Fix agghoo for tree / rpart

[agghoo.git] / test / compareToCV.R

diff --git a/test/compareToCV.R b/test/compareToCV.R
index a124dd2..276749b 100644 (file)
--- a/test/compareToCV.R
+++ b/test/compareToCV.R
@@ -101,23 +101,26 @@ compareToCV <- function(df, t_idx, task=NULL, rseed=-1, verbose=TRUE, ...) {
     task <- ifelse(is.numeric(df[,t_idx]), "regression", "classification")
   n <- nrow(df)
   test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
-  a <- agghoo(df[-test_indices,-t_idx], df[-test_indices,t_idx], task, ...)
+  data <- as.matrix(df[-test_indices,-t_idx])
+  target <- df[-test_indices,t_idx]
+  test <- as.matrix(df[test_indices,-t_idx])
+  a <- agghoo(data, target, task, ...)
   a$fit()
   if (verbose) {
     print("Parameters:")
     print(unlist(a$getParams()))
   }
-  pa <- a$predict(df[test_indices,-t_idx])
+  pa <- a$predict(test)
   err_a <- ifelse(task == "classification",
                   mean(pa != df[test_indices,t_idx]),
                   mean(abs(pa - df[test_indices,t_idx])))
   if (verbose)
     print(paste("error agghoo:", err_a))
   # Compare with standard cross-validation:
-  s <- standardCV(df[-test_indices,-t_idx], df[-test_indices,t_idx], task, ...)
+  s <- standardCV(data, target, task, ...)
   if (verbose)
     print(paste( "Parameter:", s$param ))
-  ps <- s$model(df[test_indices,-t_idx])
+  ps <- s$model(test)
   err_s <- ifelse(task == "classification",
                   mean(ps != df[test_indices,t_idx]),
                   mean(abs(ps - df[test_indices,t_idx])))
@@ -130,10 +133,15 @@ library(parallel)
 compareMulti <- function(df, t_idx, task = NULL, N = 100, nc = NA, ...) {
   if (is.na(nc))
     nc <- detectCores()
-  errors <- mclapply(1:N,
-                     function(n) {
-                       compareToCV(df, t_idx, task, n, verbose=FALSE, ...) },
-                     mc.cores = nc)
+  compareOne <- function(n) {
+    print(n)
+    compareToCV(df, t_idx, task, n, verbose=FALSE, ...)
+  }
+  errors <- if (nc >= 2) {
+    mclapply(1:N, compareOne, mc.cores = nc)
+  } else {
+    lapply(1:N, compareOne)
+  }
   print("error agghoo vs. cross-validation:")
   Reduce('+', errors) / N
 }