From: Benjamin Auder Date: Thu, 17 Jun 2021 01:19:40 +0000 (+0200) Subject: Reorganize code - unfinished: some functions not exported yet X-Git-Url: https://git.auder.net/variants/img/current/assets/css/config.php?a=commitdiff_plain;h=afa676609daba103e43d6d4654560ca4c1c9b38b;p=agghoo.git Reorganize code - unfinished: some functions not exported yet --- diff --git a/DESCRIPTION b/DESCRIPTION index 2689a20..c47391f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -32,3 +32,6 @@ Collate: 'R6_AgghooCV.R' 'R6_Model.R' 'A_NAMESPACE.R' + 'checks.R' + 'compareTo.R' + 'utils.R' diff --git a/R/R6_AgghooCV.R b/R/R6_AgghooCV.R index ed9aa5c..485c678 100644 --- a/R/R6_AgghooCV.R +++ b/R/R6_AgghooCV.R @@ -15,13 +15,11 @@ AgghooCV <- R6::R6Class("AgghooCV", #' @param task "regression" or "classification" #' @param gmodel Generic model returning a predictive function #' @param loss Function assessing the error of a prediction - initialize = function(data, target, task, gmodel, loss = NULL) { + initialize = function(data, target, task, gmodel, loss) { private$data <- data private$target <- target private$task <- task private$gmodel <- gmodel - if (is.null(loss)) - loss <- private$defaultLoss private$loss <- loss }, #' @description Fit an agghoo model. @@ -31,15 +29,10 @@ AgghooCV <- R6::R6Class("AgghooCV", #' - test_size: percentage of data in the test dataset, for MC #' (irrelevant for V-fold). Default: 0.2. \cr #' - shuffle: wether or not to shuffle data before V-fold. - #' Irrelevant for Monte-Carlo; default: TRUE - fit = function( - CV = list(type = "MC", - V = 10, - test_size = 0.2, - shuffle = TRUE) - ) { - if (!is.list(CV)) - stop("CV: list of type, V, [test_size], [shuffle]") + #' Irrelevant for Monte-Carlo; default: TRUE \cr + #' Default (if NULL): type="MC", V=10, test_size=0.2 + fit = function(CV = NULL) { + CV <- checkCV(CV) n <- nrow(private$data) shuffle_inds <- NULL if (CV$type == "vfold" && CV$shuffle) @@ -49,22 +42,14 @@ AgghooCV <- R6::R6Class("AgghooCV", for (v in seq_len(CV$V)) { # Prepare train / test data and target, from full dataset. # dataHO: "data Hold-Out" etc. - test_indices <- private$get_testIndices(CV, v, n, shuffle_inds) - dataHO <- private$data[-test_indices,] - testX <- private$data[test_indices,] - targetHO <- private$target[-test_indices] - testY <- private$target[test_indices] - # [HACK] R will cast 1-dim matrices into vectors: - if (!is.matrix(dataHO) && !is.data.frame(dataHO)) - dataHO <- as.matrix(dataHO) - if (!is.matrix(testX) && !is.data.frame(testX)) - testX <- as.matrix(testX) + test_indices <- get_testIndices(n, CV, v, shuffle_inds) + d <- splitTrainTest(private$data, private$target, test_indices) best_model <- NULL best_error <- Inf for (p in seq_len(private$gmodel$nmodels)) { - model_pred <- private$gmodel$get(dataHO, targetHO, p) - prediction <- model_pred(testX) - error <- private$loss(prediction, testY) + model_pred <- private$gmodel$get(d$dataTrain, d$targetTrain, p) + prediction <- model_pred(d$dataTest) + error <- private$loss(prediction, d$targetTest) if (error <= best_error) { newModel <- list(model=model_pred, param=private$gmodel$getParam(p)) if (error == best_error) @@ -89,7 +74,10 @@ AgghooCV <- R6::R6Class("AgghooCV", return (invisible(NULL)) } V <- length(private$pmodels) - oneLineX <- t(as.matrix(X[1,])) + oneLineX <- X[1,] + if (is.matrix(X)) + # HACK: R behaves differently with data frames and matrices. + oneLineX <- t(as.matrix(oneLineX)) if (length(private$pmodels[[1]]$model(oneLineX)) >= 2) # Soft classification: return (Reduce("+", lapply(private$pmodels, function(m) m$model(X))) / V) @@ -119,50 +107,6 @@ AgghooCV <- R6::R6Class("AgghooCV", task = NULL, gmodel = NULL, loss = NULL, - pmodels = NULL, - get_testIndices = function(CV, v, n, shuffle_inds) { - if (CV$type == "vfold") { - # Slice indices (optionnally shuffled) - first_index = round((v-1) * n / CV$V) + 1 - last_index = round(v * n / CV$V) - test_indices = first_index:last_index - if (!is.null(shuffle_inds)) - test_indices <- shuffle_inds[test_indices] - } - else - # Monte-Carlo cross-validation - test_indices = sample(n, round(n * CV$test_size)) - test_indices - }, - defaultLoss = function(y1, y2) { - if (private$task == "classification") { - if (is.null(dim(y1))) - # Standard case: "hard" classification - mean(y1 != y2) - else { - # "Soft" classification: predict() outputs a probability matrix - # In this case "target" could be in matrix form. - if (!is.null(dim(y2))) - mean(rowSums(abs(y1 - y2))) - else { - # Or not: y2 is a "factor". - y2 <- as.character(y2) - # NOTE: the user should provide target in matrix form because - # matching y2 with columns is rather inefficient! - names <- colnames(y1) - positions <- list() - for (idx in seq_along(names)) - positions[[ names[idx] ]] <- idx - mean(vapply( - seq_along(y2), - function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])), - 0)) - } - } - } - else - # Regression - mean(abs(y1 - y2)) - } + pmodels = NULL ) ) diff --git a/R/agghoo.R b/R/agghoo.R index c8765fc..48ac741 100644 --- a/R/agghoo.R +++ b/R/agghoo.R @@ -41,43 +41,18 @@ #' Journal of Machine Learning Research 22(20):1--55, 2021. #' #' @export -agghoo <- function(data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL) { +agghoo <- function( + data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL +) { # Args check: - if (!is.data.frame(data) && !is.matrix(data)) - stop("data: data.frame or matrix") - if (is.data.frame(target) || is.matrix(target)) { - if (nrow(target) != nrow(data) || ncol(target) == 1) - stop("target probability matrix does not match data size") - } - else if (!is.numeric(target) && !is.factor(target) && !is.character(target)) - stop("target: numeric, factor or character vector") - if (!is.null(task)) - task = match.arg(task, c("classification", "regression")) - if (is.character(gmodel)) - gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree")) - else if (!is.null(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.list(params) && !is.null(params)) - stop("params: numerical, character, or list (passed to model)") - if (is.function(gmodel) && !is.list(params)) - stop("params must be provided when using a custom model") - if (is.list(params) && is.null(gmodel)) - stop("model (or family) must be provided when using custom params") - if (!is.null(loss) && !is.function(loss)) - # No more checks here as well... TODO:? - stop("loss: function(y1, y2) --> Real") + checkDaTa(data, target) + task <- checkTask(task, target) + modPar <- checkModPar(gmodel, params) + loss <- checkLoss(loss, task) - if (is.null(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) + model <- Model$new(data, target, task, modPar$gmodel, modPar$params) + # Return AgghooCV object, to run and predict AgghooCV$new(data, target, task, model, loss) } diff --git a/R/checks.R b/R/checks.R new file mode 100644 index 0000000..e105dfa --- /dev/null +++ b/R/checks.R @@ -0,0 +1,100 @@ +defaultLoss_classif <- function(y1, y2) { + if (is.null(dim(y1))) + # Standard case: "hard" classification + mean(y1 != y2) + else { + # "Soft" classification: predict() outputs a probability matrix + # In this case "target" could be in matrix form. + if (!is.null(dim(y2))) + mean(rowSums(abs(y1 - y2))) + else { + # Or not: y2 is a "factor". + y2 <- as.character(y2) + # NOTE: the user should provide target in matrix form because + # matching y2 with columns is rather inefficient! + names <- colnames(y1) + positions <- list() + for (idx in seq_along(names)) + positions[[ names[idx] ]] <- idx + mean(vapply( + seq_along(y2), + function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])), + 0)) + } + } +} + +defaultLoss_regress <- function(y1, y2) { + mean(abs(y1 - y2)) +} + +# TODO: allow strings like "MSE", "abs" etc +checkLoss <- function(loss, task) { + if (!is.null(loss) && !is.function(loss)) + stop("loss: function(y1, y2) --> Real") + if (is.null(loss)) { + loss <- if (task == "classification") { + defaultLoss_classif + } else { + defaultLoss_regress + } + } + loss +} + +checkCV <- function(CV) { + if (is.null(CV)) + CV <- list(type="MC", V=10, test_size=0.2, shuffle=TRUE) + else { + if (!is.list(CV)) + stop("CV: list of type('MC'|'vfold'), V(integer, [test_size, shuffle]") + if (is.null(CV$type)) { + warning("CV$type not provided: set to MC") + CV$type <- "MC" + } + if (is.null(CV$V)) { + warning("CV$V not provided: set to 10") + CV$V <- 10 + } + if (CV$type == "MC" && is.null(CV$test_size)) + CV$test_size <- 0.2 + if (CV$type == "vfold" && is.null(CV$shuffle)) + CV$shuffle <- TRUE + } + CV +} + +checkDaTa <- function(data, target) { + if (!is.data.frame(data) && !is.matrix(data)) + stop("data: data.frame or matrix") + if (is.data.frame(target) || is.matrix(target)) { + if (!is.numeric(target)) + stop("multi-columns target must be a probability matrix") + if (nrow(target) != nrow(data) || ncol(target) == 1) + stop("target probability matrix does not match data size") + } + else if (!is.numeric(target) && !is.factor(target) && !is.character(target)) + stop("target: numeric, factor or character vector") +} + +checkTask <- function(task, target) { + if (!is.null(task)) + task <- match.arg(task, c("classification", "regression")) + task <- ifelse(is.numeric(target), "regression", "classification") +} + +checkModPar <- function(gmodel, params) { + if (is.character(gmodel)) + gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree")) + else if (!is.null(gmodel) && !is.function(gmodel)) + stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y") + if (is.numeric(params) || is.character(params)) + params <- as.list(params) + if (!is.list(params) && !is.null(params)) + stop("params: numerical, character, or list (passed to model)") + if (is.function(gmodel) && !is.list(params)) + stop("params must be provided when using a custom model") + if (is.list(params) && is.null(gmodel)) + stop("model (or family) must be provided when using custom params") + list(gmodel=gmodel, params=params) +} diff --git a/R/compareTo.R b/R/compareTo.R index aa3a4e8..00e90a9 100644 --- a/R/compareTo.R +++ b/R/compareTo.R @@ -1,86 +1,25 @@ -standardCV_core <- function(data, target, task = NULL, gmodel = NULL, params = NULL, - loss = NULL, CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE) -) { - if (!is.null(task)) - task = match.arg(task, c("classification", "regression")) - if (is.character(gmodel)) - gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree")) - if (is.numeric(params) || is.character(params)) - params <- as.list(params) - if (is.null(task)) { - if (is.numeric(target)) - task = "regression" - else - task = "classification" - } - - if (is.null(loss)) { - loss <- function(y1, y2) { - if (task == "classification") { - if (is.null(dim(y1))) - mean(y1 != y2) - else { - if (!is.null(dim(y2))) - mean(rowSums(abs(y1 - y2))) - else { - y2 <- as.character(y2) - names <- colnames(y1) - positions <- list() - for (idx in seq_along(names)) - positions[[ names[idx] ]] <- idx - mean(vapply( - seq_along(y2), - function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])), - 0)) - } - } - } - else - mean(abs(y1 - y2)) - } - } - +standardCV_core <- function(data, target, task, gmodel, params, loss, CV) { n <- nrow(data) shuffle_inds <- NULL if (CV$type == "vfold" && CV$shuffle) shuffle_inds <- sample(n, n) - get_testIndices <- function(v, shuffle_inds) { - if (CV$type == "vfold") { - first_index = round((v-1) * n / CV$V) + 1 - last_index = round(v * n / CV$V) - test_indices = first_index:last_index - if (!is.null(shuffle_inds)) - test_indices <- shuffle_inds[test_indices] - } - else - test_indices = sample(n, round(n * CV$test_size)) - test_indices - } list_testinds <- list() for (v in