_iter", "convergence_tolerance", - "reduction_factor_vec", - "n_boot_samps", - "compute_sd", - "max_batch_size", - "min_n_batches" + "reduction_factor_vec" ) ) } else { @@ -1340,11 +1389,7 @@ get_iterative_args_default <- function(internal, max_n_coalitions = max_n_coalitions, max_iter = 1, convergence_tolerance = NULL, - reduction_factor_vec = NULL, - n_boot_samps = n_boot_samps, - compute_sd = isFALSE(exact) && isFALSE(is_groupwise), - max_batch_size = max_batch_size, - min_n_batches = min_n_batches + reduction_factor_vec = NULL ) } return(ret_list) diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index 17496e6c2..e6887c8e2 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -122,7 +122,7 @@ second_path = explain( print_iter_info = TRUE, kernelSHAP_reweighting = "on_N", seed=NULL, - prev_shapr_object = first$internal$parameters$saving_path + prev_shapr_object = first$internal$parameters$output_args$saving_path ) diff --git a/man/compute_MSEv_eval_crit.Rd b/man/compute_MSEv_eval_crit.Rd index 309f50e56..27643a769 100644 --- a/man/compute_MSEv_eval_crit.Rd +++ b/man/compute_MSEv_eval_crit.Rd @@ -15,19 +15,13 @@ compute_MSEv_eval_crit( \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} \item{dt_vS}{Data.table of dimension \code{n_coalitions} times \code{n_explain + 1} containing the contribution function estimates. The first column is assumed to be named \code{id_coalition} and containing the ids of the coalitions. The last row is assumed to be the full coalition, i.e., it contains the predicted responses for the observations which are to be explained.} -\item{MSEv_uniform_comb_weights}{Logical. -If \code{TRUE} (default), then the function weights the coalitions uniformly when computing the MSEv criterion. -If \code{FALSE}, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv -criterion. -Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered.} - \item{MSEv_skip_empty_full_comb}{Logical. If \code{TRUE} (default), we exclude the empty and grand coalitions when computing the MSEv evaluation criterion. This is reasonable as they are identical for all methods, i.e., their contribution function is independent of the used method as they are special cases not diff --git a/man/compute_shapley_new.Rd b/man/compute_shapley_new.Rd index 6e3a5ce6f..3c1d249f2 100644 --- a/man/compute_shapley_new.Rd +++ b/man/compute_shapley_new.Rd @@ -10,7 +10,7 @@ compute_shapley_new(internal, dt_vS) \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} \item{dt_vS}{The contribution matrix.} } diff --git a/man/compute_vS_forecast.Rd b/man/compute_vS_forecast.Rd index 0b6d25633..d7b5ce185 100644 --- a/man/compute_vS_forecast.Rd +++ b/man/compute_vS_forecast.Rd @@ -10,7 +10,7 @@ compute_vS_forecast(internal, model, predict_model, method = "future") \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} \item{model}{Objects. The model object that ought to be explained. diff --git a/man/default_doc.Rd b/man/default_doc.Rd index 7ee25632a..1da47ca1e 100644 --- a/man/default_doc.Rd +++ b/man/default_doc.Rd @@ -10,7 +10,7 @@ default_doc(internal, model, predict_model, output_size, extra, ...) \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} \item{model}{Objects. The model object that ought to be explained. diff --git a/man/explain.Rd b/man/explain.Rd index 02cc4ab95..dc3140aba 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -9,21 +9,20 @@ explain( x_explain, x_train, approach, - paired_shap_sampling = TRUE, prediction_zero, - max_n_coalitions = NULL, iterative = NULL, + max_n_coalitions = NULL, group = NULL, + paired_shap_sampling = TRUE, n_MC_samples = 1000, + kernelSHAP_reweighting = "on_all_cond", seed = 1, - keep_samp_for_vS = FALSE, + verbose = "basic", predict_model = NULL, get_model_specs = NULL, - MSEv_uniform_comb_weights = TRUE, - verbose = "basic", - iterative_args = list(), - kernelSHAP_reweighting = "on_all_cond", prev_shapr_object = NULL, + iterative_args = list(), + output_args = list(), ... ) } @@ -46,25 +45,12 @@ All elements should, either be \code{"gaussian"}, \code{"copula"}, \code{"empiri \code{"categorical"}, \code{"timeseries"}, \code{"independence"}, \code{"regression_separate"}, or \code{"regression_surrogate"}. The two regression approaches can not be combined with any other approach. See details for more information.} -\item{paired_shap_sampling}{Logical. -If \code{TRUE} (default), paired versions of all sampled coalitions are also included in the computation. -That is, if there are 5 features and e.g. coalitions (1,3,5) are sampled, then also coalition (2,4) is used for -computing the Shapley values. This is done to reduce the variance of the Shapley value estimates.} - \item{prediction_zero}{Numeric. The prediction value for unseen data, i.e. an estimate of the expected prediction without conditioning on any features. Typically we set this value equal to the mean of the response variable in our training data, but other choices such as the mean of the predictions in the training data are also reasonable.} -\item{max_n_coalitions}{Integer. -The upper limit on the number of unique feature/group coalitions to use in the iterative procedure -(if \code{iterative = TRUE}). -If \code{iterative = FALSE} it represents the number of feature/group coalitions to use directly. -The quantity refers to the number of unique feature coalitions if \code{group = NULL}, -and group coalitions if \code{group != NULL}. -\code{max_n_coalitions = NULL} corresponds to \code{max_n_coalitions=2^n_features}.} - \item{iterative}{Logical or NULL If \code{NULL} (default), the argument is set to \code{TRUE} if there are more than 5 features/groups, and \code{FALSE} otherwise. If eventually \code{TRUE}, the Shapley values are estimated iteratively in an iterative manner. @@ -77,12 +63,25 @@ coalitions. The process is repeated until the variances are below the threshold. Specifics related to the iterative process and convergence criterion are set through \code{iterative_args}.} +\item{max_n_coalitions}{Integer. +The upper limit on the number of unique feature/group coalitions to use in the iterative procedure +(if \code{iterative = TRUE}). +If \code{iterative = FALSE} it represents the number of feature/group coalitions to use directly. +The quantity refers to the number of unique feature coalitions if \code{group = NULL}, +and group coalitions if \code{group != NULL}. +\code{max_n_coalitions = NULL} corresponds to \code{max_n_coalitions=2^n_features}.} + \item{group}{List. If \code{NULL} regular feature wise Shapley values are computed. If provided, group wise Shapley values are computed. \code{group} then has length equal to the number of groups. The list element contains character vectors with the features included in each of the different groups.} +\item{paired_shap_sampling}{Logical. +If \code{TRUE} (default), paired versions of all sampled coalitions are also included in the computation. +That is, if there are 5 features and e.g. coalitions (1,3,5) are sampled, then also coalition (2,4) is used for +computing the Shapley values. This is done to reduce the variance of the Shapley value estimates.} + \item{n_MC_samples}{Positive integer. Indicating the maximum number of samples to use in the Monte Carlo integration for every conditional expectation. For \code{approach="ctree"}, \code{n_MC_samples} corresponds to the number of samples @@ -91,13 +90,39 @@ For \code{approach="empirical"}, \code{n_MC_samples} is the \eqn{K} parameter i Aas et al. (2021), i.e. the maximum number of observations (with largest weights) that is used, see also the \code{empirical.eta} argument \code{\link[=setup_approach.empirical]{setup_approach.empirical()}}.} +\item{kernelSHAP_reweighting}{String. +How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing +the randomness and thereby the variance of the Shapley value estimates. +One of \code{'none'}, \code{'on_N'}, \code{'on_all'}, \code{'on_all_cond'} (default). +\code{'none'} means no reweighting, i.e. the sampling frequency weights are used as is. +\code{'on_coal_size'} means the sampling frequencies are averaged over all coalitions of the same size. +\code{'on_N'} means the sampling frequencies are averaged over all coalitions with the same original sampling +probabilities. +\code{'on_all'} means the original sampling probabilities are used for all coalitions. +\code{'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. +This method is preferred as it has performed the best in simulation studies.} + \item{seed}{Positive integer. Specifies the seed before any randomness based code is being run. If \code{NULL} no seed is set in the calling environment.} -\item{keep_samp_for_vS}{Logical. -Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in \code{internal$output}). -Not used for \code{approach="regression_separate"} or \code{approach="regression_surrogate"}.} +\item{verbose}{String vector or NULL. +Specifies the verbosity (printout detail level) through one or more of strings \code{"basic"}, \code{"progress"}, +\code{"convergence"}, \code{"shapley"} and \code{"vS_details"}. +\code{"basic"} (default) displays basic information about the computation which is being performed. +\verb{"progress} displays information about where in the calculation process the function currently is. +#' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are +(only when \code{iterative = TRUE}) . +\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) +\itemize{ +\item the final estimates. +\code{"vS_details"} displays information about the v_S estimates. +This is most relevant for \verb{approach \%in\% c("regression_separate", "regression_surrogate", "vaeac"}). +\code{NULL} means no printout. +Note that any combination of four strings can be used. +E.g. \code{verbose = c("basic", "vS_details")} will display basic information + details about the vS estimation process. +}} \item{predict_model}{Function. The prediction function used when \code{model} is not natively supported. @@ -121,52 +146,20 @@ If \code{NULL} (the default) internal functions are used for natively supported disabled for unsupported model classes. Can also be used to override the default function for natively supported model classes.} -\item{MSEv_uniform_comb_weights}{Logical. -If \code{TRUE} (default), then the function weights the coalitions uniformly when computing the MSEv criterion. -If \code{FALSE}, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv -criterion. -Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered.} - -\item{verbose}{String vector or NULL. -Specifies the verbosity (printout detail level) through one or more of strings \code{"basic"}, \code{"progress"}, -\code{"convergence"}, \code{"shapley"} and \code{"vS_details"}. -\code{"basic"} (default) displays basic information about the computation which is being performed. -\verb{"progress} displays information about where in the calculation process the function currently is. -#' \code{"convergence"} displays information on how close to convergence the Shapley value estimates are -(only when \code{iterative = TRUE}) . -\code{"shapley"} displays intermediate Shapley value estimates and standard deviations (only when \code{iterative = TRUE}) -\itemize{ -\item the final estimates. -\code{"vS_details"} displays information about the v_S estimates. -This is most relevant for \verb{approach \%in\% c("regression_separate", "regression_surrogate", "vaeac"}). -\code{NULL} means no printout. -Note that any combination of four strings can be used. -E.g. \code{verbose = c("basic", "vS_details")} will display basic information + details about the vS estimation process. -}} - -\item{iterative_args}{Named list. -Specifices the arguments for the iterative procedure. -See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} - -\item{kernelSHAP_reweighting}{String. -How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing -the randomness and thereby the variance of the Shapley value estimates. -One of \code{'none'}, \code{'on_N'}, \code{'on_all'}, \code{'on_all_cond'} (default). -\code{'none'} means no reweighting, i.e. the sampling frequency weights are used as is. -\code{'on_coal_size'} means the sampling frequencies are averaged over all coalitions of the same size. -\code{'on_N'} means the sampling frequencies are averaged over all coalitions with the same original sampling -probabilities. -\code{'on_all'} means the original sampling probabilities are used for all coalitions. -\code{'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. -This method is preferred as it has performed the best in simulation studies.} - \item{prev_shapr_object}{\code{shapr} object or string. If an object of class \code{shapr} is provided or string with a path to where intermediate results are strored, then the function will use the previous object to continue the computation. This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore want to continue the iterative estimation. See the vignette for examples.} +\item{iterative_args}{Named list. +Specifices the arguments for the iterative procedure. +See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} + +\item{output_args}{Named list. +Specifices the arguments for the iterative procedure. +See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} + \item{...}{ Arguments passed on to \code{\link[=setup_approach.empirical]{setup_approach.empirical}}, \code{\link[=setup_approach.independence]{setup_approach.independence}}, \code{\link[=setup_approach.gaussian]{setup_approach.gaussian}}, \code{\link[=setup_approach.copula]{setup_approach.copula}}, \code{\link[=setup_approach.ctree]{setup_approach.ctree}}, \code{\link[=setup_approach.vaeac]{setup_approach.vaeac}}, \code{\link[=setup_approach.categorical]{setup_approach.categorical}}, \code{\link[=setup_approach.regression_separate]{setup_approach.regression_separate}}, \code{\link[=setup_approach.regression_surrogate]{setup_approach.regression_surrogate}}, \code{\link[=setup_approach.timeseries]{setup_approach.timeseries}} \describe{ diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index 0fde90c39..33f1fdc5a 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -104,10 +104,6 @@ Aas et al. (2021), i.e. the maximum number of observations (with largest weights Specifies the seed before any randomness based code is being run. If \code{NULL} no seed is set in the calling environment.} -\item{keep_samp_for_vS}{Logical. -Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in \code{internal$output}). -Not used for \code{approach="regression_separate"} or \code{approach="regression_surrogate"}.} - \item{predict_model}{Function. The prediction function used when \code{model} is not natively supported. (Run \code{\link[=get_supported_models]{get_supported_models()}} for a list of natively supported models.) diff --git a/man/finalize_explanation_forecast.Rd b/man/finalize_explanation_forecast.Rd index d70fa3ca4..e4b7bb0be 100644 --- a/man/finalize_explanation_forecast.Rd +++ b/man/finalize_explanation_forecast.Rd @@ -13,7 +13,7 @@ Output from \code{\link[=compute_vS]{compute_vS()}}} \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} } \value{ Object of class \code{c("shapr", "list")}. Contains the following items: diff --git a/man/get_adaptive_arguments_default.Rd b/man/get_iterative_args_default.Rd similarity index 92% rename from man/get_adaptive_arguments_default.Rd rename to man/get_iterative_args_default.Rd index a60d0f5a7..76b45096b 100644 --- a/man/get_adaptive_arguments_default.Rd +++ b/man/get_iterative_args_default.Rd @@ -15,8 +15,7 @@ get_iterative_args_default( n_boot_samps = 100, compute_sd = isTRUE(internal$parameters$iterative), max_batch_size = 10, - min_n_batches = 10, - saving_path = tempfile("shapr_obj_", fileext = ".rds") + min_n_batches = 10 ) } \arguments{ @@ -52,10 +51,6 @@ A larger numbers requires more memory, but may have a slight computational advan \item{min_n_batches}{Integer. The minimum number of batches to split the computation into within each iteration. Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller than the number of parallel workers.} - -\item{saving_path}{String. -The path to the directory where the results of the iterative estimation procedure should be saved. -Defaults to a temporary directory.} } \description{ Function to specify arguments of the iterative estimation procedure diff --git a/man/get_output_args_default.Rd b/man/get_output_args_default.Rd new file mode 100644 index 000000000..4365118bc --- /dev/null +++ b/man/get_output_args_default.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/setup.R +\name{get_output_args_default} +\alias{get_output_args_default} +\title{Gets the default values for the output arguments} +\usage{ +get_output_args_default( + keep_samp_for_vS = FALSE, + MSEv_uniform_comb_weights = TRUE, + saving_path = tempfile("shapr_obj_", fileext = ".rds") +) +} +\arguments{ +\item{keep_samp_for_vS}{Logical. +Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in \code{internal$output}). +Not used for \code{approach="regression_separate"} or \code{approach="regression_surrogate"}.} + +\item{MSEv_uniform_comb_weights}{Logical. +If \code{TRUE} (default), then the function weights the coalitions uniformly when computing the MSEv criterion. +If \code{FALSE}, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv +criterion. +Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered.} + +\item{saving_path}{String. +The path to the directory where the results of the iterative estimation procedure should be saved. +Defaults to a temporary directory.} +} +\description{ +Gets the default values for the output arguments +} +\author{ +Martin Jullum +} diff --git a/man/regression.check_parameters.Rd b/man/regression.check_parameters.Rd index 73930ae26..55c2f3e22 100644 --- a/man/regression.check_parameters.Rd +++ b/man/regression.check_parameters.Rd @@ -10,7 +10,7 @@ regression.check_parameters(internal) \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} } \value{ The same \code{internal} list, but added logical indicator \code{internal$parameters$regression.tune} diff --git a/man/regression.get_y_hat.Rd b/man/regression.get_y_hat.Rd index ff02d4cb4..9eff9cbd5 100644 --- a/man/regression.get_y_hat.Rd +++ b/man/regression.get_y_hat.Rd @@ -10,7 +10,7 @@ regression.get_y_hat(internal, model, predict_model) \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} \item{model}{Objects. The model object that ought to be explained. diff --git a/man/regression.surrogate_aug_data.Rd b/man/regression.surrogate_aug_data.Rd index 14c97aabe..2acb6c419 100644 --- a/man/regression.surrogate_aug_data.Rd +++ b/man/regression.surrogate_aug_data.Rd @@ -20,7 +20,7 @@ regression.surrogate_aug_data( \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} \item{x}{Data.table containing the data. Either the training data or the explicands. If \code{x} is the explicands, then \code{index_features} must be provided.} diff --git a/man/setup.Rd b/man/setup.Rd index 1c4333fc8..dd5dcf98c 100644 --- a/man/setup.Rd +++ b/man/setup.Rd @@ -15,9 +15,7 @@ setup( group, n_MC_samples, seed, - keep_samp_for_vS, feature_specs, - MSEv_uniform_comb_weights = TRUE, type = "normal", horizon = NULL, y = NULL, @@ -35,6 +33,7 @@ setup( testing = FALSE, init_time = NULL, prev_shapr_object = NULL, + output_args = list(), ... ) } @@ -90,10 +89,6 @@ Aas et al. (2021), i.e. the maximum number of observations (with largest weights Specifies the seed before any randomness based code is being run. If \code{NULL} no seed is set in the calling environment.} -\item{keep_samp_for_vS}{Logical. -Indicates whether the samples used in the Monte Carlo estimation of v_S should be returned (in \code{internal$output}). -Not used for \code{approach="regression_separate"} or \code{approach="regression_surrogate"}.} - \item{feature_specs}{List. The output from \code{\link[=get_model_specs]{get_model_specs()}} or \code{\link[=get_data_specs]{get_data_specs()}}. Contains the 3 elements: \describe{ @@ -102,12 +97,6 @@ Contains the 3 elements: \item{factor_levels}{Character vector with the levels for any categorical features.} }} -\item{MSEv_uniform_comb_weights}{Logical. -If \code{TRUE} (default), then the function weights the coalitions uniformly when computing the MSEv criterion. -If \code{FALSE}, then the function use the Shapley kernel weights to weight the coalitions when computing the MSEv -criterion. -Note that the Shapley kernel weights are replaced by the sampling frequency when not all coalitions are considered.} - \item{type}{Character. Either "normal" or "forecast" corresponding to function \code{setup()} is called from, correspondingly the type of explanation that should be generated.} @@ -209,6 +198,10 @@ then the function will use the previous object to continue the computation. This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore want to continue the iterative estimation. See the vignette for examples.} +\item{output_args}{Named list. +Specifices the arguments for the iterative procedure. +See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} + \item{...}{Further arguments passed to specific approaches} } \description{ diff --git a/man/setup_computation.Rd b/man/setup_computation.Rd index ca128d70b..afd255e00 100644 --- a/man/setup_computation.Rd +++ b/man/setup_computation.Rd @@ -10,7 +10,7 @@ setup_computation(internal, model, predict_model) \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} \item{model}{Objects. The model object that ought to be explained. diff --git a/man/test_predict_model.Rd b/man/test_predict_model.Rd index 40adb5b01..b43d1f6ec 100644 --- a/man/test_predict_model.Rd +++ b/man/test_predict_model.Rd @@ -18,7 +18,7 @@ See the documentation of \code{\link[=explain]{explain()}} for details.} \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} } \description{ Model testing function diff --git a/python/shaprpy/explain.py b/python/shaprpy/explain.py index 1e0642227..fc748e08f 100644 --- a/python/shaprpy/explain.py +++ b/python/shaprpy/explain.py @@ -218,7 +218,7 @@ def batch_compute_vS(S, rinternal, model, predict_model): if regression: dt_vS = shapr.batch_prepare_vS_regression(S=S, internal=rinternal) else: - # dt_vS is either only dt_vS or a list containing dt_vS and dt if internal$parameters$keep_samp_for_vS = TRUE + # dt_vS is either only dt_vS or a list containing dt_vS and dt if internal$parameters$output_args$keep_samp_for_vS = TRUE dt_vS = batch_prepare_vS_MC(S=S, rinternal=rinternal, model=model, predict_model=predict_model) return dt_vS @@ -418,4 +418,4 @@ def change_first_underscore_to_dot(kwargs): kwargs_tmp = {} for k, v in kwargs.items(): kwargs_tmp[k.replace('_', '.', 1)] = v - return kwargs_tmp \ No newline at end of file + return kwargs_tmp diff --git a/tests/testthat/test-adaptive-setup.R b/tests/testthat/test-adaptive-setup.R index 3f8ebc3c8..f46334769 100644 --- a/tests/testthat/test-adaptive-setup.R +++ b/tests/testthat/test-adaptive-setup.R @@ -83,7 +83,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_numeric_1) + extra_estimation_args = list(min_n_batches = n_batches_non_numeric_1) ) }, error = TRUE @@ -100,7 +100,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_numeric_2) + extra_estimation_args = list(min_n_batches = n_batches_non_numeric_2) ) }, error = TRUE @@ -117,7 +117,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_integer) + extra_estimation_args = list(min_n_batches = n_batches_non_integer) ) }, error = TRUE @@ -134,7 +134,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_too_long) + extra_estimation_args = list(min_n_batches = n_batches_too_long) ) }, error = TRUE @@ -151,7 +151,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_is_NA) + extra_estimation_args = list(min_n_batches = n_batches_is_NA) ) }, error = TRUE @@ -168,7 +168,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_positive) + extra_estimation_args = list(min_n_batches = n_batches_non_positive) ) }, error = TRUE @@ -184,7 +184,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - iterative_args = list(min_n_batches = 5, max_batch_size = 10) + extra_estimation_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.empirical_n_batches_10 <- explain( @@ -194,7 +194,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - iterative_args = list(min_n_batches = 10, max_batch_size = 10) + extra_estimation_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) @@ -216,7 +216,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - iterative_args = list(min_n_batches = 5, max_batch_size = 10) + extra_estimation_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.ctree_n_batches_10 <- explain( @@ -226,7 +226,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - iterative_args = list(min_n_batches = 10, max_batch_size = 10) + extra_estimation_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index a64dc8a76..db873d269 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -1225,7 +1225,7 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - iterative_args = list(compute_sd = FALSE), + extra_estimation_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), iterative = FALSE ) @@ -1243,7 +1243,7 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - iterative_args = list(compute_sd = FALSE), + extra_estimation_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, iterative = FALSE ) diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index 75fed4089..998cd5578 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -1282,8 +1282,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`iterative_args$max_batch_size` (defaults to 10) and -`iterative_args$min_n_batches` (defaults to 10). +`extra_estimation_args$max_batch_size` (defaults to 10) and +`extra_estimation_args$min_n_batches` (defaults to 10). ## Parallelized computation diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index 980018e64..8974d49d5 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -1026,8 +1026,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`iterative_args$max_batch_size` (defaults to 10) and -`iterative_args$min_n_batches` (defaults to 10). +`extra_estimation_args$max_batch_size` (defaults to 10) and +`extra_estimation_args$min_n_batches` (defaults to 10). ## Parallelized computation From 56053decd904c954b5219529de179935278eb05c Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 22:08:19 +0200 Subject: [PATCH 09/32] man + bugfix --- NAMESPACE | 1 + R/setup.R | 6 ++--- man/explain.Rd | 3 ++- man/explain_forecast.Rd | 2 +- man/finalize_explanation_forecast.Rd | 2 +- man/get_extra_est_args_default.Rd | 37 ++++++++++++++++++++++++++++ man/get_iterative_args_default.Rd | 18 +------------- man/setup.Rd | 1 + man/vaeac_get_extra_para_default.Rd | 2 +- 9 files changed, 47 insertions(+), 25 deletions(-) create mode 100644 man/get_extra_est_args_default.Rd diff --git a/NAMESPACE b/NAMESPACE index 7a35bfa90..f20e27aa1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -69,6 +69,7 @@ export(finalize_explanation) export(finalize_explanation_forecast) export(get_cov_mat) export(get_data_specs) +export(get_extra_est_args_default) export(get_iterative_args_default) export(get_model_specs) export(get_mu_vec) diff --git a/R/setup.R b/R/setup.R index c234fa953..9c22942c9 100644 --- a/R/setup.R +++ b/R/setup.R @@ -306,13 +306,11 @@ get_parameters <- function(approach, group = group, n_MC_samples = n_MC_samples, seed = seed, - keep_samp_for_vS = keep_samp_for_vS, is_python = is_python, output_size = output_size, type = type, horizon = horizon, group_lags = group_lags, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights, verbose = verbose, kernelSHAP_reweighting = kernelSHAP_reweighting, iterative = iterative, @@ -727,7 +725,7 @@ set_output_parameters <- function(internal) { output_args <- internal$parameters$output_args # Get defaults - output_args <- utils::modifyList(get_output_args_default(internal), + output_args <- utils::modifyList(get_output_args_default(), output_args, keep.null = TRUE ) @@ -1203,7 +1201,7 @@ set_iterative_parameters <- function(internal, prev_iter_list = NULL) { n_coalitions = iterative_args$initial_n_coalitions, new_n_coalitions = iterative_args$initial_n_coalitions, exact = internal$parameters$exact, - compute_sd = internal$parameters$compute_sd, + compute_sd = internal$parameters$extra_estimation_args$compute_sd, reduction_factor = iterative_args$reduction_factor_vec[1], n_batches = set_n_batches(iterative_args$initial_n_coalitions, internal) ) diff --git a/man/explain.Rd b/man/explain.Rd index dc3140aba..917d167b0 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -21,6 +21,7 @@ explain( predict_model = NULL, get_model_specs = NULL, prev_shapr_object = NULL, + extra_estimation_args = list(), iterative_args = list(), output_args = list(), ... @@ -290,7 +291,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{iterative = TRUE} and \code{iterative_args$compute_sd=TRUE}.} +Only present when \code{extra_estimation_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index 33f1fdc5a..f37678879 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -235,7 +235,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{iterative = TRUE} and \code{iterative_args$compute_sd=TRUE}.} +Only present when \code{extra_estimation_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the diff --git a/man/finalize_explanation_forecast.Rd b/man/finalize_explanation_forecast.Rd index e4b7bb0be..1cde33aa1 100644 --- a/man/finalize_explanation_forecast.Rd +++ b/man/finalize_explanation_forecast.Rd @@ -24,7 +24,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{iterative = TRUE} and \code{iterative_args$compute_sd=TRUE}.} +Only present when \code{extra_estimation_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the diff --git a/man/get_extra_est_args_default.Rd b/man/get_extra_est_args_default.Rd new file mode 100644 index 000000000..4f7772532 --- /dev/null +++ b/man/get_extra_est_args_default.