--- /dev/null
+Package: agghoo
+Title: Aggregated Hold-out Cross Validation
+Date: 2021-06-05
+Version: 0.1-0
+Description: The 'agghoo' procedure is an alternative to usual cross-validation.
+ Instead of choosing the best model trained on V subsamples, it determines
+ a winner model for each subsample, and then aggregate the V outputs.
+ For the details, see "Aggregated hold-out" by Guillaume Maillard,
+ Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
+ published in Journal of Machine Learning Research 22(20):1--55.
+Author: Sylvain Arlot <sylvain.arlot@universite-paris-saclay.fr> [cph,ctb],
+ Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre,cph],
+ Melina Gallopin <melina.gallopin@universite-paris-saclay.fr> [cph,ctb],
+ Matthieu Lerasle <matthieu.lerasle@universite-paris-saclay.fr> [cph,ctb],
+ Guillaume Maillard <guillaume.maillard@uni.lu> [cph,ctb]
+Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
+Depends:
+ R (>= 3.5.0)
+Imports:
+ R6,
+ caret,
+ rpart,
+ randomForest
+Suggests:
+ roxygen2
+URL: https://git.auder.net/?p=agghoo.git
+License: MIT + file LICENSE
+RoxygenNote: 7.1.1
+Collate:
+ 'agghoo.R'
+ 'R6_Agghoo.R'
+ 'R6_Model.R'
+ 'A_NAMESPACE.R'
--- /dev/null
+YEAR: 2021
+COPYRIGHT HOLDER: Sylvain Arlot, Benjamin Auder, Melina Gallopin, Matthieu Lerasle, Guillaume Maillard
--- /dev/null
+# Generated by roxygen2: do not edit by hand
+
+export(Agghoo)
+export(Model)
+export(agghoo)
+export(compareToStandard)
--- /dev/null
+#' @include R6_Model.R
+#' @include R6_Agghoo.R
+#' @include agghoo.R
+NULL
--- /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$perf
+ }
+ }
+ list(model=best_model, perf=best_perf)
+ }
+ )
+)
--- /dev/null
+#' @title R6 class representing a (generic) model.
+#'
+#' @description
+#' "Model" class, containing a (generic) learning function, which from
+#' data + target [+ params] returns a prediction function X --> y.
+#' Parameters for cross-validation are either provided or estimated.
+#' Model family can be chosen among "rf", "tree", "ppr" and "knn" for now.
+#'
+#' @export
+Model <- R6::R6Class("Model",
+ public = list(
+ #' @field nmodels Number of parameters (= number of [predictive] models)
+ nmodels = NA,
+ #' @description Create a new generic model.
+ #' @param data Matrix or data.frame
+ #' @param target Vector of targets (generally numeric or factor)
+ #' @param task "regression" or "classification"
+ #' @param gmodel Generic model returning a predictive function; chosen
+ #' automatically given data and target nature if not provided.
+ #' @param params List of parameters for cross-validation (each defining a model)
+ initialize = function(data, target, task, gmodel = NA, params = NA) {
+ if (is.na(gmodel)) {
+ # (Generic) model not provided
+ all_numeric <- is.numeric(as.matrix(data))
+ if (!all_numeric)
+ # At least one non-numeric column: use random forests or trees
+ # TODO: 4 = arbitrary magic number...
+ gmodel = ifelse(ncol(data) >= 4, "rf", "tree")
+ else
+ # Numerical data
+ gmodel = ifelse(task == "regression", "ppr", "knn")
+ }
+ if (is.na(params))
+ # Here, gmodel is a string (= its family),
+ # because a custom model must be given with its parameters.
+ params <- as.list(private$getParams(gmodel, data, target))
+ private$params <- params
+ if (is.character(gmodel))
+ gmodel <- private$getGmodel(gmodel, task)
+ private$gmodel <- gmodel
+ self$nmodels <- length(private$params)
+ },
+ #' @description
+ #' Returns the model at index "index", trained on dataHO/targetHO.
+ #' index is between 1 and self$nmodels.
