From 8fba29b0b559410c9e3ae2d20df5b918823a17e3 Mon Sep 17 00:00:00 2001 From: Martin Jullum Date: Thu, 19 Dec 2024 23:45:21 +0100 Subject: [PATCH] Prep for CRAN (#428) --- DESCRIPTION | 2 +- NAMESPACE | 4 +- NEWS.md | 22 +- R/approach_empirical.R | 1 - R/approach_vaeac.R | 16 +- R/check_convergence.R | 2 +- R/compute_estimates.R | 4 +- R/explain.R | 21 - R/explain_forecast.R | 10 +- R/prepare_next_iteration.R | 2 +- R/setup.R | 76 +- R/shapley_setup.R | 8 +- R/zzz.R | 3 +- inst/model_objects/lm_model_object.rds | Bin 4934 -> 0 bytes inst/model_objects/xgboost_model_object.rds | Bin 19653 -> 0 bytes .../xgboost_model_object_cat.rds | Bin 21901 -> 0 bytes inst/model_objects/xgboost_model_object_raw | Bin 37173 -> 0 bytes .../xgboost_model_object_raw_cat | Bin 42861 -> 0 bytes inst/scripts/Beeswarm_illustration.R | 559 - ...mpare_Conditional_and_Causal_Categorical.R | 167 - .../Compare_categorical_prepare_data.R | 563 - .../scripts/Heskes_bike_rental_illustration.R | 1087 -- inst/scripts/Vaeac_compare_getitem_getbatch.R | 120 - inst/scripts/analyze_bash_test_data.R | 120 - inst/scripts/bashscript_2023.sh | 55 - inst/scripts/bashscript_2023_tmp.sh | 55 - inst/scripts/bugfix_beeswarm.R | 134 - inst/scripts/check_model_workflow.R | 169 - inst/scripts/compare_copula_in_R_and_C++.R | 1551 -- inst/scripts/compare_gaussian_in_R_and_C++.R | 2735 --- inst/scripts/compare_shap_python.R | 146 - inst/scripts/compare_shap_python_new.R | 67 - inst/scripts/create_lm_model_object.R | 12 - inst/scripts/create_xgboost_model_object.R | 26 - .../Rcpp_paired_string_coalition_sampling.R | 217 - inst/scripts/devel/Rscript_test.R | 19 - inst/scripts/devel/Rscript_test_shapr.R | 105 - inst/scripts/devel/bashscript_looping.sh | 60 - inst/scripts/devel/bashscript_looping2.sh | 50 - inst/scripts/devel/bashscript_looping_run.sh | 56 - inst/scripts/devel/compare_explain_batch.R | 172 - .../devel/compare_indep_implementations.R | 118 - inst/scripts/devel/debug_asym_vignette.R | 103 - .../demonstrate_combined_approaches_bugs.R | 139 - inst/scripts/devel/devel_batch_testing.R | 67 - inst/scripts/devel/devel_convergence_branch.R | 148 - inst/scripts/devel/devel_non_exact_grouping.R | 54 - inst/scripts/devel/devel_parallelization.R | 147 - inst/scripts/devel/devel_tmp_new_batch.R | 48 - inst/scripts/devel/devel_verbose.R | 135 - inst/scripts/devel/explain_new.R | 164 - inst/scripts/devel/future_testing.R | 56 - inst/scripts/devel/inspect_sim_res.R | 14 - inst/scripts/devel/master_res.rds | Bin 88593 -> 0 bytes inst/scripts/devel/master_res2.rds | Bin 122924 -> 0 bytes inst/scripts/devel/memory_log_test_big.csv | 15141 ---------------- .../devel/real_data_iterative_kernelshap.R | 276 - ...ata_iterative_kernelshap_analyze_results.R | 135 - inst/scripts/devel/same_seed_as_master.R | 39 - ...tive_kernelshap_lingauss_analyze_results.R | 88 - ...simtest_iterative_kernelshap_lingauss_v2.R | 261 - ...e_kernelshap_nonlingauss_analyze_results.R | 122 - .../devel/simtest_reweighting_strategies.R | 263 - ...simtest_reweighting_strategies_nonlinear.R | 182 - ..._strategies_nonlinear_nonunique_sampling.R | 217 - inst/scripts/devel/simtest_timing_to_Frida.R | 107 - .../devel/testing_explain_forevast_n_comb.R | 214 - .../testing_for_valid_defualt_n_batches.R | 54 - .../devel/testing_intermediate_saving.R | 132 - .../scripts/devel/testing_memory_monitoring.R | 98 - .../testing_n_cobinations_equal_2_power_m.R | 71 - inst/scripts/devel/testing_parallelization.R | 176 - .../devel/testing_verification_ar_model.R | 38 - inst/scripts/devel/time_series_annabelle.R | 89 - inst/scripts/devel/timing_log_test_big.csv | 1476 -- .../devel/verifying_arima_model_output.R | 76 - .../devel/visual_bug_in_Shapley_bar_plot.R | 54 - inst/scripts/empirical_memory_testing2.R | 145 - inst/scripts/example_annabelle.R | 77 - inst/scripts/example_ctree_method.R | 99 - inst/scripts/example_custom_model.R | 94 - inst/scripts/example_plot_MSEv.R | 411 - .../example_plot_SV_several_approaches.R | 186 - .../example_plot_several_vaeacs_VLB_IWAE.R | 150 - inst/scripts/explain_memory_testing.R | 113 - inst/scripts/memory_test_2023.csv | 3013 --- inst/scripts/problematic_plots_jens.R | 117 - inst/scripts/readme_example.R | 45 - inst/scripts/shap_python_script.py | 35 - inst/scripts/testing_samling_ncombinations.R | 126 - inst/scripts/time_series_annabelle.R | 89 - inst/scripts/timing_script_2023.R | 113 - inst/scripts/timing_test_2023.csv | 296 - inst/scripts/vilde/Rplot.pdf | Bin 5242 -> 0 bytes inst/scripts/vilde/Rplot01.pdf | Bin 5209 -> 0 bytes inst/scripts/vilde/Rplot02.pdf | Bin 5208 -> 0 bytes inst/scripts/vilde/airquality_example.R | 37 - inst/scripts/vilde/arrow_waterfall.pdf | Bin 8437 -> 0 bytes inst/scripts/vilde/bug_example.pdf | Bin 6150 -> 0 bytes inst/scripts/vilde/check_progress.R | 58 - .../scripts/vilde/sketch_for_waterfall_plot.R | 68 - inst/scripts/vilde/waterfall_plot.R | 79 - inst/scripts/vilde/waterfall_plot.pdf | Bin 7210 -> 0 bytes .../waterfall_plot_featurename_fixed.pdf | Bin 7221 -> 0 bytes man/create_coalition_table.Rd | 13 - man/explain.Rd | 20 - man/explain_forecast.Rd | 34 +- man/get_extra_comp_args_default.Rd | 21 + ...t_eval_crit.Rd => plot_vaeac_eval_crit.Rd} | 16 +- ...pairs.Rd => plot_vaeac_imputed_ggpairs.Rd} | 8 +- man/sample_coalition_table.Rd | 13 - man/setup.Rd | 20 - .../output_asym_caus_conf_FALSE.rds | Bin 4715 -> 4712 bytes .../output_asym_caus_conf_TRUE.rds | Bin 4670 -> 4667 bytes .../output_asym_caus_conf_mix.rds | Bin 4691 -> 4688 bytes .../output_asym_caus_conf_mix_ctree.rds | Bin 4523 -> 4521 bytes .../output_asym_caus_conf_mix_empirical.rds | Bin 4738 -> 4736 bytes .../output_asym_caus_conf_mix_n_coal.rds | Bin 4582 -> 4580 bytes .../output_asym_cond_reg.rds | Bin 5607 -> 5608 bytes .../output_asym_cond_reg_iterative.rds | Bin 6956 -> 6958 bytes .../output_asymmetric_conditional.rds | Bin 4377 -> 4376 bytes .../output_cat_asym_causal_mixed_cat_ad.rds | Bin 10485 -> 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(95%) rename man/{vaeac_plot_imputed_ggpairs.Rd => plot_vaeac_imputed_ggpairs.Rd} (97%) diff --git a/DESCRIPTION b/DESCRIPTION index f48e0ea91..b7a26a9a2 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: shapr -Version: 1.0.0.9000 +Version: 1.0.1 Title: Prediction Explanation with Dependence-Aware Shapley Values Description: Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific diff --git a/NAMESPACE b/NAMESPACE index f97e8a550..61a352917 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -74,6 +74,8 @@ export(get_supported_approaches) export(get_supported_models) export(plot_MSEv_eval_crit) export(plot_SV_several_approaches) +export(plot_vaeac_eval_crit) +export(plot_vaeac_imputed_ggpairs) export(predict_model) export(prepare_data) export(prepare_data_causal) @@ -87,8 +89,6 @@ export(shapley_setup) export(testing_cleanup) export(vaeac_get_evaluation_criteria) export(vaeac_get_extra_para_default) -export(vaeac_plot_eval_crit) -export(vaeac_plot_imputed_ggpairs) export(vaeac_train_model) export(vaeac_train_model_continue) export(weight_matrix) diff --git a/NEWS.md b/NEWS.md index b892be330..526fe534a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,24 @@ -# shapr 1.0.0 +# shapr 1.0.1 + +* Rename vaeac plotting functions [#427](https://github.com/NorskRegnesentral/shapr/pull/427)) +* Move explain() arguments `paired_shap_sampling` and `kernelSHAP_reweighting` into `extra_computation_args` [#427](https://github.com/NorskRegnesentral/shapr/pull/427)) +* Improved and unified the documentation [#427](https://github.com/NorskRegnesentral/shapr/pull/427)) +* Remove seed argument from the boostrap function as its better handled by the mother function [#427](https://github.com/NorskRegnesentral/shapr/pull/427)) +* Renamed various internal functions to be consistent with names in the rest of the package [#427](https://github.com/NorskRegnesentral/shapr/pull/427)) +* Remove MSEv from explain_forecast (as it was only supported for horizon=1). Should return in a more general manner in the future [#427](https://github.com/NorskRegnesentral/shapr/pull/427)) +* Improve efficiency of coalition sampling code and move to string sampling [#426](https://github.com/NorskRegnesentral/shapr/pull/426)) +* Bugfix `iterative = TRUE` for `explain_forecast()` which was not using coaltions from previous iterations [#426](https://github.com/NorskRegnesentral/shapr/pull/426)) +* Bugfix the handling and output with the `verbose` argument for `explain_forecast()` [#425](https://github.com/NorskRegnesentral/shapr/pull/425)) +* Improved flexibility of the beeswarm plot functionality [#424](https://github.com/NorskRegnesentral/shapr/pull/424)) +* Bugfix edge case where the `party` package returns a `constparty` object [#423](https://github.com/NorskRegnesentral/shapr/pull/423)) +* Bugfix error due to extra comma in rarely used warning [#422](https://github.com/NorskRegnesentral/shapr/pull/422)) +* Shined up the vignettes a bit [#421](https://github.com/NorskRegnesentral/shapr/pull/421)) +* Bugfix `keep_samp_for_vS` with iterative approach [#417](https://github.com/NorskRegnesentral/shapr/pull/417)) +* [Python] Brought the python code base up to speed with essentially all functionality in `explain()` in R [#416](https://github.com/NorskRegnesentral/shapr/pull/416)) +* + +# shapr 1.0.0 (GitHub only) + ### Breaking changes diff --git a/R/approach_empirical.R b/R/approach_empirical.R index c5009aaa7..4247fb7b4 100644 --- a/R/approach_empirical.R +++ b/R/approach_empirical.R @@ -53,7 +53,6 @@ setup_approach.empirical <- function(internal, empirical.cov_mat = NULL, model = NULL, predict_model = NULL, ...) { - defaults <- mget(c( "empirical.eta", "empirical.type", "empirical.fixed_sigma", "empirical.n_samples_aicc", "empirical.eval_max_aicc", "empirical.start_aicc" diff --git a/R/approach_vaeac.R b/R/approach_vaeac.R index f73ac5947..ce7984a03 100644 --- a/R/approach_vaeac.R +++ b/R/approach_vaeac.R @@ -2561,17 +2561,17 @@ Last epoch: %d. \tVLB = %.3f \tIWAE = %.3f \tIWAE_running = %.3f\n", #' ) #' #' # Call the function with the named list, will use the provided names -#' vaeac_plot_eval_crit(explanation_list = explanation_list) +#' plot_vaeac_eval_crit(explanation_list = explanation_list) #' #' # The function also works if we have only one method, #' # but then one should only look at the method plot. -#' vaeac_plot_eval_crit( +#' plot_vaeac_eval_crit( #' explanation_list = explanation_list[2], #' plot_type = "method" #' ) #' #' # Can alter the plot -#' vaeac_plot_eval_crit( +#' plot_vaeac_eval_crit( #' explanation_list = explanation_list, #' plot_from_nth_epoch = 2, #' plot_every_nth_epoch = 2, @@ -2579,7 +2579,7 @@ Last epoch: %d. \tVLB = %.3f \tIWAE = %.3f \tIWAE_running = %.3f\n", #' ) #' #' # If we only want the VLB -#' vaeac_plot_eval_crit( +#' plot_vaeac_eval_crit( #' explanation_list = explanation_list, #' criteria = "VLB", #' plot_type = "criterion" @@ -2587,7 +2587,7 @@ Last epoch: %d. \tVLB = %.3f \tIWAE = %.3f \tIWAE_running = %.3f\n", #' #' # If we want only want the criterion version #' tmp_fig_criterion <- -#' vaeac_plot_eval_crit(explanation_list = explanation_list, plot_type = "criterion") +#' plot_vaeac_eval_crit(explanation_list = explanation_list, plot_type = "criterion") #' #' # Since tmp_fig_criterion is a ggplot2 object, we can alter it #' # by, e.g,. adding points or smooths with se bands @@ -2600,7 +2600,7 @@ Last epoch: %d. \tVLB = %.3f \tIWAE = %.3f \tIWAE_running = %.3f\n", #' #' @author Lars Henry Berge Olsen #' @export -vaeac_plot_eval_crit <- function(explanation_list, +plot_vaeac_eval_crit <- function(explanation_list, plot_from_nth_epoch = 1, plot_every_nth_epoch = 1, criteria = c("VLB", "IWAE"), @@ -2773,7 +2773,7 @@ vaeac_plot_eval_crit <- function(explanation_list, #' ) #' #' # Plot the results -#' figure <- vaeac_plot_imputed_ggpairs( +#' figure <- plot_vaeac_imputed_ggpairs( #' explanation = explanation, #' which_vaeac_model = "best", #' x_true = x_train, @@ -2786,7 +2786,7 @@ vaeac_plot_eval_crit <- function(explanation_list, #' ggplot2::scale_color_manual(values = c("#E69F00", "#999999")) + #' ggplot2::scale_fill_manual(values = c("#E69F00", "#999999")) #' } -vaeac_plot_imputed_ggpairs <- function( +plot_vaeac_imputed_ggpairs <- function( explanation, which_vaeac_model = "best", x_true = NULL, diff --git a/R/check_convergence.R b/R/check_convergence.R index 4d314eb6b..2f050c5e9 100644 --- a/R/check_convergence.R +++ b/R/check_convergence.R @@ -10,7 +10,7 @@ check_convergence <- function(internal) { 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$paired_shap_sampling + paired_shap_sampling <- internal$parameters$extra_computation_args$paired_shap_sampling n_shapley_values <- internal$parameters$n_shapley_values n_sampled_coalitions <- internal$iter_list[[iter]]$n_sampled_coalitions diff --git a/R/compute_estimates.R b/R/compute_estimates.R index 562d5f56e..635bda5a0 100644 --- a/R/compute_estimates.R +++ b/R/compute_estimates.R @@ -207,8 +207,8 @@ bootstrap_shapley_inner <- function(X, n_shapley_values, shap_names, internal, d iter <- length(internal$iter_list) n_explain <- internal$parameters$n_explain - paired_shap_sampling <- internal$parameters$paired_shap_sampling - shapley_reweight <- internal$parameters$kernelSHAP_reweighting + paired_shap_sampling <- internal$parameters$extra_computation_args$paired_shap_sampling + shapley_reweight <- internal$parameters$extra_computation_args$kernelSHAP_reweighting X_org <- copy(X) diff --git a/R/explain.R b/R/explain.R index d04fc7499..52365e232 100644 --- a/R/explain.R +++ b/R/explain.R @@ -93,11 +93,6 @@ #' Note that any combination of four strings can be used. #' E.g. `verbose = c("basic", "vS_details")` will display basic information + details about the v(S)-estimation process. #' -#' @param paired_shap_sampling Logical. -#' If `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. -#' #' @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 iteratively in an iterative manner. @@ -119,18 +114,6 @@ #' @param extra_computation_args Named list. #' Specifices extra arguments related to the computation of the Shapley values. #' See [get_extra_comp_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. -#' The aim of this is to reduce the randomness and thereby the variance of the Shapley value estimates. -#' The options are one of `'none'`, `'on_N'`, `'on_all'`, `'on_all_cond'` (default). -#' `'none'` means no reweighting, i.e. the sampling frequency weights are used as is. -#' `'on_coal_size'` means the sampling frequencies are averaged over all coalitions of the same size. -#' `'on_N'` means the sampling frequencies are averaged over all coalitions with the same original sampling -#' probabilities. -#' `'on_all'` means the original sampling probabilities are used for all coalitions. -#' `'on_all_cond'` means the original sampling probabilities are used for all coalitions, while adjusting for the -#' probability that they are sampled at least once. -#' `'on_all_cond'` is preferred as it performs the best in simulation studies, see Olsen & Jullum (2024). #' #' @param prev_shapr_object `shapr` object or string. #' If an object of class `shapr` is provided, or string with a path to where intermediate results are strored, @@ -399,9 +382,7 @@ explain <- function(model, 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, @@ -432,7 +413,6 @@ explain <- function(model, x_train = x_train, x_explain = x_explain, approach = approach, - paired_shap_sampling = paired_shap_sampling, phi0 = phi0, max_n_coalitions = max_n_coalitions, group = group, @@ -442,7 +422,6 @@ explain <- function(model, verbose = verbose, iterative = iterative, iterative_args = iterative_args, - kernelSHAP_reweighting = kernelSHAP_reweighting, init_time = init_time, prev_shapr_object = prev_shapr_object, asymmetric = asymmetric, diff --git a/R/explain_forecast.R b/R/explain_forecast.R index a7c73bb5f..1fe77de42 100644 --- a/R/explain_forecast.R +++ b/R/explain_forecast.R @@ -95,8 +95,6 @@ explain_forecast <- function(model, phi0, max_n_coalitions = NULL, iterative = NULL, - iterative_args = list(), - kernelSHAP_reweighting = "on_all_cond", group_lags = TRUE, group = NULL, n_MC_samples = 1e3, @@ -104,6 +102,9 @@ explain_forecast <- function(model, predict_model = NULL, get_model_specs = NULL, verbose = "basic", + extra_computation_args = list(), + iterative_args = list(), + output_args = list(), ...) { init_time <- Sys.time() @@ -133,8 +134,6 @@ explain_forecast <- function(model, type = "forecast", horizon = horizon, iterative = iterative, - iterative_args = iterative_args, - kernelSHAP_reweighting = kernelSHAP_reweighting, init_time = init_time, y = y, xreg = xreg, @@ -145,6 +144,9 @@ explain_forecast <- function(model, group_lags = group_lags, group = group, verbose = verbose, + extra_computation_args = extra_computation_args, + iterative_args = iterative_args, + output_args = output_args, ... ) diff --git a/R/prepare_next_iteration.R b/R/prepare_next_iteration.R index d568f0427..c493388a2 100644 --- a/R/prepare_next_iteration.R +++ b/R/prepare_next_iteration.R @@ -7,7 +7,7 @@ prepare_next_iteration <- function(internal) { iter <- length(internal$iter_list) converged <- internal$iter_list[[iter]]$converged - paired_shap_sampling <- internal$parameters$paired_shap_sampling + paired_shap_sampling <- internal$parameters$extra_computation_args$paired_shap_sampling if (converged == FALSE) { diff --git a/R/setup.R b/R/setup.R index 9c5dfc70d..511185969 100644 --- a/R/setup.R +++ b/R/setup.R @@ -29,7 +29,6 @@ setup <- function(x_train, x_explain, approach, - paired_shap_sampling = TRUE, phi0, output_size = 1, max_n_coalitions, @@ -49,7 +48,6 @@ setup <- function(x_train, verbose, iterative = NULL, iterative_args = list(), - kernelSHAP_reweighting = "none", is_python = FALSE, testing = FALSE, init_time = NULL, @@ -78,7 +76,6 @@ setup <- function(x_train, internal$parameters <- get_parameters( approach = approach, - paired_shap_sampling = paired_shap_sampling, phi0 = phi0, output_size = output_size, max_n_coalitions = max_n_coalitions, @@ -95,7 +92,6 @@ setup <- function(x_train, verbose = verbose, iterative = iterative, iterative_args = iterative_args, - kernelSHAP_reweighting = kernelSHAP_reweighting, is_python = is_python, testing = testing, asymmetric = asymmetric, @@ -157,7 +153,6 @@ get_prev_internal <- function(prev_shapr_object, #' @keywords internal get_parameters <- function(approach, - paired_shap_sampling, phi0, output_size = 1, max_n_coalitions, @@ -174,7 +169,6 @@ get_parameters <- function(approach, verbose = "basic", iterative = FALSE, iterative_args = list(), - kernelSHAP_reweighting = "none", asymmetric, causal_ordering, confounding, @@ -186,10 +180,6 @@ get_parameters <- function(approach, # Check input type for approach # approach is checked more comprehensively later - if (!is.logical(paired_shap_sampling) && length(paired_shap_sampling) == 1) { - stop("`paired_shap_sampling` must be a single logical.") - } - if (!is.logical(iterative) && length(iterative) == 1) { stop("`iterative` must be a single logical.") } @@ -295,21 +285,12 @@ get_parameters <- function(approach, )) } - # type - 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( - "`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" - ) - } # Getting basic input parameters parameters <- list( approach = approach, - paired_shap_sampling = paired_shap_sampling, phi0 = phi0, max_n_coalitions = max_n_coalitions, group = group, @@ -319,7 +300,6 @@ get_parameters <- function(approach, output_size = output_size, type = type, verbose = verbose, - kernelSHAP_reweighting = kernelSHAP_reweighting, iterative = iterative, iterative_args = iterative_args, output_args = output_args, @@ -784,17 +764,6 @@ check_and_set_asymmetric <- function(internal) { # exact <- internal$parameters$exact causal_ordering <- internal$parameters$causal_ordering max_n_coalitions <- internal$parameters$max_n_coalitions - paired_shap_sampling <- internal$parameters$paired_shap_sampling - - # Check that we are not doing paired sampling - if (paired_shap_sampling) { - stop(paste0( - "Set `paired_shap_sampling = FALSE` to compute asymmetric Shapley values.\n", - "Asymmetric Shapley values do not support paired sampling as the paired ", - "coalitions will not necessarily respect the causal ordering." - )) - } - # Get the number of coalitions that respects the (partial) causal ordering internal$parameters$max_n_coalitions_causal <- get_max_n_coalitions_causal(causal_ordering = causal_ordering) @@ -1077,6 +1046,15 @@ set_extra_comp_params <- function(internal) { extra_computation_args <- trans_null_extra_est_args(extra_computation_args) + # Check that we are not doing paired sampling when computing asymmetric Shapley values + if (internal$parameters$asymmetric && extra_computation_args$paired_shap_sampling) { + stop(paste0( + "Set `paired_shap_sampling = FALSE` to compute asymmetric Shapley values.\n", + "Asymmetric Shapley values do not support paired sampling as the paired ", + "coalitions will not necessarily respect the causal ordering." + )) + } + internal$parameters$extra_computation_args <- extra_computation_args return(internal) @@ -1084,6 +1062,23 @@ set_extra_comp_params <- function(internal) { #' Gets the default values for the extra estimation arguments #' +#' @param paired_shap_sampling Logical. +#' If `TRUE` 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. +#' `TRUE` is the default and is recommended for highest accuracy. +#' For asymmetric, `FALSE` is the default and the only legal value. +#' @param kernelSHAP_reweighting String. +#' How to reweight the sampling frequency weights in the kernelSHAP solution after sampling. +#' The aim of this is to reduce the randomness and thereby the variance of the Shapley value estimates. +#' The options are one of `'none'`, `'on_N'`, `'on_all'`, `'on_all_cond'` (default). +#' `'none'` means no reweighting, i.e. the sampling frequency weights are used as is. +#' `'on_N'` means the sampling frequencies are averaged over all coalitions with the same original sampling +#' probabilities. +#' `'on_all'` means the original sampling probabilities are used for all coalitions. +#' `'on_all_cond'` means the original sampling probabilities are used for all coalitions, while adjusting for the +#' probability that they are sampled at least once. +#' `'on_all_cond'` is preferred as it performs the best in simulation studies, see Olsen & Jullum (2024). #' @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 @@ -1097,6 +1092,8 @@ set_extra_comp_params <- function(internal) { #' @export #' @author Martin Jullum get_extra_comp_args_default <- function(internal, # Only used to get the default value of compute_sd + paired_shap_sampling = isFALSE(internal$parameters$asymmetric), + kernelSHAP_reweighting = "on_all_cond", compute_sd = isFALSE(internal$parameters$exact), n_boot_samps = 100, max_batch_size = 10, @@ -1108,6 +1105,17 @@ get_extra_comp_args_default <- function(internal, # Only used to get the default check_extra_computation_args <- function(extra_computation_args) { list2env(extra_computation_args, envir = environment()) # Make accessible in the environment + # paired_shap_sampling + if (!is.logical(paired_shap_sampling) && length(paired_shap_sampling) == 1) { + stop("`paired_shap_sampling` must be a single logical.") + } + + # kernelSHAP_reweighting + if (!(length(kernelSHAP_reweighting) == 1 && kernelSHAP_reweighting %in% + c("none", "on_N", "on_all", "on_all_cond"))) { + stop("`kernelSHAP_reweighting` must be one of `none`, `on_N`, `on_all`, `on_all_cond`.\n") + } + # compute_sd if (!(is.logical(compute_sd) && length(compute_sd) == 1)) { @@ -1442,6 +1450,8 @@ compare_vecs <- function(vec1, vec2, vec_type, name1, name2) { set_iterative_parameters <- function(internal, prev_iter_list = NULL) { iterative <- internal$parameters$iterative + paired_shap_sampling <- internal$parameters$extra_computation_args$paired_shap_sampling + iterative_args <- internal$parameters$iterative_args iterative_args <- utils::modifyList(get_iterative_args_default(internal), @@ -1456,7 +1466,7 @@ set_iterative_parameters <- function(internal, prev_iter_list = NULL) { } # If paired_shap_sampling is TRUE, we need the number of coalitions to be even - if (internal$parameters$paired_shap_sampling) { + if (paired_shap_sampling) { iterative_args$initial_n_coalitions <- ceiling(iterative_args$initial_n_coalitions * 0.5) * 2 } @@ -1472,7 +1482,7 @@ set_iterative_parameters <- function(internal, prev_iter_list = NULL) { internal$iter_list <- prev_iter_list # Conveniently allow running non-iterative estimation one step further - if (isFALSE(internal$parameters$iterative)) { + if (isFALSE(iterative)) { internal$parameters$iterative_args$max_iter <- length(internal$iter_list) + 1 internal$parameters$iterative_args$n_coal_next_iter_factor_vec <- NULL } diff --git a/R/shapley_setup.R b/R/shapley_setup.R index 68e76525b..6d1628434 100644 --- a/R/shapley_setup.R +++ b/R/shapley_setup.R @@ -10,8 +10,8 @@ shapley_setup <- function(internal) { n_features <- internal$parameters$n_features approach <- internal$parameters$approach is_groupwise <- internal$parameters$is_groupwise - paired_shap_sampling <- internal$parameters$paired_shap_sampling - kernelSHAP_reweighting <- internal$parameters$kernelSHAP_reweighting + paired_shap_sampling <- internal$parameters$extra_computation_args$paired_shap_sampling + kernelSHAP_reweighting <- internal$parameters$extra_computation_args$kernelSHAP_reweighting coal_feature_list <- internal$objects$coal_feature_list causal_sampling <- internal$parameters$causal_sampling causal_ordering <- internal$parameters$causal_ordering @@ -592,8 +592,8 @@ shapley_setup_forecast <- function(internal) { n_features <- internal$parameters$n_features approach <- internal$parameters$approach is_groupwise <- internal$parameters$is_groupwise - paired_shap_sampling <- internal$parameters$paired_shap_sampling - kernelSHAP_reweighting <- internal$parameters$kernelSHAP_reweighting + paired_shap_sampling <- internal$parameters$extra_computation_args$paired_shap_sampling + kernelSHAP_reweighting <- internal$parameters$extra_computation_args$kernelSHAP_reweighting coal_feature_list <- internal$objects$coal_feature_list horizon <- internal$parameters$horizon diff --git a/R/zzz.R b/R/zzz.R index 6e027e6c0..3966dd0f6 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Functions ------------------------------------------------------------------------------------------------------- -plot_shapr <- function(x, - plot_type = "bar", - digits = 3, - index_x_explain = NULL, - top_k_features = NULL, - col = NULL, # first increasing color, then decreasing color - bar_plot_phi0 = TRUE, - bar_plot_order = "largest_first", - scatter_features = NULL, - scatter_hist = TRUE, - ...) { - if (!requireNamespace("ggplot2", quietly = TRUE)) { - stop("ggplot2 is not installed. Please run install.packages('ggplot2')") - } - if (!(plot_type %in% c("bar", "waterfall", "scatter", "beeswarm"))) { - stop(paste(plot_type, "is an invalid plot type. Try plot_type='bar', plot_type='waterfall', - plot_type='scatter', or plot_type='beeswarm'.")) - } - if (!(bar_plot_order %in% c("largest_first", "smallest_first", "original"))) { - stop(paste(bar_plot_order, "is an invalid plot order. Try bar_plot_order='largest_first', - bar_plot_order='smallest_first' or bar_plot_order='original'.")) - } - - 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 - - is_groupwise <- x$internal$parameters$is_groupwise - - # melting Kshap - shap_names <- colnames(x$shapley_values_est)[-1] - dt_shap <- round(data.table::copy(x$shapley_values_est), digits = digits) - dt_shap[, id := .I] - dt_shap_long <- data.table::melt(dt_shap, id.vars = "id", value.name = "phi") - dt_shap_long[, sign := factor(sign(phi), levels = c(1, -1), labels = c("Increases", "Decreases"))] - - # Converting and melting Xtest - if (!is_groupwise) { - desc_mat <- trimws(format(x$internal$data$x_explain, digits = digits)) - for (i in seq_len(ncol(desc_mat))) { - desc_mat[, i] <- paste0(shap_names[i], " = ", desc_mat[, i]) - } - } else { - desc_mat <- trimws(format(x$shapley_values_est[, -1], digits = digits)) - for (i in seq_len(ncol(desc_mat))) { - desc_mat[, i] <- paste0(shap_names[i]) - } - } - - dt_desc <- data.table::as.data.table(cbind(none = "none", desc_mat)) - dt_desc_long <- data.table::melt(dt_desc[, id := .I], id.vars = "id", value.name = "description") - - # Data table for plotting - dt_plot <- merge(dt_shap_long, dt_desc_long) - - # Adding the predictions - dt_pred <- data.table::data.table(id = dt_shap$id, pred = x$pred_explain) - dt_plot <- merge(dt_plot, dt_pred, by = "id") - - # Adding header for each individual plot - dt_plot[, header := paste0("id: ", id, ", pred = ", format(pred, digits = digits + 1))] - - if (plot_type == "scatter" || plot_type == "beeswarm") { - # Add feature values to data table - dt_feature_vals <- data.table::copy(x$internal$data$x_explain) - dt_feature_vals <- as.data.table(cbind(none = NA, dt_feature_vals)) - dt_feature_vals[, id := .I] - - # Deal with numeric and factor variables separately - factor_features <- dt_feature_vals[, sapply(.SD, function(x) is.factor(x) | is.character(x)), .SDcols = shap_names] - factor_features <- shap_names[factor_features] - - dt_feature_vals_long <- suppressWarnings(data.table::melt(dt_feature_vals, - id.vars = "id", - value.name = "feature_value" - )) - # this gives a warning because none-values are NA... - dt_plot <- merge(dt_plot, dt_feature_vals_long, by = c("id", "variable")) - } - - return(list(dt_plot = dt_plot, - col = col, - index_x_explain = index_x_explain, x = x, factor_features = factor_features)) -} - - -make_beeswarm_plot_old <- function(dt_plot, col, index_x_explain, x, factor_cols) { - if (!requireNamespace("ggbeeswarm", quietly = TRUE)) { - stop("geom_beeswarm is not installed. Please run install.packages('ggbeeswarm')") - } - - if (is.null(col)) { - col <- c("#F8766D", "yellow", "#00BA38") - } - if (!(length(col) %in% c(2, 3))) { - stop("'col' must be of length 2 or 3 when making beeswarm plot.") - } - - dt_plot <- dt_plot[variable != "none"] - - # Deal with factor variables - process_data <- shapr:::process_factor_data(dt_plot, factor_cols) - dt_plot <- process_data$dt_plot - - dt_train <- data.table::copy(x$internal$data$x_train) - dt_train <- suppressWarnings( # suppress warnings for coercion from int to double or to factor - data.table::melt(dt_train[, id := .I], id.vars = "id", value.name = "feature_value") - ) - dt_train <- shapr:::process_factor_data(dt_train, factor_cols)$dt_plot - dt_train[, `:=`(max = max(feature_value), min = min(feature_value)), by = variable] - dt_train <- dt_train[, .(variable, max, min)] - dt_train <- unique(dt_train) - dt_plot <- merge(dt_plot, dt_train, by = "variable") - - # scale obs. features value to their distance from min. feature value relative to the distance - # between min. and max. feature value in order to have a global color bar indicating magnitude - # of obs. feature value. - # The feature values are scaled wrt the training data - dt_plot[feature_value <= max & feature_value >= min, - feature_value_scaled := (feature_value - min) / (max - min), - by = variable - ] - dt_plot[feature_value > max, feature_value_scaled := 1] - dt_plot[feature_value < min, feature_value_scaled := 0] - - # make sure features with only one value are also scaled - dt_plot[is.nan(feature_value_scaled), feature_value_scaled := 0.5, by = variable] - - # Only plot the desired observations - dt_plot <- dt_plot[id %in% index_x_explain] - - # For factor variables, we want one line per factor level - # Give them a NA feature value to make the color grey - dt_plot[type == "factor", variable := description] - dt_plot[type == "factor", feature_value_scaled := NA] - - gg <- ggplot2::ggplot(dt_plot, ggplot2::aes(x = variable, y = phi, color = feature_value_scaled)) + - ggplot2::geom_hline(yintercept = 0, color = "grey70", linewidth = 0.5) + - ggbeeswarm::geom_beeswarm(priority = "random", cex = 0.4) + - # the cex-parameter doesnt generalize well, should use corral but not available yet.... - ggplot2::coord_flip() + - ggplot2::theme_classic() + - ggplot2::theme(panel.grid.major.y = ggplot2::element_line(colour = "grey90", linetype = "dashed")) + - ggplot2::labs(x = "", y = "Shapley value") + - ggplot2::guides(color = ggplot2::guide_colourbar( - ticks = FALSE, - barwidth = 0.5, barheight = 10 - )) - - if (length(col) == 3) { # check is col-parameter is the default - gg <- gg + - ggplot2::scale_color_gradient2( - low = col[3], mid = col[2], high = col[1], - midpoint = 0.5, - breaks = c(0, 1), - limits = c(0, 1), - labels = c("Low", "High"), - name = "Feature \n value" - ) - } else if (length(col) == 2) { # allow user to specify three colors - gg <- gg + - ggplot2::scale_color_gradient( - low = col[2], - high = col[1], - breaks = c(0, 1), - limits = c(0, 1), - labels = c("Low", "High"), - name = "Feature \n value" - ) - } - - return(gg) -} - -make_beeswarm_plot_new_cex <- function(dt_plot, col, index_x_explain, x, factor_cols) { - if (!requireNamespace("ggbeeswarm", quietly = TRUE)) { - stop("geom_beeswarm is not installed. Please run install.packages('ggbeeswarm')") - } - - if (is.null(col)) { - col <- c("#F8766D", "yellow", "#00BA38") - } - if (!(length(col) %in% c(2, 3))) { - stop("'col' must be of length 2 or 3 when making beeswarm plot.") - } - - dt_plot <- dt_plot[variable != "none"] - - # Deal with factor variables - process_data <- shapr:::process_factor_data(dt_plot, factor_cols) - dt_plot <- process_data$dt_plot - - dt_train <- data.table::copy(x$internal$data$x_train) - dt_train <- suppressWarnings( # suppress warnings for coercion from int to double or to factor - data.table::melt(dt_train[, id := .I], id.vars = "id", value.name = "feature_value") - ) - dt_train <- shapr:::process_factor_data(dt_train, factor_cols)$dt_plot - dt_train[, `:=`(max = max(feature_value), min = min(feature_value)), by = variable] - dt_train <- dt_train[, .(variable, max, min)] - dt_train <- unique(dt_train) - dt_plot <- merge(dt_plot, dt_train, by = "variable") - - # scale obs. features value to their distance from min. feature value relative to the distance - # between min. and max. feature value in order to have a global color bar indicating magnitude - # of obs. feature value. - # The feature values are scaled wrt the training data - dt_plot[feature_value <= max & feature_value >= min, - feature_value_scaled := (feature_value - min) / (max - min), - by = variable - ] - dt_plot[feature_value > max, feature_value_scaled := 1] - dt_plot[feature_value < min, feature_value_scaled := 0] - - # make sure features with only one value are also scaled - dt_plot[is.nan(feature_value_scaled), feature_value_scaled := 0.5, by = variable] - - # Only plot the desired observations - dt_plot <- dt_plot[id %in% index_x_explain] - - # For factor variables, we want one line per factor level - # Give them a NA feature value to make the color grey - dt_plot[type == "factor", variable := description] - dt_plot[type == "factor", feature_value_scaled := NA] - - gg <- ggplot2::ggplot(dt_plot, ggplot2::aes(x = variable, y = phi, color = feature_value_scaled)) + - ggplot2::geom_hline(yintercept = 0, color = "grey70", linewidth = 0.5) + - ggbeeswarm::geom_beeswarm(priority = "random", cex = 1 / length(index_x_explain)^(1/4)) + - # the cex-parameter doesnt generalize well, should use corral but not available yet.... - ggplot2::coord_flip() + - ggplot2::theme_classic() + - ggplot2::theme(panel.grid.major.y = ggplot2::element_line(colour = "grey90", linetype = "dashed")) + - ggplot2::labs(x = "", y = "Shapley value") + - ggplot2::guides(color = ggplot2::guide_colourbar( - ticks = FALSE, - barwidth = 0.5, barheight = 10 - )) - - if (length(col) == 3) { # check is col-parameter is the default - gg <- gg + - ggplot2::scale_color_gradient2( - low = col[3], mid = col[2], high = col[1], - midpoint = 0.5, - breaks = c(0, 1), - limits = c(0, 1), - labels = c("Low", "High"), - name = "Feature \n value" - ) - } else if (length(col) == 2) { # allow user to specify three colors - gg <- gg + - ggplot2::scale_color_gradient( - low = col[2], - high = col[1], - breaks = c(0, 1), - limits = c(0, 1), - labels = c("Low", "High"), - name = "Feature \n value" - ) - } - - return(gg) -} - -make_beeswarm_plot_new <- function(dt_plot, col, index_x_explain, x, factor_cols, - corral.method = "swarm", - corral.corral = "wrap", - corral.priority = "random", - corral.width = 0.75, - corral.cex = 0.75) { - if (!requireNamespace("ggbeeswarm", quietly = TRUE)) { - stop("geom_beeswarm is not installed. Please run install.packages('ggbeeswarm')") - } - - if (is.null(col)) { - col <- c("#F8766D", "yellow", "#00BA38") - } - if (!(length(col) %in% c(2, 3))) { - stop("'col' must be of length 2 or 3 when making beeswarm plot.") - } - - dt_plot <- dt_plot[variable != "none"] - - # Deal with factor variables - process_data <- shapr:::process_factor_data(dt_plot, factor_cols) - dt_plot <- process_data$dt_plot - - dt_train <- data.table::copy(x$internal$data$x_train) - dt_train <- suppressWarnings( # suppress warnings for coercion from int to double or to factor - data.table::melt(dt_train[, id := .I], id.vars = "id", value.name = "feature_value") - ) - dt_train <- shapr:::process_factor_data(dt_train, factor_cols)$dt_plot - dt_train[, `:=`(max = max(feature_value), min = min(feature_value)), by = variable] - dt_train <- dt_train[, .(variable, max, min)] - dt_train <- unique(dt_train) - dt_plot <- merge(dt_plot, dt_train, by = "variable") - - # scale obs. features value to their distance from min. feature value relative to the distance - # between min. and max. feature value in order to have a global color bar indicating magnitude - # of obs. feature value. - # The feature values are scaled wrt the training data - dt_plot[feature_value <= max & feature_value >= min, - feature_value_scaled := (feature_value - min) / (max - min), - by = variable - ] - dt_plot[feature_value > max, feature_value_scaled := 1] - dt_plot[feature_value < min, feature_value_scaled := 0] - - # make sure features with only one value are also scaled - dt_plot[is.nan(feature_value_scaled), feature_value_scaled := 0.5, by = variable] - - # Only plot the desired observations - dt_plot <- dt_plot[id %in% index_x_explain] - - # For factor variables, we want one line per factor level - # Give them a NA feature value to make the color grey - dt_plot[type == "factor", variable := description] - dt_plot[type == "factor", feature_value_scaled := NA] - - gg <- ggplot2::ggplot(dt_plot, ggplot2::aes(x = variable, y = phi, color = feature_value_scaled)) + - ggplot2::geom_hline(yintercept = 0, color = "grey70", linewidth = 0.5) + - ggbeeswarm::geom_beeswarm(method = corral.method, - corral = corral.corral, - priority = corral.priority, - corral.width = corral.width, - cex = corral.cex) + - ggplot2::coord_flip() + - ggplot2::theme_classic() + - ggplot2::theme(panel.grid.major.y = ggplot2::element_line(colour = "grey90", linetype = "dashed")) + - ggplot2::labs(x = "", y = "Shapley value") + - ggplot2::guides(color = ggplot2::guide_colourbar( - ticks = FALSE, - barwidth = 0.5, barheight = 10 - )) - - if (length(col) == 3) { # check is col-parameter is the default - gg <- gg + - ggplot2::scale_color_gradient2( - low = col[3], mid = col[2], high = col[1], - midpoint = 0.5, - breaks = c(0, 1), - limits = c(0, 1), - labels = c("Low", "High"), - name = "Feature \n value" - ) - } else if (length(col) == 2) { # allow user to specify three colors - gg <- gg + - ggplot2::scale_color_gradient( - low = col[2], - high = col[1], - breaks = c(0, 1), - limits = c(0, 1), - labels = c("Low", "High"), - name = "Feature \n value" - ) - } - - return(gg) -} - -make_beeswarm_plot_paper3 <- function(dt_plot, col, index_x_explain, x, factor_cols) { - if (!requireNamespace("ggbeeswarm", quietly = TRUE)) { - stop("geom_beeswarm is not installed. Please run install.packages('ggbeeswarm')") - } - - if (is.null(col)) { - col <- c("#F8766D", "yellow", "#00BA38") - } - if (!(length(col) %in% c(2, 3))) { - stop("'col' must be of length 2 or 3 when making beeswarm plot.") - } - - dt_plot <- dt_plot[variable != "none"] - - # Deal with factor variables - process_data <- shapr:::process_factor_data(dt_plot, factor_cols) - dt_plot <- process_data$dt_plot - - dt_train <- data.table::copy(x$internal$data$x_train) - dt_train <- suppressWarnings( # suppress warnings for coercion from int to double or to factor - data.table::melt(dt_train[, id := .I], id.vars = "id", value.name = "feature_value") - ) - dt_train <- shapr:::process_factor_data(dt_train, factor_cols)$dt_plot - dt_train[, `:=`(max = max(feature_value), min = min(feature_value)), by = variable] - dt_train <- dt_train[, .(variable, max, min)] - dt_train <- unique(dt_train) - dt_plot <- merge(dt_plot, dt_train, by = "variable") - - # scale obs. features value to their distance from min. feature value relative to the distance - # between min. and max. feature value in order to have a global color bar indicating magnitude - # of obs. feature value. - # The feature values are scaled wrt the training data - dt_plot[feature_value <= max & feature_value >= min, - feature_value_scaled := (feature_value - min) / (max - min), - by = variable - ] - dt_plot[feature_value > max, feature_value_scaled := 1] - dt_plot[feature_value < min, feature_value_scaled := 0] - - # make sure features with only one value are also scaled - dt_plot[is.nan(feature_value_scaled), feature_value_scaled := 0.5, by = variable] - - # Only plot the desired observations - dt_plot <- dt_plot[id %in% index_x_explain] - - # For factor variables, we want one line per factor level - # Give them a NA feature value to make the color grey - dt_plot[type == "factor", variable := description] - dt_plot[type == "factor", feature_value_scaled := NA] - - gg <- ggplot2::ggplot(dt_plot, ggplot2::aes(x = variable, y = phi, color = feature_value_scaled)) + - ggplot2::geom_hline(yintercept = 0, color = "grey60", linewidth = 0.5) + - #ggbeeswarm::geom_beeswarm(priority = "random", cex = 0.1) + - ggbeeswarm::geom_beeswarm(corral = "wrap", priority = "random", corral.width = 0.75) + - # the cex-parameter doesnt generalize well, should use corral but not available yet.... - ggplot2::coord_flip() + - #ggplot2::theme_classic() + - ggplot2::theme(panel.grid.major.y = ggplot2::element_line(colour = "grey75", linetype = "dashed")) + - ggplot2::labs(x = "", y = "Shapley value") + - ggplot2::guides(color = ggplot2::guide_colourbar( - ticks = FALSE, - #barwidth = 0.5, barheight = 10 - barwidth = 10, barheight = 0.5 - )) - - if (length(col) == 3) { # check is col-parameter is the default - gg <- gg + - ggplot2::scale_color_gradient2( - low = col[3], mid = col[2], high = col[1], - midpoint = 0.5, - breaks = c(0, 1), - limits = c(0, 1), - labels = c(" Low", "High "), - name = "Feature value: " - ) + - theme(legend.position = 'bottom') + - guides(fill = guide_legend(nrow = 1)) - } else if (length(col) == 2) { # allow user to specify three colors - gg <- gg + - ggplot2::scale_color_gradient( - low = col[2], - high = col[1], - breaks = c(0, 1), - limits = c(0, 1), - labels = c("Low", "High"), - name = "Feature \n value" - ) - } - - return(gg) -} - -# Run code from here ---------------------------------------------------------------------------------------------- -# Load necessary library -library(shapr) -library(xgboost) -library(data.table) -library(MASS) -library(ggplot2) -library(ggpubr) - -# Parameters -M <- 10 # Number of dimensions -N_train <- 1000 # Number of training observations -N_explain <- 5000 # Number of test observations -mu <- rep(0, M) # Mean vector, for example, a zero vector -rho <- 0.5 # Correlation coefficient (must be between -1 and 1) -beta = matrix(c(1, -2, 2, 0.5, 1.5, 0.25, 0.75, -0.5, 1, -2)[1:M]) - -# Construct the equi-correlation matrix -cov_matrix <- matrix(rho, nrow = M, ncol = M) -diag(cov_matrix) <- 1 # Set diagonal to 1 - -# Generate N observations from the multivariate normal distribution -set.seed(123) # Set seed for reproducibility -x_train <- mvrnorm(N_train, mu, cov_matrix) -x_explain <- mvrnorm(N_explain, mu, cov_matrix) - -y_train <- x_train %*% beta + rnorm(N_train, sd = 1) -y_explain <- x_explain %*% beta + rnorm(N_explain, sd = 1) - -x_train = as.data.table(x_train) -x_explain = as.data.table(x_explain) - -# Fitting a basic xgboost model to the training data -model <- xgboost::xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -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 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = p0, - max_n_coalitions = 10, # Do not need precise Shapley values to illustrate the behaviour of beeswarm plot - n_MC_samples = 10 # Do not need precise Shapley values to illustrate the behaviour of beeswarm plot -) - -# Get the objects needed to make the beeswarm plot -tmp_list = plot_shapr(explanation, plot_type = "beeswarm") - - -## Plots ----------------------------------------------------------------------------------------------------------- -# Make the old and new beeswarm plot -list_figures = lapply(c(50, 100, 1000, 5000), function(N_explain_plot) { - # Old version have problem with runaway points: see https://github.com/eclarke/ggbeeswarm?tab=readme-ov-file#corral-runaway-points - gg_old <- make_beeswarm_plot_old(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain[seq(N_explain_plot)], - x = tmp_list$x, - factor_cols = tmp_list$factor_features) - - gg_new_cex <- make_beeswarm_plot_new_cex(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain[seq(N_explain_plot)], - x = tmp_list$x, - factor_cols = tmp_list$factor_features) - - gg_new <- make_beeswarm_plot_new(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain[seq(N_explain_plot)], - x = tmp_list$x, - factor_cols = tmp_list$factor_features, - corral.corral = "wrap", # Default. Other options: "none" (default in geom_beeswarm), "gutter", "random", "omit" - corral.method = "swarm", # Default (and default in geom_beeswarm). Other options: "compactswarm", "hex", "square", "center - corral.priority = "random", # Default . Other options: "ascending" (default in geom_beeswarm), "descending", "density" - corral.width = 0.75, # Default. 0.9 is default in geom_beeswarm - corral.cex = 0.75) # Default. 1 is default in geom_beeswarm - - gg_paper3 <- make_beeswarm_plot_paper3(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain[seq(N_explain_plot)], - x = tmp_list$x, - factor_cols = tmp_list$factor_features) - return(ggpubr::ggarrange(gg_old, gg_new_cex, gg_new, gg_paper3, labels = c("Old", "New_cex", "New", "Paper3"), nrow = 1, vjust = 2)) -}) - - -# 50 -list_figures[[1]] - -# 100 -list_figures[[2]] - -# 1000 -list_figures[[3]] - -# 5000 -list_figures[[4]] - -# Plot them together -ggpubr::ggarrange(list_figures[[1]], list_figures[[2]], list_figures[[3]], list_figures[[4]], labels = c(50, 100, 1000, 5000), ncol = 1, vjust = 1) diff --git a/inst/scripts/Compare_Conditional_and_Causal_Categorical.R b/inst/scripts/Compare_Conditional_and_Causal_Categorical.R deleted file mode 100644 index f30efa475..000000000 --- a/inst/scripts/Compare_Conditional_and_Causal_Categorical.R +++ /dev/null @@ -1,167 +0,0 @@ -# In this file, we compare the causal and conditional Shapley values for a categorical dataset. -# We see that "categorical" approach sometimes produce Shapley values of the opposite sign than -# the other approaches, but this happens for both causal and conditional Shapley values. -# I.e., there is likely no mistake in the cateogical causal Shapley value code. -{ - options(digits = 5) # To avoid round off errors when printing output on different systems - - set.seed(12345) - - data <- data.table::as.data.table(airquality) - data[, Month_factor := as.factor(Month)] - data[, Ozone_sub30 := (Ozone < 30) * 1] - data[, Ozone_sub30_factor := as.factor(Ozone_sub30)] - data[, Solar.R_factor := as.factor(cut(Solar.R, 10))] - data[, Wind_factor := as.factor(round(Wind))] - - data_complete <- data[complete.cases(airquality), ] - data_complete <- data_complete[sample(seq_len(.N))] - y_var_numeric <- "Ozone" - x_var_categorical <- c("Month_factor", "Ozone_sub30_factor", "Solar.R_factor", "Wind_factor") - data_train <- head(data_complete, -10) - data_explain <- tail(data_complete, 10) - x_train_categorical <- data_train[, ..x_var_categorical] - x_explain_categorical <- data_explain[, ..x_var_categorical] - lm_formula_categorical <- as.formula(paste0(y_var_numeric, " ~ ", paste0(x_var_categorical, collapse = " + "))) - model_lm_categorical <- lm(lm_formula_categorical, data = data_complete) - p0 <- data_train[, mean(get(y_var_numeric))] -} - -# Causal Shapley values ----- -causal_independence <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "independence", - phi0 = p0, - asymmetric = FALSE, - causal_ordering = list(3:4, 2, 1), - confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE -) - -causal_categorical <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "categorical", - phi0 = p0, - asymmetric = FALSE, - causal_ordering = list(3:4, 2, 1), - confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE, - iterative = FALSE -) - -# Warning CTREE is the slowest approach by far -causal_ctree <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "ctree", - phi0 = p0, - asymmetric = FALSE, - causal_ordering = list(3:4, 2, 1), - confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE, - iterative = FALSE -) - -causal_vaeac <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "vaeac", - vaeac.epochs = 20, - phi0 = p0, - asymmetric = FALSE, - causal_ordering = list(3:4, 2, 1), - confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE, - iterative = FALSE -) - -shapr::plot_SV_several_approaches(list( - ind = causal_independence, - cat = causal_categorical, - ctree = causal_ctree, - vaeac = causal_vaeac -)) - -# Conditional Shapley values ------ -conditional_independence <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "independence", - phi0 = p0, - # asymmetric = FALSE, - # causal_ordering = list(3:4, 2, 1), - # confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE, - iterative = FALSE -) - -conditional_categorical <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "categorical", - phi0 = p0, - # asymmetric = FALSE, - # causal_ordering = list(3:4, 2, 1), - # confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE, - iterative = FALSE -) - -# Warning CTREE is the slowest approach by far -conditional_ctree <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "ctree", - phi0 = p0, - # asymmetric = FALSE, - # causal_ordering = list(3:4, 2, 1), - # confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE, - iterative = FALSE -) - -conditional_vaeac <- explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "vaeac", - vaeac.epochs = 20, - phi0 = p0, - # asymmetric = FALSE, - # causal_ordering = list(3:4, 2, 1), - # confounding = c(TRUE, FALSE, FALSE), - n_MC_samples = 50, # Just for speed - verbose = c("basic", "convergence", "shapley", "vS_details"), - keep_samp_for_vS = TRUE, - iterative = FALSE -) - -shapr::plot_SV_several_approaches(list( - ind = conditional_independence, - cat = conditional_categorical, - ctree = conditional_ctree, - vaeac = conditional_vaeac -)) diff --git a/inst/scripts/Compare_categorical_prepare_data.R b/inst/scripts/Compare_categorical_prepare_data.R deleted file mode 100644 index dd913ee4d..000000000 --- a/inst/scripts/Compare_categorical_prepare_data.R +++ /dev/null @@ -1,563 +0,0 @@ -# File with several proposals for new versions of the `compute_conditional_prob` function used by -# the categorical approach, which are much faster. -# The `compute_conditional_prob_shapr_old` computed a lot of unnecessary things, e.g., it compute the conditional -# prob for all colaitions and then threw away all results not relevant to the coalitions in the batch at the end. -# The `compute_conditional_prob_shapr_new` computes only the relevant stuff for the applicable coalitions in the batch. - -# The versions ---------------------------------------------------------------------------------------------------- -compute_conditional_prob <- function(S, index_features, x_explain, joint_probability_dt) { - - # Extract the feature names and add an id column to x_explain (copy as this changes `x_explain` outside the function) - feature_names = names(x_explain) - x_explain_copy = data.table::copy(x_explain)[,id := .I] - - # Loop over the combinations and convert to a single data table containing all the conditional probabilities - results = data.table::rbindlist(lapply(index_features, function(index_feature) { - - # Extract the feature names of the features we are to condition on - cond_cols <- feature_names[S[index_feature,] == 1] - cond_cols_with_id = c("id", cond_cols) - - # Extract the feature values to condition and including the id column - dt_conditional_feature_values = x_explain_copy[, cond_cols_with_id, with = FALSE] - - # Merge (right outer join) the joint_probability_dt data with the conditional feature values - results_id_combination = joint_probability_dt[dt_conditional_feature_values, on = cond_cols, allow.cartesian = TRUE] - - # Get the weights/conditional probabilities for each valid X_sbar conditioned on X_s for all explicands - results_id_combination[, w := joint_prob / sum(joint_prob), by = id] - results_id_combination[, c("id_all", "joint_prob") := NULL] - - # If we have a combination not in the joint prob, then we delete it - # TODO: or should we do something else? - # TODO: Comment out the printouts. Only used to debug - results_not_valid = results_id_combination[is.na(w)] - str_tmp = paste(sapply(results_not_valid$id, function(i) { - paste0("(id = ", i, ", ", paste(cond_cols, "=", results_not_valid[id == i,..cond_cols], collapse = ", "), ")") - }), collapse = ", ") - paste0("The following explicands where removed as they are not in `joint_probability_dt`: ", str_tmp, ".") - - # Return the data table where we remove the NA entries - return(results_id_combination[!is.na(w)]) - }), idcol = "id_combination", use.names = TRUE) - - # Update the index_features to their correct value - results[, id_combination := index_features[id_combination]] - - # Set id_combination and id to be the keys and the two first columns for consistency with other methods - data.table::setkeyv(results, c("id_combination", "id")) - data.table::setcolorder(results, c("id_combination", "id", feature_names)) - - return(results) -} - -compute_conditional_prob_merge <- function(S, index_features, x_explain, joint_probability_dt) { - - # Extract the feature names and add an id column to x_explain (copy as this changes `x_explain` outside the function) - feature_names = names(x_explain) - x_explain = data.table::copy(x_explain)[,id := .I] - - # Loop over the combinations and convert to a single data table containing all the conditional probabilities - results = data.table::rbindlist(lapply(index_features, function(index_feature) { - - # Extract the feature names of the features we are to condition on - cond_cols <- feature_names[S[index_feature,] == 1] - cond_cols_with_id = c("id", cond_cols) - - # Extract the feature values to condition and including the id column - dt_conditional_feature_values = x_explain[, cond_cols_with_id, with = FALSE] - - # Merge (right outer join) the joint_probability_dt data with the conditional feature values - results_id_combination <- data.table::merge.data.table(joint_probability_dt, dt_conditional_feature_values, by = cond_cols, allow.cartesian = TRUE) - - # Get the weights/conditional probabilities for each valid X_sbar conditioned on X_s for all explicands - results_id_combination[, w := joint_prob / sum(joint_prob), by = id] - results_id_combination[, c("id_all", "joint_prob") := NULL] - - # Return the data table - return(results_id_combination) - }), idcol = "id_combination", use.names = TRUE) - - # Update the index_features to their correct value - results[, id_combination := index_features[id_combination]] - - # Set id_combination and id to be the keys and the two first columns for consistency with other methods - data.table::setkeyv(results, c("id_combination", "id")) - data.table::setcolorder(results, c("id_combination", "id", feature_names)) - - return(results) -} - -compute_conditional_prob_merge_one_coalition <- function(S, index_features, x_explain, joint_probability_dt) { - if (length(index_features) != 1) stop("`index_features` must be single integer.") - - # Extract the feature names and add an id column to x_explain (copy as this changes `x_explain` outside the function) - feature_names = names(x_explain) - x_explain = data.table::copy(x_explain)[,id := .I] - - # Extract the feature names of the features we are to condition on - cond_cols <- feature_names[S[index_features,] == 1] - cond_cols_with_id = c("id", cond_cols) - - # Extract the feature values to condition and including the id column - dt_conditional_feature_values = x_explain[, cond_cols_with_id, with = FALSE] - - # Merge (right outer join) the joint_probability_dt data with the conditional feature values - results_id_combination <- data.table::merge.data.table(joint_probability_dt, dt_conditional_feature_values, by = cond_cols, allow.cartesian = TRUE) - - # Get the weights/conditional probabilities for each valid X_sbar conditioned on X_s for all explicands - results_id_combination[, w := joint_prob / sum(joint_prob), by = id] - results_id_combination[, c("id_all", "joint_prob") := NULL] - - # Set the index_features to their correct value - results_id_combination[, id_combination := index_features] - - # Set id_combination and id to be the keys and the two first columns for consistency with other methods - data.table::setkeyv(results_id_combination, c("id_combination", "id")) - data.table::setcolorder(results_id_combination, c("id_combination", "id", feature_names)) - - return(results_id_combination) -} - -compute_conditional_prob_shapr_old = function(S, index_features, x_explain, joint_probability_dt) { - - # Extract the needed objects/variables - #x_train <- internal$data$x_train - #x_explain <- internal$data$x_explain - #joint_probability_dt <- internal$parameters$categorical.joint_prob_dt - #X <- internal$objects$X - #S <- internal$objects$S - - # if (is.null(index_features)) { # 2,3 - # features <- X$features # list of [1], [2], [2, 3] - # } else { - # features <- X$features[index_features] # list of [1], - # } - feature_names <- names(x_explain) - - # 3 id columns: id, id_combination, and id_all - # id: for each x_explain observation - # id_combination: the rows of the S matrix - # id_all: identifies the unique combinations of feature values from - # the training data (not necessarily the ones in the explain data) - - - feature_conditioned <- paste0(feature_names, "_conditioned") - feature_conditioned_id <- c(feature_conditioned, "id") - - S_dt <- data.table::data.table(S) - S_dt[S_dt == 0] <- NA - S_dt[, id_combination := seq_len(nrow(S_dt))] - - data.table::setnames(S_dt, c(feature_conditioned, "id_combination")) - - # (1) Compute marginal probabilities - - # multiply table of probabilities nrow(S) times - joint_probability_mult <- joint_probability_dt[rep(id_all, nrow(S))] - - data.table::setkeyv(joint_probability_mult, "id_all") - j_S_dt <- cbind(joint_probability_mult, S_dt) # combine joint probability and S matrix - - j_S_feat <- as.matrix(j_S_dt[, feature_names, with = FALSE]) # with zeros - j_S_feat_cond <- as.matrix(j_S_dt[, feature_conditioned, with = FALSE]) - - j_S_feat[which(is.na(j_S_feat_cond))] <- NA # with NAs - j_S_feat_with_NA <- data.table::as.data.table(j_S_feat) - - # now we have a data.table with the conditioned - # features and the feature value but no ids - data.table::setnames(j_S_feat_with_NA, feature_conditioned) - - j_S_no_conditioned_features <- data.table::copy(j_S_dt) - j_S_no_conditioned_features[, (feature_conditioned) := NULL] - - # dt with conditioned features (correct values) + ids + joint_prob - j_S_all_feat <- cbind(j_S_no_conditioned_features, j_S_feat_with_NA) # features match id_all - - # compute all marginal probabilities - marg_dt <- j_S_all_feat[, .(marg_prob = sum(joint_prob)), by = feature_conditioned] - - # (2) Compute conditional probabilities - - cond_dt <- j_S_all_feat[marg_dt, on = feature_conditioned] - cond_dt[, cond_prob := joint_prob / marg_prob] - cond_dt[id_combination == 1, marg_prob := 0] - cond_dt[id_combination == 1, cond_prob := 1] - - # check marginal probabilities - cond_dt_unique <- unique(cond_dt, by = feature_conditioned) - check <- cond_dt_unique[id_combination != 1][, .(sum_prob = sum(marg_prob)), - by = "id_combination" - ][["sum_prob"]] - if (!all(round(check) == 1)) { - print("Warning - not all marginal probabilities sum to 1. There could be a problem - with the joint probabilities. Consider checking.") - } - - # make x_explain - data.table::setkeyv(cond_dt, c("id_combination", "id_all")) - x_explain_with_id <- data.table::copy(x_explain)[, id := .I] - dt_just_explain <- cond_dt[x_explain_with_id, on = feature_names] - - # this is a really important step to get the proper "w" which will be used in compute_preds() - dt_explain_just_conditioned <- dt_just_explain[, feature_conditioned_id, with = FALSE] - - cond_dt[, id_all := NULL] - dt <- cond_dt[dt_explain_just_conditioned, on = feature_conditioned, allow.cartesian = TRUE] - - # check conditional probabilities - check <- dt[id_combination != 1][, .