--- /dev/null
+#' @title R6 class representing a (generic) model.
+#'
+#' @description
+#' "Model" class, containing a (generic) learning function, which from
+#' data + target [+ params] returns a prediction function X --> y.
+#' Parameters for cross-validation are either provided or estimated.
+#' Model family can be chosen among "tree", "ppr" and "knn" for now.
+#'
+#' @importFrom FNN knn.reg
+#' @importFrom class knn
+#' @importFrom stats ppr
+#' @importFrom rpart rpart
+#'
+#' @export
+Model <- R6::R6Class("Model",
+ public = list(
+ #' @field nmodels Number of parameters (= number of [predictive] models)
+ nmodels = NA,
+ #' @description Create a new generic model.
+ #' @param data Matrix or data.frame
+ #' @param target Vector of targets (generally numeric or factor)
+ #' @param task "regression" or "classification"
+ #' @param gmodel Generic model returning a predictive function; chosen
+ #' automatically given data and target nature if not provided.
+ #' @param params List of parameters for cross-validation (each defining a model)
+ initialize = function(data, target, task, gmodel = NULL, params = NULL) {
+ if (is.null(gmodel)) {
+ # (Generic) model not provided
+ all_numeric <- is.numeric(as.matrix(data))
+ if (!all_numeric)
+ # At least one non-numeric column: use trees
+ gmodel = "tree"
+ else
+ # Numerical data
+ gmodel = ifelse(task == "regression", "ppr", "knn")
+ }
+ if (is.null(params))
+ # Here, gmodel is a string (= its family),
+ # because a custom model must be given with its parameters.
+ params <- as.list(private$getParams(gmodel, data, target, task))
+ private$params <- params
+ if (is.character(gmodel))
+ gmodel <- private$getGmodel(gmodel, task)
+ private$gmodel <- gmodel
+ self$nmodels <- length(private$params)
+ },
+ #' @description
+ #' Returns the model at index "index", trained on dataHO/targetHO.
+ #' @param dataHO Matrix or data.frame
+ #' @param targetHO Vector of targets (generally numeric or factor)
+ #' @param index Index of the model in 1...nmodels
+ get = function(dataHO, targetHO, index) {
+ private$gmodel(dataHO, targetHO, private$params[[index]])
+ },
+ #' @description
+ #' Returns the parameter at index "index".
+ #' @param index Index of the model in 1...nmodels
+ getParam = function(index) {
+ private$params[[index]]
+ }
+ ),
+ private = list(
+ # No need to expose model or parameters list
+ gmodel = NULL,
+ params = NULL,
+ # Main function: given a family, return a generic model, which in turn
+ # will output a predictive model from data + target + params.
+ getGmodel = function(family, task) {
+ if (family == "tree") {
+ function(dataHO, targetHO, param) {
+ base::require(rpart)
+ method <- ifelse(task == "classification", "class", "anova")
+ if (is.null(colnames(dataHO)))
+ colnames(dataHO) <- paste0("V", 1:ncol(dataHO))
+ df <- data.frame(cbind(dataHO, target=targetHO))
+ model <- rpart::rpart(target ~ ., df, method=method, control=list(cp=param))
+ if (task == "regression")
+ type <- "vector"
+ else {
+ if (is.null(dim(targetHO)))
+ type <- "class"
+ else
+ type <- "prob"
+ }
+ function(X) {
+ if (is.null(colnames(X)))
+ colnames(X) <- paste0("V", 1:ncol(X))
+ predict(model, as.data.frame(X), type=type)
+ }
+ }
+ }
+ else if (family == "ppr") {
+ function(dataHO, targetHO, param) {
+ model <- stats::ppr(dataHO, targetHO, nterms=param)
+ function(X) predict(model, X)
+ }
+ }
+ else if (family == "knn") {
+ if (task == "classification") {
+ function(dataHO, targetHO, param) {
+ base::require(class)
+ function(X) class::knn(dataHO, X, cl=targetHO, k=param)
+ }
+ }
+ else {
+ function(dataHO, targetHO, param) {
+ base::require(FNN)
+ function(X) FNN::knn.reg(dataHO, X, y=targetHO, k=param)$pred
+ }
+ }
+ }
+ },
+ # Return a default list of parameters, given a gmodel family
+ getParams = function(family, data, target, task) {
+ if (family == "tree") {
+ # Run rpart once to obtain a CV grid for parameter cp
+ base::require(rpart)
+ df <- data.frame(cbind(data, target=target))
+ ctrl <- list(
+ cp = 0,
+ minsplit = 2,
+ minbucket = 1,
+ xval = 0)
+ method <- ifelse(task == "classification", "class", "anova")
+ r <- rpart(target ~ ., df, method=method, control=ctrl)
+ cps <- r$cptable[-1,1]
+ if (length(cps) <= 1)
+ stop("No cross-validation possible: select another model")
+ if (length(cps) <= 11)
+ return (cps)
+ step <- (length(cps) - 1) / 10
+ cps[unique(round(seq(1, length(cps), step)))]
+ }
+ else if (family == "ppr")
+ # This is nterms in ppr() function
+ 1:10
+ else if (family == "knn") {
+ n <- nrow(data)
+ # Choose ~10 NN values
+ K <- length(unique(target))
+ if (n <= 10)
+ return (1:(n-1))
+ sqrt_n <- sqrt(n)
+ step <- (2*sqrt_n - 1) / 10
+ grid <- unique(round(seq(1, 2*sqrt_n, step)))
+ if (K == 2) {
+ # Common binary classification case: odd number of neighbors
+ for (i in 2:11) {
+ if (grid[i] %% 2 == 0)
+ grid[i] <- grid[i] + 1 #arbitrary choice
+ }
+ }
+ grid
+ }
+ }
+ )
+)
projects / agghoo.git / blobdiff