+ #' @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]])
+ }
+ ),
+ private = list(
+ # No need to expose model or parameters list
+ gmodel = NA,
+ params = NA,
+ # 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) {
+ 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))
+ function(X) predict(model, X)
+ }
+ }
+ else if (family == "rf") {
+ function(dataHO, targetHO, param) {
+ require(randomForest)
+ if (task == "classification" && !is.factor(targetHO))
+ targetHO <- as.factor(targetHO)
+ model <- randomForest::randomForest(dataHO, targetHO, mtry=param)
+ function(X) predict(model, X)
+ }
+ }
+ else if (family == "ppr") {
+ function(dataHO, targetHO, param) {
+ model <- stats::ppr(dataHO, targetHO, nterms=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)
+ }
+ }
+ },
+ # Return a default list of parameters, given a gmodel family
+ getParams = function(family, data, target) {
+ if (family == "tree") {
+ # Run rpart once to obtain a CV grid for parameter cp
+ require(rpart)
+ df <- data.frame(cbind(data, target=target))
+ ctrl <- list(
+ minsplit = 2,
+ minbucket = 1,
+ maxcompete = 0,
+ maxsurrogate = 0,
+ usesurrogate = 0,
+ xval = 0,
+ surrogatestyle = 0,
+ maxdepth = 30)
+ r <- rpart(target ~ ., df, method="class", control=ctrl)
+ cps <- r$cptable[-1,1]
+ if (length(cps) <= 11)
+ return (cps)
+ step <- (length(cps) - 1) / 10
+ cps[unique(round(seq(1, length(cps), step)))]
+ }
+ else if (family == "rf") {
+ p <- ncol(data)
+ # Use caret package to obtain the CV grid of mtry values
+ require(caret)
+ caret::var_seq(p, classification = (task == "classificaton"),
+ len = min(10, p-1))
+ }
+ else if (family == "ppr")
+ # This is nterms in ppr() function
+ 1:10
+ else if (family == "knn") {
+ n <- nrow(data)
+ # Choose ~10 NN values
+ K <- length(unique(target))
+ if (n <= 10)
+ return (1:(n-1))
+ sqrt_n <- sqrt(n)
+ step <- (2*sqrt_n - 1) / 10
+ grid <- unique(round(seq(1, 2*sqrt_n, step)))
+ if (K == 2) {
+ # Common binary classification case: odd number of neighbors
+ for (i in 2:11) {
+ if (grid[i] %% 2 == 0)
+ grid[i] <- grid[i] + 1 #arbitrary choice
+ }
+ }
+ grid
+ }
+ }
+ )
+)
--- /dev/null
+#' agghoo
+#'
+#' Run the agghoo procedure. (...)
+#'
+#' @param data Data frame or matrix containing the data in lines.
+#' @param target The target values to predict. Generally a vector.
+#' @param task "classification" or "regression". Default:
+#' regression if target is numerical, classification otherwise.
+#' @param gmodel A "generic model", which is a function returning a predict
+#' function (taking X as only argument) from the tuple
+#' (dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
+#' referring to cross-validation. Cross-validation is run on an array
+#' of 'param's. See params argument. Default: see R6::Model.
+#' @param params A list of parameters. Often, one list cell is just a
+#' numerical value, but in general it could be of any type.
+#' Default: see R6::Model.
+#' @param quality A function assessing the quality of a prediction.
+#' Arguments are y1 and y2 (comparing a prediction to known values).
+#' Default: see R6::Agghoo.
+#'
+#' @return An R6::Agghoo object.
+#'
+#' @examples
+#' # Regression:
+#' a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
+#' a_reg$fit()
+#' pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
+#' # Classification
+#' a_cla <- agghoo(iris[,-5], iris[,5])
+#' a_cla$fit(mode="standard")
+#' pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
+#'
+#' @export
+agghoo <- function(data, target, task = NA, gmodel = NA, params = NA, quality = NA) {
+ # Args check:
+ if (!is.data.frame(data) && !is.matrix(data))
+ stop("data: data.frame or matrix")
+ if (nrow(data) <= 1 || any(dim(data) == 0))
+ stop("data: non-empty, >= 2 rows")
+ if (!is.numeric(target) && !is.factor(target) && !is.character(target))
+ stop("target: numeric, factor or character vector")
+ if (!is.na(task))
+ task = match.arg(task, c("classification", "regression"))
+ if (is.character(gmodel))
+ gmodel <- match.arg("knn", "ppr", "rf")
+ else if (!is.na(gmodel) && !is.function(gmodel))
+ # No further checks here: fingers crossed :)
+ stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y")
+ if (is.numeric(params) || is.character(params))
+ params <- as.list(params)
+ if (!is.na(params) && !is.list(params))
+ stop("params: numerical, character, or list (passed to model)")
+ if (!is.na(gmodel) && !is.character(model) && is.na(params))
+ stop("params must be provided when using a custom model")
+ if (is.na(gmodel) && !is.na(params))
+ stop("model must be provided when using custom params")
+ if (!is.na(quality) && !is.function(quality))
+ # No more checks here as well... TODO:?