(sum_prob = sum(cond_prob)), - by = c("id_combination", "id") - ][["sum_prob"]] - if (!all(round(check) == 1)) { - print("Warning - not all conditional probabilities sum to 1. There could be a problem - with the joint probabilities. Consider checking.") - } - - setnames(dt, "cond_prob", "w") - data.table::setkeyv(dt, c("id_combination", "id")) - - # here we merge so that we only return the combintations found in our actual explain data - # this merge does not change the number of rows in dt - # dt <- merge(dt, x$X[, .(id_combination, n_features)], by = "id_combination") - # dt[n_features %in% c(0, ncol(x_explain)), w := 1.0] - dt[id_combination %in% c(1, 2^ncol(x_explain)), w := 1.0] - ret_col <- c("id_combination", "id", feature_names, "w") - dt_temp = dt[id_combination %in% index_features, mget(ret_col)] - - - return(dt_temp) -} - -compute_conditional_prob_shapr_new <- function(S, index_features, x_explain, joint_probability_dt) { - - # Extract the needed objects/variables - #x_train <- internal$data$x_train - #x_explain <- internal$data$x_explain - #joint_probability_dt <- internal$parameters$categorical.joint_prob_dt - #X <- internal$objects$X - #S <- internal$objects$S - - # if (is.null(index_features)) { # 2,3 - # features <- X$features # list of [1], [2], [2, 3] - # } else { - # features <- X$features[index_features] # list of [1], - # } - feature_names <- names(x_explain) - - # TODO: add - # For causal sampling, we use - # if (causal_sampling) - - # 3 id columns: id, id_combination, and id_all - # id: for each x_explain observation - # id_combination: the rows of the S matrix - # id_all: identifies the unique combinations of feature values from - # the training data (not necessarily the ones in the explain data) - - - feature_conditioned <- paste0(feature_names, "_conditioned") - feature_conditioned_id <- c(feature_conditioned, "id") - - S_dt <- data.table::data.table(S[index_features, , drop = FALSE]) - S_dt[S_dt == 0] <- NA - S_dt[, id_combination := index_features] - - data.table::setnames(S_dt, c(feature_conditioned, "id_combination")) - - # (1) Compute marginal probabilities - - # multiply table of probabilities length(index_features) times - joint_probability_mult <- joint_probability_dt[rep(id_all, length(index_features))] - - data.table::setkeyv(joint_probability_mult, "id_all") - j_S_dt <- cbind(joint_probability_mult, S_dt) # combine joint probability and S matrix - - j_S_feat <- as.matrix(j_S_dt[, feature_names, with = FALSE]) # with zeros - j_S_feat_cond <- as.matrix(j_S_dt[, feature_conditioned, with = FALSE]) - - j_S_feat[which(is.na(j_S_feat_cond))] <- NA # with NAs - j_S_feat_with_NA <- data.table::as.data.table(j_S_feat) - - # now we have a data.table with the conditioned - # features and the feature value but no ids - data.table::setnames(j_S_feat_with_NA, feature_conditioned) - - j_S_no_conditioned_features <- data.table::copy(j_S_dt) - j_S_no_conditioned_features[, (feature_conditioned) := NULL] - - # dt with conditioned features (correct values) + ids + joint_prob - j_S_all_feat <- cbind(j_S_no_conditioned_features, j_S_feat_with_NA) # features match id_all - - # compute all marginal probabilities - marg_dt <- j_S_all_feat[, .(marg_prob = sum(joint_prob)), by = feature_conditioned] - - # (2) Compute conditional probabilities - - cond_dt <- j_S_all_feat[marg_dt, on = feature_conditioned] - cond_dt[, cond_prob := joint_prob / marg_prob] - #cond_dt[id_combination == 1, marg_prob := 0] - #cond_dt[id_combination == 1, cond_prob := 1] - - # check marginal probabilities - cond_dt_unique <- unique(cond_dt, by = feature_conditioned) - check <- cond_dt_unique[id_combination != 1][, .(sum_prob = sum(marg_prob)), - by = "id_combination" - ][["sum_prob"]] - if (!all(round(check) == 1)) { - print("Warning - not all marginal probabilities sum to 1. There could be a problem - with the joint probabilities. Consider checking.") - } - - # make x_explain - data.table::setkeyv(cond_dt, c("id_combination", "id_all")) - x_explain_with_id <- data.table::copy(x_explain)[, id := .I] - - # dt_just_explain <- rbindlist(lapply(seq(length(index_features)), function(index_features_i) { - # feature_names_now = feature_names[S[index_features[index_features_i],] == 1] - # cond_dt[x_explain_with_id, on = feature_names_now] - # }), use.names = TRUE, fill = TRUE) - - dt_just_explain <- cond_dt[x_explain_with_id, on = feature_names] - - # TODO: bare legge til at cond prob er veldig veldig lav? - - - # this is a really important step to get the proper "w" which will be used in compute_preds() - dt_explain_just_conditioned <- dt_just_explain[, feature_conditioned_id, with = FALSE] - - cond_dt[, id_all := NULL] - - # dt <- rbindlist(lapply(seq(length(index_features)), function(index_features_i) { - # feature_conditioned_now = paste0(feature_names[S[index_features[index_features_i],] == 0], "_conditioned") - # cond_dt[dt_explain_just_conditioned, on = feature_conditioned_now, allow.cartesian = TRUE] - # }), use.names = TRUE, fill = TRUE) - - dt <- cond_dt[dt_explain_just_conditioned, on = feature_conditioned, allow.cartesian = TRUE] - - - # check conditional probabilities - check <- dt[id_combination != 1][, .(sum_prob = sum(cond_prob)), - by = c("id_combination", "id") - ][["sum_prob"]] - if (!all(round(check) == 1)) { - print("Warning - not all conditional probabilities sum to 1. There could be a problem - with the joint probabilities. Consider checking.") - } - - setnames(dt, "cond_prob", "w") - data.table::setkeyv(dt, c("id_combination", "id")) - - # here we merge so that we only return the combintations found in our actual explain data - # this merge does not change the number of rows in dt - # dt <- merge(dt, x$X[, .(id_combination, n_features)], by = "id_combination") - # dt[n_features %in% c(0, ncol(x_explain)), w := 1.0] - # dt[id_combination %in% c(1, 2^ncol(x_explain)), w := 1.0] - dt_temp = dt[, mget(c("id_combination", "id", feature_names, "w"))] - - return(dt_temp) -} - - -# compute_conditional_prob_shapr2 <- function(S, index_features, x_explain, joint_probability_dt) { -# # Extract the feature names -# feature_names <- names(x_explain) -# -# # Add an id column to x_explain -# x_explain = copy(x_explain)[, id := .I] -# -# # Filter the S matrix and create a data table with only relevant id_combinations -# relevant_S <- S[index_features, , drop = FALSE] -# S_dt <- data.table(relevant_S) -# S_dt[S_dt == 0] <- NA -# S_dt[, id_combination := index_features] -# -# # Define feature names with "_conditioned" -# feature_conditioned <- paste0(feature_names, "_conditioned") -# feature_conditioned_id <- c(feature_conditioned, "id") -# -# # Set column names for S_dt -# setnames(S_dt, c(feature_conditioned, "id_combination")) -# -# # Replicate the joint_probability_dt for the number of relevant id_combinations -# joint_probability_mult <- joint_probability_dt[rep(id_all, each = nrow(S_dt))] -# joint_probability_mult[, id_combination := rep(S_dt$id_combination, each = nrow(joint_probability_dt))] -# -# # Combine joint_probability_mult with S_dt -# j_S_dt <- cbind(joint_probability_mult, S_dt) -# -# # Convert features to matrix and condition them with NAs -# j_S_feat <- as.matrix(j_S_dt[, feature_names, with = FALSE]) -# j_S_feat_cond <- as.matrix(j_S_dt[, feature_conditioned, with = FALSE]) -# j_S_feat[is.na(j_S_feat_cond)] <- NA -# j_S_feat_with_NA <- as.data.table(j_S_feat) -# setnames(j_S_feat_with_NA, feature_conditioned) -# -# # Combine conditioned features with joint probabilities -# j_S_no_conditioned_features <- copy(j_S_dt) -# j_S_no_conditioned_features[, (feature_conditioned) := NULL] -# j_S_all_feat <- cbind(j_S_no_conditioned_features, j_S_feat_with_NA) -# -# # Compute marginal probabilities -# marg_dt <- j_S_all_feat[, .(marg_prob = sum(joint_prob)), by = feature_conditioned] -# -# # Compute conditional probabilities -# cond_dt <- j_S_all_feat[marg_dt, on = feature_conditioned] -# cond_dt[, cond_prob := joint_prob / marg_prob] -# cond_dt[id_combination == 1, marg_prob := 0] -# cond_dt[id_combination == 1, cond_prob := 1] -# -# # Check marginal probabilities -# cond_dt_unique <- unique(cond_dt, by = feature_conditioned) -# check <- cond_dt_unique[id_combination != 1][, .(sum_prob = sum(marg_prob)), by = "id_combination"][["sum_prob"]] -# if (!all(round(check) == 1)) { -# warning("Not all marginal probabilities sum to 1. There could be a problem with the joint probabilities. Consider checking.") -# } -# -# # Merge with x_explain -# setkeyv(cond_dt, c("id_combination", "id_all")) -# x_explain_with_id <- copy(x_explain)[, id := .I] -# dt_just_explain <- cond_dt[x_explain_with_id, on = feature_names] -# -# # Prepare the explain data -# dt_explain_just_conditioned <- dt_just_explain[, feature_conditioned_id, with = FALSE] -# cond_dt[, id_all := NULL] -# dt <- cond_dt[dt_explain_just_conditioned, on = feature_conditioned, allow.cartesian = TRUE] -# -# # Check conditional probabilities -# check <- dt[id_combination != 1][, .(sum_prob = sum(cond_prob)), by = c("id_combination", "id")][["sum_prob"]] -# if (!all(round(check) == 1)) { -# warning("Not all conditional probabilities sum to 1. There could be a problem with the joint probabilities. Consider checking.") -# } -# -# # Rename and reorder columns -# setnames(dt, "cond_prob", "w") -# setkeyv(dt, c("id_combination", "id")) -# -# # Filter and return relevant combinations -# dt[id_combination %in% c(1, 2^ncol(x_explain)), w := 1.0] -# ret_col <- c("id_combination", "id", feature_names, "w") -# dt_temp <- dt[id_combination %in% index_features, ..ret_col] -# -# return(dt_temp) -# } - -# Comparing ------------------------------------------------------------------------------------------------------- -library(data.table) - -# Need to have loaded shapr for this to work (`devtools::load_all(".")`) -explanation = explain( - model = model_lm_categorical, - x_explain = x_explain_categorical, - x_train = x_train_categorical, - approach = "categorical", - phi0 = p0, - n_batches = 1, - timing = FALSE -) - -S = explanation$internal$objects$S -joint_probability_dt = explanation$internal$parameters$categorical.joint_prob_dt -x_explain = x_explain_categorical - -# Chose any values between 2 and 15 -index_features = 2:15 - -dt = compute_conditional_prob(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt) -merge = compute_conditional_prob_merge(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt) -shapr_old = compute_conditional_prob_shapr_old(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt) -shapr_new = compute_conditional_prob_shapr_new(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt) -all.equal(dt, shapr_new) -all.equal(merge, shapr_new) -all.equal(shapr_old, shapr_new) - -# Compare with only 1 combination (dt and merge are equally fast, shapr_old is 6 times slower) -index_features = 5 -rbenchmark::benchmark(dt = compute_conditional_prob(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - merge = compute_conditional_prob_merge(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - merge_one_coalition = compute_conditional_prob_merge_one_coalition(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_old = compute_conditional_prob_shapr_old(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_new = compute_conditional_prob_shapr_new(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - replications = 500) -# FOR index_features = 2 -# test replications elapsed relative user.self sys.self user.child sys.child -# 1 dt 500 1.596 1.136 1.535 0.028 0 0 -# 2 merge 500 1.640 1.167 1.527 0.035 0 0 -# 3 merge_one_coalition 500 1.405 1.000 1.324 0.024 0 0 -# 5 shapr_new 500 6.200 4.413 6.014 0.103 0 0 -# 4 shapr_old 500 11.203 7.974 10.032 0.267 0 0 - -# FOR index_features = 5 -# test replications elapsed relative user.self sys.self user.child sys.child -# 1 dt 500 1.529 1.374 1.463 0.045 0 0 -# 2 merge 500 1.193 1.072 1.180 0.010 0 0 -# 3 merge_one_coalition 500 1.113 1.000 1.098 0.013 0 0 -# 5 shapr_new 500 5.705 5.126 5.599 0.068 0 0 -# 4 shapr_old 500 8.105 7.282 7.964 0.121 0 0 - -# FOR index_features = 12 -# test replications elapsed relative user.self sys.self user.child sys.child -# 1 dt 500 1.679 1.119 1.623 0.031 0 0 -# 2 merge 500 1.553 1.035 1.520 0.020 0 0 -# 3 merge_one_coalition 500 1.501 1.000 1.463 0.019 0 0 -# 5 shapr_new 500 5.783 3.853 5.619 0.058 0 0 -# 4 shapr_old 500 9.833 6.551 9.389 0.269 0 0 - -# FOR index_features = 12 -# test replications elapsed relative user.self sys.self user.child sys.child -# 1 dt 500 2.561 1.891 1.996 0.094 0 0 -# 2 merge 500 1.599 1.181 1.520 0.026 0 0 -# 3 merge_one_coalition 500 1.354 1.000 1.337 0.013 0 0 -# 5 shapr_new 500 5.323 3.931 5.246 0.065 0 0 -# 4 shapr_old 500 8.170 6.034 8.019 0.131 0 0 - - -merge = compute_conditional_prob_merge(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt) -merge_one_coalition = compute_conditional_prob_merge_one_coalition(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt) -all.equal(merge, merge_one_coalition) - - -# Compare with only 4 combination -index_features = c(2,6,9,12) -rbenchmark::benchmark(dt = compute_conditional_prob(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - merge = compute_conditional_prob_merge(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_old = compute_conditional_prob_shapr_old(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_new = compute_conditional_prob_shapr_new(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - replications = 100) -# test replications elapsed relative user.self sys.self user.child sys.child -# 1 dt 100 0.961 1.016 0.940 0.013 0 0 -# 2 merge 100 0.946 1.000 0.919 0.013 0 0 -# 4 shapr_new 100 1.368 1.446 1.316 0.025 0 0 -# 3 shapr_old 100 2.046 2.163 1.950 0.051 0 0 - - -# Compare with half of the combinations -index_features = seq(2, 15, 2) -rbenchmark::benchmark(dt = compute_conditional_prob(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - merge = compute_conditional_prob_merge(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_old = compute_conditional_prob_shapr_old(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_new = compute_conditional_prob_shapr_new(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - replications = 100) - -# test replications elapsed relative user.self sys.self user.child sys.child -# 1 dt 100 1.614 1.075 1.559 0.028 0 0 -# 2 merge 100 1.758 1.171 1.623 0.042 0 0 -# 4 shapr_new 100 1.501 1.000 1.437 0.033 0 0 -# 3 shapr_old 100 2.001 1.333 1.920 0.038 0 0 - -# Compare with all the combinations -index_features = seq(2, 15) -rbenchmark::benchmark(dt = compute_conditional_prob(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - merge = compute_conditional_prob_merge(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_old = compute_conditional_prob_shapr_old(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - shapr_new = compute_conditional_prob_shapr_new(S = S, index_features = index_features, x_explain = x_explain, joint_probability_dt = joint_probability_dt), - replications = 100) - -# test replications elapsed relative user.self sys.self user.child sys.child -# 1 dt 100 3.435 2.426 3.286 0.077 0 0 -# 2 merge 100 3.511 2.480 3.373 0.070 0 0 -# 4 shapr_new 100 1.416 1.000 1.363 0.026 0 0 -# 3 shapr_old 100 2.153 1.520 2.006 0.045 0 0 - - diff --git a/inst/scripts/Heskes_bike_rental_illustration.R b/inst/scripts/Heskes_bike_rental_illustration.R deleted file mode 100644 index e74e48ce9..000000000 --- a/inst/scripts/Heskes_bike_rental_illustration.R +++ /dev/null @@ -1,1087 +0,0 @@ -# This file build on Pull Request https://github.com/NorskRegnesentral/shapr/pull/273 -# 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. - -# Set to true in order to save plots in the main folder -save_plots <- FALSE - - -# Sina plot ------------------------------------------------------------------------------------------------------- -#' Make a sina plot of the Shapley values computed using shapr. -#' -#' @param explanation shapr list containing an explanation produced by shapr::explain. -#' -#' @return ggplot2 object containing the sina plot. -#' @export -#' -#' @import tidyr -#' @import shapr -#' @import ggplot2 -#' @import ggforce -#' -#' @importFrom dplyr `%>%` -#' -#' @examples -#' # set parameters and random seed -#' set.seed(2020) -#' N <- 1000 -#' m <- 4 -#' sds <- runif(4, 0.5, 1.5) -#' pars <- runif(7, -1, 1) -#' -#' # Create data from a structural equation model -#' X_1 <- rnorm(N, sd = sds[1]) -#' Z <- rnorm(N, 1) -#' X_2 <- X_1 * pars[1] + Z * pars[2] + rnorm(N, sd = sds[2]) -#' X_3 <- X_1 * pars[3] + Z * pars[4] + rnorm(N, sd = sds[3]) -#' Y <- X_1 * pars[5] + X_2 * pars[6] + X_3 * pars[7] + rnorm(N, sd = sds[4]) -#' -#' # collecting data -#' mu_A <- rep(0, m) -#' X_A <- cbind(X_1, X_2, X_3) -#' dat_A <- cbind(X_A, Y) -#' cov_A <- cov(dat_A) -#' -#' model <- lm(Y ~ . + 0 , data = as.data.frame(dat_A)) -#' explainer <- shapr::shapr(X_A, model) -#' y_mean <- mean(Y) -#' -#' explanation_classic <- shapr::explain( -#' dat_A, -#' approach = "gaussian", -#' explainer = explainer, -#' phi0 = y_mean -#' ) -#' sina_plot(explanation_classic) -#' -#' explanation_causal <- shapr::explain( -#' dat_A, -#' approach = "causal", -#' explainer = explainer, -#' phi0 = y_mean, -#' ordering = list(1, c(2, 3)) -#' ) -#' sina_plot(explanation_causal) -#' -#' @seealso \link[SHAPforxgboost]{shap.plot.summary} -#' -#' @details Function adapted from \link[SHAPforxgboost]{shap.plot.summary}. -#' Copyright © 2020 - Yang Liu & Allan Just -#' -sina_plot <- function(explanation, seed = 123) { - set.seed(seed) - - 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 - if (explanation$internal$parameters$is_groupwise) { - feature_groups = explanation$internal$parameters$group - X_values <- X_values[, lapply(feature_groups, function(cols) rowMeans(.SD[, .SD, .SDcols = cols], na.rm = TRUE))] - #setnames(X_values, names(X_values), paste0(names(X_values), "_mean")) # Rename columns to reflect mean calculations - } - - data_long <- X_values %>% - tidyr::pivot_longer(everything()) %>% - dplyr::bind_cols( - 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_est)))) %>% - dplyr::group_by(name) %>% - dplyr::arrange(name) %>% - dplyr::mutate(mean_value = mean(value)) %>% - dplyr::mutate(std_value = (value - min(value)) / (max(value) - min(value))) - - x_bound <- max(abs(max(data_long$shap)), abs(min(data_long$shap))) - - ggplot2::ggplot(data = data_long) + - ggplot2::coord_flip(ylim = c(-x_bound, x_bound)) + - ggplot2::geom_hline(yintercept = 0) + - ggforce::geom_sina( - ggplot2::aes(x = name, y = shap, color = std_value), - method = "counts", maxwidth = 0.7, alpha = 0.7 - ) + - ggplot2::theme_minimal() + ggplot2::theme( - axis.line.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(), - legend.position = "top", - legend.title = ggplot2::element_text(size = 16), legend.text = ggplot2::element_text(size = 14), - axis.title.y = ggplot2::element_text(size = 16), axis.text.y = ggplot2::element_text(size = 14), - axis.title.x = ggplot2::element_text(size = 16, vjust = -1), axis.text.x = ggplot2::element_text(size = 14) - ) + - ggplot2::scale_color_gradient( - low = "dark green" , high = "sandybrown" , - breaks = c(0, 1), labels = c(" Low", "High "), - guide = ggplot2::guide_colorbar(barwidth = 12, barheight = 0.3) - ) + - ggplot2::labs(y = "Causal Shapley value (impact on model output)", - x = "", color = "Scaled feature value ") -} - - -# 0 - Load Packages and Source Files -------------------------------------- -library(tidyverse) -library(data.table) -library(xgboost) -library(ggpubr) -library(shapr) -library(ggplot2) -library(grid) -library(gridExtra) - -if (save_plots && !dir.exists("figures")) dir.create("figures") - -# 1 - Prepare and Plot Data ----------------------------------------------- -# Can also download the data set from the source https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset -# temp <- tempfile() -# download.file("https://archive.ics.uci.edu/static/public/275/bike+sharing+dataset.zip", temp) -# bike <- read.csv(unz(temp, "day.csv")) -# unlink(temp) - - -bike <- read.csv("inst/extdata/day.csv") -# Difference in days, which takes DST into account -bike$trend <- as.numeric(difftime(bike$dteday, bike$dteday[1], units = "days")) -# bike$trend <- as.integer(difftime(bike$dteday, min(as.Date(bike$dteday)))+1)/24 -bike$cosyear <- cospi(bike$trend/365*2) -bike$sinyear <- sinpi(bike$trend/365*2) -# Unnormalize variables (see data set information in link above) -bike$temp <- bike$temp * (39 - (-8)) + (-8) -bike$atemp <- bike$atemp * (50 - (-16)) + (-16) -bike$windspeed <- 67 * bike$windspeed -bike$hum <- 100 * bike$hum - -bike_plot <- ggplot(bike, aes(x = trend, y = cnt, color = temp)) + - geom_point(size = 0.75) + scale_color_gradient(low = "blue", high = "red") + - labs(colour = "temp") + - xlab( "Days since 1 January 2011") + ylab("Number of bikes rented") + - theme_minimal() + - theme(legend.position = "right", legend.title = element_text(size = 10)) - -if (save_plots) { - ggsave("figures/bike_rental_plot.pdf", bike_plot, width = 4.5, height = 2) -} else { - print(bike_plot) -} - -x_var <- c("trend", "cosyear", "sinyear", "temp", "atemp", "windspeed", "hum") -y_var <- "cnt" - -# NOTE: Encountered RNG reproducibility issues across different systems, -# so we saved the training-test split. -# set.seed(2013) -# train_index <- caret::createDataPartition(bike$cnt, p = .8, list = FALSE, times = 1) -train_index <- readRDS("inst/extdata/train_index.rds") - -# Training data -x_train <- as.matrix(bike[train_index, x_var]) -y_train_nc <- as.matrix(bike[train_index, y_var]) # not centered -y_train <- y_train_nc - mean(y_train_nc) - -# Test data -x_explain <- as.matrix(bike[-train_index, x_var]) -y_explain_nc <- as.matrix(bike[-train_index, y_var]) # not centered -y_explain <- y_explain_nc - mean(y_train_nc) - -# Fit an XGBoost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 100, - verbose = FALSE -) -# caret::RMSE(y_explain, predict(model, x_explain)) -sqrt(mean((predict(model, x_explain) - y_explain)^2)) -phi0 <- mean(y_train) - -message("1. Prepared and plotted data, trained XGBoost model") - -# 2 - Compute Shapley Values ---------------------------------------------- -progressr::handlers("cli") -explanation_gaussian_time = system.time({ - explanation_gaussian <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = list(1:7), - confounding = FALSE, - seed = 2020, - n_samples = 50, - keep_samp_for_vS = FALSE - ) - }) -}) - -saveRDS(list(explanation_asymmetric = explanation_asymmetric, - time = explanation_asymmetric_time), - "~/CauSHAPley/inst/extdata/explanation_asymmetric_Olsen.RDS") - - -## a. We compute the causal symmetric Shapley values on a given partial order (see paper) #### -message("2a. Computing and plotting causal Shapley values") -progressr::handlers("cli") -explanation_causal_time = system.time({ - explanation_causal <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 50, - keep_samp_for_vS = FALSE, - verbose = 2, - ) - }) -}) - -set.seed(123) -sina_causal <- sina_plot(explanation_causal) -sina_causal - -# save limits of sina_causal plot for comparing against marginal and asymmetric -ylim_causal <- sina_causal$coordinates$limits$y - -sina_causal = sina_causal + - coord_flip(ylim = ylim_causal) + - ylab("Causal Shapley value (impact on model output)") - -sina_causal - -saveRDS(list(explanation = explanation_causal, - time = explanation_causal_time, - plot = sina_causal, - version = "Causal Shapley values"), - "inst/extdata/explanation_causal_Olsen.RDS") - -if (save_plots) { - ggsave("figures/sina_plot_causal.pdf", sina_causal, height = 6.5, width = 6.5) -} else { - print(sina_causal) -} - - -## b. For computing marginal Shapley values, we assume one component with confounding #### -message("2b. Computing and plotting marginal Shapley values") -explanation_marginal_time = system.time({ - explanation_marginal <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "independence", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = list(1:7), - confounding = FALSE, - seed = 2020, - n_samples = 5000, - keep_samp_for_vS = FALSE - ) - }) -}) - -set.seed(123) -sina_marginal <- sina_plot(explanation_marginal) + - coord_flip(ylim = ylim_causal) + - ylab("Marginal Shapley value (impact on model output)") - -sina_marginal - -saveRDS(list(explanation = explanation_marginal, - time = explanation_marginal_time, - plot = sina_marginal, - version = "Marginal Shapley values"), - "~/CauSHAPley/inst/extdata/explanation_marginal_Olsen.RDS") - - - -if (save_plots) { - ggsave("figures/sina_plot_marginal.pdf", sina_marginal, height = 6.5, width = 6.5) -} else { - print(sina_marginal) -} - - - - -## c. Finally, we compute the asymmetric Shapley values for the same partial order #### -message("2c. Computing and plotting asymmetric conditional Shapley values") - -progressr::handlers("cli") -explanation_asymmetric_time = system.time({ - explanation_asymmetric <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = FALSE, - seed = 2020, - n_samples = 10000, - keep_samp_for_vS = FALSE - ) - }) -}) -set.seed(123) -sina_asymmetric <- sina_plot(explanation_asymmetric) + - coord_flip(ylim = ylim_causal) + - ylab("Asymmetric conditional Shapley value (impact on model output)") - -sina_asymmetric - -saveRDS(list(explanation = explanation_asymmetric, - time = explanation_asymmetric_time, - plot = sina_asymmetric, - version = "Asymmetric conditional Shapley values"), - "~/CauSHAPley/inst/extdata/explanation_asymmetric_Olsen.RDS") - -if (save_plots) { - ggsave("figures/sina_plot_asymmetric.pdf", sina_asymmetric, height = 6.5, width = 6.5) -} else { - print(sina_asymmetric) -} - - - - - -## d. Asymmetric causal Shapley values (very similar to the conditional ones) #### -message("2d. Computing and plotting asymmetric conditional Shapley values") - -progressr::handlers("cli") -explanation_asymmetric_causal_time = system.time({ - explanation_asymmetric_causal <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 10000, - keep_samp_for_vS = FALSE - ) - }) -}) - -set.seed(123) -sina_asymmetric_causal <- sina_plot(explanation_asymmetric_causal) + - coord_flip(ylim = ylim_causal) + - ylab("Asymmetric causal Shapley value (impact on model output)") - -sina_asymmetric_causal - -saveRDS(list(explanation = explanation_asymmetric_causal, - time = explanation_asymmetric_causal_time, - plot = sina_asymmetric_causal, - version = "Asymmetric causal Shapley values"), - "~/CauSHAPley/inst/extdata/explanation_asymmetric_causal_Olsen.RDS") - - -if (save_plots) { - ggsave("figures/sina_plot_asymmetric_causal.pdf", sina_asymmetric_causal, height = 6.5, width = 6.5) -} else { - print(sina_asymmetric_causal) -} - - - - -# 2.5 Compare with old implementation ---- -save_explanation_causal = readRDS("~/CauSHAPley/inst/extdata/explanation_causal.RDS") -save_explanation_marginal = readRDS("~/CauSHAPley/inst/extdata/explanation_marginal.RDS") -save_explanation_asymmetric = readRDS("~/CauSHAPley/inst/extdata/explanation_asymmetric.RDS") -save_explanation_asymmetric_causal = readRDS("~/CauSHAPley/inst/extdata/explanation_asymmetric_causal.RDS") - -save_explanation_causal_Olsen = readRDS("~/CauSHAPley/inst/extdata/explanation_causal_Olsen.RDS") -save_explanation_marginal_Olsen = readRDS("~/CauSHAPley/inst/extdata/explanation_marginal_Olsen.RDS") -save_explanation_asymmetric_Olsen = readRDS("~/CauSHAPley/inst/extdata/explanation_asymmetric_Olsen.RDS") -save_explanation_asymmetric_causal_Olsen = readRDS("~/CauSHAPley/inst/extdata/explanation_asymmetric_causal_Olsen.RDS") - -explanation_causal = save_explanation_causal_Olsen$explanation -explanation_marginal = save_explanation_marginal_Olsen$explanation -explanation_asymmetric = save_explanation_asymmetric_Olsen$explanation -explanation_asymmetric_causal = save_explanation_asymmetric_causal_Olsen$explanation - -gridExtra::grid.arrange(save_explanation_causal$plot + ggplot2::ggtitle("Heskes et al. (2020):"), - save_explanation_causal_Olsen$plot + ggplot2::ggtitle("SHAPR:"), - top = grid::textGrob("Causal Shapley values", - gp = grid::gpar(fontsize=18,font=8))) - -# Will be a difference as we use marginal independence and they us marginal Gaussian -gridExtra::grid.arrange(save_explanation_marginal$plot + ggplot2::ggtitle("Heskes et al. (2020):"), - save_explanation_marginal_Olsen$plot + ggplot2::ggtitle("SHAPR:"), - top = grid::textGrob("Marginal Shapley values", - gp = grid::gpar(fontsize=18,font=8))) - -gridExtra::grid.arrange(save_explanation_asymmetric$plot + ggplot2::ggtitle("Heskes et al. (2020):"), - save_explanation_asymmetric_Olsen$plot + ggplot2::ggtitle("SHAPR:"), - top = grid::textGrob("Asymmetric conditional Shapley values", - gp = grid::gpar(fontsize=18,font=8))) - -gridExtra::grid.arrange(save_explanation_asymmetric_causal$plot + ggplot2::ggtitle("Heskes et al. (2020):"), - save_explanation_asymmetric_causal_Olsen$plot + ggplot2::ggtitle("SHAPR:"), - top = grid::textGrob("Asymmetric causal Shapley values", - gp = grid::gpar(fontsize=18,font=8))) - - - - -# 3 - Shapley value scatter plots (Figure 3) ------------------------------ -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$shapley_values_est$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 -) - - - -scatterplot_topleft <- - ggplot(sv_correlation_df, aes(x = sv_marg_temp, y = sv_marg_cosyear, color = temp)) + - geom_point(size = 1)+xlab("MargSV temp")+ylab( "MargSV cosyear")+ - scale_x_continuous(limits = c(-1500, 1000), breaks = c(-1000, 0, 1000)) + - scale_y_continuous(limits = c(-500, 500), breaks = c(-500, 0, 500)) + - scale_color_gradient(low="blue", high="red") + - theme_minimal() + - theme(text = element_text(size = 12), - axis.text.x = element_blank(), axis.text.y = element_text(size = 12), - axis.ticks.x = element_blank(), axis.title.x = element_blank()) - -scatterplot_topright <- - ggplot(sv_correlation_df, aes(x = sv_caus_cosyear, y = sv_marg_cosyear, color = temp)) + - geom_point(size = 1) + scale_color_gradient(low="blue", high="red") + - xlab("CauSV cosyear") + ylab("MargSV cosyear") + - scale_x_continuous(limits = c(-1500, 1000), breaks = c(-1000, 0, 1000)) + - scale_y_continuous(limits = c(-500, 500), breaks = c(-500, 0, 500)) + - theme_minimal() + - theme(text = element_text(size=12), axis.title.x = element_blank(), axis.title.y=element_blank(), - axis.text.x = element_blank(), axis.ticks.x = element_blank(), - axis.text.y = element_blank(), axis.ticks.y = element_blank()) - -scatterplot_bottomleft <- - ggplot(sv_correlation_df, aes(x = sv_marg_temp, y = sv_caus_temp, color = temp)) + - geom_point(size = 1) + scale_color_gradient(low="blue", high="red") + - ylab( "CauSV temp") + xlab("MargSV temp") + - scale_x_continuous(limits = c(-1500, 1000), breaks = c(-1000, 0, 1000)) + - scale_y_continuous(limits = c(-1000, 1000), breaks = c(-500, 0, 500)) + - theme_minimal() + - theme(text = element_text(size=12), - axis.text.x = element_text(size=12), axis.text.y = element_text(size=12)) - -scatterplot_bottomright <- - ggplot(sv_correlation_df, aes(x = sv_caus_cosyear, y = sv_caus_temp, color = temp)) + - geom_point(size = 1) + ylab("CauSV temp") + xlab( "CauSV cosyear") + - scale_x_continuous(limits = c(-1500, 1000), breaks = c(-1000, 0, 1000)) + - scale_y_continuous(limits = c(-1000, 1000), breaks = c(-500, 0, 500)) + - scale_color_gradient(low="blue", high="red")+ - theme_minimal() + - theme(text = element_text(size=12), axis.text.x=element_text(size=12), - axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank()) - -grid_top <- gridExtra::grid.arrange(scatterplot_topleft, scatterplot_topright, ncol = 2) -grid_bottom <- gridExtra::grid.arrange(scatterplot_bottomleft, scatterplot_bottomright, legend = "none") - -grid_top <- ggpubr::ggarrange(scatterplot_topleft, scatterplot_topright, legend = "none") -grid_bottom <- ggpubr::ggarrange(scatterplot_bottomleft, scatterplot_bottomright, legend = "none") - -bike_plot <- ggplot(bike, aes(x = trend, y = cnt, color = temp)) + - geom_point(size = 0.75) + scale_color_gradient(low = "blue", high = "red") + - labs(colour = "temp") + - xlab( "Days since 1 January 2011") + ylab("Number of bikes rented") + - theme_minimal() + - theme(legend.position = "right", legend.title = element_text(size = 10)) - -p1 = ggpubr::ggarrange(scatterplot_topleft, - scatterplot_topright, - scatterplot_bottomleft, - scatterplot_bottomright, - legend = "none") - -ggpubr::ggarrange(bike_plot, p1, nrow = 2, heights = c(1,2)) - -if (save_plots) { - ggsave("figures/scatter_plots_top.pdf", grid_top, width = 5, height = 1) - ggsave("figures/scatter_plots_bottom.pdf", grid_bottom, width = 5, height = 2) -} else { - print(ggpubr::ggarrange(grid_top, grid_bottom, nrow = 2)) -} - - -# 4 - Shapley value bar plots (Figure 4) ---------------------------------- -message("4. Producing bar plots comparing marginal, causal, and asymmetric conditional Shapley values") - -# Get test set index for two data points with similar temperature -# 1. 2012-10-09 (October) -# 2. 2012-12-03 (December) -features = c("cosyear", "temp") -dates = c("2012-10-09", "2012-12-03") -dates_idx = sapply(dates, function(data) which(as.integer(row.names(x_explain)) == which(bike$dteday == data))) -# predicted values for the two points -# predict(model, x_explain)[dates_idx] + mean(y_train_nc) - -explanations = list("Marginal" = explanation_marginal, "Causal" = explanation_causal) -explanations_extracted = data.table::rbindlist(lapply(seq_along(explanations), function(idx) { - explanations[[idx]]$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") -bar_plots <- ggplot(dt_all, aes(x = feature, y = value, group = interaction(Date, feature), - fill = Date, label = round(value, 2))) + - geom_col(position = "dodge") + - theme_classic() + ylab("Shapley value") + - facet_wrap(vars(type)) + theme(axis.title.x = element_blank()) + - scale_fill_manual(values = c('indianred4', 'ivory4')) + - theme(legend.position.inside = c(0.75, 0.25), axis.title = element_text(size = 20), - legend.title = element_text(size = 16), legend.text = element_text(size = 14), - axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12), - strip.text.x = element_text(size = 14)) - - -if (save_plots) { - ggsave("figures/bar_plots.pdf", bar_plots, width = 6, height = 3) -} else { - print(bar_plots) -} - - -plot_SV_several_approaches(explanations, index_explicands = dates_idx, only_these_features = features, facet_ncol = 1, - facet_scales = "free_y") - - - -# 5 - Other approaches ------------------------------------------------------------------------------------------- -approaches = c("independence", "empirical", "gaussian", "copula", "ctree", "vaeac") -n_samples_list = list("independence" = 1000, - "empirical" = 1000, - "gaussian" = 1000, - "copula" = 1000, - "ctree" = 1000, - "vaeac" = 1000) -explanation_list = list() - -for (approach_idx in seq_along(approaches)) { - -} - - - - - -progressr::handlers("cli") -explanation_asymmetric_causal_gaussian_time = system.time({ - explanation_asymmetric_causal_gaussian <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 1000, - keep_samp_for_vS = FALSE - ) - }) -}) - -progressr::handlers("cli") -explanation_asymmetric_causal_copula_time = system.time({ - explanation_asymmetric_causal_copula <- - #progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "copula", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 1000, - keep_samp_for_vS = FALSE - ) - #}) -}) - -progressr::handlers("cli") -explanation_asymmetric_causal_ctree_time = system.time({ - explanation_asymmetric_causal_ctree <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "ctree", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 500, - keep_samp_for_vS = FALSE - ) - }) -}) - - -progressr::handlers("cli") -explanation_asymmetric_causal_independence_time = system.time({ - explanation_asymmetric_causal_independence <- - #progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "independence", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 1000, - keep_samp_for_vS = FALSE - ) - #}) -}) - -progressr::handlers("cli") -explanation_asymmetric_causal_empirical_time = system.time({ - explanation_asymmetric_causal_empirical <- - #progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "empirical", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 1000, - keep_samp_for_vS = FALSE - ) - #}) -}) - -progressr::handlers("cli") -explanation_asymmetric_causal_vaeac_time = system.time({ - explanation_asymmetric_causal_vaeac <- - #progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "vaeac", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 1000, - keep_samp_for_vS = FALSE, - verbose = 2 - ) - #}) -}) - -sina_plot(explanation_asymmetric_causal_independence) -sina_plot(explanation_asymmetric_causal_empirical) -sina_plot(explanation_asymmetric_causal_gaussian) -sina_plot(explanation_asymmetric_causal_copula) -sina_plot(explanation_asymmetric_causal_ctree) -sina_plot(explanation_asymmetric_causal_vaeac) - - - - - - - - - - - - - - - -# 6 - Sampled n_combinations -------------------------------------------------------------------------------------- -explanation_asymmetric_all_gaussian2 <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = FALSE, - seed = 2020, - n_samples = 1000, - n_combinations = 10, - keep_samp_for_vS = FALSE, - n_batches = 1 - ) - }) - -explanation_asymmetric_all_gaussian$shapley_values_est - explanation_asymmetric_all_gaussian2$shapley_values_est - - -explanation_asymmetric_all_gaussian$MSEv -explanation_asymmetric_all_gaussian2$MSEv - -sina_plot(explanation_asymmetric_all_gaussian) -sina_plot(explanation_asymmetric_all_gaussian2) - - -explanation_asymmetric_gaussian <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = FALSE, - seed = 2020, - n_samples = 1000, - keep_samp_for_vS = FALSE, - n_combinations = 10 - ) - }) - - - -explanation_asymmetric_causal_gaussian -explanation_asymmetric_causal_gaussian - - - - -explanation_causal_time = system.time({ - explanation_causal <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 5000, - keep_samp_for_vS = FALSE, - verbose = 2, - ) - }) -}) - - -explanation_causal_time_sampled = system.time({ - explanation_causal_sampled <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, c(2, 3), c(4:7)), - confounding = c(FALSE, TRUE, FALSE), - seed = 2020, - n_samples = 5000, - n_combinations = 10, - keep_samp_for_vS = FALSE - ) - }) -}) - -explanation_causal_time -explanation_causal_time_sampled - -sina_plot(explanation_causal) -sina_plot(explanation_causal_sampled) - - - - -# 7 - Group ------------------------------------------------------------------------------------------------------- -# It makes sense to group the "temp" and "atemp" due to their high correlation -cor(x_train[,4], x_train[,5]) -plot(x_train[,4], x_train[,5]) -pairs(x_train) - -group_list <- list( - trend = "trend", - cosyear = "cosyear", - sinyear = "sinyear", - temp_group = c("temp", "atemp"), - windspeed = "windspeed", - hum = "hum") -causal_ordering = list("trend", c("cosyear", "sinyear"), c("temp_group", "windspeed", "hum")) -causal_ordering = list(1, 2:3, 4:6) # Equivalent to using the names (verified) -confounding = c(FALSE, TRUE, FALSE) -asymmetric = TRUE - -progressr::handlers("cli") -explanation_group_asymmetric_causal_time = system.time({ - explanation_group_asymmetric_causal <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, 2:3, 4:6), - confounding = c(FALSE, TRUE, FALSE), - group = group_list, - seed = 2020, - n_samples = 1000 - ) - }) -}) - -explanation_group_asymmetric_causal$shapley_values_est -sina_plot(explanation_group_asymmetric_causal) - -# Now we compute the group Shapley values based on only half of the coalitions -explanation_group_asymmetric_causal_sampled_time = system.time({ - explanation_group_asymmetric_causal_sampled <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(1, 2:3, 4:6), - confounding = confounding, - group = group_list, - n_combinations = explanation_group_asymmetric_causal$internal$parameters$n_combinations_causal_max/2 + 1, - seed = 2020, - n_samples = 1000 - ) - }) -}) - - -# Now we compute the group symmetric causal Shapley values -explanation_group_symmetric_causal_time = system.time({ - explanation_group_symmetric_causal <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = list(1, 2:3, 4:6), #FORTSETT HER MED Å ENDRE OG SE HVA SOM KRÆSJER - confounding = confounding, - group = group_list, - seed = 2020, - n_samples = 1000 - ) - }) -}) - -explanation_group_symmetric_causal_sampled_time = system.time({ - explanation_group_symmetric_causal_sampled <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = causal_ordering, - confounding = confounding, - group = group_list, - n_combinations = 30, - seed = 2020, - n_samples = 1000 - ) - }) -}) - -# Symmetric Conditional -progressr::handlers("cli") -explanation_group_symmetric_conditional_time = system.time({ - explanation_group_symmetric_conditional <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = NULL, - confounding = FALSE, - group = group_list, - seed = 2020, - n_samples = 1000 - ) - }) -}) - -explanation_group_symmetric_conditional_sampled_time = system.time({ - explanation_group_symmetric_conditional_sampled <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = NULL, - confounding = FALSE, - group = group_list, - n_combinations = 30, - seed = 2020, - n_samples = 1000 - ) - }) -}) - -explanation_group_asymmetric_conditional_time = system.time({ - explanation_group_asymmetric_conditional <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = list(seq_along(group_list)), - confounding = FALSE, - group = group_list, - seed = 2020, - n_samples = 1000 - ) - }) -}) -explanation_group_asymmetric_conditional$internal$objects$X - -explanation_group_asymmetric_causal_time = system.time({ - explanation_group_asymmetric_causal <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = causal_ordering, - confounding = c(FALSE, TRUE, FALSE), - group = group_list, - seed = 2020, - n_samples = 1000 - ) - }) -}) -explanation_group_asymmetric_causal$internal$objects$X - -explanation_group_asymmetric_conditional$internal$objects$S_causal_strings -explanation_group_asymmetric_causal$internal$objects$S_causal_strings -all.equal(explanation_group_asymmetric_causal$internal$objects$S_causal_strings, - explanation_group_asymmetric_conditional$internal$objects$S_causal_strings) - -explanation_group_asymmetric_conditional_sampled_time = system.time({ - explanation_group_asymmetric_conditional_sampled <- - progressr::with_progress({ - explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = TRUE, - causal_ordering = causal_ordering, - confounding = FALSE, - n_combinations = 7, - group = group_list, - seed = 2020, - n_samples = 1000 - ) - }) -}) - - -sina_plot(explanation_asymmetric_causal) -sina_plot(explanation_group_asymmetric_causal) -sina_plot(explanation_group_asymmetric_causal_sampled) - -n_index_x_explain = 6 -index_x_explain = order(y_explain)[seq(1, length(y_explain), length.out = n_index_x_explain)] -plot(explanation_group_asymmetric_causal, index_x_explain = index_x_explain) -plot(explanation_group_asymmetric_causal_sampled, index_x_explain = index_x_explain) - -plot(explanation_asymmetric_causal, plot_type = "beeswarm") - - -plot_SV_several_approaches(list(feature = explanation_asymmetric_causal), - index_explicands = index_x_explain) -plot_SV_several_approaches(list(exact = explanation_group_asymmetric_causal, - non_exact = explanation_group_asymmetric_causal_sampled), - index_explicands = index_x_explain, - include_group_feature_means = TRUE) - -plot_SV_several_approaches( - list( - GrAsymCau_exact = explanation_group_asymmetric_causal, - GrAsymCau_non_exact = explanation_group_asymmetric_causal_sampled, - GrSymCau_exact = explanation_group_symmetric_causal, - GrSymCau_non_exact = explanation_group_symmetric_causal_sampled, - GrAsymCon_exact = explanation_group_asymmetric_conditional, - GrAsymCon_non_exact = explanation_group_asymmetric_conditional_sampled, - GrSymCon_exact = explanation_group_symmetric_conditional, - GrSymCon_non_exact = explanation_group_symmetric_conditional_sampled - ), - index_explicands = index_x_explain, - brewer_palette = "Paired", - include_group_feature_means = FALSE) diff --git a/inst/scripts/Vaeac_compare_getitem_getbatch.R b/inst/scripts/Vaeac_compare_getitem_getbatch.R deleted file mode 100644 index 4d7963f55..000000000 --- a/inst/scripts/Vaeac_compare_getitem_getbatch.R +++ /dev/null @@ -1,120 +0,0 @@ -# Library --------------------------------------------------------------------------------------------------------- -library("torch") -library("rbenchmark") - -# Functions ------------------------------------------------------------------------------------------------------- -vaeac_dataset_item <- torch::dataset( - name = "vaeac_dataset", # field name The name of the `torch::dataset`. - - # description Create a new vaeac_dataset object. - # param X A torch_tensor containing the data - # param one_hot_max_sizes A torch tensor of dimension p containing the one hot sizes of the p features. - # The sizes for the continuous features can either be '0' or '1'. - initialize = function(X, one_hot_max_sizes) { - # Save the number of observations and features in X, the one hot dummy feature sizes and the dataset - self$N <- nrow(X) - self$p <- ncol(X) - self$one_hot_max_sizes <- one_hot_max_sizes - self$X <- X - }, - .getitem = function(index) self$X[index, ], # Get a single data observation based on the provided index - .length = function() nrow(self$X) # Get the number of observations in the dataset -) - -vaeac_dataset_batch <- torch::dataset( - name = "vaeac_dataset", # field name The name of the `torch::dataset`. - - # description Create a new vaeac_dataset object. - # param X A torch_tensor containing the data - # param one_hot_max_sizes A torch tensor of dimension p containing the one hot sizes of the p features. - # The sizes for the continuous features can either be '0' or '1'. - initialize = function(X, one_hot_max_sizes) { - # Save the number of observations and features in X, the one hot dummy feature sizes and the dataset - self$N <- nrow(X) - self$p <- ncol(X) - self$one_hot_max_sizes <- one_hot_max_sizes - self$X <- X - }, - .getbatch = function(index) self$X[index, , drop = FALSE], # Get a batch of data based on the provided indices - .length = function() nrow(self$X) # Get the number of observations in the dataset -) - -# Parameters ------------------------------------------------------------------------------------------------------ -p <- 10 -N <- 5000 -batch_size <- 64 -one_hot_max_sizes <- rep(1, p) - -# Create data ----------------------------------------------------------------------------------------------------- -data <- matrix(rnorm(p * N), ncol = p) - -# Comparison ------------------------------------------------------------------------------------------------------ -# Create the datasets -vaeac_ds_item <- vaeac_dataset_item(torch_tensor(data = data, dtype = torch_float()), one_hot_max_sizes) -vaeac_ds_batch <- vaeac_dataset_batch(torch_tensor(data = data, dtype = torch_float()), one_hot_max_sizes) - -# Create the dataloaders -vaeac_dl_item <- torch::dataloader(vaeac_ds_item, batch_size = batch_size, shuffle = TRUE, drop_last = FALSE) -vaeac_dl_batch <- torch::dataloader(vaeac_ds_batch, batch_size = batch_size, shuffle = TRUE, drop_last = FALSE) - -# Same length -vaeac_dl_item$.length() -vaeac_dl_batch$.length() - -## Compare values -------------------------------------------------------------------------------------------------- -# Check that .getitem and .getbatch yields the same results -torch_manual_seed(123) -vaeac_iterator_item <- vaeac_dl_item$.iter() -bi1 <- vaeac_iterator_item$.next() # batch1 -bi2 <- vaeac_iterator_item$.next() # batch2 - -torch_manual_seed(123) -vaeac_iterator_batch <- vaeac_dl_batch$.iter() -bb1 <- vaeac_iterator_batch$.next() # batch1 -bb2 <- vaeac_iterator_batch$.next() # batch2 - -all.equal(as.matrix(bi1), as.matrix(bb1)) -all.equal(as.matrix(bi2), as.matrix(bb2)) - -## Compare time ---------------------------------------------------------------------------------------------------- -# Loop over all the data and look at time -time_item <- system.time(coro::loop(for (b in vaeac_dl_item) print(b))) -time_batch <- system.time(coro::loop(for (b in vaeac_dl_batch) print(b))) -rbind(time_item, time_batch) - -# Do it more systematic -benchmark( - item = coro::loop(for (b in vaeac_dl_item) {}), - batch = coro::loop(for (b in vaeac_dl_batch) {}) -) - -# We see that batch is much faster (quite obvious in hindsight) -# p <- 5, N <- 5000, batch_size <- 64 -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 batch 100 1.684 1.000 1.665 0.013 0 0 -# 1 item 100 31.631 18.783 31.382 0.163 0 0 - -# p <- 5, N <- 5000, batch_size <- 128 -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 batch 100 1.163 1.000 1.117 0.023 0.000 0.000 -# 1 item 100 33.441 28.754 32.501 0.459 0.021 0.037 - -# p <- 10, N <- 10000, batch_size <- 64 -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 batch 100 3.732 1.000 3.551 0.064 0 0 -# 1 item 100 64.719 17.342 63.870 0.383 0 0 - -# p <- 10, N <- 10000, batch_size <- 128 -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 batch 100 1.681 1.000 1.659 0.020 0.00 0.000 -# 1 item 100 67.040 39.881 65.543 0.721 0.02 0.031 - -# p <- 15, N <- 1000, batch_size <- 64 -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 batch 100 0.528 1.000 0.525 0.003 0 0 -# 1 item 100 6.254 11.845 6.155 0.046 0 0 - -# p <- 15, N <- 1000, batch_size <- 128 -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 batch 100 0.474 1.000 0.462 0.008 0 0 -# 1 item 100 6.692 14.118 6.499 0.091 0 0 diff --git a/inst/scripts/analyze_bash_test_data.R b/inst/scripts/analyze_bash_test_data.R deleted file mode 100644 index 3cd9435e4..000000000 --- a/inst/scripts/analyze_bash_test_data.R +++ /dev/null @@ -1,120 +0,0 @@ - - -library(data.table) -### analysing bash data test - -dt_mem0 <- fread("inst/scripts/memory_test_2023_new2.csv") - -names(dt_mem0) <- c("date","time","mem_usage","rep","p","n_train","n_explain","n_batches","n_cores","approach","multicore_method","logfilename") - -#dt_mem0 <- dt_mem0[date>="2023-01-18"] - - -dt_mem0[,max_mem_usage:=max(mem_usage),by=.(rep,p,n_train,n_explain,n_batches,n_cores,approach,multicore_method,logfilename)] -dt_mem0[,n_batches_real:=pmin(2^p-2,n_batches)] - -dt_mem <- dt_mem0[mem_usage==max_mem_usage,.(date,time,mem_usage,rep,p,n_train,n_explain,n_batches_real,n_cores,approach,multicore_method)] - -dt_mem[,mem_usage_Mb:=mem_usage/1024] - -library(ggplot2) - -ggplot(dt_mem,aes(x=n_batches_real,y=mem_usage_Mb,col=as.factor(n_explain),linetype=as.factor(n_train)))+ - geom_line()+ - geom_point()+ - facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+ - scale_y_log10()+ - scale_x_log10()+ - ggtitle("Memory usage") - -ggplot(dt_mem[p<16& p>2& approach=="empirical"],aes(x=n_batches_real,y=mem_usage_Mb,col=as.factor(n_explain)))+ - geom_line()+ - geom_point()+ - facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+ - scale_y_log10()+ - scale_x_log10()+ - ggtitle("Memory usage for n_train=100") - - - -dt_mem0[p==8 & n_explain==100 & approach=="ctree"] -dt_mem0[p==16 & n_explain==100 & approach=="ctree"] - -# Wierd and inconsistent results - - -dt_time0 <- fread("inst/scripts/timing_test_2023_new2.csv") -#names(dt_time0) <- c("p","n_train","n_explain","n_batches","n_cores","approach","time","sys_time_start_explain","sys_time_end_explain", -# "secs_explain","rep","max_n","max_p","rho","sigma","mu_const","beta0","sigma_eps") - -#dt_time0 <- dt_time0[time>="2023-01-18"] - - -dt_time0[,n_batches_real:=pmin(2^p-2,n_batches)] - -dt_time <- dt_time0[,.(time,secs_explain,timing_setup,timing_test_prediction, timing_setup_computation ,timing_compute_vS ,timing_postprocessing ,timing_shapley_computation, rep,p,n_train,n_explain,n_batches_real,approach,n_coalitions)] - -dt_time[n_batches_real==1,secs_explain_singlebatch :=secs_explain] -dt_time[,secs_explain_singlebatch:=mean(secs_explain_singlebatch,na.rm=T),by=.(p,n_train,n_explain,approach,n_coalitions)] -dt_time[,secs_explain_prop_singlebatch:=secs_explain/secs_explain_singlebatch] - -ggplot(dt_time[p<14],aes(x=n_batches_real,y=secs_explain,col=as.factor(n_explain),linetype=as.factor(n_train)))+ - geom_line()+ - geom_point()+ - facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+ - #scale_y_log10()+ - scale_x_log10()+ - ggtitle("Time usage") - -ggplot(dt_time[p<14],aes(x=n_batches_real,y=secs_explain_prop_singlebatch,col=as.factor(n_explain),linetype=as.factor(n_train)))+ - geom_line()+ - geom_point()+ - facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+ - #scale_y_log10()+ - scale_x_log10()+ - ggtitle("Time usage proportional to singlebatch") - - -ggplot(dt_time[p<14],aes(x=n_batches_real,y=timing_shapley_computation,col=as.factor(n_explain),linetype=as.factor(n_train)))+ - geom_line()+ - geom_point()+ - facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+ - #scale_y_log10()+ - scale_x_log10()+ - ggtitle("Time usage") - - - - -ggplot(dt_time[p<16& p>2 & approach=="empirical"],aes(x=n_batches_real,y=secs_explain,col=as.factor(n_explain)))+ - geom_line()+ - geom_point()+ - facet_wrap(vars(approach,p),scales = "free",labeller = label_both)+ - # scale_y_log10()+ - # scale_x_log10()+ - ggtitle("Time usage for n_train=100") - - - -#### Default for ctree + gaussian: Mye å spare minnemessig + lite å tape tidsmessig -# n_batches <- (2^p-2) -# max 100, min 10 - -n_batches_fun <- function(approach,p){ - n_coalitions <- 2^p-2 - - if(approach %in% c("ctree","gaussian","copula")){ - init <- ceiling(n_coalitions/10) - floor <- max(c(10,init)) - ret <- min(c(1000,floor)) - } else { - init <- ceiling(n_coalitions/100) - floor <- max(c(2,init)) - ret <- min(c(100,floor)) - } - return(ret) -} - -n_batches_fun("empirical",10) - - diff --git a/inst/scripts/bashscript_2023.sh b/inst/scripts/bashscript_2023.sh deleted file mode 100644 index f7cd3b57f..000000000 --- a/inst/scripts/bashscript_2023.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash - -#Create array of inputs - space separator -#MJ: Define all input vectors here -script_name="timing_script_2023.R" -logfile_bash="memory_test_2023_new2.csv" -logfile_Rscript="timing_test_2023_new2.csv" - - -p_vec=(4 6 8 10 12 14 16) -n_train_vec=(100 1000) #(100 1000 1000) -n_explain_vec=(10 100) #(1 2 10 100) -n_batches_vec=(1 2 4 8 16 32 64) #(1 2 4 8 16 32) -n_cores_vec=1 #(1 2 4 8 16 24 32) -approach_vec=("empirical" "gaussian" "ctree" "copula" "independence") -multicore_method_vec=("sequential") -reps=3 - -## get length of $distro array -len_p_vec=${#p_vec[@]} -len_n_train_vec=${#n_train_vec[@]} -len_n_explain_vec=${#n_explain_vec[@]} -len_n_batches_vec=${#n_batches_vec[@]} -len_n_cores_vec=${#n_cores_vec[@]} -len_approach_vec=${#approach_vec[@]} -len_multicore_method_vec=${#multicore_method_vec[@]} - -## Use bash for loop -for (( i0=0; i0<$reps; i1++ )); do -for (( i1=0; i1<$len_p_vec; i1++ )); do -for (( i2=0; i2<$len_n_train_vec; i2++ )); do -for (( i3=0; i3<$len_n_explain_vec; i3++ )); do -for (( i4=0; i4<$len_n_batches_vec; i4++ )); do -for (( i5=0; i5<$len_n_cores_vec; i5++ )); do -for (( i6=0; i6<$len_approach_vec; i6++ )); do -for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do -running_processes=1 -start_new_script=1 -while [[ $running_processes == 1 ]] - do - if [[ $start_new_script == 1 ]] - then - sleep 5 - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash - Rscript --verbose $script_name $i0 ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_explain_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} ${multicore_method_vec[$i7]} $logfile_Rscript & - start_new_script=0 - fi - - sleep 0.5 - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash - sleep 0.5 - - running_processes=$(pgrep -f $script_name -a -c) - done -done; done; done; done; done; done; done; done diff --git a/inst/scripts/bashscript_2023_tmp.sh b/inst/scripts/bashscript_2023_tmp.sh deleted file mode 100644 index 8528564ce..000000000 --- a/inst/scripts/bashscript_2023_tmp.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash - -#Create array of inputs - space separator -#MJ: Define all input vectors here -script_name="timing_script_2023.R" -logfile_bash="memory_test_2023_tmp.csv" -logfile_Rscript="timing_test_2023_tmp.csv" - - -p_vec=(4 6 8 10 12 14 16) -n_train_vec=(100) #(100 1000 1000) -n_explain_vec=(10) #(1 2 10 100) -n_batches_vec=(1 2 4 8 16 32 64) #(1 2 4 8 16 32) -n_cores_vec=1 #(1 2 4 8 16 24 32) -approach_vec=("empirical") #"gaussian" "ctree" "copula" "independence") -multicore_method_vec=("sequential") -reps=1 - -## get length of $distro array -len_p_vec=${#p_vec[@]} -len_n_train_vec=${#n_train_vec[@]} -len_n_explain_vec=${#n_explain_vec[@]} -len_n_batches_vec=${#n_batches_vec[@]} -len_n_cores_vec=${#n_cores_vec[@]} -len_approach_vec=${#approach_vec[@]} -len_multicore_method_vec=${#multicore_method_vec[@]} - -## Use bash for loop -for (( i0=0; i0<$reps; i1++ )); do -for (( i1=0; i1<$len_p_vec; i1++ )); do -for (( i2=0; i2<$len_n_train_vec; i2++ )); do -for (( i3=0; i3<$len_n_explain_vec; i3++ )); do -for (( i4=0; i4<$len_n_batches_vec; i4++ )); do -for (( i5=0; i5<$len_n_cores_vec; i5++ )); do -for (( i6=0; i6<$len_approach_vec; i6++ )); do -for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do -running_processes=1 -start_new_script=1 -while [[ $running_processes == 1 ]] - do - if [[ $start_new_script == 1 ]] - then - sleep 5 - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash - Rscript --verbose $script_name $i0 ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_explain_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} ${multicore_method_vec[$i7]} $logfile_Rscript & - start_new_script=0 - fi - - sleep 0.5 - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_explain_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash - sleep 0.5 - - running_processes=$(pgrep -f $script_name -a -c) - done -done; done; done; done; done; done; done; done diff --git a/inst/scripts/bugfix_beeswarm.R b/inst/scripts/bugfix_beeswarm.R deleted file mode 100644 index b3ab2df0e..000000000 --- a/inst/scripts/bugfix_beeswarm.R +++ /dev/null @@ -1,134 +0,0 @@ - -library(tidyverse) -library(data.table) -library(shapr) - - -Data_O <- data.table::fread(file = "https://github.com/user-attachments/files/17777841/synthetic_data.csv") -# Remove rows with missing values -Data_O <- Data_O[complete.cases(Data_O),] -# Handle extremes of target -Data_O <- Data_O %>% filter(actief_in_inst_2022_SCH > 0.60) -Data_O$actief_in_inst_2022_SCH <- sqrt(Data_O$actief_in_inst_2022_SCH) - -# Features -check <- as.data.frame(model.matrix(~., data = Data_O[, c(3, 32:36, 38, 55:68)])) -check[] <- lapply(check, as.numeric) -check <- as.matrix(check) -check <- check[, -1] - -# Outcome variable -y <- as.numeric(Data_O$actief_in_inst_2022_SCH) - -# Split dataset into training (70%) and test (30%) sets -samp <- sample(nrow(Data_O), 0.7 * nrow(Data_O)) - -Train1 <- check[samp, ] -Train1 <- as.data.frame(Train1) - -Test1 <- check[-samp, ] -Test1 <- as.data.frame(Test1) - -Y_train <- y[samp] -Y_test <- y[-samp] - -# Train Random Forest model -rf.fit <- ranger::ranger(Y_train ~ ., - data = Train1, - mtry = 14, - max.depth = 3, - replace = FALSE, - min.node.size = 40, - sample.fraction = 0.8, - respect.unordered.factors = "order", - importance = "permutation") - -# SHAPR -p <- mean(Y_train) -library(shapr) - -progressr::handlers(global = TRUE) -explanation <- shapr::explain( - rf.fit, - Test1, - Train1, - approach = "gaussian", - max_n_coalitions = 20, - iterative_args = list(initial_n_coalitions=20), - phi0 = p -) - -library(ggplot2) -library(ggbeeswarm) - -if (requireNamespace("ggplot2", quietly = TRUE)) { - plot(explanation, plot_type = "scatter") - plot(explanation, plot_type = "beeswarm") -} - -saveRDS(explanation, "explanation.rds") - - - -explanation <- readRDS("explanation.rds") - -plot(explanation, plot_type = "beeswarm", corral = "wrap") - - - - - - - - - - - - - - - - - - - - - - - - - - -tmp_list <- plot_shapr(explanation, plot_type = "beeswarm") - - -gg_old <- make_beeswarm_plot_old(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain, - x = tmp_list$x, - factor_cols = tmp_list$factor_features) - -gg_new_cex <- make_beeswarm_plot_new_cex(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain, - x = tmp_list$x, - factor_cols = tmp_list$factor_features) - -gg_new <- make_beeswarm_plot_new(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain, - x = tmp_list$x, - factor_cols = tmp_list$factor_features, - corral.corral = "wrap", # Default. Other options: "none" (default in geom_beeswarm), "gutter", "random", "omit" - corral.method = "swarm", # Default (and default in geom_beeswarm). Other options: "compactswarm", "hex", "square", "center - corral.priority = "random", # Default . Other options: "ascending" (default in geom_beeswarm), "descending", "density" - corral.width = 0.75, # Default. 0.9 is default in geom_beeswarm - corral.cex = 0.75) # Default. 