test/compareToCV.R

   1 library(agghoo)
   2
   3 standardCV <- function(data, target, task = NULL, gmodel = NULL, params = NULL,
   4   loss = NULL, CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE)
   5 ) {
   6   if (!is.null(task))
   7     task = match.arg(task, c("classification", "regression"))
   8   if (is.character(gmodel))
   9     gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree"))
  10   if (is.numeric(params) || is.character(params))
  11     params <- as.list(params)
  12   if (is.null(task)) {
  13     if (is.numeric(target))
  14       task = "regression"
  15     else
  16       task = "classification"
  17   }
  18
  19   if (is.null(loss)) {
  20     loss <- function(y1, y2) {
  21       if (task == "classification") {
  22         if (is.null(dim(y1)))
  23           mean(y1 != y2)
  24         else {
  25           if (!is.null(dim(y2)))
  26             mean(rowSums(abs(y1 - y2)))
  27           else {
  28             y2 <- as.character(y2)
  29             names <- colnames(y1)
  30             positions <- list()
  31             for (idx in seq_along(names))
  32               positions[[ names[idx] ]] <- idx
  33             mean(vapply(
  34               seq_along(y2),
  35               function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])),
  36               0))
  37           }
  38         }
  39       }
  40     }
  41   }
  42
  43   n <- nrow(data)
  44   shuffle_inds <- NULL
  45   if (CV$type == "vfold" && CV$shuffle)
  46     shuffle_inds <- sample(n, n)
  47   get_testIndices <- function(v, shuffle_inds) {
  48     if (CV$type == "vfold") {
  49       first_index = round((v-1) * n / CV$V) + 1
  50       last_index = round(v * n / CV$V)
  51       test_indices = first_index:last_index
  52       if (!is.null(shuffle_inds))
  53         test_indices <- shuffle_inds[test_indices]
  54     }
  55     else
  56       test_indices = sample(n, round(n * CV$test_size))
  57     test_indices
  58   }
  59   list_testinds <- list()
  60   for (v in seq_len(CV$V))
  61     list_testinds[[v]] <- get_testIndices(v, shuffle_inds)
  62
  63   gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
  64   best_error <- Inf
  65   best_model <- NULL
  66   for (p in seq_len(gmodel$nmodels)) {
  67     error <- 0
  68     for (v in seq_len(CV$V)) {
  69       testIdx <- list_testinds[[v]]
  70       dataHO <- data[-testIdx,]
  71       testX <- data[testIdx,]
  72       targetHO <- target[-testIdx]
  73       testY <- target[testIdx]
  74       if (!is.matrix(dataHO) && !is.data.frame(dataHO))
  75         dataHO <- as.matrix(dataHO)
  76       if (!is.matrix(testX) && !is.data.frame(testX))
  77         testX <- as.matrix(testX)
  78       model_pred <- gmodel$get(dataHO, targetHO, p)
  79       prediction <- model_pred(testX)
  80       error <- error + loss(prediction, testY)
  81     }
  82     if (error <= best_error) {
  83       newModel <- list(model=model_pred, param=gmodel$getParam(p))
  84       if (error == best_error)
  85         best_model[[length(best_model)+1]] <- newModel
  86       else {
  87         best_model <- list(newModel)
  88         best_error <- error
  89       }
  90     }
  91   }
  92   best_model[[ sample(length(best_model), 1) ]]
  93 }
  94
  95 compareToCV <- function(df, t_idx, task=NULL, rseed=-1, verbose=TRUE) {
  96   if (rseed >= 0)
  97     set.seed(rseed)
  98   if (is.null(task))
  99     task <- ifelse(is.numeric(df[,t_idx]), "regression", "classification")
 100   n <- nrow(df)
 101   test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
 102   a <- agghoo(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
 103   a$fit()
 104   if (verbose) {
 105     print("Parameters:")
 106     print(unlist(a$getParams()))
 107   }
 108   pa <- a$predict(df[test_indices,-t_idx])
 109   err_a <- ifelse(task == "classification",
 110                   mean(pa != df[test_indices,t_idx]),
 111                   mean(abs(pa - df[test_indices,t_idx])))
 112   if (verbose)
 113     print(paste("error agghoo:", err_a))
 114   # Compare with standard cross-validation:
 115   s <- standardCV(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
 116   if (verbose)
 117     print(paste( "Parameter:", s$param ))
 118   ps <- s$model(df[test_indices,-t_idx])
 119   err_s <- ifelse(task == "classification",
 120                   mean(ps != df[test_indices,t_idx]),
 121                   mean(abs(ps - df[test_indices,t_idx])))
 122   if (verbose)
 123     print(paste("error CV:", err_s))
 124   c(err_a, err_s)
 125 }
 126
 127 library(parallel)
 128 compareMulti <- function(df, t_idx, task = NULL, N = 100, nc = NA) {
 129   if (is.na(nc))
 130     nc <- detectCores()
 131   errors <- mclapply(1:N,
 132                      function(n) {
 133                        compareToCV(df, t_idx, task, n, verbose=FALSE) },
 134                      mc.cores = nc)
 135   print("error agghoo vs. cross-validation:")
 136   Reduce('+', errors) / N
 137 }