From: Benjamin Auder <benjamin.auder@somewhere>
Date: Sat, 5 Jun 2021 17:38:39 +0000 (+0200)
Subject: First commit
X-Git-Url: https://git.auder.net/variants/Chakart/doc/scripts/img/app_dev.php?a=commitdiff_plain;h=c5946158cae8f7b9400f107c533e745f835eb20f;p=agghoo.git

First commit
---

c5946158cae8f7b9400f107c533e745f835eb20f
diff --git a/DESCRIPTION b/DESCRIPTION
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+++ b/DESCRIPTION
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+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'
diff --git a/LICENSE b/LICENSE
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+++ b/LICENSE
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+YEAR: 2021
+COPYRIGHT HOLDER: Sylvain Arlot, Benjamin Auder, Melina Gallopin, Matthieu Lerasle, Guillaume Maillard
diff --git a/NAMESPACE b/NAMESPACE
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+++ b/NAMESPACE
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+# Generated by roxygen2: do not edit by hand
+
+export(Agghoo)
+export(Model)
+export(agghoo)
+export(compareToStandard)
diff --git a/R/A_NAMESPACE.R b/R/A_NAMESPACE.R
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+++ b/R/A_NAMESPACE.R
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+#' @include R6_Model.R
+#' @include R6_Agghoo.R
+#' @include agghoo.R
+NULL
diff --git a/R/R6_Agghoo.R b/R/R6_Agghoo.R
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+++ b/R/R6_Agghoo.R
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+#' @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$perf
+        }
+      }
+      list(model=best_model, perf=best_perf)
+    }
+  )
+)
diff --git a/R/R6_Model.R b/R/R6_Model.R
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+++ b/R/R6_Model.R
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+#' @title R6 class representing a (generic) model.
+#'
+#' @description
+#' "Model" class, containing a (generic) learning function, which from
+#' data + target [+ params] returns a prediction function X --> y.
+#' Parameters for cross-validation are either provided or estimated.
+#' Model family can be chosen among "rf", "tree", "ppr" and "knn" for now.
+#'
+#' @export
+Model <- R6::R6Class("Model",
+  public = list(
+    #' @field nmodels Number of parameters (= number of [predictive] models)
+    nmodels = NA,
+    #' @description Create a new generic model.
+    #' @param data Matrix or data.frame
+    #' @param target Vector of targets (generally numeric or factor)
+    #' @param task "regression" or "classification"
+    #' @param gmodel Generic model returning a predictive function; chosen
+    #'               automatically given data and target nature if not provided.
+    #' @param params List of parameters for cross-validation (each defining a model)
+    initialize = function(data, target, task, gmodel = NA, params = NA) {
+      if (is.na(gmodel)) {
+        # (Generic) model not provided
+        all_numeric <- is.numeric(as.matrix(data))
+        if (!all_numeric)
+          # At least one non-numeric column: use random forests or trees
+          # TODO: 4 = arbitrary magic number...
+          gmodel = ifelse(ncol(data) >= 4, "rf", "tree")
+        else
+          # Numerical data
+          gmodel = ifelse(task == "regression", "ppr", "knn")
+      }
+      if (is.na(params))
+        # Here, gmodel is a string (= its family),
+        # because a custom model must be given with its parameters.
+        params <- as.list(private$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
+      }
+    }
+  )
+)
diff --git a/R/agghoo.R b/R/agghoo.R
new file mode 100644
index 0000000..4a25b17
--- /dev/null
+++ b/R/agghoo.R
@@ -0,0 +1,100 @@
+#' 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$predict(df[test_indices,-t_idx])
+  print(paste("error CV",
+              ifelse(task == "classification",
+                     mean(ps != df[test_indices,t_idx]),
+                     mean(abs(ps - df[test_indices,t_idx])))))
+}
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e932011
--- /dev/null
+++ b/README.md
@@ -0,0 +1,13 @@
+# agghoo
+
+R package for model selection based on aggregation.
+Alternative to standard corss-validation.
+
+## Install the package
+
+R CMD INSTALL .
+
+## Use the package
+
+> library(agghoo)
+> ?agghoo
diff --git a/man/Agghoo.Rd b/man/Agghoo.Rd
new file mode 100644
index 0000000..dc70db6
--- /dev/null
+++ b/man/Agghoo.Rd
@@ -0,0 +1,110 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/R6_Agghoo.R
+\name{Agghoo}
+\alias{Agghoo}
+\title{R6 class with agghoo functions fit() and predict().}
+\description{
+Class encapsulating the methods to run to obtain the best predictor
+from the list of models (see 'Model' class).
+}
+\section{Methods}{
+\subsection{Public methods}{
+\itemize{
+\item \href{#method-new}{\code{Agghoo$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$clone(deep = FALSE)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{deep}}{Whether to make a deep clone.}
+}
+\if{html}{\out{</div>}}
+}
+}
+}
diff --git a/man/Model.Rd b/man/Model.Rd
new file mode 100644
index 0000000..a16f8ae
--- /dev/null
+++ b/man/Model.Rd
@@ -0,0 +1,92 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/R6_Model.R
+\name{Model}
+\alias{Model}
+\title{R6 class representing a (generic) model.}
+\description{
+"Model" class, containing a (generic) learning function, which from
+data + target [+ params] returns a prediction function X --> y.
+Parameters for cross-validation are either provided or estimated.
+Model family can be chosen among "rf", "tree", "ppr" and "knn" for now.
+}
+\section{Public fields}{
+\if{html}{\out{<div class="r6-fields">}}
+\describe{
+\item{\code{nmodels}}{Number of parameters (= number of [predictive] models)}
+}
+\if{html}{\out{</div>}}
+}
+\section{Methods}{
+\subsection{Public methods}{
+\itemize{
+\item \href{#method-new}{\code{Model$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>}}
+}
+}
+}
diff --git a/man/agghoo.Rd b/man/agghoo.Rd
new file mode 100644
index 0000000..dea76a1
--- /dev/null
+++ b/man/agghoo.Rd
@@ -0,0 +1,47 @@
+% 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))
+
+}
diff --git a/man/compareToStandard.Rd b/man/compareToStandard.Rd
new file mode 100644
index 0000000..5787de9
--- /dev/null
+++ b/man/compareToStandard.Rd
@@ -0,0 +1,12 @@
+% 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).
+}