1 is default in geom_beeswarm - -gg_paper3 <- make_beeswarm_plot_paper3(dt_plot = tmp_list$dt_plot, - col = tmp_list$col, - index_x_explain = tmp_list$index_x_explain, - x = tmp_list$x, - factor_cols = tmp_list$factor_features) - -ggpubr::ggarrange(gg_old, gg_new_cex, gg_new, gg_paper3, labels = c("Old", "New_cex", "New", "Paper3"), nrow = 1, vjust = 2) diff --git a/inst/scripts/check_model_workflow.R b/inst/scripts/check_model_workflow.R deleted file mode 100644 index 296c090ae..000000000 --- a/inst/scripts/check_model_workflow.R +++ /dev/null @@ -1,169 +0,0 @@ -# Compare xgboost with parsnip version ---------------------------------------------------------------------------- -# Either use library(tidymodels) or separately specify the libraries -library(parsnip) -library(ggplot2) -library(recipes) -library(workflows) -library(dials) -library(hardhat) -library(workflows) -library(yardstick) - -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] - -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" -all_var <- c(y_var, x_var) - -ind_x_explain <- 1:20 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] -train <- data[-ind_x_explain, ..all_var] -test <- data[ind_x_explain, ..all_var] - -# Specifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(y_train) - -# Fitting a basic xgboost model to the training data using tidymodels -set.seed(1) -model_xgboost <- xgboost::xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 10, - verbose = FALSE -) - -set.seed(1) -model_workflow <- workflows::workflow() %>% - workflows::add_model(parsnip::boost_tree(trees = 10, engine = "xgboost", mode = "regression")) %>% - workflows::add_recipe(recipes::recipe(Ozone ~ ., data = train)) %>% - parsnip::fit(data = test) - -# See that the predictions are identical -all.equal(predict(model_workflow, x_train)$.pred, predict(model_xgboost, as.matrix(x_train))) - -explain_workflow = explain( - model = model_workflow, - x_explain = x_explain, - x_train = x_train, - approach = "empirical", - phi0 = p0, - n_batches = 4 -) - -explain_xgboost = explain( - model = model_xgboost, - x_explain = x_explain, - x_train = x_train, - approach = "empirical", - phi0 = p0, - n_batches = 4 -) - -# See that the shapley values are identical -all.equal(explain_workflow$shapley_values_est, explain_xgboost$shapley_values_est) - -# Other models in workflow --------------------------------------------------------------------------------------------- -set.seed(1) -data <- data.table::as.data.table(airquality) -data[, Month_factor := as.factor(Month)] -data[, Ozone_sub30 := (Ozone < 30) * 1] -data[, Ozone_sub30_factor := as.factor(Ozone_sub30)] -data[, Solar.R_factor := as.factor(cut(Solar.R, 10))] -data[, Wind_factor := as.factor(round(Wind))] - -data_complete <- data[complete.cases(airquality), ] -data_complete <- data_complete[sample(seq_len(.N))] # Sh - -x_var_mixed <- c("Solar.R", "Wind", "Temp", "Day", "Month_factor") -var_mixed <- c("Ozone", x_var_mixed) - -data_train <- head(data_complete, -3) -data_explain <- tail(data_complete, 3) - -x_train_mixed <- data_train[, ..x_var_mixed] -x_explain_mixed <- data_explain[, ..x_var_mixed] -train_mixed <- data_train[, ..var_mixed] - -model_decision_tree <- workflows::workflow() %>% - workflows::add_model(parsnip::decision_tree(engine = "rpart", mode = "regression")) %>% - workflows::add_recipe(recipes::recipe(Ozone ~ ., data = train_mixed) %>% - recipes::step_dummy(all_factor_predictors())) %>% - parsnip::fit(data = train_mixed) - -y_var_numeric <- "Ozone" -lm_formula_mixed <- as.formula(paste0(y_var_numeric, " ~ ", paste0(x_var_mixed, collapse = " + "))) -model_lm_mixed <- lm(lm_formula_mixed, data = data_complete) - -explain_decision_tree_ctree = explain( - model = model_decision_tree, - x_explain = x_explain_mixed, - x_train = x_train_mixed, - approach = "ctree", - phi0 = p0, - n_batches = 4 -) - -explain_decision_tree_lm = explain( - model = model_decision_tree, #model_lm_mixed - x_explain = x_explain_mixed, - x_train = x_train_mixed, - approach = "regression_separate", - regression.model = parsnip::linear_reg(), - phi0 = p0, - n_batches = 4 -) - -# - -# CV ------------------------------------------------------------------------------------------------------------------- -set.seed(1) -regression.workflow <- workflows::workflow() %>% - workflows::add_model(parsnip::rand_forest( - trees = hardhat::tune(), engine = "ranger", mode = "regression" - )) %>% - workflows::add_recipe(recipes::recipe(Ozone ~ ., data = train_mixed) %>% - recipes::step_dummy(all_factor_predictors())) - -# Add the hyperparameter tuning to the workflow -regression.results <- tune::tune_grid( - object = regression.workflow, - resamples = rsample::vfold_cv(data = train_mixed, v = 3), - grid = dials::grid_regular(dials::trees(c(50, 750)), levels = 3), - metrics = yardstick::metric_set(yardstick::rmse) -) - -# Update the workflow by finalizing it using the hyperparameters that attained the best rmse -regression.workflow <- tune::finalize_workflow(regression.workflow, tune::select_best(regression.results, "rmse")) - -# Fit the model to the augmented training data -model_rf_cv <- parsnip::fit(regression.workflow, data = train_mixed) - -# See that the model works with regression -explain_decision_model_rf_cv_rf = explain( - model = model_rf_cv, #model_lm_mixed - x_explain = x_explain_mixed, - x_train = x_train_mixed, - approach = "regression_separate", - regression.model = parsnip::rand_forest(engine = "ranger", mode = "regression"), - phi0 = p0, - n_batches = 4 -) - -# See that the model works with MC method too -explain_decision_model_rf_cv_ctree = explain( - model = model_rf_cv, #model_lm_mixed - x_explain = x_explain_mixed, - x_train = x_train_mixed, - approach = "ctree", - phi0 = p0, - n_batches = 4 -) - -# Quite similar -plot_MSEv_eval_crit(list(ctree = explain_decision_model_rf_cv_ctree, rf = explain_decision_model_rf_cv_rf)) -plot_SV_several_approaches(list(ctree = explain_decision_model_rf_cv_ctree, rf = explain_decision_model_rf_cv_rf)) - diff --git a/inst/scripts/compare_copula_in_R_and_C++.R b/inst/scripts/compare_copula_in_R_and_C++.R deleted file mode 100644 index ba676d263..000000000 --- a/inst/scripts/compare_copula_in_R_and_C++.R +++ /dev/null @@ -1,1551 +0,0 @@ -# Libraries ------------------------------------------------------------------------------------------------------- -# library(shapr) -# library(rbenchmark) -library(data.table) -devtools::load_all(".") - -# Old R code ------------------------------------------------------------------------------------------------------ -## R Old version --------------------------------------------------------------------------------------------------- -#' @inheritParams default_doc_internal -#' @rdname prepare_data -#' @export -prepare_data.copula_old <- function(internal, index_features = NULL, ...) { - X <- internal$objects$X - x_train <- internal$data$x_train - x_explain <- internal$data$x_explain - n_explain <- internal$parameters$n_explain - n_samples <- internal$parameters$n_samples - n_features <- internal$parameters$n_features - copula.mu <- internal$parameters$copula.mu - copula.cov_mat <- internal$parameters$copula.cov_mat - copula.x_explain_gaussian <- internal$data$copula.x_explain_gaussian - - x_explain0 <- as.matrix(x_explain) - dt_l <- list() - if (is.null(index_features)) { - features <- X$features - } else { - features <- X$features[index_features] - } - - for (i in seq_len(n_explain)) { - cat(sprintf("%d,", i)) - l <- lapply( - X = features, - FUN = sample_copula_old, - n_samples = n_samples, - mu = copula.mu, - cov_mat = copula.cov_mat, - m = n_features, - x_explain = x_explain0[i, , drop = FALSE], - x_train = as.matrix(x_train), - x_explain_gaussian = as.matrix(copula.x_explain_gaussian)[i, , drop = FALSE] - ) - dt_l[[i]] <- data.table::rbindlist(l, idcol = "id_coalition") - dt_l[[i]][, w := 1 / n_samples] - dt_l[[i]][, id := i] - if (!is.null(index_features)) dt_l[[i]][, id_coalition := index_features[id_coalition]] - } - - dt <- data.table::rbindlist(dt_l, use.names = TRUE, fill = TRUE) - - return(dt) -} - -#' Sample conditional variables using the Gaussian copula approach -#' -#' @param index_given Integer vector. The indices of the features to condition upon. Note that -#' `min(index_given) >= 1` and `max(index_given) <= m`. -#' @param m Positive integer. The total number of features. -#' @param x_explain_gaussian Numeric matrix. Contains the observation whose predictions ought -#' to be explained (test data), -#' after quantile-transforming them to standard Gaussian variables. -#' @param x_explain Numeric matrix. Contains the features of the observation whose -#' predictions ought to be explained (test data). -#' -#' @return data.table -#' -#' @keywords internal -#' -#' @author Martin Jullum -sample_copula_old <- function(index_given, n_samples, mu, cov_mat, m, x_explain_gaussian, x_train, x_explain) { - # Handles the unconditional and full conditional separtely when predicting - if (length(index_given) %in% c(0, m)) { - ret <- matrix(x_explain, ncol = m, nrow = 1) - } else { - dependent_ind <- (seq_len(length(mu)))[-index_given] - - tmp <- condMVNorm::condMVN( - mean = mu, - sigma = cov_mat, - dependent.ind = dependent_ind, - given.ind = index_given, - X.given = x_explain_gaussian[index_given] - ) - - ret0_z <- mvnfast::rmvn(n = n_samples, mu = tmp$condMean, sigma = tmp$condVar) - - ret0_x <- apply( - X = rbind(ret0_z, x_train[, dependent_ind, drop = FALSE]), - MARGIN = 2, - FUN = inv_gaussian_transform_old, - n_z = n_samples, - type = 7 - ) - - ret <- matrix(NA, ncol = m, nrow = n_samples) - ret[, index_given] <- rep(x_explain[index_given], each = n_samples) - ret[, dependent_ind] <- ret0_x - } - colnames(ret) <- colnames(x_explain) - return(as.data.table(ret)) -} - - -#' Transforms new data to a standardized normal distribution -#' -#' @param zx Numeric vector. The first `n_z` items are the Gaussian data, and the last part is -#' the data with the original transformation. -#' @param n_z Positive integer. Number of elements of `zx` that belongs to new data. -#' -#' @return Numeric vector of length `n_z` -#' -#' @keywords internal -#' -#' @author Martin Jullum -inv_gaussian_transform_old <- function(zx, n_z, type) { - if (n_z >= length(zx)) stop("n_z should be less than length(zx)") - ind <- 1:n_z - z <- zx[ind] - x <- zx[-ind] - u <- stats::pnorm(z) - x_new <- stats::quantile(x, probs = u, type = type) - return(as.double(x_new)) -} - -#' Transforms a sample to standardized normal distribution -#' -#' @param x Numeric vector.The data which should be transformed to a standard normal distribution. -#' -#' @return Numeric vector of length `length(x)` -#' -#' @keywords internal -#' @author Martin Jullum -gaussian_transform_old <- function(x) { - u <- rank(x) / (length(x) + 1) - z <- stats::qnorm(u) - return(z) -} - -#' Transforms new data to standardized normal (dimension 1) based on other data transformations -#' -#' @param yx Numeric vector. The first `n_y` items is the data that is transformed, and last -#' part is the data with the original transformation. -#' @param n_y Positive integer. Number of elements of `yx` that belongs to the Gaussian data. -#' -#' @return Vector of back-transformed Gaussian data -#' -#' @keywords internal -#' @author Martin Jullum -gaussian_transform_separate_old <- function(yx, n_y) { - if (n_y >= length(yx)) stop("n_y should be less than length(yx)") - ind <- 1:n_y - x <- yx[-ind] - tmp <- rank(yx)[ind] - tmp <- tmp - rank(tmp) + 0.5 - u_y <- tmp / (length(x) + 1) - z_y <- stats::qnorm(u_y) - return(z_y) -} - - -## C++ arma version ------------------------------------------------------------------------------------------------- -#' @inheritParams default_doc_internal -#' @rdname prepare_data -#' @export -#' @author Lars Henry Berge Olsen -prepare_data.copula_cpp_arma <- function(internal, index_features, ...) { - # Extract used variables - S <- internal$objects$S[index_features, , drop = FALSE] - feature_names <- internal$parameters$feature_names - n_explain <- internal$parameters$n_explain - n_samples <- internal$parameters$n_samples - n_features <- internal$parameters$n_features - n_coalitions_now <- length(index_features) - x_train_mat <- as.matrix(internal$data$x_train) - x_explain_mat <- as.matrix(internal$data$x_explain) - copula.mu <- internal$parameters$copula.mu - copula.cov_mat <- internal$parameters$copula.cov_mat - copula.x_explain_gaussian_mat <- as.matrix(internal$data$copula.x_explain_gaussian) - - # TODO: Note that `as.matrix` is not needed for `copula.x_explain_gaussian_mat` as it is already defined as a matrix - # in `setup_approach.copula`, however, it seems that Martin plans to make it into a data.table, thus, I include - # `as.matrix` as future safety. DISCUSS WITH MARTIN WHAT HIS PLANS ARE! - - # Generate the MC samples from N(0, 1) - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Use C++ to convert the MC samples to N(mu_{Sbar|S}, Sigma_{Sbar|S}), for all coalitions and explicands, - # and then transforming them back to the original scale using the inverse Gaussian transform in C++. - # The object `dt` is a 3D array of dimension (n_samples, n_explain * n_coalitions, n_features). - dt <- prepare_data_copula_cpp_arma( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat - ) - - # Reshape `dt` to a 2D array of dimension (n_samples * n_explain * n_coalitions, n_features). - dim(dt) <- c(n_coalitions_now * n_explain * n_samples, n_features) - - # Convert to a data.table and add extra identification columns - dt <- data.table::as.data.table(dt) - data.table::setnames(dt, feature_names) - dt[, id_coalition := rep(seq_len(nrow(S)), each = n_samples * n_explain)] - dt[, id := rep(seq(n_explain), each = n_samples, times = nrow(S))] - dt[, w := 1 / n_samples] - dt[, id_coalition := index_features[id_coalition]] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - return(dt) -} - - - - - -## C++ and R version ---------------------------------------------------------------------------------------------- -#' @inheritParams default_doc_internal -#' @rdname prepare_data -#' @export -#' @author Lars Henry Berge Olsen -prepare_data.copula_cpp_and_R <- function(internal, index_features, ...) { - # Extract used variables - S <- internal$objects$S[index_features, , drop = FALSE] - feature_names <- internal$parameters$feature_names - n_explain <- internal$parameters$n_explain - n_samples <- internal$parameters$n_samples - n_features <- internal$parameters$n_features - n_coalitions_now <- length(index_features) - x_train_mat <- as.matrix(internal$data$x_train) - x_explain_mat <- as.matrix(internal$data$x_explain) - copula.mu <- internal$parameters$copula.mu - copula.cov_mat <- internal$parameters$copula.cov_mat - copula.x_explain_gaussian_mat <- as.matrix(internal$data$copula.x_explain_gaussian) - - # TODO: Note that `as.matrix` is not needed for `copula.x_explain_gaussian_mat` as it is already defined as a matrix - # in `setup_approach.copula`, however, it seems that Martin plans to make it into a data.table, thus, I include - # `as.matrix` as future safety. DISCUSS WITH MARTIN WHAT HIS PLANS ARE! - - # Generate the MC samples from N(0, 1) - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Use C++ to convert the MC samples to N(mu_{Sbar|S}, Sigma_{Sbar|S}), for all coalitions and explicands, - # and then transforming them back to the original scale using the inverse Gaussian transform in C++. - # The object `dt` is a 3D array of dimension (n_samples, n_explain * n_coalitions, n_features). - dt <- prepare_data_copula_cpp_and_R( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat - ) - - # Reshape `dt` to a 2D array of dimension (n_samples * n_explain * n_coalitions, n_features). - dim(dt) <- c(n_coalitions_now * n_explain * n_samples, n_features) - - # Convert to a data.table and add extra identification columns - dt <- data.table::as.data.table(dt) - data.table::setnames(dt, feature_names) - dt[, id_coalition := rep(seq_len(nrow(S)), each = n_samples * n_explain)] - dt[, id := rep(seq(n_explain), each = n_samples, times = nrow(S))] - dt[, w := 1 / n_samples] - dt[, id_coalition := index_features[id_coalition]] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - return(dt) -} - -#' Transform data using the inverse Gaussian transformation. -#' -#' @details This function is called from `prepare_data_copula_cpp()` as the this was faster -#' -#' @param z Matrix. The data are the Gaussian Monte Carlos samples to transform. -#' @param x Matrix. The data with the original transformation. Used to conduct the transformation of `z`. -#' -#' @return Matrix of the same dimension as `z`. -#' -#' @keywords internal -#' @author Lars Henry Berge Olsen -inv_gaussian_transform_R <- function(z, x) { - u <- stats::pnorm(z) - x_new <- sapply(seq_len(ncol(u)), function(idx) quantile.type7(x[, idx], probs = u[, idx])) - return(x_new) -} - -#' Compute the quantiles using quantile type seven -#' -#' @details Using quantile type number 7 from stats::quantile. -#' -#' @param x numeric vector whose sample quantiles are wanted. -#' @param probs numeric vector of probabilities with values between zero and one. -#' -#' @return A vector of length length(`probs`) is returned. -#' -#' @keywords internal -#' @author Lars Henry Berge Olsen -quantile.type7 <- function(x, probs) { - n <- length(x) - probs <- pmax(0, pmin(1, probs)) # allow for slight overshoot - index <- 1 + (n - 1) * probs - lo <- floor(index) - hi <- ceiling(index) - x <- sort(x, partial = unique(c(lo, hi))) - qs <- x[lo] - i <- which(index > lo) - h <- (index - lo)[i] - qs[i] <- (1 - h) * qs[i] + h * x[hi[i]] - return(qs) -} - - - -# C++ with sourceRcpp --------------------------------------------------------------------------------------------- -#' @inheritParams default_doc_internal -#' @rdname prepare_data -#' @export -#' @author Lars Henry Berge Olsen -prepare_data.copula_sourceCpp <- function(internal, index_features, ...) { - # Extract used variables - S <- internal$objects$S[index_features, , drop = FALSE] - feature_names <- internal$parameters$feature_names - n_explain <- internal$parameters$n_explain - n_samples <- internal$parameters$n_samples - n_features <- internal$parameters$n_features - n_coalitions_now <- length(index_features) - x_train_mat <- as.matrix(internal$data$x_train) - x_explain_mat <- as.matrix(internal$data$x_explain) - copula.mu <- internal$parameters$copula.mu - copula.cov_mat <- internal$parameters$copula.cov_mat - copula.x_explain_gaussian_mat <- as.matrix(internal$data$copula.x_explain_gaussian) - - # Generate the MC samples from N(0, 1) - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Use C++ to convert the MC samples to N(mu_{Sbar|S}, Sigma_{Sbar|S}), for all coalitions and explicands, - # and then transforming them back to the original scale using the inverse Gaussian transform in C++. - # The object `dt` is a 3D array of dimension (n_samples, n_explain * n_coalitions, n_features). - dt <- prepare_data_copula_cpp_sourceCpp( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat - ) - - # Reshape `dt` to a 2D array of dimension (n_samples * n_explain * n_coalitions, n_features). - dim(dt) <- c(n_coalitions_now * n_explain * n_samples, n_features) - - # Convert to a data.table and add extra identification columns - dt <- data.table::as.data.table(dt) - data.table::setnames(dt, feature_names) - dt[, id_coalition := rep(seq_len(nrow(S)), each = n_samples * n_explain)] - dt[, id := rep(seq(n_explain), each = n_samples, times = nrow(S))] - dt[, w := 1 / n_samples] - dt[, id_coalition := index_features[id_coalition]] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - return(dt) -} - -Rcpp::sourceCpp( - code = ' - #include -using namespace Rcpp; - -// [[Rcpp::depends(RcppArmadillo)]] - -// Compute the quantiles using quantile type seven -// -// @details Using quantile type number seven from stats::quantile in R. -// -// @param x arma::vec. Numeric vector whose sample quantiles are wanted. -// @param probs arma::vec. Numeric vector of probabilities with values between zero and one. -// -// @return A vector of length `length(probs)` with the quantiles is returned. -// -// @keywords internal -// @author Lars Henry Berge Olsen -// [[Rcpp::export]] - arma::vec quantile_type7_cpp_sourceCpp(const arma::vec& x, const arma::vec& probs) { - int n = x.n_elem; - int m = probs.n_elem; - - // Initialize output quantile vector - arma::vec qs(m); - - // Calculate indices - arma::vec index = 1 + (n - 1) * probs; - arma::vec lo = arma::floor(index); - arma::vec hi = arma::ceil(index); - - // Sort the data - arma::vec sorted_x = arma::sort(x); - - // Calculate quantiles using quantile type seven - for (int i = 0; i < m; ++i) { - qs(i) = sorted_x(lo(i) - 1); - if (index(i) > lo(i)) { - double h = index(i) - lo(i); - qs(i) = (1 - h) * qs(i) + h * sorted_x(hi(i) - 1); - } - } - - return qs; - } - -// Transforms new data to a standardized normal distribution -// -// @details The function uses `arma::quantile(...)` which corresponds to Rs `stats::quantile(..., type = 5)`. -// -// @param z arma::mat. The data are the Gaussian Monte Carlos samples to transform. -// @param x arma::mat. The data with the original transformation. Used to conduct the transformation of `z`. -// -// @return arma::mat of the same dimension as `z` -// -// @keywords internal -// @author Lars Henry Berge Olsen -// [[Rcpp::export]] - arma::mat inv_gaussian_transform_cpp_sourceCpp(const arma::mat& z, const arma::mat& x) { - int n_features = z.n_cols; - int n_samples = z.n_rows; - arma::mat z_new(n_samples, n_features); - arma::mat u = arma::normcdf(z); - for (int feature_idx = 0; feature_idx < n_features; feature_idx++) { - z_new.col(feature_idx) = quantile_type7_cpp_sourceCpp(x.col(feature_idx), u.col(feature_idx)); - } - return z_new; - } - -// Generate (Gaussian) Copula MC samples -// -// @param MC_samples_mat arma::mat. Matrix of dimension (`n_samples`, `n_features`) containing samples from the -// univariate standard normal. -// @param x_explain_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the observations -// to explain on the original scale. -// @param x_explain_gaussian_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the -// observations to explain after being transformed using the Gaussian transform, i.e., the samples have been -// transformed to a standardized normal distribution. -// @param x_train_mat arma::mat. Matrix of dimension (`n_train`, `n_features`) containing the training observations. -// @param S arma::mat. Matrix of dimension (`n_coalitions`, `n_features`) containing binary representations of -// the used coalitions. S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones. -// This is not a problem internally in shapr as the empty and grand coalitions treated differently. -// @param mu arma::vec. Vector of length `n_features` containing the mean of each feature after being transformed -// using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution. -// @param cov_mat arma::mat. Matrix of dimension (`n_features`, `n_features`) containing the pairwise covariance -// between all pairs of features after being transformed using the Gaussian transform, i.e., the samples have been -// transformed to a standardized normal distribution. -// -// @return An arma::cube/3D array of dimension (`n_samples`, `n_explain` * `n_coalitions`, `n_features`), where -// the columns (_,j,_) are matrices of dimension (`n_samples`, `n_features`) containing the conditional Gaussian -// copula MC samples for each explicand and coalition on the original scale. -// -// @export -// @keywords internal -// @author Lars Henry Berge Olsen -// [[Rcpp::export]] - arma::cube prepare_data_copula_cpp_sourceCpp(const arma::mat& MC_samples_mat, - const arma::mat& x_explain_mat, - const arma::mat& x_explain_gaussian_mat, - const arma::mat& x_train_mat, - const arma::mat& S, - const arma::vec& mu, - const arma::mat& cov_mat) { - - int n_explain = x_explain_mat.n_rows; - int n_samples = MC_samples_mat.n_rows; - int n_features = MC_samples_mat.n_cols; - int n_coalitions = S.n_rows; - - // Initialize auxiliary matrix and result cube - arma::mat aux_mat(n_samples, n_features); - arma::cube result_cube(n_samples, n_explain*n_coalitions, n_features); - - // Iterate over the coalitions - for (int S_ind = 0; S_ind < n_coalitions; S_ind++) { - - // Get current coalition S and the indices of the features in coalition S and mask Sbar - arma::mat S_now = S.row(S_ind); - arma::uvec S_now_idx = arma::find(S_now > 0.5); - arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); - - // Extract the features we condition on, both on the original scale and the Gaussian transformed values. - arma::mat x_S_star = x_explain_mat.cols(S_now_idx); - arma::mat x_S_star_gaussian = x_explain_gaussian_mat.cols(S_now_idx); - - // Extract the mean values of the Gaussian transformed features in the two sets - arma::vec mu_S = mu.elem(S_now_idx); - arma::vec mu_Sbar = mu.elem(Sbar_now_idx); - - // Extract the relevant parts of the Gaussian transformed covariance matrix - arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); - arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); - arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); - arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); - - // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); - arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; - - // Ensure that the conditional covariance matrix is symmetric - if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { - cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); - } - - // Compute the conditional mean of Xsbar given Xs = Xs_star_gaussian, i.e., of the Gaussian transformed features - arma::mat x_Sbar_gaussian_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star_gaussian.each_row() - mu_S.t()).t(); - x_Sbar_gaussian_mean.each_col() += mu_Sbar; - - // Transform the samples to be from N(O, Sigma_{Sbar|S}) - arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); - - // Loop over the different explicands and combine the generated values with the values we conditioned on - for (int idx_now = 0; idx_now < n_explain; idx_now++) { - - // Transform the MC samples to be from N(mu_{Sbar|S}, Sigma_{Sbar|S}) for one coalition and one explicand - arma::mat MC_samples_mat_now_now = - MC_samples_mat_now + repmat(trans(x_Sbar_gaussian_mean.col(idx_now)), n_samples, 1); - - // Transform the MC to the original scale using the inverse Gaussian transform - arma::mat MC_samples_mat_now_now_trans = - inv_gaussian_transform_cpp_sourceCpp(MC_samples_mat_now_now, x_train_mat.cols(Sbar_now_idx)); - - // Insert the generate Gaussian copula MC samples and the feature values we condition on into an auxiliary matrix - aux_mat.cols(Sbar_now_idx) = MC_samples_mat_now_now_trans; - aux_mat.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); - - // Insert the auxiliary matrix into the result cube - result_cube.col(S_ind*n_explain + idx_now) = aux_mat; - } - } - - return result_cube; - } - -// Transforms a sample to standardized normal distribution -// -// @param x Numeric matrix. The data which should be transformed to a standard normal distribution. -// -// @return Numeric matrix of dimension `dim(x)` -// -// @keywords internal -// @author Lars Henry Berge Olsen -// [[Rcpp::export]] -Rcpp::NumericMatrix gaussian_transform_cpp_sourceCpp(const arma::mat& x) { - int n_obs = x.n_rows; - int n_features = x.n_cols; - - // Pre allocate the return matrix - Rcpp::NumericMatrix x_trans(n_obs, n_features); - - // Iterate over the columns, i.e., the features - for (int idx_feature = 0; idx_feature < n_features; ++idx_feature) { - // Compute the rank and transform to standardized normal distribution - arma::vec rank_now = arma::conv_to::from(arma::sort_index(arma::sort_index(x.col(idx_feature)))); - Rcpp::NumericVector u = Rcpp::wrap((rank_now + 1) / (n_obs + 1)); - x_trans(Rcpp::_, idx_feature) = Rcpp::qnorm(u); - } - - return x_trans; -} - -// Transforms new data to standardized normal (column-wise) based on other data transformations -// -// @param y arma::mat. A numeric matrix containing the data that is to be transformed. -// @param x arma::mat. A numeric matrix containing the data of the original transformation. -// -// @return An arma::mat matrix of the same dimension as `y` containing the back-transformed Gaussian data. -// -// @keywords internal -// @author Lars Henry Berge Olsen, Martin Jullum -// [[Rcpp::export]] -Rcpp::NumericMatrix gaussian_transform_separate_cpp_sourceCpp(const arma::mat& y, const arma::mat& x) { - int n_features = x.n_cols; - int n_y_rows = y.n_rows; - int n_x_rows = x.n_rows; - - // Pre allocate the return matrix - Rcpp::NumericMatrix z_y(n_y_rows, n_features); - - // Compute the transformation for each feature at the time - for (int idx_feature = 0; idx_feature < n_features; ++idx_feature) { - arma::vec yx_now = arma::join_cols(y.col(idx_feature), x.col(idx_feature)); - arma::vec rank_now_1 = arma::conv_to::from(arma::sort_index(arma::sort_index(yx_now))).head(n_y_rows); - arma::vec rank_now_2 = arma::conv_to::from(arma::sort_index(arma::sort_index(rank_now_1))); - arma::vec tmp = rank_now_1 - rank_now_2 + 0.5; - Rcpp::NumericVector u_y = Rcpp::wrap(tmp / (n_x_rows + 1)); - z_y(Rcpp::_, idx_feature) = Rcpp::qnorm(u_y); - } - - return z_y; -} - - ' -) - - - -# Old C++ code ---------------------------------------------------------------------------------------------------- -Rcpp::sourceCpp( - code = ' -// [[Rcpp::depends("RcppArmadillo")]] -#include -using namespace Rcpp; - -// Transforms new data to a standardized normal distribution -// -// @details The function uses `arma::quantile(...)` which corresponds to Rs `stats::quantile(..., type = 5)`. -// -// @param z arma::mat. The data are the Gaussian Monte Carlos samples to transform. -// @param x arma::mat. The data with the original transformation. Used to conduct the transformation of `z`. -// -// @return arma::mat of the same dimension as `z` -// -// @keywords internal -// @author Lars Henry Berge Olsen -// [[Rcpp::export]] -arma::mat inv_gaussian_transform_cpp_arma(arma::mat z, arma::mat x) { - int n_features = z.n_cols; - int n_samples = z.n_rows; - arma::mat z_new(n_samples, n_features); - arma::mat u = arma::normcdf(z); - for (int feature_idx = 0; feature_idx < n_features; feature_idx++) { - z_new.col(feature_idx) = arma::quantile(x.col(feature_idx), u.col(feature_idx)); - } - return z_new; -} - -// Generate (Gaussian) Copula MC samples -// -// @param MC_samples_mat arma::mat. Matrix of dimension (`n_samples`, `n_features`) containing samples from the -// univariate standard normal. -// @param x_explain_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the observations -// to explain on the original scale. -// @param x_explain_gaussian_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the -// observations to explain after being transformed using the Gaussian transform, i.e., the samples have been -// transformed to a standardized normal distribution. -// @param x_train_mat arma::mat. Matrix of dimension (`n_train`, `n_features`) containing the training observations. -// @param S arma::mat. Matrix of dimension (`n_coalitions`, `n_features`) containing binary representations of -// the used coalitions. S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones. -// This is not a problem internally in shapr as the empty and grand coalitions treated differently. -// @param mu arma::vec. Vector of length `n_features` containing the mean of each feature after being transformed -// using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution. -// @param cov_mat arma::mat. Matrix of dimension (`n_features`, `n_features`) containing the pairwise covariance -// between all pairs of features after being transformed using the Gaussian transform, i.e., the samples have been -// transformed to a standardized normal distribution. -// -// @return An arma::cube/3D array of dimension (`n_samples`, `n_explain` * `n_coalitions`, `n_features`), where -// the columns (_,j,_) are matrices of dimension (`n_samples`, `n_features`) containing the conditional Gaussian -// copula MC samples for each explicand and coalition on the original scale. -// -// @export -// @keywords internal -// @author Lars Henry Berge Olsen -// [[Rcpp::export]] -arma::cube prepare_data_copula_cpp_arma(arma::mat MC_samples_mat, - arma::mat x_explain_mat, - arma::mat x_explain_gaussian_mat, - arma::mat x_train_mat, - arma::mat S, - arma::vec mu, - arma::mat cov_mat) { - - int n_explain = x_explain_mat.n_rows; - int n_samples = MC_samples_mat.n_rows; - int n_features = MC_samples_mat.n_cols; - int n_coalitions = S.n_rows; - - // Initialize auxiliary matrix and result cube - arma::mat aux_mat(n_samples, n_features); - arma::cube result_cube(n_samples, n_explain*n_coalitions, n_features); - - // Iterate over the coalitions - for (int S_ind = 0; S_ind < n_coalitions; S_ind++) { - - // Get current coalition S and the indices of the features in coalition S and mask Sbar - arma::mat S_now = S.row(S_ind); - arma::uvec S_now_idx = arma::find(S_now > 0.5); - arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); - - // Extract the features we condition on, both on the original scale and the Gaussian transformed values. - arma::mat x_S_star = x_explain_mat.cols(S_now_idx); - arma::mat x_S_star_gaussian = x_explain_gaussian_mat.cols(S_now_idx); - - // Extract the mean values of the Gaussian transformed features in the two sets - arma::vec mu_S = mu.elem(S_now_idx); - arma::vec mu_Sbar = mu.elem(Sbar_now_idx); - - // Extract the relevant parts of the Gaussian transformed covariance matrix - arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); - arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); - arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); - arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); - - // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); - arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; - - // Ensure that the conditional covariance matrix is symmetric - if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { - cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); - } - - // Compute the conditional mean of Xsbar given Xs = Xs_star_gaussian, i.e., of the Gaussian transformed features - arma::mat x_Sbar_gaussian_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star_gaussian.each_row() - mu_S.t()).t(); - x_Sbar_gaussian_mean.each_col() += mu_Sbar; - - // Transform the samples to be from N(O, Sigma_{Sbar|S}) - arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); - - // Loop over the different explicands and combine the generated values with the values we conditioned on - for (int idx_now = 0; idx_now < n_explain; idx_now++) { - - // Transform the MC samples to be from N(mu_{Sbar|S}, Sigma_{Sbar|S}) for one coalition and one explicand - arma::mat MC_samples_mat_now_now = - MC_samples_mat_now + repmat(trans(x_Sbar_gaussian_mean.col(idx_now)), n_samples, 1); - - // Transform the MC to the original scale using the inverse Gaussian transform - arma::mat MC_samples_mat_now_now_trans = - inv_gaussian_transform_cpp_arma(MC_samples_mat_now_now, x_train_mat.cols(Sbar_now_idx)); - - // Insert the generate Gaussian copula MC samples and the feature values we condition on into an auxiliary matrix - aux_mat.cols(Sbar_now_idx) = MC_samples_mat_now_now_trans; - aux_mat.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); - - // Insert the auxiliary matrix into the result cube - result_cube.col(S_ind*n_explain + idx_now) = aux_mat; - } - } - - return result_cube; -} - -// Generate (Gaussian) Copula MC samples -// -// @param MC_samples_mat arma::mat. Matrix of dimension (`n_samples`, `n_features`) containing samples from the -// univariate standard normal. -// @param x_explain_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the observations -// to explain on the original scale. -// @param x_explain_gaussian_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the -// observations to explain after being transformed using the Gaussian transform, i.e., the samples have been -// transformed to a standardized normal distribution. -// @param x_train_mat arma::mat. Matrix of dimension (`n_train`, `n_features`) containing the training observations. -// @param S arma::mat. Matrix of dimension (`n_coalitions`, `n_features`) containing binary representations of -// the used coalitions. S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones. -// This is not a problem internally in shapr as the empty and grand coalitions treated differently. -// @param mu arma::vec. Vector of length `n_features` containing the mean of each feature after being transformed -// using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution. -// @param cov_mat arma::mat. Matrix of dimension (`n_features`, `n_features`) containing the pairwise covariance -// between all pairs of features after being transformed using the Gaussian transform, i.e., the samples have been -// transformed to a standardized normal distribution. -// -// @return An arma::cube/3D array of dimension (`n_samples`, `n_explain` * `n_coalitions`, `n_features`), where -// the columns (_,j,_) are matrices of dimension (`n_samples`, `n_features`) containing the conditional Gaussian -// copula MC samples for each explicand and coalition on the original scale. -// -// @export -// @keywords internal -// @author Lars Henry Berge Olsen -// [[Rcpp::export]] -arma::cube prepare_data_copula_cpp_and_R(arma::mat MC_samples_mat, - arma::mat x_explain_mat, - arma::mat x_explain_gaussian_mat, - arma::mat x_train_mat, - arma::mat S, - arma::vec mu, - arma::mat cov_mat) { - - int n_explain = x_explain_mat.n_rows; - int n_samples = MC_samples_mat.n_rows; - int n_features = MC_samples_mat.n_cols; - int n_coalitions = S.n_rows; - - // Get the R functions for computing the inverse gaussian transform - Rcpp::Function inv_gaussian_transform_R("inv_gaussian_transform_R"); - - // Initialize auxiliary matrix and result cube - arma::mat aux_mat(n_samples, n_features); - arma::cube result_cube(n_samples, n_explain*n_coalitions, n_features); - - // Iterate over the coalitions - for (int S_ind = 0; S_ind < n_coalitions; S_ind++) { - - // Get current coalition S and the indices of the features in coalition S and mask Sbar - arma::mat S_now = S.row(S_ind); - arma::uvec S_now_idx = arma::find(S_now > 0.5); - arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); - - // Extract the features we condition on, both on the original scale and the Gaussian transformed values. - arma::mat x_S_star = x_explain_mat.cols(S_now_idx); - arma::mat x_S_star_gaussian = x_explain_gaussian_mat.cols(S_now_idx); - - // Extract the mean values of the Gaussian transformed features in the two sets - arma::vec mu_S = mu.elem(S_now_idx); - arma::vec mu_Sbar = mu.elem(Sbar_now_idx); - - // Extract the relevant parts of the Gaussian transformed covariance matrix - arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); - arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); - arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); - arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); - - // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); - arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; - - // Ensure that the conditional covariance matrix is symmetric - if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { - cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); - } - - // Compute the conditional mean of Xsbar given Xs = Xs_star_gaussian, i.e., of the Gaussian transformed features - arma::mat x_Sbar_gaussian_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star_gaussian.each_row() - mu_S.t()).t(); - x_Sbar_gaussian_mean.each_col() += mu_Sbar; - - // Transform the samples to be from N(O, Sigma_{Sbar|S}) - arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); - - // Loop over the different explicands and combine the generated values with the values we conditioned on - for (int idx_now = 0; idx_now < n_explain; idx_now++) { - - // Transform the MC samples to be from N(mu_{Sbar|S}, Sigma_{Sbar|S}) for one coalition and one explicand - arma::mat MC_samples_mat_now_now = - MC_samples_mat_now + repmat(trans(x_Sbar_gaussian_mean.col(idx_now)), n_samples, 1); - - arma::mat x_train_mat_now = x_train_mat.cols(Sbar_now_idx); - //arma::mat x_train_mat_now = arma::normcdf(x_train_mat.cols(Sbar_now_idx)); - - // Transform the MC to the original scale using the inverse Gaussian transform - arma::mat MC_samples_mat_now_now_trans = - Rcpp::as(inv_gaussian_transform_R(Rcpp::wrap(MC_samples_mat_now_now), - Rcpp::wrap(x_train_mat_now))); - - // Insert the generate Gaussian copula MC samples and the feature values we condition on into an auxiliary matrix - aux_mat.cols(Sbar_now_idx) = MC_samples_mat_now_now_trans; - aux_mat.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); - - // Insert the auxiliary matrix into the result cube - result_cube.col(S_ind*n_explain + idx_now) = aux_mat; - } - } - - return result_cube; -} -') - - - - - - - - - - -# Setup ----------------------------------------------------------------------------------------------------------- -{ - n_samples <- 1000 - n_train <- 1000 - n_test <- 20 - M <- 8 - rho <- 0.5 - betas <- c(0, rep(1, M)) - - # We use the Gaussian copula approach - approach <- "copula" - - # Mean of the multivariate Gaussian distribution - mu <- rep(0, times = M) - mu <- seq(M) - - # Create the covariance matrix - sigma <- matrix(rho, ncol = M, nrow = M) # Old - for (i in seq(1, M - 1)) { - for (j in seq(i + 1, M)) { - sigma[i, j] <- sigma[j, i] <- rho^abs(i - j) - } - } - diag(sigma) <- 1 - - # Set seed for reproducibility - seed_setup <- 1996 - set.seed(seed_setup) - - # Make Gaussian data - data_train <- data.table(mvtnorm::rmvnorm(n = n_train, mean = mu, sigma = sigma)) - data_test <- data.table(mvtnorm::rmvnorm(n = n_test, mean = mu, sigma = sigma)) - colnames(data_train) <- paste("X", seq(M), sep = "") - colnames(data_test) <- paste("X", seq(M), sep = "") - - # Make the response - response_train <- as.vector(cbind(1, as.matrix(data_train)) %*% betas) - response_test <- as.vector(cbind(1, as.matrix(data_test)) %*% betas) - - # Put together the data - data_train_with_response <- copy(data_train)[, y := response_train] - data_test_with_response <- copy(data_test)[, y := response_test] - - # Fit a LM model - predictive_model <- lm(y ~ ., data = data_train_with_response) - - # Get the prediction zero, i.e., the phi0 Shapley value. - phi0 <- mean(response_train) - - model <- predictive_model - x_explain <- data_test - x_train <- data_train - keep_samp_for_vS <- FALSE - predict_model <- NULL - get_model_specs <- NULL - timing <- TRUE - n_coalitions <- NULL - group <- NULL - feature_specs <- shapr:::get_feature_specs(get_model_specs, model) - n_batches <- 1 - seed <- 1 - - internal <- setup( - x_train = x_train, - x_explain = x_explain, - approach = approach, - phi0 = phi0, - n_coalitions = n_coalitions, - group = group, - n_samples = n_samples, - n_batches = n_batches, - seed = seed, - feature_specs = feature_specs, - keep_samp_for_vS = keep_samp_for_vS, - predict_model = predict_model, - get_model_specs = get_model_specs, - timing = timing - ) - - # Gets predict_model (if not passed to explain) - predict_model <- shapr:::get_predict_model( - predict_model = predict_model, - model = model - ) - - # Sets up the Shapley (sampling) framework and prepares the - # conditional expectation computation for the chosen approach - # Note: model and predict_model are ONLY used by the AICc-methods of approach empirical to find optimal parameters - internal <- shapr:::setup_computation(internal, model, predict_model) -} - - -# Compare shapr compile and rcpp compile -------------------------------------------------------------------------- -look_at_coalitions <- seq(1, 2^M - 2) -index_features = internal$objects$S_batch$`1`[look_at_coalitions] -S <- internal$objects$S[index_features, , drop = FALSE] -feature_names <- internal$parameters$feature_names -n_explain <- internal$parameters$n_explain -n_samples <- internal$parameters$n_samples -n_features <- internal$parameters$n_features -n_coalitions_now <- length(index_features) -x_train_mat <- as.matrix(internal$data$x_train) -x_explain_mat <- as.matrix(internal$data$x_explain) -copula.mu <- internal$parameters$copula.mu -copula.cov_mat <- internal$parameters$copula.cov_mat -copula.x_explain_gaussian_mat <- as.matrix(internal$data$copula.x_explain_gaussian) - -# Generate the MC samples from N(0, 1) -MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - -# Use C++ to convert the MC samples to N(mu_{Sbar|S}, Sigma_{Sbar|S}), for all coalitions and explicands, -# and then transforming them back to the original scale using the inverse Gaussian transform in C++. -# The object `dt` is a 3D array of dimension (n_samples, n_explain * n_coalitions, n_features). -time_1 = system.time({dt_1 <- prepare_data_copula_cpp( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat -)}) -time_1 - -time_2 = system.time({dt_2 <- shapr:::prepare_data_copula_cpp( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat -)}) -time_2 - -time_3 = system.time({dt_3 <- - .Call(`_shapr_prepare_data_copula_cpp`, - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat - )}) -time_3 - -Rcpp::sourceCpp("src/Copula.cpp") -time_4 = system.time({dt_4 <- prepare_data_copula_cpp( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat -)}) -time_4 - -time_5 = system.time({dt_5 <- shapr::prepare_data_copula_cpp( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - x_explain_gaussian_mat = copula.x_explain_gaussian_mat, - x_train_mat = x_train_mat, - S = S, - mu = copula.mu, - cov_mat = copula.cov_mat -)}) - -rbind(time_1, time_2, time_3, time_4, time_5) -all.equal(dt_1, dt_2) -all.equal(dt_2, dt_3) -all.equal(dt_3, dt_4) -all.equal(dt_4, dt_5) - -# Compare prepare_data.copula ---------------------------------------------------------------------------------------- -set.seed(123) - -# Recall that old version iterate over the observations and then the coalitions. -# While the new version iterate over the coalitions and then the observations. -# The latter lets us reuse the computed conditional distributions for all observations. -look_at_coalitions <- seq(1, 2^M - 2) -# look_at_coalitions <- seq(1, 2^M - 2, 10) -# look_at_coalitions <- seq(1, 2^M - 2, 25) - -# The old R code -time_only_R <- system.time({ - res_only_R <- prepare_data.copula_old( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -time_only_R - -# The C++ code with my own quantile function -time_only_cpp <- system.time({ - res_only_cpp <- prepare_data.copula( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -data.table::setorderv(res_only_cpp, c("id", "id_coalition")) -time_only_cpp - -# The C++ code with my own quantile function -time_only_cpp_sourceCpp <- system.time({ - res_only_cpp_sourceCpp <- prepare_data.copula_sourceCpp( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -data.table::setorderv(res_only_cpp_sourceCpp, c("id", "id_coalition")) -time_only_cpp_sourceCpp - -# The C++ code with quantile functions from arma -time_only_cpp_arma <- system.time({ - res_only_cpp_arma <- prepare_data.copula_cpp_arma( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -data.table::setorderv(res_only_cpp_arma, c("id", "id_coalition")) -time_only_cpp_arma - -# The new C++ code with quantile from R -time_cpp_and_R <- system.time({ - res_cpp_and_R <- prepare_data.copula_cpp_and_R( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -data.table::setorderv(res_cpp_and_R, c("id", "id_coalition")) -time_cpp_and_R - -# Create a table of the times. Less is better -times <- rbind( - time_only_R, - time_only_cpp, - time_only_cpp_sourceCpp, - time_only_cpp_arma, - time_cpp_and_R -) -times - -# TIMES for all coalitions (254), n_samples <- 1000, n_train <- 1000, n_test <- 20, M <- 8 - -# user.self sys.self elapsed user.child sys.child -# time_only_R 65.266 2.142 70.896 0 0 -# time_only_cpp 4.622 0.393 5.212 0 0 -# time_only_cpp_sourceCpp 1.823 0.423 2.279 0 0 -# time_only_cpp_arma 23.874 0.604 27.801 0 0 -# time_cpp_and_R 6.826 1.493 8.683 0 0 - -# Relative speedup of new method -times_relative <- t(sapply(seq_len(nrow(times)), function(idx) times[1, ] / times[idx, ])) -rownames(times_relative) <- paste0(rownames(times), "_rel") -times_relative - -# RELATIVE TIMES for all coalitions, n_samples <- 1000, n_train <- 1000, n_test <- 20, M <- 8 -# user.self sys.self elapsed user.child sys.child -# time_only_R_rel 1.0000 1.0000 1.0000 NaN NaN -# time_only_cpp_rel 14.1207 5.4504 13.6025 NaN NaN -# time_only_cpp_sourceCpp_rel 35.8014 5.0638 31.1084 NaN NaN -# time_only_cpp_arma_rel 2.7338 3.5464 2.5501 NaN NaN -# time_cpp_and_R_rel 9.5614 1.4347 8.1649 NaN NaN - -# Aggregate the MC sample values for each explicand and combination -res_only_R <- res_only_R[, w := NULL] -res_only_cpp <- res_only_cpp[, w := NULL] -res_only_cpp_sourceCpp <- res_only_cpp_sourceCpp[, w := NULL] -res_only_cpp_arma <- res_only_cpp_arma[, w := NULL] -res_cpp_and_R <- res_cpp_and_R[, w := NULL] -res_only_R_agr <- res_only_R[, lapply(.SD, mean), by = c("id", "id_coalition")] -res_only_cpp_agr <- res_only_cpp[, lapply(.SD, mean), by = c("id", "id_coalition")] -res_only_cpp_sourceCpp_agr <- res_only_cpp_sourceCpp[, lapply(.SD, mean), by = c("id", "id_coalition")] -res_only_cpp_arma_agr <- res_only_cpp_arma[, lapply(.SD, mean), by = c("id", "id_coalition")] -res_cpp_and_R_agr <- res_cpp_and_R[, lapply(.SD, mean), by = c("id", "id_coalition")] - -# Difference -res_only_R_agr - res_only_cpp_agr -res_only_R_agr - res_only_cpp_sourceCpp_agr -res_only_R_agr - res_only_cpp_arma_agr -res_only_R_agr - res_cpp_and_R_agr - -# Max absolute difference -max(abs(res_only_R_agr - res_only_cpp_agr)) -max(abs(res_only_R_agr - res_only_cpp_sourceCpp_agr)) -max(abs(res_only_R_agr - res_only_cpp_arma_agr)) -max(abs(res_only_R_agr - res_cpp_and_R_agr)) - -# Max absolute relative difference -max(abs(res_only_R_agr - res_only_cpp_agr) / res_only_cpp_agr) -max(abs(res_only_R_agr - res_only_cpp_sourceCpp_agr) / res_only_cpp_sourceCpp_agr) -max(abs(res_only_R_agr - res_only_cpp_arma_agr) / res_only_cpp_arma_agr) -max(abs(res_only_R_agr - res_cpp_and_R_agr) / res_cpp_and_R_agr) - -# Compare gaussian_transform -------------------------------------------------------------------------------------- -set.seed(123) -x_temp_rows = 10000 -x_temp_cols = 20 -x_temp = matrix(rnorm(x_temp_rows*x_temp_cols), x_temp_rows, x_temp_cols) - -# Compare for equal values -system.time({gaussian_transform_R_res = apply(X = x_temp, MARGIN = 2, FUN = gaussian_transform_old)}) -system.time({gaussian_transform_cpp_res = gaussian_transform_cpp(x_temp)}) -system.time({gaussian_transform_cpp_sourceCpp_res = gaussian_transform_cpp_sourceCpp(x_temp)}) -all.equal(gaussian_transform_R_res, gaussian_transform_cpp_res) # TRUE -all.equal(gaussian_transform_R_res, gaussian_transform_cpp_sourceCpp_res) # TRUE - -# Compare time (generate new data each time such that the result is not stored in the cache) -rbenchmark::benchmark(R = apply(X = matrix(rnorm(x_temp_rows*x_temp_cols), x_temp_rows, x_temp_cols), - MARGIN = 2, - FUN = gaussian_transform_old), - cpp = gaussian_transform_cpp(matrix(rnorm(x_temp_rows*x_temp_cols), x_temp_rows, x_temp_cols)), - cpp_sourceCpp = gaussian_transform_cpp_sourceCpp(matrix(rnorm(x_temp_rows*x_temp_cols), - x_temp_rows, - x_temp_cols)), - replications = 100) -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 cpp 100 7.604 1.987 7.059 0.294 0 0 -# 3 cpp_sourceCpp 100 3.827 1.000 3.529 0.254 0 0 -# 1 R 100 6.183 1.616 4.899 0.738 0 0 - - -# Compare gaussian_transform_separate ------------------------------------------------------------------------- -set.seed(123) -x_cols = 10 -x_train_rows = 50000 -x_explain_rows = 50000 -x_train_temp = matrix(rnorm(x_train_rows*x_cols), x_train_rows, x_cols) -x_explain_temp = matrix(rnorm(x_explain_rows*x_cols), x_explain_rows, x_cols) -x_explain_train_temp = rbind(x_explain_temp, x_train_temp) - -system.time({r = apply(X = rbind(x_explain_temp, x_train_temp), - MARGIN = 2, - FUN = gaussian_transform_separate_old, - n_y = nrow(x_explain_temp))}) -system.time({cpp = gaussian_transform_separate_cpp(x_explain_temp, x_train_temp)}) -system.time({cpp_sourceCpp = gaussian_transform_separate_cpp_sourceCpp(x_explain_temp, x_train_temp)}) -all.equal(r, cpp) -all.equal(r, cpp_sourceCpp) - -# Rcpp::sourceCpp("src/Copula.cpp") # C++ code is faster when I recompile it? I don't understand. -rbenchmark::benchmark(r = apply(X = rbind(x_explain_temp, x_train_temp), - MARGIN = 2, - FUN = gaussian_transform_separate_old, - n_y = nrow(x_explain_temp)), - cpp = gaussian_transform_separate_cpp(x_explain_temp, x_train_temp), - cpp_sourceCpp = gaussian_transform_separate_cpp_sourceCpp(x_explain_temp, x_train_temp), - replications = 20) -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 cpp 20 10.933 2.352 10.082 0.179 0 0 -# 3 cpp_sourceCpp 20 4.648 1.000 4.389 0.100 0 0 -# 1 r 20 9.787 2.106 8.409 0.797 0 0 - -rbenchmark::benchmark(r = apply(X = rbind(matrix(rnorm(x_explain_rows*x_cols), x_explain_rows, x_cols), - matrix(rnorm(x_train_rows*x_cols), x_train_rows, x_cols)), - MARGIN = 2, - FUN = gaussian_transform_separate_old, - n_y = nrow(matrix(rnorm(x_explain_rows*x_cols), x_explain_rows, x_cols))), - cpp = gaussian_transform_separate_cpp(matrix(rnorm(x_explain_rows*x_cols), - x_explain_rows, - x_cols), - matrix(rnorm(x_train_rows*x_cols), x_train_rows, x_cols)), - cpp2 = .Call(`_shapr_gaussian_transform_separate_cpp`, - matrix(rnorm(x_explain_rows*x_cols), x_explain_rows, x_cols), - matrix(rnorm(x_train_rows*x_cols), x_train_rows, x_cols)), - cpp_cpp_sourceCpp = gaussian_transform_separate_cpp_sourceCpp(matrix(rnorm(x_explain_rows*x_cols), - x_explain_rows, - x_cols), - matrix(rnorm(x_train_rows*x_cols), x_train_rows, x_cols)), - replications = 20) - -# test replications elapsed relative user.self sys.self user.child sys.child -# 2 cpp 20 12.634 2.202 11.275 0.352 0.00 0.00 -# 4 cpp_cpp_sourceCpp 20 5.737 1.000 5.287 0.182 0.00 0.00 -# 3 cpp2 20 11.566 2.016 10.890 0.246 0.01 0.01 -# 1 r 20 11.937 2.081 10.232 1.027 0.00 0.00 - - - - - -# Simple C examples compile issues time -------------------------------------------------------------------------------- -sourceCpp( - code = ' -#include -// [[Rcpp::depends(RcppArmadillo)]] -// [[Rcpp::export]] -Rcpp::NumericVector addVectors(const Rcpp::NumericVector& vec1, const Rcpp::NumericVector& vec2) { - // Check if the input vectors are of the same length - if (vec1.size() != vec2.size()) { - Rcpp::stop("Vectors must be of the same length."); - } - - // Create a result vector of the same length as the input vectors - Rcpp::NumericVector result(vec1.size()); - - // Perform element-wise addition - for (int i = 0; i < vec1.size(); ++i) { - result[i] = vec1[i] + vec2[i]; - } - - return result; -} - -// [[Rcpp::export]] -Rcpp::NumericMatrix addMatrices(const Rcpp::NumericMatrix& mat1, const Rcpp::NumericMatrix& mat2) { - // Check if the input matrices have the same dimensions - if (mat1.nrow() != mat2.nrow() || mat1.ncol() != mat2.ncol()) { - Rcpp::stop("Matrices must have the same dimensions."); - } - - // Create a result matrix of the same dimensions as the input matrices - Rcpp::NumericMatrix result(mat1.nrow(), mat1.ncol()); - - // Perform element-wise addition - for (int i = 0; i < mat1.nrow(); ++i) { - for (int j = 0; j < mat1.ncol(); ++j) { - result(i, j) = mat1(i, j) + mat2(i, j); - } - } - - return result; -} - -// [[Rcpp::export]] -arma::mat addMatricesArmadillo(const arma::mat& mat1, const arma::mat& mat2) { - // Check if the input matrices have the same dimensions - if (mat1.n_rows != mat2.n_rows || mat1.n_cols != mat2.n_cols) { - Rcpp::stop("Matrices must have the same dimensions."); - } - - // Perform element-wise addition using Armadillo - arma::mat result = mat1 + mat2; - - return result; -}') - - -# !!!!!READ!!!!! -# Copy the code above into `src/Copula.cpp` and then build the package with -devtools::load_all(".") - -# Dimension of matrix -n = 1000000 -m = 100 - -# Make matrices -mat1 = matrix(rnorm(n*m), n, m) -mat2 = matrix(rnorm(n*m), n, m) - -# Time when using the compiled code using `devtools::load_all()` -shapr_vec_time = system.time({shapr_vec_res = addVectors(mat1[,1], mat2[,1])}) -shapr_mat_rcpp_time = system.time({shapr_mat_rcpp_res <- addMatrices(mat1, mat2)}) -shapr_mat_arma_time = system.time({shapr_mat_arma_res <- addMatricesArmadillo(mat1, mat2)}) - -# Then we compile with `Rcpp::compileAttributes()` -Rcpp::compileAttributes(pkgdir = ".", verbose = TRUE) -compileAttributes_vec_time = system.time({compileAttributes_vec_res = addVectors(mat1[,1], mat2[,1])}) -compileAttributes_mat_rcpp_time = system.time({compileAttributes_mat_rcpp_res <- addMatrices(mat1, mat2)}) -compileAttributes_mat_arma_time = system.time({compileAttributes_mat_arma_res <- addMatricesArmadillo(mat1, mat2)}) - -# Then we compile with `Rcpp::sourceCpp()` -# Here a shared library is built -Rcpp::sourceCpp("src/Copula.cpp", verbose = TRUE) -sourceCpp_vec_time = system.time({sourceCpp_vec_res = addVectors(mat1[,1], mat2[,1])}) -sourceCpp_mat_rcpp_time = system.time({sourceCpp_mat_rcpp_res <- addMatrices(mat1, mat2)}) -sourceCpp_mat_arma_time = system.time({sourceCpp_mat_arma_res <- addMatricesArmadillo(mat1, mat2)}) - -# Look at the times. See a drastic decrease when using sourceCpp. Half on my mac -rbind(shapr_vec_time, - compileAttributes_vec_time, - sourceCpp_vec_time) -rbind(shapr_mat_rcpp_time, - compileAttributes_mat_rcpp_time, - sourceCpp_mat_rcpp_time) -rbind(shapr_mat_arma_time, - compileAttributes_mat_arma_time, - sourceCpp_mat_arma_time) - -# All equal -all.equal(shapr_vec_res, compileAttributes_vec_res) -all.equal(shapr_vec_res, sourceCpp_vec_res) - -all.equal(shapr_mat_rcpp_res, compileAttributes_mat_rcpp_res) -all.equal(shapr_mat_rcpp_res, sourceCpp_mat_rcpp_res) - -all.equal(shapr_mat_arma_res, compileAttributes_mat_arma_res) -all.equal(shapr_mat_arma_res, sourceCpp_mat_arma_res) - - - - -# Large n_samples equal results ---------------------------------------------------------------------------------------- -{ - n_samples <- 1000000 - n_train <- 1000 - n_test <- 5 - M <- 4 - rho <- 0.5 - betas <- c(0, rep(1, M)) - - # We use the Gaussian copula approach - approach <- "copula" - - # Mean of the multivariate Gaussian distribution - mu <- rep(0, times = M) - mu <- seq(M) - - # Create the covariance matrix - sigma <- matrix(rho, ncol = M, nrow = M) # Old - for (i in seq(1, M - 1)) { - for (j in seq(i + 1, M)) { - sigma[i, j] <- sigma[j, i] <- rho^abs(i - j) - } - } - diag(sigma) <- 1 - - # Set seed for reproducibility - seed_setup <- 1996 - set.seed(seed_setup) - - # Make Gaussian data - data_train <- data.table(mvtnorm::rmvnorm(n = n_train, mean = mu, sigma = sigma)) - data_test <- data.table(mvtnorm::rmvnorm(n = n_test, mean = mu, sigma = sigma)) - colnames(data_train) <- paste("X", seq(M), sep = "") - colnames(data_test) <- paste("X", seq(M), sep = "") - - # Make the response - response_train <- as.vector(cbind(1, as.matrix(data_train)) %*% betas) - response_test <- as.vector(cbind(1, as.matrix(data_test)) %*% betas) - - # Put together the data - data_train_with_response <- copy(data_train)[, y := response_train] - data_test_with_response <- copy(data_test)[, y := response_test] - - # Fit a LM model - predictive_model <- lm(y ~ ., data = data_train_with_response) - - # Get the prediction zero, i.e., the phi0 Shapley value. - phi0 <- mean(response_train) - - model <- predictive_model - x_explain <- data_test - x_train <- data_train - keep_samp_for_vS <- FALSE - predict_model <- NULL - get_model_specs <- NULL - timing <- TRUE - n_coalitions <- NULL - group <- NULL - feature_specs <- shapr:::get_feature_specs(get_model_specs, model) - n_batches <- 1 - seed <- 1 - - internal <- setup( - x_train = x_train, - x_explain = x_explain, - approach = approach, - phi0 = phi0, - n_coalitions = n_coalitions, - group = group, - n_samples = n_samples, - n_batches = n_batches, - seed = seed, - feature_specs = feature_specs, - keep_samp_for_vS = keep_samp_for_vS, - predict_model = predict_model, - get_model_specs = get_model_specs, - timing = timing - ) - - # Gets predict_model (if not passed to explain) - predict_model <- shapr:::get_predict_model( - predict_model = predict_model, - model = model - ) - - # Sets up the Shapley (sampling) framework and prepares the - # conditional expectation computation for the chosen approach - # Note: model and predict_model are ONLY used by the AICc-methods of approach empirical to find optimal parameters - internal <- shapr:::setup_computation(internal, model, predict_model) -} - -look_at_coalitions <- seq(1, 2^M - 2) -# look_at_coalitions <- seq(1, 2^M - 2, 10) -# look_at_coalitions <- seq(1, 2^M - 2, 25) - -# The old R code -time_only_R <- system.time({ - res_only_R <- prepare_data.copula_old( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -time_only_R - -# The C++ code with my own quantile function -time_only_cpp <- system.time({ - res_only_cpp <- prepare_data.copula( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -data.table::setorderv(res_only_cpp, c("id", "id_coalition")) -time_only_cpp - -# The C++ code with my own quantile function -time_only_cpp_sourceCpp <- system.time({ - res_only_cpp_sourceCpp <- prepare_data.copula_sourceCpp( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions] - ) -}) -data.table::setorderv(res_only_cpp_sourceCpp, c("id", "id_coalition")) -time_only_cpp_sourceCpp - -# Look at the differences -# Aggregate the MC sample values for each explicand and combination -# res_only_R <- res_only_R[, w := NULL] -# res_only_cpp <- res_only_cpp[, w := NULL] -# res_only_cpp_sourceCpp <- res_only_cpp_sourceCpp[, w := NULL] -res_only_R_agr <- res_only_R[, lapply(.SD, mean), by = c("id", "id_coalition")] -res_only_cpp_agr <- res_only_cpp[, lapply(.SD, mean), by = c("id", "id_coalition")] -res_only_cpp_sourceCpp_agr <- res_only_cpp_sourceCpp[, lapply(.SD, mean), by = c("id", "id_coalition")] - -# Difference -res_only_R_agr - res_only_cpp_agr -res_only_R_agr - res_only_cpp_sourceCpp_agr - -# Max absolute difference -max(abs(res_only_R_agr - res_only_cpp_agr)) -max(abs(res_only_R_agr - res_only_cpp_sourceCpp_agr)) - -# Look at the difference in Shapley values -temp_shapley_value_func = function(dt, internal, model, predict_model) { - compute_preds( - dt, # Updating dt by reference - feature_names = internal$parameters$feature_names, - predict_model = predict_model, - model = model, - pred_cols = paste0("p_hat", seq_len(internal$parameters$output_size)), - type = internal$parameters$type, - horizon = internal$parameters$horizon, - n_endo = internal$data$n_endo, - explain_idx = internal$parameters$explain_idx, - explain_lags = internal$parameters$explain_lags, - y = internal$data$y, - xreg = internal$data$xreg - ) - dt_vS2 <- compute_MCint(dt, paste0("p_hat", seq_len(internal$parameters$output_size))) - dt_vS <- rbind(t(as.matrix(c(1, rep(phi0, n_test)))), dt_vS2, t(as.matrix(c(2^M, response_test))), - use.names = FALSE) - colnames(dt_vS) = colnames(dt_vS2) - compute_shapley(internal, dt_vS) -} - -# Compute the Shapley values -res_shapley_R = temp_shapley_value_func(data.table::copy(res_only_R), internal, model, predict_model) -res_shapley_cpp = temp_shapley_value_func(data.table::copy(res_only_cpp), internal, model, predict_model) -res_shapley_cpp_sourceCpp = temp_shapley_value_func(data.table::copy(res_only_cpp_sourceCpp), - internal, - model, - predict_model) -# Look at the difference -abs(res_shapley_R - res_shapley_cpp) -abs(res_shapley_R - res_shapley_cpp_sourceCpp) -max(abs(res_shapley_R - res_shapley_cpp)) -max(abs(res_shapley_R - res_shapley_cpp_sourceCpp)) - -# When n_samples <- 1000000, n_train <- 1000, n_test <- 5, M <- 4 -# > abs(res_shapley_R - res_shapley_cpp) -# none X1 X2 X3 X4 -# 1: 7.2140e-11 0.00056643 0.00109848 9.5478e-05 0.00043657 -# 2: 4.3903e-10 0.00179695 0.00163158 1.8549e-03 0.00202031 -# 3: 9.3072e-11 0.00142949 0.00087037 1.2457e-03 0.00180482 -# 4: 5.1367e-11 0.00079767 0.00099899 7.2505e-04 0.00052373 -# 5: 3.8260e-10 0.00032232 0.00046644 1.1651e-03 0.00102102 -# > abs(res_shapley_R - res_shapley_cpp_sourceCpp) -# none X1 X2 X3 X4 -# 1: 3.1773e-10 0.00061369 0.00096567 0.00139486 0.00174684 -# 2: 2.1354e-10 0.00164283 0.00139693 0.00051290 0.00075879 -# 3: 1.2370e-10 0.00143125 0.00066145 0.00021455 0.00055524 -# 4: 2.0396e-10 0.00090834 0.00091129 0.00077478 0.00077773 -# 5: 1.3627e-10 0.00038308 0.00033615 0.00031426 0.00026733 -# > max(abs(res_shapley_R - res_shapley_cpp)) -# [1] 0.0020203 -# > max(abs(res_shapley_R - res_shapley_cpp_sourceCpp)) -# [1] 0.0017468 diff --git a/inst/scripts/compare_gaussian_in_R_and_C++.R b/inst/scripts/compare_gaussian_in_R_and_C++.R deleted file mode 100644 index b358c9127..000000000 --- a/inst/scripts/compare_gaussian_in_R_and_C++.R +++ /dev/null @@ -1,2735 +0,0 @@ -# Libraries ------------------------------------------------------------------------------------------------------- -# library(shapr) -# library(rbenchmark) -library(data.table) - - - -# Other functions ------------------------------------------------------------------------------------------------- -#' Sample conditional Gaussian variables -#' -#' @inheritParams sample_copula -#' -#' @return data.table -#' -#' @keywords internal -#' -#' @author Martin Jullum -sample_gaussian <- function(index_given, n_samples, mu, cov_mat, m, x_explain) { - # Check input - stopifnot(is.matrix(x_explain)) - - # Handles the unconditional and full conditional separtely when predicting - cnms <- colnames(x_explain) - if (length(index_given) %in% c(0, m)) { - return(data.table::as.data.table(x_explain)) - } - - dependent_ind <- seq_along(mu)[-index_given] - x_explain_gaussian <- x_explain[index_given] - tmp <- condMVNorm::condMVN( - mean = mu, - sigma = cov_mat, - dependent.ind = dependent_ind, - given.ind = index_given, - X.given = x_explain_gaussian - ) - - # Makes the conditional covariance matrix symmetric in the rare case where numerical instability made it unsymmetric - if (!isSymmetric(tmp[["condVar"]])) { - tmp[["condVar"]] <- Matrix::symmpart(tmp$condVar) - } - - ret0 <- mvnfast::rmvn(n = n_samples, mu = tmp$condMean, sigma = tmp$condVar) - - ret <- matrix(NA, ncol = m, nrow = n_samples) - ret[, index_given] <- rep(x_explain_gaussian, each = n_samples) - ret[, dependent_ind] <- ret0 - - colnames(ret) <- cnms - return(as.data.table(ret)) -} - - -# Cpp functions --------------------------------------------------------------------------------------------------- -# #include -# #include -# using namespace Rcpp; -# -# -# //' Generate Gaussian MC samples -# //' -# //' @param MC_samples_mat matrix. Matrix of dimension `n_samples` times `n_features` containing samples from the -# //' univariate standard normal. -# //' @param x_explain_mat matrix. Matrix of dimension `n_explain` times `n_features` containing the observations -# //' to explain. -# //' @param S matrix. Matrix of dimension `n_coalitions` times `n_features` containing binary representations of -# //' the used coalitions. -# //' @param mu vector. Vector of length `n_features` containing the mean of each feature. -# //' @param cov_mat mat. Matrix of dimension `n_features` times `n_features` containing the pariwise covariance between -# //' all features. -# //' -# //' @export -# //' @keywords internal -# //' -# //' @return List of length `n_coalitions`*`n_samples`, where each entry is a matrix of dimension `n_samples` times -# //' `n_features` containing the conditional MC samples for each coalition and explicand. -# //' @author Lars Henry Berge Olsen -# // [[Rcpp::export]] -# Rcpp::List prepare_data_gaussian_cpp(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# -# // Pre-allocate result matrix -# arma::mat ret(n_samples, n_features); -# -# // Create a list containing the MC samples for all coalitions and test observations -# Rcpp::List result_list; -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < S.n_rows; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# ret.