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/setup.R +\name{get_extra_est_args_default} +\alias{get_extra_est_args_default} +\title{Gets the default values for the extra estimation arguments} +\usage{ +get_extra_est_args_default( + internal, + compute_sd = isFALSE(internal$parameters$exact), + n_boot_samps = 100, + max_batch_size = 10, + min_n_batches = 10 +) +} +\arguments{ +\item{internal}{List. +Not used directly, but passed through from \code{\link[=explain]{explain()}}.} + +\item{compute_sd}{Logical. Whether to estimate the standard deviations of the Shapley value estimates. This is TRUE +whenever sampling based kernelSHAP is applied (either iteratively or with a fixed number of coalitions).} + +\item{n_boot_samps}{Integer. The number of bootstrapped samples (i.e. samples with replacement) from the set of all +coalitions used to estimate the standard deviations of the Shapley value estimates.} + +\item{max_batch_size}{Integer. The maximum number of coalitions to estimate simultaneously within each iteration. +A larger numbers requires more memory, but may have a slight computational advantage.} + +\item{min_n_batches}{Integer. The minimum number of batches to split the computation into within each iteration. +Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller +than the number of parallel workers.} +} +\description{ +Gets the default values for the extra estimation arguments +} +\author{ +Martin Jullum +} diff --git a/man/get_iterative_args_default.Rd b/man/get_iterative_args_default.Rd index 76b45096b..f569d2e26 100644 --- a/man/get_iterative_args_default.Rd +++ b/man/get_iterative_args_default.Rd @@ -11,11 +11,7 @@ get_iterative_args_default( fixed_n_coalitions_per_iter = NULL, max_iter = 20, convergence_tolerance = 0.02, - reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)), - n_boot_samps = 100, - compute_sd = isTRUE(internal$parameters$iterative), - max_batch_size = 10, - min_n_batches = 10 + reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)) ) } \arguments{ @@ -39,18 +35,6 @@ The number of \code{n_coalitions} actually used in the next iteration is set to \code{reduction_factor_vec[i]} for iteration \code{i}. It is wise to start with smaller numbers to avoid using too many \code{n_coalitions} due to uncertain estimates in the first iterations.} - -\item{n_boot_samps}{Integer. The number of bootstrapped samples (i.e. samples with replacement) from the set of all -coalitions used to estimate the standard deviations of the Shapley value estimates.} - -\item{compute_sd}{Logical. Whether to estimate the standard deviations of the Shapley value estimates.} - -\item{max_batch_size}{Integer. The maximum number of coalitions to estimate simultaneously within each iteration. -A larger numbers requires more memory, but may have a slight computational advantage.} - -\item{min_n_batches}{Integer. The minimum number of batches to split the computation into within each iteration. -Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller -than the number of parallel workers.} } \description{ Function to specify arguments of the iterative estimation procedure diff --git a/man/setup.Rd b/man/setup.Rd index dd5dcf98c..91a332d7b 100644 --- a/man/setup.Rd +++ b/man/setup.Rd @@ -34,6 +34,7 @@ setup( init_time = NULL, prev_shapr_object = NULL, output_args = list(), + extra_estimation_args = list(), ... ) } diff --git a/man/vaeac_get_extra_para_default.Rd b/man/vaeac_get_extra_para_default.Rd index 52f385750..5337719d1 100644 --- a/man/vaeac_get_extra_para_default.Rd +++ b/man/vaeac_get_extra_para_default.Rd @@ -80,7 +80,7 @@ during the training of the vaeac model. Used in \code{\link[torch:dataloader]{to each batch when generating the Monte Carlo samples. If \code{NULL}, then the function generates the Monte Carlo samples for the provided coalitions and all explicands sent to \code{\link[=explain]{explain()}} at the time. The number of coalitions are determined by the \code{n_batches} used by \code{\link[=explain]{explain()}}. We recommend to tweak -\code{iterative_args$max_batch_size} and \code{iterative_args$min_n_batches} +\code{extra_estimation_args$max_batch_size} and \code{extra_estimation_args$min_n_batches} rather than \code{vaeac.batch_size_sampling}. Larger batch sizes are often much faster provided sufficient memory.} \item{vaeac.running_avg_n_values}{Positive integer (default is \code{5}). The number of previous IWAE values to include From 910f5b27be40fa3e84cfa404ecd682ba7cc9e456 Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 22:16:01 +0200 Subject: [PATCH 10/32] rename reduction_factor --- R/finalize_explanation.R | 2 +- R/prepare_next_iteration.R | 18 ++++++------- R/print_iter.R | 6 ++--- R/setup.R | 26 +++++++++---------- R/zzz.R | 2 +- inst/scripts/devel/devel_verbose.R | 4 +-- .../devel/testing_intermediate_saving.R | 12 ++++----- man/get_iterative_args_default.Rd | 6 ++--- tests/testthat/test-adaptive-output.R | 12 ++++----- tests/testthat/test-adaptive-setup.R | 2 +- 10 files changed, 45 insertions(+), 45 deletions(-) diff --git a/R/finalize_explanation.R b/R/finalize_explanation.R index 5596a432b..0cf24447a 100644 --- a/R/finalize_explanation.R +++ b/R/finalize_explanation.R @@ -70,7 +70,7 @@ get_iter_results <- function(iter_list) { } iter_list_to_dt <- function(iter_list, what = c( - "exact", "compute_sd", "reduction_factor", "n_coalitions", "n_batches", + "exact", "compute_sd", "n_coal_next_iter_factor", "n_coalitions", "n_batches", "converged", "converged_exact", "converged_sd", "converged_max_iter", "est_required_coalitions", "est_remaining_coalitions", "overall_conv_measure" )) { diff --git a/R/prepare_next_iteration.R b/R/prepare_next_iteration.R index 4f0c0ba40..13bd231bc 100644 --- a/R/prepare_next_iteration.R +++ b/R/prepare_next_iteration.R @@ -14,18 +14,18 @@ prepare_next_iteration <- function(internal) { next_iter_list <- list() n_shapley_values <- internal$parameters$n_shapley_values - reduction_factor_vec <- internal$parameters$iterative_args$reduction_factor_vec + n_coal_next_iter_factor_vec <- internal$parameters$iterative_args$n_coal_next_iter_factor_vec fixed_n_coalitions_per_iter <- internal$parameters$iterative_args$fixed_n_coalitions_per_iter max_n_coalitions <- internal$parameters$iterative_args$max_n_coalitions est_remaining_coalitions <- internal$iter_list[[iter]]$est_remaining_coalitions - reduction_factor <- internal$iter_list[[iter]]$reduction_factor + n_coal_next_iter_factor <- internal$iter_list[[iter]]$n_coal_next_iter_factor current_n_coalitions <- internal$iter_list[[iter]]$n_coalitions current_coal_samples <- internal$iter_list[[iter]]$coal_samples if (is.null(fixed_n_coalitions_per_iter)) { - proposal_next_n_coalitions <- current_n_coalitions + ceiling(est_remaining_coalitions * reduction_factor) + proposal_next_n_coalitions <- current_n_coalitions + ceiling(est_remaining_coalitions * n_coal_next_iter_factor) } else { proposal_next_n_coalitions <- current_n_coalitions + fixed_n_coalitions_per_iter } @@ -53,14 +53,14 @@ prepare_next_iteration <- function(internal) { next_iter_list$compute_sd <- TRUE } - if (!is.null(reduction_factor_vec[1])) { - next_iter_list$reduction_factor <- ifelse( - length(reduction_factor_vec) >= iter, - reduction_factor_vec[iter], - reduction_factor_vec[length(reduction_factor_vec)] + if (!is.null(n_coal_next_iter_factor_vec[1])) { + next_iter_list$n_coal_next_iter_factor <- ifelse( + length(n_coal_next_iter_factor_vec) >= iter, + n_coal_next_iter_factor_vec[iter], + n_coal_next_iter_factor_vec[length(n_coal_next_iter_factor_vec)] ) } else { - next_iter_list$reduction_factor <- NULL + next_iter_list$n_coal_next_iter_factor <- NULL } next_iter_list$new_n_coalitions <- next_iter_list$n_coalitions - current_n_coalitions diff --git a/R/print_iter.R b/R/print_iter.R index f491743f3..653f3a8c5 100644 --- a/R/print_iter.R +++ b/R/print_iter.R @@ -14,7 +14,7 @@ print_iter <- function(internal) { converged_max_iter <- internal$iter_list[[iter]]$converged_max_iter converged_max_n_coalitions <- internal$iter_list[[iter]]$converged_max_n_coalitions overall_conv_measure <- internal$iter_list[[iter]]$overall_conv_measure - reduction_factor <- internal$iter_list[[iter]]$reduction_factor + n_coal_next_iter_factor <- internal$iter_list[[iter]]$n_coal_next_iter_factor saving_path <- internal$parameters$output_args$saving_path convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance @@ -38,12 +38,12 @@ print_iter <- function(internal) { if (!is.null(convergence_tolerance)) { conv_nice <- signif(overall_conv_measure, 2) tol_nice <- format(signif(convergence_tolerance, 2), scientific = FALSE) - reduction_factor_nice <- format(signif(reduction_factor * 100, 2), scientific = FALSE) + n_coal_next_iter_factor_nice <- format(signif(n_coal_next_iter_factor * 100, 2), scientific = FALSE) msg <- paste0( msg, "Current convergence measure: {conv_nice} [needs {tol_nice}]\n", "Estimated remaining coalitions: {est_remaining_coalitions}\n", - "(Concervatively) adding {reduction_factor_nice}% of that ({next_new_n_coalitions} coalitions) ", + "(Concervatively) adding {n_coal_next_iter_factor_nice}% of that ({next_new_n_coalitions} coalitions) ", "in the next iteration." ) } diff --git a/R/setup.R b/R/setup.R index 9c22942c9..42a966f13 100644 --- a/R/setup.R +++ b/R/setup.R @@ -1184,7 +1184,7 @@ set_iterative_parameters <- function(internal, prev_iter_list = NULL) { # Conveniently allow running non-iterative estimation one step further if (isFALSE(internal$parameters$iterative)) { internal$parameters$iterative_args$max_iter <- length(internal$iter_list) + 1 - internal$parameters$iterative_args$reduction_factor_vec <- NULL + internal$parameters$iterative_args$n_coal_next_iter_factor_vec <- NULL } # Update convergence data with NEW iterative arguments @@ -1202,7 +1202,7 @@ set_iterative_parameters <- function(internal, prev_iter_list = NULL) { new_n_coalitions = iterative_args$initial_n_coalitions, exact = internal$parameters$exact, compute_sd = internal$parameters$extra_estimation_args$compute_sd, - reduction_factor = iterative_args$reduction_factor_vec[1], + n_coal_next_iter_factor = iterative_args$n_coal_next_iter_factor_vec[1], n_batches = set_n_batches(iterative_args$initial_n_coalitions, internal) ) } @@ -1253,12 +1253,12 @@ check_iterative_args <- function(iterative_args) { stop("`iterative_args$convergence_tolerance` must be NULL, 0, or a positive numeric.") } - # reduction_factor_vec - if (!is.null(reduction_factor_vec) && - !(all(!is.na(reduction_factor_vec)) && - all(reduction_factor_vec <= 1) && - all(reduction_factor_vec >= 0))) { - stop("`iterative_args$reduction_factor_vec` must be NULL or a vector or numerics between 0 and 1.") + # n_coal_next_iter_factor_vec + if (!is.null(n_coal_next_iter_factor_vec) && + !(all(!is.na(n_coal_next_iter_factor_vec)) && + all(n_coal_next_iter_factor_vec <= 1) && + all(n_coal_next_iter_factor_vec >= 0))) { + stop("`iterative_args$n_coal_next_iter_factor_vec` must be NULL or a vector or numerics between 0 and 1.") } } @@ -1341,10 +1341,10 @@ check_vs_prev_shapr_object <- function(internal) { #' @param convergence_tolerance Numeric. The t variable in the convergence threshold formula on page 6 in the paper #' Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' #' https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached. -#' @param reduction_factor_vec Numeric vector. The number of `n_coalitions` that must be used to reach convergence +#' @param n_coal_next_iter_factor_vec Numeric vector. The number of `n_coalitions` that must be used to reach convergence #' in the next iteration is estimated. #' The number of `n_coalitions` actually used in the next iteration is set to this estimate multiplied by -#' `reduction_factor_vec[i]` for iteration `i`. +#' `n_coal_next_iter_factor_vec[i]` for iteration `i`. #' It is wise to start with smaller numbers to avoid using too many `n_coalitions` due to uncertain estimates in #' the first iterations. #' @inheritParams default_doc_explain @@ -1365,7 +1365,7 @@ get_iterative_args_default <- function(internal, fixed_n_coalitions_per_iter = NULL, max_iter = 20, convergence_tolerance = 0.02, - reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10))) { + n_coal_next_iter_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10))) { iterative <- internal$parameters$iterative max_n_coalitions <- internal$parameters$max_n_coalitions @@ -1377,7 +1377,7 @@ get_iterative_args_default <- function(internal, "max_n_coalitions", "max_iter", "convergence_tolerance", - "reduction_factor_vec" + "n_coal_next_iter_factor_vec" ) ) } else { @@ -1387,7 +1387,7 @@ get_iterative_args_default <- function(internal, max_n_coalitions = max_n_coalitions, max_iter = 1, convergence_tolerance = NULL, - reduction_factor_vec = NULL + n_coal_next_iter_factor_vec = NULL ) } return(ret_list) diff --git a/R/zzz.R b/R/zzz.R index 57a652311..dd5585888 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -126,7 +126,7 @@ "initial_n_coalitions", "max_n_coalitions", "fixed_n_coalitions_per_iter", - "reduction_factor_vec", + "n_coal_next_iter_factor_vec", "n_boot_samps", "compute_sd", "min_n_batches", diff --git a/inst/scripts/devel/devel_verbose.R b/inst/scripts/devel/devel_verbose.R index 6f767ea17..1dd1ffc98 100644 --- a/inst/scripts/devel/devel_verbose.R +++ b/inst/scripts/devel/devel_verbose.R @@ -9,7 +9,7 @@ ex <- explain( iterative_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, - reduction_factor_vec = rep(10^(-6), 10), + n_coal_next_iter_factor_vec = rep(10^(-6), 10), max_iter = 8 ), iterative = TRUE,verbose=c("basic","progress") @@ -114,7 +114,7 @@ ex <- explain( iterative_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, - reduction_factor_vec = rep(10^(-6), 10), + n_coal_next_iter_factor_vec = rep(10^(-6), 10), max_iter = 8 ), iterative = TRUE,verbose=c("basic","convergence","shapley") diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index e6887c8e2..000d3de8f 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -9,7 +9,7 @@ aa = explain( iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.01, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 30 ), iterative = TRUE, @@ -26,7 +26,7 @@ bb = explain( iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 30 ), iterative = TRUE, @@ -51,7 +51,7 @@ full = explain( iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 7 ), iterative = TRUE, @@ -71,7 +71,7 @@ first = explain( iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 4 ), iterative = TRUE, @@ -91,7 +91,7 @@ second = explain( iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 7 ), iterative = TRUE, @@ -114,7 +114,7 @@ second_path = explain( iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 5 ), iterative = TRUE, diff --git a/man/get_iterative_args_default.Rd b/man/get_iterative_args_default.Rd index f569d2e26..3b777c6ee 100644 --- a/man/get_iterative_args_default.Rd +++ b/man/get_iterative_args_default.Rd @@ -11,7 +11,7 @@ get_iterative_args_default( fixed_n_coalitions_per_iter = NULL, max_iter = 20, convergence_tolerance = 0.02, - reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)) + n_coal_next_iter_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)) ) } \arguments{ @@ -29,10 +29,10 @@ Not used directly, but passed through from \code{\link[=explain]{explain()}}.} Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached.} -\item{reduction_factor_vec}{Numeric vector. The number of \code{n_coalitions} that must be used to reach convergence +\item{n_coal_next_iter_factor_vec}{Numeric vector. The number of \code{n_coalitions} that must be used to reach convergence in the next iteration is estimated. The number of \code{n_coalitions} actually used in the next iteration is set to this estimate multiplied by -\code{reduction_factor_vec[i]} for iteration \code{i}. +\code{n_coal_next_iter_factor_vec[i]} for iteration \code{i}. It is wise to start with smaller numbers to avoid using too many \code{n_coalitions} due to uncertain estimates in the first iterations.} } diff --git a/tests/testthat/test-adaptive-output.R b/tests/testthat/test-adaptive-output.R index e0e9887f0..655b4f60f 100644 --- a/tests/testthat/test-adaptive-output.R +++ b/tests/testthat/test-adaptive-output.R @@ -49,7 +49,7 @@ test_that("output_lm_numeric_independence_converges_maxit", { iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), iterative = TRUE, @@ -140,7 +140,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), iterative = TRUE, @@ -162,7 +162,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 5 ), iterative = TRUE, @@ -183,7 +183,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), iterative = TRUE, @@ -211,7 +211,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 5 ), iterative = TRUE, @@ -232,7 +232,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { iterative_args = list( initial_n_coalitions = 10, convergence_tolerance = 0.001, - reduction_factor_vec = rep(10^(-5), 10), + n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), iterative = TRUE, diff --git a/tests/testthat/test-adaptive-setup.R b/tests/testthat/test-adaptive-setup.R index f46334769..8b7d7b8c1 100644 --- a/tests/testthat/test-adaptive-setup.R +++ b/tests/testthat/test-adaptive-setup.R @@ -10,7 +10,7 @@ test_that("iterative_args are respected", { iterative_args = list( initial_n_coalitions = 6, convergence_tolerance = 0.0005, - reduction_factor_vec = rep(10^(-6), 10), + n_coal_next_iter_factor_vec = rep(10^(-6), 10), max_iter = 8 ), iterative = TRUE From 27fe03ce0ba5c52c66e648e060249076a7f44ef5 Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 22:33:18 +0200 Subject: [PATCH 11/32] tests --- tests/testthat/_snaps/adaptive-setup.md | 36 ++++++++++++------------- tests/testthat/test-regular-output.R | 2 +- tests/testthat/test-regular-setup.R | 6 ++--- 3 files changed, 22 insertions(+), 22 deletions(-) diff --git a/tests/testthat/_snaps/adaptive-setup.md b/tests/testthat/_snaps/adaptive-setup.md index 23bde8383..c1f4732ce 100644 --- a/tests/testthat/_snaps/adaptive-setup.md +++ b/tests/testthat/_snaps/adaptive-setup.md @@ -4,15 +4,15 @@ n_batches_non_numeric_1 <- "bla" explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_numeric_1)) + extra_estimation_args = list(min_n_batches = n_batches_non_numeric_1)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_iterative_args()`: - ! `iterative_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_estimation_args()`: + ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -20,15 +20,15 @@ n_batches_non_numeric_2 <- TRUE explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_numeric_2)) + extra_estimation_args = list(min_n_batches = n_batches_non_numeric_2)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_iterative_args()`: - ! `iterative_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_estimation_args()`: + ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -36,15 +36,15 @@ n_batches_non_integer <- 10.5 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_integer)) + extra_estimation_args = list(min_n_batches = n_batches_non_integer)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_iterative_args()`: - ! `iterative_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_estimation_args()`: + ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -52,15 +52,15 @@ n_batches_too_long <- c(1, 2) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_too_long)) + extra_estimation_args = list(min_n_batches = n_batches_too_long)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_iterative_args()`: - ! `iterative_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_estimation_args()`: + ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -68,15 +68,15 @@ n_batches_is_NA <- as.numeric(NA) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_is_NA)) + extra_estimation_args = list(min_n_batches = n_batches_is_NA)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_iterative_args()`: - ! `iterative_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_estimation_args()`: + ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -84,13 +84,13 @@ n_batches_non_positive <- 0 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - iterative_args = list(min_n_batches = n_batches_non_positive)) + extra_estimation_args = list(min_n_batches = n_batches_non_positive)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_iterative_args()`: - ! `iterative_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_estimation_args()`: + ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. diff --git a/tests/testthat/test-regular-output.R b/tests/testthat/test-regular-output.R index 6591b8368..d53ac7884 100644 --- a/tests/testthat/test-regular-output.R +++ b/tests/testthat/test-regular-output.R @@ -576,7 +576,7 @@ test_that("output_lm_numeric_independence_keep_samp_for_vS", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - keep_samp_for_vS = TRUE, + output_args = list(keep_samp_for_vS = TRUE), iterative = FALSE )), "output_lm_numeric_independence_keep_samp_for_vS" diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index db873d269..693eb1ea7 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -782,7 +782,7 @@ test_that("erroneous input: `keep_samp_for_vS`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - keep_samp_for_vS = keep_samp_for_vS_non_logical_1 + output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_1) ) }, error = TRUE @@ -799,7 +799,7 @@ test_that("erroneous input: `keep_samp_for_vS`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - keep_samp_for_vS = keep_samp_for_vS_non_logical_2 + output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_2) ) }, error = TRUE @@ -816,7 +816,7 @@ test_that("erroneous input: `keep_samp_for_vS`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - keep_samp_for_vS = keep_samp_for_vS_too_long + output_args = list(keep_samp_for_vS = keep_samp_for_vS_too_long) ) }, error = TRUE From ef49ad52e5fadd7672aecfe28b99d3dc4aa9451f Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 22:56:58 +0200 Subject: [PATCH 12/32] more tests ok --- ...m_numeric_gaussian_group_converges_tol.rds | Bin 3932 -> 3962 bytes ...lm_numeric_indep_conv_max_n_coalitions.rds | Bin 8919 -> 8810 bytes ...m_numeric_independence_cont_est_object.rds | Bin 17351 -> 17197 bytes ..._lm_numeric_independence_cont_est_path.rds | Bin 17351 -> 17197 bytes ...m_numeric_independence_converges_maxit.rds | Bin 13795 -> 13620 bytes ..._lm_numeric_independence_converges_tol.rds | Bin 9999 -> 10049 bytes ...eric_independence_converges_tol_paired.rds | Bin 9999 -> 10049 bytes ...ut_lm_numeric_independence_reach_exact.rds | Bin 8931 -> 8820 bytes .../adaptive-output/output_verbose_1.rds | Bin 12502 -> 12685 bytes .../adaptive-output/output_verbose_1_3.rds | Bin 12507 -> 12710 bytes .../adaptive-output/output_verbose_1_3_4.rds | Bin 12513 -> 12713 bytes .../output_verbose_1_3_4_5.rds | Bin 12525 -> 12725 bytes .../output_lm_categorical_lm_separate.rds | Bin 5347 -> 5433 bytes .../output_lm_categorical_lm_surrogate.rds | Bin 157968 -> 158204 bytes ...put_lm_mixed_decision_tree_cv_separate.rds | Bin 6543 -> 6902 bytes ...xed_decision_tree_cv_separate_parallel.rds | Bin 6448 -> 6812 bytes ...ut_lm_mixed_decision_tree_cv_surrogate.rds | Bin 41589 -> 42196 bytes .../output_lm_mixed_lm_separate.rds | Bin 7229 -> 7577 bytes .../output_lm_mixed_lm_surrogate.rds | Bin 340017 -> 340406 bytes .../output_lm_mixed_splines_separate.rds | Bin 22442 -> 23091 bytes .../output_lm_mixed_xgboost_separate.rds | Bin 22233 -> 22866 bytes .../output_lm_mixed_xgboost_surrogate.rds | Bin 122964 -> 120023 bytes .../output_lm_numeric_lm_separate.rds | Bin 7166 -> 7528 bytes ...output_lm_numeric_lm_separate_adaptive.rds | Bin 13789 -> 0 bytes ...utput_lm_numeric_lm_separate_iterative.rds | Bin 0 -> 14025 bytes .../output_lm_numeric_lm_separate_n_comb.rds | Bin 5693 -> 5791 bytes .../output_lm_numeric_lm_surrogate.rds | Bin 296470 -> 296936 bytes ...utput_lm_numeric_lm_surrogate_adaptive.rds | Bin 296470 -> 0 bytes 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bytes .../output_lm_numeric_comb1.rds | Bin 6425 -> 6450 bytes .../output_lm_numeric_comb2.rds | Bin 6418 -> 6438 bytes .../output_lm_numeric_comb3.rds | Bin 6344 -> 6377 bytes .../output_lm_numeric_copula.rds | Bin 6341 -> 6358 bytes .../output_lm_numeric_ctree.rds | Bin 6087 -> 6110 bytes .../output_lm_numeric_ctree_parallelized.rds | Bin 6087 -> 6110 bytes .../output_lm_numeric_empirical.rds | Bin 6280 -> 6310 bytes .../output_lm_numeric_empirical_AICc_each.rds | Bin 4172 -> 4208 bytes .../output_lm_numeric_empirical_AICc_full.rds | Bin 4172 -> 4207 bytes ...tput_lm_numeric_empirical_independence.rds | Bin 6281 -> 6312 bytes ...tput_lm_numeric_empirical_n_coalitions.rds | Bin 5639 -> 5673 bytes .../output_lm_numeric_empirical_progress.rds | Bin 6280 -> 6310 bytes .../output_lm_numeric_gaussian.rds | Bin 6244 -> 6261 bytes .../output_lm_numeric_independence.rds | Bin 6022 -> 6044 bytes ..._independence_MSEv_Shapley_weights.new.rds | Bin 0 -> 5696 bytes ...eric_independence_MSEv_Shapley_weights.rds | Bin 6025 -> 6052 bytes ..._numeric_independence_keep_samp_for_vS.rds | Bin 211387 -> 210819 bytes .../output_lm_numeric_interaction.rds | Bin 2621 -> 2637 bytes .../output_lm_numeric_vaeac.rds | Bin 6455 -> 6478 bytes .../output_lm_timeseries_method.rds | Bin 35459 -> 35575 bytes tests/testthat/_snaps/regular-setup.md | 70 +++++++++++++----- tests/testthat/test-regular-output.R | 2 +- tests/testthat/test-regular-setup.R | 6 +- 66 files changed, 56 insertions(+), 25 deletions(-) delete mode 100644 tests/testthat/_snaps/regression-output/output_lm_numeric_lm_separate_adaptive.rds create mode 100644 tests/testthat/_snaps/regression-output/output_lm_numeric_lm_separate_iterative.rds delete mode 100644 tests/testthat/_snaps/regression-output/output_lm_numeric_lm_surrogate_adaptive.rds create mode 100644 tests/testthat/_snaps/regression-output/output_lm_numeric_lm_surrogate_iterative.rds create mode 100644 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zgDX?Keq3wE4KS!`i4sZIZg$*CD-j&7Ukj-z1BDIobjb~s3UlRF5F_+0w4g=6tnt~6 z=<-*(a0TbVWITTvT)^0X)#w#_-d1^%_~4(h@SNP_5X~al+{Q042t!bsk^>HYeM}N> z@Pxu*K_{UlyyXNLjE}?Ln%rD&OODg$|2#wTwxS(@Te zCM1){3Daaip@RKFynVuQniT|(KUa062xM9_RB(8Bc6lH$&xV3gs-W^(UcD%;QZHlM eFi77FvVe;SfdM0j(+McM`_xlF+tZ^4`|=+au^EW~ From f875d64f24754ce657cc5780f96b20359d233130 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 14 Oct 2024 20:50:18 +0200 Subject: [PATCH 16/32] man + merge conflicts tails --- R/setup.R | 2 +- R/shapley_setup.R | 2 +- R/zzz.R | 2 +- man/create_coalition_table.Rd | 6 +-- man/explain.Rd | 33 ++++++------- man/get_adaptive_arguments_default.Rd | 71 --------------------------- man/prepare_data_single_coalition.Rd | 2 +- man/setup.Rd | 23 ++++----- 8 files changed, 30 insertions(+), 111 deletions(-) delete mode 100644 man/get_adaptive_arguments_default.Rd diff --git a/R/setup.R b/R/setup.R index c8e9ba8d3..ad84c7f6e 100644 --- a/R/setup.R +++ b/R/setup.R @@ -332,7 +332,7 @@ get_parameters <- function(approach, extra_computation_args = extra_computation_args, asymmetric = asymmetric, causal_ordering = causal_ordering, - confounding = confounding + confounding = confounding, testing = testing ) diff --git a/R/shapley_setup.R b/R/shapley_setup.R index 609b3fc9c..44d9b85b5 100644 --- a/R/shapley_setup.R +++ b/R/shapley_setup.R @@ -42,7 +42,7 @@ shapley_setup <- function(internal) { prev_coal_samples = prev_coal_samples, coal_feature_list = coal_feature_list, approach0 = approach, - kernelSHAP_reweighting = kernelSHAP_reweighting + kernelSHAP_reweighting = kernelSHAP_reweighting, dt_valid_causal_coalitions = dt_valid_causal_coalitions ) diff --git a/R/zzz.R b/R/zzz.R index 145e98fca..34850f93b 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -140,7 +140,7 @@ "conv_measure", "verbose", "MSEv_uniform_comb_weights", - "keep_samp_for_vS" + "keep_samp_for_vS", "S_original_names_with_id", "Sbar_features", "Sbar_now_names", diff --git a/man/create_coalition_table.Rd b/man/create_coalition_table.Rd index 36f88aff4..1b340f207 100644 --- a/man/create_coalition_table.Rd +++ b/man/create_coalition_table.Rd @@ -13,12 +13,8 @@ create_coalition_table( prev_coal_samples = NULL, coal_feature_list = as.list(seq_len(m)), approach0 = "gaussian", -<<<<<<< HEAD - kernelSHAP_reweighting = "none" -======= - shapley_reweighting = "none", + kernelSHAP_reweighting = "none", dt_valid_causal_coalitions = NULL ->>>>>>> origin/shapr-1.0.0 ) } \arguments{ diff --git a/man/explain.Rd b/man/explain.Rd index adc5ec076..eda7426d2 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -21,15 +21,12 @@ explain( predict_model = NULL, get_model_specs = NULL, prev_shapr_object = NULL, -<<<<<<< HEAD - extra_computation_args = list(), - iterative_args = list(), - output_args = list(), -======= asymmetric = FALSE, causal_ordering = NULL, confounding = NULL, ->>>>>>> origin/shapr-1.0.0 + extra_computation_args = list(), + iterative_args = list(), + output_args = list(), ... ) } @@ -159,18 +156,6 @@ then the function will use the previous object to continue the computation. This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore want to continue the iterative estimation. See the vignette for examples.} -\item{extra_computation_args}{Named list. -Specifices extra arguments related to the computation of the Shapley values. -See \code{\link[=get_extra_est_args_default]{get_extra_est_args_default()}} for description of the arguments and their default values.