seq_len(CV$V)) - list_testinds[[v]] <- get_testIndices(v, shuffle_inds) - + list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds) gmodel <- agghoo::Model$new(data, target, task, gmodel, params) best_error <- Inf best_model <- NULL for (p in seq_len(gmodel$nmodels)) { - error <- 0 - for (v in seq_len(CV$V)) { + error <- Reduce('+', lapply(seq_len(CV$V), function(v) { testIdx <- list_testinds[[v]] - dataHO <- data[-testIdx,] - testX <- data[testIdx,] - targetHO <- target[-testIdx] - testY <- target[testIdx] - if (!is.matrix(dataHO) && !is.data.frame(dataHO)) - dataHO <- as.matrix(dataHO) - if (!is.matrix(testX) && !is.data.frame(testX)) - testX <- as.matrix(testX) - model_pred <- gmodel$get(dataHO, targetHO, p) - prediction <- model_pred(testX) - error <- error + loss(prediction, testY) - } + d <- splitTrainTest(data, target, testIdx) + model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p) + prediction <- model_pred(d$dataTest) + loss(prediction, d$targetTest) + }) ) if (error <= best_error) { - newModel <- list(model=model_pred, param=gmodel$getParam(p)) + newModel <- list(model=gmodel$get(data, target, p), + param=gmodel$getParam(p)) if (error == best_error) best_model[[length(best_model)+1]] <- newModel else { @@ -89,24 +28,30 @@ standardCV_core <- function(data, target, task = NULL, gmodel = NULL, params = N } } } +#browser() best_model[[ sample(length(best_model), 1) ]] } standardCV_run <- function( - dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ... + dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ... ) { - s <- standardCV_core(dataTrain, targetTrain, ...) + args <- list(...) + task <- checkTask(args$task, targetTrain) + modPar <- checkModPar(args$gmodel, args$params) + loss <- checkLoss(args$loss, task) + s <- standardCV_core( + dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV) if (verbose) print(paste( "Parameter:", s$param )) - ps <- s$model(test) - err_s <- floss(ps, targetTest) + p <- s$model(dataTest) + err <- floss(p, targetTest) if (verbose) - print(paste("error CV:", err_s)) - invisible(c(errors, err_s)) + print(paste("error CV:", err)) + invisible(err) } agghoo_run <- function( - dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ... + dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ... ) { a <- agghoo(dataTrain, targetTrain, ...) a$fit(CV) @@ -118,27 +63,22 @@ agghoo_run <- function( err <- floss(pa, targetTest) if (verbose) print(paste("error agghoo:", err)) + invisible(err) } -# ... arguments passed to agghoo or any other procedure +# ... arguments passed to method_s (agghoo, standard CV or else) compareTo <- function( - data, target, rseed=-1, verbose=TRUE, floss=NULL, - CV = list(type = "MC", - V = 10, - test_size = 0.2, - shuffle = TRUE), - method_s=NULL, ... + data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ... ) { if (rseed >= 0) set.seed(rseed) n <- nrow(data) test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) ) - trainData <- as.matrix(data[-test_indices,]) - trainTarget <- target[-test_indices] - testData <- as.matrix(data[test_indices,]) - testTarget <- target[test_indices] + d <- splitTrainTest(data, target, test_indices) + CV <- checkCV(list(...)$CV) # Set error function to be used on model outputs (not in core method) + task <- checkTask(list(...)$task, target) if (is.null(floss)) { floss <- function(y1, y2) { ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2))) @@ -147,34 +87,33 @@ compareTo <- function( # Run (and compare) all methods: runOne <- function(o) { - o(dataTrain, dataTest, targetTrain, targetTest, verbose, CV, floss, ...) + o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, + CV, floss, verbose, ...) } + errors <- c() if (is.list(method_s)) errors <- sapply(method_s, runOne) else if (is.function(method_s)) errors <- runOne(method_s) - else - errors <- c() invisible(errors) } # Run compareTo N times in parallel +# ... : additional args to