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); // can using .fill() speed this up? -# ret.cols(Sbar_now_idx) = MC_samples_mat_now + repmat(trans(x_Sbar_mean.col(idx_now)), n_samples, 1); -# result_list.push_back(ret); -# } -# } -# -# return result_list; -# } -# -# // [[Rcpp::export]] -# Rcpp::List prepare_data_gaussian_cpp_with_wrap(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# -# // Pre-allocate result matrix -# arma::mat ret(n_samples, n_features); -# -# // Create a list containing the MC samples for all coalitions and test observations -# Rcpp::List result_list; -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < S.n_rows; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# ret.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); // can using .fill() speed this up? -# ret.cols(Sbar_now_idx) = MC_samples_mat_now + repmat(trans(x_Sbar_mean.col(idx_now)), n_samples, 1); -# result_list.push_back(Rcpp::wrap(ret)); -# } -# } -# -# return result_list; -# } -# -# // [[Rcpp::export]] -# Rcpp::List prepare_data_gaussian_cpp_v2(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# -# // Create a list containing the MC samples for all coalitions and test observations -# Rcpp::List result_list; -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < S.n_rows; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = trans(MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S)); -# -# // Loop over the different test observations and Combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# arma::mat ret(n_samples, n_features); -# ret.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); -# ret.cols(Sbar_now_idx) = trans(MC_samples_mat_now + repmat(x_Sbar_mean.col(idx_now), 1, n_samples)); -# result_list.push_back(ret); -# } -# } -# -# return result_list; -# } -# -# // [[Rcpp::export]] -# arma::mat prepare_data_gaussian_cpp_fix_large_mat(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# int n_coalitions = S.n_rows; -# -# // Pre-allocate result matrix -# arma::mat return_mat(n_coalitions*n_explain*n_samples, n_features); -# -# // Create a list containing the MC samples for all coalitions and test observations -# std::list result_list; -# // Rcpp::List result_list; -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < n_coalitions; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# // Maybe faster to create vector 0:(n_samples - 1) and then just add n_samples in each loop. -# arma::uvec row_indices_now = arma::linspace(S_ind*n_explain*n_samples + idx_now*n_samples, -# S_ind*n_explain*n_samples + idx_now*n_samples + n_samples - 1, -# n_samples); -# -# return_mat.submat(row_indices_now, S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); -# return_mat.submat(row_indices_now, Sbar_now_idx) = -# MC_samples_mat_now + repmat(trans(x_Sbar_mean.col(idx_now)), n_samples, 1); -# } -# } -# -# return return_mat; -# } -# -# // Diff in v2 is where we do the transpose -# // [[Rcpp::export]] -# arma::mat prepare_data_gaussian_cpp_fix_large_mat_v2(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# int n_coalitions = S.n_rows; -# -# // Pre-allocate result matrix -# arma::mat return_mat(n_coalitions*n_explain*n_samples, n_features); -# -# // Create a list containing the MC samples for all coalitions and test observations -# std::list result_list; -# // Rcpp::List result_list; -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < n_coalitions; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = trans(MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S)); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# // Maybe faster to create vector 0:(n_samples - 1) and then just add n_samples in each loop. -# arma::uvec row_indices_now = arma::linspace(S_ind*n_explain*n_samples + idx_now*n_samples, -# S_ind*n_explain*n_samples + idx_now*n_samples + n_samples - 1, -# n_samples); -# -# return_mat.submat(row_indices_now, S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); -# return_mat.submat(row_indices_now, Sbar_now_idx) = -# trans(MC_samples_mat_now + repmat(x_Sbar_mean.col(idx_now), 1, n_samples)); -# } -# } -# -# return return_mat; -# } -# -# // [[Rcpp::export]] -# arma::cube prepare_data_gaussian_cpp_fix_cube(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# int n_coalitions = S.n_rows; -# -# // Pre-allocate result matrix -# arma::mat aux_mat(n_samples, n_features); -# arma::cube result_cube(n_samples, n_features, n_explain*n_coalitions); -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < n_coalitions; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# aux_mat.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); // can using .fill() speed this up? -# aux_mat.cols(Sbar_now_idx) = MC_samples_mat_now + repmat(trans(x_Sbar_mean.col(idx_now)), n_samples, 1); -# result_cube.slice(S_ind*n_explain + idx_now) = aux_mat; -# } -# } -# -# return result_cube; -# } -# -# // [[Rcpp::export]] -# arma::cube prepare_data_gaussian_cpp_fix_cube_v2(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# int n_coalitions = S.n_rows; -# -# // Pre-allocate result matrix -# arma::mat aux_mat(n_samples, n_features); -# arma::cube result_cube(n_samples, n_explain*n_coalitions, n_features); -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < n_coalitions; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# aux_mat.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); // can using .fill() speed this up? -# aux_mat.cols(Sbar_now_idx) = MC_samples_mat_now + repmat(trans(x_Sbar_mean.col(idx_now)), n_samples, 1); -# result_cube.col(S_ind*n_explain + idx_now) = aux_mat; -# } -# } -# -# return result_cube; -# } -# -# // [[Rcpp::export]] -# Rcpp::List prepare_data_gaussian_cpp_fix_list_of_lists_of_matrices(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# -# // Pre-allocate result matrix -# arma::mat aux_mat(n_samples, n_features); -# -# // Create a list containing lists that contian the MC samples for all coalitions and test observations in each matrix -# Rcpp::List result_list(S.n_rows); -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < S.n_rows; S_ind++) { -# -# Rcpp::List result_list_now(n_explain); -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# aux_mat.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); // can using .fill() speed this up? -# aux_mat.cols(Sbar_now_idx) = MC_samples_mat_now + repmat(trans(x_Sbar_mean.col(idx_now)), n_samples, 1); -# result_list_now[idx_now] = aux_mat; -# } -# result_list[S_ind] = result_list_now; -# } -# -# return result_list; -# } -# -# // [[Rcpp::export]] -# std::list prepare_data_gaussian_cpp_fix_std_list(arma::mat MC_samples_mat, -# arma::mat x_explain_mat, -# arma::mat S, -# arma::vec mu, -# arma::mat cov_mat) { -# int n_explain = x_explain_mat.n_rows; -# int n_samples = MC_samples_mat.n_rows; -# int n_features = MC_samples_mat.n_cols; -# -# // Pre-allocate result matrix -# arma::mat aux_mat(n_samples, n_features); -# -# // Create a list containing the MC samples for all coalitions and test observations -# std::list result_list; -# -# // Iterate over the coalitions -# for (int S_ind = 0; S_ind < S.n_rows; S_ind++) { -# -# // TODO: REMOVE IN FINAL VERSION Small printout -# Rcpp::Rcout << S_ind + 1 << ","; -# -# // Get current coalition S and the indices of the features in coalition S and mask Sbar -# arma::mat S_now = S.row(S_ind); -# arma::uvec S_now_idx = arma::find(S_now > 0.5); // må finnes en bedre løsning her -# arma::uvec Sbar_now_idx = arma::find(S_now < 0.5); -# -# // Extract the features we condition on -# arma::mat x_S_star = x_explain_mat.cols(S_now_idx); -# -# // Extract the mean values for the features in the two sets -# arma::vec mu_S = mu.elem(S_now_idx); -# arma::vec mu_Sbar = mu.elem(Sbar_now_idx); -# -# // Extract the relevant parts of the covariance matrix -# arma::mat cov_mat_SS = cov_mat.submat(S_now_idx, S_now_idx); -# arma::mat cov_mat_SSbar = cov_mat.submat(S_now_idx, Sbar_now_idx); -# arma::mat cov_mat_SbarS = cov_mat.submat(Sbar_now_idx, S_now_idx); -# arma::mat cov_mat_SbarSbar = cov_mat.submat(Sbar_now_idx, Sbar_now_idx); -# -# // Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix -# arma::mat cov_mat_SbarS_cov_mat_SS_inv = cov_mat_SbarS * inv(cov_mat_SS); -# arma::mat cond_cov_mat_Sbar_given_S = cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv * cov_mat_SSbar; -# -# // Ensure that the conditional covariance matrix is symmetric -# if (!cond_cov_mat_Sbar_given_S.is_symmetric()) { -# cond_cov_mat_Sbar_given_S = arma::symmatl(cond_cov_mat_Sbar_given_S); -# } -# -# // Compute the conditional mean of Xsbar given Xs = Xs_star -# arma::mat x_Sbar_mean = cov_mat_SbarS_cov_mat_SS_inv * (x_S_star.each_row() - mu_S.t()).t(); // Can we speed it up by reducing the number of transposes? -# x_Sbar_mean.each_col() += mu_Sbar; -# -# // Transform the samples to be from N(O, Sigma_Sbar|S) -# arma::mat MC_samples_mat_now = MC_samples_mat.cols(Sbar_now_idx) * arma::chol(cond_cov_mat_Sbar_given_S); -# -# // Loop over the different test observations and combine the generated values with the values we conditioned on -# for (int idx_now = 0; idx_now < n_explain; idx_now++) { -# aux_mat.cols(S_now_idx) = repmat(x_S_star.row(idx_now), n_samples, 1); // can using .fill() speed this up? -# aux_mat.cols(Sbar_now_idx) = MC_samples_mat_now + repmat(trans(x_Sbar_mean.col(idx_now)), n_samples, 1); -# result_list.push_back(aux_mat); -# } -# } -# -# return result_list; -# } - - - -# Old and new version --------------------------------------------------------------------------------------------- -prepare_data_gaussian_old <- function(internal, index_features = NULL, ...) { - x_train <- internal$data$x_train - x_explain <- internal$data$x_explain - n_explain <- internal$parameters$n_explain - gaussian.cov_mat <- internal$parameters$gaussian.cov_mat - n_samples <- internal$parameters$n_samples - gaussian.mu <- internal$parameters$gaussian.mu - n_features <- internal$parameters$n_features - - X <- internal$objects$X - - x_explain0 <- as.matrix(x_explain) - dt_l <- list() - - if (is.null(index_features)) { - features <- X$features - } else { - features <- X$features[index_features] - } - - for (i in seq_len(n_explain)) { - cat(sprintf("%d,", i)) - l <- lapply( - X = features, - FUN = sample_gaussian, #shapr:::sample_gaussian, - n_samples = n_samples, - mu = gaussian.mu, - cov_mat = gaussian.cov_mat, - m = n_features, - x_explain = x_explain0[i, , drop = FALSE] - ) - - dt_l[[i]] <- data.table::rbindlist(l, idcol = "id_coalition") - dt_l[[i]][, w := 1 / n_samples] - dt_l[[i]][, id := i] - if (!is.null(index_features)) dt_l[[i]][, id_coalition := index_features[id_coalition]] - } - - dt <- data.table::rbindlist(dt_l, use.names = TRUE, fill = TRUE) - return(dt) -} - - -# In this version we improve the method by only computing the conditional covariance matrices once. -prepare_data_gaussian_new_v1 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% (t(x_S_star) - mu_S) - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - B <- matrix(nrow = n_samples, ncol = sum(Sbar_now)) - class(B) <- "numeric" - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Sample the MC samples from the conditional Gaussian distribution for one test observation. - .Call("rmvnCpp", - n_ = n_samples, - mu_ = x_Sbar_mean[, idx_now], - sigma_ = cond_cov_mat_Sbar_given_S, - ncores_ = 1, - isChol_ = FALSE, - A_ = B, - PACKAGE = "mvnfast" - ) - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_explain_mat[idx_now, S_now]), each = n_samples) - ret[, Sbar_now] <- B - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -# This is similar to v1, but we compute the Cholensky decomposition only once for each coalitions. -# In v1, it is computed n_explain times. -prepare_data_gaussian_new_v2 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% (t(x_S_star) - mu_S) - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - B <- matrix(nrow = n_samples, ncol = sum(Sbar_now)) - class(B) <- "numeric" - - # Compute the Cholensky decomposition - cond_cov_mat_Sbar_given_S_chol <- chol(cond_cov_mat_Sbar_given_S) - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Sample the MC samples from the conditional Gaussian distribution for one test observation. - .Call("rmvnCpp", - n_ = n_samples, - mu_ = x_Sbar_mean[, idx_now], - sigma_ = cond_cov_mat_Sbar_given_S_chol, - ncores_ = 1, - isChol_ = TRUE, - A_ = B, - PACKAGE = "mvnfast" - ) - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_explain_mat[idx_now, S_now]), each = n_samples) - ret[, Sbar_now] <- B - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -# Here we improve the method speed by only sampling once per coalition -# and only add the test-observation-dependent mean in a secondary call. -prepare_data_gaussian_new_v3 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% (t(x_S_star) - mu_S) - - # rbenchmark::benchmark( - # t(sweep(x_S_star, 2, mu_S, FUN = "-")), - # t(x_S_star) - mu_S) - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - B <- matrix(nrow = n_samples, ncol = sum(Sbar_now)) - class(B) <- "numeric" - - .Call("rmvnCpp", - n_ = n_samples, - mu_ = rep(0, length(mu_Sbar)), - sigma_ = cond_cov_mat_Sbar_given_S, - ncores_ = 1, - isChol_ = FALSE, - A_ = B, - PACKAGE = "mvnfast" - ) - - # Transpose her and untranspose later for faster matrix addition in `t(B + x_Sbar_mean[, idx_now])` - # as it seems to be faster than using `sweep(B, 2, x_Sbar_mean[, idx_now], FUN = "+")` on the - # original B (i.e., not transposed B). - B <- t(B) - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_explain_mat[idx_now, S_now]), each = n_samples) - ret[, Sbar_now] <- t(B + x_Sbar_mean[, idx_now]) - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -# Same as v3, but we now use R to compute Cholensky -prepare_data_gaussian_new_v4 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% (t(x_S_star) - mu_S) - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - B <- matrix(nrow = n_samples, ncol = sum(Sbar_now)) - class(B) <- "numeric" - - .Call("rmvnCpp", - n_ = n_samples, - mu_ = rep(0, length(mu_Sbar)), - sigma_ = chol(cond_cov_mat_Sbar_given_S), - ncores_ = 1, - isChol_ = TRUE, - A_ = B, - PACKAGE = "mvnfast" - ) - - # Transpose her and untranspose later for faster matrix addition in `t(B + x_Sbar_mean[, idx_now])` - # as it seems to be faster than using `sweep(B, 2, x_Sbar_mean[, idx_now], FUN = "+")` on the - # original B (i.e., not transposed B). - B <- t(B) - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_explain_mat[idx_now, S_now]), each = n_samples) - ret[, Sbar_now] <- t(B + x_Sbar_mean[, idx_now]) - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -# Here we only want to generate the data once. So we generate n_samples from N(0, I), -# and then use Cholensky to transform to N(O, Sigma_{Sbar|S}), and then add the means. -prepare_data_gaussian_new_v5 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - B <- matrix(nrow = n_samples, ncol = n_features) - class(B) <- "numeric" - - .Call("rmvnCpp", - n_ = n_samples, - mu_ = rep(0, n_features), - sigma_ = diag(n_features), - ncores_ = 1, - isChol_ = TRUE, - A_ = B, - PACKAGE = "mvnfast" - ) - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% t(sweep(x_S_star, 2, mu_S, FUN = "-")) - - # Transform the samples to be from N(O, Sigma_Sbar|S) - # Transpose her and untranspose later for faster matrix addition in `t(B + x_Sbar_mean[, idx_now])` - # as it seems to be faster than using `sweep(B, 2, x_Sbar_mean[, idx_now], FUN = "+")` on the - # original B (i.e., not transposed B). - B_now <- t(B[, Sbar_now] %*% chol(cond_cov_mat_Sbar_given_S)) - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_explain_mat[idx_now, S_now]), each = n_samples) - ret[, Sbar_now] <- t(B_now + x_Sbar_mean[, idx_now]) - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - # B <- matrix(nrow = n_samples, ncol = n_features) - # class(B) <- "numeric" - - # .Call("rmvnCpp", - # n_ = n_samples, - # mu_ = rep(0, n_features), - # sigma_ = diag(n_features), - # ncores_ = 1, - # isChol_ = TRUE, - # A_ = B, - # PACKAGE = "mvnfast" - # ) - - B <- matrix(rnorm(n_samples*n_features),nrow = n_samples, ncol = n_features) - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% t(sweep(x_S_star, 2, mu_S, FUN = "-")) - - - - - - # Transform the samples to be from N(O, Sigma_Sbar|S) - # Transpose her and untranspose later for faster matrix addition in `t(B + x_Sbar_mean[, idx_now])` - # as it seems to be faster than using `sweep(B, 2, x_Sbar_mean[, idx_now], FUN = "+")` on the - # original B (i.e., not transposed B). - B_now <- t(B[, Sbar_now] %*% chol(cond_cov_mat_Sbar_given_S)) - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_explain_mat[idx_now, S_now]), each = n_samples) - ret[, Sbar_now] <- t(B_now + x_Sbar_mean[, idx_now]) - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_v2 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - # B <- matrix(nrow = n_samples, ncol = n_features) - # class(B) <- "numeric" - - # .Call("rmvnCpp", - # n_ = n_samples, - # mu_ = rep(0, n_features), - # sigma_ = diag(n_features), - # ncores_ = 1, - # isChol_ = TRUE, - # A_ = B, - # PACKAGE = "mvnfast" - # ) - - B <- matrix(rnorm(n_samples*n_features),nrow = n_samples, ncol = n_features) - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% (t(x_S_star) - mu_S) - - - # Transform the samples to be from N(O, Sigma_Sbar|S) - # Transpose her and untranspose later for faster matrix addition in `t(B + x_Sbar_mean[, idx_now])` - # as it seems to be faster than using `sweep(B, 2, x_Sbar_mean[, idx_now], FUN = "+")` on the - # original B (i.e., not transposed B). - B_now <- t(B[, Sbar_now] %*% chol(cond_cov_mat_Sbar_given_S)) - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_S_star[idx_now,]), each = n_samples) - ret[, Sbar_now] <- t(B_now + x_Sbar_mean[, idx_now]) - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - - - -prepare_data_gaussian_new_v5_rnorm_cpp <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp - result_list <- prepare_data_gaussian_cpp( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - - dt = as.data.table(do.call(rbind, result_list)) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_cpp_with_wrap <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp - result_list <- prepare_data_gaussian_cpp_with_wrap( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - - dt = as.data.table(do.call(rbind, result_list)) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - - -prepare_data_gaussian_new_v5_rnorm_cpp_v2 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp - result_list <- prepare_data_gaussian_cpp_v2( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - - dt = as.data.table(do.call(rbind, result_list)) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_cpp_fix_large_mat <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples from N(0, 1) - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp to create the data table with the MC samples for all explicands and coalitions - dt <- as.data.table( - prepare_data_gaussian_cpp_fix_large_mat( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - ) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_cpp_fix_large_mat_v2 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples from N(0, 1) - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp to create the data table with the MC samples for all explicands and coalitions - dt <- as.data.table( - prepare_data_gaussian_cpp_fix_large_mat_v2( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - ) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp - result_list <- prepare_data_gaussian_cpp_fix_list_of_lists_of_matrices( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - - # Here we first put the inner list together and then the whole thing. Maybe exist another faster way! - dt = as.data.table(do.call(rbind, lapply(result_list, function(inner_list) do.call(rbind, inner_list)))) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_cpp_fix_cube <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp - result_cube <- prepare_data_gaussian_cpp_fix_cube( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - - # Reshape the 3D array to 2D - # This is slower - # dt = as.data.table(matrix(aperm(result_cube, c(1, 3, 2)), - # nrow = prod(dim(result_cube)[-2]), - # ncol = dim(result_cube)[2])) - dims = dim(result_cube) - result_cube = aperm(result_cube, c(1, 3, 2)) - dim(result_cube) <- c(prod(dims[-2]), dims[2]) - dt = as.data.table(result_cube) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_cpp_fix_cube_v2 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - n_coalitions_now <- length(index_features) - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp - dt <- prepare_data_gaussian_cpp_fix_cube_v2( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - - # Reshape and convert to data.table - dim(dt) = c(n_coalitions_now*n_explain*n_samples, n_features) - print(system.time({dt = as.data.table(dt)}, gcFirst = FALSE)) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -prepare_data_gaussian_new_v5_rnorm_cpp_fix_std_list <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - - # Generate the MC samples - MC_samples_mat <- matrix(rnorm(n_samples * n_features), nrow = n_samples, ncol = n_features) - - # Call cpp - result_list <- prepare_data_gaussian_cpp_fix_std_list( - MC_samples_mat = MC_samples_mat, - x_explain_mat = x_explain_mat, - S = S, - mu = mu, - cov_mat = cov_mat) - - # FIND A BETTER WAY TO DO THIS - for (i in seq(length(result_list))) { - dim(result_list[[i]]) = c(n_samples, n_features) - } - - # Here we first put the inner list together and then the whole thing. Maybe exist another faster way! - dt = as.data.table(do.call(rbind, result_list)) - setnames(dt, feature_names) - dt[, "id_coalition" := rep(seq(nrow(S)), each = n_samples * n_explain)] - dt[, "id" := rep(seq(n_explain), each = n_samples, times = nrow(S))] - data.table::setcolorder(dt, c("id_coalition", "id", feature_names)) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -# Here we only want to generate the data once. So we generate n_samples*n_batches from N(0, I), -# and then use Cholensky to transform to N(O, Sigma_{Sbar|S}), and then add the means. -prepare_data_gaussian_new_v6 <- function(internal, index_features, ...) { - # This function assumes that index_features will never include the empty and - # grand coalitions. This is valid 21/11/23 as `batch_prepare_vS()` removes the - # grand coalition before calling the `prepare_data()` function and the empty - # coalition is never included in the `internal$objects$S_batch` list. - - # Extract objects that we are going to use - x_explain <- internal$data$x_explain - S <- internal$objects$S - mu <- internal$parameters$gaussian.mu - cov_mat <- internal$parameters$gaussian.cov_mat - x_explain_mat <- as.matrix(internal$data$x_explain) - n_explain <- internal$parameters$n_explain - n_features <- internal$parameters$n_features - n_samples <- internal$parameters$n_samples - feature_names <- internal$parameters$feature_names - n_coalitions <- internal$parameters$n_coalitions - - # Extract the relevant coalitions specified in `index_features` from `S`. - # This will always be called as `index_features` is never NULL. - S <- if (!is.null(index_features)) S[index_features, , drop = FALSE] - n_coalitions_in_this_batch <- nrow(S) - - # Allocate an empty matrix used in mvnfast:::rmvnCpp to store the generated MC samples. - B <- matrix(nrow = n_samples * n_coalitions_in_this_batch, ncol = n_features) - class(B) <- "numeric" - - .Call("rmvnCpp", - n_ = n_samples * n_coalitions_in_this_batch, - mu_ = rep(0, n_features), - sigma_ = diag(n_features), - ncores_ = 1, - isChol_ = TRUE, - A_ = B, - PACKAGE = "mvnfast" - ) - - # Indices of the start for the combinations - B_indices <- n_samples * (seq(0, n_coalitions_in_this_batch)) + 1 - - # Generate a data table containing all Monte Carlo samples for all test observations and coalitions - dt <- data.table::rbindlist( - # Iterate over the coalitions - lapply( - seq_len(nrow(S)), - function(S_ind) { - # This function generates the conditional samples Xsbar | Xs = Xs_star - # and combine those values with the unconditional values. - cat(sprintf("%d,", S_ind)) - - # Get boolean representations if the features are in the S and the Sbar sets - S_now <- as.logical(S[S_ind, ]) - Sbar_now <- !as.logical(S[S_ind, ]) - - # Remove: - # Do not need to treat the empty and grand coalitions different as they will never be present - # if (sum(S_now) %in% c(0, n_features)) { - # return(data.table::as.data.table(cbind("id" = seq(n_explain), x_explain))) - # } - - # Extract the features we condition on - x_S_star <- x_explain_mat[, S_now, drop = FALSE] - - # Extract the mean values for the features in the two sets - mu_S <- mu[S_now] - mu_Sbar <- mu[Sbar_now] - - # Extract the relevant parts of the covariance matrix - cov_mat_SS <- cov_mat[S_now, S_now, drop = FALSE] - cov_mat_SSbar <- cov_mat[S_now, Sbar_now, drop = FALSE] - cov_mat_SbarS <- cov_mat[Sbar_now, S_now, drop = FALSE] - cov_mat_SbarSbar <- cov_mat[Sbar_now, Sbar_now, drop = FALSE] - - # Compute the covariance matrix multiplication factors/terms and the conditional covariance matrix - cov_mat_SbarS_cov_mat_SS_inv <- cov_mat_SbarS %*% solve(cov_mat_SS) - cond_cov_mat_Sbar_given_S <- cov_mat_SbarSbar - cov_mat_SbarS_cov_mat_SS_inv %*% cov_mat_SSbar - - # Ensure that the conditional covariance matrix symmetric in the - # rare case where numerical instability made it unsymmetrical. - if (!isSymmetric(cond_cov_mat_Sbar_given_S)) { - cond_cov_mat_Sbar_given_S <- Matrix::symmpart(cond_cov_mat_Sbar_given_S) - } - - # Compute the conditional mean of Xsbar given Xs = Xs_star - x_Sbar_mean <- mu_Sbar + cov_mat_SbarS_cov_mat_SS_inv %*% t(sweep(x_S_star, 2, mu_S, FUN = "-")) - - # Transform the samples to be from N(O, Sigma_Sbar|S) - # Extract the relevant samples for this combination - # Transpose her and untranspose later for faster matrix addition in `t(B + x_Sbar_mean[, idx_now])` - # as it seems to be faster than using `sweep(B, 2, x_Sbar_mean[, idx_now], FUN = "+")` on the - # original B (i.e., not transposed B). - B_now <- t(B[B_indices[S_ind]:(B_indices[S_ind + 1] - 1), Sbar_now] %*% chol(cond_cov_mat_Sbar_given_S)) - - # Create a data.table containing the MC samples for all test observations for one coalition - data.table::rbindlist( - - # Loop over the different test observations - lapply(seq(n_explain), function(idx_now) { - # Combine the generated values with the values we conditioned on - ret <- matrix(NA, ncol = n_features, nrow = n_samples) - ret[, S_now] <- rep(c(x_explain_mat[idx_now, S_now]), each = n_samples) - ret[, Sbar_now] <- t(B_now + x_Sbar_mean[, idx_now]) - - # Set names of the columns and convert to a data.table - colnames(ret) <- feature_names - as.data.table(ret) - }), - use.names = TRUE, idcol = "id", fill = TRUE - ) - } - ), - idcol = "id_coalition" - ) - - # Update the id_coalition. This will always be called as `index_features` is never NULL. - if (!is.null(index_features)) dt[, id_coalition := index_features[id_coalition]] - - # Add uniform weights - dt[, w := 1 / n_samples] - - # Remove: - # This is not needed when we assume that the empty and grand coalitions will never be present - # dt[id_coalition %in% c(1, n_coalitions), w := 1] - - # Return the MC samples - return(dt) -} - -# Compare the methods --------------------------------------------------------------------------------------------- - -## Setup ----------------------------------------------------------------------------------------------------------- - -{ - n_samples <- 1000 - # n_samples <- 25000 - n_train <- 1000 - n_test <- 100 - M <- 8 - rho <- 0.5 - betas <- c(0, rep(1, M)) - - # We use the Gaussian approach - approach <- "gaussian" - - # Mean of the multivariate Gaussian distribution - mu <- rep(0, times = M) - mu <- seq(M) - - # Create the covariance matrix - sigma <- matrix(rho, ncol = M, nrow = M) # Old - for (i in seq(1, M - 1)) { - for (j in seq(i + 1, M)) { - sigma[i, j] <- sigma[j, i] <- rho^abs(i - j) - } - } - diag(sigma) <- 1 - - # Set seed for reproducibility - seed_setup <- 1996 - set.seed(seed_setup) - - # Make Gaussian data - data_train <- data.table(mvtnorm::rmvnorm(n = n_train, mean = mu, sigma = sigma)) - data_test <- data.table(mvtnorm::rmvnorm(n = n_test, mean = mu, sigma = sigma)) - colnames(data_train) <- paste("X", seq(M), sep = "") - colnames(data_test) <- paste("X", seq(M), sep = "") - - # Make the response - response_train <- as.vector(cbind(1, as.matrix(data_train)) %*% betas) - response_test <- as.vector(cbind(1, as.matrix(data_test)) %*% betas) - - # Put together the data - data_train_with_response <- copy(data_train)[, y := response_train] - data_test_with_response <- copy(data_test)[, y := response_test] - - # Fit a LM model - predictive_model <- lm(y ~ ., data = data_train_with_response) - - # Get the prediction zero, i.e., the phi0 Shapley value. - phi0 <- mean(response_train) - - model <- predictive_model - x_explain <- data_test - x_train <- data_train - keep_samp_for_vS <- FALSE - predict_model <- NULL - get_model_specs <- NULL - timing <- TRUE - n_coalitions <- NULL - group <- NULL - feature_specs <- get_feature_specs(get_model_specs, model) - n_batches <- 1 - seed <- 1 - - internal <- setup( - x_train = x_train, - x_explain = x_explain, - approach = approach, - phi0 = phi0, - n_coalitions = n_coalitions, - group = group, - n_samples = n_samples, - n_batches = n_batches, - seed = seed, - feature_specs = feature_specs, - keep_samp_for_vS = keep_samp_for_vS, - predict_model = predict_model, - get_model_specs = get_model_specs, - timing = timing, - gaussian.mu = mu, - gaussian.cov_mat = sigma - ) - - # Gets predict_model (if not passed to explain) - predict_model <- get_predict_model( - predict_model = predict_model, - model = model - ) - - # Sets up the Shapley (sampling) framework and prepares the - # conditional expectation computation for the chosen approach - # Note: model and predict_model are ONLY used by the AICc-methods of approach empirical to find optimal parameters - internal <- setup_computation(internal, model, predict_model) -} - - - -## Compare time ---------------------------------------------------------------------------------------------------- - -# Recall that old version iterate over the observations and then the coalitions. -# While the new version iterate over the coalitions and then the observations. -# The latter lets us reuse the computed conditional distributions for all observations. -look_at_coalitions <- seq(1, 2^M - 2) -#look_at_coalitions <- seq(1, 2^M - 2, 10) -#look_at_coalitions <- seq(1, 2^M - 2, 25) -time_old <- system.time({ - res_old <- prepare_data_gaussian_old(internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_old <- NULL -# Set to NULL as it is many GB when we look at many combinations in one batch and the methods slow down due to -# little available memory. The same below. - -time_new_v1 <- system.time({ - res_new_v1 <- prepare_data_gaussian_new_v1( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v1 <- NULL - -time_new_v2 <- system.time({ - res_new_v2 <- prepare_data_gaussian_new_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v2 <- NULL - -time_new_v3 <- system.time({ - res_new_v3 <- prepare_data_gaussian_new_v3( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v3 <- NULL - -time_new_v4 <- system.time({ - res_new_v4 <- prepare_data_gaussian_new_v4( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v4 <- NULL - -time_new_v5 <- system.time({ - res_new_v5 <- prepare_data_gaussian_new_v5( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5 <- NULL - -time_new_v5_rnorm <- system.time({ - res_new_v5_rnorm <- prepare_data_gaussian_new_v5_rnorm( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm <- NULL - -time_new_v5_rnorm_v2 <- system.time({ - res_new_v5_rnorm_v2 <- prepare_data_gaussian_new_v5_rnorm_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_v2 <- NULL - -time_new_v5_rnorm_cpp <- system.time({ - res_new_v5_rnorm_cpp <- prepare_data_gaussian_new_v5_rnorm_cpp( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp <- NULL - -time_new_v5_rnorm_cpp_with_wrap <- system.time({ - res_new_v5_rnorm_cpp_with_wrap <- prepare_data_gaussian_new_v5_rnorm_cpp_with_wrap( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_with_wrap <- NULL - -time_new_v5_rnorm_cpp_v2 <- system.time({ - res_new_v5_rnorm_cpp_v2 <- prepare_data_gaussian_new_v5_rnorm_cpp_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_v2 <- NULL - -time_new_v5_rnorm_cpp_fix_large_mat <- system.time({ - res_new_v5_rnorm_cpp_fix_large_mat <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_large_mat( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_fix_large_mat <- NULL - -time_new_v5_rnorm_cpp_fix_large_mat_v2 <- system.time({ - res_new_v5_rnorm_cpp_fix_large_mat_v2 <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_large_mat_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_fix_large_mat_v2 <- NULL - -time_new_v5_rnorm_cpp_fix_cube <- system.time({ - res_new_v5_rnorm_cpp_fix_cube <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_cube( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_fix_cube <- NULL - -time_new_v5_rnorm_cpp_fix_cube_v2 <- system.time({ - res_new_v5_rnorm_cpp_fix_cube_v2 <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_cube_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_fix_cube_v2 <- NULL - -time_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices <- system.time({ - res_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices <- NULL - -time_new_v5_rnorm_cpp_fix_std_list <- system.time({ - res_new_v5_rnorm_cpp_fix_std_list <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_std_list( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v5_rnorm_cpp_fix_std_list <- NULL - -time_new_v6 <- system.time({ - res_new_v6 <- prepare_data_gaussian_new_v6( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalitions])}) -res_new_v6 <- NULL - -# Create a table of the times. Less is better -times <- rbind(time_old, - time_new_v1, - time_new_v2, - time_new_v3, - time_new_v4, - time_new_v5, - time_new_v5_rnorm, - time_new_v5_rnorm_v2, - time_new_v5_rnorm_cpp, - time_new_v5_rnorm_cpp_with_wrap, - time_new_v5_rnorm_cpp_v2, - time_new_v5_rnorm_cpp_fix_large_mat, - time_new_v5_rnorm_cpp_fix_large_mat_v2, - time_new_v5_rnorm_cpp_fix_cube, - time_new_v5_rnorm_cpp_fix_cube_v2, - time_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices, - time_new_v5_rnorm_cpp_fix_std_list, - time_new_v6) -times - -# Look at the relative time compared to the old method. Larger value is better. -# Tells us how many times faster the new version is. -times_relative <- t(sapply(seq_len(nrow(times)), function(idx) times[1, ] / times[idx, ])) -rownames(times_relative) <- paste0(rownames(times), "_rel") -times_relative - -# ALL COALITIONS (look_at_coalitions = seq(1, 2^M-2)) -# user.self sys.self elapsed user.child sys.child -# time_old 38.663 3.654 43.044 0.000 0.000 -# time_new_v1 14.693 3.539 18.709 0.000 0.000 -# time_new_v2 15.545 3.897 19.966 0.012 0.032 -# time_new_v3 13.476 3.838 17.812 0.000 0.000 -# time_new_v4 14.085 4.858 19.718 0.015 0.033 -# time_new_v5 13.508 4.104 18.148 0.000 0.000 -# time_new_v5_rnorm 13.107 4.178 17.705 0.000 0.000 -# time_new_v5_rnorm_v2 13.309 4.458 18.233 0.010 0.023 -# time_new_v5_rnorm_cpp 44.782 5.589 51.849 0.000 0.000 -# time_new_v5_rnorm_cpp_with_wrap 45.816 4.799 51.979 0.021 0.070 -# time_new_v5_rnorm_cpp_v2 44.997 6.513 52.931 0.000 0.000 -# time_new_v5_rnorm_cpp_fix_large_mat 5.594 2.142 7.831 0.000 0.000 -# time_new_v5_rnorm_cpp_fix_large_mat_v2 6.160 2.112 8.499 0.000 0.000 -# time_new_v5_rnorm_cpp_fix_cube 5.607 2.745 8.558 0.000 0.000 -# time_new_v5_rnorm_cpp_fix_cube_v2 4.621 2.121 6.862 0.000 0.000 -# time_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices 6.016 3.687 10.469 0.000 0.000 -# time_new_v5_rnorm_cpp_fix_std_list 5.407 3.272 8.841 0.000 0.000 -# time_new_v6 13.540 4.267 18.361 0.000 0.000 -# user.self sys.self elapsed user.child sys.child -# time_old_rel 1.00000 1.00000 1.00000 NaN NaN -# time_new_v1_rel 2.63139 1.03250 2.30071 NaN NaN -# time_new_v2_rel 2.48717 0.93764 2.15586 0 0 -# time_new_v3_rel 2.86903 0.95206 2.41657 NaN NaN -# time_new_v4_rel 2.74498 0.75216 2.18298 0 0 -# time_new_v5_rel 2.86223 0.89035 2.37183 NaN NaN -# time_new_v5_rnorm_rel 2.94980 0.87458 2.43118 NaN NaN -# time_new_v5_rnorm_v2_rel 2.90503 0.81965 2.36077 0 0 -# time_new_v5_rnorm_cpp_rel 0.86336 0.65378 0.83018 NaN NaN -# time_new_v5_rnorm_cpp_with_wrap_rel 0.84388 0.76141 0.82810 0 0 -# time_new_v5_rnorm_cpp_v2_rel 0.85924 0.56103 0.81321 NaN NaN -# time_new_v5_rnorm_cpp_fix_large_mat_rel 6.91151 1.70588 5.49662 NaN NaN -# time_new_v5_rnorm_cpp_fix_large_mat_v2_rel 6.27646 1.73011 5.06460 NaN NaN -# time_new_v5_rnorm_cpp_fix_cube_rel 6.89549 1.33115 5.02968 NaN NaN -# time_new_v5_rnorm_cpp_fix_cube_v2_rel 8.36680 1.72277 6.27281 NaN NaN -# time_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices_rel 6.42670 0.99105 4.11157 NaN NaN -# time_new_v5_rnorm_cpp_fix_std_list_rel 7.15055 1.11675 4.86868 NaN NaN -# time_new_v6_rel 2.85547 0.85634 2.34432 NaN NaN - - -# 26 coalitions (look_at_coalitions = seq(1, 2^M-2, 10)) -# user.self sys.self elapsed user.child sys.child -# time_old 25.913 2.797 30.399 0 0 -# time_new_v1 7.071 1.624 8.997 0 0 -# time_new_v2 6.653 1.461 8.244 0 0 -# time_new_v3 5.700 1.690 7.521 0 0 -# time_new_v4 5.877 1.826 7.852 0 0 -# time_new_v5 5.522 1.594 7.286 0 0 -# time_new_v6 5.559 1.668 7.335 0 0 -# user.self sys.self elapsed user.child sys.child -# time_old_rel 1.0000 1.0000 1.0000 NaN NaN -# time_new_v1_rel 3.6647 1.7223 3.3788 NaN NaN -# time_new_v2_rel 3.8949 1.9144 3.6874 NaN NaN -# time_new_v3_rel 4.5461 1.6550 4.0419 NaN NaN -# time_new_v4_rel 4.4092 1.5318 3.8715 NaN NaN -# time_new_v5_rel 4.6927 1.7547 4.1722 NaN NaN -# time_new_v6_rel 4.6614 1.6769 4.1444 NaN NaN - - -# 11 coalitions (look_at_coalitions = seq(1, 2^M-2, 25)) -# user.self sys.self elapsed user.child sys.child -# time_old 11.251 1.187 12.961 0.000 0.000 -# time_new_v1 3.273 0.873 4.306 0.000 0.000 -# time_new_v2 3.043 0.690 4.011 0.000 0.000 -# time_new_v3 2.677 0.794 3.587 0.000 0.000 -# time_new_v4 2.598 0.759 3.460 0.000 0.000 -# time_new_v5 2.574 0.752 3.613 0.000 0.000 -# time_new_v6 2.303 0.669 3.009 0.000 0.000 -# user.self sys.self elapsed user.child sys.child -# time_old_rel 1.0000 1.0000 1.0000 NaN NaN -# time_new_v1_rel 3.4375 1.3597 3.0100 NaN NaN -# time_new_v2_rel 3.6973 1.7203 3.2314 NaN NaN -# time_new_v3_rel 4.2028 1.4950 3.6133 NaN NaN -# time_new_v4_rel 4.3306 1.5639 3.7460 NaN NaN -# time_new_v5_rel 4.3710 1.5785 3.5873 NaN NaN -# time_new_v6_rel 4.8854 1.7743 4.3074 NaN NaN - - -## Compare mean ---------------------------------------------------------------------------------------------------- -look_at_coalition <- 25 -one_coalition_time_old <- system.time({ - one_coalition_res_old <- prepare_data_gaussian_old( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) -one_coalition_time_old2 <- system.time({ - one_coalition_res_old2 <- prepare_data_gaussian_old( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -one_coalition_time_new_v1 <- system.time({ - one_coalition_res_new_v1 <- prepare_data_gaussian_new_v1( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -one_coalition_time_new_v2 <- system.time({ - one_coalition_res_new_v2 <- prepare_data_gaussian_new_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -one_coalition_time_new_v3 <- system.time({ - one_coalition_res_new_v3 <- prepare_data_gaussian_new_v3( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -one_coalition_time_new_v4 <- system.time({ - one_coalition_res_new_v4 <- prepare_data_gaussian_new_v4( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -one_coalition_time_new_v5 <- system.time({ - one_coalition_res_new_v5 <- prepare_data_gaussian_new_v5( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm <- system.time({ - one_coalition_res_new_v5_rnorm <- prepare_data_gaussian_new_v5_rnorm( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_v2 <- system.time({ - one_coalition_res_new_v5_rnorm_v2 <- prepare_data_gaussian_new_v5_rnorm_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp <- system.time({ - one_coalition_res_new_v5_rnorm_cpp <- prepare_data_gaussian_new_v5_rnorm_cpp( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_with_wrap <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_with_wrap <- prepare_data_gaussian_new_v5_rnorm_cpp_with_wrap( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_v2 <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_v2 <- prepare_data_gaussian_new_v5_rnorm_cpp_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_fix_large_mat <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_fix_large_mat <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_large_mat( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_fix_large_mat_v2 <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_fix_large_mat_v2 <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_large_mat_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_fix_cube <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_fix_cube <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_cube( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_fix_cube_v2 <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_fix_cube_v2 <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_cube_v2( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -set.seed(123) -one_coalition_time_new_v5_rnorm_cpp_fix_std_list <- system.time({ - one_coalition_res_new_v5_rnorm_cpp_fix_std_list <- prepare_data_gaussian_new_v5_rnorm_cpp_fix_std_list( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -one_coalition_time_new_v6 <- system.time({ - one_coalition_res_new_v6 <- prepare_data_gaussian_new_v6( - internal = internal, - index_features = internal$objects$S_batch$`1`[look_at_coalition])}) - -rbind(one_coalition_time_old, - one_coalition_time_old2, - one_coalition_time_new_v1, - one_coalition_time_new_v2, - one_coalition_time_new_v3, - one_coalition_time_new_v4, - one_coalition_time_new_v5, - one_coalition_time_new_v5_rnorm, - one_coalition_time_new_v5_rnorm_v2, - one_coalition_time_new_v5_rnorm_cpp, - one_coalition_time_new_v5_rnorm_cpp_with_wrap, - one_coalition_time_new_v5_rnorm_cpp_v2, - one_coalition_time_new_v5_rnorm_cpp_fix_large_mat, - one_coalition_time_new_v5_rnorm_cpp_fix_large_mat_v2, - one_coalition_time_new_v5_rnorm_cpp_fix_cube, - one_coalition_time_new_v5_rnorm_cpp_fix_cube_v2, - one_coalition_time_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices, - one_coalition_time_new_v5_rnorm_cpp_fix_std_list, - one_coalition_time_new_v6) - -internal$objects$S[internal$objects$S_batch$`1`[look_at_coalition], , drop = FALSE] -means_old <- one_coalition_res_old[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_old2 <- one_coalition_res_old2[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v1 <- one_coalition_res_new_v1[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v2 <- one_coalition_res_new_v2[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v3 <- one_coalition_res_new_v3[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v4 <- one_coalition_res_new_v4[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5 <- one_coalition_res_new_v5[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm <- one_coalition_res_new_v5_rnorm[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_v2 <- one_coalition_res_new_v5_rnorm_v2[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp <- one_coalition_res_new_v5_rnorm_cpp[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_with_wrap <- one_coalition_res_new_v5_rnorm_cpp_with_wrap[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_v2 <- one_coalition_res_new_v5_rnorm_cpp_v2[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_fix_large_mat <- one_coalition_res_new_v5_rnorm_cpp_fix_large_mat[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_fix_large_mat_v2 <- one_coalition_res_new_v5_rnorm_cpp_fix_large_mat_v2[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_fix_cube <- one_coalition_res_new_v5_rnorm_cpp_fix_cube[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_fix_cube_v2 <- one_coalition_res_new_v5_rnorm_cpp_fix_cube_v2[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_fix_list_of_lists_of_matrices <- one_coalition_res_new_v5_rnorm_cpp_fix_list_of_lists_of_matrices[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v5_rnorm_cpp_fix_std_list <- one_coalition_res_new_v5_rnorm_cpp_fix_std_list[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] -means_v6 <- one_coalition_res_new_v6[, lapply(.SD, mean), .SDcols = paste0("X", seq(M)), by = list(id_coalition, id)] - -# They are all in the same ballpark, so the differences are due to sampling. -# This is supported by the fact that mean_old and mean_old2 use the same old code, and the difference there is the -# same as for the new methods. -# A larger n_samples makes these closer to 0 (I have done that and for other means too) -max(abs(means_old - means_old2)) -max(abs(means_old - means_v1)) -max(abs(means_old - means_v2)) -max(abs(means_old - means_v3)) -max(abs(means_old - means_v4)) -max(abs(means_old - means_v5)) -max(abs(means_old - means_v5_rnorm)) -max(abs(means_old - means_v5_rnorm_v2)) -max(abs(means_old - means_v5_rnorm_cpp)) -max(abs(means_old - means_v5_rnorm_cpp_with_wrap)) -max(abs(means_old - means_v5_rnorm_cpp_v2)) -max(abs(means_old - means_v5_rnorm_cpp_fix_large_mat)) -max(abs(means_old - means_v5_rnorm_cpp_fix_large_mat_v2)) -max(abs(means_old - means_v5_rnorm_cpp_fix_cube)) -max(abs(means_old - means_v5_rnorm_cpp_fix_cube_v2)) -max(abs(means_old - means_v5_rnorm_cpp_fix_list_of_lists_of_matrices)) -max(abs(means_old - means_v5_rnorm_cpp_fix_std_list)) -max(abs(means_old - means_v6)) - - - diff --git a/inst/scripts/compare_shap_python.R b/inst/scripts/compare_shap_python.R deleted file mode 100644 index ebc39e2c3..000000000 --- a/inst/scripts/compare_shap_python.R +++ /dev/null @@ -1,146 +0,0 @@ -#### NOTE: THIS COMPARISON WAS DONE BASED ON OLD VERSION OF BOTH SHAPR AND SHAP, AND MAY NO LONGER REPRESENT THE -#### ACTUAL PERFORMANCE DIFFERENCE. THE COMPARISON SHOULD BE UPDATED WITH UP-TO-DATE VERSIONS. - - -library(MASS) -library(xgboost) -library(shapr) -library(data.table) - -# Python settings -# Using the virtual environment here "../../Python/.venv/bin/python", as set by -#Sys.setenv(RETICULATE_PYTHON = "../../Python/.venv/bin/python") in the .Rprofile -library(reticulate) - -# Install some packages -#py_install("xgboost") -#py_install("shap") -#py_install("pandas") - -data("Boston") - -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -x_train <- as.matrix(tail(Boston[, x_var], -6)) -y_train <- tail(Boston[, y_var], -6) -x_test <- as.matrix(head(Boston[, x_var], 6)) - -# Creating a larger test data set (600 observations) for more realistic function time calls. -# Modifying x_test to repeat the 6 test observations 100 times -x_test = rep(1,100) %x% x_test -colnames(x_test) <- colnames(x_train) - -# Reading the R format version of the xgboost model to avoid crash reading same xgboost model in R and Python -model <- readRDS(system.file("model_objects", "xgboost_model_object.rds", package = "shapr")) - -pred_test <- predict(model,x_test) - -# Spedifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(predict(model,x_train))# adjustment from the standard mean(y_train) to comply with the shap implementation - -time_R_start <- proc.time() -# Prepare the data for explanation -explainer <- shapr(x_train, model) - -time_R_prepare <- proc.time() - -# 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_independence <- explain(x_test, explainer, approach = "independence", phi0 = p0) - -time_R_indep0 <- proc.time() - -explanation_largesigma <- explain(x_test, explainer, approach = "empirical", type = "fixed_sigma", - fixed_sigma_vec = 10000, w_threshold = 1, phi0 = p0) - -time_R_largesigma0 <- proc.time() - -time_R_indep <- time_R_indep0 - time_R_start -time_R_largesigma <- (time_R_largesigma0 - time_R_indep0) + (time_R_prepare- time_R_start) - -# Printing the Shapley values for the test data -Kshap_indep <- explanation_independence$dt -Kshap_largesigma <- explanation_largesigma$dt - -head(Kshap_indep) -#> Kshap_indep -# none lstat rm dis indus -#1: 22,41355 7,116128 0,5203017 -1,91427784 3,1657530 -#2: 22,41355 2,173011 -1,2201068 -0,47653736 0,3620256 -#3: 22,41355 8,280909 3,7869719 -1,96968536 0,6037250 -#4: 22,41355 8,384073 2,9590225 -2,19376523 1,8672685 -#5: 22,41355 4,212031 3,8319436 -0,06695137 1,3392699 -#6: 22,41355 3,295275 -1,2450126 -0,70618891 1,0924035 - -head(Kshap_largesigma) -#> Kshap_largesigma -# none lstat rm dis indus -#1: 22,41355 7,116128 0,5203018 -1,9142779 3,1657530 -#2: 22,41355 2,173011 -1,2201069 -0,4765373 0,3620255 -#3: 22,41355 8,280910 3,7869718 -1,9696854 0,6037249 -#4: 22,41355 8,384073 2,9590226 -2,1937652 1,8672685 -#5: 22,41355 4,212031 3,8319435 -0,0669514 1,3392700 -#6: 22,41355 3,295275 -1,2450126 -0,7061889 1,0924036 - - -# Checking the difference between the methods -mean(abs(as.matrix(Kshap_indep)-as.matrix(Kshap_largesigma))) -#[1] 8.404487e-08 # Numerically identical - - - -#### Running shap from Python #### -reticulate::py_run_file(system.file("scripts", "shap_python_script.py", package = "shapr")) -# Writes Python objects to the list py # - -# Checking that the predictions are identical -sum((pred_test-py$py_pred_test)^2) - -head(Kshap_indep) -#> Kshap_indep -# none lstat rm dis indus -#1: 22,41355 7,116128 0,5203017 -1,91427784 3,1657530 -#2: 22,41355 2,173011 -1,2201068 -0,47653736 0,3620256 -#3: 22,41355 8,280909 3,7869719 -1,96968536 0,6037250 -#4: 22,41355 8,384073 2,9590225 -2,19376523 1,8672685 -#5: 22,41355 4,212031 3,8319436 -0,06695137 1,3392699 -#6: 22,41355 3,295275 -1,2450126 -0,70618891 1,0924035 - -head(py$Kshap_shap) -#> py$Kshap_shap -# none lstat rm dis indus -#1 22,41355 7,116128 0,5203018 -1,91427784 3,1657530 -#2 22,41355 2,173011 -1,2201069 -0,47653727 0,3620255 -#3 22,41355 8,280910 3,7869719 -1,96968537 0,6037250 -#4 22,41355 8,384073 2,9590226 -2,19376508 1,8672686 -#5 22,41355 4,212031 3,8319435 -0,06695135 1,3392701 -#6 22,41355 3,295275 -1,2450126 -0,70618891 1,0924036 - - -# Checking difference between our R implementtaion and the shap implementation i Python -mean(abs(as.matrix(Kshap_indep)-as.matrix(py$Kshap_shap))) -#[1] 1,300368e-07 # Numerically identical - -# Checking the running time of the different methods -time_R_indep[3] -time_R_largesigma[3] -py$time_py - -#> time_R_indep[3] -#elapsed -#7,417 -#> time_R_largesigma[3] -#elapsed -#6,271 -#> py$time_py -#[1] 21,23536 - -# Our R implementation is about 3 times faster than the the shap package on this task. -# Might be some overhead by calling Python from R, but it shouldn't be even close to that much. - - - - - - diff --git a/inst/scripts/compare_shap_python_new.R b/inst/scripts/compare_shap_python_new.R deleted file mode 100644 index 5e51120f4..000000000 --- a/inst/scripts/compare_shap_python_new.R +++ /dev/null @@ -1,67 +0,0 @@ -library(MASS) -library(xgboost) -library(shapr) -library(data.table) - -# Python settings -# Using the virtual environment here "../../Python/.venv/bin/python", as set by -#Sys.setenv(RETICULATE_PYTHON = "../../Python/.venv/bin/python") in the .Rprofile -library(reticulate) - -# Install some packages -#py_install("xgboost") -#py_install("shap") -#py_install("pandas") - -data("Boston") - -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -x_train <- as.matrix(tail(Boston[, x_var], -6)) -y_train <- tail(Boston[, y_var], -6) -x_test <- as.matrix(head(Boston[, x_var], 6)) - -# Creating a larger test data set (600 observations) for more realistic function time calls. -# Modifying x_test to repeat the 6 test observations 100 times -x_test = rep(1,100) %x% x_test -colnames(x_test) <- colnames(x_train) - -# Reading the R format version of the xgboost model to avoid crash reading same xgboost model in R and Python -model <- readRDS(system.file("model_objects", "xgboost_model_object.rds", package = "shapr")) - -pred_test <- predict(model,x_test) - -# Spedifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(predict(model,x_train))# adjustment from the standard mean(y_train) to comply with the shap implementation - -time_R_start <- proc.time() - -# 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_independence <- explain(model = model,x_explain = x_test,x_train=x_train, - approach = "independence", phi0 = p0,n_batches = 1) - -time_R_indep0 <- proc.time() - - -explanation_largesigma <- explain(model = model,x_explain = x_test,x_train=x_train, - approach = "empirical",empirical.type="fixed_sigma",empirical.fixed_sigma=10000,empirical.eta=1, - phi0 = p0,n_batches=1) - - -time_R_largesigma0 <- proc.time() - -(time_R_indep <- time_R_indep0 - time_R_start) -(time_R_largesigma <- time_R_largesigma0 - time_R_indep0) - -# Printing the Shapley values for the test data -Kshap_indep <- explanation_independence$shapley_values_est -Kshap_largesigma <- explanation_largesigma$shapley_values_est - -Kshap_indep -Kshap_largesigma - -mean(abs(as.matrix(Kshap_indep)-as.matrix(Kshap_largesigma))) - -reticulate::py_run_file(system.file("scripts", "shap_python_script.py", package = "shapr")) diff --git a/inst/scripts/create_lm_model_object.R b/inst/scripts/create_lm_model_object.R deleted file mode 100644 index b895f3374..000000000 --- a/inst/scripts/create_lm_model_object.R +++ /dev/null @@ -1,12 +0,0 @@ -# Load data ----------- -data("Boston", package = "MASS") -df <- tail(Boston, 50) - -# Fit linear model -set.seed(123) -model <- lm(medv ~ lstat + rm + dis + indus, data = df) - -saveRDS(object = model, "inst/model_objects/lm_model_object.rds") - -# Used for testing as well, so need a copy un the testthat directory -saveRDS(object = model, "tests/testthat/model_objects/lm_model_object.rds") diff --git a/inst/scripts/create_xgboost_model_object.R b/inst/scripts/create_xgboost_model_object.R deleted file mode 100644 index 7bcb8bbf2..000000000 --- a/inst/scripts/create_xgboost_model_object.R +++ /dev/null @@ -1,26 +0,0 @@ -library(xgboost) - -data("Boston") - -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -x_train <- as.matrix(tail(Boston[, x_var], -6)) -y_train <- tail(Boston[, y_var], -6) -x_test <- as.matrix(head(Boston[, x_var], 6)) - -# Creating a larger test data set (300 observations) for more realistic function time calls. -# Modifying x_test to repeat the 6 test observations 50 times -x_test = rep(1,50) %x% x_test -colnames(x_test) <- colnames(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20 -) - -saveRDS(model,file = "inst/model_objects/xgboost_model_object.rds") - -xgb.save(model=model,fname = "inst/model_objects/xgboost_model_object_raw") diff --git a/inst/scripts/devel/Rcpp_paired_string_coalition_sampling.R b/inst/scripts/devel/Rcpp_paired_string_coalition_sampling.R deleted file mode 100644 index dd7a81963..000000000 --- a/inst/scripts/devel/Rcpp_paired_string_coalition_sampling.R +++ /dev/null @@ -1,217 +0,0 @@ -# Below we include other versions of the C++ function that we have tried. -# However, they were slower than the new version. - - -# #include -# #include -# #include -# #include -# using namespace Rcpp; -# // [[Rcpp::export]] -# IntegerVector get_complement(int m, IntegerVector k) { -# // Create a set with all integers from 1 to m -# std::set all_numbers; -# for (int i = 1; i <= m; ++i) { -# all_numbers.insert(i); -# } -# -# // Erase elements that are present in k -# for (int i = 0; i < k.size(); ++i) { -# all_numbers.erase(k[i]); -# } -# -# // Convert the set to an IntegerVector -# IntegerVector complement(all_numbers.begin(), all_numbers.end()); -# -# return complement; -# } -# -# //' @keywords internal -# // [[Rcpp::export]] -# CharacterVector sample_coalitions_cpp_str_paired1(int m, IntegerVector n_features, bool paired_shap_sampling = true) { -# -# int n = n_features.length(); -# int result_size = paired_shap_sampling ? 