avoid under and overflows in stacking

R/loo_model_weights.R

@@ -257,15 +257,12 @@ stacking_weights <-
       stop("At least two models are required for stacking weights.")
     }
 
-    exp_lpd_point <- exp(lpd_point)
     negative_log_score_loo <- function(w) {
       # objective function: log score
       stopifnot(length(w) == K - 1)
       w_full <- c(w, 1 - sum(w))
-      sum <- 0
-      for (i in 1:N) {
-        sum <- sum + log(exp(lpd_point[i, ]) %*% w_full)
-      }
+      # avoid over- and underflows using log weights and rowLogSumExps
+      sum <- sum(matrixStats::rowLogSumExps(sweep(lpd_point[1:N,], 2, log(w_full), '+')))
       return(-as.numeric(sum))
     }
 
@@ -274,11 +271,11 @@ stacking_weights <-
       stopifnot(length(w) == K - 1)
       w_full <- c(w, 1 - sum(w))
       grad <- rep(0, K - 1)
+      # avoid over- and underflows using log weights, rowLogSumExps,
+      # and by subtracting the row maximum of lpd_point
+      mlpd <- matrixStats::rowMaxs(lpd_point)
       for (k in 1:(K - 1)) {
-        for (i in 1:N) {
-          grad[k] <- grad[k] +
-            (exp_lpd_point[i, k] - exp_lpd_point[i, K]) / (exp_lpd_point[i,]  %*% w_full)
-        }
+        grad[k] <- sum((exp(lpd_point[, k] - mlpd) - exp(lpd_point[, K] - mlpd)) / exp(matrixStats::rowLogSumExps(sweep(lpd_point, 2, log(w_full), '+')) - mlpd))
       }
       return(-grad)
     }