X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=agghoo.Rcheck%2F00_pkg_src%2Fagghoo%2FR%2FR6_AgghooCV.R;fp=agghoo.Rcheck%2F00_pkg_src%2Fagghoo%2FR%2FR6_AgghooCV.R;h=0000000000000000000000000000000000000000;hp=328c1413bc340d38c179bc7f591ff9243a013271;hb=16906f6e8c432b811ddf99da1b18a2a357a75235;hpb=97f16440280a40a49c4898a75942e374880bfca3

diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_AgghooCV.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_AgghooCV.R
deleted file mode 100644
index 328c141..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_AgghooCV.R
+++ /dev/null
@@ -1,115 +0,0 @@
-#' @title R6 class with agghoo functions fit() and predict().
-#'
-#' @description
-#' Class encapsulating the methods to run to obtain the best predictor
-#' from the list of models (see 'Model' class).
-#'
-#' @importFrom R6 R6Class
-#'
-#' @export
-AgghooCV <- R6::R6Class("AgghooCV",
-  public = list(
-    #' @description Create a new AgghooCV object.
-    #' @param data Matrix or data.frame
-    #' @param target Vector of targets (generally numeric or factor)
-    #' @param task "regression" or "classification".
-    #'             Default: classification if target not numeric.
-    #' @param gmodel Generic model returning a predictive function
-    #'               Default: tree if mixed data, knn/ppr otherwise.
-    #' @param loss Function assessing the error of a prediction
-    #'             Default: error rate or mean(abs(error)).
-    initialize = function(data, target, task, gmodel, loss) {
-      private$data <- data
-      private$target <- target
-      private$task <- task
-      private$gmodel <- gmodel
-      private$loss <- loss
-    },
-    #' @description Fit an agghoo model.
-    #' @param CV List describing cross-validation to run. Slots: \cr
-    #'          - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
-    #'          - V: number of runs (default: 10) \cr
-    #'          - test_size: percentage of data in the test dataset, for MC
-    #'            (irrelevant for V-fold). Default: 0.2. \cr
-    #'          - shuffle: wether or not to shuffle data before V-fold.
-    #'            Irrelevant for Monte-Carlo; default: TRUE \cr
-    #'        Default (if NULL): type="MC", V=10, test_size=0.2
-    fit = function(CV = NULL) {
-      CV <- checkCV(CV)
-      n <- nrow(private$data)
-      shuffle_inds <- NULL
-      if (CV$type == "vfold" && CV$shuffle)
-        shuffle_inds <- sample(n, n)
-      # Result: list of V predictive models (+ parameters for info)
-      private$pmodels <- list()
-      for (v in seq_len(CV$V)) {
-        # Prepare train / test data and target, from full dataset.
-        # dataHO: "data Hold-Out" etc.
-        test_indices <- get_testIndices(n, CV, v, shuffle_inds)
-        d <- splitTrainTest(private$data, private$target, test_indices)
-        best_model <- NULL
-        best_error <- Inf
-        for (p in seq_len(private$gmodel$nmodels)) {
-          model_pred <- private$gmodel$get(d$dataTrain, d$targetTrain, p)
-          prediction <- model_pred(d$dataTest)
-          error <- private$loss(prediction, d$targetTest)
-          if (error <= best_error) {
-            newModel <- list(model=model_pred, param=private$gmodel$getParam(p))
-            if (error == best_error)
-              best_model[[length(best_model)+1]] <- newModel
-            else {
-              best_model <- list(newModel)
-              best_error <- error
-            }
-          }
-        }
-        # Choose a model at random in case of ex-aequos
-        private$pmodels[[v]] <- best_model[[ sample(length(best_model),1) ]]
-      }
-    },
-    #' @description Predict an agghoo model (after calling fit())
-    #' @param X Matrix or data.frame to predict
-    predict = function(X) {
-      if (!is.matrix(X) && !is.data.frame(X))
-        stop("X: matrix or data.frame")
-      if (!is.list(private$pmodels)) {
-        print("Please call $fit() method first")
-        return (invisible(NULL))
-      }
-      V <- length(private$pmodels)
-      oneLineX <- X[1,]
-      if (is.matrix(X))
-        # HACK: R behaves differently with data frames and matrices.
-        oneLineX <- t(as.matrix(oneLineX))
-      if (length(private$pmodels[[1]]$model(oneLineX)) >= 2)
-        # Soft classification:
-        return (Reduce("+", lapply(private$pmodels, function(m) m$model(X))) / V)
-      n <- nrow(X)
-      all_predictions <- as.data.frame(matrix(nrow=n, ncol=V))
-      for (v in 1:V)
-        all_predictions[,v] <- private$pmodels[[v]]$model(X)
-      if (private$task == "regression")
-        # Easy case: just average each row
-        return (rowMeans(all_predictions))
-      # "Hard" classification:
-      apply(all_predictions, 1, function(row) {
-        t <- table(row)
-        # Next lines in case of ties (broken at random)
-        tmax <- max(t)
-        sample( names(t)[which(t == tmax)], 1 )
-      })
-    },
-    #' @description Return the list of V best parameters (after calling fit())
-    getParams = function() {
-      lapply(private$pmodels, function(m) m$param)
-    }
-  ),
-  private = list(
-    data = NULL,
-    target = NULL,
-    task = NULL,
-    gmodel = NULL,
-    loss = NULL,
-    pmodels = NULL
-  )
-)