2 * n : n; -# CharacterVector result(result_size); -# -# for (int i = 0; i < n; i++) { -# -# int s = n_features[i]; -# IntegerVector k = sample(m, s); -# std::sort(k.begin(), k.end()); -# -# // Convert sampled features to a comma-separated string -# std::stringstream ss; -# for (int j = 0; j < s; j++) { -# if (j != 0) { -# ss << ","; -# } -# ss << k[j]; -# } -# result[i * (paired_shap_sampling ? 2 : 1)] = ss.str(); -# -# if (paired_shap_sampling) { -# // Get complement and convert to string -# IntegerVector complement = get_complement(m, k); -# std::stringstream paired_ss; -# for (int j = 0; j < complement.size(); j++) { -# if (j != 0) { -# paired_ss << ","; -# } -# paired_ss << complement[j]; -# } -# result[i * 2 + 1] = paired_ss.str(); -# } -# } -# -# return result; -# } -# -# -# //' @keywords internal -# // [[Rcpp::export]] -# CharacterVector sample_coalitions_cpp_str_paired2(int m, IntegerVector n_features, bool paired_shap_sampling = true) { -# -# int n = n_features.length(); -# int result_size = paired_shap_sampling ? 2 * n : n; -# CharacterVector result(result_size); -# -# for (int i = 0; i < n; i++) { -# -# int s = n_features[i]; -# IntegerVector k = sample(m, s); -# std::sort(k.begin(), k.end()); -# -# // Convert sampled features to a comma-separated string -# std::stringstream ss; -# for (int j = 0; j < s; j++) { -# if (j != 0) { -# ss << ","; -# } -# ss << k[j]; -# } -# result[i * (paired_shap_sampling ? 2 : 1)] = ss.str(); -# -# if (paired_shap_sampling) { -# // Collect integers from 1 to m not in k -# std::stringstream paired_ss; -# for (int j = 1; j <= m; j++) { -# if (std::find(k.begin(), k.end(), j) == k.end()) { -# if (paired_ss.tellp() > 0) { -# paired_ss << ","; -# } -# paired_ss << j; -# } -# } -# result[i * 2 + 1] = paired_ss.str(); -# } -# } -# -# return result; -# } -# -# -# //' @keywords internal -# // [[Rcpp::export]] -# CharacterVector sample_coalitions_cpp_str_paired3(int m, IntegerVector n_features, bool paired_shap_sampling = true) { -# -# int n = n_features.size(); -# int result_size = paired_shap_sampling ? 2 * n : n; -# CharacterVector result(result_size); -# -# for (int i = 0; i < n; i++) { -# -# int s = n_features[i]; -# IntegerVector k = sample(m, s); -# std::sort(k.begin(), k.end()); -# -# // Convert sampled features to a comma-separated string -# std::stringstream ss; -# for (int j = 0; j < s; j++) { -# if (j != 0) { -# ss << ","; -# } -# ss << k[j]; -# } -# result[i * (paired_shap_sampling ? 2 : 1)] = ss.str(); -# -# if (paired_shap_sampling) { -# // Use a boolean vector to mark presence of elements in k -# std::vector present(m + 1, false); -# for (int idx = 0; idx < s; idx++) { -# present[k[idx]] = true; -# } -# -# // Build the complement string -# std::stringstream paired_ss; -# for (int j = 1; j <= m; j++) { -# if (!present[j]) { -# if (paired_ss.tellp() > 0) { -# paired_ss << ","; -# } -# paired_ss << j; -# } -# } -# result[i * 2 + 1] = paired_ss.str(); -# } -# } -# -# return result; -# } -# -# -# -# -# //' @keywords internal -# // [[Rcpp::export]] -# CharacterVector sample_coalitions_cpp_str_paired4(int m, IntegerVector n_features, bool paired_shap_sampling = true) { -# -# int n = n_features.size(); -# int result_size = paired_shap_sampling ? 2 * n : n; -# CharacterVector result(result_size); -# -# for (int i = 0; i < n; i++) { -# -# int s = n_features[i]; -# IntegerVector k = sample(m, s); -# std::sort(k.begin(), k.end()); -# -# // Use a boolean vector to mark presence of elements in k -# std::vector present(m + 1, false); -# for (int idx = 0; idx < s; idx++) { -# present[k[idx]] = true; -# } -# -# // Generate both the ss and paired_ss strings in a single pass -# std::stringstream ss; -# std::stringstream paired_ss; -# bool first_ss = true; -# bool first_paired_ss = true; -# -# for (int j = 1; j <= m; j++) { -# if (present[j]) { -# if (!first_ss) { -# ss << ","; -# } else { -# first_ss = false; -# } -# ss << j; -# } else if (paired_shap_sampling) { -# if (!first_paired_ss) { -# paired_ss << ","; -# } else { -# first_paired_ss = false; -# } -# paired_ss << j; -# } -# } -# -# result[i * (paired_shap_sampling ? 2 : 1)] = ss.str(); -# -# if (paired_shap_sampling) { -# result[i * 2 + 1] = paired_ss.str(); -# } -# } -# -# return result; -# } diff --git a/inst/scripts/devel/Rscript_test.R b/inst/scripts/devel/Rscript_test.R deleted file mode 100644 index b0bc1da33..000000000 --- a/inst/scripts/devel/Rscript_test.R +++ /dev/null @@ -1,19 +0,0 @@ - - -args <- commandArgs(trailingOnly = TRUE) - -p <- as.numeric(args[1]) -n_train <- as.numeric(args[2]) -n_test <- as.numeric(args[3]) -n_batches <- as.numeric(args[4]) -n_cores <- as.numeric(args[5]) -approach <- args[6] - -print(.libPaths()) - -print(p) -print(n_train) -print(n_test) -print(n_batches) -print(n_cores) -print(approach) diff --git a/inst/scripts/devel/Rscript_test_shapr.R b/inst/scripts/devel/Rscript_test_shapr.R deleted file mode 100644 index 03380a6ed..000000000 --- a/inst/scripts/devel/Rscript_test_shapr.R +++ /dev/null @@ -1,105 +0,0 @@ -#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library") -sys_time_initial <- Sys.time() - -# libraries -library(shapr) -library(future) -library(MASS) -library(microbenchmark) -library(data.table) - -# Initial setup -max_n <- 10^5 -max_p <- 13 -rho <- 0.3 -sigma <- 1 -mu_const <- 0 -beta0 <- 1 -sigma_eps <- 1 - -mu <- rep(mu_const,max_p) -beta <- c(beta0,seq_len(max_p)/max_p) -Sigma <- matrix(rho,max_p,max_p) -diag(Sigma) <- sigma - -set.seed(123) -x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma) -y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps) - -# Arguments form bash -args <- commandArgs(trailingOnly = TRUE) -if(length(args)==0) args = c(1,10,1000,100,6,2,"empirical","multisession","test.csv") - -this_rep <- as.numeric(args[1]) -p <- as.numeric(args[2]) -n_train <- as.numeric(args[3]) -n_test <- as.numeric(args[4]) -n_batches <- as.numeric(args[5]) -n_cores <- as.numeric(args[6]) -approach <- args[7] -multicore_method <- args[8] -logfilename <- args[9] - -set.seed(123) - - -these_p <- sample.int(max_p,size=p) -these_train <- sample.int(max_n,size=n_train) -these_test <- sample.int(max_n,size=n_test) - -x_train <- as.data.frame(x_all[these_train,these_p]) -x_test <- as.data.frame(x_all[these_test,these_p]) - -colnames(x_test) <- colnames(x_train) <- paste0("X",seq_len(p)) - -y_train <- y_all[these_train] - -xy_train <- cbind(x_train,y=y_train) - -model <- lm(formula = y~.,data=xy_train) - -sys_time_start_shapr <- Sys.time() -explainer <- shapr(x_train, model) -sys_time_end_shapr <- Sys.time() - -phi0 <- mean(y_train) - -n_batches_use <- min(nrow(explainer$S),n_batches) - -future::plan(multicore_method,workers=n_cores) - -sys_time_start_explain <- Sys.time() -explanation <- explain( - x_test, - approach = approach, - explainer = explainer, - phi0 = phi0, - n_batches = n_batches_use -) -sys_time_end_explain <- Sys.time() -future::plan(sequential) # To close multisessions etc - -timing <- list(p = p, - n_train = n_train, - n_test = n_test, - n_batches = n_batches, - n_cores = n_cores, - approach = approach, - sys_time_initial = as.character(sys_time_initial), - sys_time_start_shapr = as.character(sys_time_start_shapr), - sys_time_end_shapr = as.character(sys_time_end_shapr), - sys_time_start_explain = as.character(sys_time_start_explain), - sys_time_end_explain = as.character(sys_time_end_explain), - secs_shapr = as.double(difftime(sys_time_end_shapr,sys_time_start_shapr),units="secs"), - secs_explain = as.double(difftime(sys_time_end_explain,sys_time_start_explain),units="secs"), - this_rep = this_rep, - max_n = max_n, - max_p = max_p, - rho = rho, - sigma = sigma, - mu_const = mu_const, - beta0 = beta0, - sigma_eps = sigma_eps) - -#print(unlist(timing)) -data.table::fwrite(timing,logfilename,append = T) diff --git a/inst/scripts/devel/bashscript_looping.sh b/inst/scripts/devel/bashscript_looping.sh deleted file mode 100644 index 6834840fc..000000000 --- a/inst/scripts/devel/bashscript_looping.sh +++ /dev/null @@ -1,60 +0,0 @@ -#!/bin/bash - -#Create array of inputs - space separator -#MJ: Define all input vectors here -p_vec=(10 3 4 5 6 7 8 9 10) -n_train_vec=(1000 10000) -n_test_vec=(100 10 20 100) -n_batches_vec=(1 2 4 8 16 24 32) -n_cores_vec=(1 2 4 8 16 24 32) -approach_vec=("empirical" "gaussian" "ctree") - - -## get length of $distro array -len_p_vec=${#p_vec[@]} -len_n_train_vec=${#n_train_vec[@]} -len_n_test_vec=${#n_test_vec[@]} -len_n_batches_vec=${#n_batches_vec[@]} -len_n_cores_vec=${#n_cores_vec[@]} -len_approach_vec=${#approach_vec[@]} - - -## Use bash for loop -for (( i1=0; i1<$len_p_vec; i1++ )); do -for (( i2=0; i2<$len_n_train_vec; i2++ )); do -for (( i3=0; i3<$len_n_test_vec; i3++ )); do -for (( i4=0; i4<$len_n_batches_vec; i4++ )); do -for (( i5=0; i5<$len_n_cores_vec; i5++ )); do -for (( i6=0; i6<$len_approach_vec; i6++ )); do -# CURRENT STARTS - Rscript --verbose Rscript_test_shapr.R ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} -# CURRENT ENDS -done; done; done; done; done; done - - -# SOMETHING LIKE THIS??? -# #START -run=true -new=true -while run - do - if (("$new")); then - Rscript --verbose Rscript_test_shapr.R ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} & - new=false - else - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -k -c pss -P 4.1.1 | tail -n 1)" | tee -a logfile2 - sleep 2 - fi - if (___RSCRIPT IS DONE___); then - run=false - fi - done -## END - - - -while true - do - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -k -c pss -P 4.1.1 | tail -n 1)" | tee -a logfile2 - sleep 2 -done diff --git a/inst/scripts/devel/bashscript_looping2.sh b/inst/scripts/devel/bashscript_looping2.sh deleted file mode 100644 index 138d37ca9..000000000 --- a/inst/scripts/devel/bashscript_looping2.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash - -#Create array of inputs - space separator -#MJ: Define all input vectors here -script_name="Rscript_test_shapr.R" -logfile_bash="memory_log.csv" -logfile_Rscript="timing_log.csv" - - -p_vec=(2 3 4 5) # 6 7 8 9 10) -n_train_vec=(1000 10000) -n_test_vec=(20) #(10 20 100) -n_batches_vec=(12) #(1 2 4 8 16 24 32) -n_cores_vec=(2 12) #(1 2 4 8 16 24 32) -approach_vec= gaussian #("empirical" "gaussian" "ctree") -multicore_method_vec= ("multisession" "multicore") - - -## get length of $distro array -len_p_vec=${#p_vec[@]} -len_n_train_vec=${#n_train_vec[@]} -len_n_test_vec=${#n_test_vec[@]} -len_n_batches_vec=${#n_batches_vec[@]} -len_n_cores_vec=${#n_cores_vec[@]} -len_approach_vec=${#approach_vec[@]} -len_multicore_method_vec=${#multicore_method_vec[@]} - - -## Use bash for loop -for (( i1=0; i1<$len_p_vec; i1++ )); do -for (( i2=0; i2<$len_n_train_vec; i2++ )); do -for (( i3=0; i3<$len_n_test_vec; i3++ )); do -for (( i4=0; i4<$len_n_batches_vec; i4++ )); do -for (( i5=0; i5<$len_n_cores_vec; i5++ )); do -for (( i6=0; i6<$len_approach_vec; i6++ )); do -for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do -running_processes=1 -start_new_script=1 -while [[ $running_processes == 1 ]] - do - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_test_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash - sleep 1 - if [[ $start_new_script == 1 ]] - then - Rscript --verbose $script_name ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} $logfile_Rscript & - start_new_script=0 - fi - running_processes=$(pgrep -f $script_name -a -c) - done -done; done; done; done; done; done diff --git a/inst/scripts/devel/bashscript_looping_run.sh b/inst/scripts/devel/bashscript_looping_run.sh deleted file mode 100644 index f37c34854..000000000 --- a/inst/scripts/devel/bashscript_looping_run.sh +++ /dev/null @@ -1,56 +0,0 @@ -#!/bin/bash - -#Create array of inputs - space separator -#MJ: Define all input vectors here -script_name="Rscript_test_shapr.R" -logfile_bash="memory_log_test_big.csv" -logfile_Rscript="timing_log_test_big.csv" - - -p_vec=(8 9 10 11 12 13) -n_train_vec=(1000 10000) -n_test_vec=(100) -n_batches_vec=(1 2 4 8 16 24 32) -n_cores_vec=(1 2 4 8 16 24 32) -approach_vec=("empirical" "gaussian" "ctree") -multicore_method_vec=("multisession" "multicore") -reps=5 - -## get length of $distro array -len_p_vec=${#p_vec[@]} -len_n_train_vec=${#n_train_vec[@]} -len_n_test_vec=${#n_test_vec[@]} -len_n_batches_vec=${#n_batches_vec[@]} -len_n_cores_vec=${#n_cores_vec[@]} -len_approach_vec=${#approach_vec[@]} -len_multicore_method_vec=${#multicore_method_vec[@]} - - -## Use bash for loop -for (( i0=0; i1<$reps; i1++ )); do -for (( i1=0; i1<$len_p_vec; i1++ )); do -for (( i2=0; i2<$len_n_train_vec; i2++ )); do -for (( i3=0; i3<$len_n_test_vec; i3++ )); do -for (( i4=0; i4<$len_n_batches_vec; i4++ )); do -for (( i5=0; i5<$len_n_cores_vec; i5++ )); do -for (( i6=0; i6<$len_approach_vec; i6++ )); do -for (( i7=0; i7<$len_multicore_method_vec; i7++ )); do -running_processes=1 -start_new_script=1 -while [[ $running_processes == 1 ]] - do - if [[ $start_new_script == 1 ]] - then - sleep 5 - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_test_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash - Rscript --verbose $script_name $i0 ${p_vec[$i1]} ${n_train_vec[$i2]} ${n_test_vec[$i3]} ${n_batches_vec[$i4]} ${n_cores_vec[$i5]} ${approach_vec[$i6]} ${multicore_method_vec[$i7]} $logfile_Rscript & - start_new_script=0 - fi - - sleep 0.5 - echo "$(date '+%Y-%m-%d, %H:%M:%S,') $(smem -t -c pss -P 4.1.1 | tail -n 1), $i0, ${p_vec[$i1]}, ${n_train_vec[$i2]}, ${n_test_vec[$i3]}, ${n_batches_vec[$i4]}, ${n_cores_vec[$i5]}, ${approach_vec[$i6]}, ${multicore_method_vec[$i7]}, $logfile_Rscript" | tee -a $logfile_bash - sleep 0.5 - - running_processes=$(pgrep -f $script_name -a -c) - done -done; done; done; done; done; done; done; done diff --git a/inst/scripts/devel/compare_explain_batch.R b/inst/scripts/devel/compare_explain_batch.R deleted file mode 100644 index 48544bd80..000000000 --- a/inst/scripts/devel/compare_explain_batch.R +++ /dev/null @@ -1,172 +0,0 @@ - -library(xgboost) -#library(shapr) -library(data.table) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus")#,"nox","age","tax","ptratio") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) - -# THIS IS GENERATED FROM MASTER BRANCH -# Prepare the data for explanation -library(shapr) -explainer <- shapr(x_train, model,n_coalitions = 100) -p = mean(y_train) -gauss = explain(x_test, explainer, "gaussian", phi0 = p, n_samples = 10000) -emp = explain(x_test, explainer, "empirical", phi0 = p, n_samples = 10000) -copula = explain(x_test, explainer, "copula", phi0 = p, n_samples = 10000) -indep = explain(x_test, explainer, "independence", phi0 = p, n_samples = 10000) -comb = explain(x_test, explainer, c("gaussian", "gaussian", "empirical", "empirical"), phi0 = p, n_samples = 10000) -ctree = explain(x_test, explainer, "ctree", mincriterion = 0.95, phi0 = p, n_samples = 10000) -ctree2 = explain(x_test, explainer, "ctree", mincriterion = c(0.95, 0.95, 0.95, 0.95), phi0 = p, n_samples = 10000) -#saveRDS(list(gauss = gauss, empirical = emp, copula = copula, indep = indep, comb = comb, ctree = ctree, ctree_comb = ctree2), file = "inst/scripts/devel/master_res2.rds") -# saveRDS(list(ctree = ctree, ctree_comb = ctree2), file = "inst/scripts/devel/master_res_ctree.rds") - - -detach("package:shapr", unload = TRUE) -devtools::load_all() -nobs = 6 -x_test <- as.matrix(Boston[1:nobs, x_var]) -explainer <- shapr(x_train, model,n_coalitions = 100) -p = mean(y_train) -gauss = explain(x_test, explainer, "gaussian", phi0 = p, n_samples = 10000, n_batches = 1) -emp = explain(x_test, explainer, "empirical", phi0 = p, n_samples = 10000, n_batches = 1) -copula = explain(x_test, explainer, "copula", phi0 = p, n_samples = 10000, n_batches = 1) -indep = explain(x_test, explainer, "independence", phi0 = p, n_samples = 10000, n_batches = 1) -comb = explain(x_test, explainer, c("gaussian", "gaussian", "empirical", "empirical"), phi0 = p, n_samples = 10000, n_batches = 1) -ctree = explain(x_test, explainer, "ctree", mincriterion = 0.95, phi0 = p, n_samples = 10000, n_batches = 1) -ctree2 = explain(x_test, explainer, "ctree", mincriterion = c(0.95, 0.95, 0.95, 0.95), phi0 = p, n_samples = 10000, n_batches = 1) - -res = readRDS("inst/scripts/devel/master_res2.rds") - -# Compare res -all.equal(res$gauss$dt, gauss$dt) # TRUE -all.equal(res$empirical$dt, emp$dt) # TRUE - -res$comb$dt -comb$dt - -# With batches -gauss_b = explain(x_test, explainer, "gaussian", phi0 = p, n_samples = 10000, n_batches = 3) -emp_b = explain(x_test, explainer, "empirical", phi0 = p, n_samples = 10000, n_batches = 3) - -gauss_b$dt -res$gauss$dt - -emp_b$dt -res$empirical$dt - -#### MJ stuff here: - -explain.independence2 <- function(x, explainer, approach, phi0, - n_samples = 1e3, n_batches = 1, seed = 1, only_return_contrib_dt = FALSE, ...) { - - - if (!is.null(seed)) set.seed(seed) - - # Add arguments to explainer object - explainer$x_test <- as.matrix(preprocess_data(x, explainer$feature_specs)$x_dt) - explainer$approach <- approach - explainer$n_samples <- n_samples - - r <- prepare_and_predict(explainer, n_batches, phi0, only_return_contrib_dt, ...) -} - - -prepare_data.independence2 <- function(x, index_features = NULL, ...) { - id <- id_coalition <- w <- NULL # due to NSE notes in R CMD check - - if (is.null(index_features)) { - index_features <- x$X[, .I] - } - - S <- x$S[index_features, ] - x_train <- as.matrix(x$x_train) - n_train <- nrow(x_train) - n_samples <- min(x$n_samples, n_train) - - index_s <- rep(seq(nrow(S)), each = n_samples) - w <- 1 / x$n_samples - - n_col <- nrow(x$x_test) - - dt_l <- list() - for (i in seq(n_col)) { - x_test <- x$x_test[i, , drop = FALSE] - - # sampling index_xtrain - index_xtrain <- c(replicate(nrow(S), sample(x = seq(n_train), size = n_samples, replace = F))) - - # Generate data used for prediction - dt_p <- observation_impute_cpp( - index_xtrain = index_xtrain, - index_s = index_s, - xtrain = x_train, - xtest = x_test, - S = S - ) - - # Add keys - dt_l[[i]] <- data.table::as.data.table(dt_p) - data.table::setnames(dt_l[[i]], colnames(x_train)) - dt_l[[i]][, id_coalition := index_s] - dt_l[[i]][, w := w] # IS THIS NECESSARY? - dt_l[[i]][, id := i] - } - - - dt <- data.table::rbindlist(dt_l, use.names = TRUE, fill = TRUE) - return(dt) -} - - - - -# Using independence with n_samples > nrow(x_train) such that no sampling is performed - -indep1 = explain(x_test, explainer, "independence", phi0 = p, n_samples = 10000, n_batches = 1) -indep2 = explain(x_test, explainer, "independence2", phi0 = p, n_samples = 10000, n_batches = 1) - -all.equal(indep1,indep2) # TRUE - -indep1_batch_2 = explain(x_test, explainer, "independence", phi0 = p, n_samples = 10000, n_batches = 2) - -all.equal(indep1,indep1_batch_2) # TRUE - -indep1_batch_5 = explain(x_test, explainer, "independence", phi0 = p, n_samples = 10000, n_batches = 5) - -all.equal(indep1,indep1_batch_5) # TRUE - -comb_indep_1_batch_1 = explain(x_test, explainer, c("independence", "independence", "independence", "independence"), phi0 = p, n_samples = 10000, n_batches = 1) - -all.equal(indep1,comb_indep_1_batch_1) # TRUE - -comb_indep_1_batch_2 = explain(x_test, explainer, c("independence", "independence", "independence", "independence"), phi0 = p, n_samples = 10000, n_batches = 2) - -all.equal(indep1,comb_indep_1_batch_2) # TRUE - -comb_indep_1_2_batch_1 = explain(x_test, explainer, c("independence", "independence", "independence2", "independence2"), phi0 = p, n_samples = 10000, n_batches = 1) - -all.equal(indep1,comb_indep_1_2_batch_1) #TRUE - -comb_indep_1_2_batch_2 = explain(x_test, explainer, c("independence", "independence", "independence2", "independence2"), phi0 = p, n_samples = 10000, n_batches = 2) - -all.equal(indep1,comb_indep_1_2_batch_2) #TRUE - -comb_indep_1_2_batch_5 = explain(x_test, explainer, c("independence", "independence", "independence2", "independence2"), phi0 = p, n_samples = 10000, n_batches = 5) - -all.equal(indep1,comb_indep_1_2_batch_5) #TRUE - diff --git a/inst/scripts/devel/compare_indep_implementations.R b/inst/scripts/devel/compare_indep_implementations.R deleted file mode 100644 index ae035b492..000000000 --- a/inst/scripts/devel/compare_indep_implementations.R +++ /dev/null @@ -1,118 +0,0 @@ -library(xgboost) -library(shapr) - -data("Boston", package = "MASS") - -x_var <- c("crim", "zn", "indus","chas", "nox", "rm", "age", "dis", "rad", "tax", "ptratio", "black", "lstat")[1:6] -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) - -x_test <- x_test[rep(1,1000),] - -# Looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) - -# Prepare the data for explanation -explainer <- shapr(x_train, model) - -# Specifying the phi_0, i.e. the expected prediction without any features -p <- 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) -t_old <- proc.time() -explanation_old <- explain( - x_test, - approach = "empirical", - type = "independence", - explainer = explainer, - phi0 = p, seed=111,n_samples = 100 -) -print(proc.time()-t_old) -#user system elapsed -#64.228 2.829 16.455 - -t_new <- proc.time() -explanation_new <- explain( - x_test, - approach = "independence", - explainer = explainer, - phi0 = p,seed = 111,n_samples = 100 - ) -print(proc.time()-t_new) -#user system elapsed -#10.376 0.731 4.907 - -colMeans(explanation_old$dt) -colMeans(explanation_new$dt) -#> colMeans(explanation_old$dt) -#none crim zn indus chas nox rm -#22.4459999 0.5606242 0.2137357 -0.3738064 -0.2214088 -0.3129846 0.9761603 -#> colMeans(explanation_new$dt) -#none crim zn indus chas nox rm -#22.4459999 0.5638315 0.2087042 -0.3697038 -0.2155639 -0.3173748 0.9724273 - -t_old <- proc.time() -explanation_full_old <- explain( - x_test, - approach = "empirical", - type = "independence", - explainer = explainer, - phi0 = p, seed=111 -) -print(proc.time()-t_old) -#user system elapsed -#96.064 6.679 29.782 - -t_new <- proc.time() -explanation_full_new <- explain( - x_test, - approach = "independence", - explainer = explainer, - phi0 = p,seed = 111 -) -print(proc.time()-t_new) -#user system elapsed -#40.363 5.978 16.982 - -explanation_full_old$dt -explanation_full_new$dt - -#> explanation_full_old$dt -#none crim zn indus chas nox rm -#1: 22.446 0.5669854 0.2103575 -0.3720833 -0.2213789 -0.3109162 0.9693561 -#2: 22.446 0.5669846 0.2103578 -0.3720834 -0.2213790 -0.3109160 0.9693565 -#3: 22.446 0.5669852 0.2103576 -0.3720835 -0.2213789 -0.3109162 0.9693562 -#4: 22.446 0.5669855 0.2103575 -0.3720834 -0.2213790 -0.3109163 0.9693562 -#5: 22.446 0.5669851 0.2103576 -0.3720833 -0.2213794 -0.3109161 0.9693566 -#--- -# 996: 22.446 0.5669856 0.2103575 -0.3720833 -0.2213791 -0.3109163 0.9693562 -#997: 22.446 0.5669851 0.2103575 -0.3720833 -0.2213790 -0.3109162 0.9693563 -#998: 22.446 0.5669854 0.2103576 -0.3720834 -0.2213790 -0.3109163 0.9693562 -#999: 22.446 0.5669853 0.2103575 -0.3720832 -0.2213791 -0.3109162 0.9693562 -#1000: 22.446 0.5669846 0.2103577 -0.3720833 -0.2213790 -0.3109161 0.9693565 -#> explanation_full_new$dt -#none crim zn indus chas nox rm -#1: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#2: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#3: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#4: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#5: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#--- -# 996: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#997: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#998: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#999: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 -#1000: 22.446 0.5669853 0.2103576 -0.3720834 -0.221379 -0.3109162 0.9693563 - diff --git a/inst/scripts/devel/debug_asym_vignette.R b/inst/scripts/devel/debug_asym_vignette.R deleted file mode 100644 index a40741b6c..000000000 --- a/inst/scripts/devel/debug_asym_vignette.R +++ /dev/null @@ -1,103 +0,0 @@ -setwd("vignettes") - -library(ggplot2) -library(xgboost) -library(data.table) -library(shapr) - -# Additional packages which are only used for plotting in this vignette. -# There are not listed as dependencies is shapr -library(GGally) -library(ggpubr) -library(gridExtra) - - - -# Ensure that shapr's functions are prioritzed, otherwise we need to use the `shapr::` -# prefix when calling explain(). The `conflicted` package is imported by `tidymodels`. -conflicted::conflicts_prefer(shapr::explain, shapr::prepare_data) - - -# Set up the data -# Can also download the data set from the source https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset -# temp <- tempfile() -# download.file("https://archive.ics.uci.edu/static/public/275/bike+sharing+dataset.zip", temp) -# bike <- read.csv(unz(temp, "day.csv")) -# unlink(temp) -bike <- read.csv("../inst/extdata/day.csv") -# Difference in days, which takes DST into account -bike$trend <- as.numeric(difftime(bike$dteday, bike$dteday[1], units = "days")) -bike$cosyear <- cospi(bike$trend / 365 * 2) -bike$sinyear <- sinpi(bike$trend / 365 * 2) -# Unnormalize variables (see data set information in link above) -bike$temp <- bike$temp * (39 - (-8)) + (-8) -bike$atemp <- bike$atemp * (50 - (-16)) + (-16) -bike$windspeed <- 67 * bike$windspeed -bike$hum <- 100 * bike$hum - - - - -x_var <- c("trend", "cosyear", "sinyear", "temp", "atemp", "windspeed", "hum") -y_var <- "cnt" - -# NOTE: To avoid RNG reproducibility issues across different systems, we -# load the training-test split from a file. 80% training and 20% test -train_index <- readRDS("../inst/extdata/train_index.rds") - -# Training data -x_train <- as.matrix(bike[train_index, x_var]) -y_train_nc <- as.matrix(bike[train_index, y_var]) # not centered -y_train <- y_train_nc - mean(y_train_nc) - - -x_explain <- as.matrix(bike[-train_index, x_var]) -y_explain_nc <- as.matrix(bike[-train_index, y_var]) # not centered -y_explain <- y_explain_nc - mean(y_train_nc) - -# Get 6 explicands to plot the Shapley values of with a wide spread in their predicted outcome -n_index_x_explain <- 6 -index_x_explain <- order(y_explain)[seq(1, length(y_explain), length.out = n_index_x_explain)] -y_explain[index_x_explain] - -# Fit an XGBoost model to the training data -model <- xgboost::xgboost( - data = x_train, - label = y_train, - nround = 100, - verbose = FALSE -) - -# Save the phi0 -phi0 <- mean(y_train) - - - - -group_list <- list( - trend0 = "trend", - cosyear0 = "cosyear", - sinyear0 = "sinyear", - temp_group0 = c("temp", "atemp"), - windspeed0 = "windspeed", - hum0 = "hum" -) - -causal_ordering_group <- - list("trend0", c("cosyear0", "sinyear0"), c("temp_group0", "windspeed0", "hum0")) -confounding <- c(FALSE, TRUE, FALSE) - -debugonce(explain) - -explain( - model = model, - x_train = x_train, - x_explain = x_explain, - approach = "gaussian", - phi0 = phi0, - asymmetric = FALSE, - causal_ordering = causal_ordering_group, - confounding = confounding, - n_MC_samples = 1000, - group = group_list -) diff --git a/inst/scripts/devel/demonstrate_combined_approaches_bugs.R b/inst/scripts/devel/demonstrate_combined_approaches_bugs.R deleted file mode 100644 index bafa0bab3..000000000 --- a/inst/scripts/devel/demonstrate_combined_approaches_bugs.R +++ /dev/null @@ -1,139 +0,0 @@ -# Use the data objects from the helper-lm.R file. -# Here we want to illustrate three bugs related to combined approaches (before the bugfix) - - -# First we see that setting `n_batches` lower than the number of unique approaches -# produce some inconsistencies in shapr. -# After the bugfix, we force the user to choose a valid value for `n_batches`. -explanation_1 = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = c("independence", "empirical", "gaussian", "copula", "empirical"), - phi0 = p0, - n_batches = 3, - timing = FALSE, - seed = 1) - -# It says shapr is using 3 batches -explanation_1$internal$parameters$n_batches - -# But shapr has actually used 4. -# This is because shapr can only handle one type of approach for each batch. -# Hence, the number of batches must be at least as large as the number of unique approaches. -# (excluding the last approach which is not used, as we then condition on all features) -length(explanation_1$internal$objects$S_batch) - -# Note that after the bugfix, we give an error if `n_batches` < # unique approaches. - - - - - -# Second we look at at another situation where # unique approaches is two and we set `n_batches` = 2, -# but shapr still use three batches. This is due to how shapr decides how many batches each approach -# should get. Right now it decided based on the proportion of the number of coalitions each approach -# is responsible. In this setting, independence is responsible for 5 coalitions and ctree for 25 coalitions, -# So, initially shapr sets that ctree should get the two batches while independence gets 0, but this -# is than changed to 1 without considering that it now breaks the consistency with the `n_batches`. -# This is done in the function `create_S_batch_new()` in setup_computation.R. -explanation_2 = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = c("independence", "ctree", "ctree", "ctree" ,"ctree"), - phi0 = p0, - n_batches = 2, - timing = FALSE, - seed = 1) - -# It says shapr is using 2 batches -explanation_2$internal$parameters$n_batches - -# But shapr has actually used 3 -length(explanation_2$internal$objects$S_batch) - -# These are equal after the bugfix - - -# Same type of bug but in the opposite direction -explanation_3 = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = c("independence", "ctree", "ctree", "ctree" ,"ctree"), - phi0 = p0, - n_batches = 15, - timing = FALSE, - seed = 1) - -# It says shapr is using 15 batches -explanation_3$internal$parameters$n_batches - -# It says shapr is using 14 batches -length(explanation_3$internal$objects$S_batch) - -# These are equal after the bugfix - - - - - - -# Bug number three caused shapr to not to be reproducible as seting the seed did not work for combined approaches. -# This was due to a `set.seed(NULL)` which ruins all of the earlier set.seed procedures. - - -# Check that setting the seed works for a combination of approaches -# Here `n_batches` is set to `4`, so one batch for each method, -# i.e., no randomness. -# In the first example we get no bug as there is no randomness in assigning the batches. -explanation_combined_1 = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = c("independence", "empirical", "gaussian", "copula", "empirical"), - phi0 = p0, - timing = FALSE, - seed = 1) - -explanation_combined_2 = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = c("independence", "empirical", "gaussian", "copula", "empirical"), - phi0 = p0, - timing = FALSE, - seed = 1) - -# Check that they are equal -all.equal(explanation_combined_1, explanation_combined_2) - - -# Here `n_batches` is set to `10`, so NOT one batch for each method, -# i.e., randomness in assigning the batches. -explanation_combined_3 = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = c("independence", "empirical", "gaussian", "copula", "ctree"), - phi0 = p0, - timing = FALSE, - seed = 1) - -explanation_combined_4 = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = c("independence", "empirical", "gaussian", "copula", "ctree"), - phi0 = p0, - timing = FALSE, - seed = 1) - -# Check that they are not equal -all.equal(explanation_combined_3, explanation_combined_4) -explanation_combined_3$internal$objects$X -explanation_combined_4$internal$objects$X - -# These are equal after the bugfix - diff --git a/inst/scripts/devel/devel_batch_testing.R b/inst/scripts/devel/devel_batch_testing.R deleted file mode 100644 index 20c1063f3..000000000 --- a/inst/scripts/devel/devel_batch_testing.R +++ /dev/null @@ -1,67 +0,0 @@ - -#remotes::install_github("NorskRegnesentral/shapr") # Installs GitHub version of shapr - -library(shapr) -library(data.table) -library(MASS) -library(Matrix) - -# Just sample some data to work with -m <- 9 -n_train <- 10000 -n_explain <- 10 -rho_1 <- 0.5 -rho_2 <- 0 -rho_3 <- 0.4 -Sigma_1 <- matrix(rho_1, m/3, m/3) + diag(m/3) * (1 - rho_1) -Sigma_2 <- matrix(rho_2, m/3, m/3) + diag(m/3) * (1 - rho_2) -Sigma_3 <- matrix(rho_3, m/3, m/3) + diag(m/3) * (1 - rho_3) -Sigma <- as.matrix(bdiag(Sigma_1, Sigma_2, Sigma_3)) -mu <- rep(0,m) - -set.seed(123) - - - -x_train <- as.data.table(MASS::mvrnorm(n_train,mu,Sigma)) -x_explain <- as.data.table(MASS::mvrnorm(n_explain,mu,Sigma)) - -names(x_train) <- paste0("VV",1:m) -names(x_explain) <- paste0("VV",1:m) - -beta <- c(4:1, rep(0, m - 4)) -alpha <- 1 -y_train <- as.vector(alpha + as.matrix(x_train) %*% beta + rnorm(n_train, 0, 1)) -y_explain <- alpha + as.matrix(x_explain) %*% beta + rnorm(n_explain, 0, 1) - -xy_train <- cbind(y_train, x_train) - -p0 <- mean(y_train) - -# We need to pass a model object and a proper prediction function to shapr for it to work, but it can be anything as we don't use it -model <- lm(y_train ~ ., data = x_train) - -### First run proper shapr call on this -library(progressr) -library(future) -# Not necessary, and only apply to the explain() call below -progressr::handlers(global = TRUE) # For progress bars -#future::plan(multisession, workers = 2) # Parallized computations -#future::plan(sequential) - -expl <- explain(model = model, - x_explain= x_explain, - x_train = x_train, - approach = "ctree", - phi0 = p0, - n_batches = 100, - n_samples = 1000, - iterative = TRUE, - print_iter_info = TRUE, - print_shapleyres = TRUE) - - -n_combinations <- 5 -max_batch_size <- 10 -min_n_batches <- 10 - diff --git a/inst/scripts/devel/devel_convergence_branch.R b/inst/scripts/devel/devel_convergence_branch.R deleted file mode 100644 index 313a28698..000000000 --- a/inst/scripts/devel/devel_convergence_branch.R +++ /dev/null @@ -1,148 +0,0 @@ -library(xgboost) -#library(shapr) - -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] -data[,new1 :=sqrt(Wind*Ozone)] -data[,new2 :=sqrt(Wind*Temp)] -data[,new3 :=sqrt(Wind*Day)] -data[,new4 :=sqrt(Wind*Solar.R)] -data[,new5 :=rnorm(.N)] -data[,new6 :=rnorm(.N)] -data[,new7 :=rnorm(.N)] - - -x_var <- c("Solar.R", "Wind", "Temp", "Month","Day","new1","new2","new3","new4","new5")#"new6","new7") -y_var <- "Ozone" - -ind_x_explain <- 1:20 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# Looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -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_iterative <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - max_n_coalitions = 500, - phi0 = p0, - iterative = TRUE, - print_shapleyres = TRUE, # tmp - print_iter_info = TRUE, # tmp - kernelSHAP_reweighting = "on_N" -) - -explanation_iterative <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "ctree", - n_coalitions = 500, - phi0 = p0, - iterative = TRUE, - print_shapleyres = TRUE, # tmp - print_iter_info = TRUE, # tmp - kernelSHAP_reweighting = "on_N" -) - - -explanation_noniterative <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - n_coalitions = 400, - phi0 = p0, - iterative = FALSE -) - - -explanation_iterative <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - n_coalitions = 500, - phi0 = p0, - iterative = TRUE, - iterative_args = list(initial_n_coalitions=10,convergence_tol=0.0001), - print_shapleyres = TRUE, # tmp - print_iter_info = TRUE, # tmp - kernelSHAP_reweighting = "on_N" -) - - - - - - - - - - -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_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_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_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - explanation_iterative$internal$output$iter_objects$dt_iter_convergence_res[[5+i]],type="l",col=1+i) -} - - -plot(explanation_iterative$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_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_iterative$internal$output$iter_objects$dt_iter_convergence_res$n_current_samples, - explanation_iterative$internal$output$iter_objects$dt_iter_shapley_sd[explain_id==i,Solar.R],type="l",col=1+i) -} - - - -lines(explanation_iterative$internal$output$dt_iter_convergence_res$n_current_samples, - sd_full0/sqrt(explanation_iterative$internal$output$dt_iter_convergence_res$n_current_samples),type="l",col=2) - - -plot(explanation_iterative$internal$output$dt_iter_convergence_res$estimated_required_samples) - -explanation_regular <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - n_coalitions = NULL, - phi0 = p0, - iterative = FALSE -) - diff --git a/inst/scripts/devel/devel_non_exact_grouping.R b/inst/scripts/devel/devel_non_exact_grouping.R deleted file mode 100644 index d5e29e3b0..000000000 --- a/inst/scripts/devel/devel_non_exact_grouping.R +++ /dev/null @@ -1,54 +0,0 @@ - -### NOTE: THIS DOES NO LONGER WORK AS WE SWITCH TO exact when a large n_coalitions is used, but the checks -### confirms the code works as intended. - -library(xgboost) -library(shapr) -library(data.table) - -#### Testing grouping function - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus","age","tax","ptratio","black","nox") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) - -# Looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) - -group <- list(A=x_var[1:3],B=x_var[4:5],C=x_var[7],D=x_var[c(6,8)],E=x_var[9]) - -explainer1 <- shapr(x_train, model,group = group,n_coalitions=10^ 6) - -explainer2 <- shapr(x_train, model,group = group) - -explanation1 <- explain( - x_test, - approach = "independence", - explainer = explainer1, - phi0 = p -) - -explanation2 <- explain( - x_test, - approach = "independence", - explainer = explainer2, - phi0 = p -) - - -explanation2$dt-explanation1$dt # All small differences! - - diff --git a/inst/scripts/devel/devel_parallelization.R b/inst/scripts/devel/devel_parallelization.R deleted file mode 100644 index 21aa964cc..000000000 --- a/inst/scripts/devel/devel_parallelization.R +++ /dev/null @@ -1,147 +0,0 @@ -library(xgboost) -library(shapr) -library(future) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus","rad","tax","ptratio","black","zn","crim") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:20, x_var]) - -# Looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) - -# Prepare the data for explanation -explainer <- shapr(x_train, model) - -# Specifying the phi_0, i.e. the expected prediction without any features -p <- mean(y_train) - - -# No specification (sequential) -start <- proc.time() -explanation0 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -time0 <- stop-start - - -# Sequential -start <- proc.time() -future::plan("sequential") -explanation1 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -time1 <- stop-start - -# Try to set multicore (in Rstudio this is disabled so falls back to sequential) -start <- proc.time() -future::plan("multicore",workers=5) ## defaults to availableCores() workers -explanation2 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -time2 <- stop-start - -# Multisession with 2 workers -start <- proc.time() -future::plan("multisession",workers = 2) ## defaults to availableCores() workers -explanation3 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -time3 <- stop-start - -# Multisession with 5 workers -start <- proc.time() -future::plan("multisession",workers=5) ## defaults to availableCores() workers -explanation4 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -time4 <- stop-start - -# Multisession with 10 workers -start <- proc.time() -future::plan("multisession",workers=10) ## defaults to availableCores() workers -explanation5 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -time5 <- stop-start - -# Multisession with 20 workers -start <- proc.time() -future::plan("multisession",workers=20) -explanation6 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -time6 <- stop-start - -# Trying to set up a cluster and run it there -start <- proc.time() -cl <- parallel::makeCluster(c(rep("hpc01",5),rep("hpc02",5),rep("hpc03",6)), - rscript = "/nr/prog/AppServerDefaults/Ubuntu_18.04_x86_64/bin/Rscript") -plan(cluster, workers = cl) -#plan(remote, workers = cl) -explanation7 <- explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 32 -) -stop <- proc.time() -parallel::stopCluster(cl) -time7 <- stop-start - - - - - -# Printing the Shapley values for the test data. -# For more information about the interpretation of the values in the table, see ?shapr::explain. -head(explanation0$dt,2) -head(explanation1$dt,2) -head(explanation2$dt,2) -head(explanation3$dt,2) -head(explanation4$dt,2) -head(explanation5$dt,2) -head(explanation6$dt,2) -head(explanation7$dt,2) - -cbind(time0,time1,time2,time3,time4,time5,time6,time7) diff --git a/inst/scripts/devel/devel_tmp_new_batch.R b/inst/scripts/devel/devel_tmp_new_batch.R deleted file mode 100644 index 37950b3a3..000000000 --- a/inst/scripts/devel/devel_tmp_new_batch.R +++ /dev/null @@ -1,48 +0,0 @@ - - - -explainer <- explain_setup( - x_test, - approach = c("empirical","empirical","gaussian","copula"), - explainer = explainer, - phi0 = p, - n_batches = 4 -) - - -explainer$approach = c("empirical","empirical","gaussian","copula") - - - - -explainer$X[,randomorder:=sample(.N)] -setorder(explainer$X,randomorder) - -aa <- explainer$X[!is.na(approach)][order(randomorder)][order(shapley_weight),batch:=ceiling(.I/.N*n_batches_per_approach)] - -aa <- explainer$X[!is.na(approach)][order(randomorder)][order(shapley_weight),batch:=ceiling(.I/.N*5)] - - -bb <- explainer$X[!is.na(approach)][rank(shapley_weight,ties.method = "random")] - - -explainer$X[] - - -n_batches <- max(1, floor(length(index_S) / no_samples * n_batches)) - - -S_per_apprach <- - - findInterval(x, quantile(x,type=5), rightmost.closed=TRUE) - -# It is fast -set.seed(1) -DT <- data.table(x=rep(rnorm(5),2)) - -library(microbenchmark) - - -microbenchmark( - order = DT[order(x),bin:=ceiling(.I/.N*5)], - findInterval = DT[, b2 :=findInterval(x, quantile(x,type=5), rightmost.closed=TRUE)],times=20 ) diff --git a/inst/scripts/devel/devel_verbose.R b/inst/scripts/devel/devel_verbose.R deleted file mode 100644 index ad4a2fb7d..000000000 --- a/inst/scripts/devel/devel_verbose.R +++ /dev/null @@ -1,135 +0,0 @@ -ex <- explain( - testing = TRUE, - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - max_n_coalitions = 30, - iterative_args = list( - initial_n_coalitions = 6, - convergence_tol = 0.0005, - n_coal_next_iter_factor_vec = rep(10^(-6), 10), - max_iter = 8 - ), - iterative = TRUE,verbose=c("basic","progress") -) - -ex <- explain( - testing = TRUE, - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "regression_separate", - phi0 = p0, - max_n_coalitions = 30, - iterative = TRUE,verbose=c("vS_details") -) -ex <- explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "regression_separate", - phi0 = p0, - max_n_coalitions = 30, - 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) -) - -ex <- explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "regression_surrogate", - phi0 = p0, - max_n_coalitions = 30, - 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) -) - - -future::plan("multisession", workers = 4) -progressr::handlers(global = TRUE) - - -ex <- explain( - testing = TRUE, - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "vaeac", - phi0 = p0, - max_n_coalitions = 30, - iterative = FALSE,verbose=c("basic","progress","vS_details"), - n_MC_samples = 100, - vaeac.epochs = 3 -) - -ex2 <- explain( - testing = TRUE, - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "vaeac", - phi0 = p0, - max_n_coalitions = 30, - iterative = FALSE,verbose=c("basic","progress","vS_details"), - n_MC_samples = 100, - vaeac.extra_parameters = list( - vaeac.pretrained_vaeac_model = ex$internal$parameters$vaeac - ) -) - - - -vaeac.extra_parameters = list( - vaeac.pretrained_vaeac_model = explanation$internal$parameters$vaeac -) - - -ex <- explain( - testing = TRUE, - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "regression_separate", - phi0 = p0, - max_n_coalitions = 30, - iterative = FALSE,verbose=c("basic") -) - - -ex <- explain( - testing = TRUE, - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "empirical", - phi0 = p0, - max_n_coalitions = 30, - iterative_args = list( - initial_n_coalitions = 6, - convergence_tol = 0.0005, - n_coal_next_iter_factor_vec = rep(10^(-6), 10), - max_iter = 8 - ), - iterative = TRUE,verbose=c("basic","convergence","shapley") -) - - -explain( - testing = TRUE, - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - iterative = TRUE, - iterative_args <- list(n_initial_) - verbose = c("basic"), - paired_shap_sampling = TRUE -) diff --git a/inst/scripts/devel/explain_new.R b/inst/scripts/devel/explain_new.R deleted file mode 100644 index 1e86d1276..000000000 --- a/inst/scripts/devel/explain_new.R +++ /dev/null @@ -1,164 +0,0 @@ -library(xgboost) -library(shapr) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) - -# Looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) - -# Prepare the data for explanation -explainer <- shapr(x_train,model) -explainer2 <- shapr(x_train,model,is_python=T) -explainer3 <- shapr(x_train,is_python=T) - - -# Specifying the phi_0, i.e. the expected prediction without any features -p <- 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) - -##### TESTS #### - -explanation_new <- explain_new( - x_test, - approach = "gaussian", - explainer = explainer1, - phi0 = p, - n_samples = 5*10^5,n_batches = 1 -) - -explanation_new$dt_shapley -#none lstat rm dis indus -#1: 22.446 5.190027 -0.9981141 0.4190562 4.2444812 -#2: 22.446 1.828362 -1.3269640 -0.2576771 0.5622163 -#3: 22.446 5.447883 4.6641813 -0.1288418 0.6862445 -#4: 22.446 5.617564 2.5234393 0.6614170 2.1817247 -#5: 22.446 1.715925 5.0150390 0.8001951 1.7526796 -#6: 22.446 2.592836 -2.6699632 0.6478134 1.8333372 - -explanation_new <- explain_new( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p, - n_samples = 10^5,n_batches = 4 -) - -explanation_new$dt_shapley -#none lstat rm dis indus -#1: 22.446 5.194753 -0.9882765 0.4020399 4.2469342 -#2: 22.446 1.809298 -1.3121239 -0.2562250 0.5649886 -#3: 22.446 5.447172 4.6691463 -0.1240268 0.6771758 -#4: 22.446 5.626358 2.5125083 0.6672642 2.1780147 -#5: 22.446 1.712585 5.0095470 0.8175396 1.7441671 -#6: 22.446 2.590425 -2.6672009 0.6596539 1.8211454 - -explanation_new <- explain_new( - x_test, - approach = "empirical", - explainer = explainer, - phi0 = p, - n_samples = 10^5,n_batches = 1 -) - -explanation_new$dt_shapley -#none lstat rm dis indus -#1: 22.446 5.2632030 -1.2526613 0.2920444 4.5528644 -#2: 22.446 0.1671901 -0.7088401 0.9689005 0.3786871 -#3: 22.446 5.9888022 5.5450858 0.5660134 -1.4304351 -#4: 22.446 8.2142204 0.7507572 0.1893366 1.8298304 -#5: 22.446 0.5059898 5.6875103 0.8432238 2.2471150 -#6: 22.446 1.9929673 -3.6001958 0.8601984 3.1510531 - -explanation_new <- explain_new( - x_test, - approach = "empirical", - explainer = explainer, - phi0 = p, - n_samples = 10^5,n_batches = 4 -) - -explanation_new$dt_shapley -#none lstat rm dis indus -#1: 22.446 5.2632030 -1.2526613 0.2920444 4.5528644 -#2: 22.446 0.1671901 -0.7088401 0.9689005 0.3786871 -#3: 22.446 5.9888022 5.5450858 0.5660134 -1.4304351 -#4: 22.446 8.2142204 0.7507572 0.1893366 1.8298304 -#5: 22.446 0.5059898 5.6875103 0.8432238 2.2471150 -#6: 22.446 1.9929673 -3.6001958 0.8601984 3.1510531 - -#### TESTS ENDS ##### - -# -# print(explanation$dt) -# -# setup <- explain_setup( -# x_test, -# approach = "gaussian", -# explainer = explainer, -# phi0 = p -# ) -# -# str(explainer,max.level = 1) -# str(setup,max.level=1) -# -explainer <- explain_setup( - x_test, - approach = "empirical", - explainer = explainer, - phi0 = p, - n_batches = 4 - ) - -explainer0 <- explain_setup( - x_test, - approach = c("empirical","copula","ctree","gaussian"), - explainer = explainer, - phi0 = p, - n_batches = 7 -) - -explainer0$X - -# -# -# dt <- future.apply::future_lapply(X = explainer$S_batch, -# FUN = batch_prepare_vS, -# explainer = explainer, -# future.seed = explainer$seed) -# dt <- batch_prepare_vS(explainer$S_batch[[4]],explainer) -# -# -# explanation_new <- explain_new( -# x_test, -# approach = "gaussian", -# explainer = explainer, -# phi0 = p, -# n_samples = 10^5 -# ) - - - -prepare_data(explainer, index_features = explainer$S_batch[[1]]) -# 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$dt) - -# Finally we plot the resulting explanations -plot(explanation) diff --git a/inst/scripts/devel/future_testing.R b/inst/scripts/devel/future_testing.R deleted file mode 100644 index 6d6734f76..000000000 --- a/inst/scripts/devel/future_testing.R +++ /dev/null @@ -1,56 +0,0 @@ - -plan(multisession, workers = 5) # Adjust the number of workers as needed -plan(sequential) # Adjust the number of workers as needed - -fun <- function(x) { - print(x) - if(z==0){ - if(x==5){ - Sys.sleep(1) - z <<- 100 - } - return(x+z) - } else { - return(NA) - } -} - -z <- 0 - - - - -plan(multisession, workers = 5) -plan(multicore, workers = 5) - -plan(sequential) - -fun2 <- function(x){ - x^2 -} - - -start <- proc.time() -for(i in 1:100){ - future.apply::future_lapply(1:10, fun2) -} -print(proc.time()-start) -#user system elapsed -#14.985 0.045 20.323 - -start <- proc.time() -for(i in 1:10){ - future.apply::future_lapply(rep(1:10,10), fun2) -} -print(proc.time()-start) -#user system elapsed -#1.504 0.005 2.009 - -start <- proc.time() -aa=future.apply::future_lapply(rep(1:10,100), fun2) -print(proc.time()-start) -#user system elapsed -#0.146 0.000 0.202 - - - diff --git a/inst/scripts/devel/inspect_sim_res.R b/inst/scripts/devel/inspect_sim_res.R deleted file mode 100644 index 3f5c3993a..000000000 --- a/inst/scripts/devel/inspect_sim_res.R +++ /dev/null @@ -1,14 +0,0 @@ -logfile_bash="memory_log_test_big.csv" -logfile_Rscript="timing_log_test_big.csv" - - -bash <- fread(file.path("inst/scripts/devel/",logfile_bash)) -Rscript <- fread(file.path("inst/scripts/devel/",logfile_Rscript)) - -names(bash) <- c("date","time","mem","p","n_train","n_test","n_batches","n_cores","approach","multicore_method","logfilename") - - -bash[,mem_MB:=mem/1024] - -bash[,list(max_mem_MB=max(mem_MB)),by=c("p","n_train","n_test","n_batches","n_cores","approach","multicore_method")] -str(bash) diff --git a/inst/scripts/devel/master_res.rds b/inst/scripts/devel/master_res.rds deleted file mode 100644 index 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timing_log_test_big.csv -2022-01-23, 15:45:03, 119422040 , 0, 13, 10000, 100, 32, 32, gaussian, multicore, timing_log_test_big.csv -2022-01-23, 15:47:02, 133338388 , 0, 13, 10000, 100, 32, 32, ctree, multisession, timing_log_test_big.csv -2022-01-23, 15:49:26, 127923326 , 0, 13, 10000, 100, 32, 32, ctree, multisession, timing_log_test_big.csv -2022-01-23, 15:51:35, 132161860 , 0, 13, 10000, 100, 32, 32, ctree, multicore, timing_log_test_big.csv -2022-01-23, 15:53:26, 126884391 , 0, 13, 10000, 100, 32, 32, ctree, multicore, timing_log_test_big.csv diff --git a/inst/scripts/devel/real_data_iterative_kernelshap.R b/inst/scripts/devel/real_data_iterative_kernelshap.R deleted file mode 100644 index 0e33ae141..000000000 --- a/inst/scripts/devel/real_data_iterative_kernelshap.R +++ /dev/null @@ -1,276 +0,0 @@ - -### Upcoming generalization: - -#1. Use non-linear truth (xgboost or so) -#2. Even more features - -print(Sys.time()) -library(data.table) -library(shapr) -library(ranger) - -# Give me some credit data set -gmc <- read.table("/nr/project/stat//BigInsight//Projects//Explanations//Counterfactual_kode//Carla_datasets//GiveMeSomeCredit-training.csv",header=TRUE, sep=",") -foo <- apply(gmc,1,sum) -ind <- which(is.na(foo)) -gmc <- gmc[-ind,] - - -nobs <- dim(gmc)[1] -ind <- sample(x=nobs, size=round(0.75*nobs)) -gmcTrain <- gmc[ind,-1] -gmcTest <- gmc[-ind,-1] -gmcTrain <- as.data.table(gmcTrain) -gmcTest <- as.data.table(gmcTest) - -integer_columns <- sapply(gmcTrain, is.integer) # Identify integer columns -integer_columns = integer_columns[2:length(integer_columns)] -gmcTrain[, c("RevolvingUtilizationOfUnsecuredLines", "age", -"NumberOfTime30.59DaysPastDueNotWorse", "DebtRatio", "MonthlyIncome", -"NumberOfOpenCreditLinesAndLoans", "NumberOfTimes90DaysLate", -"NumberRealEstateLoansOrLines", "NumberOfTime60.89DaysPastDueNotWorse", "NumberOfDependents"):= -lapply(.SD, as.numeric), .SDcols = c("RevolvingUtilizationOfUnsecuredLines", "age", -"NumberOfTime30.59DaysPastDueNotWorse", "DebtRatio", "MonthlyIncome", -"NumberOfOpenCreditLinesAndLoans", "NumberOfTimes90DaysLate", -"NumberRealEstateLoansOrLines", "NumberOfTime60.89DaysPastDueNotWorse", "NumberOfDependents")] -integer_columns <- sapply(gmcTest, is.integer) # Identify integer columns -integer_columns = integer_columns[2:length(integer_columns)] -gmcTest[, c("RevolvingUtilizationOfUnsecuredLines", "age", -"NumberOfTime30.59DaysPastDueNotWorse", "DebtRatio", "MonthlyIncome", -"NumberOfOpenCreditLinesAndLoans", "NumberOfTimes90DaysLate", -"NumberRealEstateLoansOrLines", "NumberOfTime60.89DaysPastDueNotWorse", "NumberOfDependents"):= -lapply(.SD, as.numeric), .SDcols = c("RevolvingUtilizationOfUnsecuredLines", "age", -"NumberOfTime30.59DaysPastDueNotWorse", "DebtRatio", "MonthlyIncome", -"NumberOfOpenCreditLinesAndLoans", "NumberOfTimes90DaysLate", -"NumberRealEstateLoansOrLines", "NumberOfTime60.89DaysPastDueNotWorse", "NumberOfDependents")] - -# model <- ranger(SeriousDlqin2yrs ~ ., data = gmcTrain, num.trees = 500, num.threads = 6, -# verbose = TRUE, -# probability = FALSE, -# importance = "impurity", -# mtry = sqrt(11), -# seed = 3045) -library(hmeasure) -#pred.rf <- predict(model, data = gmcTest) -#results <- HMeasure(unlist(as.vector(gmcTest[,1])),pred.rf$predictions,threshold=0.15) -#results$metrics$AUC - -y_train = gmcTrain$SeriousDlqin2yrs -x_train = gmcTrain[,-1] -y_explain = gmcTest$SeriousDlqin2yrs -x_explain = gmcTest[,-1] - -set.seed(123) -model <- xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 50, - verbose = FALSE,params = list(objective = "binary:logistic") -) -pred.xgb <- predict(model, newdata = as.matrix(x_explain)) -results <- HMeasure(as.vector(y_explain),pred.xgb,threshold=0.15) -results$metrics$AUC - - -set.seed(123) - -inds_train = sample(1:nrow(x_train), 9000) -x_train = x_train[inds_train,] -y_train = y_train[inds_train] - -m = ncol(x_train) - - -p0 <- mean(y_train) -mu = colMeans(x_train) -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/" -kernelSHAP_reweighting_strategy = "none" - -predict_model_xgb <- function(object,newdata){ - xgboost:::predict.xgb.Booster(object,as.matrix(newdata)) -} - - -preds_explain <- predict_model_xgb(model,x_explain) -head(order(-preds_explain),50) -inds_1 <- head(order(-preds_explain),50) -set.seed(123) -inds_2 <- sample(which(preds_explain>quantile(preds_explain,0.9) & preds_explain 0.05 -shapley_threshold_val <- 0.02 -shapley_threshold_prob <- 0.2 - -source("inst/scripts/devel/iterative_kernelshap_sourcefuncs.R") - -testObs_computed_vec <- inds# seq_len(n_explain) -runres_list <- runcomps_list <- list() - -cutoff_feats = colnames(x_train) - -run_obj_list <- list() -for(kk in seq_along(testObs_computed_vec)){ - testObs_computed <- testObs_computed_vec[kk] - full_pred <- predict_model_xgb(model,x_explain)[testObs_computed] - shapsum_other_features <- 0 - - - run <- iterative_kshap_func(model,x_explain,x_train, - testObs_computed = testObs_computed, - cutoff_feats = cutoff_feats, - initial_n_coalitions = 50, - full_pred = full_pred, - shapsum_other_features = shapsum_other_features, - p0 = p0, - predict_model = predict_model_xgb, - shapley_threshold_val = shapley_threshold_val, - shapley_threshold_prob = shapley_threshold_prob, - approach = approach, - kernelSHAP_reweighting_strategy = kernelSHAP_reweighting_strategy) - - runres_list[[kk]] <- run$kshap_final - runcomps_list[[kk]] <- sum(sapply(run$keep_list,"[[","no_computed_combinations")) - run_obj_list[[kk]] <- run - print(kk) - print(Sys.time()) -} - -est <- rbindlist(runres_list) -est[,other_features:=NULL] -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() -for (i in seq_along(testObs_computed_vec)){ - expl_approx_obj <- shapr::explain(model = model, - x_explain= x_explain[testObs_computed_vec[i],], - x_train = x_train, - approach = approach, - phi0 = p0, - n_coalitions = runcomps_list[[i]]) - 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_est - -colnames(expl_approx) <- colnames(truth) -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)) -mae_vec <- colMeans(abs(est-truth)) - -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_", kernelSHAP_reweighting_strategy, ".RData")) - -hist(unlist(runcomps_list),breaks = 20) - -summary(unlist(runcomps_list)) - - -run$kshap_final -sum(unlist(run$kshap_final)) -full_pred - -print(Sys.time()) - -# TODO: Må finne ut av hvorfor det ikke gir korrekt sum her... -# Hvis det er noen variabler som ble ekskludert, så må jeg legge til disse i summen for å få prediksjonen til modellen. -# for(i in 1:18){ -# print(sum(unlist(run$keep_list[[i]]$kshap_est_dt[,-1]))+run$keep_list[[i]]$shap_it_excluded_features) -# #print(run$keep_list[[i]]$shap_it_excluded_features) -# } - -# run$kshap_it_est_dt - - - -# run$kshap_final -# expl$shapley_values_est - - - - -# kshap_final <- copy(run$kshap_est_dt_list[,-1]) -# setnafill(kshap_final,"locf") -# kshap_final[.N,] # final estimate - -# sum(unlist(kshap_final[.N,])) - -# sum(unlist(expl$shapley_values_est[testObs_computed,])) - - - - - - - - - - -# cutoff_feats <- paste0("VV",1:6) -# testObs_computed <- 5 - -# full_pred <- predict(model,x_explain)[5] -# p0 <- mean(y_train) -# pred_not_to_decompose <- sum(expl$shapley_values_est[5,VV7:VV9]) - - -# run_minor <- iterative_kshap_func(model,x_explain,x_train, -# testObs_computed = 5, -# cutoff_feats = cutoff_feats, -# full_pred = full_pred, -# pred_not_to_decompose = pred_not_to_decompose, -# p0 = p0, -# predict_model = predict.lm,shapley_threshold_val = 0) - - -# aa=run$keep_list[[8]]$dt_vS - -# bb=run_minor$keep_list[[6]]$dt_vS -# setnames(bb,"p_hat_1","p_hat_1_approx") - -# cc=merge(aa,bb) -# cc[,diff:=p_hat_1-p_hat_1_approx] - - -# TODO: - -# 1. Run example with gaussian features where the truth is known in advance in a large setting, with e.g. 12 features or so. I want the estimate -# both for the full 12 features, and for subsets where one is removed. -# 2. - -# Utfordringer: -# 1. Hvordan justere vekter og samplingrutine fra subset S når man allerede har et utvalg sampler (som også er noe biased). -# 2. Bruker altså E[f(x1=x1*,x2,x3=x3*,x4)|x1=x1*] som proxy for E[f(x1=x1*,x2,x3=x3*,x4)|x1=x1*,x3=x3*], -#men hva med E[f(x1=x1*,x2,x3,x4=x4*)|x1=x1*,x4=x4*]? Burde jeg bruke den for -#E[f(x1=x1*,x2,x3=x3*,x4=x4*)|x1=x1*,x4=x4*]? -# 3. Når jeg fjerner en variabel (som har lite å si), så settes shapley-verdien til det den har per da. MEN den verdien vil trolig være noe biased fordi den fjernes første gangen den går over terskelverdiene -# jeg har satt for ekskludering. - diff --git a/inst/scripts/devel/real_data_iterative_kernelshap_analyze_results.R b/inst/scripts/devel/real_data_iterative_kernelshap_analyze_results.R deleted file mode 100644 index 866d28bf9..000000000 --- a/inst/scripts/devel/real_data_iterative_kernelshap_analyze_results.R +++ /dev/null @@ -1,135 +0,0 @@ -library(data.table) -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_", kernelSHAP_reweighting_strategy, ".RData")) - - - -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_", 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)) -mae_vec <- colMeans(abs(exact_vals - iterative_vals)) - -bias_vec_approx <- colMeans(exact_vals - approx_vals) -rmse_vec_approx <- sqrt(colMeans((exact_vals - approx_vals)^2)) -mae_vec_approx <- colMeans(abs(exact_vals - approx_vals)) - -treeshap_vals <- as.data.table(predict(model,newdata=as.matrix(x_explain),predcontrib = TRUE)) -setnames(treeshap_vals,"BIAS","none") -setcolorder(treeshap_vals,"none") -head(treeshap_vals) -mae_vec_treeshap <- colMeans(abs(exact_vals - treeshap_vals)) -mean(mae_vec_treeshap[-1]) - - -library(ggplot2) - -# Create a data frame for the bar plot - -# MAE -df <- data.frame(matrix(0, length(mae_vec)*2, 3)) -colnames(df) <- c("MAE", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- mae_vec_approx -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- mae_vec -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) -df <- as.data.table(df) -df[,features0:=.GRP,by="features"] -df[,features1:=paste0("VV",features0)] -df[,features1:=factor(features1,levels=c(paste0("VV",1:11)))] - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features1, y = MAE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "mae_comparison.png"), plot = p, width = 10, height = 5) - - -runcomps_list - -df = data.frame(matrix(0, length(runcomps_list), 1)) -colnames(df) <- c("n_rows") -df$n_rows <- as.numeric(runcomps_list) - -p <- ggplot(df, aes(n_rows)) + - geom_histogram() -ggsave(paste0(sim_results_folder, "n_rows.png"), plot = p,width = 10, height = 5) - - - - - - - - - -# RMSE -df <- data.frame(matrix(0, length(rmse_vec)*2, 3)) -colnames(df) <- c("RMSE", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- rmse_vec_approx -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- rmse_vec -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = RMSE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "rmse_comparison.png"), plot = p) - - -# Bias -df <- data.frame(matrix(0, length(bias_vec)*2, 3)) -colnames(df) <- c("abs_bias", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- abs(bias_vec_approx) -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- abs(bias_vec) -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = abs_bias, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "bias_comparison.png"), plot = p) - -# Number of sample used -runcomps_list - -df = data.frame(matrix(0, length(runcomps_list), 1)) -colnames(df) <- c("n_rows") -df$n_rows <- as.numeric(runcomps_list) - -p <- ggplot(df, aes(n_rows)) + - geom_histogram() -ggsave(paste0(sim_results_folder, "n_rows.png"), plot = p) - -#### Just looking at the largest predictions - -preds <- rowSums(exact_vals) - -these <- head(order(-preds),10) - -preds[these]-rowSums(iterative_vals)[these] - -bias_vec <- colMeans(exact_vals[these] - iterative_vals[these]) -rmse_vec <- sqrt(colMeans((exact_vals[these] - iterative_vals[these])^2)) -mae_vec <- colMeans(abs(exact_vals[these] - iterative_vals[these])) - -bias_vec_approx <- colMeans(exact_vals[these] - approx_vals[these]) -rmse_vec_approx <- sqrt(colMeans((exact_vals[these] - approx_vals[these])^2)) -mae_vec_approx <- colMeans(abs(exact_vals[these] - approx_vals[these])) - - - diff --git a/inst/scripts/devel/same_seed_as_master.R b/inst/scripts/devel/same_seed_as_master.R deleted file mode 100644 index 4460b7e62..000000000 --- a/inst/scripts/devel/same_seed_as_master.R +++ /dev/null @@ -1,39 +0,0 @@ -library(xgboost) -#library(shapr) -library(data.table) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus")#,"nox","age","tax","ptratio") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) -# THIS IS GENERATED FROM MASTER BRANCH -# Prepare the data for explanation -explainer <- shapr(x_train, model,n_coalitions = 100) -p = mean(y_train) -gauss = explain(x_test, explainer, "gaussian", phi0 = p, n_samples = 10000) -emp = explain(x_test, explainer, "empirical", phi0 = p, n_samples = 10000) -copula = explain(x_test, explainer, "copula", phi0 = p, n_samples = 10000) -indep = explain(x_test, explainer, "independence", phi0 = p, n_samples = 10000) -comb = explain(x_test, explainer, c("gaussian", "gaussian", "empirical", "empirical"), phi0 = p, n_samples = 10000) -ctree = explain(x_test, explainer, "ctree", mincriterion = 0.95, phi0 = p, n_samples = 10000) -ctree2 = explain(x_test, explainer, "ctree", mincriterion = c(0.95, 0.95, 0.95, 0.95), phi0 = p, n_samples = 10000) - - -# results from master - -res_master = readRDS("inst/scripts/devel/master_res.rds") - -all.equal(comb$dt, res_master$comb$dt) #TRUE -all.equal(comb$p, res_master$comb$p) #TRUE diff --git a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_analyze_results.R b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_analyze_results.R deleted file mode 100644 index ac35df40c..000000000 --- a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_analyze_results.R +++ /dev/null @@ -1,88 +0,0 @@ -library(data.table) -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,"_",kernelSHAP_reweighting_strategy, ".RData")) - - -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,"_",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)) -mae_vec <- colMeans(abs(exact_vals - iterative_vals)) - -bias_vec_approx <- colMeans(exact_vals - approx_vals) -rmse_vec_approx <- sqrt(colMeans((exact_vals - approx_vals)^2)) -mae_vec_approx <- colMeans(abs(exact_vals - approx_vals)) - -library(ggplot2) - -# Create a data frame for the bar plot - -# MAE -df <- data.frame(matrix(0, length(mae_vec)*2, 3)) -colnames(df) <- c("MAE", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- mae_vec_approx -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- mae_vec -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) -df <- as.data.table(df) -df[,features:=factor(features,levels=c("phi0",paste0("VV",1:12)))] - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = MAE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "mae_comparison.png"), plot = p, width = 10, height = 5) - - -# RMSE -df <- data.frame(matrix(0, length(rmse_vec)*2, 3)) -colnames(df) <- c("RMSE", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- rmse_vec_approx -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- rmse_vec -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = RMSE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "rmse_comparison.png"), plot = p) - - -# Bias -df <- data.frame(matrix(0, length(bias_vec)*2, 3)) -colnames(df) <- c("abs_bias", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- abs(bias_vec_approx) -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- abs(bias_vec) -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = abs_bias, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "bias_comparison.png"), plot = p) - -# Number of sample used -runcomps_list - -df = data.frame(matrix(0, length(runcomps_list), 1)) -colnames(df) <- c("n_rows") -df$n_rows <- as.numeric(runcomps_list) - -p <- ggplot(df, aes(n_rows)) + - geom_histogram() -ggsave(paste0(sim_results_folder, "n_rows.png"), plot = p) - diff --git a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R b/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R deleted file mode 100644 index afbd2467f..000000000 --- a/inst/scripts/devel/simtest_iterative_kernelshap_lingauss_v2.R +++ /dev/null @@ -1,261 +0,0 @@ - -### Upcoming generalization: - -#1. Use non-linear truth (xgboost or so) -#2. Even more features - - -library(data.table) -library(MASS) -library(Matrix) -library(shapr) -library(future) -library(xgboost) - -shapley_threshold_prob <- 0.2 -shapley_threshold_val <- 0.1 - -m <- 12 -n_train <- 5000 -n_explain <- 100 -rho_1 <- 0.5 -rho_2 <- 0.5 -rho_3 <- 0.5 -rho_4 <- 0 -Sigma_1 <- matrix(rho_1, m/4, m/4) + diag(m/4) * (1 - rho_1) -Sigma_2 <- matrix(rho_2, m/4, m/4) + diag(m/4) * (1 - rho_2) -Sigma_3 <- matrix(rho_3, m/4, m/4) + diag(m/4) * (1 - rho_3) -Sigma_4 <- matrix(rho_4, m/4, m/4) + diag(m/4) * (1 - rho_4) - -Sigma <- as.matrix(bdiag(Sigma_1, Sigma_2, Sigma_3, Sigma_4)) -mu <- rep(0,m) - -library(corrplot) -corrplot(Sigma) -set.seed(123) - - -x_train <- as.data.table(MASS::mvrnorm(n_train,mu,Sigma)) -x_explain <- as.data.table(MASS::mvrnorm(n_explain,mu,Sigma)) - -names(x_train) <- paste0("VV",1:m) -names(x_explain) <- paste0("VV",1:m) - - -beta <- c(5:1, rep(0, m - 5)) -alpha <- 1 -y_train <- as.vector(alpha + as.matrix(x_train) %*% beta + rnorm(n_train, 0, 1)) -y_explain <- alpha + as.matrix(x_explain) %*% beta + rnorm(n_explain, 0, 1) - -xy_train <- cbind(y_train, x_train) - -set.seed(123) - -model <- lm(y_train ~ .,data = xy_train) - -pred_train <- predict(model, x_train) -plot(unlist(x_train[,1]),pred_train) -plot(unlist(x_train[,2]),pred_train) -plot(unlist(x_train[,3]),pred_train) -plot(unlist(x_train[,4]),pred_train) -plot(unlist(x_train[,5]),pred_train) -plot(unlist(x_train[,6]),pred_train) - -this_order <- order(unlist(x_train[,1])) - -plot(unlist(x_train[this_order,1]),pred_train[this_order],type="l") - -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/" -kernelSHAP_reweighting_strategy = "none" - -set.seed(465132) -inds = 1:n_explain -progressr::handlers(global = TRUE) -expl <- shapr::explain(model = model, - x_explain= x_explain[inds,], - x_train = x_train, - approach = "gaussian", - phi0 = p0,Sigma=Sigma,mu=mu) - -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) - - -### Need to create an lm analogoue to pred_mod_xgb here - - -set.seed(123) - - - -# These are the parameters for for interative_kshap_func -n_samples <- 1000 -approach = "gaussian" - -# Reduce if < 10% prob of shapval > 0.2 - -source("inst/scripts/devel/iterative_kernelshap_sourcefuncs.R") - -testObs_computed_vec <- inds# seq_len(n_explain) - -# Using threshold: 0.1 -runres_list <- runcomps_list <- list() -for(kk in testObs_computed_vec){ - testObs_computed <- testObs_computed_vec[kk] - full_pred <- predict(model,x_explain)[testObs_computed] - shapsum_other_features <- 0 - - - run <- iterative_kshap_func(model,x_explain,x_train, - testObs_computed = testObs_computed, - cutoff_feats = cutoff_feats, - initial_n_coalitions = 50, - full_pred = full_pred, - shapsum_other_features = shapsum_other_features, - p0 = p0, - predict_model = predict.lm, - shapley_threshold_val = shapley_threshold_val, - shapley_threshold_prob = shapley_threshold_prob, - approach = approach, - n_samples = n_samples, - gaussian.mu = mu, - gaussian.cov_mat = Sigma, - 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) -} - -est <- rbindlist(runres_list) -est[,other_features:=NULL] -fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) - - - - -truth <- expl$shapley_values_est - -expl_approx <- matrix(0, nrow = length(inds), ncol = m+1) -expl_approx_obj_list <- list() -for (i in testObs_computed_vec){ - expl_approx_obj <- shapr::explain(model = model, - x_explain= x_explain[inds[i],], - x_train = x_train, - approach = "gaussian", - phi0 = p0, - n_coalitions = runcomps_list[[i]], - Sigma=Sigma,mu=mu) - expl_approx[i,] = unlist(expl_approx_obj$shapley_values_est) - expl_approx_obj_list[[i]] <- expl_approx_obj -} -expl_approx <- as.data.table(expl_approx) -colnames(expl_approx) <- colnames(truth) -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)) -mae_vec <- colMeans(abs(est-truth)) - -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,"_",kernelSHAP_reweighting_strategy, ".RData")) - -hist(unlist(runcomps_list),breaks = 20) - -summary(unlist(runcomps_list)) - - -run$kshap_final -sum(unlist(run$kshap_final)) -full_pred - - - - - - - - -# TODO: Må finne ut av hvorfor det ikke gir korrekt sum her... -# Hvis det er noen variabler som ble ekskludert, så må jeg legge til disse i summen for å få prediksjonen til modellen. -# for(i in 1:18){ -# print(sum(unlist(run$keep_list[[i]]$kshap_est_dt[,-1]))+run$keep_list[[i]]$shap_it_excluded_features) -# #print(run$keep_list[[i]]$shap_it_excluded_features) -# } - -# run$kshap_it_est_dt - - - -# run$kshap_final -# expl$shapley_values_est - - - - -# kshap_final <- copy(run$kshap_est_dt_list[,-1]) -# setnafill(kshap_final,"locf") -# kshap_final[.N,] # final estimate - -# sum(unlist(kshap_final[.N,])) - -# sum(unlist(expl$shapley_values_est[testObs_computed,])) - - - - - - - - - - -# cutoff_feats <- paste0("VV",1:6) -# testObs_computed <- 5 - -# full_pred <- predict(model,x_explain)[5] -# p0 <- mean(y_train) -# pred_not_to_decompose <- sum(expl$shapley_values_est[5,VV7:VV9]) - - -# run_minor <- iterative_kshap_func(model,x_explain,x_train, -# testObs_computed = 5, -# cutoff_feats = cutoff_feats, -# full_pred = full_pred, -# pred_not_to_decompose = pred_not_to_decompose, -# p0 = p0, -# predict_model = predict.lm,shapley_threshold_val = 0) - - -# aa=run$keep_list[[8]]$dt_vS - -# bb=run_minor$keep_list[[6]]$dt_vS -# setnames(bb,"p_hat_1","p_hat_1_approx") - -# cc=merge(aa,bb) -# cc[,diff:=p_hat_1-p_hat_1_approx] - - -# TODO: - -# 1. Run example with gaussian features where the truth is known in advance in a large setting, with e.g. 12 features or so. I want the estimate -# both for the full 12 features, and for subsets where one is removed. -# 2. - -# Utfordringer: -# 1. Hvordan justere vekter og samplingrutine fra subset S når man allerede har et utvalg sampler (som også er noe biased). -# 2. Bruker altså E[f(x1=x1*,x2,x3=x3*,x4)|x1=x1*] som proxy for E[f(x1=x1*,x2,x3=x3*,x4)|x1=x1*,x3=x3*], -#men hva med E[f(x1=x1*,x2,x3,x4=x4*)|x1=x1*,x4=x4*]? Burde jeg bruke den for -#E[f(x1=x1*,x2,x3=x3*,x4=x4*)|x1=x1*,x4=x4*]? -# 3. Når jeg fjerner en variabel (som har lite å si), så settes shapley-verdien til det den har per da. MEN den verdien vil trolig være noe biased fordi den fjernes første gangen den går over terskelverdiene -# jeg har satt for ekskludering. - diff --git a/inst/scripts/devel/simtest_iterative_kernelshap_nonlingauss_analyze_results.R b/inst/scripts/devel/simtest_iterative_kernelshap_nonlingauss_analyze_results.R deleted file mode 100644 index 9888f57f1..000000000 --- a/inst/scripts/devel/simtest_iterative_kernelshap_nonlingauss_analyze_results.R +++ /dev/null @@ -1,122 +0,0 @@ -library(data.table) -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,"_",kernelSHAP_reweighting_strategy, ".RData")) - - -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,"_",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)) -mae_vec <- colMeans(abs(exact_vals - iterative_vals)) - -bias_vec_approx <- colMeans(exact_vals - approx_vals) -rmse_vec_approx <- sqrt(colMeans((exact_vals - approx_vals)^2)) -mae_vec_approx <- colMeans(abs(exact_vals - approx_vals)) - -mean(mae_vec[-1]) -mean(mae_vec_approx[-1]) - -treeshap_vals <- as.data.table(predict(model,newdata=as.matrix(x_explain),predcontrib = TRUE)) -setnames(treeshap_vals,"BIAS","none") -setcolorder(treeshap_vals,"none") -head(treeshap_vals) -mae_vec_treeshap <- colMeans(abs(exact_vals - treeshap_vals)) -mean(mae_vec_treeshap[-1]) - -library(ggplot2) - -# Create a data frame for the bar plot - -# MAE -df <- data.frame(matrix(0, length(mae_vec)*2, 3)) -colnames(df) <- c("MAE", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- mae_vec_approx -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- mae_vec -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) -df <- as.data.table(df) -dt_treeshap <- data.frame(MAE=mae_vec_treeshap,approach="TreeSHAP",features=names(mae_vec_treeshap)) -df <- rbind(df,dt_treeshap) - -df[,features:=factor(features,levels=c("phi0",paste0("VV",1:12)))] - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = MAE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "mae_comparison.png"), plot = p, width = 10, height = 5) - - - - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = MAE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "mae_comparison.png"), plot = p) - - -# RMSE -df <- data.frame(matrix(0, length(rmse_vec)*2, 3)) -colnames(df) <- c("RMSE", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- rmse_vec_approx -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- rmse_vec -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) -df[,features:=factor(features,levels=c("phi0",paste0("VV",1:12)))] - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = MAE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "mae_comparison.png"), plot = p, width = 10, height = 5) - - - - - - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = RMSE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "rmse_comparison.png"), plot = p) - - -# Bias -df <- data.frame(matrix(0, length(bias_vec)*2, 3)) -colnames(df) <- c("abs_bias", "approach", "features") -# rownames(df) <- names(exact_vals) -df[1:length(exact_vals), 1] <- abs(bias_vec_approx) -df[1:length(exact_vals), 2] <- rep("approx", length(exact_vals)) -df[(length(exact_vals)+1):nrow(df), 1] <- abs(bias_vec) -df[(length(exact_vals)+1):nrow(df), 2] <- rep("iterative", length(exact_vals)) -df[, 3] <- rep(names(exact_vals), 2) -df[,features:=factor(features,levels=c("phi0",paste0("VV",1:12)))] - -# Create the bar plot using ggplot -p <- ggplot(df, aes(x = features, y = MAE, fill = approach)) + - geom_col(position = "dodge") -ggsave(paste(sim_results_folder, "mae_comparison.png"), plot = p, width = 10, height = 5) - - -# Number of sample used -runcomps_list - -df = data.frame(matrix(0, length(runcomps_list), 1)) -colnames(df) <- c("n_rows") -df$n_rows <- as.numeric(runcomps_list) - -p <- ggplot(df, aes(n_rows)) + - geom_histogram() -ggsave(paste0(sim_results_folder, "n_rows.png"), plot = p) - diff --git a/inst/scripts/devel/simtest_reweighting_strategies.R b/inst/scripts/devel/simtest_reweighting_strategies.R deleted file mode 100644 index 3f6a1e3df..000000000 --- a/inst/scripts/devel/simtest_reweighting_strategies.R +++ /dev/null @@ -1,263 +0,0 @@ -### Upcoming generalization: - -#1. Use non-linear truth (xgboost or so) -#2. Even more features - - -library(data.table) -library(MASS) -library(Matrix) -library(shapr) -library(future) -library(xgboost) - -m <- 12 -n_train <- 5000 -n_explain <- 5 -rho_1 <- 0.9 -rho_2 <- 0.6 -rho_3 <- 0.3 -rho_4 <- 0.1 -Sigma_1 <- matrix(rho_1, m/4, m/4) + diag(m/4) * (1 - rho_1) -Sigma_2 <- matrix(rho_2, m/4, m/4) + diag(m/4) * (1 - rho_2) -Sigma_3 <- matrix(rho_3, m/4, m/4) + diag(m/4) * (1 - rho_3) -Sigma_4 <- matrix(rho_4, m/4, m/4) + diag(m/4) * (1 - rho_4) - -Sigma <- as.matrix(bdiag(Sigma_1, Sigma_2, Sigma_3, Sigma_4)) -mu <- rep(0,m) - -set.seed(123) - - -x_train <- as.data.table(MASS::mvrnorm(n_train,mu,Sigma)) -x_explain <- as.data.table(MASS::mvrnorm(n_explain,mu,Sigma)) - -names(x_train) <- paste0("VV",1:m) -names(x_explain) <- paste0("VV",1:m) - - -beta <- rnorm(m) -alpha <- 1 -y_train <- as.vector(alpha + as.matrix(x_train) %*% beta + rnorm(n_train, 0, 1)) -y_explain <- alpha + as.matrix(x_explain) %*% beta + rnorm(n_explain, 0, 1) - -xy_train <- cbind(y_train, x_train) - -set.seed(123) - -model <- lm(y_train ~ .,data = xy_train) - -p0 <- mean(y_train) - - -### First run proper shapr call on this - -kernelSHAP_reweighting_strategy = "none" - -set.seed(465132) -progressr::handlers(global = TRUE) -expl <- shapr::explain(model = model, - x_explain= x_explain, - x_train = x_train, - approach = "gaussian", - n_batches=100,n_samples = 10000, - phi0 = p0,Sigma=Sigma,mu=mu) - -dt_vS_map <- merge(expl$internal$iter_list[[1]]$coalition_map,expl$internal$output$dt_vS,by="id_coalition")[,-"id_coalition"] - - -kernelSHAP_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond") - -n_coalitions_vec <- c(50,100,200,400,800,1200,1600,2000,2400,2800,3200,3600,4000) - -reps <- 100 - -paired_shap_sampling_vec <- c(FALSE,TRUE) - -res_list <- list() - -for(i0 in seq_along(paired_shap_sampling_vec)){ - - for(i in seq_len(reps)){ - - for(ii in seq_along(n_coalitions_vec)){ - - this_seed <- 1+i - this_n_coalitions <- n_coalitions_vec[ii] - this_paired_shap_sampling <- paired_shap_sampling_vec[i0] - - this <- shapr::explain(model = model, - x_explain= x_explain, - x_train = x_train, - approach = "gaussian", - n_samples = 10, # Never used - n_batches=10, - phi0 = p0, - Sigma=Sigma, - mu=mu, - seed = this_seed, - max_n_coalitions = this_n_coalitions, - kernelSHAP_reweighting = "none", - paired_shap_sampling = this_paired_shap_sampling) - - this0_X <- this$internal$objects$X - - - exact_dt_vS <- merge(this$internal$iter_list[[1]]$coalition_map,dt_vS_map,by="coalitions_str") - setorder(exact_dt_vS,id_coalition) - - - for(iii in seq_along(kernelSHAP_reweighting_strategy_vec)){ - this_kernelSHAP_reweighting_strategy <- kernelSHAP_reweighting_strategy_vec[iii] - - this_X <- copy(this0_X) - - shapr:::kernelSHAP_reweighting(this_X,reweight=this_kernelSHAP_reweighting_strategy) - - this_W <- weight_matrix( - X = this_X, - normalize_W_weights = TRUE - ) - - shap_dt0 <- as.data.table(cbind(seq_len(n_explain),t(this_W%*%as.matrix(exact_dt_vS[,-c("coalitions_str","id_coalition")])))) - names(shap_dt0) <- names(this$shapley_values_est) - - 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)) - this_RMSE <- sqrt(mean(this_diff^2)) - - res_vec <- data.table(n_coalitions = this_n_coalitions, - paired_shap_sampling = this_paired_shap_sampling, - kernelSHAP_reweighting_strategy = this_kernelSHAP_reweighting_strategy, - seed = this_seed, - bias=this_bias, - var = this_var, - MAE = this_MAE, - RMSE = this_RMSE) - - res_list[[length(res_list)+1]] <- copy(res_vec) - - } - - } - - print(i) - - } - -} - - -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,kernelSHAP_reweighting_strategy)] - -library(ggplot2) - -ggplot(resres[paired_shap_sampling==TRUE],aes(x=n_coalitions,y=MAE,col=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ - geom_line() - - - -#### OLD #### - -### Need to create an lm analogoue to pred_mod_xgb here - - -set.seed(123) - - - -# These are the parameters for for interative_kshap_func -n_samples <- 1000 -approach = "gaussian" - -# Reduce if < 10% prob of shapval > 0.2 - -source("inst/scripts/devel/iterative_kernelshap_sourcefuncs.R") - -testObs_computed_vec <- inds# seq_len(n_explain) - -# Using threshold: 0.1 -runres_list <- runcomps_list <- list() -for(kk in testObs_computed_vec){ - testObs_computed <- testObs_computed_vec[kk] - full_pred <- predict(model,x_explain)[testObs_computed] - shapsum_other_features <- 0 - - - run <- iterative_kshap_func(model,x_explain,x_train, - testObs_computed = testObs_computed, - cutoff_feats = cutoff_feats, - initial_n_combinations = 50, - full_pred = full_pred, - shapsum_other_features = shapsum_other_features, - p0 = p0, - predict_model = predict.lm, - shapley_threshold_val = shapley_threshold_val, - shapley_threshold_prob = shapley_threshold_prob, - approach = approach, - n_samples = n_samples, - gaussian.mu = mu, - gaussian.cov_mat = Sigma, - kernelSHAP_reweighting_strategy = kernelSHAP_reweighting_strategy) - runres_list[[kk]] <- run$kshap_final - runcomps_list[[kk]] <- sum(sapply(run$keep_list,"[[","no_computed_combinations")) - print(kk) -} - -est <- rbindlist(runres_list) -est[,other_features:=NULL] -fwrite(est,paste0(sim_results_saving_folder,"iterative_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) - - - - -truth <- expl$shapley_values_est - -expl_approx <- matrix(0, nrow = length(inds), ncol = m+1) -expl_approx_obj_list <- list() -for (i in testObs_computed_vec){ - expl_approx_obj <- shapr::explain(model = model, - x_explain= x_explain[inds[i],], - x_train = x_train, - approach = "gaussian", - phi0 = p0, - n_combinations = runcomps_list[[i]], - Sigma=Sigma,mu=mu) - expl_approx[i,] = unlist(expl_approx_obj$shapley_values_est) - expl_approx_obj_list[[i]] <- expl_approx_obj -} -expl_approx <- as.data.table(expl_approx) -colnames(expl_approx) <- colnames(truth) -fwrite(expl_approx,paste0(sim_results_saving_folder,"approx_shapley_values_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".csv")) - -bias_vec <- colMeans(est-truth) -rmse_vec <- sqrt(colMeans((est-truth)^2)) -mae_vec <- colMeans(abs(est-truth)) - -bias_vec_approx <- colMeans(expl_approx-truth) -rmse_vec_approx <- sqrt(colMeans((expl_approx-truth)^2)) -mae_vec_approx <- colMeans(abs(expl_approx-truth)) - -save.image(paste0(sim_results_saving_folder, "iterative_kernelshap_",shapley_threshold_val,"_",kernelSHAP_reweighting_strategy, ".RData")) - -hist(unlist(runcomps_list),breaks = 20) - -summary(unlist(runcomps_list)) - - -run$kshap_final -sum(unlist(run$kshap_final)) -full_pred - - - - - - - diff --git a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R b/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R deleted file mode 100644 index c7ab347b7..000000000 --- a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear.R +++ /dev/null @@ -1,182 +0,0 @@ -### Upcoming generalization: - -#1. Use non-linear truth (xgboost or so) -#2. Even more features - - -library(data.table) -library(MASS) -library(Matrix) -library(shapr) -library(future) -library(xgboost) - -m <- 12 -n_train <- 5000 -n_explain <- 5 -rho_1 <- 0.9 -rho_2 <- 0.6 -rho_3 <- 0.3 -rho_4 <- 0.1 -Sigma_1 <- matrix(rho_1, m/4, m/4) + diag(m/4) * (1 - rho_1) -Sigma_2 <- matrix(rho_2, m/4, m/4) + diag(m/4) * (1 - rho_2) -Sigma_3 <- matrix(rho_3, m/4, m/4) + diag(m/4) * (1 - rho_3) -Sigma_4 <- matrix(rho_4, m/4, m/4) + diag(m/4) * (1 - rho_4) - -Sigma <- as.matrix(bdiag(Sigma_1, Sigma_2, Sigma_3, Sigma_4)) -mu <- rep(0,m) - -set.seed(123) - - -x_train <- as.data.table(MASS::mvrnorm(n_train,mu,Sigma)) -x_explain <- as.data.table(MASS::mvrnorm(n_explain,mu,Sigma)) - -names(x_train) <- paste0("VV",1:m) -names(x_explain) <- paste0("VV",1:m) - - -g <- function(a,b){ - a*b+a*b^2+a^2*b -} - -beta <- c(0.2, -0.8, 1.0, 0.5, -0.8, rep(0, m - 5)) -gamma <- c(0.8,-1) -alpha <- 1 -y_train <- alpha + - as.vector(as.matrix(cos(x_train))%*%beta) + - unlist(gamma[1]*g(x_train[,1],x_train[,2])) + - unlist(gamma[1]*g(x_train[,3],x_train[,4])) + - rnorm(n_train, 0, 1) -y_explain <- alpha + - as.vector(as.matrix(cos(x_explain))%*%beta) + - unlist(gamma[1]*g(x_explain[,1],x_explain[,2])) + - unlist(gamma[1]*g(x_explain[,3],x_explain[,4])) + - rnorm(n_train, 0, 1) - -xy_train <- cbind(y_train, x_train) - -set.seed(123) -model <- xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 50, - verbose = FALSE -) - -p0 <- mean(y_train) - - -### First run proper shapr call on this - -kernelSHAP_reweighting_strategy = "none" - -set.seed(465132) -progressr::handlers(global = TRUE) -expl <- shapr::explain(model = model, - x_explain= x_explain, - x_train = x_train, - approach = "gaussian", - n_batches=100,n_samples = 10000, - phi0 = p0,Sigma=Sigma,mu=mu) - -dt_vS_map <- merge(expl$internal$iter_list[[1]]$coalition_map,expl$internal$output$dt_vS,by="id_coalition")[,-"id_coalition"] - - -kernelSHAP_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond") - -n_coalitions_vec <- c(50,100,200,400,800,1200,1600,2000,2400,2800,3200,3600,4000) - -reps <- 100 - -paired_shap_sampling_vec <- c(FALSE,TRUE) - -res_list <- list() - -for(i0 in seq_along(paired_shap_sampling_vec)){ - - for(i in seq_len(reps)){ - - for(ii in seq_along(n_coalitions_vec)){ - - this_seed <- 1+i - this_n_coalitions <- n_coalitions_vec[ii] - this_paired_shap_sampling <- paired_shap_sampling_vec[i0] - - this <- shapr::explain(model = model, - x_explain= x_explain, - x_train = x_train, - approach = "gaussian", - n_samples = 10, # Never used - n_batches=10, - phi0 = p0, - Sigma=Sigma, - mu=mu, - seed = this_seed, - max_n_coalitions = this_n_coalitions, - kernelSHAP_reweighting = "none", - paired_shap_sampling = this_paired_shap_sampling) - - this0_X <- this$internal$objects$X - - - exact_dt_vS <- merge(this$internal$iter_list[[1]]$coalition_map,dt_vS_map,by="coalitions_str") - setorder(exact_dt_vS,id_coalition) - - - for(iii in seq_along(kernelSHAP_reweighting_strategy_vec)){ - this_kernelSHAP_reweighting_strategy <- kernelSHAP_reweighting_strategy_vec[iii] - - this_X <- copy(this0_X) - - shapr:::kernelSHAP_reweighting(this_X,reweight=this_kernelSHAP_reweighting_strategy) - - this_W <- weight_matrix( - X = this_X, - normalize_W_weights = TRUE - ) - - shap_dt0 <- as.data.table(cbind(seq_len(n_explain),t(this_W%*%as.matrix(exact_dt_vS[,-c("coalitions_str","id_coalition")])))) - names(shap_dt0) <- names(this$shapley_values_est) - - 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)) - this_RMSE <- sqrt(mean(this_diff^2)) - - res_vec <- data.table(n_coalitions = this_n_coalitions, - paired_shap_sampling = this_paired_shap_sampling, - kernelSHAP_reweighting_strategy = this_kernelSHAP_reweighting_strategy, - seed = this_seed, - bias=this_bias, - var = this_var, - MAE = this_MAE, - RMSE = this_RMSE) - - res_list[[length(res_list)+1]] <- copy(res_vec) - - } - - } - - print(i) - - } - -} - - -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,kernelSHAP_reweighting_strategy)] - -library(ggplot2) - -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=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 deleted file mode 100644 index 84f9e71c4..000000000 --- a/inst/scripts/devel/simtest_reweighting_strategies_nonlinear_nonunique_sampling.R +++ /dev/null @@ -1,217 +0,0 @@ -### Upcoming generalization: - -#1. Use non-linear truth (xgboost or so) -#2. Even more features - - -library(data.table) -library(MASS) -library(Matrix) -library(shapr) -library(future) -library(xgboost) - -m <- 12 -n_train <- 5000 -n_explain <- 5 -rho_1 <- 0.9 -rho_2 <- 0.6 -rho_3 <- 0.3 -rho_4 <- 0.1 -Sigma_1 <- matrix(rho_1, m/4, m/4) + diag(m/4) * (1 - rho_1) -Sigma_2 <- matrix(rho_2, m/4, m/4) + diag(m/4) * (1 - rho_2) -Sigma_3 <- matrix(rho_3, m/4, m/4) + diag(m/4) * (1 - rho_3) -Sigma_4 <- matrix(rho_4, m/4, m/4) + diag(m/4) * (1 - rho_4) - -Sigma <- as.matrix(bdiag(Sigma_1, Sigma_2, Sigma_3, Sigma_4)) -mu <- rep(0,m) - -set.seed(123) - - -x_train <- as.data.table(MASS::mvrnorm(n_train,mu,Sigma)) -x_explain <- as.data.table(MASS::mvrnorm(n_explain,mu,Sigma)) - -names(x_train) <- paste0("VV",1:m) -names(x_explain) <- paste0("VV",1:m) - - -g <- function(a,b){ - a*b+a*b^2+a^2*b -} - -beta <- c(0.2, -0.8, 1.0, 0.5, -0.8, rep(0, m - 5)) -gamma <- c(0.8,-1) -alpha <- 1 -y_train <- alpha + - as.vector(as.matrix(cos(x_train))%*%beta) + - unlist(gamma[1]*g(x_train[,1],x_train[,2])) + - unlist(gamma[1]*g(x_train[,3],x_train[,4])) + - rnorm(n_train, 0, 1) -y_explain <- alpha + - as.vector(as.matrix(cos(x_explain))%*%beta) + - unlist(gamma[1]*g(x_explain[,1],x_explain[,2])) + - unlist(gamma[1]*g(x_explain[,3],x_explain[,4])) + - rnorm(n_train, 0, 1) - -xy_train <- cbind(y_train, x_train) - -set.seed(123) -model <- xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 50, - verbose = FALSE -) - -p0 <- mean(y_train) - - -### First run proper shapr call on this - -kernelSHAP_reweighting_strategy = "none" - -set.seed(465132) -progressr::handlers(global = TRUE) -expl <- shapr::explain(model = model, - x_explain= x_explain, - x_train = x_train, - approach = "gaussian", - n_batches=100,n_samples = 10000, - phi0 = p0,Sigma=Sigma,mu=mu) - -dt_vS_map <- merge(expl$internal$iter_list[[1]]$coalition_map,expl$internal$output$dt_vS,by="id_coalition")[,-"id_coalition"] - - -kernelSHAP_reweighting_strategy_vec <- c("none","on_N","on_coal_size","on_all","on_all_cond","on_all_cond_paired","comb") - -n_coalitions_vec <- c(50,100,200,400,800,1200,1600,2000,2400,2800,3200,3600,4000) - -reps <- 200 - -paired_shap_sampling_vec <- c(FALSE,TRUE) - -res_list <- weight_list <- list() - -for(ii in seq_along(n_coalitions_vec)){ - - for(i0 in seq_along(paired_shap_sampling_vec)){ - - for(i in seq_len(reps)){ - - this_seed <- 10000+1+i - this_n_coalitions <- n_coalitions_vec[ii] - this_paired_shap_sampling <- paired_shap_sampling_vec[i0] - - this <- shapr::explain(model = model, - x_explain= x_explain, - x_train = x_train, - approach = "gaussian", - n_samples = 10, # Never used - n_batches=10, - phi0 = p0, - Sigma=Sigma, - mu=mu, - seed = this_seed, - max_n_coalitions = this_n_coalitions, - kernelSHAP_reweighting = "none", - unique_sampling = TRUE, - paired_shap_sampling = this_paired_shap_sampling) - - this0_X <- this$internal$objects$X - - - exact_dt_vS <- merge(this$internal$iter_list[[1]]$coalition_map,dt_vS_map,by="coalitions_str") - setorder(exact_dt_vS,id_coalition) - - - for(iii in seq_along(kernelSHAP_reweighting_strategy_vec)){ - this_kernelSHAP_reweighting_strategy <- kernelSHAP_reweighting_strategy_vec[iii] - - this_X <- copy(this0_X) - - shapr:::kernelSHAP_reweighting(this_X,reweight=this_kernelSHAP_reweighting_strategy) - - this_W <- weight_matrix( - X = this_X, - normalize_W_weights = TRUE - ) - - shap_dt0 <- as.data.table(cbind(seq_len(n_explain),t(this_W%*%as.matrix(exact_dt_vS[,-c("coalitions_str","id_coalition")])))) - names(shap_dt0) <- names(this$shapley_values_est) - - 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)) - this_RMSE <- sqrt(mean(this_diff^2)) - - res_vec <- data.table(n_coalitions = this_n_coalitions, - paired_shap_sampling = this_paired_shap_sampling, - kernelSHAP_reweighting_strategy = this_kernelSHAP_reweighting_strategy, - seed = this_seed, - bias=this_bias, - var = this_var, - MAE = this_MAE, - RMSE = this_RMSE) - - res_list[[length(res_list)+1]] <- copy(res_vec) - - # weight_dt <- unique(this_X[,.(coalition_size,shapley_weight)][,shapley_weight:=mean(shapley_weight),by=coalition_size][]) - weight_dt <- this_X[,.(coalition_size,shapley_weight)][,head(.SD,1),by=coalition_size] - - weight_dt[,n_coalitions:=this_n_coalitions] - weight_dt[,paired_shap_sampling:=this_paired_shap_sampling] - weight_dt[,kernelSHAP_reweighting_strategy:=this_kernelSHAP_reweighting_strategy] - weight_dt[,seed:=this_seed] - - weight_list[[length(weight_list)+1]] <- copy(weight_dt) - - - } - - } - - print(i) - - } - - print(n_coalitions_vec[ii]) -} - - -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,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=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ - geom_line() - -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=kernelSHAP_reweighting_strategy,linetype= paired_shap_sampling))+ - geom_line()+scale_y_log10() - - - - -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,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,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,kernelSHAP_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 deleted file mode 100644 index acc7e3e2a..000000000 --- a/inst/scripts/devel/simtest_timing_to_Frida.R +++ /dev/null @@ -1,107 +0,0 @@ -library(data.table) -library(MASS) -library(Matrix) -library(shapr) -library(future) -library(xgboost) - -shapley_threshold_prob <- 0.2 -shapley_threshold_val <- 0.1 - -m <- 12 -n_train <- 5000 -n_explain <- 100 -rho_1 <- 0.5 -rho_2 <- 0.5 -rho_3 <- 0.5 -rho_4 <- 0 -Sigma_1 <- matrix(rho_1, m/4, m/4) + diag(m/4) * (1 - rho_1) -Sigma_2 <- matrix(rho_2, m/4, m/4) + diag(m/4) * (1 - rho_2) -Sigma_3 <- matrix(rho_3, m/4, m/4) + diag(m/4) * (1 - rho_3) -Sigma_4 <- matrix(rho_4, m/4, m/4) + diag(m/4) * (1 - rho_4) - -Sigma <- as.matrix(bdiag(Sigma_1, Sigma_2, Sigma_3, Sigma_4)) -mu <- rep(0,m) - -set.seed(123) - - -x_train <- as.data.table(MASS::mvrnorm(n_train,mu,Sigma)) -x_explain <- as.data.table(MASS::mvrnorm(n_explain,mu,Sigma)) - -names(x_train) <- paste0("VV",1:m) -names(x_explain) <- paste0("VV",1:m) - - -g <- function(a,b){ - a*b+a*b^2+a^2*b -} - -beta <- c(0.2, -0.8, 1.0, 0.5, -0.8, rep(0, m - 5)) -gamma <- c(0.8,-1) -alpha <- 1 -y_train <- alpha + - as.vector(as.matrix(cos(x_train))%*%beta) + - unlist(gamma[1]*g(x_train[,1],x_train[,2])) + - unlist(gamma[1]*g(x_train[,3],x_train[,4])) + - rnorm(n_train, 0, 1) -y_explain <- alpha + - as.vector(as.matrix(cos(x_explain))%*%beta) + - unlist(gamma[1]*g(x_explain[,1],x_explain[,2])) + - unlist(gamma[1]*g(x_explain[,3],x_explain[,4])) + - rnorm(n_train, 0, 1) - -xy_train <- cbind(y_train, x_train) - -set.seed(123) -model <- xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 50, - verbose = FALSE -) - -pred_train <- predict(model, as.matrix(x_train)) - -this_order <- order(unlist(x_train[,1])) - -plot(unlist(x_train[this_order,1]),pred_train[this_order],type="l") - -p0 <- mean(y_train) - - -### First run proper shapr call on this - - -set.seed(465132) -inds = 1:5#1:n_explain - -expl <- explain( - model = model, - x_explain= x_explain[inds,], - x_train = x_train, - approach = "gaussian", - phi0 = p0, - n_coalitions = 100, - Sigma=Sigma, - mu=mu, - iterative = TRUE, - unique_sampling = FALSE, - iterative_args = list(initial_n_coalitions = 50, - fixed_n_coalitions_per_iter = 50, - max_iter = 10, - convergence_tol = 10^(-10), - compute_sd = TRUE), - kernelSHAP_reweighting = "none", - print_iter_info = TRUE -) - -# Number of (non-unique) coalitions per iteration -sapply(expl$internal$iter_list,function(dt) dt$X[,sum(sample_freq)]) - -# Timing of main function call -expl$timing$main_timing_secs - -# Timings per iteration -expl$timing$iter_timing_secs_dt[] - diff --git a/inst/scripts/devel/testing_explain_forevast_n_comb.R b/inst/scripts/devel/testing_explain_forevast_n_comb.R deleted file mode 100644 index 03bea2181..000000000 --- a/inst/scripts/devel/testing_explain_forevast_n_comb.R +++ /dev/null @@ -1,214 +0,0 @@ - - -h3test <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 3, - approach = "empirical", - phi0 = p0_ar[1:3], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = i, - n_coalitions = 300 -) - -h2test <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 2, - approach = "empirical", - phi0 = p0_ar[1:2], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = i, - n_coalitions = 10^7 -) - -h1test <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 1, - approach = "empirical", - phi0 = p0_ar[1], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = i, - n_coalitions = 10^7 -) - -w <- h3test$internal$objects$X_list[[1]][["shapley_weight"]] - -w[-c(1, length(w))] <- w[-c(1, length(w))] / sum(w[-c(1, length(w))]) -h3test$internal$objects$X_list[[1]][,shapley_weight_norm := w] - - -w <- h1test$internal$objects$X_list[[1]][["shapley_weight"]] - -w[-c(1, length(w))] <- w[-c(1, length(w))] / sum(w[-c(1, length(w))]) -h1test$internal$objects$X_list[[1]][,shapley_weight_norm := w] - - -w2 <- h1full$internal$objects$X_list[[1]][["shapley_weight"]] - -w2[-c(1, length(w2))] <- w2[-c(1, length(w2))] / sum(w2[-c(1, length(w2))]) -h1full$internal$objects$X_list[[1]][,shapley_weight_norm := w2] - -h1test$internal$objects$X_list[[1]][-c(1,.N),] -h1full$internal$objects$X_list[[1]][-c(1,.N),] -h3test$internal$objects$X_list[[1]][-c(1,.N),] - - -ncomb <- 50 - -reps <- 10 - -set.seed(123) -h3full <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 3, - approach = "empirical", - phi0 = p0_ar[1:3], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = 1) - -set.seed(123) -h1full <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 1, - approach = "empirical", - phi0 = p0_ar[1], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = 1) - - - -h1list <- h2list <- h3list <- list() -for (i in 1:reps){ - h3list[[i]] <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 3, - approach = "empirical", - phi0 = p0_ar[1:3], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = i, - n_coalitions = ncomb - ) - - h2list[[i]] <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 2, - approach = "empirical", - phi0 = p0_ar[1:2], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = i, - n_coalitions = ncomb - ) - - h1list[[i]] <- explain_forecast(model = model_arima_temp, - y = data[1:150, "Temp"], - xreg = data[, "Wind"], - train_idx = 2:148, - explain_idx = 149:150, - explain_y_lags = 2, - explain_xreg_lags = 2, - horizon = 1, - approach = "empirical", - phi0 = p0_ar[1], - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - seed = i, - n_coalitions = min(ncomb,31) - ) - - print(i) -} - - - -cols_horizon1 <- h3full$internal$objects$cols_per_horizon[[1]] -cols_horizon2 <- h3full$internal$objects$cols_per_horizon[[2]] -cols_horizon3 <- h3full$internal$objects$cols_per_horizon[[3]] - -h1mean1 <- h2mean1 <- h2mean2 <- h3mean1 <- h3mean2 <- h3mean3 <- list() -for(i in 1:reps){ - h1mean1[[i]] <- as.matrix(h1list[[i]]$shapley_values_est[horizon==1, ..cols_horizon1]) - - h2mean1[[i]] <- as.matrix(h2list[[i]]$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_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]) - -} - -# Horizon 1 -Reduce("+", h1mean1) / reps -Reduce("+", h2mean1) / reps -Reduce("+", h3mean1) / reps -h3full$shapley_values_est[horizon==1,..cols_horizon1] - -# Horizon 2 -Reduce("+", h2mean2) / reps -Reduce("+", h3mean2) / reps -h3full$shapley_values_est[horizon==2,..cols_horizon2] - -# Horizon 3 -Reduce("+", h3mean3) / reps -h3full$shapley_values_est[horizon==3,..cols_horizon3] - - - -expect_equal(h2$shapley_values_est[horizon==1, ..cols_horizon1], - h1$shapley_values_est[horizon==1,..cols_horizon1]) - -expect_equal(h3$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$shapley_values_est[horizon==2, ..cols_horizon2], - h2$shapley_values_est[horizon==2,..cols_horizon2]) diff --git a/inst/scripts/devel/testing_for_valid_defualt_n_batches.R b/inst/scripts/devel/testing_for_valid_defualt_n_batches.R deleted file mode 100644 index a097fe73c..000000000 --- a/inst/scripts/devel/testing_for_valid_defualt_n_batches.R +++ /dev/null @@ -1,54 +0,0 @@ -# In this code we demonstrate that (before the bugfix) the `explain()` function -# does not enter the exact mode when n_coalitions is larger than or equal to 2^m. -# The mode is only changed if n_coalitions is strictly larger than 2^m. -# This means that we end up with using all coalitions when n_coalitions is 2^m, -# but use not the exact Shapley kernel weights. -# Bugfix replaces `>` with `=>`in the places where the code tests if -# n_coalitions is larger than or equal to 2^m. Then the text/messages printed by -# shapr and the code correspond. - -library(xgboost) -library(data.table) - -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] - -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" - -ind_x_explain <- 1:6 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# Fitting a basic xgboost model to the training data -model <- xgboost::xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(y_train) - -# Shapr sets the default number of batches to be 10 for this dataset for the -# "ctree", "gaussian", and "copula" approaches. Thus, setting `n_coalitions` -# to any value lower of equal to 10 causes the error. -any_number_equal_or_below_10 = 8 - -# Before the bugfix, shapr:::check_n_batches() throws the error: -# Error in check_n_batches(internal) : -# `n_batches` (10) must be smaller than the number feature combinations/`n_coalitions` (8) -# Bug only occures for "ctree", "gaussian", and "copula" as they are treated different in -# `get_default_n_batches()`, I am not certain why. Ask Martin about the logic behind that. -explanation <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - n_samples = 2, # Low value for fast computations - approach = "gaussian", - phi0 = p0, - n_coalitions = any_number_equal_or_below_10 -) diff --git a/inst/scripts/devel/testing_intermediate_saving.R b/inst/scripts/devel/testing_intermediate_saving.R deleted file mode 100644 index 85981c381..000000000 --- a/inst/scripts/devel/testing_intermediate_saving.R +++ /dev/null @@ -1,132 +0,0 @@ - - -aa = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - iterative_args = list( - initial_n_coalitions = 10, - convergence_tol = 0.01, - n_coal_next_iter_factor_vec = rep(10^(-5), 10), - max_iter = 30 - ), - iterative = TRUE, - print_shapleyres = TRUE, - print_iter_info = TRUE,kernelSHAP_reweighting = "on_N" -) - -bb = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - iterative_args = list( - initial_n_coalitions = 10, - convergence_tol = 0.001, - n_coal_next_iter_factor_vec = rep(10^(-5), 10), - max_iter = 30 - ), - iterative = TRUE, - print_shapleyres = TRUE, - print_iter_info = TRUE,kernelSHAP_reweighting = "on_N",prev_shapr_object = aa -) - - - - -##### Reproducable results setting seed outside, and not setting it inside of explain (+ an seed-independent approach) -# Add something like this - - -set.seed(123) -full = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - iterative_args = list( - initial_n_coalitions = 10, - convergence_tol = 0.001, - n_coal_next_iter_factor_vec = rep(10^(-5), 10), - max_iter = 7 - ), - iterative = TRUE, - print_shapleyres = TRUE, - print_iter_info = TRUE, - kernelSHAP_reweighting = "on_N", - seed=NULL -) - -set.seed(123) -first = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - iterative_args = list( - initial_n_coalitions = 10, - convergence_tol = 0.001, - n_coal_next_iter_factor_vec = rep(10^(-5), 10), - max_iter = 4 - ), - iterative = TRUE, - print_shapleyres = TRUE, - print_iter_info = TRUE, - kernelSHAP_reweighting = "on_N", - seed=NULL -) - - -second = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - iterative_args = list( - initial_n_coalitions = 10, - convergence_tol = 0.001, - n_coal_next_iter_factor_vec = rep(10^(-5), 10), - max_iter = 7 - ), - iterative = TRUE, - print_shapleyres = TRUE, - print_iter_info = TRUE, - kernelSHAP_reweighting = "on_N", - seed=NULL, - prev_shapr_object = first -) - - - -# This cannot be tested, I think. -second_path = explain( - model = model_lm_numeric, - x_explain = x_explain_numeric, - x_train = x_train_numeric, - approach = "independence", - phi0 = p0, - iterative_args = list( - initial_n_coalitions = 10, - convergence_tol = 0.001, - n_coal_next_iter_factor_vec = rep(10^(-5), 10), - max_iter = 5 - ), - iterative = TRUE, - print_shapleyres = TRUE, - print_iter_info = TRUE, - kernelSHAP_reweighting = "on_N", - seed=NULL, - prev_shapr_object = first$internal$parameters$output_args$saving_path -) - - -# Identical results -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/testing_memory_monitoring.R b/inst/scripts/devel/testing_memory_monitoring.R deleted file mode 100644 index f161c3d2e..000000000 --- a/inst/scripts/devel/testing_memory_monitoring.R +++ /dev/null @@ -1,98 +0,0 @@ - - -library(shapr) -library(future) -library(MASS) -library(microbenchmark) -library(data.table) -library(peakRAM) - -p_vec <- 20#2:10 -n_train_vec <- 1000 -n_test_vec <- 100#c(2,10,20) -n_batches_vec <- c(1,2,4,8,16,24,32)#seq(2,20,by=5) -n_cores_vec <- c(1,2,4,8,16,24,32)#c(1,seq(2,32,by=5)) -approach_vec <- c("empirical","gaussian","ctree")#rev(c("empirical","gaussian")) -reps <- 2 - -max_n <- 10^5 -max_p <- 20 -rho <- 0.3 -Sigma <- matrix(rho,max_p,max_p) -diag(Sigma) <- 1 -mu <- rep(0,max_p) -beta <- c(1,seq_len(max_p)/max_p) -sigma_eps <- 1 - -set.seed(123) -x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma) -y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps) - - - -set.seed(123) -these_p <- sample.int(max_p,size=p_vec[1]) -these_train <- sample.int(max_n,size=n_train_vec[1]) -these_test <- sample.int(max_n,size=n_test_vec[1]) - -x_train <- as.data.frame(x_all[these_train,these_p]) -x_test <- as.data.frame(x_all[these_test,these_p]) - -y_train <- y_all[these_train] - -xy_train <- cbind(x_train,y=y_train) - -model <- lm(formula = y~.,data=xy_train) - -explainer <- shapr(x_train, model,n_coalitions = 1000) -p <- mean(y_train) - - -future::plan("multicore",workers=4) -#future::plan("multisession",workers=4) -#future::plan("sequential") - -peakRAM(explain( - x_test, - approach = "gaussian", - explainer = explainer, - phi0 = p,n_batches = 4) - ) - -# , -# explain( -# x_test, -# approach = "empirical", -# explainer = explainer, -# phi0 = p,n_batches = 2), -# explain( -# x_test, -# approach = "empirical", -# explainer = explainer, -# phi0 = p,n_batches = 4)) - -# explain( -# x_test, -# approach = "empirical", -# explainer = explainer, -# phi0 = p,n_batches = 8), -# explain( -# x_test, -# approach = "empirical", -# explainer = explainer, -# phi0 = p,n_batches = 16), -# explain( -# x_test, -# approach = "empirical", -# explainer = explainer, -# phi0 = p,n_batches = 32) -# ) - -# s <- proc.time() -# explain( -# x_test, -# approach = "empirical", -# explainer = explainer, -# phi0 = p,n_batches = 32) -# print(proc.time()-s) -# diff --git a/inst/scripts/devel/testing_n_cobinations_equal_2_power_m.R b/inst/scripts/devel/testing_n_cobinations_equal_2_power_m.R deleted file mode 100644 index ee8a01e3f..000000000 --- a/inst/scripts/devel/testing_n_cobinations_equal_2_power_m.R +++ /dev/null @@ -1,71 +0,0 @@ -# In this code we demonstrate that (before the bugfix) the `explain()` function -# does not enter the exact mode when n_coalitions is larger than or equal to 2^m. -# The mode is only changed if n_coalitions is strictly larger than 2^m. -# This means that we end up with using all coalitions when n_coalitions is 2^m, -# but use not the exact Shapley kernel weights. -# Bugfix replaces `>` with `=>`in the places where the code tests if -# n_coalitions is larger than or equal to 2^m. Then the text/messages printed by -# shapr and the code correspond. - -library(xgboost) -library(data.table) - -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] - -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" - -ind_x_explain <- 1:6 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# Fitting a basic xgboost model to the training data -model <- xgboost::xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(y_train) - -# Computing the conditional Shapley values using the gaussian approach -explanation_exact <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - n_samples = 2, # Low value for fast computations - n_batches = 1, # Not related to the bug - approach = "gaussian", - phi0 = p0, - n_coalitions = NULL -) - -# Computing the conditional Shapley values using the gaussian approach -explanation_should_also_be_exact <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - n_samples = 2, # Low value for fast computations - n_batches = 1, # Not related to the bug - approach = "gaussian", - phi0 = p0, - n_coalitions = 2^ncol(x_explain) -) - -# see that both `explain()` objects have the same number of combinations -explanation_exact$internal$parameters$n_coalitions -explanation_should_also_be_exact$internal$parameters$n_coalitions - -# But the first one of them is exact and the other not. -explanation_exact$internal$parameters$exact -explanation_should_also_be_exact$internal$parameters$exact - -# Can also see that the Shapley weights differ, as the first one uses -# the correct values, while the other one uses the sampling frequency. -explanation_exact$internal$objects$X$shapley_weight -explanation_should_also_be_exact$internal$objects$X$shapley_weight diff --git a/inst/scripts/devel/testing_parallelization.R b/inst/scripts/devel/testing_parallelization.R deleted file mode 100644 index 3f82541f2..000000000 --- a/inst/scripts/devel/testing_parallelization.R +++ /dev/null @@ -1,176 +0,0 @@ - - -library(shapr) -library(future) -library(MASS) -library(microbenchmark) -library(data.table) - -p_vec <- 10#2:10 -n_train_vec <- 1000 -n_test_vec <- 100#c(2,10,20) -n_batches_vec <- c(1,2,4,8,16,24,32)#seq(2,20,by=5) -n_cores_vec <- c(1,2,4,8,16,24,32)#c(1,seq(2,32,by=5)) -approach_vec <- c("empirical","gaussian","ctree")#rev(c("empirical","gaussian")) -reps <- 2 - -max_n <- 10^5 -max_p <- 10 -rho <- 0.3 -Sigma <- matrix(rho,max_p,max_p) -diag(Sigma) <- 1 -mu <- rep(0,max_p) -beta <- c(1,seq_len(max_p)/max_p) -sigma_eps <- 1 - -set.seed(123) -x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma) -y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps) - - -res_dt <- as.data.table(expand.grid(p = p_vec, - n_train = n_train_vec, - n_test = n_test_vec, - n_batches = n_batches_vec, - n_cores = n_cores_vec, - approach = approach_vec)) - -res_dt[,n_cores:=ifelse(n_cores>n_batches,n_batches,n_cores)] -res_dt <- unique(res_dt) - -res_dt[,approach:=as.character(approach)] -res_dt[,time_median:=as.numeric(NA)] -res_dt[,time_min:=as.numeric(NA)] -res_dt[,mem_alloc:=as.numeric(NA)] - - -for(i in seq_len(nrow(res_dt))){ -#for(i in sample.int(nrow(res_dt),10)){ - - set.seed(123) - these_p <- sample.int(max_p,size=res_dt[i,p]) - these_train <- sample.int(max_n,size=res_dt[i,n_train]) - these_test <- sample.int(max_n,size=res_dt[i,n_test]) - - x_train <- as.data.frame(x_all[these_train,these_p]) - x_test <- as.data.frame(x_all[these_test,these_p]) - - y_train <- y_all[these_train] - - xy_train <- cbind(x_train,y=y_train) - - model <- lm(formula = y~.,data=xy_train) - - explainer <- shapr(x_train, model) - p <- mean(y_train) - - - n_batches_use <- min(nrow(explainer$S),res_dt[i,n_batches]) - n_cores_use <- res_dt[i,n_cores] - approach_use <- res_dt[i,approach] - - #future::plan("multicore",workers=n_cores_use) - future::plan("multisession",workers=n_cores_use) - - - res0 <- bench::mark({ - explanation <- explain( - x_test, - approach = approach_use, - explainer = explainer, - phi0 = p,n_batches = n_batches_use - )},iterations = reps,time_unit ='s',memory = F, - min_time = Inf - ) - - res_dt[i,c("time_median","time_min","mem_alloc"):= list(res0$median,res0$min,res0$mem_alloc/1024^2),] - - # res_dt[p==res_dt[i,p] & - # n_train == res_dt[i,n_train] & - # n_test == res_dt[i,n_test] & - # n_cores == res_dt[i,n_cores] & - # n_batches == res_dt[i,n_batches] & - # approach == approach_use, - # res:=res0$time[3]/10^6 - # ] - - print(res_dt[i]) -} - -setkey(res_dt,time_median) - -#res_dt[approach=="gaussian"] - - - - -# p n_train n_test n_batches n_cores approach time_median time_min mem_alloc -# 1: 10 1000 100 5 10 empirical 8.264136 8.199610 NA -# 2: 10 1000 100 5 5 empirical 8.277614 8.224627 NA -# 3: 10 1000 100 5 15 empirical 8.351432 8.189444 NA -# 4: 10 1000 100 5 20 empirical 8.394858 8.317760 NA -# 5: 10 1000 100 5 30 empirical 8.496488 8.453119 NA -# 6: 10 1000 100 10 5 empirical 10.534386 10.523246 NA -# 7: 10 1000 100 10 10 empirical 11.659772 11.659772 NA -# 8: 10 1000 100 10 15 empirical 11.767503 11.767503 NA -# 9: 10 1000 100 10 30 empirical 11.835323 11.835323 NA -# 10: 10 1000 100 10 20 empirical 11.902262 11.902262 NA -# 11: 10 1000 100 20 5 empirical 14.750653 14.718519 NA -# 12: 10 1000 100 20 30 empirical 15.426510 15.398783 NA -# 13: 10 1000 100 20 15 empirical 15.426532 15.388514 NA -# 14: 10 1000 100 20 20 empirical 15.468479 15.426808 NA -# 15: 10 1000 100 20 10 empirical 15.564483 15.536153 NA -# 16: 10 1000 100 10 2 empirical 16.275958 16.155311 NA -# 17: 10 1000 100 5 2 empirical 16.520838 16.484130 NA -# 18: 10 1000 100 20 2 empirical 22.812822 22.733153 NA -# 19: 10 1000 100 5 1 empirical 32.814998 32.723445 NA -# 20: 10 1000 100 10 1 empirical 33.740455 33.284869 NA -# 21: 10 1000 100 10 30 gaussian 42.697496 42.123002 NA -# 22: 10 1000 100 10 15 gaussian 43.153707 42.400444 NA -# 23: 10 1000 100 10 20 gaussian 43.331616 42.330915 NA -# 24: 10 1000 100 10 10 gaussian 43.601197 42.580585 NA -# 25: 10 1000 100 20 10 gaussian 43.713152 42.444733 NA -# 26: 10 1000 100 20 1 empirical 44.970672 44.957254 NA -# 27: 10 1000 100 20 15 gaussian 48.515789 48.364623 NA -# 28: 10 1000 100 20 30 gaussian 48.980771 48.716296 NA -# 29: 10 1000 100 20 20 gaussian 49.048357 48.585454 NA -# 30: 10 1000 100 5 10 gaussian 49.929313 49.906563 NA -# 31: 10 1000 100 5 5 gaussian 49.952981 49.428697 NA -# 32: 10 1000 100 20 5 gaussian 49.954880 49.645313 NA -# 33: 10 1000 100 5 30 gaussian 50.220795 49.894032 NA -# 34: 10 1000 100 5 20 gaussian 50.480277 50.116526 NA -# 35: 10 1000 100 5 15 gaussian 50.616905 50.517388 NA -# 36: 10 1000 100 10 5 gaussian 50.739175 48.893451 NA -# 37: 10 1000 100 20 20 ctree 79.067415 79.060347 NA -# 38: 10 1000 100 20 30 ctree 79.178795 78.830831 NA -# 39: 10 1000 100 20 10 ctree 80.194740 76.259531 NA -# 40: 10 1000 100 10 20 ctree 84.368049 83.086716 NA -# 41: 10 1000 100 10 10 ctree 84.583532 84.125999 NA -# 42: 10 1000 100 20 15 ctree 85.021570 84.921147 NA -# 43: 10 1000 100 10 30 ctree 86.293475 83.902999 NA -# 44: 10 1000 100 10 15 ctree 86.549406 85.115549 NA -# 45: 10 1000 100 20 5 ctree 92.955276 92.538537 NA -# 46: 10 1000 100 10 5 ctree 94.816191 92.215222 NA -# 47: 10 1000 100 5 15 ctree 94.846974 94.641546 NA -# 48: 10 1000 100 10 2 gaussian 95.399388 95.341892 NA -# 49: 10 1000 100 5 20 ctree 95.887569 95.437676 NA -# 50: 10 1000 100 5 5 ctree 95.938850 93.705034 NA -# 51: 10 1000 100 5 30 ctree 96.015618 92.434543 NA -# 52: 10 1000 100 5 10 ctree 96.238056 94.071784 NA -# 53: 10 1000 100 20 2 gaussian 96.379812 95.719475 NA -# 54: 10 1000 100 5 2 gaussian 109.674539 108.807517 NA -# 55: 10 1000 100 10 1 gaussian 189.596560 188.909395 NA -# 56: 10 1000 100 5 1 gaussian 191.256527 191.157274 NA -# 57: 10 1000 100 20 1 gaussian 196.929709 196.358810 NA -# 58: 10 1000 100 10 2 ctree 200.709682 200.523174 NA -# 59: 10 1000 100 20 2 ctree 200.942230 200.570071 NA -# 60: 10 1000 100 5 2 ctree 237.327601 236.488531 NA -# 61: 10 1000 100 10 1 ctree 395.500767 393.656852 NA -# 62: 10 1000 100 5 1 ctree 402.635571 401.290227 NA -# 63: 10 1000 100 20 1 ctree 403.930240 403.903723 NA -# p n_train n_test n_batches n_cores approach time_median time_min mem_alloc - - - - - diff --git a/inst/scripts/devel/testing_verification_ar_model.R b/inst/scripts/devel/testing_verification_ar_model.R deleted file mode 100644 index 6cf50f894..000000000 --- a/inst/scripts/devel/testing_verification_ar_model.R +++ /dev/null @@ -1,38 +0,0 @@ -library(data.table) -options(digits = 5) # To avoid round off errors when printing output on different systems -set.seed(123) - -data <- as.data.table(matrix(rnorm(100*3),ncol=3))# first column is noise, the other two are xregs - -# Create AR(1)-structure -y <- rep(0,100) -y[1] <- data[1,1]/5+data[1,2]+data[1,3] -for(i in 2:100){ - y[i] <- y[i-1]+data[i,1]/5+data[i,2]+data[i,3] -} -y <- unlist(y) -plot(y) - -dat <- data.table(y=y,xreg1=unlist(data[,2]),xreg2=unlist(data[,3])) - -model_arima_temp <- arima(dat$y, c(2,1,0), xreg=dat[,2:3]) - - -set.seed(123) -exp <- explain_forecast(model = model_arima_temp, - y = dat$y, - xreg = dat[, 2:3],#dat[, 2:3], - train_idx = 10:50, - explain_idx = 71:72, - explain_y_lags = 0, - explain_xreg_lags = c(0,0), - horizon = 2, - approach = "empirical", - phi0 = c(0,0), - group_lags = FALSE, - n_batches = 1, - timing = FALSE, - n_coalitions = 50 -) - - diff --git a/inst/scripts/devel/time_series_annabelle.R b/inst/scripts/devel/time_series_annabelle.R deleted file mode 100644 index 59e33379d..000000000 --- a/inst/scripts/devel/time_series_annabelle.R +++ /dev/null @@ -1,89 +0,0 @@ -library(data.table) -library(shapr) - -devtools::load_all() - -set.seed(1) -n_train = 1000 -n_test = 6 -n_features = 200 -# x = rnorm((n_train + n_test) * (n_features + 5), mean = 1, sd = 2) -# x = matrix(x, nrow = n_train + n_test, byrow = T) -# x1 = t(apply(x, 1, cumsum)) -# x = data.table(x[, c(1:n_features, n_features + 5)]) - -alpha <- 1 -beta <- 0 -theta <- 0.8 - -data = NULL -for(n in 1:(n_train + n_test)){ - set.seed(n) - e <- rnorm(n_features + 6, mean = 0, sd = 1) - - m_1 <- 0 - for(i in 2:length(e)){ - m_1[i] <- alpha + beta * i + theta * m_1[i - 1] + e[i] - } - data = rbind(data, m_1) -} - - -x <- data[, c(2:(n_features + 1), n_features + 5)] -x <- data.table(x) - -plot(ts((t(x)[,1]))) -points(ts((t(x)[,1])), pch = 19) - -Q1_days <- 1:(floor(n_features / 4)) -Q2_days <- 1:(floor(n_features / 4)) + max(Q1_days) -Q3_days <- 1:(floor(n_features / 4)) + max(Q2_days) -Q4_days <- (max(Q3_days) + 1):n_features - -group <- list(Q1 = paste0("V", Q1_days), - Q2 = paste0("V", Q2_days), - Q3 = paste0("V", Q3_days), - Q4 = paste0("V", Q4_days)) - -response = paste0("V", n_features + 1) -formula = as.formula(paste0(response, "~ ", paste0("V", 1:n_features, collapse = " + "))) - -model = lm(formula, data = x) - -x_all <- x[, 1:n_features] -y_all <- x[[response]] - -all_pred <- predict(model, x_all) -mean((all_pred-y_all)^2) -# [1] 1.8074 - -# --------------- - -x_explain = x_all[-c(1:n_train), ] -x_train = x_all[1:n_train, ] - -p0 <- mean(y_all[-c(1:n_train)]) - -# --------------- - -explanation_group <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "timeseries", - phi0 = p0, - group = group, - timeseries.fixed_sigma = 2 - # timeseries.bounds = c(-1, 2) -) - -explanation_group -# none Q1 Q2 Q3 Q4 -# 1: 5.1217 -0.0019489 0.201396 -0.208099 0.74808 -# 2: 5.1217 0.0164650 -0.148537 0.639499 0.38405 -# 3: 5.1217 -0.4625373 0.564378 -0.281495 0.61380 -# 4: 5.1217 -0.1859842 -0.121323 0.048696 -0.25682 -# 5: 5.1217 -1.2290037 -0.896415 1.096474 -0.10961 -# 6: 5.1217 -0.0435240 -0.049311 0.898789 -1.36716 - -plot(explanation_group, plot_phi0 = F) diff --git a/inst/scripts/devel/timing_log_test_big.csv b/inst/scripts/devel/timing_log_test_big.csv deleted file mode 100644 index 06084d07a..000000000 --- a/inst/scripts/devel/timing_log_test_big.csv +++ /dev/null @@ -1,1476 +0,0 @@ -p,n_train,n_test,n_batches,n_cores,approach,sys_time_initial,sys_time_start_shapr,sys_time_end_shapr,sys_time_start_explain,sys_time_end_explain,secs_shapr,secs_explain,this_rep,max_n,max_p,rho,sigma,mu_const,beta0,sigma_eps -8,1000,100,1,1,empirical,2022-01-21 19:45:21,2022-01-21 19:45:22,2022-01-21 19:45:22,2022-01-21 19:45:22,2022-01-21 19:45:28,0.0679423809051514,6.21460437774658,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,1,empirical,2022-01-21 19:45:34,2022-01-21 19:45:34,2022-01-21 19:45:34,2022-01-21 19:45:34,2022-01-21 19:45:41,0.0655725002288818,6.15326380729675,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,1,gaussian,2022-01-21 19:45:46,2022-01-21 19:45:47,2022-01-21 19:45:47,2022-01-21 19:45:47,2022-01-21 19:46:31,0.0679314136505127,43.818482875824,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,1,gaussian,2022-01-21 19:46:37,2022-01-21 19:46:38,2022-01-21 19:46:38,2022-01-21 19:46:38,2022-01-21 19:47:22,0.0672848224639893,44.5887775421143,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,1,ctree,2022-01-21 19:47:28,2022-01-21 19:47:28,2022-01-21 19:47:28,2022-01-21 19:47:29,2022-01-21 19:49:00,0.0699443817138672,91.2768745422363,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,1,ctree,2022-01-21 19:49:06,2022-01-21 19:49:06,2022-01-21 19:49:07,2022-01-21 19:49:07,2022-01-21 19:50:38,0.0691261291503906,91.5022351741791,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,2,empirical,2022-01-21 19:50:44,2022-01-21 19:50:44,2022-01-21 19:50:45,2022-01-21 19:50:45,2022-01-21 19:50:52,0.0679514408111572,6.59367156028748,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,2,empirical,2022-01-21 19:50:58,2022-01-21 19:50:58,2022-01-21 19:50:58,2022-01-21 19:50:59,2022-01-21 19:51:07,0.0681295394897461,8.33681225776672,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,4,empirical,2022-01-21 19:51:29,2022-01-21 19:51:30,2022-01-21 19:51:30,2022-01-21 19:51:31,2022-01-21 19:51:39,0.0709364414215088,7.74393963813782,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,4,empirical,2022-01-21 19:51:36,2022-01-21 19:51:37,2022-01-21 19:51:37,2022-01-21 19:51:37,2022-01-21 19:51:46,0.0693521499633789,9.35610723495483,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,2,gaussian,2022-01-21 19:51:04,2022-01-21 19:51:04,2022-01-21 19:51:05,2022-01-21 19:51:05,2022-01-21 19:51:54,0.0730493068695068,48.3370258808136,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,2,gaussian,2022-01-21 19:51:10,2022-01-21 19:51:11,2022-01-21 19:51:11,2022-01-21 19:51:11,2022-01-21 19:52:01,0.070913553237915,50.250979423523,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,8,empirical,2022-01-21 19:52:09,2022-01-21 19:52:10,2022-01-21 19:52:10,2022-01-21 19:52:11,2022-01-21 19:52:20,0.0875692367553711,8.68195867538452,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,8,empirical,2022-01-21 