#' Parameters for cross-validation are either provided or estimated.
#' Model family can be chosen among "rf", "tree", "ppr" and "knn" for now.
#'
+#' @importFrom FNN knn.reg
+#' @importFrom class knn
+#' @importFrom stats ppr
+#' @importFrom randomForest randomForest
+#' @importFrom rpart rpart
+#' @importFrom caret var_seq
+#'
#' @export
Model <- R6::R6Class("Model",
public = list(
#' @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 = NA, params = NA) {
- if (is.na(gmodel)) {
+ 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)
# Numerical data
gmodel = ifelse(task == "regression", "ppr", "knn")
}
- if (is.na(params))
+ 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$getParams(gmodel, data, target))
),
private = list(
# No need to expose model or parameters list
- gmodel = NA,
- params = NA,
+ gmodel = NULL,
+ params = NULL,
# Main function: given a family, return a generic model, which in turn
# will output a predictive model from data + target + params.
getGmodel = function(family, task) {
require(rpart)
method <- ifelse(task == "classification", "class", "anova")
df <- data.frame(cbind(dataHO, target=targetHO))
- model <- rpart(target ~ ., df, method=method, control=list(cp=param))
+ model <- rpart::rpart(target ~ ., df, method=method, control=list(cp=param))
function(X) predict(model, X)
}
}
}
}
else if (family == "knn") {
- function(dataHO, targetHO, param) {
- require(class)
- function(X) class::knn(dataHO, X, cl=targetHO, k=param)
+ if (task == "classification") {
+ function(dataHO, targetHO, param) {
+ require(class)
+ function(X) class::knn(dataHO, X, cl=targetHO, k=param)
+ }
+ }
+ else {
+ function(dataHO, targetHO, param) {
+ require(FNN)
+ function(X) FNN::knn.reg(dataHO, X, y=targetHO, k=param)$pred
+ }
}
}
},