+ stop("quality: function(y1, y2) --> Real")
+
+ if (is.na(task)) {
+ if (is.numeric(target))
+ task = "regression"
+ else
+ task = "classification"
+ }
+ # Build Model object (= list of parameterized models)
+ model <- Model$new(data, target, task, gmodel, params)
+ # Return Agghoo object, to run and predict
+ Agghoo$new(data, target, task, model, quality)
+}
+
+#' compareToStandard
+#'
+#' Temporary function to compare agghoo to CV
+#' (TODO: extended, in another file, more tests - when faster code).
+#'
+#' @export
+compareToStandard <- function(df, t_idx, task = NA, rseed = -1) {
+ if (rseed >= 0)
+ set.seed(rseed)
+ if (is.na(task))
+ task <- ifelse(is.numeric(df[,t_idx]), "regression", "classification")
+ n <- nrow(df)
+ test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
+ a <- agghoo(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
+ a$fit(mode="agghoo") #default mode
+ pa <- a$predict(df[test_indices,-t_idx])
+ print(paste("error agghoo",
+ ifelse(task == "classification",
+ mean(p != df[test_indices,t_idx]),
+ mean(abs(pa - df[test_indices,t_idx])))))
+ # Compare with standard cross-validation:
+ a$fit(mode="standard")
+ ps <- a$predict(df[test_indices,-t_idx])
+ print(paste("error CV",
+ ifelse(task == "classification",
+ mean(ps != df[test_indices,t_idx]),
+ mean(abs(ps - df[test_indices,t_idx])))))
+}
--- /dev/null
+# agghoo
+
+R package for model selection based on aggregation.
+Alternative to standard corss-validation.
+
+## Install the package
+
+R CMD INSTALL .
+
+## Use the package
+
+> library(agghoo)
+> ?agghoo
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/R6_Agghoo.R
+\name{Agghoo}
+\alias{Agghoo}
+\title{R6 class with agghoo functions fit() and predict().}
+\description{
+Class encapsulating the methods to run to obtain the best predictor
+from the list of models (see 'Model' class).
+}
+\section{Methods}{
+\subsection{Public methods}{
+\itemize{
+\item \href{#method-new}{\code{Agghoo$new()}}
+\item \href{#method-fit}{\code{Agghoo$fit()}}
+\item \href{#method-predict}{\code{Agghoo$predict()}}
+\item \href{#method-clone}{\code{Agghoo$clone()}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-new"></a>}}
+\if{latex}{\out{\hypertarget{method-new}{}}}
+\subsection{Method \code{new()}}{
+Create a new Agghoo object.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{Agghoo$new(data, target, task, gmodel, quality = NA)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{data}}{Matrix or data.frame}
+
+\item{\code{target}}{Vector of targets (generally numeric or factor)}
+
+\item{\code{task}}{"regression" or "classification"}
+
+\item{\code{gmodel}}{Generic model returning a predictive function}
+
+\item{\code{quality}}{Function assessing the quality of a prediction;
+quality(y1, y2) --> real number}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-fit"></a>}}
+\if{latex}{\out{\hypertarget{method-fit}{}}}
+\subsection{Method \code{fit()}}{
+Fit an agghoo model.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{Agghoo$fit(
+ CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE),
+ mode = "agghoo"
+)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{CV}}{List describing cross-validation to run. Slots:
+- type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
+- V: number of runs (default: 10)
+- test_size: percentage of data in the test dataset, for MC
+ (irrelevant for V-fold). Default: 0.2.
+- shuffle: wether or not to shuffle data before V-fold.