19:52:16,2022-01-21 19:52:17,2022-01-21 19:52:17,2022-01-21 19:52:17,2022-01-21 19:52:27,0.0852441787719727,9.46109437942505,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,4,gaussian,2022-01-21 19:51:43,2022-01-21 19:51:43,2022-01-21 19:51:43,2022-01-21 19:51:44,2022-01-21 19:52:35,0.10053014755249,51.1583552360535,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,4,gaussian,2022-01-21 19:51:49,2022-01-21 19:51:50,2022-01-21 19:51:50,2022-01-21 19:51:50,2022-01-21 19:52:42,0.0782136917114258,52.3956470489502,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,16,empirical,2022-01-21 19:52:52,2022-01-21 19:52:52,2022-01-21 19:52:52,2022-01-21 19:52:54,2022-01-21 19:53:05,0.0900194644927979,11.7862379550934,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,16,empirical,2022-01-21 19:53:00,2022-01-21 19:53:00,2022-01-21 19:53:00,2022-01-21 19:53:00,2022-01-21 19:53:12,0.103877544403076,11.3551757335663,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,8,gaussian,2022-01-21 19:52:23,2022-01-21 19:52:24,2022-01-21 19:52:24,2022-01-21 19:52:25,2022-01-21 19:53:22,0.0887646675109863,57.4617612361908,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,2,ctree,2022-01-21 19:51:17,2022-01-21 19:51:17,2022-01-21 19:51:17,2022-01-21 19:51:18,2022-01-21 19:53:30,0.0736520290374756,131.975045681,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,8,gaussian,2022-01-21 19:52:30,2022-01-21 19:52:31,2022-01-21 19:52:31,2022-01-21 19:52:31,2022-01-21 19:53:30,0.0811092853546143,59.2656891345978,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,2,ctree,2022-01-21 19:51:23,2022-01-21 19:51:23,2022-01-21 19:51:23,2022-01-21 19:51:24,2022-01-21 19:53:46,0.0772199630737305,142.687088012695,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,24,empirical,2022-01-21 19:53:39,2022-01-21 19:53:40,2022-01-21 19:53:40,2022-01-21 19:53:41,2022-01-21 19:53:53,0.0883660316467285,11.9152135848999,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,24,empirical,2022-01-21 19:53:48,2022-01-21 19:53:49,2022-01-21 19:53:49,2022-01-21 19:53:49,2022-01-21 19:54:00,0.0703213214874268,11.1572158336639,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,16,gaussian,2022-01-21 19:53:08,2022-01-21 19:53:08,2022-01-21 19:53:08,2022-01-21 19:53:10,2022-01-21 19:54:11,0.0837068557739258,61.6379418373108,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,4,ctree,2022-01-21 19:51:56,2022-01-21 19:51:56,2022-01-21 19:51:57,2022-01-21 19:51:57,2022-01-21 19:54:15,0.0905437469482422,138.026141881943,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,16,gaussian,2022-01-21 19:53:16,2022-01-21 19:53:16,2022-01-21 19:53:16,2022-01-21 19:53:16,2022-01-21 19:54:19,0.0814304351806641,62.1634771823883,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,4,ctree,2022-01-21 19:52:03,2022-01-21 19:52:03,2022-01-21 19:52:03,2022-01-21 19:52:03,2022-01-21 19:54:35,0.0948200225830078,152.007485628128,0,1e+05,13,0.3,1,0,1,1 -8,1000,100,1,32,empirical,2022-01-21 19:54:32,2022-01-21 19:54:33,2022-01-21 19:54:33,2022-01-21 19:54:34,2022-01-21 19:54:48,0.0968301296234131,13.2652621269226,0,1e+05,13,0.3,1,0,1,1 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01:09:18,2022-01-22 01:09:19,2022-01-22 01:09:27,2022-01-22 01:11:15,0.608818769454956,107.476801633835,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,24,gaussian,2022-01-22 01:10:05,2022-01-22 01:10:08,2022-01-22 01:10:09,2022-01-22 01:10:09,2022-01-22 01:11:18,0.640880584716797,68.3192081451416,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,16,ctree,2022-01-22 01:06:52,2022-01-22 01:06:54,2022-01-22 01:06:55,2022-01-22 01:06:55,2022-01-22 01:11:24,0.543595552444458,268.161667823792,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,16,ctree,2022-01-22 01:06:01,2022-01-22 01:06:04,2022-01-22 01:06:05,2022-01-22 01:06:11,2022-01-22 01:11:46,0.677626848220825,335.324812412262,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,4,ctree,2022-01-22 00:57:52,2022-01-22 00:57:55,2022-01-22 00:57:55,2022-01-22 00:57:59,2022-01-22 01:12:10,0.375102758407593,850.867648124695,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,2,empirical,2022-01-22 00:56:06,2022-01-22 00:56:07,2022-01-22 00:56:07,2022-01-22 00:56:08,2022-01-22 01:13:04,0.0894236564636231,1016.30822968483,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,32,empirical,2022-01-22 01:12:22,2022-01-22 01:12:26,2022-01-22 01:12:26,2022-01-22 01:12:28,2022-01-22 01:13:42,0.62375283241272,74.4546775817871,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,24,ctree,2022-01-22 01:11:28,2022-01-22 01:11:29,2022-01-22 01:11:30,2022-01-22 01:11:30,2022-01-22 01:13:43,0.480569124221802,133.051090240479,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,24,ctree,2022-01-22 01:11:10,2022-01-22 01:11:11,2022-01-22 01:11:12,2022-01-22 01:11:16,2022-01-22 01:14:05,0.300862550735474,169.09902882576,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,32,empirical,2022-01-22 01:11:51,2022-01-22 01:11:53,2022-01-22 01:11:54,2022-01-22 01:11:58,2022-01-22 01:14:35,0.436030387878418,157.347808122635,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,32,gaussian,2022-01-22 01:13:57,2022-01-22 01:13:59,2022-01-22 01:14:00,2022-01-22 01:14:01,2022-01-22 01:14:59,0.61557149887085,58.2910091876984,0,1e+05,13,0.3,1,0,1,1 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01:14:48,2022-01-22 01:14:54,2022-01-22 01:16:42,0.87923264503479,107.627303123474,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,2,ctree,2022-01-22 00:56:38,2022-01-22 00:56:39,2022-01-22 00:56:39,2022-01-22 00:56:41,2022-01-22 01:18:04,0.118601560592651,1283.59071850777,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,4,empirical,2022-01-22 01:17:57,2022-01-22 01:17:58,2022-01-22 01:17:59,2022-01-22 01:17:59,2022-01-22 01:22:29,0.272570610046387,270.00651550293,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,1,empirical,2022-01-22 00:54:41,2022-01-22 00:54:42,2022-01-22 00:54:42,2022-01-22 00:54:42,2022-01-22 01:22:58,0.172960996627808,1695.70917034149,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,4,gaussian,2022-01-22 01:18:09,2022-01-22 01:18:11,2022-01-22 01:18:11,2022-01-22 01:18:14,2022-01-22 01:23:28,0.243377923965454,314.42053771019,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,8,empirical,2022-01-22 01:19:39,2022-01-22 01:19:41,2022-01-22 01:19:41,2022-01-22 01:19:42,2022-01-22 01:23:30,0.67338228225708,228.16231918335,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,1,empirical,2022-01-22 00:54:57,2022-01-22 00:54:58,2022-01-22 00:54:59,2022-01-22 00:54:59,2022-01-22 01:23:39,0.145205020904541,1719.32288908958,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,8,gaussian,2022-01-22 01:20:08,2022-01-22 01:20:11,2022-01-22 01:20:12,2022-01-22 01:20:18,2022-01-22 01:23:47,0.779103994369507,208.754786729813,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,4,gaussian,2022-01-22 01:18:23,2022-01-22 01:18:25,2022-01-22 01:18:25,2022-01-22 01:18:25,2022-01-22 01:24:04,0.166044473648071,338.406321763992,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,8,gaussian,2022-01-22 01:20:42,2022-01-22 01:20:45,2022-01-22 01:20:45,2022-01-22 01:20:46,2022-01-22 01:24:16,0.684644222259522,210.197974205017,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,2,empirical,2022-01-22 01:17:00,2022-01-22 01:17:01,2022-01-22 01:17:01,2022-01-22 01:17:01,2022-01-22 01:24:19,0.156692266464233,437.709321022034,0,1e+05,13,0.3,1,0,1,1 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01:26:02,2022-01-22 01:26:03,2022-01-22 01:26:05,2022-01-22 01:28:02,0.930123090744019,117.771133184433,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,16,empirical,2022-01-22 01:23:29,2022-01-22 01:23:32,2022-01-22 01:23:32,2022-01-22 01:23:40,2022-01-22 01:28:14,0.515447616577148,274.445198297501,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,8,ctree,2022-01-22 01:22:27,2022-01-22 01:22:31,2022-01-22 01:22:32,2022-01-22 01:22:34,2022-01-22 01:30:22,0.95165228843689,468.72024679184,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,8,ctree,2022-01-22 01:21:30,2022-01-22 01:21:34,2022-01-22 01:21:35,2022-01-22 01:21:44,2022-01-22 01:31:04,0.981574535369873,560.419023513794,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,24,empirical,2022-01-22 01:29:22,2022-01-22 01:29:25,2022-01-22 01:29:25,2022-01-22 01:29:26,2022-01-22 01:31:23,0.737452745437622,116.889065504074,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,4,empirical,2022-01-22 01:17:45,2022-01-22 01:17:47,2022-01-22 01:17:47,2022-01-22 01:17:50,2022-01-22 01:31:49,0.201099872589111,839.631712436676,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,1,gaussian,2022-01-22 01:16:04,2022-01-22 01:16:06,2022-01-22 01:16:07,2022-01-22 01:16:07,2022-01-22 01:31:54,0.317160129547119,946.763425827026,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,16,ctree,2022-01-22 01:28:06,2022-01-22 01:28:08,2022-01-22 01:28:09,2022-01-22 01:28:09,2022-01-22 01:32:22,0.370973587036133,252.829816818237,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,1,gaussian,2022-01-22 01:16:23,2022-01-22 01:16:24,2022-01-22 01:16:24,2022-01-22 01:16:25,2022-01-22 01:32:32,0.259876728057861,967.826942682266,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,16,ctree,2022-01-22 01:27:09,2022-01-22 01:27:12,2022-01-22 01:27:13,2022-01-22 01:27:21,2022-01-22 01:32:34,0.809694051742554,312.536510705948,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,24,gaussian,2022-01-22 01:30:27,2022-01-22 01:30:30,2022-01-22 01:30:31,2022-01-22 01:30:40,2022-01-22 01:32:53,0.975281715393066,133.432362556458,0,1e+05,13,0.3,1,0,1,1 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01:33:24,2022-01-22 01:33:24,2022-01-22 01:33:29,2022-01-22 01:36:30,0.282434701919556,181.30904173851,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,24,ctree,2022-01-22 01:33:01,2022-01-22 01:33:02,2022-01-22 01:33:03,2022-01-22 01:33:03,2022-01-22 01:36:46,0.29549241065979,223.263365983963,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,32,gaussian,2022-01-22 01:35:39,2022-01-22 01:35:42,2022-01-22 01:35:43,2022-01-22 01:35:44,2022-01-22 01:36:47,0.593777418136597,62.2703466415405,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,32,gaussian,2022-01-22 01:34:48,2022-01-22 01:34:51,2022-01-22 01:34:52,2022-01-22 01:35:00,2022-01-22 01:36:49,1.072110414505,109.304872989655,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,24,ctree,2022-01-22 01:32:39,2022-01-22 01:32:40,2022-01-22 01:32:40,2022-01-22 01:32:44,2022-01-22 01:36:52,0.229806184768677,247.217267990112,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,32,ctree,2022-01-22 01:36:58,2022-01-22 01:36:59,2022-01-22 01:36:59,2022-01-22 01:37:00,2022-01-22 01:38:20,0.140708446502686,80.3763806819916,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,2,ctree,2022-01-22 01:17:35,2022-01-22 01:17:36,2022-01-22 01:17:36,2022-01-22 01:17:37,2022-01-22 01:38:37,0.154358386993408,1260.15559983254,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,32,ctree,2022-01-22 01:36:45,2022-01-22 01:36:46,2022-01-22 01:36:46,2022-01-22 01:36:49,2022-01-22 01:38:37,0.238625526428223,108.586423873901,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,1,ctree,2022-01-22 00:55:46,2022-01-22 00:55:47,2022-01-22 00:55:47,2022-01-22 00:55:47,2022-01-22 01:38:46,0.137654066085815,2579.23980212212,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,24,1,ctree,2022-01-22 00:55:57,2022-01-22 00:55:58,2022-01-22 00:55:58,2022-01-22 00:55:58,2022-01-22 01:38:47,0.102860450744629,2569.46539926529,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,2,empirical,2022-01-22 01:16:52,2022-01-22 01:16:53,2022-01-22 01:16:53,2022-01-22 01:16:54,2022-01-22 01:38:55,0.100463628768921,1320.99148344994,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,1,empirical,2022-01-22 01:37:20,2022-01-22 01:37:22,2022-01-22 01:37:22,2022-01-22 01:37:22,2022-01-22 01:39:05,0.34293794631958,102.677614450455,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,1,empirical,2022-01-22 01:37:56,2022-01-22 01:37:59,2022-01-22 01:37:59,2022-01-22 01:38:00,2022-01-22 01:39:15,0.538186073303223,75.0914919376373,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,2,empirical,2022-01-22 01:39:07,2022-01-22 01:39:07,2022-01-22 01:39:07,2022-01-22 01:39:08,2022-01-22 01:39:58,0.151895761489868,50.6365342140198,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,2,empirical,2022-01-22 01:38:59,2022-01-22 01:39:00,2022-01-22 01:39:00,2022-01-22 01:39:01,2022-01-22 01:40:07,0.12798285484314,65.1254358291626,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,2,ctree,2022-01-22 01:17:25,2022-01-22 01:17:26,2022-01-22 01:17:26,2022-01-22 01:17:28,2022-01-22 01:40:09,0.188083648681641,1360.05182433128,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,4,empirical,2022-01-22 01:39:57,2022-01-22 01:39:58,2022-01-22 01:39:58,2022-01-22 01:39:58,2022-01-22 01:40:59,0.134471893310547,60.5440368652344,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,4,empirical,2022-01-22 01:39:48,2022-01-22 01:39:49,2022-01-22 01:39:49,2022-01-22 01:39:51,2022-01-22 01:41:07,0.168824911117554,76.013284444809,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,8,empirical,2022-01-22 01:40:55,2022-01-22 01:40:56,2022-01-22 01:40:56,2022-01-22 01:40:56,2022-01-22 01:42:12,0.159416913986206,75.9276638031006,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,8,empirical,2022-01-22 01:40:44,2022-01-22 01:40:45,2022-01-22 01:40:45,2022-01-22 01:40:47,2022-01-22 01:42:19,0.288350582122803,91.5134017467499,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,16,empirical,2022-01-22 01:41:57,2022-01-22 01:41:58,2022-01-22 01:41:58,2022-01-22 01:42:01,2022-01-22 01:43:42,0.214263916015625,101.665854930878,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,16,empirical,2022-01-22 01:42:12,2022-01-22 01:42:14,2022-01-22 01:42:14,2022-01-22 01:42:14,2022-01-22 01:43:51,0.115813732147217,97.0400414466858,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,24,empirical,2022-01-22 01:43:33,2022-01-22 01:43:34,2022-01-22 01:43:35,2022-01-22 01:43:39,2022-01-22 01:45:29,0.321691751480103,109.873937129974,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,24,empirical,2022-01-22 01:43:51,2022-01-22 01:43:52,2022-01-22 01:43:52,2022-01-22 01:43:52,2022-01-22 01:45:39,0.241383790969849,106.319730520248,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,1,empirical,2022-01-22 01:15:18,2022-01-22 01:15:20,2022-01-22 01:15:20,2022-01-22 01:15:21,2022-01-22 01:46:11,0.387478828430176,1850.03498315811,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,1,empirical,2022-01-22 01:15:38,2022-01-22 01:15:41,2022-01-22 01:15:41,2022-01-22 01:15:42,2022-01-22 01:46:36,0.496640205383301,1853.96723556519,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,32,empirical,2022-01-22 01:45:50,2022-01-22 01:45:52,2022-01-22 01:45:52,2022-01-22 01:45:56,2022-01-22 01:47:57,0.39066481590271,121.211019992828,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,1,32,empirical,2022-01-22 01:46:18,2022-01-22 01:46:19,2022-01-22 01:46:19,2022-01-22 01:46:20,2022-01-22 01:48:31,0.385215997695923,131.002678155899,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,1,ctree,2022-01-22 01:16:36,2022-01-22 01:16:36,2022-01-22 01:16:36,2022-01-22 01:16:37,2022-01-22 01:51:13,0.144690990447998,2075.92581558228,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,1,empirical,2022-01-22 01:48:49,2022-01-22 01:48:51,2022-01-22 01:48:51,2022-01-22 01:48:52,2022-01-22 01:51:29,0.344641208648682,157.168018341064,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,1,empirical,2022-01-22 01:49:19,2022-01-22 01:49:21,2022-01-22 01:49:21,2022-01-22 01:49:22,2022-01-22 01:51:58,0.386120557785034,156.260537624359,0,1e+05,13,0.3,1,0,1,1 -9,10000,100,32,1,ctree,2022-01-22 01:16:45,2022-01-22 01:16:45,2022-01-22 01:16:45,2022-01-22 01:16:45,2022-01-22 01:52:19,0.109169960021973,2133.36749792099,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,2,empirical,2022-01-22 01:52:06,2022-01-22 01:52:08,2022-01-22 01:52:08,2022-01-22 01:52:10,2022-01-22 01:53:38,0.232431173324585,87.2419457435608,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,2,empirical,2022-01-22 01:52:39,2022-01-22 01:52:40,2022-01-22 01:52:40,2022-01-22 01:52:41,2022-01-22 01:53:41,0.205278396606445,60.3414344787598,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,4,empirical,2022-01-22 01:55:33,2022-01-22 01:55:34,2022-01-22 01:55:35,2022-01-22 01:55:40,2022-01-22 01:57:15,0.23747444152832,95.2346889972687,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,4,empirical,2022-01-22 01:56:13,2022-01-22 01:56:15,2022-01-22 01:56:15,2022-01-22 01:56:16,2022-01-22 01:57:22,0.3500075340271,66.3186814785004,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,8,empirical,2022-01-22 01:59:13,2022-01-22 01:59:14,2022-01-22 01:59:15,2022-01-22 01:59:18,2022-01-22 02:00:43,0.362035751342773,85.0005538463593,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,8,empirical,2022-01-22 01:59:47,2022-01-22 01:59:48,2022-01-22 01:59:49,2022-01-22 01:59:49,2022-01-22 02:00:57,0.305766344070435,67.8213777542114,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,16,empirical,2022-01-22 02:02:51,2022-01-22 02:02:52,2022-01-22 02:02:53,2022-01-22 02:02:56,2022-01-22 02:04:21,0.224912881851196,85.0768251419068,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,16,empirical,2022-01-22 02:03:20,2022-01-22 02:03:22,2022-01-22 02:03:22,2022-01-22 02:03:22,2022-01-22 02:04:30,0.235606670379639,67.4479074478149,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,24,empirical,2022-01-22 02:06:00,2022-01-22 02:06:02,2022-01-22 02:06:02,2022-01-22 02:06:05,2022-01-22 02:07:47,0.38435173034668,101.877918243408,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,24,empirical,2022-01-22 02:06:37,2022-01-22 02:06:38,2022-01-22 02:06:38,2022-01-22 02:06:39,2022-01-22 02:07:49,0.330096244812012,70.4685909748077,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,32,empirical,2022-01-22 02:09:43,2022-01-22 02:09:45,2022-01-22 02:09:45,2022-01-22 02:09:50,2022-01-22 02:11:19,0.3041090965271,88.6841707229614,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,2,32,empirical,2022-01-22 02:10:18,2022-01-22 02:10:20,2022-01-22 02:10:20,2022-01-22 02:10:21,2022-01-22 02:12:07,0.270083665847778,106.64613199234,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,1,empirical,2022-01-22 02:13:10,2022-01-22 02:13:11,2022-01-22 02:13:12,2022-01-22 02:13:12,2022-01-22 02:16:42,0.249302387237549,210.346002101898,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,1,empirical,2022-01-22 02:13:48,2022-01-22 02:13:53,2022-01-22 02:13:53,2022-01-22 02:13:54,2022-01-22 02:17:22,0.260806798934937,208.156105279922,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,2,empirical,2022-01-22 02:16:54,2022-01-22 02:16:55,2022-01-22 02:16:56,2022-01-22 02:16:56,2022-01-22 02:17:46,0.376712083816528,50.1760177612305,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,2,empirical,2022-01-22 02:16:26,2022-01-22 02:16:28,2022-01-22 02:16:28,2022-01-22 02:16:31,2022-01-22 02:18:17,0.314223527908325,105.823621749878,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,4,empirical,2022-01-22 02:19:16,2022-01-22 02:19:17,2022-01-22 02:19:17,2022-01-22 02:19:19,2022-01-22 02:20:18,0.204575061798096,58.8597540855408,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,4,empirical,2022-01-22 02:20:08,2022-01-22 02:20:09,2022-01-22 02:20:09,2022-01-22 02:20:10,2022-01-22 02:20:32,0.178043127059937,22.7060980796814,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,8,empirical,2022-01-22 02:22:28,2022-01-22 02:22:29,2022-01-22 02:22:30,2022-01-22 02:22:33,2022-01-22 02:23:42,0.180992364883423,69.2829351425171,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,8,empirical,2022-01-22 02:22:58,2022-01-22 02:23:01,2022-01-22 02:23:01,2022-01-22 02:23:02,2022-01-22 02:23:49,0.512573003768921,46.6333136558533,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,16,empirical,2022-01-22 02:26:42,2022-01-22 02:26:43,2022-01-22 02:26:43,2022-01-22 02:26:43,2022-01-22 02:27:57,0.306085348129272,73.472410440445,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,16,empirical,2022-01-22 02:26:14,2022-01-22 02:26:16,2022-01-22 02:26:16,2022-01-22 02:26:19,2022-01-22 02:28:41,0.154666185379028,142.405807495117,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,24,empirical,2022-01-22 02:30:39,2022-01-22 02:30:41,2022-01-22 02:30:42,2022-01-22 02:30:46,2022-01-22 02:31:54,0.37065052986145,68.0808618068695,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,24,empirical,2022-01-22 02:31:21,2022-01-22 02:31:23,2022-01-22 02:31:23,2022-01-22 02:31:24,2022-01-22 02:32:24,0.326589107513428,60.2514832019806,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,32,empirical,2022-01-22 02:36:09,2022-01-22 02:36:11,2022-01-22 02:36:12,2022-01-22 02:36:12,2022-01-22 02:36:55,0.437098979949951,43.1515862941742,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,4,ctree,2022-01-22 02:21:59,2022-01-22 02:22:00,2022-01-22 02:22:01,2022-01-22 02:22:01,2022-01-22 02:37:54,0.479377508163452,952.167794942856,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,32,empirical,2022-01-22 02:35:37,2022-01-22 02:35:40,2022-01-22 02:35:41,2022-01-22 02:36:06,2022-01-22 02:38:01,0.623153924942017,114.225393533707,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,2,empirical,2022-01-22 02:44:37,2022-01-22 02:44:38,2022-01-22 02:44:39,2022-01-22 02:44:39,2022-01-22 02:45:15,0.204447269439697,35.7962510585785,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,24,ctree,2022-01-22 02:34:23,2022-01-22 02:34:25,2022-01-22 02:34:25,2022-01-22 02:34:26,2022-01-22 02:45:17,0.404924869537354,650.690148353577,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,1,empirical,2022-01-22 02:40:44,2022-01-22 02:40:46,2022-01-22 02:40:46,2022-01-22 02:40:47,2022-01-22 02:46:43,0.344234466552734,356.187374591827,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,2,empirical,2022-01-22 02:44:10,2022-01-22 02:44:11,2022-01-22 02:44:11,2022-01-22 02:44:16,2022-01-22 02:47:17,0.33298659324646,180.438060998917,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,1,empirical,2022-01-22 02:41:27,2022-01-22 02:41:28,2022-01-22 02:41:28,2022-01-22 02:41:28,2022-01-22 02:47:33,0.211798667907715,364.525406360626,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,4,empirical,2022-01-22 02:47:19,2022-01-22 02:47:22,2022-01-22 02:47:22,2022-01-22 02:47:23,2022-01-22 02:47:55,0.26728343963623,31.9319834709168,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,4,empirical,2022-01-22 02:46:44,2022-01-22 02:46:45,2022-01-22 02:46:45,2022-01-22 02:46:48,2022-01-22 02:48:38,0.163733720779419,109.999467134476,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,4,32,ctree,2022-01-22 02:39:23,2022-01-22 02:39:25,2022-01-22 02:39:25,2022-01-22 02:39:25,2022-01-22 02:50:23,0.28056001663208,657.837820529938,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,8,empirical,2022-01-22 02:49:44,2022-01-22 02:49:46,2022-01-22 02:49:46,2022-01-22 02:49:49,2022-01-22 02:51:03,0.378391742706299,74.2485461235046,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,8,empirical,2022-01-22 02:50:27,2022-01-22 02:50:29,2022-01-22 02:50:29,2022-01-22 02:50:29,2022-01-22 02:51:11,0.288201093673706,41.4705760478973,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,16,empirical,2022-01-22 02:55:09,2022-01-22 02:55:16,2022-01-22 02:55:17,2022-01-22 02:55:19,2022-01-22 02:56:18,1.01343655586243,59.1026678085327,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,16,empirical,2022-01-22 02:53:58,2022-01-22 02:54:02,2022-01-22 02:54:02,2022-01-22 02:54:09,2022-01-22 02:56:40,0.692918300628662,150.14314699173,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,4,gaussian,2022-01-22 02:47:53,2022-01-22 02:47:54,2022-01-22 02:47:54,2022-01-22 02:47:57,2022-01-22 02:57:14,0.211225271224976,556.901450157166,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,8,gaussian,2022-01-22 02:51:45,2022-01-22 02:51:47,2022-01-22 02:51:48,2022-01-22 02:51:49,2022-01-22 02:57:59,0.486351490020752,370.704549789429,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,4,gaussian,2022-01-22 02:48:16,2022-01-22 02:48:19,2022-01-22 02:48:19,2022-01-22 02:48:20,2022-01-22 02:58:59,0.472658157348633,639.603382587433,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,24,empirical,2022-01-22 02:59:08,2022-01-22 02:59:10,2022-01-22 02:59:11,2022-01-22 02:59:17,2022-01-22 03:01:00,0.499860525131226,103.697935581207,0,1e+05,13,0.3,1,0,1,1 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02:49:10,2022-01-22 02:49:10,2022-01-22 03:11:49,0.193442821502686,1359.09394526482,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,24,ctree,2022-01-22 03:03:08,2022-01-22 03:03:11,2022-01-22 03:03:12,2022-01-22 03:03:13,2022-01-22 03:12:57,0.896903514862061,583.915278196335,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,32,ctree,2022-01-22 03:08:40,2022-01-22 03:08:43,2022-01-22 03:08:43,2022-01-22 03:08:44,2022-01-22 03:13:43,0.509770631790161,298.823926687241,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,4,ctree,2022-01-22 02:48:40,2022-01-22 02:48:42,2022-01-22 02:48:42,2022-01-22 02:48:46,2022-01-22 03:14:14,0.184979677200317,1528.42556667328,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,2,empirical,2022-01-22 03:13:34,2022-01-22 03:13:36,2022-01-22 03:13:36,2022-01-22 03:13:37,2022-01-22 03:14:40,0.389299392700195,63.1538534164429,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,24,ctree,2022-01-22 03:02:02,2022-01-22 03:02:06,2022-01-22 03:02:06,2022-01-22 03:02:14,2022-01-22 03:15:31,0.577649831771851,796.840286016464,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,32,ctree,2022-01-22 03:07:46,2022-01-22 03:07:49,2022-01-22 03:07:50,2022-01-22 03:07:57,2022-01-22 03:16:19,0.64387583732605,502.070887327194,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,4,empirical,2022-01-22 03:15:51,2022-01-22 03:15:52,2022-01-22 03:15:53,2022-01-22 03:15:53,2022-01-22 03:16:34,0.39448881149292,40.8158376216888,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,8,1,gaussian,2022-01-22 02:42:52,2022-01-22 02:42:54,2022-01-22 02:42:54,2022-01-22 02:42:54,2022-01-22 03:17:06,0.308839797973633,2051.89924573898,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,2,empirical,2022-01-22 03:13:06,2022-01-22 03:13:07,2022-01-22 03:13:08,2022-01-22 03:13:11,2022-01-22 03:17:13,0.289663314819336,242.524348497391,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,4,empirical,2022-01-22 03:15:32,2022-01-22 03:15:34,2022-01-22 03:15:34,2022-01-22 03:15:37,2022-01-22 03:17:50,0.209911584854126,132.961334228516,0,1e+05,13,0.3,1,0,1,1 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03:21:07,2022-01-22 03:21:13,2022-01-22 03:22:44,0.596693277359009,91.0323338508606,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,16,empirical,2022-01-22 03:22:11,2022-01-22 03:22:15,2022-01-22 03:22:16,2022-01-22 03:22:17,2022-01-22 03:22:53,0.781660795211792,36.5028355121613,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,8,gaussian,2022-01-22 03:18:36,2022-01-22 03:18:39,2022-01-22 03:18:39,2022-01-22 03:18:43,2022-01-22 03:23:57,0.515699148178101,314.193381071091,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,8,gaussian,2022-01-22 03:19:02,2022-01-22 03:19:04,2022-01-22 03:19:04,2022-01-22 03:19:05,2022-01-22 03:24:36,0.454651355743408,330.895944356918,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,4,gaussian,2022-01-22 03:16:09,2022-01-22 03:16:10,2022-01-22 03:16:10,2022-01-22 03:16:13,2022-01-22 03:24:56,0.529733896255493,523.202776432037,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,4,gaussian,2022-01-22 03:16:31,2022-01-22 03:16:33,2022-01-22 03:16:33,2022-01-22 03:16:34,2022-01-22 03:25:52,0.366849422454834,558.215231180191,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,16,gaussian,2022-01-22 03:24:16,2022-01-22 03:24:19,2022-01-22 03:24:20,2022-01-22 03:24:22,2022-01-22 03:27:59,0.906281471252441,217.435868263245,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,24,empirical,2022-01-22 03:28:37,2022-01-22 03:28:40,2022-01-22 03:28:41,2022-01-22 03:28:42,2022-01-22 03:29:09,0.51256799697876,27.5531740188599,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,24,empirical,2022-01-22 03:27:57,2022-01-22 03:27:59,2022-01-22 03:27:59,2022-01-22 03:28:05,2022-01-22 03:29:21,0.314983606338501,75.8100733757019,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,8,ctree,2022-01-22 03:20:12,2022-01-22 03:20:14,2022-01-22 03:20:15,2022-01-22 03:20:16,2022-01-22 03:34:56,0.736507892608643,880.105220556259,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,16,ctree,2022-01-22 03:26:53,2022-01-22 03:27:05,2022-01-22 03:27:07,2022-01-22 03:27:08,2022-01-22 03:34:59,1.68570113182068,471.237874746323,0,1e+05,13,0.3,1,0,1,1 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03:32:44,2022-01-22 03:33:09,2022-01-22 03:39:51,1.05011582374573,402.112931966782,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,24,ctree,2022-01-22 03:34:38,2022-01-22 03:34:40,2022-01-22 03:34:40,2022-01-22 03:34:41,2022-01-22 03:40:17,0.312652111053467,336.034219503403,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,32,gaussian,2022-01-22 03:36:21,2022-01-22 03:36:23,2022-01-22 03:36:23,2022-01-22 03:36:28,2022-01-22 03:40:17,0.323652505874634,228.625579595566,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,32,gaussian,2022-01-22 03:37:14,2022-01-22 03:37:18,2022-01-22 03:37:19,2022-01-22 03:37:21,2022-01-22 03:40:39,0.98668909072876,197.94747376442,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,4,ctree,2022-01-22 03:17:16,2022-01-22 03:17:18,2022-01-22 03:17:18,2022-01-22 03:17:19,2022-01-22 03:41:33,0.258048534393311,1453.51244306564,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,32,ctree,2022-01-22 03:40:10,2022-01-22 03:40:12,2022-01-22 03:40:12,2022-01-22 03:40:13,2022-01-22 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04:33:29,2022-01-22 04:33:47,2022-01-22 04:44:01,0.610734462738037,614.076422452927,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,32,ctree,2022-01-22 04:42:26,2022-01-22 04:42:27,2022-01-22 04:42:28,2022-01-22 04:42:28,2022-01-22 04:45:14,0.215010643005371,165.800561189652,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,4,ctree,2022-01-22 04:16:39,2022-01-22 04:16:41,2022-01-22 04:16:41,2022-01-22 04:16:42,2022-01-22 04:45:46,0.502440214157105,1744.52444386482,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,32,ctree,2022-01-22 04:40:36,2022-01-22 04:40:42,2022-01-22 04:40:43,2022-01-22 04:40:56,2022-01-22 04:45:55,1.08466792106628,299.564634084702,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,4,ctree,2022-01-22 04:16:15,2022-01-22 04:16:17,2022-01-22 04:16:17,2022-01-22 04:16:21,2022-01-22 04:46:58,0.19093656539917,1836.83525872231,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,16,1,ctree,2022-01-22 03:12:14,2022-01-22 03:12:15,2022-01-22 03:12:15,2022-01-22 03:12:16,2022-01-22 04:47:21,0.14710259437561,5704.73913574219,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,1,gaussian,2022-01-22 04:12:36,2022-01-22 04:12:39,2022-01-22 04:12:39,2022-01-22 04:12:40,2022-01-22 04:49:25,0.554125308990479,2204.86403536797,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,1,gaussian,2022-01-22 04:11:57,2022-01-22 04:12:00,2022-01-22 04:12:01,2022-01-22 04:12:02,2022-01-22 04:50:23,0.468873977661133,2301.6923494339,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,2,ctree,2022-01-22 04:14:48,2022-01-22 04:14:49,2022-01-22 04:14:49,2022-01-22 04:14:49,2022-01-22 04:51:20,0.188418626785278,2191.39847159386,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,32,2,ctree,2022-01-22 04:14:36,2022-01-22 04:14:38,2022-01-22 04:14:38,2022-01-22 04:14:41,2022-01-22 04:59:38,0.2060866355896,2697.50974178314,0,1e+05,13,0.3,1,0,1,1 -10,1000,100,24,1,ctree,2022-01-22 03:42:22,2022-01-22 03:42:23,2022-01-22 03:42:23,2022-01-22 03:42:24,2022-01-22 05:08:25,0.160650491714478,5161.34821200371,0,1e+05,13,0.3,1,0,1,1 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06:32:04,2022-01-22 06:32:04,2022-01-22 06:32:05,2022-01-22 06:38:00,0.454639911651611,355.269972085953,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,24,empirical,2022-01-22 06:38:22,2022-01-22 06:38:24,2022-01-22 06:38:24,2022-01-22 06:38:29,2022-01-22 06:42:58,0.280443906784058,269.579582214356,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,24,empirical,2022-01-22 06:39:01,2022-01-22 06:39:08,2022-01-22 06:39:08,2022-01-22 06:39:09,2022-01-22 06:43:09,0.167315721511841,240.405614614487,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,8,empirical,2022-01-22 06:25:44,2022-01-22 06:25:46,2022-01-22 06:25:46,2022-01-22 06:25:47,2022-01-22 06:43:24,0.417171239852905,1057.1796169281,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,32,empirical,2022-01-22 06:45:28,2022-01-22 06:45:30,2022-01-22 06:45:30,2022-01-22 06:45:31,2022-01-22 06:48:32,0.452541828155518,180.846414089203,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,32,empirical,2022-01-22 06:44:41,2022-01-22 06:44:42,2022-01-22 06:44:42,2022-01-22 06:45:03,2022-01-22 06:49:37,0.1501145362854,274.034828186035,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,24,ctree,2022-01-22 06:43:55,2022-01-22 06:44:00,2022-01-22 06:44:01,2022-01-22 06:44:02,2022-01-22 06:51:36,1.01762580871582,454.107393741608,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,32,ctree,2022-01-22 06:50:12,2022-01-22 06:50:14,2022-01-22 06:50:15,2022-01-22 06:50:16,2022-01-22 06:53:17,0.931843996047974,181.305830955505,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,16,32,ctree,2022-01-22 06:48:51,2022-01-22 06:48:59,2022-01-22 06:49:00,2022-01-22 06:49:17,2022-01-22 06:54:07,0.874159574508667,290.188654899597,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,24,8,gaussian,2022-01-22 06:57:18,2022-01-22 06:57:21,2022-01-22 06:57:21,2022-01-22 06:57:27,2022-01-22 07:05:28,0.39708685874939,481.765605211258,0,1e+05,13,0.3,1,0,1,1 -10,10000,100,24,24,empirical,2022-01-22 07:10:57,2022-01-22 07:10:59,2022-01-22 07:10:59,2022-01-22 07:11:00,2022-01-22 07:16:01,0.440148115158081,300.573085784912,0,1e+05,13,0.3,1,0,1,1 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10:48:58,2022-01-22 10:48:58,2022-01-22 10:48:58,2022-01-22 10:51:14,0.139031171798706,135.397399425507,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,4,empirical,2022-01-22 10:53:03,2022-01-22 10:53:05,2022-01-22 10:53:05,2022-01-22 10:53:05,2022-01-22 10:54:18,0.278486728668213,73.3427712917328,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,4,empirical,2022-01-22 10:52:10,2022-01-22 10:52:11,2022-01-22 10:52:11,2022-01-22 10:52:18,2022-01-22 10:58:41,0.130438804626465,382.672886133194,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,8,empirical,2022-01-22 10:57:32,2022-01-22 10:57:34,2022-01-22 10:57:35,2022-01-22 10:57:36,2022-01-22 10:59:06,0.52803373336792,90.3082437515259,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,2,empirical,2022-01-22 10:48:34,2022-01-22 10:48:37,2022-01-22 10:48:37,2022-01-22 10:48:53,2022-01-22 10:59:39,0.414208889007568,645.782402992249,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,8,empirical,2022-01-22 10:56:32,2022-01-22 10:56:35,2022-01-22 10:56:36,2022-01-22 10:56:42,2022-01-22 10:59:51,0.761104345321655,189.024942874908,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,16,empirical,2022-01-22 11:01:40,2022-01-22 11:01:44,2022-01-22 11:01:45,2022-01-22 11:01:46,2022-01-22 11:03:15,0.884268999099731,89.2238826751709,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,16,empirical,2022-01-22 11:00:52,2022-01-22 11:00:56,2022-01-22 11:00:56,2022-01-22 11:01:03,2022-01-22 11:03:38,0.683446884155273,155.434013128281,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,1,empirical,2022-01-22 10:45:51,2022-01-22 10:45:53,2022-01-22 10:45:53,2022-01-22 10:45:54,2022-01-22 11:03:45,0.428142547607422,1071.30545711517,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,1,empirical,2022-01-22 10:43:56,2022-01-22 10:44:01,2022-01-22 10:44:01,2022-01-22 10:44:03,2022-01-22 11:05:44,0.606060981750488,1301.2333483696,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,24,24,empirical,2022-01-22 11:10:19,2022-01-22 11:10:22,2022-01-22 11:10:23,2022-01-22 11:10:24,2022-01-22 11:11:22,0.765867710113525,57.6616673469544,0,1e+05,13,0.3,1,0,1,1 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11:36:54,2022-01-22 11:36:55,2022-01-22 11:36:55,2022-01-22 11:38:44,0.279355049133301,108.533129453659,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,4,empirical,2022-01-22 11:38:22,2022-01-22 11:38:22,2022-01-22 11:38:23,2022-01-22 11:38:23,2022-01-22 11:39:54,0.222272157669067,91.0347166061401,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,8,empirical,2022-01-22 11:40:18,2022-01-22 11:40:20,2022-01-22 11:40:21,2022-01-22 11:40:21,2022-01-22 11:42:05,0.549343824386597,103.083040714264,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,8,empirical,2022-01-22 11:39:50,2022-01-22 11:39:51,2022-01-22 11:39:52,2022-01-22 11:39:56,2022-01-22 11:43:21,0.221651554107666,205.402804136276,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,4,empirical,2022-01-22 11:38:09,2022-01-22 11:38:10,2022-01-22 11:38:10,2022-01-22 11:38:12,2022-01-22 11:43:31,0.168190956115723,318.949892759323,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,16,empirical,2022-01-22 11:45:31,2022-01-22 11:45:36,2022-01-22 11:45:37,2022-01-22 11:45:38,2022-01-22 11:47:05,1.00594711303711,86.7194800376892,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,16,empirical,2022-01-22 11:44:20,2022-01-22 11:44:22,2022-01-22 11:44:23,2022-01-22 11:44:30,2022-01-22 11:47:06,0.523659706115723,156.78627872467,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,2,empirical,2022-01-22 11:36:32,2022-01-22 11:36:34,2022-01-22 11:36:34,2022-01-22 11:36:37,2022-01-22 11:47:31,0.165961503982544,654.281175374985,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,24,empirical,2022-01-22 11:54:52,2022-01-22 11:54:57,2022-01-22 11:54:58,2022-01-22 11:55:00,2022-01-22 11:56:24,1.14682364463806,83.9669575691223,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,24,empirical,2022-01-22 11:53:45,2022-01-22 11:53:49,2022-01-22 11:53:52,2022-01-22 11:54:02,2022-01-22 11:56:55,2.42309260368347,173.221267461777,0,1e+05,13,0.3,1,0,1,1 -11,1000,100,32,1,empirical,2022-01-22 11:32:41,2022-01-22 11:32:44,2022-01-22 11:32:44,2022-01-22 11:32:45,2022-01-22 11:57:30,0.692607164382935,1484.48212981224,0,1e+05,13,0.3,1,0,1,1 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17:15:04,2022-01-22 17:15:04,2022-01-22 17:15:05,2022-01-22 17:20:47,0.349697113037109,342.335664749146,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,4,4,empirical,2022-01-22 17:18:13,2022-01-22 17:18:15,2022-01-22 17:18:15,2022-01-22 17:18:16,2022-01-22 17:22:24,0.327213048934937,248.467654705048,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,2,empirical,2022-01-22 17:51:26,2022-01-22 17:51:28,2022-01-22 17:51:28,2022-01-22 17:51:29,2022-01-22 17:57:17,0.341325283050537,347.88488650322,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,2,empirical,2022-01-22 17:50:45,2022-01-22 17:50:47,2022-01-22 17:50:47,2022-01-22 17:50:50,2022-01-22 17:58:58,0.566452026367188,487.494567871094,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,1,empirical,2022-01-22 17:45:30,2022-01-22 17:45:35,2022-01-22 17:45:36,2022-01-22 17:45:36,2022-01-22 17:59:05,0.363771200180054,808.551822900772,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,4,empirical,2022-01-22 17:54:54,2022-01-22 17:54:55,2022-01-22 17:54:56,2022-01-22 17:55:00,2022-01-22 17:59:07,0.538132190704346,246.953627109528,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,4,empirical,2022-01-22 17:55:29,2022-01-22 17:55:31,2022-01-22 17:55:31,2022-01-22 17:55:32,2022-01-22 17:59:40,0.322488069534302,248.050733804703,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,1,empirical,2022-01-22 17:46:27,2022-01-22 17:46:30,2022-01-22 17:46:30,2022-01-22 17:46:31,2022-01-22 18:00:03,0.636071443557739,811.378388404846,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,8,empirical,2022-01-22 17:58:58,2022-01-22 17:59:01,2022-01-22 17:59:01,2022-01-22 17:59:05,2022-01-22 18:01:51,0.477749347686768,166.109085559845,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,8,empirical,2022-01-22 17:59:37,2022-01-22 17:59:40,2022-01-22 17:59:41,2022-01-22 17:59:41,2022-01-22 18:02:13,0.694354295730591,151.790697574615,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,16,empirical,2022-01-22 18:04:30,2022-01-22 18:04:34,2022-01-22 18:04:35,2022-01-22 18:04:41,2022-01-22 18:08:19,0.872366189956665,217.755168676376,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,16,empirical,2022-01-22 18:05:50,2022-01-22 18:05:53,2022-01-22 18:05:54,2022-01-22 18:05:55,2022-01-22 18:09:42,0.94979190826416,226.951153039932,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,24,empirical,2022-01-22 18:14:15,2022-01-22 18:14:19,2022-01-22 18:14:19,2022-01-22 18:14:20,2022-01-22 18:17:42,0.739112854003906,201.530950307846,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,8,32,empirical,2022-01-22 18:22:49,2022-01-22 18:22:52,2022-01-22 18:22:53,2022-01-22 18:22:54,2022-01-22 18:26:05,1.13713765144348,190.900821208954,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,2,empirical,2022-01-22 18:36:57,2022-01-22 18:36:59,2022-01-22 18:37:00,2022-01-22 18:37:00,2022-01-22 18:42:14,0.579819202423096,313.423630237579,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,4,empirical,2022-01-22 18:41:08,2022-01-22 18:41:10,2022-01-22 18:41:11,2022-01-22 18:41:11,2022-01-22 18:44:36,0.601732969284058,204.581836462021,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,4,empirical,2022-01-22 18:40:26,2022-01-22 18:40:31,2022-01-22 18:40:31,2022-01-22 18:40:36,2022-01-22 18:46:19,0.438101053237915,343.334706306458,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,2,empirical,2022-01-22 18:36:26,2022-01-22 18:36:28,2022-01-22 18:36:29,2022-01-22 18:36:32,2022-01-22 18:47:34,0.373157739639282,662.379744052887,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,8,empirical,2022-01-22 18:44:40,2022-01-22 18:44:43,2022-01-22 18:44:43,2022-01-22 18:44:48,2022-01-22 18:48:23,0.592904329299927,214.69881272316,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,8,empirical,2022-01-22 18:45:36,2022-01-22 18:45:39,2022-01-22 18:45:39,2022-01-22 18:45:40,2022-01-22 18:49:58,0.501163959503174,257.76019525528,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,1,empirical,2022-01-22 18:30:00,2022-01-22 18:30:03,2022-01-22 18:30:04,2022-01-22 18:30:06,2022-01-22 18:53:43,0.874485969543457,1417.68450570107,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,16,empirical,2022-01-22 18:50:26,2022-01-22 18:50:36,2022-01-22 18:50:38,2022-01-22 18:50:53,2022-01-22 18:53:52,1.89371299743652,178.651078939438,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,16,empirical,2022-01-22 18:51:27,2022-01-22 18:51:34,2022-01-22 18:51:35,2022-01-22 18:51:37,2022-01-22 18:53:54,0.81693696975708,137.530794858933,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,1,empirical,2022-01-22 18:31:23,2022-01-22 18:31:26,2022-01-22 18:31:27,2022-01-22 18:31:27,2022-01-22 18:55:31,0.658213615417481,1443.05756092072,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,24,empirical,2022-01-22 19:01:47,2022-01-22 19:01:54,2022-01-22 19:01:56,2022-01-22 19:01:59,2022-01-22 19:05:18,2.0645956993103,199.504716157913,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,16,32,empirical,2022-01-22 19:14:53,2022-01-22 19:14:58,2022-01-22 19:15:00,2022-01-22 19:15:01,2022-01-22 19:18:06,1.19364953041077,185.220926761627,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,2,empirical,2022-01-22 19:33:57,2022-01-22 19:33:58,2022-01-22 19:33:59,2022-01-22 19:33:59,2022-01-22 19:39:19,0.302567005157471,320.244542598724,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,4,empirical,2022-01-22 19:37:55,2022-01-22 19:37:57,2022-01-22 19:37:57,2022-01-22 19:37:58,2022-01-22 19:42:18,0.411279439926147,260.641203165054,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,8,empirical,2022-01-22 19:42:00,2022-01-22 19:42:06,2022-01-22 19:42:06,2022-01-22 19:42:06,2022-01-22 19:45:33,0.25588870048523,206.726095199585,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,4,empirical,2022-01-22 19:37:12,2022-01-22 19:37:16,2022-01-22 19:37:18,2022-01-22 19:37:22,2022-01-22 19:45:47,2.54053568840027,505.435400724411,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,8,empirical,2022-01-22 19:41:42,2022-01-22 19:41:53,2022-01-22 19:41:54,2022-01-22 19:42:22,2022-01-22 19:50:08,1.03908658027649,465.98491859436,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,16,empirical,2022-01-22 19:47:32,2022-01-22 19:47:35,2022-01-22 19:47:36,2022-01-22 19:47:42,2022-01-22 19:51:34,0.818039178848267,232.413943529129,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,16,empirical,2022-01-22 19:49:46,2022-01-22 19:49:49,2022-01-22 19:49:50,2022-01-22 19:49:50,2022-01-22 19:52:20,0.785335063934326,149.103640794754,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,2,empirical,2022-01-22 19:33:33,2022-01-22 19:33:34,2022-01-22 19:33:35,2022-01-22 19:33:40,2022-01-22 19:53:07,0.177771806716919,1167.03664064407,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,24,empirical,2022-01-22 19:56:10,2022-01-22 19:56:20,2022-01-22 19:56:22,2022-01-22 19:56:33,2022-01-22 20:01:29,1.43938851356506,296.15135717392,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,24,empirical,2022-01-22 19:58:25,2022-01-22 19:58:31,2022-01-22 19:58:33,2022-01-22 19:58:35,2022-01-22 20:01:30,1.93052744865417,174.487854003906,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,1,empirical,2022-01-22 19:27:44,2022-01-22 19:27:49,2022-01-22 19:27:50,2022-01-22 19:27:51,2022-01-22 20:05:03,1.09261703491211,2231.47869825363,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,32,empirical,2022-01-22 20:10:44,2022-01-22 20:10:56,2022-01-22 20:10:58,2022-01-22 20:11:57,2022-01-22 20:16:59,2.12010335922241,301.716490030289,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,24,32,empirical,2022-01-22 20:13:31,2022-01-22 20:13:39,2022-01-22 20:13:41,2022-01-22 20:13:44,2022-01-22 20:17:08,2.00761938095093,204.593585729599,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,2,empirical,2022-01-22 20:33:33,2022-01-22 20:33:35,2022-01-22 20:33:36,2022-01-22 20:33:36,2022-01-22 20:38:37,0.481948614120483,300.715023756027,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,4,empirical,2022-01-22 20:37:41,2022-01-22 20:37:44,2022-01-22 20:37:58,2022-01-22 20:37:58,2022-01-22 20:40:02,13.9503815174103,123.559878826141,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,4,empirical,2022-01-22 20:37:17,2022-01-22 20:37:18,2022-01-22 20:37:18,2022-01-22 20:37:21,2022-01-22 20:45:11,0.256648778915405,470.425592660904,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,8,empirical,2022-01-22 20:42:08,2022-01-22 20:42:14,2022-01-22 20:42:14,2022-01-22 20:42:15,2022-01-22 20:45:14,0.838595390319824,179.268458604813,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,8,empirical,2022-01-22 20:41:11,2022-01-22 20:41:12,2022-01-22 20:41:13,2022-01-22 20:41:18,2022-01-22 20:47:44,0.301345109939575,385.813750505447,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,16,empirical,2022-01-22 20:49:31,2022-01-22 20:49:35,2022-01-22 20:49:36,2022-01-22 20:49:37,2022-01-22 20:51:36,0.850199699401856,119.07880282402,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,16,empirical,2022-01-22 20:48:08,2022-01-22 20:48:12,2022-01-22 20:48:13,2022-01-22 20:48:20,2022-01-22 20:51:37,1.39012312889099,197.221575498581,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,2,empirical,2022-01-22 20:33:10,2022-01-22 20:33:17,2022-01-22 20:33:18,2022-01-22 20:33:22,2022-01-22 20:54:38,0.360357284545898,1276.76272368431,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,24,empirical,2022-01-22 20:58:40,2022-01-22 20:58:56,2022-01-22 20:58:56,2022-01-22 20:58:58,2022-01-22 21:01:11,0.342028379440308,133.387796640396,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,24,empirical,2022-01-22 20:56:27,2022-01-22 20:56:32,2022-01-22 20:56:34,2022-01-22 20:56:44,2022-01-22 21:03:28,1.55437779426575,403.614232540131,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,1,empirical,2022-01-22 20:28:23,2022-01-22 20:28:30,2022-01-22 20:28:32,2022-01-22 20:28:33,2022-01-22 21:15:47,1.68647599220276,2833.6894595623,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,32,empirical,2022-01-22 21:13:30,2022-01-22 21:13:42,2022-01-22 21:13:43,2022-01-22 21:13:44,2022-01-22 21:15:49,0.637606859207153,124.904546737671,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,32,empirical,2022-01-22 21:11:23,2022-01-22 21:11:40,2022-01-22 21:11:41,2022-01-22 21:12:14,2022-01-22 21:20:10,1.17925953865051,476.281431674957,0,1e+05,13,0.3,1,0,1,1 -12,1000,100,32,1,empirical,2022-01-22 20:25:06,2022-01-22 20:25:12,2022-01-22 20:25:14,2022-01-22 20:25:17,2022-01-22 21:29:23,1.76262354850769,3846.27600169182,0,1e+05,13,0.3,1,0,1,1 -13,1000,100,1,2,empirical,2022-01-23 05:20:17,2022-01-23 05:20:17,2022-01-23 05:20:17,2022-01-23 05:20:18,2022-01-23 05:24:06,0.130674123764038,228.618875026703,0,1e+05,13,0.3,1,0,1,1 -13,1000,100,16,16,empirical,2022-01-23 07:46:35,2022-01-23 07:46:42,2022-01-23 07:46:43,2022-01-23 07:46:43,2022-01-23 07:53:27,0.78925633430481,403.846161127091,0,1e+05,13,0.3,1,0,1,1 -13,1000,100,16,32,empirical,2022-01-23 08:11:19,2022-01-23 08:11:30,2022-01-23 08:11:33,2022-01-23 08:11:34,2022-01-23 08:17:42,2.57792806625366,367.90896821022,0,1e+05,13,0.3,1,0,1,1 -13,1000,100,24,4,empirical,2022-01-23 08:33:22,2022-01-23 08:33:26,2022-01-23 08:33:27,2022-01-23 08:33:31,2022-01-23 08:51:04,0.321960210800171,1053.26301193237,0,1e+05,13,0.3,1,0,1,1 -13,1000,100,32,8,empirical,2022-01-23 09:46:17,2022-01-23 09:46:20,2022-01-23 09:46:21,2022-01-23 09:46:21,2022-01-23 09:58:47,1.05202484130859,745.312472581863,0,1e+05,13,0.3,1,0,1,1 -13,1000,100,32,16,empirical,2022-01-23 09:54:20,2022-01-23 09:54:23,2022-01-23 09:54:24,2022-01-23 09:54:24,2022-01-23 10:00:31,0.986858129501343,366.976919174194,0,1e+05,13,0.3,1,0,1,1 -13,1000,100,32,4,empirical,2022-01-23 09:40:45,2022-01-23 09:40:46,2022-01-23 09:40:46,2022-01-23 09:40:49,2022-01-23 10:01:17,0.369621515274048,1228.32821273804,0,1e+05,13,0.3,1,0,1,1 diff --git a/inst/scripts/devel/verifying_arima_model_output.R b/inst/scripts/devel/verifying_arima_model_output.R deleted file mode 100644 index 47ce0641d..000000000 --- a/inst/scripts/devel/verifying_arima_model_output.R +++ /dev/null @@ -1,76 +0,0 @@ -library(shapr) - -options(digits = 5) # To avoid round off errors when printing output on different systems -set.seed(123) - -n <- 10^3 - -xreg <- cbind(rnorm(n,mean=1,sd=1), - rnorm(n,mean=2,sd=1)) - -noise <- rnorm(n,mean=0,sd=0.5) - -# Create AR(1)-structure -beta <- c(1.5,0) -alpha <- 0.5 # AR-coefficient -mu <- 1 - -y <- rep(0,n) -y[1] <- mu +beta[1]*xreg[1,1]+beta[2]*xreg[1,2]+noise[1] - - -for(i in 2:n){ - y[i] <- mu +alpha*y[i-1]+beta[1]*xreg[i,1]+beta[2]*xreg[i,2]+noise[i] -} -plot(y,type="l") - -# In practice this model is y = 1 + y[i] + 1.5*xreg1 with independent features - -#model_arima_temp <- arima(y, c(3,1,2), xreg=xreg) -model_arima_temp <- arima(y, c(1,0,0), xreg=xreg) - -colnames(xreg) <- c("var1","var2") - -train_idx <- 1:(n-10) -explain_idx <- n-5:4 - - -set.seed(123) -exp <- explain_forecast(model = model_arima_temp, - y = y, - xreg = xreg, - train_idx = train_idx, - explain_idx = explain_idx, - explain_y_lags = 1, - explain_xreg_lags = c(0,1), - horizon = 1, - approach = "empirical", - phi0 = rep(mean(y),1), - group_lags = FALSE, - n_batches = 1) - -# These two should be approximately equal -# For y -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$shapley_values_est$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_est$var2.F1 -0 -#[1] 0.011555 0.031911 -#[1] 0 - - -# Close enough (maybe increase sample size n to make sure they converge as they should?) - - - diff --git a/inst/scripts/devel/visual_bug_in_Shapley_bar_plot.R b/inst/scripts/devel/visual_bug_in_Shapley_bar_plot.R deleted file mode 100644 index f9189e480..000000000 --- a/inst/scripts/devel/visual_bug_in_Shapley_bar_plot.R +++ /dev/null @@ -1,54 +0,0 @@ -# In this file we illustrate a visual bug that appears (before the bugfix) -# when the features values of the x_explain are of different order for the -# different observations. -# The visual bug in `plot.shapr()` gave extra whitespace in the strings "feature = value". -# The bugfix adds trim whitespace to remove redundant whitespace. -# Before the bugfix we got extra whitespace for those features that have different numbers -# of digits. E.g., if a feature is "1000" for one observation and "5" for another, then we -# would previously get the strings "1000" and " 5", i.e., the latter/smaller one got three -# extra white spaces. We illustrate this below. - -library(xgboost) -library(data.table) - -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] - -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" - -ind_x_explain <- 1:6 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# Fitting a basic xgboost model to the training data -model <- xgboost::xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(y_train) - -# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using -# the gaussian approach -explanation <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = p0, - n_samples = 10, - keep_samp_for_vS = TRUE -) - -# Overwrite the feature value for one of the explicands (x_explain) with -# many digits to make the visual bug easier to spot -explanation$internal$data$x_explain$Wind[4] = 200000 - -# Make the plot. Note that `Wind = ...