--- /dev/null
+#' @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
+ )
+)