X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=R%2FcompareTo.R;fp=R%2FcompareTo.R;h=00e90a9ea7ddaac9ea3a24326ceb21d5eb0e01d0;hp=aa3a4e8b7f4bf68004831e598aa8fe561e34a148;hb=afa676609daba103e43d6d4654560ca4c1c9b38b;hpb=43a6578d444f388d72755e74c7eed74f3af638ec

diff --git a/R/compareTo.R b/R/compareTo.R
index aa3a4e8..00e90a9 100644
--- a/R/compareTo.R
+++ b/R/compareTo.R
@@ -1,86 +1,25 @@
-standardCV_core <- 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))
-    }
-  }
-
+standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
   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$V))
-    list_testinds[[v]] <- get_testIndices(v, shuffle_inds)
-
+    list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
   gmodel <- agghoo::Model$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)) {
+    error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
       testIdx <- list_testinds[[v]]
-      dataHO <- data[-testIdx,]
-      testX <- data[testIdx,]
-      targetHO <- target[-testIdx]
-      testY <- target[testIdx]
-      if (!is.matrix(dataHO) && !is.data.frame(dataHO))
-        dataHO <- as.matrix(dataHO)
-      if (!is.matrix(testX) && !is.data.frame(testX))
-        testX <- as.matrix(testX)
-      model_pred <- gmodel$get(dataHO, targetHO, p)
-      prediction <- model_pred(testX)
-      error <- error + loss(prediction, testY)
-    }
+      d <- splitTrainTest(data, target, testIdx)
+      model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
+      prediction <- model_pred(d$dataTest)
+      loss(prediction, d$targetTest)
+    }) )
     if (error <= best_error) {
-      newModel <- list(model=model_pred, param=gmodel$getParam(p))
+      newModel <- list(model=gmodel$get(data, target, p),
+                       param=gmodel$getParam(p))
       if (error == best_error)
         best_model[[length(best_model)+1]] <- newModel
       else {
@@ -89,24 +28,30 @@ standardCV_core <- function(data, target, task = NULL, gmodel = NULL, params = N
       }
     }
   }
+#browser()
   best_model[[ sample(length(best_model), 1) ]]
 }
 
 standardCV_run <- function(
-  dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ...
+  dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
 ) {
-  s <- standardCV_core(dataTrain, targetTrain, ...)
+  args <- list(...)
+  task <- checkTask(args$task, targetTrain)
+  modPar <- checkModPar(args$gmodel, args$params)
+  loss <- checkLoss(args$loss, task)
+  s <- standardCV_core(
+    dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
   if (verbose)
     print(paste( "Parameter:", s$param ))
-  ps <- s$model(test)
-  err_s <- floss(ps, targetTest)
+  p <- s$model(dataTest)
+  err <- floss(p, targetTest)
   if (verbose)
-    print(paste("error CV:", err_s))
-  invisible(c(errors, err_s))
+    print(paste("error CV:", err))
+  invisible(err)
 }
 
 agghoo_run <- function(
-  dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ...
+  dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
 ) {
   a <- agghoo(dataTrain, targetTrain, ...)
   a$fit(CV)
@@ -118,27 +63,22 @@ agghoo_run <- function(
   err <- floss(pa, targetTest)
   if (verbose)
     print(paste("error agghoo:", err))
+  invisible(err)
 }
 
-# ... arguments passed to agghoo or any other procedure
+# ... arguments passed to method_s (agghoo, standard CV or else)
 compareTo <- function(
-  data, target, rseed=-1, verbose=TRUE, floss=NULL,
-  CV = list(type = "MC",
-            V = 10,
-            test_size = 0.2,
-            shuffle = TRUE),
-  method_s=NULL, ...
+  data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ...
 ) {
   if (rseed >= 0)
     set.seed(rseed)
   n <- nrow(data)
   test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
-  trainData <- as.matrix(data[-test_indices,])
-  trainTarget <- target[-test_indices]
-  testData <- as.matrix(data[test_indices,])
-  testTarget <- target[test_indices]
+  d <- splitTrainTest(data, target, test_indices)
+  CV <- checkCV(list(...)$CV)
 
   # Set error function to be used on model outputs (not in core method)
+  task <- checkTask(list(...)$task, target)
   if (is.null(floss)) {
     floss <- function(y1, y2) {
       ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2)))
@@ -147,34 +87,33 @@ compareTo <- function(
 
   # Run (and compare) all methods:
   runOne <- function(o) {
-    o(dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ...)
+    o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest,
+      CV, floss, verbose, ...)
   }
+  errors <- c()
   if (is.list(method_s))
     errors <- sapply(method_s, runOne)
   else if (is.function(method_s))
     errors <- runOne(method_s)
-  else
-    errors <- c()
   invisible(errors)
 }
 
 # Run compareTo N times in parallel
+# ... : additional args to be passed to method_s
 compareMulti <- function(
-  data, target, N = 100, nc = NA,
-  CV = list(type = "MC",
-            V = 10,
-            test_size = 0.2,
-            shuffle = TRUE),
-  method_s=NULL, ...
+  data, target, method_s, N=100, nc=NA, floss=NULL, ...
 ) {
+  require(parallel)
   if (is.na(nc))
     nc <- parallel::detectCores()
+
+  # "One" comparison for each method in method_s (list)
   compareOne <- function(n) {
     print(n)
-    compareTo(data, target, n, verbose=FALSE, CV, method_s, ...)
+    compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
   }
+
   errors <- if (nc >= 2) {
-    require(parallel)
     parallel::mclapply(1:N, compareOne, mc.cores = nc)
   } else {
     lapply(1:N, compareOne)
@@ -182,5 +121,3 @@ compareMulti <- function(
   print("Errors:")
   Reduce('+', errors) / N
 }
-
-# TODO: unfinished !