` has a lot of whitespace now. -plot(explanation) diff --git a/inst/scripts/empirical_memory_testing2.R b/inst/scripts/empirical_memory_testing2.R deleted file mode 100644 index 84e1f863f..000000000 --- a/inst/scripts/empirical_memory_testing2.R +++ /dev/null @@ -1,145 +0,0 @@ -#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library") -sys_time_initial <- Sys.time() - -# libraries -library(shapr) -library(future) -library(MASS) -library(microbenchmark) -library(data.table) -library(profmem) - -# Initial setup -max_n <- 10^5 -max_p <- 16 -rho <- 0.3 -sigma <- 1 -mu_const <- 0 -beta0 <- 1 -sigma_eps <- 1 - -mu <- rep(mu_const,max_p) -beta <- c(beta0,seq_len(max_p)/max_p) -Sigma <- matrix(rho,max_p,max_p) -diag(Sigma) <- sigma - -set.seed(123) -x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma) -y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps) - -# Arguments from bash -#args <- commandArgs(trailingOnly = TRUE) -#if(length(args)==0) args = c(1,10,1000,100,10,1,"empirical","sequential","timing_test_2023.csv") - - -this_rep <- 1 -p <- 6 -n_train <- 100 -n_explain <- 100 -n_batches <- 100 -n_cores <- 1 -approach <- "empirical" -multicore_method <- "sequential" -logfilename <- "bla" - -set.seed(123) - - -these_p <- sample.int(max_p,size=p) -these_train <- sample.int(max_n,size=n_train) -these_explain <- sample.int(max_n,size=n_explain) - -x_train <- as.data.frame(x_all[these_train,these_p,drop=F]) -x_explain <- as.data.frame(x_all[these_explain,these_p,drop=F]) - -colnames(x_explain) <- colnames(x_train) <- paste0("X",seq_len(p)) - -y_train <- y_all[these_train] - -xy_train <- cbind(x_train,y=y_train) - -model <- lm(formula = y~.,data=xy_train) - -phi0 <- mean(y_train) - -n_batches_use <- min(2^p-2,n_batches) - - -explanation_many <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = approach, - n_batches = n_batches_use, - phi0 = phi0 - ) - - -#explanation_single <- explain( -# model = model, -# x_explain = x_explain, -# x_train = x_train, -# approach = approach, -# n_batches = 1, -# phi0 = phi0 -#) - - - -#S_batch_many <- copy(explanation_many$internal$objects$S_batch) -#internal_many <- copy(explanation_many$internal) - -#S_batch_single <- list(`1`=sort(unlist(copy(explanation_many$internal$objects$S_batch),use.names=FALSE))) -#internal_single <- copy(explanation_many$internal) - -feature_specs <- shapr:::get_feature_specs(NULL, model) - - -internal <- setup( - x_train = x_train, - x_explain = x_explain, - approach = approach, - phi0 = phi0, - n_coalitions = 2^p, - group = NULL, - n_samples = 1e3, - n_batches = n_batches_use, - seed = 123, - keep_samp_for_vS = FALSE, - feature_specs = feature_specs) - -internal <- setup_computation(internal, model, NULL) - -S_batch_many <- internal$objects$S_batch -S_batch_single <- list(`1`=sort(unlist(copy(S_batch_many),use.names=FALSE))) - -testfunc <- function(S, internal) { - dt <- shapr:::batch_prepare_vS(S = S, internal = internal) # Make it optional to store and return the dt_list - return(S) -} - -internal$parameters$empirical.fixed_sigma <- rep(0.1,2) - -#pp_many <- profmem({ -s <- proc.time() -ret <- future.apply::future_lapply( - X = S_batch_many, - FUN = testfunc, - internal = internal) -proc.time()-s -#},threshold=10^4) - -#pp_single <- profmem({ -s <- proc.time() - ret <- future.apply::future_lapply( - X = S_batch_single, - FUN = testfunc, - internal = internal) -proc.time()-s -#},threshold=10^4) - -plot(pp_many$bytes) -points(pp_single$bytes,col=2) - -sum(pp_many$bytes) -sum(pp_single$bytes) diff --git a/inst/scripts/example_annabelle.R b/inst/scripts/example_annabelle.R deleted file mode 100644 index b2cad4031..000000000 --- a/inst/scripts/example_annabelle.R +++ /dev/null @@ -1,77 +0,0 @@ -library(shapr) -library(data.table) -library(MASS) - -# ------------------------------ - -Boston$rad <- as.factor(Boston$rad) -Boston$chas <- as.factor(Boston$chas) -x_var <- c("rad", "chas") -y_var <- "medv" - -ind_x_test <- 1:4 -train <- Boston[-ind_x_test, c(x_var, y_var)] -x_test <- Boston[ind_x_test, x_var] -x_train = train[, x_var] - -model <- lm(medv ~ rad + chas, data = train) - -# ------------------------------ -# To test the categorical method when we know the results - -data = fread("../shapr/data.csv") -data$feat_1_ = factor(data$feat_1_) -data$feat_2_ = factor(data$feat_2_) -data$feat_3_ = factor(data$feat_3_) - -x_train = data[1:1000, c("feat_1_", "feat_2_", "feat_3_")] -x_test = data[1001:1005, c("feat_1_", "feat_2_", "feat_3_")] - -joint_prob_dt = fread("../shapr/joint_prob_dt.csv") - -p <- mean(data[1:1000,][['response']]) - -joint_prob_dt[, feat_1_ := as.factor(feat_1_)] -joint_prob_dt[, feat_2_ := as.factor(feat_2_)] -joint_prob_dt[, feat_3_ := as.factor(feat_3_)] - -train = data[1:1000,] - -model <- lm(response ~ feat_1_ + feat_2_ + feat_3_, data = train) - -# ------------------------------ - -temp = explain( - x_train = x_train, - x_explain = x_test, - model = model, - approach = "categorical", - phi0 = p, - joint_probability_dt = joint_prob_dt -) -print(temp) -# none rad chas -# 1: -0.030511 13.231 10.887 -# 2: -0.030511 15.709 11.035 -# 3: -0.030511 15.709 11.035 -# 4: -0.030511 16.624 10.883 - -# Without joint prob dt -# none feat_1_ feat_2_ feat_3_ -# 1: -0.030516 0.20455 0.29895 0.1381985 -# 2: -0.030516 0.23079 0.35300 -0.0480793 -# 3: -0.030516 0.13084 0.32979 -0.8297798 -# 4: -0.030516 0.23133 -0.88754 0.1923399 -# 5: -0.030516 0.27954 -0.84447 -0.0049256 - -# With joint prob dt -# none feat_1_ feat_2_ feat_3_ -# 1: -0.03051645 0.2211416 0.3030599 0.1174976222 -# 2: -0.03051648 0.2312988 0.3611456 -0.0567361622 -# 3: -0.03051644 0.1437691 0.3371903 -0.8501081617 -# 4: -0.03051647 0.2446707 -0.8627886 0.1542449764 -# 5: -0.03051649 0.2140973 -0.7843376 0.0003764934 - -# none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor -# 1: 40.752 6.1998 7.8422 2.852 70.2288 -# 2: 40.752 -3.7270 9.8283 5.626 4.1224 diff --git a/inst/scripts/example_ctree_method.R b/inst/scripts/example_ctree_method.R deleted file mode 100644 index 6765a989c..000000000 --- a/inst/scripts/example_ctree_method.R +++ /dev/null @@ -1,99 +0,0 @@ -library(shapr) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -#### 1) Example with just continuous features #### - -x_train <- as.matrix(tail(Boston[, x_var], -6)) -y_train <- tail(Boston[, y_var], -6) -x_test <- as.matrix(head(Boston[, x_var], 6)) - -# Just looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost::xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) - -# Prepare the data for explanation -explainer <- shapr(x_train, model) - -# Spedifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(y_train) - -# Computing the actual Shapley values with kernelSHAP accounting for feature dependence using -# the ctree approach with default mincriterion = 0.95, minsplit = 20, minbucket = 7, -# and sample = TRUE -explanation <- explain(x_test, explainer, - approach = "ctree", - phi0 = p0) - -# Printing the Shapley values for the test data -explanation$dt - -# Finally we plot the resulting explanations -plot(explanation) - - -#### 2) Example with mixed continuous and categorical features #### -library(shapr) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -x_train <- as.matrix(tail(Boston[, x_var], -6)) -y_train <- tail(Boston[, y_var], -6) -x_test <- as.matrix(head(Boston[, x_var], 6)) - -x_train_cat <- as.data.frame(x_train) -x_test_cat <- as.data.frame(x_test) - -# convert to factors for illustational purpose -x_train_cat$rm <- factor(round(x_train_cat$rm)) -x_test_cat$rm <- factor(round(x_test_cat$rm), levels = c(8, 9, 7, 4, 5, 6)) - -# Make sure they have the same levels! -print(levels(x_train_cat$rm)) -print(levels(x_test_cat$rm)) - -# -- special function when using categorical data + xgboost -dummylist <- make_dummies(traindata = x_train_cat, testdata = x_test_cat) - -x_train_dummy <- dummylist$train_dummies -x_test_dummy <- dummylist$test_dummies - -# Fitting a basic xgboost model to the training data -model_cat <- xgboost::xgboost( - data = x_train_dummy, - label = y_train, - nround = 20, - verbose = FALSE -) -model_cat$feature_specs <- dummylist$feature_specs - -explainer_cat <- shapr(dummylist$traindata_new, model_cat) - -# Specifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(y_train) - -# dummylist$testdata_new$rm - -explanation_cat <- explain( - dummylist$testdata_new, - approach = "ctree", - explainer = explainer_cat, - phi0 = p0 -) - -# Plot the resulting explanations for observations 1 and 6, excluding -# the no-covariate effect -plot(explanation_cat) diff --git a/inst/scripts/example_custom_model.R b/inst/scripts/example_custom_model.R deleted file mode 100644 index c2a476a31..000000000 --- a/inst/scripts/example_custom_model.R +++ /dev/null @@ -1,94 +0,0 @@ -library(gbm) -library(shapr) - -# Load data -data("Boston", package = "MASS") - -# Create test- and training data -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) - -form = as.formula(paste0(y_var,"~",paste0(x_var,collapse="+"))) - -library(gbm) - -xy_train <- data.frame(x_train,medv = y_train) - - -# Fitting a gbm model -set.seed(825) -model <- gbm::gbm( - form, - data = xy_train, - distribution = "gaussian" -) - -#### Full feature versions of the three required model functions #### - -predict_model.gbm <- function(x, newdata) { - - if (!requireNamespace('gbm', quietly = TRUE)) { - stop('The gbm package is required for predicting train models') - } - - model_type <- ifelse( - x$distribution$name %in% c("bernoulli","adaboost"), - "classification", - "regression" - ) - if (model_type == "classification") { - - predict(x, as.data.frame(newdata), type = "response",n.trees = x$n.trees) - } else { - - predict(x, as.data.frame(newdata),n.trees = x$n.trees) - } -} - -get_model_specs.gbm <- function(x){ - feature_specs = list() - feature_specs$labels <- labels(x$Terms) - m <- length(feature_specs$labels) - - feature_specs$classes <- attr(x$Terms,"dataClasses")[-1] - feature_specs$factor_levels <- setNames(vector("list", m), feature_specs$labels) - feature_specs$factor_levels[feature_specs$classes=="factor"] <- NA # the model object doesn't contain factor levels info - - return(feature_specs) -} - -# Prepare the data for explanation -set.seed(123) -explainer <- shapr(xy_train, model) -p0 <- mean(xy_train[,y_var]) -explanation <- explain(x_test, explainer, approach = "empirical", phi0 = p0) -# Plot results -plot(explanation) - - -# Minimal version of the three required model functions -# Note: Working only for this exact version of the model class -# Avoiding to define get_model_specs skips all feature -# consistency checking between your data and model - -# Removing the previously defined functions to simulate a fresh start -rm(predict_model.gbm) -rm(get_model_specs.gbm) - - -predict_model.gbm <- function(x, newdata) { - predict(x, as.data.frame(newdata),n.trees = x$n.trees) -} - - -# Prepare the data for explanation -set.seed(123) -explainer <- shapr(x_train, model) -p0 <- mean(xy_train[,y_var]) -explanation <- explain(x_test, explainer, approach = "empirical", phi0 = p0) -# Plot results -plot(explanation) diff --git a/inst/scripts/example_plot_MSEv.R b/inst/scripts/example_plot_MSEv.R deleted file mode 100644 index 725b1d896..000000000 --- a/inst/scripts/example_plot_MSEv.R +++ /dev/null @@ -1,411 +0,0 @@ -# Setup example --------------------------------------------------------------------------------------------------- -# Load necessary libraries -library(xgboost) -library(data.table) -library(shapr) -library(ggplot2) - -# Get the data -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] - -#' Define the features and the response -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" - -# Split data into test and training data set -ind_x_explain <- 1:25 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# Fitting a basic xgboost model to the training data -model <- xgboost::xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -phi0 <- mean(y_train) - -# Independence approach -explanation_independence <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "independence", - phi0 = phi0, - n_samples = 1e2 -) - -# Empirical approach -explanation_empirical <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "empirical", - phi0 = phi0, - n_samples = 1e2 -) - -# Gaussian 1e1 approach -explanation_gaussian_1e1 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 1e1 -) - -# Gaussian 1e2 approach -explanation_gaussian_1e2 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 1e2 -) - -# ctree approach -explanation_ctree <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "ctree", - phi0 = phi0, - n_samples = 1e2 -) - -# Combined approach -explanation_combined <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = c("gaussian", "independence", "ctree"), - phi0 = phi0, - n_samples = 1e2 -) - -# Create a list of explanations without names -explanation_list_unnamed <- list( - explanation_independence, - explanation_empirical, - explanation_gaussian_1e1, - explanation_gaussian_1e2, - explanation_ctree, - explanation_combined -) - -# Create a list of explanations with names -explanation_list_named <- list( - "Ind." = explanation_independence, - "Emp." = explanation_empirical, - "Gaus. 1e1" = explanation_gaussian_1e1, - "Gaus. 1e2" = explanation_gaussian_1e2, - "Ctree" = explanation_ctree, - "Combined" = explanation_combined -) - - - -# Plots ----------------------------------------------------------------------------------------------------------- -# Create the default MSEv plot -MSEv_figure <- plot_MSEv_eval_crit(explanation_list_named) -MSEv_figure - -# For long method names, one can rotate them or put them on different lines (or both) -MSEv_figure + ggplot2::guides(x = ggplot2::guide_axis(angle = 45)) -MSEv_figure + ggplot2::guides(x = ggplot2::guide_axis(n.dodge = 2)) - -# The function sets default names based on the used approach when an unnamed list is provided -plot_MSEv_eval_crit(explanation_list_unnamed) + ggplot2::guides(x = ggplot2::guide_axis(angle = 45)) - -# Can move the legend around or simply remove it -MSEv_figure + - ggplot2::theme(legend.position = "bottom") + - ggplot2::guides(fill = ggplot2::guide_legend(nrow = 2, ncol = 3)) -MSEv_figure + ggplot2::theme(legend.position = "none") - -# Change the size of the title or simply remove it -MSEv_figure + ggplot2::theme(plot.title = ggplot2::element_text(size = 10)) -MSEv_figure + ggplot2::labs(title = NULL) - -# Change the theme and color scheme -MSEv_figure + ggplot2::theme_minimal() + - ggplot2::scale_fill_brewer(palette = "Paired") - -# Can add the height of the bars as text. Remove the error bars. -bar_text_n_decimals <- 1 -MSEv_figure_wo_CI <- plot_MSEv_eval_crit(explanation_list_named, CI_level = NULL) -MSEv_figure_wo_CI + - ggplot2::geom_text( - ggplot2::aes(label = sprintf( - paste("%.", sprintf("%d", bar_text_n_decimals), "f", sep = ""), - round(MSEv, bar_text_n_decimals) - )), - vjust = 1.75, - hjust = NA, - color = "black", - position = ggplot2::position_dodge(0.9), - size = 5 - ) - -# Rotate the plot -MSEv_figure + - ggplot2::scale_x_discrete(limits = rev(levels(MSEv_figure$data$Method))) + - ggplot2::coord_flip() - -# All of these can be combined -MSEv_figure_wo_CI + - ggplot2::scale_x_discrete(limits = rev(levels(MSEv_figure_wo_CI$data$Method))) + - ggplot2::coord_flip() + - ggplot2::scale_fill_discrete() + #' Default ggplot2 palette - ggplot2::theme_minimal() + #' This must be set before the other theme call - ggplot2::theme( - plot.title = ggplot2::element_text(size = 10), - legend.position = "bottom" - ) + - ggplot2::guides(fill = ggplot2::guide_legend(nrow = 1, ncol = 6)) + - ggplot2::geom_text( - ggplot2::aes(label = sprintf( - paste("%.", sprintf("%d", bar_text_n_decimals), "f", sep = ""), - round(MSEv, bar_text_n_decimals) - )), - vjust = NA, # These must be changed for different figure sizes - hjust = 1.15, # These must be changed for different figure sizes - color = "black", - position = ggplot2::position_dodge(0.9), - size = 5 - ) - -# or with the CI -MSEv_figure + - ggplot2::scale_x_discrete(limits = rev(levels(MSEv_figure$data$Method))) + - ggplot2::coord_flip() + - ggplot2::scale_fill_discrete() + #' Default ggplot2 palette - ggplot2::theme_minimal() + #' This must be set before the other theme call - ggplot2::theme( - plot.title = ggplot2::element_text(size = 10), - legend.position = "bottom" - ) + - ggplot2::guides(fill = ggplot2::guide_legend(nrow = 1, ncol = 6)) + - ggplot2::geom_text( - ggplot2::aes(label = sprintf( - paste("%.", sprintf("%d", bar_text_n_decimals), "f", sep = ""), - round(MSEv, bar_text_n_decimals) - )), - vjust = -1, # These must be changed for different figure sizes - hjust = 1.15, # These must be changed for different figure sizes - color = "black", - position = ggplot2::position_dodge(0.9), - size = 5 - ) - - - -# Can also create plots where we look at the MSEv criterion averaged only over the combinations or observations. -# Note that we can also alter the design of these plots as we did above. -MSEv_figures <- plot_MSEv_eval_crit( - explanation_list_named, - plot_type = c("overall", "comb", "explicand")) -MSEv_figures$MSEv_bar -MSEv_figures$MSEv_combination_bar -MSEv_figures$MSEv_explicand_bar - -# When there are many combinations or observations, then it can be easier to look at line plots -MSEv_figures$MSEv_combination_line_point -MSEv_figures$MSEv_explicand_line_point - -# We can specify which test observations or combinations to plot -plot_MSEv_eval_crit(explanation_list_named, - plot_type = "explicand", - index_x_explain = c(1, 3:4, 6) -)$MSEv_explicand_bar -plot_MSEv_eval_crit(explanation_list_named, - plot_type = "comb", - id_coalition = c(3, 4, 9, 13:15) -)$MSEv_combination_bar - - -# To rotate the combination plot, we need to alter the order of the methods to get them in the same order as before -MSEv_combination <- plot_MSEv_eval_crit( - explanation_list_named, - plot_type = "comb", - id_coalition = c(3, 4, 9, 13:15) -)$MSEv_combination_bar -MSEv_combination$data$Method <- factor(MSEv_combination$data$Method, levels = rev(levels(MSEv_combination$data$Method))) -MSEv_combination + - ggplot2::scale_x_discrete(limits = rev(unique(MSEv_combination$data$id_coalition))) + - ggplot2::scale_fill_discrete(breaks = rev(levels(MSEv_combination$data$Method)), direction = -1) + - ggplot2::coord_flip() - - -# Rotate and with text, but without CI -MSEv_combination_wo_CI <- plot_MSEv_eval_crit( - explanation_list_named, - plot_type = "comb", - id_coalition = c(3, 4, 9, 13:15), - CI_level = NULL -)$MSEv_combination_bar -MSEv_combination_wo_CI$data$Method <- factor(MSEv_combination_wo_CI$data$Method, - levels = rev(levels(MSEv_combination_wo_CI$data$Method)) -) -MSEv_combination_wo_CI + - ggplot2::scale_x_discrete(limits = rev(unique(MSEv_combination_wo_CI$data$id_coalition))) + - ggplot2::scale_fill_brewer( - breaks = rev(levels(MSEv_combination_wo_CI$data$Method)), - palette = "Paired", - direction = -1 - ) + - ggplot2::coord_flip() + - ggplot2::theme_minimal() + #' This must be set before the other theme call - ggplot2::theme( - plot.title = ggplot2::element_text(size = 10), - legend.position = "bottom" - ) + - ggplot2::guides(fill = ggplot2::guide_legend(nrow = 1, ncol = 6)) + - ggplot2::geom_text( - ggplot2::aes( - label = sprintf( - paste("%.", sprintf("%d", bar_text_n_decimals), "f", sep = ""), - round(MSEv, bar_text_n_decimals) - ), - group = Method - ), - hjust = 1.2, - vjust = NA, - color = "white", - position = ggplot2::position_dodge(MSEv_combination_wo_CI$layers[[1]]$geom_params$width), - size = 3 - ) - -# Check for same combinations ------------------------------------------------------------------------------------ -explanation_gaussian_seed_1 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10, - n_coalitions = 10, - seed = 1 -) - -explanation_gaussian_seed_1_V2 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10, - n_coalitions = 10, - seed = 1 -) - -explanation_gaussian_seed_2 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10, - n_coalitions = 10, - seed = 2 -) - -explanation_gaussian_seed_3 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10, - n_coalitions = 10, - seed = 3 -) - -# Explanations based on different combinations -explanation_gaussian_seed_1$internal$objects$X$features -explanation_gaussian_seed_2$internal$objects$X$features -explanation_gaussian_seed_3$internal$objects$X$features - -# Will give an error due to different combinations -plot_MSEv_eval_crit(list( - "Seed1" = explanation_gaussian_seed_1, - "Seed1_V2" = explanation_gaussian_seed_1_V2, - "Seed2" = explanation_gaussian_seed_2, - "Seed3" = explanation_gaussian_seed_3 -)) - - - -# Different explicands -------------------------------------------------------------------------------------------- -explanation_gaussian_all <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10 -) - -explanation_gaussian_only_5 <- explain( - model = model, - x_explain = x_explain[1:5, ], - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10 -) - -# Will give an error due to different explicands -plot_MSEv_eval_crit(list( - "All_explicands" = explanation_gaussian_all, - "Five_explicands" = explanation_gaussian_only_5 -)) - - -# Different feature names ---------------------------------------------------------------------------------------------- -explanation_gaussian <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10 -) - -explanation_gaussian_copy <- copy(explanation_gaussian_all) -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( - "Original" = explanation_gaussian, - "Reversed_feature_names" = explanation_gaussian_copy -)) - - - -# Missing MSEv ---------------------------------------------------------------------------------------------------- -explanation_gaussian <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 10 -) - -explanation_gaussian_copy <- copy(explanation_gaussian_all) -explanation_gaussian_copy$MSEv <- NULL - -# Will give an error due to missing MSEv -plot_MSEv_eval_crit(list( - "Original" = explanation_gaussian, - "Missing_MSEv" = explanation_gaussian_copy -)) diff --git a/inst/scripts/example_plot_SV_several_approaches.R b/inst/scripts/example_plot_SV_several_approaches.R deleted file mode 100644 index 564e4c133..000000000 --- a/inst/scripts/example_plot_SV_several_approaches.R +++ /dev/null @@ -1,186 +0,0 @@ -# Setup ----------------------------------------------------------------------------------------------------------- -# Load necessary libraries -library(xgboost) -library(data.table) - -# Get the data -data("airquality") -data = data.table::as.data.table(airquality) -data = data[complete.cases(data), ] - -# Define the features and the response -x_var = c("Solar.R", "Wind", "Temp", "Month") -y_var = "Ozone" - -# Split data into test and training data set -ind_x_explain = 1:12 -x_train = data[-ind_x_explain, ..x_var] -y_train = data[-ind_x_explain, get(y_var)] -x_explain = data[ind_x_explain, ..x_var] - -# Fitting a basic xgboost model to the training data -model = xgboost::xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -phi0 = mean(y_train) - -# Independence approach -explanation_independence = explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "independence", - phi0 = phi0, - n_samples = 1e2 -) - -# Empirical approach -explanation_empirical = explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "empirical", - phi0 = phi0, - n_samples = 1e2 -) - -# Gaussian 1e1 approach -explanation_gaussian_1e1 = explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 1e1 -) - -# Gaussian 1e2 approach -explanation_gaussian_1e2 = explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "gaussian", - phi0 = phi0, - n_samples = 1e2 -) - -# Combined approach -explanation_combined = explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = c("gaussian", "ctree", "empirical"), - phi0 = phi0, - n_samples = 1e2 -) - -# Create a list of explanations with names -explanation_list = list( - "Ind." = explanation_independence, - "Emp." = explanation_empirical, - "Gaus. 1e1" = explanation_gaussian_1e1, - "Gaus. 1e2" = explanation_gaussian_1e2, - "Combined" = explanation_combined -) - - -# Plots ----------------------------------------------------------------------------------------------------------- -# The function uses the provided names. -plot_SV_several_approaches(explanation_list) - -# We can change the number of columns in the grid of plots and add other visual alterations -plot_SV_several_approaches(explanation_list, - facet_ncol = 3, - facet_scales = "free_y", - add_zero_line = TRUE, - digits = 2, - brewer_palette = "Paired", - geom_col_width = 0.6) + - ggplot2::theme_minimal() + - ggplot2::theme(legend.position = "bottom", plot.title = ggplot2::element_text(size = 0)) - - -# We can specify which explicands to plot to get less chaotic plots and make the bars vertical -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - horizontal_bars = FALSE, - axis_labels_rotate_angle = 45) - - -# We can change the order of the features by specifying the order using the `only_these_features` parameter. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R", "Month", "Wind")) - -# We can also remove certain features if we are not interested in them or want to focus on, e.g., two features. -# The function will give a message to if the user specifies non-valid feature names. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R"), - plot_phi0 = TRUE) - -# We can specify which explicands to plot to get less chaotic plots. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10)) - -# We can make the bars vertical by setting `horizontal_bars = FALSE`. -# Will then get message about long label names on the x-axis and how to fix it. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - horizontal_bars = FALSE) - -# We can change the order of the features by specifying the order using the `only_these_features` parameter. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R", "Month", "Wind")) - -# We can also remove certain features if we are not interested in them or want to focus on, e.g., two features. -# The function will give a message to if the user specifies non-valid feature names. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R")) - -# To more easily compare the magnitude of the Shapley values for different explicands we can fix the x-axis -# by specifying that only the scales on the y-axis are to be free. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R"), - facet_scales = "free_y") - -# If we rather want vertical bars and fix the y-axis, then we specify that the scales are only free on the x-axis. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R"), - facet_scales = "free_x", - axis_labels_rotate_angle = 0, - horizontal_bars = FALSE) - -# By default the function does not plot the phi0, but we can change that by setting `plot_phi0 = TRUE`. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R"), - plot_phi0 = TRUE) - -# Or we can include "none" in the `only_these_features` parameter. Note that phi0 will always be the first bars. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - only_these_features = c("Temp", "Solar.R", "none")) - -# We can add a line at the Shapley value of zero and ensure non overlapping labels by setting `axis_labels_n_dodge`. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - add_zero_line = TRUE, - axis_labels_n_dodge = 2, - horizontal_bars = FALSE) - -# We can increase the space between the features to make it easier to distinguish them from each other -# by lowering `geom_col_width`. Note that default is 0.85. -plot_SV_several_approaches(explanation_list, - index_explicands = c(1:2, 5, 10), - geom_col_width = 0.6) - diff --git a/inst/scripts/example_plot_several_vaeacs_VLB_IWAE.R b/inst/scripts/example_plot_several_vaeacs_VLB_IWAE.R deleted file mode 100644 index 85e9e3914..000000000 --- a/inst/scripts/example_plot_several_vaeacs_VLB_IWAE.R +++ /dev/null @@ -1,150 +0,0 @@ -library(xgboost) -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] - -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" - -ind_x_explain <- 1:6 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 100, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -p0 <- mean(y_train) - -# Train several different NN -explanation_paired_sampling_TRUE <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = approach, - phi0 = p0, - n_batches = 2, - n_samples = 1, #' As we are only interested in the training of the vaeac - vaeac.epochs = 25, #' Should be higher in applications. - vaeac.n_vaeacs_initialize = 5, - vaeac.extra_parameters = list( - vaeac.paired_sampling = TRUE, - vaeac.verbose = TRUE - ) -) - -explanation_paired_sampling_FALSE <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = approach, - phi0 = p0, - n_batches = 2, - n_samples = 1, #' As we are only interested in the training of the vaeac - vaeac.epochs = 25, #' Should be higher in applications. - vaeac.n_vaeacs_initialize = 5, - vaeac.extra_parameters = list( - vaeac.paired_sampling = FALSE, - vaeac.verbose = TRUE - ) -) - -# Other networks have 4.76 times more parameters. -explanation_paired_sampling_FALSE_small <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = approach, - phi0 = p0, - n_batches = 2, - n_samples = 1, #' As we are only interested in the training of the vaeac - vaeac.epochs = 25, #' Should be higher in applications. - vaeac.width = 16, #' Default is 32 - vaeac.depth = 2, #' Default is 3 - vaeac.latent_dim = 4, #' Default is 8 - vaeac.n_vaeacs_initialize = 5, - vaeac.extra_parameters = list( - vaeac.paired_sampling = FALSE, - vaeac.verbose = TRUE - ) -) - -explanation_paired_sampling_TRUE_small <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = approach, - phi0 = p0, - n_batches = 2, - n_samples = 1, #' As we are only interested in the training of the vaeac - vaeac.epochs = 25, #' Should be higher in applications. - vaeac.width = 16, #' Default is 32 - vaeac.depth = 2, #' Default is 3 - vaeac.latent_dim = 4, #' Default is 8 - vaeac.n_vaeacs_initialize = 5, - vaeac.extra_parameters = list( - vaeac.paired_sampling = TRUE, - vaeac.verbose = TRUE - ) -) - -# Collect the explanation objects in an unnamed list -explanation_list_unnamed <- list( - explanation_paired_sampling_FALSE, - explanation_paired_sampling_FALSE_small, - explanation_paired_sampling_TRUE, - explanation_paired_sampling_TRUE_small -) - -# Collect the explanation objects in an named list -explanation_list_named <- list( - "Regular samp. & large NN" = explanation_paired_sampling_FALSE, - "Regular samp. & small NN" = explanation_paired_sampling_FALSE_small, - "Paired samp. & large NN" = explanation_paired_sampling_TRUE, - "Paired samp. & small NN" = explanation_paired_sampling_TRUE_small -) - -# Call the function with the unnamed list, will create names -vaeac_plot_eval_crit(explanation_list = explanation_list_unnamed) - -# Call the function with the named list, will use the provided names -# See that the paired samplign often produce more stable results -vaeac_plot_eval_crit(explanation_list = explanation_list_named) - -# The function also works if we have only one method, but then one should only look at the method plot -vaeac_plot_eval_crit(explanation_list = list("Paired samp. & large NN" = explanation_paired_sampling_TRUE), - plot_type = "method") - -# Can alter the plot -vaeac_plot_eval_crit( - explanation_list = explanation_list_named, - plot_from_nth_epoch = 5, - plot_every_nth_epoch = 3, - facet_wrap_scales = "free" -) - -# If we want only want the criterion version -tmp_fig_criterion = vaeac_plot_eval_crit( - explanation_list = explanation_list_named, - plot_type = "criterion") - -# We can add points -tmp_fig_criterion + ggplot2::geom_point(shape = "circle", size = 1, ggplot2::aes(col = Method)) - -# If we rather want smooths with se bands -tmp_fig_criterion$layers[[1]] = NULL -tmp_fig_criterion + ggplot2::geom_smooth(method = "loess", formula = y ~ x, se = TRUE) + - ggplot2::scale_color_brewer(palette = "Set1") + - ggplot2::theme_minimal() - -# If we only want the VLB -vaeac_plot_eval_crit( - explanation_list = explanation_list_named, - criteria = "VLB", - plot_type = "criterion") diff --git a/inst/scripts/explain_memory_testing.R b/inst/scripts/explain_memory_testing.R deleted file mode 100644 index d9e35e7eb..000000000 --- a/inst/scripts/explain_memory_testing.R +++ /dev/null @@ -1,113 +0,0 @@ -#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library") -sys_time_initial <- Sys.time() - -# libraries -library(shapr) -library(future) -library(MASS) -library(microbenchmark) -library(data.table) -library(profmem) - -# Initial setup -max_n <- 10^5 -max_p <- 16 -rho <- 0.3 -sigma <- 1 -mu_const <- 0 -beta0 <- 1 -sigma_eps <- 1 - -mu <- rep(mu_const,max_p) -beta <- c(beta0,seq_len(max_p)/max_p) -Sigma <- matrix(rho,max_p,max_p) -diag(Sigma) <- sigma - -set.seed(123) -x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma) -y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps) - -# Arguments from bash -#args <- commandArgs(trailingOnly = TRUE) -#if(length(args)==0) args = c(1,10,1000,100,10,1,"empirical","sequential","timing_test_2023.csv") - - -this_rep <- 1 -p <- 4 -n_train <- 1000 -n_explain <- 50 -n_batches <- 10 -n_cores <- 1 -approach <- "empirical" -multicore_method <- "sequential" -logfilename <- "bla" - -set.seed(123) - - -these_p <- sample.int(max_p,size=p) -these_train <- sample.int(max_n,size=n_train) -these_explain <- sample.int(max_n,size=n_explain) - -x_train <- as.data.frame(x_all[these_train,these_p,drop=F]) -x_explain <- as.data.frame(x_all[these_explain,these_p,drop=F]) - -colnames(x_explain) <- colnames(x_train) <- paste0("X",seq_len(p)) - -y_train <- y_all[these_train] - -xy_train <- cbind(x_train,y=y_train) - -model <- lm(formula = y~.,data=xy_train) - -phi0 <- mean(y_train) - -n_batches_use <- min(2^p-2,n_batches) - - -sys_time_start_explain <- Sys.time() - -pp.old <- profmem({ -explanation <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = approach, - n_batches = n_batches_use, - phi0 = phi0 -) -},threshold=10^4) - -sys_time_end_explain <- Sys.time() - -pp[!is.na(pp$bytes) &pp$bytes >6*10^5,] - -plot(pp$bytes) -points(pp.old$bytes,col=2) -#"bytes"] -pp.old[!is.na(pp.old$bytes) &pp.old$bytes >6*10^5,"bytes"] - -secs_explain <- as.double(difftime(sys_time_end_explain,sys_time_start_explain),units="secs") -print(secs_explain) - -timing <- list(p = p, - n_train = n_train, - n_explain = n_explain, - n_batches = n_batches, - n_cores = n_cores, - approach = approach, - sys_time_initial = as.character(sys_time_initial), - sys_time_start_explain = as.character(sys_time_start_explain), - sys_time_end_explain = as.character(sys_time_end_explain), - secs_explain = secs_explain, - this_rep = this_rep, - max_n = max_n, - max_p = max_p, - rho = rho, - sigma = sigma, - mu_const = mu_const, - beta0 = beta0, - sigma_eps = sigma_eps) - -#print(unlist(timing)) -data.table::fwrite(timing,logfilename,append = T) diff --git a/inst/scripts/memory_test_2023.csv b/inst/scripts/memory_test_2023.csv deleted file mode 100644 index 4daa220ed..000000000 --- a/inst/scripts/memory_test_2023.csv +++ /dev/null @@ -1,3013 +0,0 @@ -2023-01-17, 15:06:10, 6610 , 0, 2, 100, 10, 1, 1, empirical, sequential, timing_test_2023.csv -2023-01-17, 15:06:10, 106097 , 0, 2, 100, 10, 1, 1, empirical, sequential, timing_test_2023.csv -2023-01-17, 15:06:16, 6610 , 0, 2, 100, 10, 1, 1, gaussian, sequential, timing_test_2023.csv -2023-01-17, 15:06:16, 106358 , 0, 2, 100, 10, 1, 1, gaussian, sequential, timing_test_2023.csv -2023-01-17, 15:06:22, 6614 , 0, 2, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv -2023-01-17, 15:06:23, 106451 , 0, 2, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv -2023-01-17, 15:06:24, 255488 , 0, 2, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv -2023-01-17, 15:06:29, 6603 , 0, 2, 100, 10, 1, 1, independence, sequential, timing_test_2023.csv -2023-01-17, 15:06:30, 106143 , 0, 2, 100, 10, 1, 1, independence, sequential, timing_test_2023.csv -2023-01-17, 15:06:35, 6610 , 0, 2, 100, 10, 2, 1, empirical, sequential, timing_test_2023.csv -2023-01-17, 15:06:36, 106392 , 0, 2, 100, 10, 2, 1, empirical, sequential, timing_test_2023.csv -2023-01-17, 15:06:42, 6603 , 0, 2, 100, 10, 2, 1, gaussian, sequential, timing_test_2023.csv -2023-01-17, 15:06:42, 106387 , 0, 2, 100, 10, 2, 1, gaussian, sequential, timing_test_2023.csv -2023-01-17, 15:06:48, 6605 , 0, 2, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv -2023-01-17, 15:06:48, 105688 , 0, 2, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv -2023-01-17, 15:06:49, 254789 , 0, 2, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv -2023-01-17, 15:06:55, 6612 , 0, 2, 100, 10, 2, 1, independence, sequential, timing_test_2023.csv -2023-01-17, 15:06:56, 106091 , 0, 2, 100, 10, 2, 1, independence, sequential, timing_test_2023.csv -2023-01-17, 15:07:01, 6613 , 0, 2, 100, 10, 4, 1, empirical, sequential, timing_test_2023.csv -2023-01-17, 15:07:02, 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timing_test_2023.csv -2023-01-18, 15:53:46, 122348 , 0, 4, 100, 10, 1, 1, gaussian, sequential, timing_test_2023.csv -2023-01-18, 15:53:52, 6623 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:53:52, 133068 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:53:53, 256802 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:53:54, 6613 , 0, 4, 100, 10, 1, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:54:00, 6609 , 0, 4, 100, 10, 1, 1, independence, sequential, timing_test_2023.csv -2023-01-18, 15:54:00, 133109 , 0, 4, 100, 10, 1, 1, independence, sequential, timing_test_2023.csv -2023-01-18, 15:54:06, 6605 , 0, 4, 100, 10, 2, 1, empirical, sequential, timing_test_2023.csv -2023-01-18, 15:54:07, 122717 , 0, 4, 100, 10, 2, 1, empirical, sequential, timing_test_2023.csv -2023-01-18, 15:54:12, 6614 , 0, 4, 100, 10, 2, 1, gaussian, sequential, timing_test_2023.csv -2023-01-18, 15:54:13, 133271 , 0, 4, 100, 10, 2, 1, gaussian, sequential, timing_test_2023.csv -2023-01-18, 15:54:18, 6599 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:54:19, 133701 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:54:20, 256149 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:54:21, 6603 , 0, 4, 100, 10, 2, 1, ctree, sequential, timing_test_2023.csv -2023-01-18, 15:54:27, 6609 , 0, 4, 100, 10, 2, 1, independence, sequential, timing_test_2023.csv -2023-01-18, 15:54:27, 122741 , 0, 4, 100, 10, 2, 1, independence, sequential, timing_test_2023.csv diff --git a/inst/scripts/problematic_plots_jens.R b/inst/scripts/problematic_plots_jens.R deleted file mode 100644 index 176af6a9f..000000000 --- a/inst/scripts/problematic_plots_jens.R +++ /dev/null @@ -1,117 +0,0 @@ -devtools::load_all() -library(data.table) -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" - -ind_x_explain <- 1:100 -x_train <- data[, ..x_var] -y_train <- data[, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# convert to factors -data[,Month_factor := as.factor(month.abb[Month])] -# data[, Temp_factor := fcase(Temp < 71, "low", -# Temp %between% c(71, 84), "medium", -# Temp > 84, "high")] -# data[, Temp_factor := as.factor(Temp_factor)] - - - -data[, Temp_factor := as.factor(round(Temp, -1))] -data_train_cat <- copy(data) -data_explain_cat <- data[ind_x_explain,] - -x_var_cat <- c("Solar.R", "Wind", "Temp_factor", "Month_factor") -x_train_cat <- data_train_cat[, ..x_var_cat] - - -# Example 1 - No errors ------------------------------------------------------------------------------------------- -x_explain_cat <- data_explain_cat[, ..x_var_cat] -# x_explain_cat[, Wind := 10] -#x_explain_cat[, Month_factor := Month_factor[1]] -lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + "))) -model_lm_cat <- lm(lm_formula,data_train_cat) - -p0 <- mean(y_train) -explanation_cat <- explain( - model = model_lm_cat, - x_explain = x_explain_cat, - x_train = x_train_cat, - approach = "ctree", - phi0 = p0 -) - - -plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter") -plot(explanation_cat, plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE) - - - -# Example 2 - One factor value ----------------------------------------------------------------------------------- -x_explain_cat <- data_explain_cat[, ..x_var_cat] -x_explain_cat[, Month_factor := Month_factor[1]] -lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + "))) -model_lm_cat <- lm(lm_formula,data_train_cat) - -p0 <- mean(y_train) -explanation_cat <- explain( - model = model_lm_cat, - x_explain = x_explain_cat, - x_train = x_train_cat, - approach = "ctree", - phi0 = p0 -) - -# Works fine -plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter") -# Wrong x-labels due to breaks being different from when scatter_hist = TRUE -plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE) - - - -# Example 3 - few test observations ------------------------------------------------------------------------------ - -x_explain_cat <- data_explain_cat[, ..x_var_cat] -x_explain_cat <- x_explain_cat[1:3, ] -lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + "))) -model_lm_cat <- lm(lm_formula,data_train_cat) - -p0 <- mean(y_train) -explanation_cat <- explain( - model = model_lm_cat, - x_explain = x_explain_cat, - x_train = x_train_cat, - approach = "ctree", - phi0 = p0 -) - -# Only 4 ticks in the x-axis for the factor -plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter") -# Wrong x-labels due to breaks being different from when scatter_hist = TRUE -plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE) - - -# Example 4 - few observations - to many x-ticks with same label ----------------------------------------- - -x_explain_cat <- data_explain_cat[, ..x_var_cat] -x_explain_cat <- x_explain_cat[1:4, ] -lm_formula <- as.formula(paste0(y_var, " ~ ", paste0(x_var_cat, collapse = " + "))) -model_lm_cat <- lm(lm_formula,data_train_cat) - -p0 <- mean(y_train) -explanation_cat <- explain( - model = model_lm_cat, - x_explain = x_explain_cat, - x_train = x_train_cat, - approach = "ctree", - phi0 = p0 -) - -# Duplicated labels on the x-axis -plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter") -# Wrong x-labels due to breaks being different from when scatter_hist = TRUE -plot(explanation_cat, bar_plot_phi0 = FALSE, plot_type = "scatter", scatter_hist = FALSE) - diff --git a/inst/scripts/readme_example.R b/inst/scripts/readme_example.R deleted file mode 100644 index 9d63bc1a1..000000000 --- a/inst/scripts/readme_example.R +++ /dev/null @@ -1,45 +0,0 @@ -library(xgboost) -library(shapr) - -data("airquality") -data <- data.table::as.data.table(airquality) -data <- data[complete.cases(data), ] - -x_var <- c("Solar.R", "Wind", "Temp", "Month") -y_var <- "Ozone" - -ind_x_explain <- 1:6 -x_train <- data[-ind_x_explain, ..x_var] -y_train <- data[-ind_x_explain, get(y_var)] -x_explain <- data[ind_x_explain, ..x_var] - -# Looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = as.matrix(x_train), - label = y_train, - nround = 20, - verbose = FALSE -) - -# Specifying the phi_0, i.e. the expected prediction without any features -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 <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "empirical", - phi0 = p0 -) - -# 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_est) - -# Finally we plot the resulting explanations -plot(explanation) diff --git a/inst/scripts/shap_python_script.py b/inst/scripts/shap_python_script.py deleted file mode 100644 index 4c1fdc13a..000000000 --- a/inst/scripts/shap_python_script.py +++ /dev/null @@ -1,35 +0,0 @@ -#### Python code #### -import xgboost as xgb -import shap -import numpy as np -import pandas as pd -import time - -model = xgb.Booster() # init model -model.load_model("inst/model_objects/xgboost_model_object_raw") - -## kernel shap sends data as numpy array which has no column names, so we fix it -def xgb_predict(data_asarray): - data_asDmatrix = xgb.DMatrix(data_asarray) - return model.predict(data_asDmatrix) - -py_pred_test = xgb_predict(r.x_test) # Test predictions in python - -# - -#### Applying kernelshap - -time_py_start = time.perf_counter() - -shap_kernel_explainer = shap.KernelExplainer(xgb_predict, r.x_train) -Kshap_shap0 = shap_kernel_explainer.shap_values(r.x_test,nsamples = int(100000),l1_reg=0) - -time_py_end = time.perf_counter() - -time_py = time_py_end-time_py_start - -getattr(shap_kernel_explainer,'expected_value') # This is phi0, not used at all below - -Kshap_shap = pd.DataFrame(Kshap_shap0,columns = r.x_var) - -Kshap_shap.insert(0,"none",getattr(shap_kernel_explainer,'expected_value'),True) # Adding the none column diff --git a/inst/scripts/testing_samling_ncombinations.R b/inst/scripts/testing_samling_ncombinations.R deleted file mode 100644 index d11220a4f..000000000 --- a/inst/scripts/testing_samling_ncombinations.R +++ /dev/null @@ -1,126 +0,0 @@ -library(xgboost) -#library(shapr) -# remotes::install_github("NorskRegnesentral/shapr@devel") -library(shapr) -library(data.table) -n = c(100, 1000, 2000) -p = c(5, 10, 10) -n_coalitions = c(20, 800, 800) - -res = list() -for (i in seq_along(n)) { - set.seed(123) - cat("n =", n[i], "p =", p[i], "n_coalitions =", n_coalitions[i], "\n") - x_train = data.table(matrix(rnorm(n[i]*p[i]), nrow = n[i], ncol = p[i])) - x_test = data.table(matrix(rnorm(10*p[i]), nrow = 10, ncol = p[i])) - beta = rnorm(p[i]) - y = rnorm(n[i], as.matrix(x_train) %*% beta) - dt = data.table(cbind(x_train, data.table(y=y))) - model = lm(y ~ ., data = dt) - p_mean = mean(y) - - res[[i]] = bench::mark( - x = shapr::explain( - x_train, - x_test, - model = model, - approach = "empirical", - phi0 = p_mean, - n_coalitions = n_coalitions[i] - ) - ) -} - -devtools::load_all() -res2 = list() -for (i in seq_along(n)) { - - - set.seed(123) - cat("n =", n[i], "p =", p[i], "n_coalitions =", n_coalitions[i], "\n") - x_train = data.table(matrix(rnorm(n[i] * p[i]), nrow = n[i], ncol = p[i])) - x_test = data.table(matrix(rnorm(10 * p[i]), nrow = 10, ncol = p[i])) - beta = rnorm(p[i]) - y = rnorm(n[i], as.matrix(x_train) %*% beta) - dt = data.table(cbind(x_train, data.table(y = y))) - model = lm(y ~ ., data = dt) - p_mean = mean(y) - - res2[[i]] = bench::mark( - explain( - x_train, - x_test, - model = model, - approach = "empirical", - phi0 = p_mean, - n_coalitions = n_coalitions[i] - ) - ) -} - -saveRDS(res, "inst/scripts/testing_samling_ncombinations.rds") -saveRDS(res2, "inst/scripts/testing_samling_ncombinations2.rds") - - - -i = 2 -set.seed(123) -cat("n =", n[i], "p =", p[i], "n_coalitions =", n_coalitions[i], "\n") -x_train = data.table(matrix(rnorm(n[i] * p[i]), nrow = n[i], ncol = p[i])) -x_test = data.table(matrix(rnorm(10 * p[i]), nrow = 10, ncol = p[i])) -beta = rnorm(p[i]) -y = rnorm(n[i], as.matrix(x_train) %*% beta) -dt = data.table(cbind(x_train, data.table(y = y))) -model = lm(y ~ ., data = dt) -p_mean = mean(y) -x1 = Sys.time() -system.time({res = explain( - x_train, - x_test, - model = model, - approach = "empirical", - phi0 = p_mean, - n_coalitions = 1000 -)}) - -devtools::load_all() -system.time({res2 = explain( - x_train, - x_test, - model = model, - approach = "empirical", - phi0 = p_mean, - n_coalitions = 800 -)}) - - - -system.time({res3 = explain( - x_train, - x_test, - model = model, - approach = "empirical", - phi0 = p_mean, - n_coalitions = NULL -)}) - -x2 = Sys.time() -x2-x1 -# devel branch user system elapsed -# 2.43 0.25 2.56 - - -library(profvis) - -res = profvis({res = explain( - x_train, - x_test, - model = model, - approach = "empirical", - phi0 = p_mean, - n_coalitions = n_coalitions[i] -)}) -res - -time2 - time1 -time4 - time3 diff --git a/inst/scripts/time_series_annabelle.R b/inst/scripts/time_series_annabelle.R deleted file mode 100644 index 59e33379d..000000000 --- a/inst/scripts/time_series_annabelle.R +++ /dev/null @@ -1,89 +0,0 @@ -library(data.table) -library(shapr) - -devtools::load_all() - -set.seed(1) -n_train = 1000 -n_test = 6 -n_features = 200 -# x = rnorm((n_train + n_test) * (n_features + 5), mean = 1, sd = 2) -# x = matrix(x, nrow = n_train + n_test, byrow = T) -# x1 = t(apply(x, 1, cumsum)) -# x = data.table(x[, c(1:n_features, n_features + 5)]) - -alpha <- 1 -beta <- 0 -theta <- 0.8 - -data = NULL -for(n in 1:(n_train + n_test)){ - set.seed(n) - e <- rnorm(n_features + 6, mean = 0, sd = 1) - - m_1 <- 0 - for(i in 2:length(e)){ - m_1[i] <- alpha + beta * i + theta * m_1[i - 1] + e[i] - } - data = rbind(data, m_1) -} - - -x <- data[, c(2:(n_features + 1), n_features + 5)] -x <- data.table(x) - -plot(ts((t(x)[,1]))) -points(ts((t(x)[,1])), pch = 19) - -Q1_days <- 1:(floor(n_features / 4)) -Q2_days <- 1:(floor(n_features / 4)) + max(Q1_days) -Q3_days <- 1:(floor(n_features / 4)) + max(Q2_days) -Q4_days <- (max(Q3_days) + 1):n_features - -group <- list(Q1 = paste0("V", Q1_days), - Q2 = paste0("V", Q2_days), - Q3 = paste0("V", Q3_days), - Q4 = paste0("V", Q4_days)) - -response = paste0("V", n_features + 1) -formula = as.formula(paste0(response, "~ ", paste0("V", 1:n_features, collapse = " + "))) - -model = lm(formula, data = x) - -x_all <- x[, 1:n_features] -y_all <- x[[response]] - -all_pred <- predict(model, x_all) -mean((all_pred-y_all)^2) -# [1] 1.8074 - -# --------------- - -x_explain = x_all[-c(1:n_train), ] -x_train = x_all[1:n_train, ] - -p0 <- mean(y_all[-c(1:n_train)]) - -# --------------- - -explanation_group <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = "timeseries", - phi0 = p0, - group = group, - timeseries.fixed_sigma = 2 - # timeseries.bounds = c(-1, 2) -) - -explanation_group -# none Q1 Q2 Q3 Q4 -# 1: 5.1217 -0.0019489 0.201396 -0.208099 0.74808 -# 2: 5.1217 0.0164650 -0.148537 0.639499 0.38405 -# 3: 5.1217 -0.4625373 0.564378 -0.281495 0.61380 -# 4: 5.1217 -0.1859842 -0.121323 0.048696 -0.25682 -# 5: 5.1217 -1.2290037 -0.896415 1.096474 -0.10961 -# 6: 5.1217 -0.0435240 -0.049311 0.898789 -1.36716 - -plot(explanation_group, plot_phi0 = F) diff --git a/inst/scripts/timing_script_2023.R b/inst/scripts/timing_script_2023.R deleted file mode 100644 index 31c258d98..000000000 --- a/inst/scripts/timing_script_2023.R +++ /dev/null @@ -1,113 +0,0 @@ -#.libPaths("/disk/home/jullum/R/x86_64-pc-linux-gnu-library/4.1","/opt/R/4.1.1/lib/R/library") -sys_time_initial <- Sys.time() - -# libraries -library(shapr) -library(future) -library(MASS) -library(microbenchmark) -library(data.table) - -# Initial setup -max_n <- 10^5 -max_p <- 16 -rho <- 0.3 -sigma <- 1 -mu_const <- 0 -beta0 <- 1 -sigma_eps <- 1 - -mu <- rep(mu_const,max_p) -beta <- c(beta0,seq_len(max_p)/max_p) -Sigma <- matrix(rho,max_p,max_p) -diag(Sigma) <- sigma - -set.seed(123) -x_all <- MASS::mvrnorm(max_n,mu = mu,Sigma = Sigma) -y_all <- as.vector(cbind(1,x_all)%*%beta)+rnorm(max_n,mean = 0,sd = sigma_eps) - -# Arguments from bash -args <- commandArgs(trailingOnly = TRUE) -if(length(args)==0) args = c(0,4,100,10,16,1,"empirical","sequential","timing_test_2023_new2.csv") - - -this_rep <- as.numeric(args[1]) -p <- as.numeric(args[2]) -n_train <- as.numeric(args[3]) -n_explain <- as.numeric(args[4]) -n_batches <- as.numeric(args[5]) -n_cores <- as.numeric(args[6]) -approach <- args[7] -multicore_method <- args[8] -logfilename <- args[9] - -set.seed(123) - - -these_p <- sample.int(max_p,size=p) -these_train <- sample.int(max_n,size=n_train) -these_explain <- sample.int(max_n,size=n_explain) - -x_train <- as.data.frame(x_all[these_train,these_p,drop=F]) -x_explain <- as.data.frame(x_all[these_explain,these_p,drop=F]) - -colnames(x_explain) <- colnames(x_train) <- paste0("X",seq_len(p)) - -y_train <- y_all[these_train] - -xy_train <- cbind(x_train,y=y_train) - -model <- lm(formula = y~.,data=xy_train) - -phi0 <- mean(y_train) - -n_batches_use <- min(2^p-2,n_batches) - - -sys_time_start_explain <- Sys.time() - -explanation <- explain( - model = model, - x_explain = x_explain, - x_train = x_train, - approach = approach, - n_batches = n_batches_use, - phi0 = phi0, - n_coalitions = 10^4 -) - -sys_time_end_explain <- Sys.time() - -secs_explain <- as.double(difftime(sys_time_end_explain,sys_time_start_explain),units="secs") -print(secs_explain) - -explanation$timing$timing_secs - -timing <- list(p = p, - n_train = n_train, - n_explain = n_explain, - n_batches = n_batches, - n_cores = n_cores, - approach = approach, - n_coalitions = explanation$internal$parameters$used_n_coalitions, - sys_time_initial = as.character(sys_time_initial), - sys_time_start_explain = as.character(sys_time_start_explain), - sys_time_end_explain = as.character(sys_time_end_explain), - secs_explain = secs_explain, - this_rep = this_rep, - max_n = max_n, - max_p = max_p, - rho = rho, - sigma = sigma, - mu_const = mu_const, - beta0 = beta0, - sigma_eps = sigma_eps, - timing_setup = explanation$timing$timing_secs["setup"], - timing_test_prediction = explanation$timing$timing_secs["test_prediction"], - timing_setup_computation = explanation$timing$timing_secs["setup_computation"], - timing_compute_vS = explanation$timing$timing_secs["compute_vS"], - timing_postprocessing = explanation$timing$timing_secs["postprocessing"], - timing_shapley_computation = explanation$timing$timing_secs["shapley_computation"]) - -#print(unlist(timing)) -data.table::fwrite(timing,logfilename,append = T) diff --git a/inst/scripts/timing_test_2023.csv b/inst/scripts/timing_test_2023.csv deleted file mode 100644 index ee6e1a6e9..000000000 --- a/inst/scripts/timing_test_2023.csv +++ /dev/null @@ -1,296 +0,0 @@ -2,100,10,1,1,empirical,2023-01-17 15:06:10,2023-01-17 15:06:10,2023-01-17 15:06:10,0.316483736038208,0,1e+05,13,0.3,1,0,1,1 -2,100,10,1,1,gaussian,2023-01-17 15:06:16,2023-01-17 15:06:16,2023-01-17 15:06:17,0.311131954193115,0,1e+05,13,0.3,1,0,1,1 -2,100,10,1,1,ctree,2023-01-17 15:06:22,2023-01-17 15:06:23,2023-01-17 15:06:24,1.30662298202515,0,1e+05,13,0.3,1,0,1,1 -2,100,10,1,1,independence,2023-01-17 15:06:29,2023-01-17 15:06:30,2023-01-17 15:06:30,0.276997566223145,0,1e+05,13,0.3,1,0,1,1 -2,100,10,2,1,empirical,2023-01-17 15:06:36,2023-01-17 15:06:36,2023-01-17 15:06:36,0.355516910552979,0,1e+05,13,0.3,1,0,1,1 -2,100,10,2,1,gaussian,2023-01-17 15:06:42,2023-01-17 15:06:42,2023-01-17 15:06:42,0.342517852783203,0,1e+05,13,0.3,1,0,1,1 -2,100,10,2,1,ctree,2023-01-17 15:06:48,2023-01-17 15:06:48,2023-01-17 15:06:50,1.31686520576477,0,1e+05,13,0.3,1,0,1,1 -2,100,10,2,1,independence,2023-01-17 15:06:55,2023-01-17 15:06:56,2023-01-17 15:06:56,0.289998054504395,0,1e+05,13,0.3,1,0,1,1 -2,100,10,4,1,empirical,2023-01-17 15:07:01,2023-01-17 15:07:02,2023-01-17 15:07:02,0.357321500778198,0,1e+05,13,0.3,1,0,1,1 -2,100,10,4,1,gaussian,2023-01-17 15:07:08,2023-01-17 15:07:08,2023-01-17 15:07:08,0.335101366043091,0,1e+05,13,0.3,1,0,1,1 -2,100,10,4,1,ctree,2023-01-17 15:07:14,2023-01-17 15:07:14,2023-01-17 15:07:15,1.33222341537476,0,1e+05,13,0.3,1,0,1,1 -2,100,10,4,1,independence,2023-01-17 15:07:21,2023-01-17 15:07:21,2023-01-17 15:07:22,0.287325859069824,0,1e+05,13,0.3,1,0,1,1 -2,100,10,8,1,empirical,2023-01-17 15:07:27,2023-01-17 15:07:28,2023-01-17 15:07:28,0.358258008956909,0,1e+05,13,0.3,1,0,1,1 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-2,100,100,8,1,gaussian,2023-01-17 15:10:19,2023-01-17 15:10:19,2023-01-17 15:10:20,0.685787200927734,0,1e+05,13,0.3,1,0,1,1 -2,100,100,8,1,ctree,2023-01-17 15:10:26,2023-01-17 15:10:26,2023-01-17 15:10:29,2.14231562614441,0,1e+05,13,0.3,1,0,1,1 -2,100,100,8,1,independence,2023-01-17 15:10:34,2023-01-17 15:10:35,2023-01-17 15:10:35,0.55462646484375,0,1e+05,13,0.3,1,0,1,1 -2,100,100,16,1,empirical,2023-01-17 15:10:41,2023-01-17 15:10:41,2023-01-17 15:10:42,1.07916855812073,0,1e+05,13,0.3,1,0,1,1 -2,100,100,16,1,gaussian,2023-01-17 15:10:48,2023-01-17 15:10:48,2023-01-17 15:10:49,0.720760345458984,0,1e+05,13,0.3,1,0,1,1 -2,100,100,16,1,ctree,2023-01-17 15:10:55,2023-01-17 15:10:55,2023-01-17 15:10:58,2.09656834602356,0,1e+05,13,0.3,1,0,1,1 -2,100,100,16,1,independence,2023-01-17 15:11:03,2023-01-17 15:11:04,2023-01-17 15:11:04,0.545693635940552,0,1e+05,13,0.3,1,0,1,1 -2,100,100,32,1,empirical,2023-01-17 15:11:10,2023-01-17 15:11:10,2023-01-17 15:11:11,1.08544635772705,0,1e+05,13,0.3,1,0,1,1 -2,100,100,32,1,gaussian,2023-01-17 15:11:17,2023-01-17 15:11:17,2023-01-17 15:11:18,0.70287299156189,0,1e+05,13,0.3,1,0,1,1 -2,100,100,32,1,ctree,2023-01-17 15:11:24,2023-01-17 15:11:25,2023-01-17 15:11:27,2.15682244300842,0,1e+05,13,0.3,1,0,1,1 -2,100,100,32,1,independence,2023-01-17 15:11:32,2023-01-17 15:11:33,2023-01-17 15:11:33,0.541534423828125,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,1,1,empirical,2023-01-17 15:11:39,2023-01-17 15:11:39,2023-01-17 15:11:39,0.383393049240112,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,1,1,gaussian,2023-01-17 15:11:45,2023-01-17 15:11:45,2023-01-17 15:11:46,0.33311915397644,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,1,1,ctree,2023-01-17 15:11:51,2023-01-17 15:11:51,2023-01-17 15:11:53,1.37176656723022,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,1,1,independence,2023-01-17 15:11:58,2023-01-17 15:11:59,2023-01-17 15:11:59,0.312752962112427,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,2,1,empirical,2023-01-17 15:12:04,2023-01-17 15:12:05,2023-01-17 15:12:05,0.411961317062378,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,2,1,gaussian,2023-01-17 15:12:11,2023-01-17 15:12:11,2023-01-17 15:12:11,0.358123779296875,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,2,1,ctree,2023-01-17 15:12:17,2023-01-17 15:12:17,2023-01-17 15:12:19,1.42444825172424,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,2,1,independence,2023-01-17 15:12:24,2023-01-17 15:12:24,2023-01-17 15:12:25,0.341542482376099,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,4,1,empirical,2023-01-17 15:12:30,2023-01-17 15:12:31,2023-01-17 15:12:31,0.410071849822998,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,4,1,gaussian,2023-01-17 15:12:36,2023-01-17 15:12:37,2023-01-17 15:12:37,0.364272117614746,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,4,1,ctree,2023-01-17 15:12:43,2023-01-17 15:12:43,2023-01-17 15:12:44,1.40929126739502,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,4,1,independence,2023-01-17 15:12:50,2023-01-17 15:12:50,2023-01-17 15:12:51,0.344814777374268,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,8,1,empirical,2023-01-17 15:12:56,2023-01-17 15:12:56,2023-01-17 15:12:57,0.414499998092651,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,8,1,gaussian,2023-01-17 15:13:02,2023-01-17 15:13:03,2023-01-17 15:13:03,0.362139225006104,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,8,1,ctree,2023-01-17 15:13:08,2023-01-17 15:13:09,2023-01-17 15:13:10,1.43294835090637,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,8,1,independence,2023-01-17 15:13:16,2023-01-17 15:13:16,2023-01-17 15:13:16,0.333022832870483,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,16,1,empirical,2023-01-17 15:13:22,2023-01-17 15:13:22,2023-01-17 15:13:23,0.407428741455078,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,16,1,gaussian,2023-01-17 15:13:28,2023-01-17 15:13:28,2023-01-17 15:13:29,0.381525278091431,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,16,1,ctree,2023-01-17 15:13:34,2023-01-17 15:13:35,2023-01-17 15:13:36,1.39694333076477,0,1e+05,13,0.3,1,0,1,1 -2,1000,10,16,1,independence,2023-01-17 15:13:42,2023-01-17 15:13:42,2023-01-17 15:13:42,0.338482618331909,0,1e+05,13,0.3,1,0,1,1 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15:14:36,0.501956939697266,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,2,1,empirical,2023-01-17 15:14:41,2023-01-17 15:14:42,2023-01-17 15:14:43,1.21703958511353,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,2,1,gaussian,2023-01-17 15:14:49,2023-01-17 15:14:49,2023-01-17 15:14:50,0.712919473648071,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,2,1,ctree,2023-01-17 15:14:56,2023-01-17 15:14:56,2023-01-17 15:14:59,2.19771456718445,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,2,1,independence,2023-01-17 15:15:04,2023-01-17 15:15:05,2023-01-17 15:15:05,0.627683877944946,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,4,1,empirical,2023-01-17 15:15:12,2023-01-17 15:15:12,2023-01-17 15:15:13,1.21958661079407,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,4,1,gaussian,2023-01-17 15:15:19,2023-01-17 15:15:19,2023-01-17 15:15:20,0.711959362030029,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,4,1,ctree,2023-01-17 15:15:26,2023-01-17 15:15:26,2023-01-17 15:15:29,2.21992778778076,0,1e+05,13,0.3,1,0,1,1 -2,1000,100,4,1,independence,2023-01-17 15:15:34,2023-01-17 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a/inst/scripts/vilde/airquality_example.R +++ /dev/null @@ -1,37 +0,0 @@ - -data("airquality") -airquality <- airquality[complete.cases(airquality), ] -# Split data into test- and training data -train <- head(airquality, -50) -test <- tail(airquality, 50) - -# Fit a linear model -model <- lm(Ozone ~ Solar.R + Wind+ Temp + Month, data = x_train) - -p <- mean(train$Ozone) - -x <- explain( - train, - test, - model = model, - approach = "empirical", - phi0 = p -) - -if (requireNamespace("ggplot2", quietly = TRUE)) { - # The default plotting option is a bar plot of the Shapley values - # We draw bar plots for the first 4 observations - plot(x, index_x_explain = 1:4) - - # We can also make waterfall plots - plot(x, plot_type = "waterfall", index_x_explain = 1:4) - plot(x, plot_type = "waterfall", index_x_explain = 1:4, top_k_features = 2) # top_k_features = 2 shows the 2 features with largest contribution - - # Or scatter plots showing the distribution of the shapley values and feature 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zz9tOSxamA~|r#&QZnc|dFmi#)oTr7KRBT&v}n=MB=Xes_Q9cSKB zk<-NNwKd**mhy4a=pA}qtDdW+uj%`m*-80oosZ|p>}?;G;JTHZ@s{}0$4A#fB!5)e zYycD#Hxy)d;cp`4kh7HnN~rULkmfb6?})CbVc0&;jAjpiumDiW_SpYSdnzrfxJya+ z-1>BIW!kUJ{|qe7Bq#OL`rr&#@x<;$+xzqJZPX6-{lO1&whBkvN6ki|K8vE89_LM& zehd-~JC!@aN|_;R-&qg7ZWkvSn6adbrC7iI%ZeC@Hn zbY4Ob&+qyO4?O(h(fe1^KXbHD?srgF7bJoT_`4~9aNK{_f&DQkM=Egu2t+0R_XB`P zNl8cn907mFz<9Nr@Blpjj)5TfOU8f3z|su%0Tcy|DUzuU=Uu{ z{_mK~KVni4$Un{}B`t-&LHw@8Vh}FwC=6b#2LMf60#W$)1pu`@9ntu!4`CiaV>B8I cAc*L{X5)iJV6eX?BL$TLQ}OYs8)#7dA4>z{SpWb4 diff --git a/inst/scripts/vilde/check_progress.R b/inst/scripts/vilde/check_progress.R deleted file mode 100644 index ec3da4887..000000000 --- a/inst/scripts/vilde/check_progress.R +++ /dev/null @@ -1,58 +0,0 @@ -library(progressr) -library(future.apply) -library(xgboost) -library(shapr) -library(data.table) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus", "age", "ptratio") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-15, x_var]) -y_train <- Boston[-1:-15, y_var] -x_test <- as.matrix(Boston[1:100, x_var]) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) -p <- mean(y_train) - -plan(multisession, workers=3) - -# when we simply call explain(), no progress bar is shown -x <- explain(x_train, x_test, model, approach="gaussian", phi0=p, n_batches = 4) - -# the handler specifies what kind of progress bar is shown -# Wrapping explain() in with_progress() gives a progress bar when calling explain() -handlers("txtprogressbar") -x <- with_progress( - explain(x_train, x_test, model, approach="empirical", phi0=p, n_batches = 5) - ) - -# with global=TRUE the progress bar is displayed whenever the explain-function is called, and there is no need to use with_progress() -handlers(global = TRUE) -x <- explain(x_train, x_test, model, approach="gaussian", phi0=p, n_batches = 4) - -# there are different options for what kind of progress bar should be displayed -handlers("txtprogressbar") #this is the default -x <- explain(x_train, x_test, model, approach="independence", phi0=p, n_batches = 4) - -handlers("progress") -x <- explain(x_train, x_test, model, approach="independence", phi0=p, n_batches = 4) - -# you can edit the symbol used to draw completed progress in the progress bar (as well as other features) with handler_progress() -handlers(handler_progress(complete = "#")) -x <- explain(x_train, x_test, model, approach="copula", phi0=p, n_batches = 4) - -plan("sequential") - -handlers("progress") -x <- explain(x_train, x_test, model, approach=c(rep("ctree",4),"independence","independence"), phi0=p, n_batches = 4) - - - diff --git a/inst/scripts/vilde/sketch_for_waterfall_plot.R b/inst/scripts/vilde/sketch_for_waterfall_plot.R deleted file mode 100644 index e31971a1a..000000000 --- a/inst/scripts/vilde/sketch_for_waterfall_plot.R +++ /dev/null @@ -1,68 +0,0 @@ -library(xgboost) -library(shapr) -library(ggplot2) -library(data.table) - -data("Boston", package = "MASS") - -x_var <- c("lstat", "rm", "dis", "indus") -y_var <- "medv" - -x_train <- as.matrix(Boston[-1:-6, x_var]) -y_train <- Boston[-1:-6, y_var] -x_test <- as.matrix(Boston[1:6, x_var]) - -# Looking at the dependence between the features -cor(x_train) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) -p <- mean(y_train) - -# Prepare the data for explanation -res <- explain_final(x_train,x_test,model,approach="independence",phi0=p,n_batches = 4) -plot(res) - -i<- 1 # index for observation we want to plot -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$shapley_values_est[i,])[1] -observed <- res$pred_explain[i] - -dt[, sign := ifelse(shapley_value > 0, "Increases", "Decreases")] -dt[, rank := frank(abs(shapley_value))] -setorder(dt, rank) -dt[, end := cumsum(shapley_value)+expected] -dt[, start := c(expected, head(end, -1))] -dt[, description := factor(feat_name, levels = unique(feat_name[order(abs(shapley_value))]))] -dt - -p <- ggplot(dt, aes(x = description, fill = sign)) + - geom_rect(aes(x=description, xmin = rank - 0.45, xmax = rank + 0.45, ymin = end,ymax = start)) + - scale_fill_manual(values=c("steelblue", "lightsteelblue")) + - geom_segment(x=-0.1, xend = 0.56, y=expected, yend=expected, linetype="dashed", col="dark grey") + - labs( - y = "Feature contribution", - x = "Feature", - fill = "", - title = "Shapley value prediction explanation" - ) + - geom_text(aes(label = format(shapley_value,digits=2), x=rank, y=start + (end-start)/2)) + - annotate("text",label=paste0("E(italic(f(x)))==", format(expected,digits=3)), y=expected, x=-Inf,parse = TRUE) + - coord_flip(clip = 'off', xlim=c(0.5, 4)) + - theme(plot.margin = unit(c(1,1,3,1), "lines")) + - geom_segment(x=-0.1, xend = 4.46, y=observed, yend=observed, linetype="dashed", col="dark grey") + - annotate("text",label=paste0("italic(f(x))==", format(observed,digits=3)), y=observed, x=Inf, parse = TRUE) + - geom_segment(aes(x=ifelse(rank==last(rank), as.numeric(rank), as.numeric(rank)-0.45), xend = ifelse(rank==last(rank), as.numeric(rank), as.numeric(rank)+1.45), - y=end, yend=end), linetype="dashed", col="dark grey") -p - - - diff --git a/inst/scripts/vilde/waterfall_plot.R b/inst/scripts/vilde/waterfall_plot.R deleted file mode 100644 index 5035d2528..000000000 --- a/inst/scripts/vilde/waterfall_plot.R +++ /dev/null @@ -1,79 +0,0 @@ -library(xgboost) -library(shapr) -library(ggplot2) -library(data.table) - -#test plotting w Boston data -data("Boston", package = "MASS") -x_var <- c("lstat", "rm", "dis", "indus", "crim", "age") -y_var <- "medv" -b <- 150 -x_train <- as.matrix(Boston[-1:-b, x_var]) -y_train <- Boston[-1:-b, y_var] -x_test <- as.matrix(Boston[1:b, x_var]) - -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) -p <- mean(y_train) -x <- explain_final(x_train,x_test,model,approach="independence",phi0=p,n_batches = 4) -plot.shapr(x, - plot_type = "bar", - digits = 3, - plot_phi0 = TRUE, - index_x_explain = NULL, - top_k_features = NULL, - col = c("#00BA38","#F8766D"), #first increasing color, then decreasing color - plot_order = "largest_first", - features_to_plot = NULL, - histogram = TRUE, - ) - -# data("AdultUCI", package = "arules") -# names(AdultUCI) <- gsub("-","_",names(AdultUCI)) -# data <- na.omit(AdultUCI) -# data$income <-ifelse(data$income==2,1,0) -# x_var <- c("age", "workclass", "hours_per_week","native_country") -# y_var <- "income" -# x_train <- as.matrix(data[-1:-b, x_var]) -# y_train <- data[-1:-b, y_var] -# x_test <- as.matrix(data[1:b, x_var]) - -#test plotting with simulated data -test <- data.frame(x1 = rnorm(5000, mean=10, sd=4), - x2 = rnorm(5000, mean=-60, sd=2), - x3 = rnorm(5000, mean=100, sd=1), - x4 = rnorm(5000, mean=0, sd=1), - y = rnorm(5000, mean=-5, sd=2)) - -x_var <- c("x1", "x2", "x3", "x4") -y_var <- "y" -b <- 350 -x_train <- as.matrix(test[-1:-b, x_var]) -y_train <- test[-1:-b, y_var] -x_test <- as.matrix(test[1:b, x_var]) - -# Fitting a basic xgboost model to the training data -model <- xgboost( - data = x_train, - label = y_train, - nround = 20, - verbose = FALSE -) -p <- mean(y_train) - -plot.shapr(x, - plot_type = "bar", - digits = 3, - plot_phi0 = TRUE, - index_x_explain = NULL, - top_k_features = NULL, - col = c("#00BA38","#F8766D"), #first increasing color, then decreasing color - plot_order = "largest_first", - features_to_plot = NULL, - histogram = TRUE - ) - diff --git a/inst/scripts/vilde/waterfall_plot.pdf b/inst/scripts/vilde/waterfall_plot.pdf deleted file mode 100644 index bdae1d34afffc13ea9c5e6521e54fdd5dc0ca9b3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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