possible leaks from data.table

R/melt_aorsf.R

@@ -0,0 +1,39 @@
+
+# need to make this to avoid possible memory leak in data.table melt
+
+melt_aorsf <-
+ function(data,
+          id.vars,
+          measure.vars,
+          variable.name = "variable",
+          value.name = "value") {
+  if (!is.data.frame(data)) {
+   stop("Input 'data' must be a data frame.")
+  }
+
+  if (!is.character(id.vars)) {
+   stop("Input 'id.vars' must be a character vector.")
+  }
+
+  if (!is.character(measure.vars)) {
+   stop("Input 'measure.vars' must be a character vector.")
+  }
+
+  # Select id variables and measure variables
+  id_data <- data[id.vars]
+  measure_data <- data[measure.vars]
+
+  # Create a sequence variable to represent the variable names
+  variable_data <- rep(names(measure_data), each = nrow(data))
+
+  # Reshape the data
+  long_data <- data.frame(id_data,
+                          variable = variable_data,
+                          value = unlist(measure_data, use.names = FALSE))
+
+  names(long_data)[names(long_data) == 'variable'] <- variable.name
+  names(long_data)[names(long_data) == 'value'] <- value.name
+
+  return(long_data)
+ }
+

R/orsf_pd.R

@@ -606,14 +606,25 @@ orsf_pred_dependence <- function(object,
    else
     colnames(pd_vals[[i]][[j]]) <- c(paste(1:nrow(x_new)))
 
-   pd_vals[[i]][[j]] <- as.data.table(pd_vals[[i]][[j]],
-                                      keep.rownames = 'pred_horizon')
-
-   if(type_output == 'ice')
-    pd_vals[[i]][[j]] <- melt(data = pd_vals[[i]][[j]],
-                              id.vars = 'pred_horizon',
-                              variable.name = 'id_row',
-                              value.name = 'pred')
+   ph <- rownames(pd_vals[[i]][[j]])
+
+   pd_vals[[i]][[j]] <- as.data.frame(pd_vals[[i]][[j]])
+
+   rownames(pd_vals[[i]][[j]]) <- NULL
+
+   pd_vals[[i]][[j]][['pred_horizon']] <- ph
+
+   if(type_output == 'ice'){
+
+    pd_vals[[i]][[j]] <- melt_aorsf(
+     data = pd_vals[[i]][[j]],
+     id.vars = 'pred_horizon',
+     variable.name = 'id_row',
+     value.name = 'pred',
+     measure.vars = setdiff(names(pd_vals[[i]][[j]]), 'pred_horizon'))
+
+   }
+
 
   }
 

cran-comments.md

@@ -1,5 +1,7 @@
 ## Version 0.1.1
 
+Update, October 25: Thank you for helping me with this. I have tidied up threads and avoided usage of the `data.table` functions that were creating possible memory leaks.  I have checked this submission locally with valgrind and on rhub, with both indicating 0 memory leaks. However, if this submission does not pass on your end, I would like to request an extension on the October 28th deadline.
+
 Update, October 21: I have updated the submission to fix memory leaks. Many of the leaks were caused by my omission of a virtual de-constructor for derived classes or by omission of a delete statement for dynamically allocated memory. I apologize for these oversights. After reviewing, you may still see a possible memory leak from `orsf_ice` functions. From what I can tell, this possible leak could either be measurement error or could be attributed to `data.table`. I do not think it's from `aorsf`.
 
 Initial submission: This version is being submitted to CRAN early due to a memory error that was identified in version 1.0.0. I apologize for the oversight. As `aorsf` would be removed from CRAN if the issue is not fixed before October 28, I would like to request an expedited submission. I have run the current submission's tests and examples with valgrind to ensure the memory error has been fixed. 

src/Makevars.win

@@ -1,3 +1,2 @@
-CXX_STD = CXX14
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
 PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)