+ Irrelevant for Monte-Carlo; default: TRUE}
+
+\item{\code{mode}}{"agghoo" or "standard" (for usual cross-validation)}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-predict"></a>}}
+\if{latex}{\out{\hypertarget{method-predict}{}}}
+\subsection{Method \code{predict()}}{
+Predict an agghoo model (after calling fit())
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{Agghoo$predict(X, weight = "uniform")}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{X}}{Matrix or data.frame to predict}
+
+\item{\code{weight}}{"uniform" (default) or "quality" to weight votes or
+average models performances (TODO: bad idea?!)}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-clone"></a>}}
+\if{latex}{\out{\hypertarget{method-clone}{}}}
+\subsection{Method \code{clone()}}{
+The objects of this class are cloneable with this method.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{Agghoo$clone(deep = FALSE)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{deep}}{Whether to make a deep clone.}
+}
+\if{html}{\out{</div>}}
+}
+}
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/R6_Model.R
+\name{Model}
+\alias{Model}
+\title{R6 class representing a (generic) model.}
+\description{
+"Model" class, containing a (generic) learning function, which from
+data + target [+ params] returns a prediction function X --> y.
+Parameters for cross-validation are either provided or estimated.
+Model family can be chosen among "rf", "tree", "ppr" and "knn" for now.
+}
+\section{Public fields}{
+\if{html}{\out{<div class="r6-fields">}}
+\describe{
+\item{\code{nmodels}}{Number of parameters (= number of [predictive] models)}
+}
+\if{html}{\out{</div>}}
+}
+\section{Methods}{
+\subsection{Public methods}{
+\itemize{
+\item \href{#method-new}{\code{Model$new()}}
+\item \href{#method-get}{\code{Model$get()}}
+\item \href{#method-clone}{\code{Model$clone()}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-new"></a>}}
+\if{latex}{\out{\hypertarget{method-new}{}}}
+\subsection{Method \code{new()}}{
+Create a new generic model.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{Model$new(data, target, task, gmodel = NA, params = NA)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{data}}{Matrix or data.frame}
+
+\item{\code{target}}{Vector of targets (generally numeric or factor)}
+
+\item{\code{task}}{"regression" or "classification"}
+
+\item{\code{gmodel}}{Generic model returning a predictive function; chosen
+automatically given data and target nature if not provided.}
+
+\item{\code{params}}{List of parameters for cross-validation (each defining a model)}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-get"></a>}}
+\if{latex}{\out{\hypertarget{method-get}{}}}
+\subsection{Method \code{get()}}{
+Returns the model at index "index", trained on dataHO/targetHO.
+index is between 1 and self$nmodels.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{Model$get(dataHO, targetHO, index)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{dataHO}}{Matrix or data.frame}
+
+\item{\code{targetHO}}{Vector of targets (generally numeric or factor)}
+
+\item{\code{index}}{Index of the model in 1...nmodels}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-clone"></a>}}
+\if{latex}{\out{\hypertarget{method-clone}{}}}
+\subsection{Method \code{clone()}}{
+The objects of this class are cloneable with this method.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{Model$clone(deep = FALSE)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{deep}}{Whether to make a deep clone.}
+}
+\if{html}{\out{</div>}}
+}
+}
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/agghoo.R
+\name{agghoo}
+\alias{agghoo}
+\title{agghoo}
+\usage{
+agghoo(data, target, task = NA, gmodel = NA, params = NA, quality = NA)
+}
+\arguments{
+\item{data}{Data frame or matrix containing the data in lines.}
+
+\item{target}{The target values to predict. Generally a vector.}
+
+\item{task}{"classification" or "regression". Default:
+regression if target is numerical, classification otherwise.}
+
+\item{gmodel}{A "generic model", which is a function returning a predict
+function (taking X as only argument) from the tuple
+(dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
+referring to cross-validation. Cross-validation is run on an array
+of 'param's. See params argument. Default: see R6::Model.}
+
+\item{params}{A list of parameters. Often, one list cell is just a
+numerical value, but in general it could be of any type.
+Default: see R6::Model.}
+
+\item{quality}{A function assessing the quality of a prediction.
+Arguments are y1 and y2 (comparing a prediction to known values).
+Default: see R6::Agghoo.}
+}
+\value{
+An R6::Agghoo object.
+}
+\description{
+Run the agghoo procedure. (...)
+}
+\examples{
+# Regression:
+a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
+a_reg$fit()
+pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
+# Classification
+a_cla <- agghoo(iris[,-5], iris[,5])
+a_cla$fit(mode="standard")
+pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
+
+}
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/agghoo.R
+\name{compareToStandard}
+\alias{compareToStandard}
+\title{compareToStandard}
+\usage{
+compareToStandard(df, t_idx, task = NA, rseed = -1)
+}
+\description{
+Temporary function to compare agghoo to CV
+(TODO: extended, in another file, more tests - when faster code).
+}
projects / agghoo.git / commitdiff