} - -\item{iterative_args}{Named list. -Specifices the arguments for the iterative procedure. -See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} - -\item{output_args}{Named list. -Specifices certain arguments related to the output of the function. -See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} - \item{asymmetric}{Logical. Not applicable for (regular) non-causal or asymmetric explanations. If \code{FALSE} (default), \code{explain} computes regular symmetric Shapley values, @@ -207,6 +192,18 @@ specified, then \code{explain} computes asymmetric/symmetric causal Shapley valu \code{asymmetric}. The \code{approach} cannot be \code{regression_separate} and \code{regression_surrogate} as the regression-based approaches are not applicable to the causal Shapley value methodology.} +\item{extra_computation_args}{Named list. +Specifices extra arguments related to the computation of the Shapley values. +See \code{\link[=get_extra_est_args_default]{get_extra_est_args_default()}} for description of the arguments and their default values.} + +\item{iterative_args}{Named list. +Specifices the arguments for the iterative procedure. +See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} + +\item{output_args}{Named list. +Specifices certain arguments related to the output of the function. +See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} + \item{...}{ Arguments passed on to \code{\link[=setup_approach.empirical]{setup_approach.empirical}}, \code{\link[=setup_approach.independence]{setup_approach.independence}}, \code{\link[=setup_approach.gaussian]{setup_approach.gaussian}}, \code{\link[=setup_approach.copula]{setup_approach.copula}}, \code{\link[=setup_approach.ctree]{setup_approach.ctree}}, \code{\link[=setup_approach.vaeac]{setup_approach.vaeac}}, \code{\link[=setup_approach.categorical]{setup_approach.categorical}}, \code{\link[=setup_approach.regression_separate]{setup_approach.regression_separate}}, \code{\link[=setup_approach.regression_surrogate]{setup_approach.regression_surrogate}}, \code{\link[=setup_approach.timeseries]{setup_approach.timeseries}} \describe{ diff --git a/man/get_adaptive_arguments_default.Rd b/man/get_adaptive_arguments_default.Rd deleted file mode 100644 index 11c655e89..000000000 --- a/man/get_adaptive_arguments_default.Rd +++ /dev/null @@ -1,71 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/setup.R -\name{get_adaptive_arguments_default} -\alias{get_adaptive_arguments_default} -\title{Function to specify arguments of the adaptive estimation procedure} -\usage{ -get_adaptive_arguments_default( - internal, - initial_n_coalitions = ceiling(min(200, internal$parameters$max_n_coalitions_causal, - internal$parameters$max_n_coalitions, max(5, internal$parameters$n_features, - (2^internal$parameters$n_features)/10))), - fixed_n_coalitions_per_iter = NULL, - max_iter = 20, - convergence_tolerance = 0.02, - reduction_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)), - n_boot_samps = 100, - compute_sd = isTRUE(internal$parameters$adaptive), - max_batch_size = 10, - min_n_batches = 10, - saving_path = tempfile("shapr_obj_", fileext = ".rds") -) -} -\arguments{ -\item{internal}{List. -Not used directly, but passed through from \code{\link[=explain]{explain()}}.} - -\item{initial_n_coalitions}{Integer. Number of coalitions to use in the first estimation iteration.} - -\item{fixed_n_coalitions_per_iter}{Integer. Number of \code{n_coalitions} to use in each iteration. -\code{NULL} (default) means setting it based on estimates based on a set convergence threshold.} - -\item{max_iter}{Integer. Maximum number of estimation iterations} - -\item{convergence_tolerance}{Numeric. The t variable in the convergence threshold formula on page 6 in the paper -Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' -https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached.} - -\item{reduction_factor_vec}{Numeric vector. The number of \code{n_coalitions} that must be used to reach convergence -in the next iteration is estimated. -The number of \code{n_coalitions} actually used in the next iteration is set to this estimate multiplied by -\code{reduction_factor_vec[i]} for iteration \code{i}. -It is wise to start with smaller numbers to avoid using too many \code{n_coalitions} due to uncertain estimates in -the first iterations.} - -\item{n_boot_samps}{Integer. The number of bootstrapped samples (i.e. samples with replacement) from the set of all -coalitions used to estimate the standard deviations of the Shapley value estimates.} - -\item{compute_sd}{Logical. Whether to estimate the standard deviations of the Shapley value estimates.} - -\item{max_batch_size}{Integer. The maximum number of coalitions to estimate simultaneously within each iteration. -A larger numbers requires more memory, but may have a slight computational advantage.} - -\item{min_n_batches}{Integer. The minimum number of batches to split the computation into within each iteration. -Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller -than the number of parallel workers.} - -\item{saving_path}{String. -The path to the directory where the results of the adaptive estimation procedure should be saved. -Defaults to a temporary directory.} -} -\description{ -Function to specify arguments of the adaptive estimation procedure -} -\details{ -The functions sets default values for the adaptive estimation procedure, according to the function defaults. -If the argument \code{adaptive} of \code{\link[=explain]{explain()}} is FALSE, it sets parameters corresponding to the use of a -non-adaptive estimation procedure -} -\author{ -Martin Jullum -} diff --git a/man/prepare_data_single_coalition.Rd b/man/prepare_data_single_coalition.Rd index e40830a1c..9bd170b2b 100644 --- a/man/prepare_data_single_coalition.Rd +++ b/man/prepare_data_single_coalition.Rd @@ -10,7 +10,7 @@ prepare_data_single_coalition(internal, index_features) \item{internal}{List. Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, \code{iter_timing_list} and \code{iter_results}.} +\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} } \description{ The \code{\link[=prepare_data.categorical]{prepare_data.categorical()}} function is slow when evaluated for a single coalition. diff --git a/man/setup.Rd b/man/setup.Rd index 5fd7e33c5..28b96bfb4 100644 --- a/man/setup.Rd +++ b/man/setup.Rd @@ -33,14 +33,11 @@ setup( testing = FALSE, init_time = NULL, prev_shapr_object = NULL, -<<<<<<< HEAD - output_args = list(), - extra_computation_args = list(), -======= asymmetric = FALSE, causal_ordering = NULL, confounding = NULL, ->>>>>>> origin/shapr-1.0.0 + output_args = list(), + extra_computation_args = list(), ... ) } @@ -205,14 +202,6 @@ then the function will use the previous object to continue the computation. This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore want to continue the iterative estimation. See the vignette for examples.} -\item{output_args}{Named list. -Specifices certain arguments related to the output of the function. -See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} - -\item{extra_computation_args}{Named list. -Specifices extra arguments related to the computation of the Shapley values. -See \code{\link[=get_extra_est_args_default]{get_extra_est_args_default()}} for description of the arguments and their default values.} - \item{asymmetric}{Logical. Not applicable for (regular) non-causal or asymmetric explanations. If \code{FALSE} (default), \code{explain} computes regular symmetric Shapley values, @@ -249,6 +238,14 @@ specified, then \code{explain} computes asymmetric/symmetric causal Shapley valu \code{asymmetric}. The \code{approach} cannot be \code{regression_separate} and \code{regression_surrogate} as the regression-based approaches are not applicable to the causal Shapley value methodology.} +\item{output_args}{Named list. +Specifices certain arguments related to the output of the function. +See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} + +\item{extra_computation_args}{Named list. +Specifices extra arguments related to the computation of the Shapley values. +See \code{\link[=get_extra_est_args_default]{get_extra_est_args_default()}} for description of the arguments and their default values.} + \item{...}{Further arguments passed to specific approaches} } \description{ From 45ee664e7fb3154b9a9032df9a44f4e66980af80 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 14 Oct 2024 21:06:07 +0200 Subject: [PATCH 17/32] snap updates --- tests/testthat/_snaps/regular-setup.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tests/testthat/_snaps/regular-setup.md b/tests/testthat/_snaps/regular-setup.md index 282297281..a22c6a710 100644 --- a/tests/testthat/_snaps/regular-setup.md +++ b/tests/testthat/_snaps/regular-setup.md @@ -232,6 +232,11 @@ explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = approach_non_character, prediction_zero = p0) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition Error in `check_approach()`: ! `approach` must be one of the following: 'categorical', 'copula', 'ctree', 'empirical', 'gaussian', 'independence', 'regression_separate', 'regression_surrogate', 'timeseries', 'vaeac'. @@ -244,6 +249,11 @@ explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = approach_incorrect_length, prediction_zero = p0) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition Error in `check_approach()`: ! `approach` must be one of the following: 'categorical', 'copula', 'ctree', 'empirical', 'gaussian', 'independence', 'regression_separate', 'regression_surrogate', 'timeseries', 'vaeac'. @@ -256,6 +266,11 @@ explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = approach_incorrect_character, prediction_zero = p0) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition Error in `check_approach()`: ! `approach` must be one of the following: 'categorical', 'copula', 'ctree', 'empirical', 'gaussian', 'independence', 'regression_separate', 'regression_surrogate', 'timeseries', 'vaeac'. From 9a693d92fc99758e928c7692f011e0e21449e746 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 14 Oct 2024 21:22:35 +0200 Subject: [PATCH 18/32] regular test files --- R/setup.R | 1 + ...utput_custom_lm_numeric_independence_1.rds | Bin 6044 -> 5816 bytes ...utput_custom_lm_numeric_independence_2.rds | Bin 6044 -> 5816 bytes ...utput_custom_xgboost_mixed_dummy_ctree.rds | Bin 6124 -> 5911 bytes .../output_lm_categorical_ctree.rds | Bin 4179 -> 4252 bytes .../output_lm_categorical_independence.rds | Bin 4105 -> 4172 bytes .../output_lm_categorical_method.rds | Bin 4554 -> 4640 bytes .../output_lm_categorical_vaeac.rds | Bin 4550 -> 4621 bytes .../regular-output/output_lm_mixed_comb.rds | Bin 6181 -> 5958 bytes .../regular-output/output_lm_mixed_ctree.rds | Bin 6161 -> 5950 bytes .../output_lm_mixed_independence.rds | Bin 6103 -> 5879 bytes .../regular-output/output_lm_mixed_vaeac.rds | Bin 6535 -> 6309 bytes .../output_lm_numeric_comb1.rds | Bin 6450 -> 6231 bytes .../output_lm_numeric_comb2.rds | Bin 6438 -> 6232 bytes .../output_lm_numeric_comb3.rds | Bin 6377 -> 6153 bytes .../output_lm_numeric_copula.rds | Bin 6358 -> 6152 bytes .../output_lm_numeric_ctree.rds | Bin 6110 -> 5887 bytes .../output_lm_numeric_ctree_parallelized.rds | Bin 6110 -> 5887 bytes .../output_lm_numeric_empirical.rds | Bin 6310 -> 6093 bytes .../output_lm_numeric_empirical_AICc_each.rds | Bin 4209 -> 4305 bytes .../output_lm_numeric_empirical_AICc_full.rds | Bin 4210 -> 4305 bytes ...tput_lm_numeric_empirical_independence.rds | Bin 6311 -> 6093 bytes ...tput_lm_numeric_empirical_n_coalitions.rds | Bin 5673 -> 5712 bytes .../output_lm_numeric_empirical_progress.rds | Bin 6310 -> 6093 bytes .../output_lm_numeric_gaussian.rds | Bin 6260 -> 6051 bytes .../output_lm_numeric_independence.rds | Bin 6044 -> 5812 bytes ...eric_independence_MSEv_Shapley_weights.rds | Bin 6051 -> 5823 bytes ..._numeric_independence_keep_samp_for_vS.rds | Bin 210819 -> 210677 bytes .../output_lm_numeric_interaction.rds | Bin 2637 -> 2734 bytes .../output_lm_numeric_vaeac.rds | Bin 6477 -> 6251 bytes .../output_lm_timeseries_method.rds | Bin 35574 -> 35710 bytes tests/testthat/_snaps/regular-setup.md | 15 --------------- 32 files changed, 1 insertion(+), 15 deletions(-) diff --git a/R/setup.R b/R/setup.R index ad84c7f6e..d97e03fd5 100644 --- a/R/setup.R +++ b/R/setup.R @@ -186,6 +186,7 @@ get_parameters <- function(approach, is_python, output_args = list(), extra_computation_args = list(), + testing = FALSE, ...) { # Check input type for approach diff --git a/tests/testthat/_snaps/regular-output/output_custom_lm_numeric_independence_1.rds b/tests/testthat/_snaps/regular-output/output_custom_lm_numeric_independence_1.rds index bd6b103cd3ef1d1d24678943fb880752049145ae..dcb74033f4d6d229a6a519448951970e1607d1ef 100644 GIT binary patch literal 5816 zcmV;p7DwqHiwFP!000001MNKtcofCbvzraMLqb9z9AP;{ZW6)`+et_OA%T!^$h9oV 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..._lm_numeric_independence_cont_est_path.rds | Bin 17197 -> 17295 bytes ...m_numeric_independence_converges_maxit.rds | Bin 13620 -> 13171 bytes ..._lm_numeric_independence_converges_tol.rds | Bin 10049 -> 9871 bytes ...eric_independence_converges_tol_paired.rds | Bin 10049 -> 9871 bytes ...ut_lm_numeric_independence_reach_exact.rds | Bin 8820 -> 8735 bytes .../adaptive-output/output_verbose_1.rds | Bin 12686 -> 12487 bytes .../adaptive-output/output_verbose_1_3.rds | Bin 12710 -> 12491 bytes .../adaptive-output/output_verbose_1_3_4.rds | Bin 12714 -> 12495 bytes .../output_verbose_1_3_4_5.rds | Bin 12725 -> 12508 bytes .../output_lm_categorical_lm_separate.rds | Bin 5433 -> 5500 bytes .../output_lm_categorical_lm_surrogate.rds | Bin 158204 -> 158258 bytes ...put_lm_mixed_decision_tree_cv_separate.rds | Bin 6901 -> 6666 bytes ...xed_decision_tree_cv_separate_parallel.rds | Bin 6811 -> 6572 bytes ...ut_lm_mixed_decision_tree_cv_surrogate.rds | Bin 42196 -> 41880 bytes .../output_lm_mixed_lm_separate.rds | Bin 7576 -> 7360 bytes .../output_lm_mixed_lm_surrogate.rds | Bin 340407 -> 340121 bytes .../output_lm_mixed_splines_separate.rds | Bin 23089 -> 22845 bytes .../output_lm_mixed_xgboost_separate.rds | Bin 22863 -> 22614 bytes .../output_lm_mixed_xgboost_surrogate.rds | Bin 120023 -> 119815 bytes .../output_lm_numeric_lm_separate.rds | Bin 7527 -> 7303 bytes ...utput_lm_numeric_lm_separate_iterative.rds | Bin 14024 -> 13747 bytes .../output_lm_numeric_lm_separate_n_comb.rds | Bin 5791 -> 5841 bytes .../output_lm_numeric_lm_surrogate.rds | Bin 296936 -> 296691 bytes ...tput_lm_numeric_lm_surrogate_iterative.rds | Bin 296936 -> 296691 bytes .../output_lm_numeric_lm_surrogate_n_comb.rds | Bin 107364 -> 107415 bytes ...m_numeric_lm_surrogate_reg_surr_n_comb.rds | Bin 107363 -> 107415 bytes tests/testthat/_snaps/regression-setup.md | 10 ---------- 30 files changed, 10 deletions(-) diff --git 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zS4zF@Yd*6GX6pCzlG5wS?gzFX@xdOwr$mv&$M5GH`4xwq3csUK*8Ya~J`AAc^ITst h2-3;o6_xlXWRW?`IP?(&TRy(NfjQ=$`q3b~`9F0kKY0KE diff --git a/tests/testthat/_snaps/asymmetric-causal-output.md b/tests/testthat/_snaps/asymmetric-causal-output.md index cd886263a..0177b8b4d 100644 --- a/tests/testthat/_snaps/asymmetric-causal-output.md +++ b/tests/testthat/_snaps/asymmetric-causal-output.md @@ -9,7 +9,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -36,7 +36,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -63,7 +63,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -94,7 +94,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -119,7 +119,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind, Temp, Month, Day} @@ -146,7 +146,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind, Temp, Month, Day} @@ -173,7 +173,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -201,7 +201,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -229,7 +229,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -253,7 +253,7 @@ Message * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -281,7 +281,7 @@ * Model class: * Approach: empirical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -309,7 +309,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -337,7 +337,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day} @@ -364,7 +364,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day} @@ -391,7 +391,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day} @@ -418,7 +418,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 * Causal ordering: {A, B}, {C} @@ -445,7 +445,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 * Causal ordering: {A}, {B}, {C} @@ -494,7 +494,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor} @@ -521,7 +521,7 @@ * Model class: * Approach: ctree - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor} @@ -570,7 +570,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -598,7 +598,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -626,7 +626,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -657,7 +657,7 @@ * Model class: * Approach: categorical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 2 * Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor}, @@ -684,7 +684,7 @@ * Model class: * Approach: categorical - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 * Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor}, @@ -725,7 +725,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 * Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor}, diff --git a/tests/testthat/_snaps/asymmetric-causal-output/output_asym_caus_conf_FALSE.rds b/tests/testthat/_snaps/asymmetric-causal-output/output_asym_caus_conf_FALSE.rds index 504c99d71afe74f1abe8bf9eb608fd32753c19ec..e57be87ecd6149a77b5d4e281643d8d3cb89b090 100644 GIT binary patch delta 4623 zcmV+q67cQzB=sbJABzY80000000Zqj4R}?>m2+R-%X$-eWfB00s zs#V%WQCDrDZlQc#vNQLdbMM@F_vHtYC~V%xFf(V)nRDiU%*>fHb0<@mP6$aM?i3eE 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znWfz}{&mY1JdwQ2#t(eqW4r%;yRH9!-u0TCa#8!!7oy+JF!-~Q`?7oWE0_qv-~3Ke(S<%;Lt*?MKOO@I93 z%7$&vd-|V}`Zu4d!r!A60y#Xl#b0)v1Nj2Tmq1=i@XzGK4_<{e1Z@dp3iK^yB3hdd ztUYLh7>l4!Diiq%@>pvY+AhX0=*!AP$bvl9T8B1|u@CydGLf{+2i8cOD;P7O?<^B> z3-VZNE6y>Dwb19Dg^~G0Y|cz#{}zvw_@TZ=Ri#;pd+;dvDIF0m!iS5mXN$DOc!yOi z#8GoOa4VY9Z*@J0YxL8w=a)FK7SKfpPl*rHS{L)gK~s}jhCPqlhb@M;i;v<~I|y+D zpu=hd)zD|S99Uf~K)LXdfew*-h|=)0aVO>k??q0G%Xq)aGrpG;Rkt2+)~OgM-YDmQ z;|Z * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of group-wise Shapley values: 4 * Number of observations to explain: 2 diff --git a/tests/testthat/_snaps/regression-output.md b/tests/testthat/_snaps/regression-output.md index 4f1db6b3f..73230c664 100644 --- a/tests/testthat/_snaps/regression-output.md +++ b/tests/testthat/_snaps/regression-output.md @@ -9,7 +9,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -47,7 +47,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -68,7 +68,7 @@ Message * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -93,7 +93,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -118,7 +118,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -143,7 +143,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -168,7 +168,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -193,7 +193,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -218,7 +218,7 @@ * Model class: * Approach: regression_separate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -243,11 +243,11 @@ Condition Warning in `check_and_set_iterative()`: - iterative estimation of Shapley values are not supported for approach = regression_surrogate. Setting iterative = FALSE. + Iterative estimation of Shapley values are not supported for approach = regression_surrogate. Setting iterative = FALSE. Message * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -272,7 +272,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -293,7 +293,7 @@ Message * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -314,7 +314,7 @@ Message * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -339,7 +339,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -364,7 +364,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -389,7 +389,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -414,7 +414,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 diff --git a/tests/testthat/_snaps/regression-output/output_lm_categorical_lm_separate.rds b/tests/testthat/_snaps/regression-output/output_lm_categorical_lm_separate.rds index f708d6f75ad5fd2504b8544990a34f179dbf3e56..69d40701375155749e4a113ab7175a98dfd6d44b 100644 GIT binary patch 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Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -67,7 +67,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -100,7 +100,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -134,7 +134,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -381,7 +381,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -410,7 +410,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 @@ -925,7 +925,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -952,7 +952,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 @@ -980,7 +980,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -999,7 +999,7 @@ Message * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -1022,7 +1022,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 From 0461950b1d7742c2a4b863d4ac2e133321cb2ced Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 15:06:47 +0200 Subject: [PATCH 29/32] style --- tests/testthat/test-forecast-setup.R | 47 ++++++++++++++-------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/tests/testthat/test-forecast-setup.R b/tests/testthat/test-forecast-setup.R index 8c3c21bc6..00da0fe0a 100644 --- a/tests/testthat/test-forecast-setup.R +++ b/tests/testthat/test-forecast-setup.R @@ -151,31 +151,32 @@ test_that("erroneous input: `prediction_zero`", { test_that("erroneous input: `max_n_coalitions`", { set.seed(123) - expect_snapshot({ - # Too low max_n_coalitions (smaller than # features) - horizon <- 3 - explain_y_lags <- 2 - explain_xreg_lags <- 2 + expect_snapshot( + { + # Too low max_n_coalitions (smaller than # features) + horizon <- 3 + explain_y_lags <- 2 + explain_xreg_lags <- 2 - n_coalitions <- horizon + explain_y_lags + explain_xreg_lags - 1 + n_coalitions <- horizon + explain_y_lags + explain_xreg_lags - 1 - explain_forecast( - testing = TRUE, - model = model_arima_temp, - y = data_arima[1:150, "Temp"], - xreg = data_arima[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = explain_y_lags, - explain_xreg_lags = explain_xreg_lags, - horizon = horizon, - approach = "independence", - prediction_zero = p0_ar, - max_n_coalitions = n_coalitions, - group_lags = FALSE - ) - }, - error = TRUE + explain_forecast( + testing = TRUE, + model = model_arima_temp, + y = data_arima[1:150, "Temp"], + xreg = data_arima[, "Wind"], + train_idx = 2:148, + explain_idx = 149:150, + explain_y_lags = explain_y_lags, + explain_xreg_lags = explain_xreg_lags, + horizon = horizon, + approach = "independence", + prediction_zero = p0_ar, + max_n_coalitions = n_coalitions, + group_lags = FALSE + ) + }, + error = TRUE ) From 6e970a05c48eef8310cbed77cb5745876123c04a Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 16:11:02 +0200 Subject: [PATCH 30/32] shapley_values -> shapley_values_est --- R/explain.R | 6 ++-- R/finalize_explanation.R | 2 +- R/plot.R | 14 ++++---- R/print.R | 2 +- README.Rmd | 2 +- README.md | 2 +- inst/scripts/Beeswarm_illustration.R | 6 ++-- .../scripts/Heskes_bike_rental_illustration.R | 20 ++++++------ inst/scripts/check_model_workflow.R | 2 +- inst/scripts/compare_shap_python_new.R | 4 +-- .../devel/real_data_iterative_kernelshap.R | 12 +++---- ...simtest_iterative_kernelshap_lingauss_v2.R | 12 +++---- .../devel/simtest_reweighting_strategies.R | 8 ++--- ...simtest_reweighting_strategies_nonlinear.R | 4 +-- ..._strategies_nonlinear_nonunique_sampling.R | 4 +-- .../devel/testing_explain_forevast_n_comb.R | 30 +++++++++--------- .../devel/testing_intermediate_saving.R | 6 ++-- .../devel/verifying_arima_model_output.R | 6 ++-- inst/scripts/example_plot_MSEv.R | 2 +- inst/scripts/readme_example.R | 2 +- .../scripts/vilde/sketch_for_waterfall_plot.R | 6 ++-- man/explain.Rd | 6 ++-- man/explain_forecast.Rd | 2 +- man/finalize_explanation_forecast.Rd | 6 ++-- python/shaprpy/explain.py | 2 +- ...m_numeric_gaussian_group_converges_tol.rds | Bin 4093 -> 4095 bytes ...lm_numeric_indep_conv_max_n_coalitions.rds | Bin 8932 -> 8935 bytes ...m_numeric_independence_cont_est_object.rds | Bin 17327 -> 17330 bytes ..._lm_numeric_independence_cont_est_path.rds | Bin 17327 -> 17330 bytes ...m_numeric_independence_converges_maxit.rds | Bin 13748 -> 13751 bytes ..._lm_numeric_independence_converges_tol.rds | Bin 10174 -> 10178 bytes ...eric_independence_converges_tol_paired.rds | Bin 10174 -> 10178 bytes ...ut_lm_numeric_independence_reach_exact.rds | Bin 8941 -> 8943 bytes .../adaptive-output/output_verbose_1.rds | Bin 12897 -> 12899 bytes .../adaptive-output/output_verbose_1_3.rds | Bin 12901 -> 12904 bytes .../adaptive-output/output_verbose_1_3_4.rds | Bin 12905 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.../understanding_shapr_regression.Rmd.orig | 4 +-- vignettes/understanding_shapr_vaeac.Rmd | 8 ++--- vignettes/understanding_shapr_vaeac.Rmd.orig | 8 ++--- 129 files changed, 138 insertions(+), 138 deletions(-) diff --git a/R/explain.R b/R/explain.R index dfe204a4c..c6ad43706 100644 --- a/R/explain.R +++ b/R/explain.R @@ -209,7 +209,7 @@ #' #' @return Object of class `c("shapr", "list")`. Contains the following items: #' \describe{ -#' \item{shapley_values}{data.table with the estimated Shapley values with explained observation in the rows and +#' \item{shapley_values_est}{data.table with the estimated Shapley values with explained observation in the rows and #' features along the columns. #' The column `none` is the prediction not devoted to any of the features (given by the argument `prediction_zero`)} #' \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. @@ -315,7 +315,7 @@ #' ) #' #' # Print the Shapley values -#' print(explain1$shapley_values) +#' print(explain1$shapley_values_est) #' #' # Plot the results #' if (requireNamespace("ggplot2", quietly = TRUE)) { @@ -335,7 +335,7 @@ #' prediction_zero = p, #' n_MC_samples = 1e2 #' ) -#' print(explain_groups$shapley_values) +#' print(explain_groups$shapley_values_est) #' #' # Separate and surrogate regression approaches with linear regression models. #' # More complex regression models can be used, and we can use CV to diff --git a/R/finalize_explanation.R b/R/finalize_explanation.R index 765f07345..b820c4297 100644 --- a/R/finalize_explanation.R +++ b/R/finalize_explanation.R @@ -255,7 +255,7 @@ finalize_explanation_forecast <- function(vS_list, internal) { # Temporary used internal$output <- processed_vS_list output <- list( - shapley_values = dt_shapley, + shapley_values_est = dt_shapley, internal = internal, pred_explain = p ) diff --git a/R/plot.R b/R/plot.R index c7f8fa363..13eb328a5 100644 --- a/R/plot.R +++ b/R/plot.R @@ -186,7 +186,7 @@ plot.shapr <- function(x, } # Remove the explain_id column - x$shapley_values <- x$shapley_values[, -"explain_id"] + x$shapley_values_est <- x$shapley_values_est[, -"explain_id"] if (is.null(index_x_explain)) index_x_explain <- seq(x$internal$parameters$n_explain) if (is.null(top_k_features)) top_k_features <- x$internal$parameters$n_features + 1 @@ -229,7 +229,7 @@ plot.shapr <- function(x, # melting Kshap shap_names <- x$internal$parameters$shap_names - dt_shap <- round(data.table::copy(x$shapley_values), digits = digits) + dt_shap <- round(data.table::copy(x$shapley_values_est), digits = digits) dt_shap[, id := .I] dt_shap_long <- data.table::melt(dt_shap, id.vars = "id", value.name = "phi") dt_shap_long[, sign := factor(sign(phi), levels = c(1, -1), labels = c("Increases", "Decreases"))] @@ -241,7 +241,7 @@ plot.shapr <- function(x, desc_mat[, i] <- paste0(shap_names[i], " = ", desc_mat[, i]) } } else { - desc_mat <- trimws(format(x$shapley_values[, -c("explain_id", "none")], digits = digits)) + desc_mat <- trimws(format(x$shapley_values_est[, -c("explain_id", "none")], digits = digits)) for (i in seq_len(ncol(desc_mat))) { desc_mat[, i] <- paste0(shap_names[i]) } @@ -1165,7 +1165,7 @@ MSEv_check_explanation_list <- function(explanation_list) { if (any(names(explanation_list) == "")) stop("All the entries in `explanation_list` must be named.") # Check that all explanation objects use the same column names for the Shapley values - if (length(unique(lapply(explanation_list, function(explanation) colnames(explanation$shapley_values)))) != 1) { + if (length(unique(lapply(explanation_list, function(explanation) colnames(explanation$shapley_values_est)))) != 1) { stop("The Shapley value feature names are not identical in all objects in the `explanation_list`.") } @@ -1586,7 +1586,7 @@ plot_SV_several_approaches <- function(explanation_list, if (any(names(explanation_list) == "")) stop("All the entries in `explanation_list` must be named.") # Check that the column names for the Shapley values are the same for all explanations in the `explanation_list` - if (length(unique(lapply(explanation_list, function(explanation) colnames(explanation$shapley_values)))) != 1) { + if (length(unique(lapply(explanation_list, function(explanation) colnames(explanation$shapley_values_est)))) != 1) { stop("The Shapley value feature names are not identical in all objects in the `explanation_list`.") } @@ -1708,7 +1708,7 @@ update_only_these_features <- function(explanation_list, # Update the `only_these_features` parameter vector based on `plot_phi0` or in case it is NULL # Get the common feature names for all explanation objects (including `none`) and one without `none` - feature_names_with_none <- colnames(explanation_list[[1]]$shapley_values)[-1] + feature_names_with_none <- colnames(explanation_list[[1]]$shapley_values_est)[-1] feature_names_without_none <- feature_names_with_none[feature_names_with_none != "none"] # Only keep the desired features/columns @@ -1759,7 +1759,7 @@ extract_Shapley_values_dt <- function(explanation_list, lapply( explanation_list, function(explanation) { - data.table::copy(explanation$shapley_values)[, c(".id", ".pred") := list(.I, explanation$pred_explain)] + data.table::copy(explanation$shapley_values_est)[, c(".id", ".pred") := list(.I, explanation$pred_explain)] } ), use.names = TRUE, diff --git a/R/print.R b/R/print.R index 5937e1b42..573cc36e6 100644 --- a/R/print.R +++ b/R/print.R @@ -1,6 +1,6 @@ #' @export print.shapr <- function(x, digits = 4, ...) { - shap <- copy(x$shapley_values) + shap <- copy(x$shapley_values_est) shap_names <- x$internal$parameters$shap_names cols <- c("none", shap_names) shap[, (cols) := lapply(.SD, round, digits = digits + 2), .SDcols = cols] diff --git a/README.Rmd b/README.Rmd index d645cceaa..d7511c54a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -176,7 +176,7 @@ explanation <- explain( # Printing the Shapley values for the test data. # For more information about the interpretation of the values in the table, see ?shapr::explain. -print(explanation$shapley_values) +print(explanation$shapley_values_est) # Finally we plot the resulting explanations plot(explanation) diff --git a/README.md b/README.md index 7d22b1316..203526d60 100644 --- a/README.md +++ b/README.md @@ -236,7 +236,7 @@ explanation <- explain( # Printing the Shapley values for the test data. # For more information about the interpretation of the values in the table, see ?shapr::explain. -print(explanation$shapley_values) +print(explanation$shapley_values_est) #> none Solar.R Wind Temp Month #> 1: 43.08571 13.2117337 4.785645 -25.57222 -5.599230 #> 2: 43.08571 -9.9727747 5.830694 -11.03873 -7.829954 diff --git a/inst/scripts/Beeswarm_illustration.R b/inst/scripts/Beeswarm_illustration.R index a3fdfaab4..83fedf42f 100644 --- a/inst/scripts/Beeswarm_illustration.R +++ b/inst/scripts/Beeswarm_illustration.R @@ -28,8 +28,8 @@ plot_shapr <- function(x, is_groupwise <- x$internal$parameters$is_groupwise # melting Kshap - shap_names <- colnames(x$shapley_values)[-1] - dt_shap <- round(data.table::copy(x$shapley_values), digits = digits) + shap_names <- colnames(x$shapley_values_est)[-1] + dt_shap <- round(data.table::copy(x$shapley_values_est), digits = digits) dt_shap[, id := .I] dt_shap_long <- data.table::melt(dt_shap, id.vars = "id", value.name = "phi") dt_shap_long[, sign := factor(sign(phi), levels = c(1, -1), labels = c("Increases", "Decreases"))] @@ -41,7 +41,7 @@ plot_shapr <- function(x, desc_mat[, i] <- paste0(shap_names[i], " = ", desc_mat[, i]) } } else { - desc_mat <- trimws(format(x$shapley_values[, -1], digits = digits)) + desc_mat <- trimws(format(x$shapley_values_est[, -1], digits = digits)) for (i in seq_len(ncol(desc_mat))) { desc_mat[, i] <- paste0(shap_names[i]) } diff --git a/inst/scripts/Heskes_bike_rental_illustration.R b/inst/scripts/Heskes_bike_rental_illustration.R index 3b795148e..9fb9d2722 100644 --- a/inst/scripts/Heskes_bike_rental_illustration.R +++ b/inst/scripts/Heskes_bike_rental_illustration.R @@ -72,7 +72,7 @@ save_plots <- FALSE sina_plot <- function(explanation, seed = 123) { set.seed(seed) - shapley_values <- explanation$shapley_values[, -"none", drop = FALSE] + shapley_values_est <- explanation$shapley_values_est[, -"none", drop = FALSE] X_values <- explanation$internal$data$x_explain # If we are doing group Shapley, then we compute the mean feature value for each group for each explicand @@ -85,12 +85,12 @@ sina_plot <- function(explanation, seed = 123) { data_long <- X_values %>% tidyr::pivot_longer(everything()) %>% dplyr::bind_cols( - explanation$shapley_values %>% + explanation$shapley_values_est %>% dplyr::select(-none) %>% tidyr::pivot_longer(everything()) %>% dplyr::select(-name) %>% dplyr::rename(shap = value)) %>% - dplyr::mutate(name = factor(name, levels = rev(names(explanation$shapley_values)))) %>% + dplyr::mutate(name = factor(name, levels = rev(names(explanation$shapley_values_est)))) %>% dplyr::group_by(name) %>% dplyr::arrange(name) %>% dplyr::mutate(mean_value = mean(value)) %>% @@ -454,10 +454,10 @@ gridExtra::grid.arrange(save_explanation_asymmetric_causal$plot + ggplot2::ggtit message("3. Producing scatter plots comparing marginal and causal Shapley values on the test set") sv_correlation_df <- data.frame( temp = x_explain[, "temp"], - sv_marg_cosyear = explanation_marginal$shapley_values$cosyear, - sv_caus_cosyear = explanation_causal$shapley_values$cosyear, - sv_marg_temp = explanation_marginal$shapley_values$temp, - sv_caus_temp = explanation_causal$shapley_values$temp + sv_marg_cosyear = explanation_marginal$shapley_values_est$cosyear, + sv_caus_cosyear = explanation_causal$shapley_values_est$cosyear, + sv_marg_temp = explanation_marginal$shapley_values_est$temp, + sv_caus_temp = explanation_causal$shapley_values_est$temp ) @@ -547,7 +547,7 @@ dates_idx = sapply(dates, function(data) which(as.integer(row.names(x_explain)) explanations = list("Marginal" = explanation_marginal, "Causal" = explanation_causal) explanations_extracted = data.table::rbindlist(lapply(seq_along(explanations), function(idx) { - explanations[[idx]]$shapley_values[dates_idx, ..features][, `:=` (Date = dates, type = names(explanations)[idx])] + explanations[[idx]]$shapley_values_est[dates_idx, ..features][, `:=` (Date = dates, type = names(explanations)[idx])] })) dt_all = data.table::melt(explanations_extracted, id.vars = c("Date", "type"), variable.name = "feature") @@ -756,7 +756,7 @@ explanation_asymmetric_all_gaussian2 <- ) }) -explanation_asymmetric_all_gaussian$shapley_values - explanation_asymmetric_all_gaussian2$shapley_values +explanation_asymmetric_all_gaussian$shapley_values_est - explanation_asymmetric_all_gaussian2$shapley_values_est explanation_asymmetric_all_gaussian$MSEv @@ -880,7 +880,7 @@ explanation_group_asymmetric_causal_time = system.time({ }) }) -explanation_group_asymmetric_causal$shapley_values +explanation_group_asymmetric_causal$shapley_values_est sina_plot(explanation_group_asymmetric_causal) # Now we compute the group Shapley values based on only half of the coalitions diff --git a/inst/scripts/check_model_workflow.R b/inst/scripts/check_model_workflow.R index 01799eae1..ef76a36ff 100644 --- a/inst/scripts/check_model_workflow.R +++ b/inst/scripts/check_model_workflow.R @@ -64,7 +64,7 @@ explain_xgboost = explain( ) # See that the shapley values are identical -all.equal(explain_workflow$shapley_values, explain_xgboost$shapley_values) +all.equal(explain_workflow$shapley_values_est, explain_xgboost$shapley_values_est) # Other models in workflow --------------------------------------------------------------------------------------------- set.seed(1) diff --git a/inst/scripts/compare_shap_python_new.R b/inst/scripts/compare_shap_python_new.R index c15fed9d6..b8a0a2b33 100644 --- a/inst/scripts/compare_shap_python_new.R +++ b/inst/scripts/compare_shap_python_new.R @@ -56,8 +56,8 @@ time_R_largesigma0 <- proc.time() (time_R_largesigma <- time_R_largesigma0 - time_R_indep0) # Printing the Shapley values for the test data -Kshap_indep <- explanation_independence$shapley_values -Kshap_largesigma <- explanation_largesigma$shapley_values +Kshap_indep <- explanation_independence$shapley_values_est +Kshap_largesigma <- explanation_largesigma$shapley_values_est Kshap_indep Kshap_largesigma diff --git a/inst/scripts/devel/real_data_iterative_kernelshap.R b/inst/scripts/devel/real_data_iterative_kernelshap.R index 6cb2d9973..9d6404f99 100644 --- a/inst/scripts/devel/real_data_iterative_kernelshap.R +++ b/inst/scripts/devel/real_data_iterative_kernelshap.R @@ -109,7 +109,7 @@ expl <- shapr::explain(model = model, approach = "ctree", prediction_zero = p0 ) -fwrite(expl$shapley_values,paste0(sim_results_saving_folder,"exact_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) +fwrite(expl$shapley_values_est,paste0(sim_results_saving_folder,"exact_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) print(Sys.time()) # These are the parameters for for iterative_kshap_func @@ -170,11 +170,11 @@ for (i in seq_along(testObs_computed_vec)){ approach = approach, prediction_zero = p0, n_coalitions = runcomps_list[[i]]) - expl_approx[i,] = unlist(expl_approx_obj$shapley_values) + expl_approx[i,] = unlist(expl_approx_obj$shapley_values_est) expl_approx_obj_list[[i]] <- expl_approx_obj } expl_approx <- as.data.table(expl_approx) -truth <- expl$shapley_values +truth <- expl$shapley_values_est colnames(expl_approx) <- colnames(truth) fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_", kernelSHAP_reweighting_strategy, ".csv")) @@ -212,7 +212,7 @@ print(Sys.time()) # run$kshap_final -# expl$shapley_values +# expl$shapley_values_est @@ -223,7 +223,7 @@ print(Sys.time()) # sum(unlist(kshap_final[.N,])) -# sum(unlist(expl$shapley_values[testObs_computed,])) +# sum(unlist(expl$shapley_values_est[testObs_computed,])) @@ -239,7 +239,7 @@ print(Sys.time()) # full_pred <- predict(model,x_explain)[5] # p0 <- mean(y_train) -# pred_not_to_decompose <- sum(expl$shapley_values[5,VV7:VV9]) +# pred_not_to_decompose <- sum(expl$shapley_values_est[5,VV7:VV9]) # run_minor <- iterative_kshap_func(model,x_explain,x_train, diff --git a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R index 852175d8f..1d88192c5 100644 --- a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R +++ b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R @@ -82,7 +82,7 @@ expl <- shapr::explain(model = model, approach = "gaussian", prediction_zero = p0,Sigma=Sigma,mu=mu) -fwrite(expl$shapley_values,paste0(sim_results_saving_folder,"exact_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) +fwrite(expl$shapley_values_est,paste0(sim_results_saving_folder,"exact_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) cutoff_feats <- paste0("VV",1:12) @@ -140,7 +140,7 @@ fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_ -truth <- expl$shapley_values +truth <- expl$shapley_values_est expl_approx <- matrix(0, nrow = length(inds), ncol = m+1) expl_approx_obj_list <- list() @@ -152,7 +152,7 @@ for (i in testObs_computed_vec){ prediction_zero = p0, n_coalitions = runcomps_list[[i]], Sigma=Sigma,mu=mu) - expl_approx[i,] = unlist(expl_approx_obj$shapley_values) + expl_approx[i,] = unlist(expl_approx_obj$shapley_values_est) expl_approx_obj_list[[i]] <- expl_approx_obj } expl_approx <- as.data.table(expl_approx) @@ -197,7 +197,7 @@ full_pred # run$kshap_final -# expl$shapley_values +# expl$shapley_values_est @@ -208,7 +208,7 @@ full_pred # sum(unlist(kshap_final[.N,])) -# sum(unlist(expl$shapley_values[testObs_computed,])) +# sum(unlist(expl$shapley_values_est[testObs_computed,])) @@ -224,7 +224,7 @@ full_pred # full_pred <- predict(model,x_explain)[5] # p0 <- mean(y_train) -# pred_not_to_decompose <- sum(expl$shapley_values[5,VV7:VV9]) +# pred_not_to_decompose <- sum(expl$shapley_values_est[5,VV7:VV9]) # run_minor <- iterative_kshap_func(model,x_explain,x_train, diff --git a/inst/scripts/devel/simtest_reweighting_strategies.R b/inst/scripts/devel/simtest_reweighting_strategies.R index 950315ac4..3a5a24f0c 100644 --- a/inst/scripts/devel/simtest_reweighting_strategies.R +++ b/inst/scripts/devel/simtest_reweighting_strategies.R @@ -120,9 +120,9 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ ) shap_dt0 <- as.data.table(cbind(seq_len(n_explain),t(this_W%*%as.matrix(exact_dt_vS[,-c("coalitions_str","id_coalition")])))) - names(shap_dt0) <- names(this$shapley_values) + names(shap_dt0) <- names(this$shapley_values_est) - this_diff <- unlist(shap_dt0[,-c(1,2)]-expl$shapley_values[,-c(1,2)]) + this_diff <- unlist(shap_dt0[,-c(1,2)]-expl$shapley_values_est[,-c(1,2)]) this_bias <- mean(this_diff) this_var <- var(this_diff) this_MAE <- mean(abs(this_diff)) @@ -217,7 +217,7 @@ fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_ -truth <- expl$shapley_values +truth <- expl$shapley_values_est expl_approx <- matrix(0, nrow = length(inds), ncol = m+1) expl_approx_obj_list <- list() @@ -229,7 +229,7 @@ for (i in testObs_computed_vec){ prediction_zero = p0, n_combinations = runcomps_list[[i]], Sigma=Sigma,mu=mu) - expl_approx[i,] = unlist(expl_approx_obj$shapley_values) + expl_approx[i,] = unlist(expl_approx_obj$shapley_values_est) expl_approx_obj_list[[i]] <- expl_approx_obj } expl_approx <- as.data.table(expl_approx) diff --git a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R index d04203af4..0c1c8b521 100644 --- a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R +++ b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R @@ -137,9 +137,9 @@ for(i0 in seq_along(paired_shap_sampling_vec)){ ) shap_dt0 <- as.data.table(cbind(seq_len(n_explain),t(this_W%*%as.matrix(exact_dt_vS[,-c("coalitions_str","id_coalition")])))) - names(shap_dt0) <- names(this$shapley_values) + names(shap_dt0) <- names(this$shapley_values_est) - this_diff <- unlist(shap_dt0[,-c(1,2)]-expl$shapley_values[,-c(1,2)]) + this_diff <- unlist(shap_dt0[,-c(1,2)]-expl$shapley_values_est[,-c(1,2)]) this_bias <- mean(this_diff) this_var <- var(this_diff) this_MAE <- mean(abs(this_diff)) diff --git a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R index 4739b5429..cc8341f92 100644 --- a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R +++ b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R @@ -138,9 +138,9 @@ for(ii in seq_along(n_coalitions_vec)){ ) shap_dt0 <- as.data.table(cbind(seq_len(n_explain),t(this_W%*%as.matrix(exact_dt_vS[,-c("coalitions_str","id_coalition")])))) - names(shap_dt0) <- names(this$shapley_values) + names(shap_dt0) <- names(this$shapley_values_est) - this_diff <- unlist(shap_dt0[,-c(1,2)]-expl$shapley_values[,-c(1,2)]) + this_diff <- unlist(shap_dt0[,-c(1,2)]-expl$shapley_values_est[,-c(1,2)]) this_bias <- mean(this_diff) this_var <- var(this_diff) this_MAE <- mean(abs(this_diff)) diff --git a/inst/scripts/devel/testing_explain_forevast_n_comb.R b/inst/scripts/devel/testing_explain_forevast_n_comb.R index 23517f2c9..43aeb6ced 100644 --- a/inst/scripts/devel/testing_explain_forevast_n_comb.R +++ b/inst/scripts/devel/testing_explain_forevast_n_comb.R @@ -175,14 +175,14 @@ cols_horizon3 <- h3full$internal$objects$cols_per_horizon[[3]] h1mean1 <- h2mean1 <- h2mean2 <- h3mean1 <- h3mean2 <- h3mean3 <- list() for(i in 1:reps){ - h1mean1[[i]] <- as.matrix(h1list[[i]]$shapley_values[horizon==1, ..cols_horizon1]) + h1mean1[[i]] <- as.matrix(h1list[[i]]$shapley_values_est[horizon==1, ..cols_horizon1]) - h2mean1[[i]] <- as.matrix(h2list[[i]]$shapley_values[horizon==1, ..cols_horizon1]) - h2mean2[[i]] <- as.matrix(h2list[[i]]$shapley_values[horizon==2, ..cols_horizon2]) + h2mean1[[i]] <- as.matrix(h2list[[i]]$shapley_values_est[horizon==1, ..cols_horizon1]) + h2mean2[[i]] <- as.matrix(h2list[[i]]$shapley_values_est[horizon==2, ..cols_horizon2]) - h3mean1[[i]] <- as.matrix(h3list[[i]]$shapley_values[horizon==1, ..cols_horizon1]) - h3mean2[[i]] <- as.matrix(h3list[[i]]$shapley_values[horizon==2, ..cols_horizon2]) - h3mean3[[i]] <- as.matrix(h3list[[i]]$shapley_values[horizon==3, ..cols_horizon3]) + h3mean1[[i]] <- as.matrix(h3list[[i]]$shapley_values_est[horizon==1, ..cols_horizon1]) + h3mean2[[i]] <- as.matrix(h3list[[i]]$shapley_values_est[horizon==2, ..cols_horizon2]) + h3mean3[[i]] <- as.matrix(h3list[[i]]$shapley_values_est[horizon==3, ..cols_horizon3]) } @@ -190,25 +190,25 @@ for(i in 1:reps){ Reduce("+", h1mean1) / reps Reduce("+", h2mean1) / reps Reduce("+", h3mean1) / reps -h3full$shapley_values[horizon==1,..cols_horizon1] +h3full$shapley_values_est[horizon==1,..cols_horizon1] # Horizon 2 Reduce("+", h2mean2) / reps Reduce("+", h3mean2) / reps -h3full$shapley_values[horizon==2,..cols_horizon2] +h3full$shapley_values_est[horizon==2,..cols_horizon2] # Horizon 3 Reduce("+", h3mean3) / reps -h3full$shapley_values[horizon==3,..cols_horizon3] +h3full$shapley_values_est[horizon==3,..cols_horizon3] -expect_equal(h2$shapley_values[horizon==1, ..cols_horizon1], - h1$shapley_values[horizon==1,..cols_horizon1]) +expect_equal(h2$shapley_values_est[horizon==1, ..cols_horizon1], + h1$shapley_values_est[horizon==1,..cols_horizon1]) -expect_equal(h3$shapley_values[horizon==1, ..cols_horizon1], - h1$shapley_values[horizon==1,..cols_horizon1]) +expect_equal(h3$shapley_values_est[horizon==1, ..cols_horizon1], + h1$shapley_values_est[horizon==1,..cols_horizon1]) cols_horizon2 <- h2$internal$objects$cols_per_horizon[[2]] -expect_equal(h3$shapley_values[horizon==2, ..cols_horizon2], - h2$shapley_values[horizon==2,..cols_horizon2]) +expect_equal(h3$shapley_values_est[horizon==2, ..cols_horizon2], + h2$shapley_values_est[horizon==2,..cols_horizon2]) diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index 4cf3376fc..7214a2eac 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -127,6 +127,6 @@ second_path = explain( # Identical results -all.equal(full$shapley_values,second$shapley_values) # TRUE -all.equal(full$shapley_values,second2$shapley_values) # TRUE -all.equal(full$shapley_values,second_path$shapley_values) # TRUE +all.equal(full$shapley_values_est,second$shapley_values_est) # TRUE +all.equal(full$shapley_values_est,second2$shapley_values_est) # TRUE +all.equal(full$shapley_values_est,second_path$shapley_values_est) # TRUE diff --git a/inst/scripts/devel/verifying_arima_model_output.R b/inst/scripts/devel/verifying_arima_model_output.R index 7a63bcbf5..4f27f3c40 100644 --- a/inst/scripts/devel/verifying_arima_model_output.R +++ b/inst/scripts/devel/verifying_arima_model_output.R @@ -51,20 +51,20 @@ exp <- explain_forecast(model = model_arima_temp, # These two should be approximately equal # For y -exp$shapley_values$Y1.1 +exp$shapley_values_est$Y1.1 model_arima_temp$coef[1]*(y[explain_idx]-mean(y)) #[1] -0.13500 0.20643 #[1] -0.079164 0.208118 # for xreg1 -exp$shapley_values$var1.F1 +exp$shapley_values_est$var1.F1 model_arima_temp$coef[3]*(xreg[explain_idx+1,1]-mean(xreg[,1])) #[1] -0.030901 1.179386 #[1] -0.12034 1.19589 # for xreg2 -exp$shapley_values$var2.F1 +exp$shapley_values_est$var2.F1 0 #[1] 0.011555 0.031911 #[1] 0 diff --git a/inst/scripts/example_plot_MSEv.R b/inst/scripts/example_plot_MSEv.R index 05381b580..51fd56ee2 100644 --- a/inst/scripts/example_plot_MSEv.R +++ b/inst/scripts/example_plot_MSEv.R @@ -381,7 +381,7 @@ explanation_gaussian <- explain( ) explanation_gaussian_copy <- copy(explanation_gaussian_all) -colnames(explanation_gaussian_copy$shapley_values) <- rev(colnames(explanation_gaussian_copy$shapley_values)) +colnames(explanation_gaussian_copy$shapley_values_est) <- rev(colnames(explanation_gaussian_copy$shapley_values_est)) # Will give an error due to different feature names plot_MSEv_eval_crit(list( diff --git a/inst/scripts/readme_example.R b/inst/scripts/readme_example.R index 480f599d7..b9e8852a9 100644 --- a/inst/scripts/readme_example.R +++ b/inst/scripts/readme_example.R @@ -39,7 +39,7 @@ explanation <- explain( # Printing the Shapley values for the test data. # For more information about the interpretation of the values in the table, see ?shapr::explain. -print(explanation$shapley_values) +print(explanation$shapley_values_est) # Finally we plot the resulting explanations plot(explanation) diff --git a/inst/scripts/vilde/sketch_for_waterfall_plot.R b/inst/scripts/vilde/sketch_for_waterfall_plot.R index dc9e9278f..f8572fb55 100644 --- a/inst/scripts/vilde/sketch_for_waterfall_plot.R +++ b/inst/scripts/vilde/sketch_for_waterfall_plot.R @@ -29,11 +29,11 @@ res <- explain_final(x_train,x_test,model,approach="independence",prediction_zer plot(res) i<- 1 # index for observation we want to plot -dt <- data.table(feat_name = paste0(colnames(res$shapley_values[,-1]), " = ", format(res$internal$data$x_explain[i,], 2) ), - shapley_value = as.numeric(res$shapley_values[i,-1]) +dt <- data.table(feat_name = paste0(colnames(res$shapley_values_est[,-1]), " = ", format(res$internal$data$x_explain[i,], 2) ), + shapley_value = as.numeric(res$shapley_values_est[i,-1]) ) dt -expected <- as.numeric(res$shapley_values[i,])[1] +expected <- as.numeric(res$shapley_values_est[i,])[1] observed <- res$pred_explain[i] dt[, sign := ifelse(shapley_value > 0, "Increases", "Decreases")] diff --git a/man/explain.Rd b/man/explain.Rd index eda7426d2..b1e3e2454 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -328,7 +328,7 @@ This is useful if the underlying time series are scaled between 0 and 1, for exa \value{ Object of class \code{c("shapr", "list")}. Contains the following items: \describe{ -\item{shapley_values}{data.table with the estimated Shapley values with explained observation in the rows and +\item{shapley_values_est}{data.table with the estimated Shapley values with explained observation in the rows and features along the columns. The column \code{none} is the prediction not devoted to any of the features (given by the argument \code{prediction_zero})} \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. @@ -461,7 +461,7 @@ explain5 <- explain( ) # Print the Shapley values -print(explain1$shapley_values) +print(explain1$shapley_values_est) # Plot the results if (requireNamespace("ggplot2", quietly = TRUE)) { @@ -481,7 +481,7 @@ explain_groups <- explain( prediction_zero = p, n_MC_samples = 1e2 ) -print(explain_groups$shapley_values) +print(explain_groups$shapley_values_est) # Separate and surrogate regression approaches with linear regression models. # More complex regression models can be used, and we can use CV to diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index 860aa14ac..db66aba7a 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -260,7 +260,7 @@ This is useful if the underlying time series are scaled between 0 and 1, for exa \value{ Object of class \code{c("shapr", "list")}. Contains the following items: \describe{ -\item{shapley_values}{data.table with the estimated Shapley values with explained observation in the rows and +\item{shapley_values_est}{data.table with the estimated Shapley values with explained observation in the rows and features along the columns. The column \code{none} is the prediction not devoted to any of the features (given by the argument \code{prediction_zero})} \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. diff --git a/man/finalize_explanation_forecast.Rd b/man/finalize_explanation_forecast.Rd index 5269efd9c..5ed40b0f4 100644 --- a/man/finalize_explanation_forecast.Rd +++ b/man/finalize_explanation_forecast.Rd @@ -18,7 +18,7 @@ The list contains one or more of the elements \code{parameters}, \code{data}, \c \value{ Object of class \code{c("shapr", "list")}. Contains the following items: \describe{ -\item{shapley_values}{data.table with the estimated Shapley values with explained observation in the rows and +\item{shapley_values_est}{data.table with the estimated Shapley values with explained observation in the rows and features along the columns. The column \code{none} is the prediction not devoted to any of the features (given by the argument \code{prediction_zero})} \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. @@ -151,7 +151,7 @@ explain5 <- explain( ) # Print the Shapley values -print(explain1$shapley_values) +print(explain1$shapley_values_est) # Plot the results if (requireNamespace("ggplot2", quietly = TRUE)) { @@ -171,7 +171,7 @@ explain_groups <- explain( prediction_zero = p, n_MC_samples = 1e2 ) -print(explain_groups$shapley_values) +print(explain_groups$shapley_values_est) # Separate and surrogate regression approaches with linear regression models. # More complex regression models can be used, and we can use CV to diff --git a/python/shaprpy/explain.py b/python/shaprpy/explain.py index fc748e08f..ef3f1e147 100644 --- a/python/shaprpy/explain.py +++ b/python/shaprpy/explain.py @@ -195,7 +195,7 @@ def explain( routput = 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shapley values for different )) # Same Shapley values expect_equal( - explain.empirical_n_batches_5$shapley_values, - explain.empirical_n_batches_10$shapley_values + explain.empirical_n_batches_5$shapley_values_est, + explain.empirical_n_batches_10$shapley_values_est ) # approach "ctree" is seed dependent @@ -236,7 +236,7 @@ test_that("different n_batches gives same/different shapley values for different )) # NEITHER same Shapley values expect_false(identical( - explain.ctree_n_batches_5$shapley_values, - explain.ctree_n_batches_10$shapley_values + explain.ctree_n_batches_5$shapley_values_est, + explain.ctree_n_batches_10$shapley_values_est )) }) diff --git a/tests/testthat/test-forecast-output.R b/tests/testthat/test-forecast-output.R index 32b79b251..1f90c9b3c 100644 --- a/tests/testthat/test-forecast-output.R +++ b/tests/testthat/test-forecast-output.R @@ -205,19 +205,19 @@ test_that("ARIMA gives the same output with different horizons", { cols_horizon1 <- h2$internal$objects$cols_per_horizon[[1]] expect_equal( - h2$shapley_values[horizon == 1, ..cols_horizon1], - h1$shapley_values[horizon == 1, ..cols_horizon1] + h2$shapley_values_est[horizon == 1, ..cols_horizon1], + h1$shapley_values_est[horizon == 1, ..cols_horizon1] ) expect_equal( - h3$shapley_values[horizon == 1, ..cols_horizon1], - h1$shapley_values[horizon == 1, ..cols_horizon1] + h3$shapley_values_est[horizon == 1, ..cols_horizon1], + h1$shapley_values_est[horizon == 1, ..cols_horizon1] ) cols_horizon2 <- h2$internal$objects$cols_per_horizon[[2]] expect_equal( - h3$shapley_values[horizon == 2, ..cols_horizon2], - h2$shapley_values[horizon == 2, ..cols_horizon2] + h3$shapley_values_est[horizon == 2, ..cols_horizon2], + h2$shapley_values_est[horizon == 2, ..cols_horizon2] ) }) @@ -278,17 +278,17 @@ test_that("ARIMA gives the same output with different horizons with grouping", { ) expect_equal( - h2$shapley_values[horizon == 1], - h1$shapley_values[horizon == 1] + h2$shapley_values_est[horizon == 1], + h1$shapley_values_est[horizon == 1] ) expect_equal( - h3$shapley_values[horizon == 1], - h1$shapley_values[horizon == 1] + h3$shapley_values_est[horizon == 1], + h1$shapley_values_est[horizon == 1] ) expect_equal( - h3$shapley_values[horizon == 2], - h2$shapley_values[horizon == 2] + h3$shapley_values_est[horizon == 2], + h2$shapley_values_est[horizon == 2] ) }) diff --git a/tests/testthat/test-regular-output.R b/tests/testthat/test-regular-output.R index d7d4122d5..d9c5cba9f 100644 --- a/tests/testthat/test-regular-output.R +++ b/tests/testthat/test-regular-output.R @@ -450,8 +450,8 @@ test_that("output_custom_lm_numeric_independence_2", { # Check that the printed Shapley values are identical expect_equal( - custom$shapley_values, - native$shapley_values + custom$shapley_values_est, + native$shapley_values_est ) }) diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index 802f2f905..b2cb77e22 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -1323,12 +1323,12 @@ test_that("data feature ordering is output_lm_numeric_column_order", { # Same Shapley values, but different order expect_false(identical( - explain.original$shapley_values, - explain.new_data_feature_order$shapley_values + explain.original$shapley_values_est, + explain.new_data_feature_order$shapley_values_est )) expect_equal( - explain.original$shapley_values[, mget(sort(names(explain.original$shapley_values)))], - explain.new_data_feature_order$shapley_values[, mget(sort(names(explain.new_data_feature_order$shapley_values)))] + explain.original$shapley_values_est[, mget(sort(names(explain.original$shapley_values_est)))], + explain.new_data_feature_order$shapley_values_est[, mget(sort(names(explain.new_data_feature_order$shapley_values_est)))] ) # Same Shapley values in same order @@ -1579,7 +1579,7 @@ test_that("vaeac_set_seed_works", { ) # Check for equal Shapley values - expect_equal(explanation_vaeac_1$shapley_values, explanation_vaeac_2$shapley_values) + expect_equal(explanation_vaeac_1$shapley_values_est, explanation_vaeac_2$shapley_values_est) }) test_that("vaeac_pretreained_vaeac_model", { @@ -1624,7 +1624,7 @@ test_that("vaeac_pretreained_vaeac_model", { ) # Check for equal Shapley values - expect_equal(explanation_vaeac_1$shapley_values, explanation_pretrained_vaeac$shapley_values) + expect_equal(explanation_vaeac_1$shapley_values_est, explanation_pretrained_vaeac$shapley_values_est) #### We can also do this by reusing the vaeac model PATH # Get the pre-trained vaeac model path @@ -1647,7 +1647,7 @@ test_that("vaeac_pretreained_vaeac_model", { ) # Check for equal Shapley values - expect_equal(explanation_vaeac_1$shapley_values, explanation_pretrained_vaeac$shapley_values) + expect_equal(explanation_vaeac_1$shapley_values_est, explanation_pretrained_vaeac$shapley_values_est) }) @@ -1682,5 +1682,5 @@ test_that("feature wise and groupwise computations are identical", { # Checking equality in the list with all final and intermediate results - expect_equal(expl_feat$shapley_values, expl_group$shapley_values) + expect_equal(expl_feat$shapley_values_est, expl_group$shapley_values_est) }) diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index 5d0690e7c..f556d6298 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -494,7 +494,7 @@ explanation <- explain( # Printing the Shapley values for the test data. # For more information about the interpretation of the values in the table, see ?shapr::explain. -print(explanation$shapley_values) +print(explanation$shapley_values_est) #> explain_id none Solar.R Wind Temp Month #> #> 1: 1 43.086 13.21173 4.7856 -25.572 -5.5992 @@ -1814,7 +1814,7 @@ explanation_tidymodels <- explain( #> ℹ Using 16 of 16 coalitions. # See that the Shapley value explanations are identical too -all.equal(explanation$shapley_values, explanation_tidymodels$shapley_values) +all.equal(explanation$shapley_values_est, explanation_tidymodels$shapley_values_est) #> [1] TRUE ``` @@ -2298,7 +2298,7 @@ explanation_forecast <- explain_forecast( #> max_n_coalitions is NULL or larger than or 2^n_features = 32, #> and is therefore set to 2^n_features = 32. -print(explanation_forecast$shapley_values) +print(explanation_forecast$shapley_values_est) #> explain_idx horizon none Temp.1 Temp.2 Wind.1 Wind.F1 Wind.F2 #> #> 1: 151 1 77.96 -0.67793 -0.67340 -1.2688 0.493408 NA diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index b6e84c3ff..30ce83a7c 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -491,7 +491,7 @@ explanation <- explain( # Printing the Shapley values for the test data. # For more information about the interpretation of the values in the table, see ?shapr::explain. -print(explanation$shapley_values) +print(explanation$shapley_values_est) # Plot the resulting explanations for observations 1 and 6 plot(explanation, bar_plot_phi0 = FALSE, index_x_explain = c(1, 6)) @@ -1429,7 +1429,7 @@ explanation_tidymodels <- explain( ) # See that the Shapley value explanations are identical too -all.equal(explanation$shapley_values, explanation_tidymodels$shapley_values) +all.equal(explanation$shapley_values_est, explanation_tidymodels$shapley_values_est) ``` ## The parameters of the `vaeac` approach @@ -1788,7 +1788,7 @@ explanation_forecast <- explain_forecast( group_lags = FALSE ) -print(explanation_forecast$shapley_values) +print(explanation_forecast$shapley_values_est) ``` diff --git a/vignettes/understanding_shapr_asymmetric_causal.Rmd b/vignettes/understanding_shapr_asymmetric_causal.Rmd index 8e089841f..711654140 100644 --- a/vignettes/understanding_shapr_asymmetric_causal.Rmd +++ b/vignettes/understanding_shapr_asymmetric_causal.Rmd @@ -1337,10 +1337,10 @@ sv_framework_2_str <- "Causal SV" # Set up the data.frame we are going to plot sv_correlation_df <- data.frame( color = x_explain[, color], - sv_framework_1_feature_1 = sv_framework_1$shapley_values[[feature_1]], - sv_framework_2_feature_1 = sv_framework_2$shapley_values[[feature_1]], - sv_framework_1_feature_2 = sv_framework_1$shapley_values[[feature_2]], - sv_framework_2_feature_2 = sv_framework_2$shapley_values[[feature_2]] + sv_framework_1_feature_1 = sv_framework_1$shapley_values_est[[feature_1]], + sv_framework_2_feature_1 = sv_framework_2$shapley_values_est[[feature_1]], + sv_framework_1_feature_2 = sv_framework_1$shapley_values_est[[feature_2]], + sv_framework_2_feature_2 = sv_framework_2$shapley_values_est[[feature_2]] ) # Make the plots @@ -1501,7 +1501,7 @@ explanations <- list( # Extract the relevant Shapley values explanations_extracted <- data.table::rbindlist(lapply(seq_along(explanations), function(idx) { - explanations[[idx]]$shapley_values[ + explanations[[idx]]$shapley_values_est[ dates_idx, ..features ][, `:=`(Date = dates, type = names(explanations)[idx])] })) diff --git a/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig b/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig index f7ddbc9e3..66f19c49d 100644 --- a/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig +++ b/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig @@ -845,10 +845,10 @@ sv_framework_2_str <- "Causal SV" # Set up the data.frame we are going to plot sv_correlation_df <- data.frame( color = x_explain[, color], - sv_framework_1_feature_1 = sv_framework_1$shapley_values[[feature_1]], - sv_framework_2_feature_1 = sv_framework_2$shapley_values[[feature_1]], - sv_framework_1_feature_2 = sv_framework_1$shapley_values[[feature_2]], - sv_framework_2_feature_2 = sv_framework_2$shapley_values[[feature_2]] + sv_framework_1_feature_1 = sv_framework_1$shapley_values_est[[feature_1]], + sv_framework_2_feature_1 = sv_framework_2$shapley_values_est[[feature_1]], + sv_framework_1_feature_2 = sv_framework_1$shapley_values_est[[feature_2]], + sv_framework_2_feature_2 = sv_framework_2$shapley_values_est[[feature_2]] ) # Make the plots @@ -1006,7 +1006,7 @@ explanations <- list( # Extract the relevant Shapley values explanations_extracted <- data.table::rbindlist(lapply(seq_along(explanations), function(idx) { - explanations[[idx]]$shapley_values[ + explanations[[idx]]$shapley_values_est[ dates_idx, ..features ][, `:=`(Date = dates, type = names(explanations)[idx])] })) diff --git a/vignettes/understanding_shapr_regression.Rmd b/vignettes/understanding_shapr_regression.Rmd index 9d2ceaeb1..788f33526 100644 --- a/vignettes/understanding_shapr_regression.Rmd +++ b/vignettes/understanding_shapr_regression.Rmd @@ -1434,8 +1434,8 @@ future::plan(future::sequential) # To return to non-parallel computation # Check that we get identical Shapley value explanations all.equal( - explanation_list$sur_rf_cv$shapley_values, - explanation_list$sur_rf_cv_par$shapley_values + explanation_list$sur_rf_cv$shapley_values_est, + explanation_list$sur_rf_cv_par$shapley_values_est ) #> [1] TRUE ``` diff --git a/vignettes/understanding_shapr_regression.Rmd.orig b/vignettes/understanding_shapr_regression.Rmd.orig index 9d9f32ef6..c9f5f53b7 100644 --- a/vignettes/understanding_shapr_regression.Rmd.orig +++ b/vignettes/understanding_shapr_regression.Rmd.orig @@ -892,8 +892,8 @@ future::plan(future::sequential) # To return to non-parallel computation # Check that we get identical Shapley value explanations all.equal( - explanation_list$sur_rf_cv$shapley_values, - explanation_list$sur_rf_cv_par$shapley_values + explanation_list$sur_rf_cv$shapley_values_est, + explanation_list$sur_rf_cv_par$shapley_values_est ) ``` diff --git a/vignettes/understanding_shapr_vaeac.Rmd b/vignettes/understanding_shapr_vaeac.Rmd index 76464eada..dcf30355e 100644 --- a/vignettes/understanding_shapr_vaeac.Rmd +++ b/vignettes/understanding_shapr_vaeac.Rmd @@ -172,7 +172,7 @@ We can look at the Shapley values. ``` r # Printing and ploting the Shapley values. # See ?shapr::explain for interpretation of the values. -print(explanation$shapley_values) +print(explanation$shapley_values_est) #> explain_id none Solar.R Wind Temp Month #> #> 1: 1 43.086 4.35827 -0.49487 -16.7173 0.55352 @@ -219,7 +219,7 @@ expl_pretrained_vaeac <- explain( #> and is therefore set to 2^n_features = 16. # Check that this version provides the same Shapley values -all.equal(explanation$shapley_values, expl_pretrained_vaeac$shapley_values) +all.equal(explanation$shapley_values_est, expl_pretrained_vaeac$shapley_values_est) #> [1] TRUE ``` @@ -251,7 +251,7 @@ expl_pretrained_vaeac_path <- explain( #> and is therefore set to 2^n_features = 16. # Check that this version provides the same Shapley values -all.equal(explanation$shapley_values, expl_pretrained_vaeac_path$shapley_values) +all.equal(explanation$shapley_values_est, expl_pretrained_vaeac_path$shapley_values_est) #> [1] TRUE ``` @@ -555,7 +555,7 @@ progressr::with_progress({ #> '/tmp/RtmpIQRVZ2/X2024.10.04.14.57.33.088772_n_features_4_n_train_105_depth_3_width_32_latent_8_lr_0.001_epoch_best.pt' #> '/tmp/RtmpIQRVZ2/X2024.10.04.14.57.33.088772_n_features_4_n_train_105_depth_3_width_32_latent_8_lr_0.001_epoch_best_running.pt' #> '/tmp/RtmpIQRVZ2/X2024.10.04.14.57.33.088772_n_features_4_n_train_105_depth_3_width_32_latent_8_lr_0.001_epoch_last.pt' -all.equal(expl_with_messages$shapley_values, expl_with_progressr$shapley_values) +all.equal(expl_with_messages$shapley_values_est, expl_with_progressr$shapley_values_est) #> [1] TRUE ``` diff --git a/vignettes/understanding_shapr_vaeac.Rmd.orig b/vignettes/understanding_shapr_vaeac.Rmd.orig index 63ee5cc63..20499c9b7 100644 --- a/vignettes/understanding_shapr_vaeac.Rmd.orig +++ b/vignettes/understanding_shapr_vaeac.Rmd.orig @@ -177,7 +177,7 @@ We can look at the Shapley values. ```{r first-vaeac-plots, cache = TRUE} # Printing and ploting the Shapley values. # See ?shapr::explain for interpretation of the values. -print(explanation$shapley_values) +print(explanation$shapley_values_est) plot(explanation) ``` @@ -208,7 +208,7 @@ expl_pretrained_vaeac <- explain( ) # Check that this version provides the same Shapley values -all.equal(explanation$shapley_values, expl_pretrained_vaeac$shapley_values) +all.equal(explanation$shapley_values_est, expl_pretrained_vaeac$shapley_values_est) ``` ## Pre-trained vaeac (path) {#pretrained_vaeac_path} @@ -233,7 +233,7 @@ expl_pretrained_vaeac_path <- explain( ) # Check that this version provides the same Shapley values -all.equal(explanation$shapley_values, expl_pretrained_vaeac_path$shapley_values) +all.equal(explanation$shapley_values_est, expl_pretrained_vaeac_path$shapley_values_est) ``` @@ -397,7 +397,7 @@ progressr::with_progress({ vaeac.n_vaeacs_initialize = 2 ) }) -all.equal(expl_with_messages$shapley_values, expl_with_progressr$shapley_values) +all.equal(expl_with_messages$shapley_values_est, expl_with_progressr$shapley_values_est) ``` ## Continue the training of the vaeac approach {#continue_training} From e47f90025e1034ce6a6e3585c584d957509aa22c Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 16:15:54 +0200 Subject: [PATCH 31/32] lintr --- tests/testthat/test-regular-setup.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index b2cb77e22..a42314511 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -1303,7 +1303,7 @@ test_that("data feature ordering is output_lm_numeric_column_order", { prediction_zero = p0 ) - explain.new_data_feature_order <- explain( + ex.new_data_feature_order <- explain( testing = TRUE, model = model_lm_numeric, x_explain = rev(x_explain_numeric), @@ -1324,11 +1324,11 @@ test_that("data feature ordering is output_lm_numeric_column_order", { # Same Shapley values, but different order expect_false(identical( explain.original$shapley_values_est, - explain.new_data_feature_order$shapley_values_est + ex.new_data_feature_order$shapley_values_est )) expect_equal( explain.original$shapley_values_est[, mget(sort(names(explain.original$shapley_values_est)))], - explain.new_data_feature_order$shapley_values_est[, mget(sort(names(explain.new_data_feature_order$shapley_values_est)))] + ex.new_data_feature_order$shapley_values_est[, mget(sort(names(ex.new_data_feature_order$shapley_values_est)))] ) # Same Shapley values in same order From 4391a7d17a8e9efb0a2dd58cb6afa1fd5f7bef6d Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 16:25:40 +0200 Subject: [PATCH 32/32] checks --- R/compute_estimates.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/compute_estimates.R b/R/compute_estimates.R index c69eef24a..34c14c826 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -282,7 +282,7 @@ bootstrap_shapley <- function(internal, dt_vS, n_boot_samps = 100, seed = 123) { shap_names <- internal$parameters$horizon_features[[i]] } dt_cols <- c(1, seq_len(n_explain) + (i - 1) * n_explain + 1) - dt_vS_this <- dt_vS[, ..dt_cols] + dt_vS_this <- dt_vS[, dt_cols, with = FALSE] result[[i]] <- bootstrap_shapley_inner(X, n_shapley_values, shap_names, internal, dt_vS_this, n_boot_samps, seed) } result <- rbindlist(result, fill = TRUE)

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b/tests/testthat/_snaps/regression-setup.md @@ -129,7 +129,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -271,7 +271,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -295,7 +295,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -319,7 +319,7 @@ * Model class: * Approach: regression_surrogate - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 diff --git a/tests/testthat/_snaps/regular-output.md b/tests/testthat/_snaps/regular-output.md index 97ae4d330..632383c8f 100644 --- a/tests/testthat/_snaps/regular-output.md +++ b/tests/testthat/_snaps/regular-output.md @@ -9,7 +9,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -34,7 +34,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -59,7 +59,7 @@ * Model class: * Approach: empirical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -80,7 +80,7 @@ Message * Model class: * Approach: empirical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -110,7 +110,7 @@ Message * Model class: * Approach: empirical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -161,7 +161,7 @@ Message * Model class: * Approach: empirical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -182,7 +182,7 @@ Message * Model class: * Approach: empirical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -207,7 +207,7 @@ * Model class: * Approach: gaussian - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -232,7 +232,7 @@ * Model class: * Approach: copula - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -257,7 +257,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -282,7 +282,7 @@ * Model class: * Approach: vaeac - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -307,7 +307,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -332,7 +332,7 @@ * Model class: * Approach: vaeac - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -357,7 +357,7 @@ * Model class: * Approach: categorical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -382,7 +382,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 3 @@ -407,7 +407,7 @@ * Model class: * Approach: timeseries - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of group-wise Shapley values: 4 * Number of observations to explain: 2 @@ -431,7 +431,7 @@ * Model class: * Approach: gaussian, empirical, ctree, and independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -456,7 +456,7 @@ * Model class: * Approach: ctree, copula, independence, and copula - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -481,7 +481,7 @@ * Model class: * Approach: independence, empirical, gaussian, and empirical - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -506,7 +506,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -531,7 +531,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -556,7 +556,7 @@ * Model class: * Approach: vaeac - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -581,7 +581,7 @@ * Model class: * Approach: ctree, independence, ctree, and independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -606,7 +606,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -634,7 +634,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -662,7 +662,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -687,7 +687,7 @@ * Model class: * Approach: independence - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 2 * Number of observations to explain: 3 @@ -712,7 +712,7 @@ * Model class: * Approach: ctree - * iterative estimation: FALSE + * Iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -737,7 +737,7 @@ * Model 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z{2NCzcO8nHm?9_tx3;$${ZDB7J@en%p3nDRYI|q%_Y6`g$5IPRw}6c2kI7-~+Ku@x zMDjJ+|McI#GfF+w+D=S3wwdt!yZ)ZpZS^j2`7S*@%9^vUJZp-PU5B5f9m=1vg-2(Yr#*Y`(0>$}}R#Pu%!vsSiQ z42`2}$RmyFn^RI<^t^Lx1cupW?mTmoKj?l$q0~zRF4t?Sx{|1A#CaeGe literal 0 HcmV?d00001 diff --git a/tests/testthat/test-asymmetric-causal-output.R b/tests/testthat/test-asymmetric-causal-output.R index 305024c30..9fb4668c8 100644 --- a/tests/testthat/test-asymmetric-causal-output.R +++ b/tests/testthat/test-asymmetric-causal-output.R @@ -461,7 +461,7 @@ test_that("output_categorical_asym_causal_mixed_cat", { causal_ordering = list(3:4, 2, 1), confounding = c(TRUE, FALSE, FALSE), n_MC_samples = 5, # Just for speed - keep_samp_for_vS = TRUE + output_args = list(keep_samp_for_vS = TRUE) ), "output_categorical_asym_causal_mixed_cat" ) diff --git a/tests/testthat/test-regular-output.R b/tests/testthat/test-regular-output.R index f820cb917..d7d4122d5 100644 --- a/tests/testthat/test-regular-output.R +++ b/tests/testthat/test-regular-output.R @@ -566,7 +566,7 @@ test_that("output_lm_numeric_empirical_progress", { }) -# Just checking that internal$output$dt_samp_for_vS keep_samp_for_vS +# Just checking that internal$output$dt_samp_for_vS works test_that("output_lm_numeric_independence_keep_samp_for_vS", { expect_snapshot_rds( (out <- explain( From 9c08800e0dd75cc250af776f50e34e39f7b60f56 Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 14:15:00 +0200 Subject: [PATCH 26/32] more test files --- ...m_numeric_gaussian_group_converges_tol.rds | Bin 4064 -> 4098 bytes ...tput_categorical_asym_causal_mixed_cat.rds | Bin 5012 -> 6888 bytes .../output_sym_caus_conf_TRUE_group.rds | Bin 4331 -> 4365 bytes .../output_sym_caus_conf_mix_group.rds | Bin 4312 -> 4337 bytes ...tput_sym_caus_conf_mix_group_iterative.rds | Bin 4317 -> 4315 bytes tests/testthat/_snaps/forecast-output.md | 18 +++++----- .../forecast_output_ar_numeric.rds | Bin 3053 -> 3095 bytes .../forecast_output_arima_numeric.rds | Bin 22491 -> 22611 bytes .../forecast_output_arima_numeric_no_lags.rds | Bin 3682 -> 3727 bytes .../forecast_output_arima_numeric_no_xreg.rds | Bin 2813 -> 2860 bytes ...st_output_forecast_ARIMA_group_numeric.rds | Bin 4710 -> 4754 bytes tests/testthat/_snaps/forecast-setup.md | 32 +++--------------- .../output_lm_timeseries_method.rds | Bin 35663 -> 35761 bytes tests/testthat/test-forecast-setup.R | 4 ++- 14 files changed, 16 insertions(+), 38 deletions(-) diff --git a/tests/testthat/_snaps/adaptive-output/output_lm_numeric_gaussian_group_converges_tol.rds b/tests/testthat/_snaps/adaptive-output/output_lm_numeric_gaussian_group_converges_tol.rds index 7ce9cd8c5745c240bc10324265c72603b94d2c33..f5b0cdb59497d4a9b1ae024a6ab16c5fa24fe536 100644 GIT binary patch literal 4098 zcmV+d5dH5TiwFP!000001MM6ObX3K4cC+6=LLeaoNFeg@1CdYQ91sip66B+p00u$v z^L$BOlC8UMm)#c#SWxSyrC4gM*4A2oZI7NFd-RB>lvYf{YU`(JkD|v<{UH<;tWZ6G zN4lMP@7{T{Z{H&Z5~V%a!)0df+_`h-&dj|t^Ja2UJ|QHJ`0~6Y&rhSof8nzEXU?R! 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feature-wise Shapley values: 9 * Number of observations to explain: 2 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 10 of 512 coalitions, 10 new. @@ -120,11 +120,11 @@ * Model class: * Approach: empirical - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of group-wise Shapley values: 10 * Number of observations to explain: 2 - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 10 of 1024 coalitions, 10 new. @@ -158,7 +158,7 @@ * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 2 * Number of observations to explain: 2 @@ -189,7 +189,7 @@ * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of 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zBKC=Z4D5x9Jq0GMWA+^0>cQfaDplFIVUALd&@DbRUA()rSojwu{80xMPa_rwXiX67 zLbFUOo2NZb6zwV>+7~|?ZHaNqcK+4~jnM3zH_D)OVqO$k`zd#DHZ%uqv_?l{#&?r! zdy3gUHIKN9wJREFrB0cCB|`1DC^X6eZW~;22@7gV4iLql{PTX*sMy9Rws-x{xr2tE zWwS<>oMCHDvVE5 zVBoNM(NkH&+`Z}3&W>XK&8R#2*m8`G8JITxm#~w}-hCe*zPV`)Q^k)yI5Tw?)39)2 zSVFl6(>0rSy?gtEA#7&(_H{YijdKl{S@#dB z%x4btA6-FbC$mYMgDU7ej=AuW!_s~6LFVe>UK8U#GqTZ>t(W>xE0{gHH=}Cn Date: Fri, 18 Oct 2024 14:50:10 +0200 Subject: [PATCH 27/32] convergence_tol and Iterative_estimation --- R/check_convergence.R | 6 ++--- R/cli.R | 2 +- R/print_iter.R | 8 +++---- R/setup.R | 22 +++++++++---------- R/zzz.R | 2 +- inst/scripts/devel/devel_convergence_branch.R | 2 +- inst/scripts/devel/devel_verbose.R | 4 ++-- inst/scripts/devel/simtest_timing_to_Frida.R | 2 +- .../devel/testing_intermediate_saving.R | 12 +++++----- man/get_iterative_args_default.Rd | 4 ++-- tests/testthat/test-adaptive-output.R | 18 +++++++-------- tests/testthat/test-adaptive-setup.R | 6 ++--- tests/testthat/test-forecast-output.R | 2 +- vignettes/understanding_shapr.Rmd | 6 ++--- vignettes/understanding_shapr.Rmd.orig | 6 ++--- 15 files changed, 51 insertions(+), 51 deletions(-) diff --git a/R/check_convergence.R b/R/check_convergence.R index d90e277a0..b260d9a77 100644 --- a/R/check_convergence.R +++ b/R/check_convergence.R @@ -7,7 +7,7 @@ check_convergence <- function(internal) { iter <- length(internal$iter_list) - convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance + convergence_tol <- internal$parameters$iterative_args$convergence_tol max_iter <- internal$parameters$iterative_args$max_iter max_n_coalitions <- internal$parameters$iterative_args$max_n_coalitions paired_shap_sampling <- internal$parameters$paired_shap_sampling @@ -32,11 +32,11 @@ check_convergence <- function(internal) { converged_sd <- FALSE } else { converged_exact <- FALSE - if (!is.null(convergence_tolerance)) { + if (!is.null(convergence_tol)) { dt_shapley_est0[, maxval := max(.SD, na.rm = TRUE), .SDcols = -c(1, 2), by = .I] dt_shapley_est0[, minval := min(.SD, na.rm = TRUE), .SDcols = -c(1, 2), by = .I] dt_shapley_est0[, max_sd0 := max_sd0] - dt_shapley_est0[, req_samples := (max_sd0 / ((maxval - minval) * convergence_tolerance))^2] + dt_shapley_est0[, req_samples := (max_sd0 / ((maxval - minval) * convergence_tol))^2] dt_shapley_est0[, conv_measure := max_sd0 / ((maxval - minval) * sqrt(n_sampled_coalitions))] dt_shapley_est0[, req_samples := min(req_samples, 2^n_shapley_values - 2)] diff --git a/R/cli.R b/R/cli.R index 78e8f3b5a..18af9f4d2 100644 --- a/R/cli.R +++ b/R/cli.R @@ -22,7 +22,7 @@ cli_startup <- function(internal, model, verbose) { line_vec <- "Model class: {.cls {class(model)}}" line_vec <- c(line_vec, "Approach: {.emph {approach}}") - line_vec <- c(line_vec, "iterative estimation: {.emph {iterative}}") + line_vec <- c(line_vec, "Iterative estimation: {.emph {iterative}}") line_vec <- c(line_vec, "Number of {.emph {feat_group_txt}} Shapley values: {n_shapley_values}") line_vec <- c(line_vec, "Number of observations to explain: {n_explain}") if (isTRUE(asymmetric)) { diff --git a/R/print_iter.R b/R/print_iter.R index 653f3a8c5..174eea7ab 100644 --- a/R/print_iter.R +++ b/R/print_iter.R @@ -17,11 +17,11 @@ print_iter <- function(internal) { n_coal_next_iter_factor <- internal$iter_list[[iter]]$n_coal_next_iter_factor saving_path <- internal$parameters$output_args$saving_path - convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance + convergence_tol <- internal$parameters$iterative_args$convergence_tol testing <- internal$parameters$testing if ("convergence" %in% verbose) { - convergence_tolerance <- internal$parameters$iterative_args$convergence_tolerance + convergence_tol <- internal$parameters$iterative_args$convergence_tol current_n_coalitions <- internal$iter_list[[iter]]$n_coalitions est_remaining_coalitions <- internal$iter_list[[iter]]$est_remaining_coalitions @@ -35,9 +35,9 @@ print_iter <- function(internal) { if (isFALSE(converged)) { msg <- "Not converged after {current_n_coalitions} coalitions:\n" - if (!is.null(convergence_tolerance)) { + if (!is.null(convergence_tol)) { conv_nice <- signif(overall_conv_measure, 2) - tol_nice <- format(signif(convergence_tolerance, 2), scientific = FALSE) + tol_nice <- format(signif(convergence_tol, 2), scientific = FALSE) n_coal_next_iter_factor_nice <- format(signif(n_coal_next_iter_factor * 100, 2), scientific = FALSE) msg <- paste0( msg, diff --git a/R/setup.R b/R/setup.R index 3119bdea9..be00abb95 100644 --- a/R/setup.R +++ b/R/setup.R @@ -1147,7 +1147,7 @@ check_and_set_iterative <- function(internal) { if (isTRUE(iterative)) { warning( paste0( - "iterative estimation of Shapley values are not supported for approach = ", + "Iterative estimation of Shapley values are not supported for approach = ", paste0(unsupported, collapse = ", "), ". Setting iterative = FALSE." ) ) @@ -1530,12 +1530,12 @@ check_iterative_args <- function(iterative_args) { stop("`iterative_args$max_iter` must be NULL, Inf or a single positive integer.") } - # convergence_tolerance - if (!is.null(convergence_tolerance) && - !(length(convergence_tolerance) == 1 && - !is.na(convergence_tolerance) && - convergence_tolerance >= 0)) { - stop("`iterative_args$convergence_tolerance` must be NULL, 0, or a positive numeric.") + # convergence_tol + if (!is.null(convergence_tol) && + !(length(convergence_tol) == 1 && + !is.na(convergence_tol) && + convergence_tol >= 0)) { + stop("`iterative_args$convergence_tol` must be NULL, 0, or a positive numeric.") } # n_coal_next_iter_factor_vec @@ -1623,7 +1623,7 @@ check_vs_prev_shapr_object <- function(internal) { #' @param initial_n_coalitions Integer. Number of coalitions to use in the first estimation iteration. #' @param fixed_n_coalitions_per_iter Integer. Number of `n_coalitions` to use in each iteration. #' `NULL` (default) means setting it based on estimates based on a set convergence threshold. -#' @param convergence_tolerance Numeric. The t variable in the convergence threshold formula on page 6 in the paper +#' @param convergence_tol Numeric. The t variable in the convergence threshold formula on page 6 in the paper #' Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' #' https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached. #' @param n_coal_next_iter_factor_vec Numeric vector. The number of `n_coalitions` that must be used to reach @@ -1649,7 +1649,7 @@ get_iterative_args_default <- function(internal, ), fixed_n_coalitions_per_iter = NULL, max_iter = 20, - convergence_tolerance = 0.02, + convergence_tol = 0.02, n_coal_next_iter_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10))) { iterative <- internal$parameters$iterative max_n_coalitions <- internal$parameters$max_n_coalitions @@ -1661,7 +1661,7 @@ get_iterative_args_default <- function(internal, "fixed_n_coalitions_per_iter", "max_n_coalitions", "max_iter", - "convergence_tolerance", + "convergence_tol", "n_coal_next_iter_factor_vec" ) ) @@ -1671,7 +1671,7 @@ get_iterative_args_default <- function(internal, fixed_n_coalitions_per_iter = NULL, max_n_coalitions = max_n_coalitions, max_iter = 1, - convergence_tolerance = NULL, + convergence_tol = NULL, n_coal_next_iter_factor_vec = NULL ) } diff --git a/R/zzz.R b/R/zzz.R index 34850f93b..a1458be87 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -136,7 +136,7 @@ "cond", "tmp_coalitions", "max_iter", - "convergence_tolerance", + "convergence_tol", "conv_measure", "verbose", "MSEv_uniform_comb_weights", diff --git a/inst/scripts/devel/devel_convergence_branch.R b/inst/scripts/devel/devel_convergence_branch.R index ea1d33b3c..9d022ba17 100644 --- a/inst/scripts/devel/devel_convergence_branch.R +++ b/inst/scripts/devel/devel_convergence_branch.R @@ -83,7 +83,7 @@ explanation_iterative <- explain( n_coalitions = 500, prediction_zero = p0, iterative = TRUE, - iterative_args = list(initial_n_coalitions=10,convergence_tolerance=0.0001), + iterative_args = list(initial_n_coalitions=10,convergence_tol=0.0001), print_shapleyres = TRUE, # tmp print_iter_info = TRUE, # tmp kernelSHAP_reweighting = "on_N" diff --git a/inst/scripts/devel/devel_verbose.R b/inst/scripts/devel/devel_verbose.R index 1dd1ffc98..d9f0a54fc 100644 --- a/inst/scripts/devel/devel_verbose.R +++ b/inst/scripts/devel/devel_verbose.R @@ -8,7 +8,7 @@ ex <- explain( max_n_coalitions = 30, iterative_args = list( initial_n_coalitions = 6, - convergence_tolerance = 0.0005, + convergence_tol = 0.0005, n_coal_next_iter_factor_vec = rep(10^(-6), 10), max_iter = 8 ), @@ -113,7 +113,7 @@ ex <- explain( max_n_coalitions = 30, iterative_args = list( initial_n_coalitions = 6, - convergence_tolerance = 0.0005, + convergence_tol = 0.0005, n_coal_next_iter_factor_vec = rep(10^(-6), 10), max_iter = 8 ), diff --git a/inst/scripts/devel/simtest_timing_to_Frida.R b/inst/scripts/devel/simtest_timing_to_Frida.R index e221358a4..93fc801bf 100644 --- a/inst/scripts/devel/simtest_timing_to_Frida.R +++ b/inst/scripts/devel/simtest_timing_to_Frida.R @@ -90,7 +90,7 @@ expl <- explain( iterative_args = list(initial_n_coalitions = 50, fixed_n_coalitions_per_iter = 50, max_iter = 10, - convergence_tolerance = 10^(-10), + convergence_tol = 10^(-10), compute_sd = TRUE), kernelSHAP_reweighting = "none", print_iter_info = TRUE diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R index 000d3de8f..4cf3376fc 100644 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ b/inst/scripts/devel/testing_intermediate_saving.R @@ -8,7 +8,7 @@ aa = explain( prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.01, + convergence_tol = 0.01, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 30 ), @@ -25,7 +25,7 @@ bb = explain( prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 30 ), @@ -50,7 +50,7 @@ full = explain( prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 7 ), @@ -70,7 +70,7 @@ first = explain( prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 4 ), @@ -90,7 +90,7 @@ second = explain( prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 7 ), @@ -113,7 +113,7 @@ second_path = explain( prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 5 ), diff --git a/man/get_iterative_args_default.Rd b/man/get_iterative_args_default.Rd index 1b9e06d13..ca995d454 100644 --- a/man/get_iterative_args_default.Rd +++ b/man/get_iterative_args_default.Rd @@ -10,7 +10,7 @@ get_iterative_args_default( (2^internal$parameters$n_features)/10))), fixed_n_coalitions_per_iter = NULL, max_iter = 20, - convergence_tolerance = 0.02, + convergence_tol = 0.02, n_coal_next_iter_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10)) ) } @@ -25,7 +25,7 @@ Not used directly, but passed through from \code{\link[=explain]{explain()}}.} \item{max_iter}{Integer. Maximum number of estimation iterations} -\item{convergence_tolerance}{Numeric. The t variable in the convergence threshold formula on page 6 in the paper +\item{convergence_tol}{Numeric. The t variable in the convergence threshold formula on page 6 in the paper Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached.} diff --git a/tests/testthat/test-adaptive-output.R b/tests/testthat/test-adaptive-output.R index 655b4f60f..20b132075 100644 --- a/tests/testthat/test-adaptive-output.R +++ b/tests/testthat/test-adaptive-output.R @@ -28,7 +28,7 @@ test_that("output_lm_numeric_independence_converges_tol", { prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.1 + convergence_tol = 0.1 ), iterative = TRUE, verbose = c("convergence", "shapley") @@ -48,7 +48,7 @@ test_that("output_lm_numeric_independence_converges_maxit", { prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), @@ -95,7 +95,7 @@ test_that("output_lm_numeric_gaussian_group_converges_tol", { prediction_zero = p0, iterative_args = list( initial_n_coalitions = 5, - convergence_tolerance = 0.1 + convergence_tol = 0.1 ), iterative = TRUE, verbose = c("convergence", "shapley") @@ -115,7 +115,7 @@ test_that("output_lm_numeric_independence_converges_tol_paired", { prediction_zero = p0, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.1 + convergence_tol = 0.1 ), iterative = TRUE, verbose = c("convergence", "shapley"), @@ -139,7 +139,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { paired_shap_sampling = FALSE, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), @@ -161,7 +161,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { paired_shap_sampling = FALSE, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 5 ), @@ -182,7 +182,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { paired_shap_sampling = FALSE, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), @@ -210,7 +210,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { paired_shap_sampling = FALSE, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 5 ), @@ -231,7 +231,7 @@ test_that("output_lm_numeric_independence_saving_and_cont_est", { paired_shap_sampling = FALSE, iterative_args = list( initial_n_coalitions = 10, - convergence_tolerance = 0.001, + convergence_tol = 0.001, n_coal_next_iter_factor_vec = rep(10^(-5), 10), max_iter = 8 ), diff --git a/tests/testthat/test-adaptive-setup.R b/tests/testthat/test-adaptive-setup.R index ea3cc35c6..4d06339e2 100644 --- a/tests/testthat/test-adaptive-setup.R +++ b/tests/testthat/test-adaptive-setup.R @@ -9,7 +9,7 @@ test_that("iterative_args are respected", { max_n_coalitions = 30, iterative_args = list( initial_n_coalitions = 6, - convergence_tolerance = 0.0005, + convergence_tol = 0.0005, n_coal_next_iter_factor_vec = rep(10^(-6), 10), max_iter = 8 ), @@ -43,7 +43,7 @@ test_that("iterative feature wise and groupwise computations identical", { prediction_zero = p0, iterative_args = list( initial_n_coalitions = 5, - convergence_tolerance = 0.1 + convergence_tol = 0.1 ), iterative = TRUE ) @@ -59,7 +59,7 @@ test_that("iterative feature wise and groupwise computations identical", { prediction_zero = p0, iterative_args = list( initial_n_coalitions = 5, - convergence_tolerance = 0.1 + convergence_tol = 0.1 ), iterative = TRUE ) diff --git a/tests/testthat/test-forecast-output.R b/tests/testthat/test-forecast-output.R index 13b6bba4f..32b79b251 100644 --- a/tests/testthat/test-forecast-output.R +++ b/tests/testthat/test-forecast-output.R @@ -79,7 +79,7 @@ test_that("forecast_output_arima_numeric_iterative_groups", { group_lags = TRUE, max_n_coalitions = 150, iterative = TRUE, - iterative_args = list(initial_n_coalitions = 10, convergence_tolerance = 7e-3) + iterative_args = list(initial_n_coalitions = 10, convergence_tol = 7e-3) ), "forecast_output_arima_numeric_iterative_groups" ) diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index 7249415b8..5d0690e7c 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -111,7 +111,7 @@ The reason for this is that the number of possible coalitions grows exponentiall These defaults can be overridden by setting the `iterative` argument to `TRUE` or `FALSE`. When using the `iterative` argument, the estimation for an observation is stopped when all Shapley value standard deviations are below `t` times the range of the Shapley values. -The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `iterative_args$convergence_tolerance` argument, see [iterative estimation](#iterative) for more details. +The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `iterative_args$convergence_tol` argument, see [iterative estimation](#iterative) for more details. Since the iterativeness default changes based on the number of features (or feature groups), the default is also to have no upper bound on the number of coalitions considered. @@ -1215,7 +1215,7 @@ ex <- explain( approach = "gaussian", prediction_zero = p0, iterative = TRUE, - iterative_args = list(convergence_tolerance = 0.1) + iterative_args = list(convergence_tol = 0.1) ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -2011,7 +2011,7 @@ ex_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 25, - iterative_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold + iterative_args = list(convergence_tol = 0.005), # Decrease the convergence threshold prev_shapr_object = ex_init ) #> Note: Feature classes extracted from the model contains NA. diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index 60cee65f5..b6e84c3ff 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -126,7 +126,7 @@ The reason for this is that the number of possible coalitions grows exponentiall These defaults can be overridden by setting the `iterative` argument to `TRUE` or `FALSE`. When using the `iterative` argument, the estimation for an observation is stopped when all Shapley value standard deviations are below `t` times the range of the Shapley values. -The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `iterative_args$convergence_tolerance` argument, see [iterative estimation](#iterative) for more details. +The `t` value controls the convergence tolerance, defaults to 0.02, and can be set through the `iterative_args$convergence_tol` argument, see [iterative estimation](#iterative) for more details. Since the iterativeness default changes based on the number of features (or feature groups), the default is also to have no upper bound on the number of coalitions considered. @@ -997,7 +997,7 @@ ex <- explain( approach = "gaussian", prediction_zero = p0, iterative = TRUE, - iterative_args = list(convergence_tolerance = 0.1) + iterative_args = list(convergence_tol = 0.1) ) ``` @@ -1576,7 +1576,7 @@ ex_further <- explain( approach = "gaussian", prediction_zero = p0, max_n_coalitions = 25, - iterative_args = list(convergence_tolerance = 0.005), # Decrease the convergence threshold + iterative_args = list(convergence_tol = 0.005), # Decrease the convergence threshold prev_shapr_object = ex_init ) From 834e69b11cf9e001b1265af5b6ac0cdfe25677dc Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 15:05:49 +0200 Subject: [PATCH 28/32] tests --- R/finalize_explanation.R | 2 +- tests/testthat/_snaps/adaptive-output.md | 10 +-- ...m_numeric_gaussian_group_converges_tol.rds | Bin 4098 -> 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...st_output_forecast_ARIMA_group_numeric.rds | Bin 4754 -> 4748 bytes tests/testthat/_snaps/forecast-setup.md | 2 +- tests/testthat/_snaps/regression-output.md | 36 +++++------ .../output_lm_categorical_lm_separate.rds | Bin 5500 -> 5548 bytes .../output_lm_categorical_lm_surrogate.rds | Bin 158258 -> 158311 bytes ...put_lm_mixed_decision_tree_cv_separate.rds | Bin 6666 -> 7020 bytes ...xed_decision_tree_cv_separate_parallel.rds | Bin 6572 -> 6925 bytes ...ut_lm_mixed_decision_tree_cv_surrogate.rds | Bin 41880 -> 42299 bytes .../output_lm_mixed_lm_separate.rds | Bin 7360 -> 7705 bytes .../output_lm_mixed_lm_surrogate.rds | Bin 340121 -> 340508 bytes .../output_lm_mixed_splines_separate.rds | Bin 22845 -> 23208 bytes .../output_lm_mixed_xgboost_separate.rds | Bin 22614 -> 22979 bytes .../output_lm_mixed_xgboost_surrogate.rds | Bin 119815 -> 120140 bytes .../output_lm_numeric_lm_separate.rds | Bin 7303 -> 7650 bytes ...utput_lm_numeric_lm_separate_iterative.rds | Bin 13747 -> 14150 bytes .../output_lm_numeric_lm_separate_n_comb.rds | Bin 5841 -> 5911 bytes .../output_lm_numeric_lm_surrogate.rds | Bin 296691 -> 297039 bytes ...tput_lm_numeric_lm_surrogate_iterative.rds | Bin 296691 -> 297039 bytes .../output_lm_numeric_lm_surrogate_n_comb.rds | Bin 107415 -> 107477 bytes ...m_numeric_lm_surrogate_reg_surr_n_comb.rds | Bin 107415 -> 107477 bytes tests/testthat/_snaps/regression-setup.md | 8 +-- tests/testthat/_snaps/regular-output.md | 60 +++++++++--------- ...utput_custom_lm_numeric_independence_1.rds | Bin 5816 -> 6169 bytes ...utput_custom_lm_numeric_independence_2.rds | Bin 5816 -> 6169 bytes ...utput_custom_xgboost_mixed_dummy_ctree.rds | Bin 5911 -> 6263 bytes .../output_lm_categorical_ctree.rds | Bin 4252 -> 4304 bytes .../output_lm_categorical_independence.rds | Bin 4172 -> 4220 bytes .../output_lm_categorical_method.rds | Bin 4640 -> 4686 bytes .../output_lm_categorical_vaeac.rds | Bin 4621 -> 4669 bytes .../regular-output/output_lm_mixed_comb.rds | Bin 5958 -> 6308 bytes .../regular-output/output_lm_mixed_ctree.rds | Bin 5950 -> 6296 bytes .../output_lm_mixed_independence.rds | Bin 5879 -> 6234 bytes .../regular-output/output_lm_mixed_vaeac.rds | Bin 6309 -> 6659 bytes .../output_lm_numeric_comb1.rds | Bin 6231 -> 6577 bytes .../output_lm_numeric_comb2.rds | Bin 6232 -> 6568 bytes .../output_lm_numeric_comb3.rds | Bin 6153 -> 6501 bytes .../output_lm_numeric_copula.rds | Bin 6152 -> 6493 bytes .../output_lm_numeric_ctree.rds | Bin 5887 -> 6237 bytes .../output_lm_numeric_ctree_parallelized.rds | Bin 5887 -> 6237 bytes .../output_lm_numeric_empirical.rds | Bin 6093 -> 6440 bytes .../output_lm_numeric_empirical_AICc_each.rds | Bin 4305 -> 4332 bytes .../output_lm_numeric_empirical_AICc_full.rds | Bin 4305 -> 4336 bytes ...tput_lm_numeric_empirical_independence.rds | Bin 6093 -> 6445 bytes ...tput_lm_numeric_empirical_n_coalitions.rds | Bin 5712 -> 5801 bytes .../output_lm_numeric_empirical_progress.rds | Bin 6093 -> 6440 bytes .../output_lm_numeric_gaussian.rds | Bin 6051 -> 6399 bytes .../output_lm_numeric_independence.rds | Bin 5812 -> 6169 bytes ...eric_independence_MSEv_Shapley_weights.rds | Bin 5823 -> 6174 bytes ..._numeric_independence_keep_samp_for_vS.rds | Bin 210677 -> 210915 bytes .../output_lm_numeric_interaction.rds | Bin 2734 -> 2737 bytes .../output_lm_numeric_vaeac.rds | Bin 6251 -> 6604 bytes .../output_lm_timeseries_method.rds | Bin 35761 -> 35759 bytes tests/testthat/_snaps/regular-setup.md | 22 +++---- 101 files changed, 102 insertions(+), 102 deletions(-) diff --git a/R/finalize_explanation.R b/R/finalize_explanation.R index 5ea9e82c6..765f07345 100644 --- a/R/finalize_explanation.R +++ b/R/finalize_explanation.R @@ -48,7 +48,7 @@ finalize_explanation <- function(internal) { iterative_results <- get_iter_results(internal$iter_list) output <- list( - shapley_values = dt_shapley_est, + shapley_values_est = dt_shapley_est, shapley_values_sd = dt_shapley_sd, pred_explain = p, MSEv = MSEv, diff --git a/tests/testthat/_snaps/adaptive-output.md b/tests/testthat/_snaps/adaptive-output.md index 86175b4d2..72239e8d0 100644 --- a/tests/testthat/_snaps/adaptive-output.md +++ b/tests/testthat/_snaps/adaptive-output.md @@ -9,7 +9,7 @@ * Model class: * Approach: independence - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -621,7 +621,7 @@ * Model class: * Approach: gaussian - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -659,7 +659,7 @@ * Model class: * Approach: gaussian - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -725,7 +725,7 @@ * Model class: * Approach: gaussian - * iterative estimation: TRUE + * Iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of 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zc$+Hgjb38JQ6Dw$Mv2|CpT;(DroK!T7;YDz?nQ|x7V-lZPl7H{0K5Y;{*_(bgyi0H2T`O<#3|wy Date: Fri, 18 Oct 2024 13:26:10 +0200 Subject: [PATCH 23/32] man --- NAMESPACE | 4 ---- man/compute_vS_forecast.Rd | 28 ---------------------------- man/explain_forecast.Rd | 29 ----------------------------- 3 files changed, 61 deletions(-) delete mode 100644 man/compute_vS_forecast.Rd diff --git a/NAMESPACE b/NAMESPACE index 63d0d352d..fad90c328 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -65,11 +65,7 @@ export(create_coalition_table) export(explain) export(explain_forecast) export(finalize_explanation) -<<<<<<< HEAD export(finalize_explanation_forecast) -======= -export(get_adaptive_arguments_default) ->>>>>>> origin/shapr-1.0.0 export(get_cov_mat) export(get_data_specs) export(get_extra_est_args_default) diff --git a/man/compute_vS_forecast.Rd b/man/compute_vS_forecast.Rd deleted file mode 100644 index d7b5ce185..000000000 --- a/man/compute_vS_forecast.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/compute_vS.R -\name{compute_vS_forecast} -\alias{compute_vS_forecast} -\title{Computes \code{v(S)} for all features subsets \code{S}.} -\usage{ -compute_vS_forecast(internal, model, predict_model, method = "future") -} -\arguments{ -\item{internal}{List. -Holds all parameters, data, functions and computed objects used within \code{\link[=explain]{explain()}} -The list contains one or more of the elements \code{parameters}, \code{data}, \code{objects}, \code{iter_list}, \code{timing_list}, -\code{main_timing_list}, \code{output}, and \code{iter_timing_list}.} - -\item{model}{Objects. -The model object that ought to be explained. -See the documentation of \code{\link[=explain]{explain()}} for details.} - -\item{predict_model}{Function. -The prediction function used when \code{model} is not natively supported. -See the documentation of \code{\link[=explain]{explain()}} for details.} - -\item{method}{Character -Indicates whether the lappy method (default) or loop method should be used.} -} -\description{ -Computes \code{v(S)} for all features subsets \code{S}. -} diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index 402fbc24c..a78a53eb4 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -84,35 +84,6 @@ The quantity refers to the number of unique feature coalitions if \code{group = and group coalitions if \code{group != NULL}. \code{max_n_coalitions = NULL} corresponds to \code{max_n_coalitions=2^n_features}.} -\item{adaptive}{Logical or NULL -If \code{NULL} (default), the argument is set to \code{TRUE} if there are more than 5 features/groups, and \code{FALSE} otherwise. -If eventually \code{TRUE}, the Shapley values are estimated adaptively in an iterative manner. -This provides sufficiently accurate Shapley value estimates faster. -First an initial number of coalitions is sampled, then bootsrapping is used to estimate the variance of the Shapley -values. -A convergence criterion is used to determine if the variances of the Shapley values are sufficently small. -If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more -coalitions. -The process is repeated until the variances are below the threshold. -Specifics related to the adaptive process and convergence criterion are set through \code{adaptive_arguments}.} - -\item{adaptive_arguments}{Named list. -Specifices the arguments for the adaptive procedure. -See \code{\link[=get_adaptive_arguments_default]{get_adaptive_arguments_default()}} for description of the arguments and their default values.} - -\item{shapley_reweighting}{String. -How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing -the randomness and thereby the variance of the Shapley value estimates. -One of \code{'none'}, \code{'on_N'}, \code{'on_all'}, \code{'on_all_cond'} (default). -\code{'none'} means no reweighting, i.e. the sampling frequency weights are used as is. -\code{'on_coal_size'} means the sampling frequencies are averaged over all coalitions of the same size. -\code{'on_N'} means the sampling frequencies are averaged over all coalitions with the same original sampling -probabilities. -\code{'on_all'} means the original sampling probabilities are used for all coalitions. -\code{'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. -This method is preferred as it has performed the best in simulation studies.} - \item{group_lags}{Logical. If \code{TRUE} all lags of each variable are grouped together and explained as a group. If \code{FALSE} all lags of each variable are explained individually.} From 664a2b0cec920b437c61a7ffa81b8f4452cad365 Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 13:29:36 +0200 Subject: [PATCH 24/32] find-replace for forecast on new names --- R/compute_estimates.R | 2 +- R/explain_forecast.R | 12 ++++---- R/setup.R | 2 +- R/shapley_setup.R | 2 +- ...mpare_Conditional_and_Causal_Categorical.R | 14 ++++----- .../scripts/Heskes_bike_rental_illustration.R | 2 +- man/explain_forecast.Rd | 6 ++-- tests/testthat/_snaps/forecast-output.md | 4 +-- tests/testthat/test-forecast-output.R | 30 +++++++++---------- .../understanding_shapr_asymmetric_causal.Rmd | 8 ++--- ...rstanding_shapr_asymmetric_causal.Rmd.orig | 6 ++-- 11 files changed, 44 insertions(+), 44 deletions(-) diff --git a/R/compute_estimates.R b/R/compute_estimates.R index a19c254bd..c69eef24a 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -303,7 +303,7 @@ bootstrap_shapley_inner <- function(X, n_shapley_values, shap_names, internal, d n_explain <- internal$parameters$n_explain paired_shap_sampling <- internal$parameters$paired_shap_sampling - shapley_reweight <- internal$parameters$shapley_reweighting + shapley_reweight <- internal$parameters$kernelSHAP_reweighting X_org <- copy(X) diff --git a/R/explain_forecast.R b/R/explain_forecast.R index a41a8dd82..f3f8ea5dc 100644 --- a/R/explain_forecast.R +++ b/R/explain_forecast.R @@ -95,9 +95,9 @@ explain_forecast <- function(model, approach, prediction_zero, max_n_coalitions = NULL, - adaptive = NULL, - adaptive_arguments = list(), - shapley_reweighting = "on_all_cond", + iterative = NULL, + iterative_args = list(), + kernelSHAP_reweighting = "on_all_cond", group_lags = TRUE, group = NULL, n_MC_samples = 1e3, @@ -133,9 +133,9 @@ explain_forecast <- function(model, feature_specs = feature_specs, type = "forecast", horizon = horizon, - adaptive = adaptive, - adaptive_arguments = adaptive_arguments, - shapley_reweighting = shapley_reweighting, + iterative = iterative, + iterative_args = iterative_args, + kernelSHAP_reweighting = kernelSHAP_reweighting, init_time = init_time, y = y, xreg = xreg, diff --git a/R/setup.R b/R/setup.R index b62c6ea2c..3119bdea9 100644 --- a/R/setup.R +++ b/R/setup.R @@ -1261,7 +1261,7 @@ check_computability <- function(internal) { if (isTRUE(causal_sampling) && !is.null(max_n_coalitions_causal) && max_n_coalitions_causal > 1000) { warning( paste0( - "Due to computation time, we strongly recommend enabling adaptive estimation with adaptive = TRUE ", + "Due to computation time, we strongly recommend enabling iterative estimation with iterative = TRUE ", "when the number of valid causal coalitions are more than 1000 due to the long causal sampling time. \n" ) ) diff --git a/R/shapley_setup.R b/R/shapley_setup.R index 2d15cac2c..bfd6e7c8f 100644 --- a/R/shapley_setup.R +++ b/R/shapley_setup.R @@ -707,7 +707,7 @@ shapley_setup_forecast <- function(internal) { prev_coal_samples = prev_coal_samples, coal_feature_list = this_coal_feature_list, approach0 = approach, - shapley_reweighting = shapley_reweighting + kernelSHAP_reweighting = kernelSHAP_reweighting ) W_list[[i]] <- weight_matrix( diff --git a/inst/scripts/Compare_Conditional_and_Causal_Categorical.R b/inst/scripts/Compare_Conditional_and_Causal_Categorical.R index c633c6631..502b16e47 100644 --- a/inst/scripts/Compare_Conditional_and_Causal_Categorical.R +++ b/inst/scripts/Compare_Conditional_and_Causal_Categorical.R @@ -54,7 +54,7 @@ causal_categorical <- explain( n_MC_samples = 50, # Just for speed verbose = c("basic", "convergence", "shapley", "vS_details"), keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE ) # Warning CTREE is the slowest approach by far @@ -70,7 +70,7 @@ causal_ctree <- explain( n_MC_samples = 50, # Just for speed verbose = c("basic", "convergence", "shapley", "vS_details"), keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE ) causal_vaeac <- explain( @@ -86,7 +86,7 @@ causal_vaeac <- explain( n_MC_samples = 50, # Just for speed verbose = c("basic", "convergence", "shapley", "vS_details"), keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE ) shapr::plot_SV_several_approaches(list( @@ -109,7 +109,7 @@ conditional_independence <- explain( n_MC_samples = 50, # Just for speed verbose = c("basic", "convergence", "shapley", "vS_details"), keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE ) conditional_categorical <- explain( @@ -124,7 +124,7 @@ conditional_categorical <- explain( n_MC_samples = 50, # Just for speed verbose = c("basic", "convergence", "shapley", "vS_details"), keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE ) # Warning CTREE is the slowest approach by far @@ -140,7 +140,7 @@ conditional_ctree <- explain( n_MC_samples = 50, # Just for speed verbose = c("basic", "convergence", "shapley", "vS_details"), keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE ) conditional_vaeac <- explain( @@ -156,7 +156,7 @@ conditional_vaeac <- explain( n_MC_samples = 50, # Just for speed verbose = c("basic", "convergence", "shapley", "vS_details"), keep_samp_for_vS = TRUE, - adaptive = FALSE + iterative = FALSE ) shapr::plot_SV_several_approaches(list( diff --git a/inst/scripts/Heskes_bike_rental_illustration.R b/inst/scripts/Heskes_bike_rental_illustration.R index 2fcc9dbd2..3b795148e 100644 --- a/inst/scripts/Heskes_bike_rental_illustration.R +++ b/inst/scripts/Heskes_bike_rental_illustration.R @@ -1,5 +1,5 @@ # This file build on Pull Request https://github.com/NorskRegnesentral/shapr/pull/273 -# This file does not run on the adaptive version. +# This file does not run on the iterative version. # The point of the file was to replicate the plot values that Heskes obtained in their implementation # to validate my implementation. diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index a78a53eb4..38f33f0a9 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -16,9 +16,9 @@ explain_forecast( approach, prediction_zero, max_n_coalitions = NULL, - adaptive = NULL, - adaptive_arguments = list(), - shapley_reweighting = "on_all_cond", + iterative = NULL, + iterative_args = list(), + kernelSHAP_reweighting = "on_all_cond", group_lags = TRUE, group = NULL, n_MC_samples = 1000, diff --git a/tests/testthat/_snaps/forecast-output.md b/tests/testthat/_snaps/forecast-output.md index 000bc2510..c266237aa 100644 --- a/tests/testthat/_snaps/forecast-output.md +++ b/tests/testthat/_snaps/forecast-output.md @@ -68,7 +68,7 @@ 5: 0.5630 6: -0.7615 -# forecast_output_arima_numeric_adaptive +# forecast_output_arima_numeric_iterative Code (out <- code) @@ -110,7 +110,7 @@ 5: -1.5436 -0.5418 2.8952 6: -0.6202 -0.8545 0.4549 -# forecast_output_arima_numeric_adaptive_groups +# forecast_output_arima_numeric_iterative_groups Code (out <- code) diff --git a/tests/testthat/test-forecast-output.R b/tests/testthat/test-forecast-output.R index 876d09615..13b6bba4f 100644 --- a/tests/testthat/test-forecast-output.R +++ b/tests/testthat/test-forecast-output.R @@ -33,13 +33,13 @@ test_that("forecast_output_arima_numeric", { prediction_zero = p0_ar, group_lags = FALSE, max_n_coalitions = 150, - adaptive = FALSE + iterative = FALSE ), "forecast_output_arima_numeric" ) }) -test_that("forecast_output_arima_numeric_adaptive", { +test_that("forecast_output_arima_numeric_iterative", { expect_snapshot_rds( explain_forecast( testing = TRUE, @@ -55,14 +55,14 @@ test_that("forecast_output_arima_numeric_adaptive", { prediction_zero = p0_ar, group_lags = FALSE, max_n_coalitions = 150, - adaptive = TRUE, - adaptive_arguments = list(initial_n_coalitions = 10) + iterative = TRUE, + iterative_args = list(initial_n_coalitions = 10) ), - "forecast_output_arima_numeric_adaptive" + "forecast_output_arima_numeric_iterative" ) }) -test_that("forecast_output_arima_numeric_adaptive_groups", { +test_that("forecast_output_arima_numeric_iterative_groups", { expect_snapshot_rds( explain_forecast( testing = TRUE, @@ -78,10 +78,10 @@ test_that("forecast_output_arima_numeric_adaptive_groups", { prediction_zero = p0_ar, group_lags = TRUE, max_n_coalitions = 150, - adaptive = TRUE, - adaptive_arguments = list(initial_n_coalitions = 10, convergence_tolerance = 7e-3) + iterative = TRUE, + iterative_args = list(initial_n_coalitions = 10, convergence_tolerance = 7e-3) ), - "forecast_output_arima_numeric_adaptive_groups" + "forecast_output_arima_numeric_iterative_groups" ) }) @@ -163,7 +163,7 @@ test_that("ARIMA gives the same output with different horizons", { group_lags = FALSE, n_batches = 1, max_n_coalitions = 200, - adaptive = FALSE + iterative = FALSE ) @@ -182,7 +182,7 @@ test_that("ARIMA gives the same output with different horizons", { group_lags = FALSE, n_batches = 1, max_n_coalitions = 100, - adaptive = FALSE + iterative = FALSE ) h1 <- explain_forecast( @@ -200,7 +200,7 @@ test_that("ARIMA gives the same output with different horizons", { group_lags = FALSE, n_batches = 1, max_n_coalitions = 50, - adaptive = FALSE + iterative = FALSE ) cols_horizon1 <- h2$internal$objects$cols_per_horizon[[1]] @@ -237,7 +237,7 @@ test_that("ARIMA gives the same output with different horizons with grouping", { group_lags = TRUE, n_batches = 1, max_n_coalitions = 50, - adaptive = FALSE + iterative = FALSE ) @@ -256,7 +256,7 @@ test_that("ARIMA gives the same output with different horizons with grouping", { group_lags = TRUE, n_batches = 1, max_n_coalitions = 50, - adaptive = FALSE + iterative = FALSE ) h1 <- explain_forecast( @@ -274,7 +274,7 @@ test_that("ARIMA gives the same output with different horizons with grouping", { group_lags = TRUE, n_batches = 1, max_n_coalitions = 50, - adaptive = FALSE + iterative = FALSE ) expect_equal( diff --git a/vignettes/understanding_shapr_asymmetric_causal.Rmd b/vignettes/understanding_shapr_asymmetric_causal.Rmd index 59839defe..8e089841f 100644 --- a/vignettes/understanding_shapr_asymmetric_causal.Rmd +++ b/vignettes/understanding_shapr_asymmetric_causal.Rmd @@ -613,7 +613,7 @@ explanation_asym_con[["gaussian"]] <- explain( #> ── Iteration 1 ────────────────────────────────────────────────────────────────────────────────────────────────── #> ℹ Using 13 of 20 coalitions, 13 new. -explanation_asym_con[["gaussian_non_adaptive"]] <- explain( +explanation_asym_con[["gaussian_non_iterative"]] <- explain( model = model, x_train = x_train, x_explain = x_explain, @@ -624,7 +624,7 @@ explanation_asym_con[["gaussian_non_adaptive"]] <- explain( asymmetric = TRUE, causal_ordering = causal_ordering, confounding = NULL, # Default value - adaptive = FALSE + iterative = FALSE ) #> Note: Feature classes extracted from the model contains NA. #> Assuming feature classes from the data are correct. @@ -722,7 +722,7 @@ instead of all $128$ coalitions (see code below). print_MSEv_scores_and_time(explanation_asym_con) #> MSEv MSEv_sd Time (secs) #> gaussian 330603.3 36828.70 1.66 -#> gaussian_non_adaptive 306457.7 35411.60 1.52 +#> gaussian_non_iterative 306457.7 35411.60 1.52 #> ctree 260562.1 29428.95 8.75 #> xgboost 307562.1 39362.81 1.60 @@ -1015,7 +1015,7 @@ explanation_sym_cau[["gaussian"]] <- explain( asymmetric = FALSE, causal_ordering = list(1, 2:3, 4:7), confounding = c(FALSE, TRUE, FALSE), - adaptive = FALSE, # Set to FALSE to get a single iteration to illustrate sampling steps below + iterative = FALSE, # Set to FALSE to get a single iteration to illustrate sampling steps below exact = TRUE ) #> Note: Feature classes extracted from the model contains NA. diff --git a/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig b/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig index f9de45bea..f7ddbc9e3 100644 --- a/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig +++ b/vignettes/understanding_shapr_asymmetric_causal.Rmd.orig @@ -490,7 +490,7 @@ explanation_asym_con[["gaussian"]] <- explain( confounding = NULL # Default value ) -explanation_asym_con[["gaussian_non_adaptive"]] <- explain( +explanation_asym_con[["gaussian_non_iterative"]] <- explain( model = model, x_train = x_train, x_explain = x_explain, @@ -501,7 +501,7 @@ explanation_asym_con[["gaussian_non_adaptive"]] <- explain( asymmetric = TRUE, causal_ordering = causal_ordering, confounding = NULL, # Default value - adaptive = FALSE + iterative = FALSE ) explanation_asym_con[["ctree"]] <- explain( @@ -675,7 +675,7 @@ explanation_sym_cau[["gaussian"]] <- explain( asymmetric = FALSE, causal_ordering = list(1, 2:3, 4:7), confounding = c(FALSE, TRUE, FALSE), - adaptive = FALSE, # Set to FALSE to get a single iteration to illustrate sampling steps below + iterative = FALSE, # Set to FALSE to get a single iteration to illustrate sampling steps below exact = TRUE ) From f1ba7b62513c8a08106792a92d1b6a6d12f72208 Mon Sep 17 00:00:00 2001 From: Martin Date: Fri, 18 Oct 2024 13:47:23 +0200 Subject: [PATCH 25/32] more fixing --- man/explain_forecast.Rd | 29 ++++++++++++++++++ ...utput_sym_caus_conf_mix_group_adaptive.rds | Bin 4284 -> 0 bytes ...forecast_output_arima_numeric_adaptive.rds | Bin 52964 -> 0 bytes ...t_output_arima_numeric_adaptive_groups.rds | Bin 26789 -> 0 bytes ...orecast_output_arima_numeric_iterative.rds | Bin 0 -> 51400 bytes ..._output_arima_numeric_iterative_groups.rds | Bin 0 -> 26793 bytes .../testthat/test-asymmetric-causal-output.R | 2 +- tests/testthat/test-regular-output.R | 2 +- 8 files changed, 31 insertions(+), 2 deletions(-) delete mode 100644 tests/testthat/_snaps/asymmetric-causal-output/output_sym_caus_conf_mix_group_adaptive.rds delete mode 100644 tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_adaptive.rds delete mode 100644 tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_adaptive_groups.rds create mode 100644 tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_iterative.rds create mode 100644 tests/testthat/_snaps/forecast-output/forecast_output_arima_numeric_iterative_groups.rds diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index 38f33f0a9..860aa14ac 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -84,6 +84,35 @@ The quantity refers to the number of unique feature coalitions if \code{group = and group coalitions if \code{group != NULL}. \code{max_n_coalitions = NULL} corresponds to \code{max_n_coalitions=2^n_features}.