From 63cc2033ecca6caa9a7ca3595a2ab7bf15bb030d Mon Sep 17 00:00:00 2001
From: byron jaeger <byron.jaeger@gmail.com>
Date: Fri, 12 Jan 2024 08:53:22 -0500
Subject: [PATCH] more careful checks for time to train

---
 CRAN-SUBMISSION |  4 ++--
 R/orsf.R        | 24 +++++++++++++++++++-----
 R/orsf_update.R |  2 ++
 src/Tree.cpp    |  1 +
 4 files changed, 24 insertions(+), 7 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index a835f801..bba7e17f 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.1.2
-Date: 2024-01-11 14:20:18 UTC
-SHA: 48469549668880b518502d598e4f56765bf4ef1d
+Date: 2024-01-12 02:20:10 UTC
+SHA: 8cdb854819420d52a14b685653b47d2a55d2c7a8
diff --git a/R/orsf.R b/R/orsf.R
index c003f86a..4b073abf 100644
--- a/R/orsf.R
+++ b/R/orsf.R
@@ -431,8 +431,9 @@ orsf_train <- function(object, attach_data = TRUE){
 #' @param object an untrained `aorsf` object
 #'
 #' @param n_tree_subset (*integer*)  how many trees should be fit in order
-#'   to estimate the time needed to train `object`. The default value is 50,
-#'   as this usually gives a good enough approximation.
+#'   to estimate the time needed to train `object`. The default value is 10%
+#'   of the trees specified in `object`. I.e., if `object` has `n_tree` of
+#'   500, then the default value `n_tree_subset` is 50.
 #'
 #' @return a [difftime] object.
 #'
@@ -444,8 +445,8 @@ orsf_train <- function(object, attach_data = TRUE){
 #' object <- orsf(pbc_orsf, Surv(time, status) ~ . - id,
 #'                n_tree = 10, no_fit = TRUE)
 #'
-#' # approximate the time it will take to grow 500 trees
-#' time_estimated <- orsf_time_to_train(object)
+#' # approximate the time it will take to grow 10 trees
+#' time_estimated <- orsf_time_to_train(object, n_tree_subset=1)
 #'
 #' print(time_estimated)
 #'
@@ -462,10 +463,23 @@ orsf_train <- function(object, attach_data = TRUE){
 #' abs(time_true - time_estimated)
 #'
 
-orsf_time_to_train <- function(object, n_tree_subset = 50){
+orsf_time_to_train <- function(object, n_tree_subset = NULL){
 
  n_tree_original <- object$n_tree
 
+ if(n_tree_original == 1){
+  stop("Cannot estimate time to train for a forest with 1 tree.",
+       call. = FALSE)
+ }
+
+ n_tree_subset <- n_tree_subset %||% ceiling(n_tree_original * 0.10)
+
+ if (n_tree_subset >= n_tree_original){
+  msg <- paste0("n_tree_subset (", n_tree_subset, ")",
+                "must be < n_tree_original (", n_tree_original, ").")
+  stop(msg, call. = FALSE)
+ }
+
  time_train_start <- Sys.time()
 
  object$train(n_tree = n_tree_subset)
diff --git a/R/orsf_update.R b/R/orsf_update.R
index 4b27a131..f135e947 100644
--- a/R/orsf_update.R
+++ b/R/orsf_update.R
@@ -67,6 +67,7 @@
 #'
 #' @examples
 #'
+#' \dontrun{
 #' # initial fit has mtry of 5
 #' fit <- orsf(pbc_orsf, time + status ~ . -id)
 #'
@@ -75,6 +76,7 @@
 #'
 #' # prevent dynamic updates by specifying inputs you want to freeze.
 #' fit_newer <- orsf_update(fit_new, mtry = 2)
+#' }
 #'
 #'
 orsf_update <- function(object,
diff --git a/src/Tree.cpp b/src/Tree.cpp
index 4a296214..d0577695 100644
--- a/src/Tree.cpp
+++ b/src/Tree.cpp
@@ -121,6 +121,7 @@
   this->data = data;
   this->n_cols_total = data->n_cols_x;
   this->n_rows_total = data->n_rows;
+
   this->seed = seed;
   this->mtry = mtry;
   this->sample_with_replacement = sample_with_replacement;