X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=test%2FcompareToCV.R;fp=test%2FcompareToCV.R;h=255c70c78fcf4661ae698bbdde818a16df03d85a;hp=0000000000000000000000000000000000000000;hb=504afaadc783916dc126fb87ab9e067f302eb2c5;hpb=15f48abea9c631d52317ff157c8af0dd4c7a67d3

diff --git a/test/compareToCV.R b/test/compareToCV.R
new file mode 100644
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--- /dev/null
+++ b/test/compareToCV.R
@@ -0,0 +1,137 @@
+library(agghoo)
+
+standardCV <- function(data, target, task = NULL, gmodel = NULL, params = NULL,
+  loss = NULL, CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE)
+) {
+  if (!is.null(task))
+    task = match.arg(task, c("classification", "regression"))
+  if (is.character(gmodel))
+    gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree"))
+  if (is.numeric(params) || is.character(params))
+    params <- as.list(params)
+  if (is.null(task)) {
+    if (is.numeric(target))
+      task = "regression"
+    else
+      task = "classification"
+  }
+
+  if (is.null(loss)) {
+    loss <- function(y1, y2) {
+      if (task == "classification") {
+        if (is.null(dim(y1)))
+          mean(y1 != y2)
+        else {
+          if (!is.null(dim(y2)))
+            mean(rowSums(abs(y1 - y2)))
+          else {
+            y2 <- as.character(y2)
+            names <- colnames(y1)
+            positions <- list()
+            for (idx in seq_along(names))
+              positions[[ names[idx] ]] <- idx
+            mean(vapply(
+              seq_along(y2),
+              function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])),
+              0))
+          }
+        }
+      }
+    }
+  }
+
+  n <- nrow(data)
+  shuffle_inds <- NULL
+  if (CV$type == "vfold" && CV$shuffle)
+    shuffle_inds <- sample(n, n)
+  get_testIndices <- function(v, shuffle_inds) {
+    if (CV$type == "vfold") {
+      first_index = round((v-1) * n / CV$V) + 1
+      last_index = round(v * n / CV$V)
+      test_indices = first_index:last_index
+      if (!is.null(shuffle_inds))
+        test_indices <- shuffle_inds[test_indices]
+    }
+    else
+      test_indices = sample(n, round(n * CV$test_size))
+    test_indices
+  }
+  list_testinds <- list()
+  for (v in seq_len(CV$V))
+    list_testinds[[v]] <- get_testIndices(v, shuffle_inds)
+
+  gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
+  best_error <- Inf
+  best_model <- NULL
+  for (p in seq_len(gmodel$nmodels)) {
+    error <- 0
+    for (v in seq_len(CV$V)) {
+      testIdx <- list_testinds[[v]]
+      dataHO <- data[-testIdx,]
+      testX <- data[testIdx,]
+      targetHO <- target[-testIdx]
+      testY <- target[testIdx]
+      if (!is.matrix(dataHO) && !is.data.frame(dataHO))
+        dataHO <- as.matrix(dataHO)
+      if (!is.matrix(testX) && !is.data.frame(testX))
+        testX <- as.matrix(testX)
+      model_pred <- gmodel$get(dataHO, targetHO, p)
+      prediction <- model_pred(testX)
+      error <- error + loss(prediction, testY)
+    }
+    if (error <= best_error) {
+      newModel <- list(model=model_pred, param=gmodel$getParam(p))
+      if (error == best_error)
+        best_model[[length(best_model)+1]] <- newModel
+      else {
+        best_model <- list(newModel)
+        best_error <- error
+      }
+    }
+  }
+  best_model[[ sample(length(best_model), 1) ]]
+}
+
+compareToCV <- function(df, t_idx, task=NULL, rseed=-1, verbose=TRUE) {
+  if (rseed >= 0)
+    set.seed(rseed)
+  if (is.null(task))
+    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)
+  a$fit()
+  if (verbose) {
+    print("Parameters:")
+    print(unlist(a$getParams()))
+  }
+  pa <- a$predict(df[test_indices,-t_idx])
+  err_a <- ifelse(task == "classification",
+                  mean(pa != df[test_indices,t_idx]),
+                  mean(abs(pa - df[test_indices,t_idx])))
+  if (verbose)
+    print(paste("error agghoo:", err_a))
+  # Compare with standard cross-validation:
+  s <- standardCV(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
+  if (verbose)
+    print(paste( "Parameter:", s$param ))
+  ps <- s$model(df[test_indices,-t_idx])
+  err_s <- ifelse(task == "classification",
+                  mean(ps != df[test_indices,t_idx]),
+                  mean(abs(ps - df[test_indices,t_idx])))
+  if (verbose)
+    print(paste("error CV:", err_s))
+  c(err_a, err_s)
+}
+
+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)
+  print("error agghoo vs. cross-validation:")
+  Reduce('+', errors) / N
+}