reverting memory allocation changes because the error was in the inpu…

…t genotype data, i.e. duplicated samples names resulting in huge genotype matrices

R/cross_validation.R

@@ -327,8 +327,6 @@ fn_cv_1 = function(i, list_merged, df_params, mat_idx_shuffle, vec_set_partition
 #'      using the sommer R package (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4894563/).
 #'      https://link.springer.com/protocol/10.1007/978-1-62703-447-0_13
 #' @param max_mem_Gb maximum memory in gigabytes available for computation (Default=15)
-#' @param bool_across_pop for across population cross-validation? This will use a data size multiplier of 40 if true,
-#'      otherwise a multiplier of 10 will be used for within population cross-validation (Default=TRUE)
 #' @param verbose show cross-validation parameter preparation messages? (Default=FALSE)
 #' @returns
 #'  - Ok:
@@ -356,7 +354,7 @@ fn_cv_1 = function(i, list_merged, df_params, mat_idx_shuffle, vec_set_partition
 #' @export
 fn_cross_validation_preparation = function(list_merged, cv_type=1, n_folds=10, n_reps=10, 
     vec_models_to_test=c("ridge","lasso","elastic_net","Bayes_A","Bayes_B","Bayes_C","gBLUP"),
-    max_mem_Gb=15, bool_across_pop=TRUE, verbose=FALSE)
+    max_mem_Gb=15, verbose=FALSE)
 {
     ###################################################
     ### TEST
@@ -420,18 +418,12 @@ fn_cross_validation_preparation = function(list_merged, cv_type=1, n_folds=10, n
         df_params = df_params[order(df_params$rep), ]
         df_params = df_params[order(df_params$fold), ]
         ### Estimate the maximum number of threads which can be used without running out of memory
-        if (bool_across_pop) {
-            memory_multiplier = 40
-        } else {
-            memory_multiplier = 10
-        }
         list_mem = fn_estimate_memory_footprint(
             X=list_merged,
             n_models=length(vec_models_to_test),
             n_folds=n_folds,
             n_reps=n_reps, 
             memory_requested_Gb=max_mem_Gb,
-            memory_multiplier=memory_multiplier,
             verbose=verbose)
     } else if (cv_type == 2) {
         ############################################
@@ -701,7 +693,6 @@ fn_cross_validation_within_population = function(list_merged, n_folds=10, n_reps
             n_reps=n_reps, 
             vec_models_to_test=vec_models_to_test,
             max_mem_Gb=max_mem_Gb,
-            bool_across_pop=FALSE,
             verbose=verbose)
         if (methods::is(list_cv_params, "gpError")) {
             error = chain(list_cv_params, methods::new("gpError",

R/io.R

@@ -2376,7 +2376,6 @@ fn_estimate_memory_footprint = function(X, n_models=7, n_folds=10, n_reps=10,
     size_total = size_X * n_models * n_folds * n_reps * memory_multiplier
     n_threads = floor(as.numeric(gsub(" bytes", "",  size_RAM / (size_X * memory_multiplier))))
     if (verbose) {
-        print(paste0("Dimensions of G: = ", paste(dim(X$G), collapse=" x ")))
         print(paste0("Size of X: = ", format(size_X, units="b")))
         print(paste0("n_models = ", n_models, "; n_folds = ", n_folds, "; n_reps = ", n_reps))
         print(paste0("Total memory requested = ",