} +\item{iterative}{Logical or NULL +If \code{NULL} (default), the argument is set to \code{TRUE} if there are more than 5 features/groups, and \code{FALSE} otherwise. +If eventually \code{TRUE}, the Shapley values are estimated iteratively in an iterative manner. +This provides sufficiently accurate Shapley value estimates faster. +First an initial number of coalitions is sampled, then bootsrapping is used to estimate the variance of the Shapley +values. +A convergence criterion is used to determine if the variances of the Shapley values are sufficently small. +If the variances are too high, we estimate the number of required samples to reach convergence, and thereby add more +coalitions. +The process is repeated until the variances are below the threshold. +Specifics related to the iterative process and convergence criterion are set through \code{iterative_args}.} + +\item{iterative_args}{Named list. +Specifices the arguments for the iterative procedure. +See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} + +\item{kernelSHAP_reweighting}{String. +How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing +the randomness and thereby the variance of the Shapley value estimates. +One of \code{'none'}, \code{'on_N'}, \code{'on_all'}, \code{'on_all_cond'} (default). +\code{'none'} means no reweighting, i.e. the sampling frequency weights are used as is. +\code{'on_coal_size'} means the sampling frequencies are averaged over all coalitions of the same size. +\code{'on_N'} means the sampling frequencies are averaged over all coalitions with the same original sampling +probabilities. +\code{'on_all'} means the original sampling probabilities are used for all coalitions. +\code{'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. +This method is preferred as it has performed the best in simulation studies.} + \item{group_lags}{Logical. 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zCkPT5XxLa(Gw-0ILrL(Qc^%l4kPn_UEe^hV{`!_!iO$5l2fHZ0swn66%9`>#IV6qD zys9|+hfQ7VmKfzU_El|QU3s`ZhIG&;@$2JHai=ug^MT4t!1!Q(Skr`TrV-^^ZfvAb jF{+^U9ZWM;tqx^hXWg-r4C;J_^?y}N@Ww)d;L862V0+f@ diff --git a/tests/testthat/_snaps/regression-setup.md b/tests/testthat/_snaps/regression-setup.md index 1d0444f4d..f0dc46c87 100644 --- a/tests/testthat/_snaps/regression-setup.md +++ b/tests/testthat/_snaps/regression-setup.md @@ -5,11 +5,6 @@ x_train = x_train_numeric, prediction_zero = p0, approach = c( "regression_surrogate", "gaussian", "independence", "empirical"), iterative = FALSE) - Message - Success with message: - max_n_coalitions is NULL or larger than or 2^n_features = 32, - and is therefore set to 2^n_features = 32. - Condition Error in `check_approach()`: ! The `regression_separate` and `regression_surrogate` approaches cannot be combined with other approaches. @@ -20,11 +15,6 @@ explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, prediction_zero = p0, approach = c( "regression_separate", "gaussian", "independence", "empirical"), iterative = FALSE) - Message - Success with message: - max_n_coalitions is NULL or larger than or 2^n_features = 32, - and is therefore set to 2^n_features = 32. - Condition Error in `check_approach()`: ! The `regression_separate` and `regression_surrogate` approaches cannot be combined with other approaches. From a9d9297a04b4083938a465fd0d89a92f27f9d153 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 14 Oct 2024 21:37:09 +0200 Subject: [PATCH 20/32] asym-causal test updates --- R/setup.R | 4 +-- .../testthat/test-asymmetric-causal-output.R | 22 ++++++------ tests/testthat/test-asymmetric-causal-setup.R | 36 +++++++++---------- 3 files changed, 31 insertions(+), 31 deletions(-) diff --git a/R/setup.R b/R/setup.R index d97e03fd5..d7a95e8a1 100644 --- a/R/setup.R +++ b/R/setup.R @@ -1189,7 +1189,7 @@ check_computability <- function(internal) { paste0( "Due to computation time, we recommend not computing asymmetric Shapley values exactly \n", "with all valid causal coalitions (", max_n_coalitions_causal, ") when larger than 5000.\n", - "Consider reducing max_n_coalitions and enabling adaptive estimation with adaptive = TRUE.\n" + "Consider reducing max_n_coalitions and enabling iterative estimation with iterative = TRUE.\n" ) ) } @@ -1220,7 +1220,7 @@ check_computability <- function(internal) { paste0( "Due to computation time, we recommend not computing causal Shapley values exactly \n", "with all valid causal coalitions when there are more than 1000 due to the long causal sampling time. \n", - "Consider reducing max_n_coalitions and enabling adaptive estimation with adaptive = TRUE.\n" + "Consider reducing max_n_coalitions and enabling iterative estimation with iterative = TRUE.\n" ) } } else { diff --git a/tests/testthat/test-asymmetric-causal-output.R b/tests/testthat/test-asymmetric-causal-output.R index 0ae98f763..2e045c21c 100644 --- a/tests/testthat/test-asymmetric-causal-output.R +++ b/tests/testthat/test-asymmetric-causal-output.R @@ -37,7 +37,7 @@ test_that("output_asym_cond_reg", { ) }) -test_that("output_asym_cond_reg_adaptive", { +test_that("output_asym_cond_reg_iterative", { expect_snapshot_rds( explain( testing = TRUE, @@ -51,9 +51,9 @@ test_that("output_asym_cond_reg_adaptive", { causal_ordering = list(1:2, 3, 4:5), confounding = NULL, paired_shap_sampling = FALSE, - adaptive = TRUE + iterative = TRUE ), - "output_asym_cond_reg_adaptive" + "output_asym_cond_reg_iterative" ) }) @@ -323,7 +323,7 @@ test_that("output_sym_caus_conf_mix_group", { ) }) -test_that("output_sym_caus_conf_mix_group_adaptive", { +test_that("output_sym_caus_conf_mix_group_iterative", { expect_snapshot_rds( explain( testing = TRUE, @@ -338,9 +338,9 @@ test_that("output_sym_caus_conf_mix_group_adaptive", { group = list("A" = c("Solar.R"), B = c("Wind", "Temp"), C = c("Month", "Day")), n_MC_samples = 5, # Just for speed, verbose = c("convergence"), - adaptive = TRUE + iterative = TRUE ), - "output_sym_caus_conf_mix_group_adaptive" + "output_sym_caus_conf_mix_group_iterative" ) }) @@ -367,7 +367,7 @@ test_that("output_mixed_sym_caus_conf_TRUE", { ) }) -test_that("output_mixed_sym_caus_conf_TRUE_adaptive", { +test_that("output_mixed_sym_caus_conf_TRUE_iterative", { expect_snapshot_rds( explain( testing = TRUE, @@ -380,9 +380,9 @@ test_that("output_mixed_sym_caus_conf_TRUE_adaptive", { causal_ordering = list(1:2, 3, 4:5), confounding = TRUE, n_MC_samples = 5, # Just for speed - adaptive = TRUE + iterative = TRUE ), - "output_mixed_sym_caus_conf_TRUE_adaptive" + "output_mixed_sym_caus_conf_TRUE_iterative" ) }) @@ -439,7 +439,7 @@ test_that("output_mixed_asym_cond_reg", { causal_ordering = list(1:2, 3, 4:5), paired_shap_sampling = FALSE, confounding = NULL, - adaptive = TRUE + iterative = TRUE ), "output_mixed_asym_cond_reg" ) @@ -482,7 +482,7 @@ test_that("output_cat_asym_causal_mixed_cat_ad", { causal_ordering = list(3:4, 2, 1), confounding = c(TRUE, FALSE, FALSE), n_MC_samples = 5, # Just for speed - adaptive = TRUE + iterative = TRUE ), "output_cat_asym_causal_mixed_cat_ad" ) diff --git a/tests/testthat/test-asymmetric-causal-setup.R b/tests/testthat/test-asymmetric-causal-setup.R index b535b077b..3a17d9d78 100644 --- a/tests/testthat/test-asymmetric-causal-setup.R +++ b/tests/testthat/test-asymmetric-causal-setup.R @@ -14,7 +14,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list(1:6), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -33,7 +33,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list(1:5, 5), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -52,7 +52,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list(2:5, 5), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -71,7 +71,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list(1:2, 4), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -90,7 +90,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list("Solar.R", "Wind", "Temp", "Month", "Day", "Invalid feature name"), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -109,7 +109,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list("Solar.R", "Wind", "Temp", "Month", "Day", "Day"), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -128,7 +128,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list("Solar.R", "Wind", "Temp", "Day", "Day"), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -147,7 +147,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { causal_ordering = list("Solar.R", "Wind"), confounding = NULL, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -167,7 +167,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { confounding = NULL, approach = "gaussian", group = list("A" = c("Solar.R", "Wind"), B = "Temp", C = c("Month", "Day")), - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -187,7 +187,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { confounding = NULL, approach = "gaussian", group = list("A" = c("Solar.R", "Wind"), B = "Temp", C = c("Month", "Day")), - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -207,7 +207,7 @@ test_that("asymmetric erroneous input: `causal_ordering`", { confounding = NULL, approach = "gaussian", group = list("A" = c("Solar.R", "Wind"), B = "Temp", C = c("Month", "Day")), - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -231,7 +231,7 @@ test_that("asymmetric erroneous input: `approach`", { causal_ordering = list(1:2, 3:4, 5), confounding = TRUE, approach = c("gaussian", "independence", "empirical", "gaussian"), - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -254,7 +254,7 @@ test_that("asymmetric erroneous input: `asymmetric`", { causal_ordering = list(1:2, 3:4, 5), confounding = TRUE, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -273,7 +273,7 @@ test_that("asymmetric erroneous input: `asymmetric`", { causal_ordering = list(1:2, 3:4, 5), confounding = TRUE, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -292,7 +292,7 @@ test_that("asymmetric erroneous input: `asymmetric`", { causal_ordering = list(1:2, 3:4, 5), confounding = TRUE, approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE @@ -315,8 +315,8 @@ test_that("asymmetric erroneous input: `confounding`", { asymmetric = FALSE, causal_ordering = list(1:2, 3:4, 5), confounding = c("A", "B", "C"), - approach = "gaussian", , - adaptive = FALSE + approach = "gaussian", + iterative = FALSE ) }, error = TRUE @@ -335,7 +335,7 @@ test_that("asymmetric erroneous input: `confounding`", { causal_ordering = list(1:2, 3:4, 5), confounding = c(TRUE, FALSE), approach = "gaussian", - adaptive = FALSE + iterative = FALSE ) }, error = TRUE From e72e78969c3952b6911f209de6125dfe7301c818 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 14 Oct 2024 21:42:57 +0200 Subject: [PATCH 21/32] asym tests --- .../_snaps/asymmetric-causal-output.md | 64 +++++++++--------- .../output_asym_caus_conf_FALSE.rds | Bin 4715 -> 4726 bytes .../output_asym_caus_conf_TRUE.rds | Bin 4668 -> 4688 bytes .../output_asym_caus_conf_mix.rds | Bin 4683 -> 4700 bytes .../output_asym_caus_conf_mix_ctree.rds | Bin 4463 -> 4532 bytes .../output_asym_caus_conf_mix_empirical.rds | Bin 4686 -> 4748 bytes .../output_asym_caus_conf_mix_n_coal.rds | Bin 4663 -> 4696 bytes .../output_asym_cond_reg.rds | Bin 5597 -> 5615 bytes .../output_asym_cond_reg_iterative.rds | Bin 0 -> 6967 bytes .../output_asymmetric_conditional.rds | Bin 4317 -> 4378 bytes .../output_cat_asym_causal_mixed_cat_ad.rds | Bin 10804 -> 10866 bytes ...tput_categorical_asym_causal_mixed_cat.rds | Bin 6850 -> 5012 bytes ...ut_categorical_asym_causal_mixed_ctree.rds | Bin 4758 -> 4792 bytes .../output_mixed_asym_cond_reg.rds | Bin 6951 -> 7025 bytes .../output_mixed_sym_caus_conf_TRUE.rds | Bin 6712 -> 7197 bytes ...put_mixed_sym_caus_conf_TRUE_iterative.rds | Bin 0 -> 22511 bytes .../output_mixed_sym_caus_conf_mixed.rds | Bin 4503 -> 4583 bytes .../output_mixed_sym_caus_conf_mixed_2.rds | Bin 4557 -> 4581 bytes .../output_sym_caus_conf_FALSE.rds | Bin 7282 -> 7406 bytes .../output_sym_caus_conf_TRUE.rds | Bin 7179 -> 7307 bytes .../output_sym_caus_conf_TRUE_group.rds | Bin 4331 -> 4367 bytes .../output_sym_caus_conf_mix.rds | Bin 7223 -> 7348 bytes .../output_sym_caus_conf_mix_group.rds | Bin 4305 -> 4340 bytes ...tput_sym_caus_conf_mix_group_iterative.rds | Bin 0 -> 4317 bytes .../output_symmetric_conditional.rds | Bin 6075 -> 6438 bytes .../output_symmetric_marginal_gaussian.rds | Bin 6975 -> 6901 bytes ...output_symmetric_marginal_independence.rds | Bin 6407 -> 6689 bytes .../_snaps/asymmetric-causal-setup.md | 34 +++++----- 28 files changed, 49 insertions(+), 49 deletions(-) create mode 100644 tests/testthat/_snaps/asymmetric-causal-output/output_asym_cond_reg_iterative.rds create mode 100644 tests/testthat/_snaps/asymmetric-causal-output/output_mixed_sym_caus_conf_TRUE_iterative.rds create mode 100644 tests/testthat/_snaps/asymmetric-causal-output/output_sym_caus_conf_mix_group_iterative.rds diff --git a/tests/testthat/_snaps/asymmetric-causal-output.md b/tests/testthat/_snaps/asymmetric-causal-output.md index 69d88372c..6282245de 100644 --- a/tests/testthat/_snaps/asymmetric-causal-output.md +++ b/tests/testthat/_snaps/asymmetric-causal-output.md @@ -9,7 +9,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -36,7 +36,7 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -52,7 +52,7 @@ 2: 2 42.44 5.546 -6.262 -4.518 -6.664 -1.982 3: 3 42.44 9.720 -32.555 7.270 -3.377 1.374 -# output_asym_cond_reg_adaptive +# output_asym_cond_reg_iterative Code (out <- code) @@ -63,13 +63,13 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 * Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day} - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 8 coalitions, 5 new. @@ -94,7 +94,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 @@ -119,7 +119,7 @@ * Model class: * Approach: independence - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind, Temp, Month, Day} @@ -146,7 +146,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind, Temp, Month, Day} @@ -173,7 +173,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -201,7 +201,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -229,7 +229,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -253,7 +253,7 @@ Message * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -281,7 +281,7 @@ * Model class: * Approach: empirical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -309,7 +309,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -337,7 +337,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day} @@ -364,7 +364,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day} @@ -391,7 +391,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day} @@ -418,7 +418,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 * Causal ordering: {A, B}, {C} @@ -445,7 +445,7 @@ * Model class: * Approach: gaussian - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of group-wise Shapley values: 3 * Number of observations to explain: 3 * Causal ordering: {A}, {B}, {C} @@ -461,7 +461,7 @@ 2: 2 42.44 3.126 -6.343 -10.662 3: 3 42.44 5.310 -17.036 -5.842 -# output_sym_caus_conf_mix_group_adaptive +# output_sym_caus_conf_mix_group_iterative Code (out <- code) @@ -494,7 +494,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor} @@ -510,7 +510,7 @@ 2: 2 42.44 4.729 -11.40 -7.837 1.6971 -2.570 3: 3 42.44 3.010 -23.62 3.218 4.8728 1.922 -# output_mixed_sym_caus_conf_TRUE_adaptive +# output_mixed_sym_caus_conf_TRUE_iterative Code (out <- code) @@ -521,13 +521,13 @@ * Model class: * Approach: ctree - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor} * Components with confounding: {Solar.R, Wind}, {Temp}, {Day, Month_factor} - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 32 coalitions, 5 new. @@ -570,7 +570,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -598,7 +598,7 @@ * Model class: * Approach: ctree - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 @@ -626,13 +626,13 @@ * Model class: * Approach: regression_separate - * Adaptive estimation: TRUE + * iterative estimation: TRUE * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Number of asymmetric coalitions: 8 * Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor} - -- Adaptive computation started -- + -- iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 5 of 8 coalitions, 5 new. @@ -657,7 +657,7 @@ * Model class: * Approach: categorical - * Adaptive estimation: FALSE + * iterative estimation: FALSE * Number of feature-wise Shapley values: 4 * Number of observations to explain: 2 * Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor}, @@ -684,14 +684,14 @@ * Model class: * Approach: categorical - 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explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - keep_samp_for_vS = keep_samp_for_vS_non_logical_1) + output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_1)) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition - Error in `get_parameters()`: - ! `keep_samp_for_vS` must be single logical. + Error in `check_output_args()`: + ! `output_args$keep_samp_for_vS` must be single logical. --- @@ -608,10 +613,15 @@ keep_samp_for_vS_non_logical_2 <- NULL explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - keep_samp_for_vS = keep_samp_for_vS_non_logical_2) + output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_2)) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition - Error in `get_parameters()`: - ! `keep_samp_for_vS` must be single logical. + Error in `check_output_args()`: + ! `output_args$keep_samp_for_vS` must be single logical. --- @@ -619,10 +629,15 @@ keep_samp_for_vS_too_long <- c(TRUE, FALSE) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - keep_samp_for_vS = keep_samp_for_vS_too_long) + output_args = list(keep_samp_for_vS = keep_samp_for_vS_too_long)) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition - Error in `get_parameters()`: - ! `keep_samp_for_vS` must be single logical. + Error in `check_output_args()`: + ! `output_args$keep_samp_for_vS` must be single logical. # erroneous input: `MSEv_uniform_comb_weights` @@ -630,10 +645,15 @@ MSEv_uniform_comb_weights_nl_1 <- "bla" explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1) + output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1)) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition - Error in `get_parameters()`: - ! `MSEv_uniform_comb_weights` must be single logical. + Error in `check_output_args()`: + ! `output_args$MSEv_uniform_comb_weights` must be single logical. --- @@ -641,10 +661,15 @@ MSEv_uniform_comb_weights_nl_2 <- NULL explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2) + output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2)) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition - Error in `get_parameters()`: - ! `MSEv_uniform_comb_weights` must be single logical. + Error in `check_output_args()`: + ! `output_args$MSEv_uniform_comb_weights` must be single logical. --- @@ -652,10 +677,15 @@ MSEv_uniform_comb_weights_long <- c(TRUE, FALSE) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long) + output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long)) + Message + Success with message: + max_n_coalitions is NULL or larger than or 2^n_features = 32, + and is therefore set to 2^n_features = 32. + Condition - Error in `get_parameters()`: - ! `MSEv_uniform_comb_weights` must be single logical. + Error in `check_output_args()`: + ! `output_args$MSEv_uniform_comb_weights` must be single logical. # erroneous input: `predict_model` @@ -978,7 +1008,7 @@ Code explanation_equal <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, iterative_args = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, extra_estimation_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), iterative = FALSE) Message @@ -997,7 +1027,7 @@ Code explanation_larger <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, iterative_args = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, extra_estimation_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, iterative = FALSE) Message diff --git a/tests/testthat/test-regular-output.R b/tests/testthat/test-regular-output.R index d53ac7884..f820cb917 100644 --- a/tests/testthat/test-regular-output.R +++ b/tests/testthat/test-regular-output.R @@ -24,7 +24,7 @@ test_that("output_lm_numeric_independence_MSEv_Shapley_weights", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - MSEv_uniform_comb_weights = FALSE, + output_args = list(MSEv_uniform_comb_weights = FALSE), iterative = FALSE ), "output_lm_numeric_independence_MSEv_Shapley_weights" diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index 693eb1ea7..ac157bf51 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -837,7 +837,7 @@ test_that("erroneous input: `MSEv_uniform_comb_weights`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1 + output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1) ) }, error = TRUE @@ -854,7 +854,7 @@ test_that("erroneous input: `MSEv_uniform_comb_weights`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2 + output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2) ) }, error = TRUE @@ -871,7 +871,7 @@ test_that("erroneous input: `MSEv_uniform_comb_weights`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long + output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long) ) }, error = TRUE From 99b6c6ea4e84cbd98f92a976655844946773cddd Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 23:27:13 +0200 Subject: [PATCH 13/32] extra_estimation_args -> extra_computation_args ++ --- R/approach_vaeac.R | 2 +- R/compute_estimates.R | 2 +- R/explain.R | 11 ++++-- R/setup.R | 52 ++++++++++++------------- man/explain.Rd | 10 +++-- man/explain_forecast.Rd | 2 +- man/finalize_explanation_forecast.Rd | 2 +- man/setup.Rd | 8 +++- man/vaeac_get_extra_para_default.Rd | 2 +- tests/testthat/_snaps/adaptive-setup.md | 36 ++++++++--------- tests/testthat/_snaps/regular-setup.md | 4 +- tests/testthat/test-adaptive-setup.R | 20 +++++----- tests/testthat/test-regular-setup.R | 4 +- vignettes/understanding_shapr.Rmd | 4 +- vignettes/understanding_shapr.Rmd.orig | 4 +- 15 files changed, 87 insertions(+), 76 deletions(-) diff --git a/R/approach_vaeac.R b/R/approach_vaeac.R index 9a6e9e9fb..02b2c394b 100644 --- a/R/approach_vaeac.R +++ b/R/approach_vaeac.R @@ -1656,7 +1656,7 @@ vaeac_check_parameters <- function(x_train, #' each batch when generating the Monte Carlo samples. If `NULL`, then the function generates the Monte Carlo samples #' for the provided coalitions and all explicands sent to [shapr::explain()] at the time. #' The number of coalitions are determined by the `n_batches` used by [shapr::explain()]. We recommend to tweak -#' `extra_estimation_args$max_batch_size` and `extra_estimation_args$min_n_batches` +#' `extra_computation_args$max_batch_size` and `extra_computation_args$min_n_batches` #' rather than `vaeac.batch_size_sampling`. Larger batch sizes are often much faster provided sufficient memory. #' @param vaeac.running_avg_n_values Positive integer (default is `5`). The number of previous IWAE values to include #' when we compute the running means of the IWAE criterion. diff --git a/R/compute_estimates.R b/R/compute_estimates.R index 4f2c747c3..fcc5241cd 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -16,7 +16,7 @@ compute_estimates <- function(internal, vS_list) { iter <- length(internal$iter_list) compute_sd <- internal$iter_list[[iter]]$compute_sd - n_boot_samps <- internal$parameters$extra_estimation_args$n_boot_samps + n_boot_samps <- internal$parameters$extra_computation_args$n_boot_samps processed_vS_list <- postprocess_vS_list( vS_list = vS_list, diff --git a/R/explain.R b/R/explain.R index 58cb1a9f1..4a4abaeef 100644 --- a/R/explain.R +++ b/R/explain.R @@ -112,8 +112,11 @@ #' Specifices the arguments for the iterative procedure. #' See [shapr::get_iterative_args_default()] for description of the arguments and their default values. #' @param output_args Named list. -#' Specifices the arguments for the iterative procedure. +#' Specifices certain arguments related to the output of the function. #' See [shapr::get_output_args_default()] for description of the arguments and their default values. +#' @param extra_computation_args Named list. +#' Specifices extra arguments related to the computation of the Shapley values. +#' See [shapr::get_extra_est_args_default()] for description of the arguments and their default values. #' @param kernelSHAP_reweighting String. #' How to reweight the sampling frequency weights in the kernelSHAP solution after sampling, with the aim of reducing #' the randomness and thereby the variance of the Shapley value estimates. @@ -168,7 +171,7 @@ #' \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. #' Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by #' definition 0 when all coalitions is used. -#' Only present when `extra_estimation_args$compute_sd=TRUE`.} +#' Only present when `extra_computation_args$compute_sd=TRUE`.} #' \item{internal}{List with the different parameters, data, functions and other output used internally.} #' \item{pred_explain}{Numeric vector with the predictions for the explained observations} #' \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the @@ -352,7 +355,7 @@ explain <- function(model, predict_model = NULL, get_model_specs = NULL, prev_shapr_object = NULL, - extra_estimation_args = list(), + extra_computation_args = list(), iterative_args = list(), output_args = list(), ...) { # ... is further arguments passed to specific approaches @@ -388,7 +391,7 @@ explain <- function(model, init_time = init_time, prev_shapr_object = prev_shapr_object, output_args = output_args, - extra_estimation_args = extra_estimation_args, + extra_computation_args = extra_computation_args, ... ) diff --git a/R/setup.R b/R/setup.R index 42a966f13..58ae3a4fa 100644 --- a/R/setup.R +++ b/R/setup.R @@ -52,7 +52,7 @@ setup <- function(x_train, init_time = NULL, prev_shapr_object = NULL, output_args = list(), - extra_estimation_args = list(), + extra_computation_args = list(), ...) { internal <- list() @@ -93,7 +93,7 @@ setup <- function(x_train, is_python = is_python, testing = testing, output_args = output_args, - extra_estimation_args = extra_estimation_args, + extra_computation_args = extra_computation_args, ... ) @@ -177,7 +177,7 @@ get_parameters <- function(approach, testing, is_python, output_args = list(), - extra_estimation_args = list(), + extra_computation_args = list(), ...) { # Check input type for approach @@ -195,8 +195,8 @@ get_parameters <- function(approach, if (!is.list(output_args)) { stop("`output_args` must be a list.") } - if (!is.list(extra_estimation_args)) { - stop("`extra_estimation_args` must be a list.") + if (!is.list(extra_computation_args)) { + stop("`extra_computation_args` must be a list.") } @@ -316,7 +316,7 @@ get_parameters <- function(approach, iterative = iterative, iterative_args = iterative_args, output_args = output_args, - extra_estimation_args = extra_estimation_args, + extra_computation_args = extra_computation_args, testing = testing ) @@ -797,20 +797,20 @@ check_output_args <- function(output_args){ #' @keywords internal set_extra_estimation_params <- function(internal) { - extra_estimation_args <- internal$parameters$extra_estimation_args + extra_computation_args <- internal$parameters$extra_computation_args # Get defaults - extra_estimation_args <- utils::modifyList(get_extra_est_args_default(internal), - extra_estimation_args, + extra_computation_args <- utils::modifyList(get_extra_est_args_default(internal), + extra_computation_args, keep.null = TRUE ) # Check the output_args elements - check_extra_estimation_args(extra_estimation_args) + check_extra_computation_args(extra_computation_args) - extra_estimation_args <- trans_null_extra_est_args(extra_estimation_args) + extra_computation_args <- trans_null_extra_est_args(extra_computation_args) - internal$parameters$extra_estimation_args <- extra_estimation_args + internal$parameters$extra_computation_args <- extra_computation_args return(internal) } @@ -837,13 +837,13 @@ get_extra_est_args_default <- function(internal, # Only used to get the default return(mget(methods::formalArgs(get_extra_est_args_default)[-1])) # [-1] to exclude internal } -check_extra_estimation_args <- function(extra_estimation_args){ - list2env(extra_estimation_args, envir = environment()) # Make accessible in the environment +check_extra_computation_args <- function(extra_computation_args){ + list2env(extra_computation_args, envir = environment()) # Make accessible in the environment # compute_sd if (!