X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=test%2FcompareToCV.R;fp=test%2FcompareToCV.R;h=0000000000000000000000000000000000000000;hp=276749ba6b0a0d384b27f88cdc72eccdf3b789c6;hb=17ea2f13e0c32c107db20677750bd7a98bb7e0f8;hpb=afa676609daba103e43d6d4654560ca4c1c9b38b

diff --git a/test/compareToCV.R b/test/compareToCV.R
deleted file mode 100644
index 276749b..0000000
--- a/test/compareToCV.R
+++ /dev/null
@@ -1,147 +0,0 @@
-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))
-          }
-        }
-      }
-      else
-        mean(abs(y1 - y2))
-    }
-  }
-
-  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)) )
-  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$predict(test)
-  err_a <- ifelse(task == "classification",
-                  mean(pa != df[test_indices,t_idx]),
-                  mean(abs(pa - df[test_indices,t_idx])))
-  if (verbose)
-    print(paste("error agghoo:", err_a))
-  # Compare with standard cross-validation:
-  s <- standardCV(data, target, task, ...)
-  if (verbose)
-    print(paste( "Parameter:", s$param ))
-  ps <- s$model(test)
-  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))
-  invisible(c(err_a, err_s))
-}
-
-library(parallel)
-compareMulti <- function(df, t_idx, task = NULL, N = 100, nc = NA, ...) {
-  if (is.na(nc))
-    nc <- detectCores()
-  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
-}