+++ /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).
-#'
-#' @export
-Agghoo <- R6::R6Class("Agghoo",
- public = list(
- #' @description Create a new Agghoo object.
- #' @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
- #' @param quality Function assessing the quality of a prediction;
- #' quality(y1, y2) --> real number
- initialize = function(data, target, task, gmodel, quality = NA) {
- private$data <- data
- private$target <- target
- private$task <- task
- private$gmodel <- gmodel
- if (is.na(quality)) {
- quality <- function(y1, y2) {
- # NOTE: if classif output is a probability matrix, adapt.
- if (task == "classification")
- mean(y1 == y2)
- else
- atan(1.0 / (mean(abs(y1 - y2) + 0.01))) #experimental...
- }
- }
- private$quality <- quality
- },
- #' @description Fit an agghoo model.
- #' @param CV List describing cross-validation to run. Slots:
- #' - type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
- #' - V: number of runs (default: 10)
- #' - test_size: percentage of data in the test dataset, for MC
- #' (irrelevant for V-fold). Default: 0.2.
- #' - shuffle: wether or not to shuffle data before V-fold.
- #' Irrelevant for Monte-Carlo; default: TRUE
- #' @param mode "agghoo" or "standard" (for usual cross-validation)
- fit = function(
- CV = list(type = "MC",
- V = 10,
- test_size = 0.2,
- shuffle = TRUE),
- mode="agghoo"
- ) {
- if (!is.list(CV))
- stop("CV: list of type, V, [test_size], [shuffle]")
- n <- nrow(private$data)
- shuffle_inds <- NA
- if (CV$type == "vfold" && CV$shuffle)
- shuffle_inds <- sample(n, n)
- if (mode == "agghoo") {
- vperfs <- list()
- for (v in 1:CV$V) {
- test_indices <- private$get_testIndices(CV, v, n, shuffle_inds)
- vperf <- private$get_modelPerf(test_indices)
- vperfs[[v]] <- vperf
- }
- private$run_res <- vperfs
- }
- else {
- # Standard cross-validation
- best_index = 0
- best_perf <- -1
- for (p in 1:private$gmodel$nmodels) {
- tot_perf <- 0
- for (v in 1:CV$V) {
- test_indices <- private$get_testIndices(CV, v, n, shuffle_inds)
- perf <- private$get_modelPerf(test_indices, p)
- tot_perf <- tot_perf + perf / CV$V
- }
- if (tot_perf > best_perf) {
- # TODO: if ex-aequos: models list + choose at random
- best_index <- p
- best_perf <- tot_perf
- }
- }
- best_model <- private$gmodel$get(private$data, private$target, best_index)
- private$run_res <- list( list(model=best_model, perf=best_perf) )
- }
- },
- #' @description Predict an agghoo model (after calling fit())
- #' @param X Matrix or data.frame to predict
- #' @param weight "uniform" (default) or "quality" to weight votes or
- #' average models performances (TODO: bad idea?!)
- predict = function(X, weight="uniform") {
- if (!is.list(private$run_res) || is.na(private$run_res)) {
- print("Please call $fit() method first")
- return
- }
- V <- length(private$run_res)
- if (V == 1)
- # Standard CV:
- return (private$run_res[[1]]$model(X))
- # Agghoo:
- if (weight == "uniform")
- weights <- rep(1 / V, V)
- else {
- perfs <- sapply(private$run_res, function(item) item$perf)
- perfs[perfs < 0] <- 0 #TODO: show a warning (with count of < 0...)
- total_weight <- sum(perfs) #TODO: error if total_weight == 0
- weights <- perfs / total_weight
- }
- n <- nrow(X)
- # TODO: detect if output = probs matrix for classif (in this case, adapt?)
- # prediction agghoo "probabiliste" pour un nouveau x :
- # argMax({ predict(m_v, x), v in 1..V }) ...
- if (private$task == "classification") {
- votes <- as.list(rep(NA, n))
- parse_numeric <- FALSE
- }
- else
- preds <- matrix(0, nrow=n, ncol=V)
- for (v in 1:V) {
- predictions <- private$run_res[[v]]$model(X)
- if (private$task == "regression")
- preds <- cbind(preds, weights[v] * predictions)
- else {
- if (!parse_numeric && is.numeric(predictions))
- parse_numeric <- TRUE
- for (i in 1:n) {
- if (!is.list(votes[[i]]))
- votes[[i]] <- list()
- index <- as.character(predictions[i])
- if (is.null(votes[[i]][[index]]))
- votes[[i]][[index]] <- 0
- votes[[i]][[index]] <- votes[[i]][[index]] + weights[v]
- }
- }
- }
- if (private$task == "regression")
- return (rowSums(preds))
- res <- c()
- for (i in 1:n) {
- # TODO: if ex-aequos, random choice...
- ind_max <- which.max(unlist(votes[[i]]))
- pred_class <- names(votes[[i]])[ind_max]
- if (parse_numeric)
- pred_class <- as.numeric(pred_class)
- res <- c(res, pred_class)
- }
- res
- }
- ),
- private = list(
- data = NA,
- target = NA,
- task = NA,
- gmodel = NA,
- quality = NA,
- run_res = NA,
- get_testIndices = function(CV, v, n, shuffle_inds) {
- if (CV$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 (CV$shuffle)
- test_indices <- shuffle_inds[test_indices]
- }
- else
- test_indices = sample(n, round(n * CV$test_size))
- test_indices
- },
- get_modelPerf = function(test_indices, p=0) {
- getOnePerf <- function(p) {
- model_pred <- private$gmodel$get(dataHO, targetHO, p)
- prediction <- model_pred(testX)
- perf <- private$quality(prediction, testY)
- list(model=model_pred, perf=perf)
- }
- dataHO <- private$data[-test_indices,]
- testX <- private$data[test_indices,]
- targetHO <- private$target[-test_indices]
- testY <- private$target[test_indices]
- if (p >= 1)
- # Standard CV: one model at a time
- return (getOnePerf(p)$perf)
- # Agghoo: loop on all models
- best_model = NULL
- best_perf <- -1
- for (p in 1:private$gmodel$nmodels) {
- model_perf <- getOnePerf(p)
- if (model_perf$perf > best_perf) {
- # TODO: if ex-aequos: models list + choose at random
- best_model <- model_perf$model
- best_perf <- model_perf$perf
- }
- }
- list(model=best_model, perf=best_perf)
- }
- )
-)
projects / agghoo.git / blobdiff