(is.logical(compute_sd) && length(compute_sd) == 1)) { - stop("`extra_estimation_args$compute_sd` must be single logical.") + stop("`extra_computation_args$compute_sd` must be single logical.") } # n_boot_samps @@ -851,7 +851,7 @@ check_extra_estimation_args <- function(extra_estimation_args){ length(n_boot_samps) == 1 && !is.na(n_boot_samps) && n_boot_samps > 0)) { - stop("`extra_estimation_args$n_boot_samps` must be a single positive integer.") + stop("`extra_computation_args$n_boot_samps` must be a single positive integer.") } # max_batch_size @@ -860,7 +860,7 @@ check_extra_estimation_args <- function(extra_estimation_args){ length(max_batch_size) == 1 && !is.na(max_batch_size) && max_batch_size > 0)) { - stop("`extra_estimation_args$max_batch_size` must be NULL, Inf or a single positive integer.") + stop("`extra_computation_args$max_batch_size` must be NULL, Inf or a single positive integer.") } # min_n_batches @@ -869,19 +869,19 @@ check_extra_estimation_args <- function(extra_estimation_args){ length(min_n_batches) == 1 && !is.na(min_n_batches) && min_n_batches > 0)) { - stop("`extra_estimation_args$min_n_batches` must be NULL or a single positive integer.") + stop("`extra_computation_args$min_n_batches` must be NULL or a single positive integer.") } } -trans_null_extra_est_args <- function(extra_estimation_args) { - list2env(extra_estimation_args, envir = environment()) +trans_null_extra_est_args <- function(extra_computation_args) { + list2env(extra_computation_args, envir = environment()) # Translating NULL to always return n_batches = 1 (if just one approach) - extra_estimation_args$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) - extra_estimation_args$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) + extra_computation_args$min_n_batches <- ifelse(is.null(min_n_batches), 1, min_n_batches) + extra_computation_args$max_batch_size <- ifelse(is.null(max_batch_size), Inf, max_batch_size) - return(extra_estimation_args) + return(extra_computation_args) } @@ -1201,7 +1201,7 @@ set_iterative_parameters <- function(internal, prev_iter_list = NULL) { n_coalitions = iterative_args$initial_n_coalitions, new_n_coalitions = iterative_args$initial_n_coalitions, exact = internal$parameters$exact, - compute_sd = internal$parameters$extra_estimation_args$compute_sd, + compute_sd = internal$parameters$extra_computation_args$compute_sd, n_coal_next_iter_factor = iterative_args$n_coal_next_iter_factor_vec[1], n_batches = set_n_batches(iterative_args$initial_n_coalitions, internal) ) @@ -1274,8 +1274,8 @@ trans_null_iterative_args <- function(iterative_args) { set_n_batches <- function(n_coalitions, internal) { - min_n_batches <- internal$parameters$extra_estimation_args$min_n_batches - max_batch_size <- internal$parameters$extra_estimation_args$max_batch_size + min_n_batches <- internal$parameters$extra_computation_args$min_n_batches + max_batch_size <- internal$parameters$extra_computation_args$max_batch_size n_unique_approaches <- internal$parameters$n_unique_approaches diff --git a/man/explain.Rd b/man/explain.Rd index 917d167b0..32f31d449 100644 --- a/man/explain.Rd +++ b/man/explain.Rd @@ -21,7 +21,7 @@ explain( predict_model = NULL, get_model_specs = NULL, prev_shapr_object = NULL, - extra_estimation_args = list(), + extra_computation_args = list(), iterative_args = list(), output_args = list(), ... @@ -153,12 +153,16 @@ then the function will use the previous object to continue the computation. This is useful if the computation is interrupted or you want higher accuracy than already obtained, and therefore want to continue the iterative estimation. See the vignette for examples.} +\item{extra_computation_args}{Named list. +Specifices extra arguments related to the computation of the Shapley values. +See \code{\link[=get_extra_est_args_default]{get_extra_est_args_default()}} for description of the arguments and their default values.} + \item{iterative_args}{Named list. Specifices the arguments for the iterative procedure. See \code{\link[=get_iterative_args_default]{get_iterative_args_default()}} for description of the arguments and their default values.} \item{output_args}{Named list. -Specifices the arguments for the iterative procedure. +Specifices certain arguments related to the output of the function. See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} \item{...}{ @@ -291,7 +295,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{extra_estimation_args$compute_sd=TRUE}.} +Only present when \code{extra_computation_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index f37678879..dd26c6595 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -235,7 +235,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{extra_estimation_args$compute_sd=TRUE}.} +Only present when \code{extra_computation_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the diff --git a/man/finalize_explanation_forecast.Rd b/man/finalize_explanation_forecast.Rd index 1cde33aa1..fc423f4c6 100644 --- a/man/finalize_explanation_forecast.Rd +++ b/man/finalize_explanation_forecast.Rd @@ -24,7 +24,7 @@ The column \code{none} is the prediction not devoted to any of the features (giv \item{shapley_values_sd}{data.table with the standard deviation of the Shapley values reflecting the uncertainty. Note that this only reflects the coalition sampling part of the kernelSHAP procedure, and is therefore by definition 0 when all coalitions is used. -Only present when \code{extra_estimation_args$compute_sd=TRUE}.} +Only present when \code{extra_computation_args$compute_sd=TRUE}.} \item{internal}{List with the different parameters, data, functions and other output used internally.} \item{pred_explain}{Numeric vector with the predictions for the explained observations} \item{MSEv}{List with the values of the MSEv evaluation criterion for the approach. See the diff --git a/man/setup.Rd b/man/setup.Rd index 91a332d7b..be3396bfb 100644 --- a/man/setup.Rd +++ b/man/setup.Rd @@ -34,7 +34,7 @@ setup( init_time = NULL, prev_shapr_object = NULL, output_args = list(), - extra_estimation_args = list(), + extra_computation_args = list(), ... ) } @@ -200,9 +200,13 @@ This is useful if the computation is interrupted or you want higher accuracy tha want to continue the iterative estimation. See the vignette for examples.} \item{output_args}{Named list. -Specifices the arguments for the iterative procedure. +Specifices certain arguments related to the output of the function. See \code{\link[=get_output_args_default]{get_output_args_default()}} for description of the arguments and their default values.} +\item{extra_computation_args}{Named list. +Specifices extra arguments related to the computation of the Shapley values. +See \code{\link[=get_extra_est_args_default]{get_extra_est_args_default()}} for description of the arguments and their default values.} + \item{...}{Further arguments passed to specific approaches} } \description{ diff --git a/man/vaeac_get_extra_para_default.Rd b/man/vaeac_get_extra_para_default.Rd index 5337719d1..8b54f75f9 100644 --- a/man/vaeac_get_extra_para_default.Rd +++ b/man/vaeac_get_extra_para_default.Rd @@ -80,7 +80,7 @@ during the training of the vaeac model. Used in \code{\link[torch:dataloader]{to each batch when generating the Monte Carlo samples. If \code{NULL}, then the function generates the Monte Carlo samples for the provided coalitions and all explicands sent to \code{\link[=explain]{explain()}} at the time. The number of coalitions are determined by the \code{n_batches} used by \code{\link[=explain]{explain()}}. We recommend to tweak -\code{extra_estimation_args$max_batch_size} and \code{extra_estimation_args$min_n_batches} +\code{extra_computation_args$max_batch_size} and \code{extra_computation_args$min_n_batches} rather than \code{vaeac.batch_size_sampling}. Larger batch sizes are often much faster provided sufficient memory.} \item{vaeac.running_avg_n_values}{Positive integer (default is \code{5}). The number of previous IWAE values to include diff --git a/tests/testthat/_snaps/adaptive-setup.md b/tests/testthat/_snaps/adaptive-setup.md index c1f4732ce..1b331af40 100644 --- a/tests/testthat/_snaps/adaptive-setup.md +++ b/tests/testthat/_snaps/adaptive-setup.md @@ -4,15 +4,15 @@ n_batches_non_numeric_1 <- "bla" explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_numeric_1)) + extra_computation_args = list(min_n_batches = n_batches_non_numeric_1)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_extra_estimation_args()`: - ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_computation_args()`: + ! `extra_computation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -20,15 +20,15 @@ n_batches_non_numeric_2 <- TRUE explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_numeric_2)) + extra_computation_args = list(min_n_batches = n_batches_non_numeric_2)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_extra_estimation_args()`: - ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_computation_args()`: + ! `extra_computation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -36,15 +36,15 @@ n_batches_non_integer <- 10.5 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_integer)) + extra_computation_args = list(min_n_batches = n_batches_non_integer)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_extra_estimation_args()`: - ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_computation_args()`: + ! `extra_computation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -52,15 +52,15 @@ n_batches_too_long <- c(1, 2) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_too_long)) + extra_computation_args = list(min_n_batches = n_batches_too_long)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_extra_estimation_args()`: - ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_computation_args()`: + ! `extra_computation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -68,15 +68,15 @@ n_batches_is_NA <- as.numeric(NA) explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_is_NA)) + extra_computation_args = list(min_n_batches = n_batches_is_NA)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_extra_estimation_args()`: - ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_computation_args()`: + ! `extra_computation_args$min_n_batches` must be NULL or a single positive integer. --- @@ -84,13 +84,13 @@ n_batches_non_positive <- 0 explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_positive)) + extra_computation_args = list(min_n_batches = n_batches_non_positive)) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, and is therefore set to 2^n_features = 32. Condition - Error in `check_extra_estimation_args()`: - ! `extra_estimation_args$min_n_batches` must be NULL or a single positive integer. + Error in `check_extra_computation_args()`: + ! `extra_computation_args$min_n_batches` must be NULL or a single positive integer. diff --git a/tests/testthat/_snaps/regular-setup.md b/tests/testthat/_snaps/regular-setup.md index a25366ff9..a22c6a710 100644 --- a/tests/testthat/_snaps/regular-setup.md +++ b/tests/testthat/_snaps/regular-setup.md @@ -1008,7 +1008,7 @@ Code explanation_equal <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, extra_estimation_args = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, extra_computation_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), iterative = FALSE) Message @@ -1027,7 +1027,7 @@ Code explanation_larger <- explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian", - prediction_zero = p0, n_MC_samples = 2, seed = 123, extra_estimation_args = list( + prediction_zero = p0, n_MC_samples = 2, seed = 123, extra_computation_args = list( compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, iterative = FALSE) Message diff --git a/tests/testthat/test-adaptive-setup.R b/tests/testthat/test-adaptive-setup.R index 8b7d7b8c1..ea3cc35c6 100644 --- a/tests/testthat/test-adaptive-setup.R +++ b/tests/testthat/test-adaptive-setup.R @@ -83,7 +83,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_numeric_1) + extra_computation_args = list(min_n_batches = n_batches_non_numeric_1) ) }, error = TRUE @@ -100,7 +100,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_numeric_2) + extra_computation_args = list(min_n_batches = n_batches_non_numeric_2) ) }, error = TRUE @@ -117,7 +117,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_integer) + extra_computation_args = list(min_n_batches = n_batches_non_integer) ) }, error = TRUE @@ -134,7 +134,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_too_long) + extra_computation_args = list(min_n_batches = n_batches_too_long) ) }, error = TRUE @@ -151,7 +151,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_is_NA) + extra_computation_args = list(min_n_batches = n_batches_is_NA) ) }, error = TRUE @@ -168,7 +168,7 @@ test_that("erroneous input: `min_n_batches`", { x_train = x_train_numeric, approach = "independence", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = n_batches_non_positive) + extra_computation_args = list(min_n_batches = n_batches_non_positive) ) }, error = TRUE @@ -184,7 +184,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = 5, max_batch_size = 10) + extra_computation_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.empirical_n_batches_10 <- explain( @@ -194,7 +194,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "empirical", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = 10, max_batch_size = 10) + extra_computation_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) @@ -216,7 +216,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = 5, max_batch_size = 10) + extra_computation_args = list(min_n_batches = 5, max_batch_size = 10) ) explain.ctree_n_batches_10 <- explain( @@ -226,7 +226,7 @@ test_that("different n_batches gives same/different shapley values for different x_train = x_train_numeric, approach = "ctree", prediction_zero = p0, - extra_estimation_args = list(min_n_batches = 10, max_batch_size = 10) + extra_computation_args = list(min_n_batches = 10, max_batch_size = 10) ) # Difference in the objects (n_batches and related) diff --git a/tests/testthat/test-regular-setup.R b/tests/testthat/test-regular-setup.R index ac157bf51..802f2f905 100644 --- a/tests/testthat/test-regular-setup.R +++ b/tests/testthat/test-regular-setup.R @@ -1225,7 +1225,7 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - extra_estimation_args = list(compute_sd = FALSE), + extra_computation_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric), iterative = FALSE ) @@ -1243,7 +1243,7 @@ test_that("Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weight prediction_zero = p0, n_MC_samples = 2, # Low value for fast computations seed = 123, - extra_estimation_args = list(compute_sd = FALSE), + extra_computation_args = list(compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1, iterative = FALSE ) diff --git a/vignettes/understanding_shapr.Rmd b/vignettes/understanding_shapr.Rmd index 998cd5578..be634eac7 100644 --- a/vignettes/understanding_shapr.Rmd +++ b/vignettes/understanding_shapr.Rmd @@ -1282,8 +1282,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`extra_estimation_args$max_batch_size` (defaults to 10) and -`extra_estimation_args$min_n_batches` (defaults to 10). +`extra_computation_args$max_batch_size` (defaults to 10) and +`extra_computation_args$min_n_batches` (defaults to 10). ## Parallelized computation diff --git a/vignettes/understanding_shapr.Rmd.orig b/vignettes/understanding_shapr.Rmd.orig index 8974d49d5..60cee65f5 100644 --- a/vignettes/understanding_shapr.Rmd.orig +++ b/vignettes/understanding_shapr.Rmd.orig @@ -1026,8 +1026,8 @@ computations (the computation of v(S)) in sequential batches with different feature subsets $S$, the memory usage can be significantly reduces. The user can control the number of batches by setting the two arguments -`extra_estimation_args$max_batch_size` (defaults to 10) and -`extra_estimation_args$min_n_batches` (defaults to 10). +`extra_computation_args$max_batch_size` (defaults to 10) and +`extra_computation_args$min_n_batches` (defaults to 10). ## Parallelized computation From 62cca509623a40e238898b91453e3cc8eda0215d Mon Sep 17 00:00:00 2001 From: Martin Date: Tue, 8 Oct 2024 23:36:01 +0200 Subject: [PATCH 14/32] check fixes --- R/explain_forecast.R | 1 - R/setup.R | 84 +++++++++--------- R/zzz.R | 4 +- ..._independence_MSEv_Shapley_weights.new.rds | Bin 5696 -> 0 bytes ...eric_independence_MSEv_Shapley_weights.rds | Bin 6052 -> 5696 bytes 5 files changed, 43 insertions(+), 46 deletions(-) delete mode 100644 tests/testthat/_snaps/regular-output/output_lm_numeric_independence_MSEv_Shapley_weights.new.rds diff --git a/R/explain_forecast.R b/R/explain_forecast.R index 08e03012a..e06ef8e58 100644 --- a/R/explain_forecast.R +++ b/R/explain_forecast.R @@ -99,7 +99,6 @@ explain_forecast <- function(model, group = NULL, n_MC_samples = 1e3, seed = 1, - keep_samp_for_vS = FALSE, predict_model = NULL, get_model_specs = NULL, verbose = "basic", diff --git a/R/setup.R b/R/setup.R index 58ae3a4fa..f7edbc2e0 100644 --- a/R/setup.R +++ b/R/setup.R @@ -721,13 +721,12 @@ check_max_n_coalitions_fc <- function(internal) { #' @author Martin Jullum #' @keywords internal set_output_parameters <- function(internal) { - output_args <- internal$parameters$output_args # Get defaults output_args <- utils::modifyList(get_output_args_default(), - output_args, - keep.null = TRUE + output_args, + keep.null = TRUE ) check_output_args(output_args) @@ -754,18 +753,18 @@ set_output_parameters <- function(internal) { #' @author Martin Jullum get_output_args_default <- function(keep_samp_for_vS = FALSE, MSEv_uniform_comb_weights = TRUE, - saving_path = tempfile("shapr_obj_", fileext = ".rds")){ + saving_path = tempfile("shapr_obj_", fileext = ".rds")) { return(mget(methods::formalArgs(get_output_args_default))) } -check_output_args <- function(output_args){ +check_output_args <- function(output_args) { list2env(output_args, envir = environment()) # Make accessible in the environment # Check the output_args elements # keep_samp_for_vS if (!(is.logical(keep_samp_for_vS) && - length(keep_samp_for_vS) == 1)) { + length(keep_samp_for_vS) == 1)) { stop("`output_args$keep_samp_for_vS` must be single logical.") } @@ -776,7 +775,7 @@ check_output_args <- function(output_args){ # saving_path if (!(is.character(saving_path) && - length(saving_path) == 1)) { + length(saving_path) == 1)) { stop("`output_args$saving_path` must be a single character.") } @@ -789,20 +788,18 @@ check_output_args <- function(output_args){ ) ) } - } #' @author Martin Jullum #' @keywords internal set_extra_estimation_params <- function(internal) { - extra_computation_args <- internal$parameters$extra_computation_args # Get defaults extra_computation_args <- utils::modifyList(get_extra_est_args_default(internal), - extra_computation_args, - keep.null = TRUE + extra_computation_args, + keep.null = TRUE ) # Check the output_args elements @@ -833,45 +830,44 @@ get_extra_est_args_default <- function(internal, # Only used to get the default compute_sd = isFALSE(internal$parameters$exact), n_boot_samps = 100, max_batch_size = 10, - min_n_batches = 10){ + min_n_batches = 10) { return(mget(methods::formalArgs(get_extra_est_args_default)[-1])) # [-1] to exclude internal } -check_extra_computation_args <- function(extra_computation_args){ +check_extra_computation_args <- function(extra_computation_args) { list2env(extra_computation_args, envir = environment()) # Make accessible in the environment # compute_sd if (!(is.logical(compute_sd) && - length(compute_sd) == 1)) { + length(compute_sd) == 1)) { stop("`extra_computation_args$compute_sd` must be single logical.") } # n_boot_samps if (!(is.wholenumber(n_boot_samps) && - length(n_boot_samps) == 1 && - !is.na(n_boot_samps) && - n_boot_samps > 0)) { + length(n_boot_samps) == 1 && + !is.na(n_boot_samps) && + n_boot_samps > 0)) { stop("`extra_computation_args$n_boot_samps` must be a single positive integer.") } # max_batch_size if (!is.null(max_batch_size) && - !((is.wholenumber(max_batch_size) || is.infinite(max_batch_size)) && - length(max_batch_size) == 1 && - !is.na(max_batch_size) && - max_batch_size > 0)) { + !((is.wholenumber(max_batch_size) || is.infinite(max_batch_size)) && + length(max_batch_size) == 1 && + !is.na(max_batch_size) && + max_batch_size > 0)) { stop("`extra_computation_args$max_batch_size` must be NULL, Inf or a single positive integer.") } # min_n_batches if (!is.null(min_n_batches) && - !(is.wholenumber(min_n_batches) && - length(min_n_batches) == 1 && - !is.na(min_n_batches) && - min_n_batches > 0)) { + !(is.wholenumber(min_n_batches) && + length(min_n_batches) == 1 && + !is.na(min_n_batches) && + min_n_batches > 0)) { stop("`extra_computation_args$min_n_batches` must be NULL or a single positive integer.") } - } trans_null_extra_est_args <- function(extra_computation_args) { @@ -1260,7 +1256,6 @@ check_iterative_args <- function(iterative_args) { all(n_coal_next_iter_factor_vec >= 0))) { stop("`iterative_args$n_coal_next_iter_factor_vec` must be NULL or a vector or numerics between 0 and 1.") } - } trans_null_iterative_args <- function(iterative_args) { @@ -1330,7 +1325,8 @@ check_vs_prev_shapr_object <- function(internal) { # Get functions ======================================================================================================== #' Function to specify arguments of the iterative estimation procedure #' -#' @details The functions sets default values for the iterative estimation procedure, according to the function defaults. +#' @details The functions sets default values for the iterative estimation procedure, according to the function +#' defaults. #' If the argument `iterative` of [shapr::explain()] is FALSE, it sets parameters corresponding to the use of a #' non-iterative estimation procedure #' @@ -1341,8 +1337,8 @@ check_vs_prev_shapr_object <- function(internal) { #' @param convergence_tolerance Numeric. The t variable in the convergence threshold formula on page 6 in the paper #' Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' #' https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached. -#' @param n_coal_next_iter_factor_vec Numeric vector. The number of `n_coalitions` that must be used to reach convergence -#' in the next iteration is estimated. +#' @param n_coal_next_iter_factor_vec Numeric vector. The number of `n_coalitions` that must be used to reach +#' convergence in the next iteration is estimated. #' The number of `n_coalitions` actually used in the next iteration is set to this estimate multiplied by #' `n_coal_next_iter_factor_vec[i]` for iteration `i`. #' It is wise to start with smaller numbers to avoid using too many `n_coalitions` due to uncertain estimates in @@ -1352,20 +1348,20 @@ check_vs_prev_shapr_object <- function(internal) { #' @export #' @author Martin Jullum get_iterative_args_default <- function(internal, - initial_n_coalitions = ceiling( - min( - 200, - max( - 5, - internal$parameters$n_features, - (2^internal$parameters$n_features) / 10 - ) - ) - ), - fixed_n_coalitions_per_iter = NULL, - max_iter = 20, - convergence_tolerance = 0.02, - n_coal_next_iter_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10))) { + initial_n_coalitions = ceiling( + min( + 200, + max( + 5, + internal$parameters$n_features, + (2^internal$parameters$n_features) / 10 + ) + ) + ), + fixed_n_coalitions_per_iter = NULL, + max_iter = 20, + convergence_tolerance = 0.02, + n_coal_next_iter_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10))) { iterative <- internal$parameters$iterative max_n_coalitions <- internal$parameters$max_n_coalitions diff --git a/R/zzz.R b/R/zzz.R index dd5585888..b4bfecdcc 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -138,7 +138,9 @@ "max_iter", "convergence_tolerance", "conv_measure", - "verbose" + "verbose", + "MSEv_uniform_comb_weights", + "keep_samp_for_vS" ) ) diff --git a/tests/testthat/_snaps/regular-output/output_lm_numeric_independence_MSEv_Shapley_weights.new.rds b/tests/testthat/_snaps/regular-output/output_lm_numeric_independence_MSEv_Shapley_weights.new.rds deleted file mode 100644 index f5262328e3e31770f41fcf2e5898a6703f5b2806..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5696 zcmV-G7Qg8qiwFP!000001MNKrcvQvKcQ>0}+&~C~8mb6{l0fL}q!C(x6pARWo8%^0 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.../output_lm_mixed_lm_surrogate.rds | Bin 340406 -> 340407 bytes .../output_lm_mixed_splines_separate.rds | Bin 23091 -> 23089 bytes .../output_lm_mixed_xgboost_separate.rds | Bin 22866 -> 22863 bytes .../output_lm_mixed_xgboost_surrogate.rds | Bin 120023 -> 120023 bytes .../output_lm_numeric_lm_separate.rds | Bin 7528 -> 7527 bytes ...utput_lm_numeric_lm_separate_iterative.rds | Bin 14025 -> 14024 bytes .../output_lm_numeric_lm_separate_n_comb.rds | Bin 5791 -> 5791 bytes .../output_lm_numeric_lm_surrogate.rds | Bin 296936 -> 296936 bytes ...tput_lm_numeric_lm_surrogate_iterative.rds | Bin 296936 -> 296936 bytes .../output_lm_numeric_lm_surrogate_n_comb.rds | Bin 107365 -> 107364 bytes ...m_numeric_lm_surrogate_reg_surr_n_comb.rds | Bin 107364 -> 107363 bytes ...utput_custom_lm_numeric_independence_1.rds | Bin 6044 -> 6044 bytes ...utput_custom_lm_numeric_independence_2.rds | Bin 6044 -> 6044 bytes ...utput_custom_xgboost_mixed_dummy_ctree.rds | Bin 6125 -> 6124 bytes .../output_lm_categorical_ctree.rds | Bin 4179 -> 4179 bytes .../output_lm_categorical_independence.rds | Bin 4105 -> 4105 bytes .../output_lm_categorical_method.rds | Bin 4555 -> 4554 bytes .../output_lm_categorical_vaeac.rds | Bin 4550 -> 4550 bytes .../regular-output/output_lm_mixed_comb.rds | Bin 6181 -> 6181 bytes .../regular-output/output_lm_mixed_ctree.rds | Bin 6161 -> 6161 bytes .../output_lm_mixed_independence.rds | Bin 6104 -> 6103 bytes .../regular-output/output_lm_mixed_vaeac.rds | Bin 6535 -> 6535 bytes .../output_lm_numeric_comb1.rds | Bin 6450 -> 6450 bytes .../output_lm_numeric_comb2.rds | Bin 6438 -> 6438 bytes .../output_lm_numeric_comb3.rds | Bin 6377 -> 6377 bytes .../output_lm_numeric_copula.rds | Bin 6358 -> 6358 bytes .../output_lm_numeric_ctree.rds | Bin 6110 -> 6110 bytes .../output_lm_numeric_ctree_parallelized.rds | Bin 6110 -> 6110 bytes .../output_lm_numeric_empirical.rds | Bin 6310 -> 6310 bytes .../output_lm_numeric_empirical_AICc_each.rds | Bin 4208 -> 4209 bytes .../output_lm_numeric_empirical_AICc_full.rds | Bin 4207 -> 4210 bytes ...tput_lm_numeric_empirical_independence.rds | Bin 6312 -> 6311 bytes ...tput_lm_numeric_empirical_n_coalitions.rds | Bin 5673 -> 5673 bytes .../output_lm_numeric_empirical_progress.rds | Bin 6310 -> 6310 bytes .../output_lm_numeric_gaussian.rds | Bin 6261 -> 6260 bytes .../output_lm_numeric_independence.rds | Bin 6044 -> 6044 bytes ...eric_independence_MSEv_Shapley_weights.rds | Bin 5696 -> 6051 bytes ..._numeric_independence_keep_samp_for_vS.rds | Bin 210819 -> 210819 bytes .../output_lm_numeric_interaction.rds | Bin 2637 -> 2637 bytes .../output_lm_numeric_vaeac.rds | Bin 6478 -> 6477 bytes .../output_lm_timeseries_method.rds | Bin 35575 -> 35574 bytes 63 files changed, 6 insertions(+), 6 deletions(-) diff --git a/R/compute_vS.R b/R/compute_vS.R index ead0aaf94..204333449 100644 --- a/R/compute_vS.R +++ b/R/compute_vS.R @@ -115,7 +115,8 @@ batch_compute_vS <- function(S, internal, model, predict_model, p = NULL) { if (regression) { dt_vS <- batch_prepare_vS_regression(S = S, internal = internal) } else { - # Here dt_vS is either only dt_vS or a list containing dt_vS and dt if internal$parameters$output_args$keep_samp_for_vS = TRUE + # Here dt_vS is either only dt_vS or a list containing dt_vS and dt if + # internal$parameters$output_args$keep_samp_for_vS = TRUE dt_vS <- batch_prepare_vS_MC(S = S, internal = internal, model = model, predict_model = predict_model) } diff --git a/R/explain_forecast.R b/R/explain_forecast.R index e06ef8e58..c6994369c 100644 --- a/R/explain_forecast.R +++ b/R/explain_forecast.R @@ -130,7 +130,6 @@ explain_forecast <- function(model, # n_batches = n_batches, # TODO: This is not used anymore, but the code does not use the iterative version of it # either I think... I have now just set it to always 10 in the create_S_batches_forecast function. seed = seed, - keep_samp_for_vS = keep_samp_for_vS, feature_specs = feature_specs, type = "forecast", horizon = horizon, diff --git a/man/explain_forecast.Rd b/man/explain_forecast.Rd index dd26c6595..b729c3c5c 100644 --- a/man/explain_forecast.Rd +++ b/man/explain_forecast.Rd @@ -20,7 +20,6 @@ explain_forecast( group = NULL, n_MC_samples = 1000, seed = 1, - keep_samp_for_vS = FALSE, predict_model = NULL, get_model_specs = NULL, verbose = "basic", diff --git a/man/get_iterative_args_default.Rd b/man/get_iterative_args_default.Rd index 3b777c6ee..1b9e06d13 100644 --- a/man/get_iterative_args_default.Rd +++ b/man/get_iterative_args_default.Rd @@ -29,8 +29,8 @@ Not used directly, but passed through from \code{\link[=explain]{explain()}}.} Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached.} -\item{n_coal_next_iter_factor_vec}{Numeric vector. The number of \code{n_coalitions} that must be used to reach convergence -in the next iteration is estimated. +\item{n_coal_next_iter_factor_vec}{Numeric vector. The number of \code{n_coalitions} that must be used to reach +convergence in the next iteration is estimated. The number of \code{n_coalitions} actually used in the next iteration is set to this estimate multiplied by \code{n_coal_next_iter_factor_vec[i]} for iteration \code{i}. It is wise to start with smaller numbers to avoid using too many \code{n_coalitions} due to uncertain estimates in @@ -40,7 +40,8 @@ the first iterations.} Function to specify arguments of the iterative estimation procedure } \details{ -The functions sets default values for the iterative estimation procedure, according to the function defaults. +The functions sets default values for the iterative estimation procedure, according to the function +defaults. 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--- a/tests/testthat/_snaps/regression-setup.md +++ b/tests/testthat/_snaps/regression-setup.md @@ -3,7 +3,8 @@ Code explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric, prediction_zero = p0, approach = c( - "regression_surrogate", "gaussian", "independence", "empirical"), iterative = FALSE) + "regression_surrogate", "gaussian", "independence", "empirical"), + iterative = FALSE) Message Success with message: max_n_coalitions is NULL or larger than or 2^n_features = 32, diff --git a/tests/testthat/_snaps/regular-output/output_custom_lm_numeric_independence_1.rds b/tests/testthat/_snaps/regular-output/output_custom_lm_numeric_independence_1.rds index 6c92a9af1ce85847d7e6deff9088b17d8141b27a..0fb10fe66d31d9f28d0026fd76fda189dcf0ee2a 100644 GIT binary patch literal 6044 zcmV;N7h~ujiwFP!000001MNKtcvMBwZzhxbU;-f!A|cBWQMp67;dK%cKu92jORix^ zCduezW|$0tu=(WmQ;K2Vqe_E=9auKs;6j7DZW5NVdCQ)q6cJ 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