be passed to method_s compareMulti <- function( - data, target, N = 100, nc = NA, - CV = list(type = "MC", - V = 10, - test_size = 0.2, - shuffle = TRUE), - method_s=NULL, ... + data, target, method_s, N=100, nc=NA, floss=NULL, ... ) { + require(parallel) if (is.na(nc)) nc <- parallel::detectCores() + + # "One" comparison for each method in method_s (list) compareOne <- function(n) { print(n) - compareTo(data, target, n, verbose=FALSE, CV, method_s, ...) + compareTo(data, target, method_s, n, floss, verbose=FALSE, ...) } + errors <- if (nc >= 2) { - require(parallel) parallel::mclapply(1:N, compareOne, mc.cores = nc) } else { lapply(1:N, compareOne) @@ -182,5 +121,3 @@ compareMulti <- function( print("Errors:") Reduce('+', errors) / N } - -# TODO: unfinished ! diff --git a/R/utils.R b/R/utils.R new file mode 100644 index 0000000..fa3a9df --- /dev/null +++ b/R/utils.R @@ -0,0 +1,28 @@ +get_testIndices <- function(n, CV, v, shuffle_inds) { + if (CV$type == "vfold") { + # Slice indices (optionnally shuffled) + first_index = round((v-1) * n / CV$V) + 1 + last_index = round(v * n / CV$V) + test_indices = first_index:last_index + if (!is.null(shuffle_inds)) + test_indices <- shuffle_inds[test_indices] + } + else + # Monte-Carlo cross-validation + test_indices = sample(n, round(n * CV$test_size)) + test_indices +} + +splitTrainTest <- function(data, target, testIdx) { + dataTrain <- data[-testIdx,] + targetTrain <- target[-testIdx] + dataTest <- data[testIdx,] + targetTest <- target[testIdx] + # [HACK] R will cast 1-dim matrices into vectors: + if (!is.matrix(dataTrain) && !is.data.frame(dataTrain)) + dataTrain <- as.matrix(dataTrain) + if (!is.matrix(dataTest) && !is.data.frame(dataTest)) + dataTest <- as.matrix(dataTest) + list(dataTrain=dataTrain, targetTrain=targetTrain, + dataTest=dataTest, targetTest=targetTest) +} diff --git a/man/AgghooCV.Rd b/man/AgghooCV.Rd index 75ce9db..5122236 100644 --- a/man/AgghooCV.Rd +++ b/man/AgghooCV.Rd @@ -23,7 +23,7 @@ from the list of models (see 'Model' class). \subsection{Method \code{new()}}{ Create a new AgghooCV object. \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss = NULL)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{AgghooCV$new(data, target, task, gmodel, loss)}\if{html}{\out{
}} } \subsection{Arguments}{ @@ -48,19 +48,20 @@ Create a new AgghooCV object. \subsection{Method \code{fit()}}{ Fit an agghoo model. \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{AgghooCV$fit(CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE))}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{AgghooCV$fit(CV = NULL)}\if{html}{\out{
}} } \subsection{Arguments}{ \if{html}{\out{
}} \describe{ \item{\code{CV}}{List describing cross-validation to run. Slots: \cr -- type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr -- V: number of runs (default: 10) \cr -- test_size: percentage of data in the test dataset, for MC \cr - (irrelevant for V-fold). Default: 0.2. -- shuffle: wether or not to shuffle data before V-fold. - Irrelevant for Monte-Carlo; default: TRUE} + - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr + - V: number of runs (default: 10) \cr + - test_size: percentage of data in the test dataset, for MC + (irrelevant for V-fold). Default: 0.2. \cr + - shuffle: wether or not to shuffle data before V-fold. + Irrelevant for Monte-Carlo; default: TRUE \cr +Default (if NULL): type="MC", V=10, test_size=0.2} } \if{html}{\out{
}} } diff --git a/man/agghoo.Rd b/man/agghoo.Rd index 179d309..38730eb 100644 --- a/man/agghoo.Rd +++ b/man/agghoo.Rd @@ -33,7 +33,7 @@ loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.} An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData) } \description{ -Run the agghoo procedure (or standard cross-validation). +Run the (core) agghoo procedure. Arguments specify the list of models, their parameters and the cross-validation settings, among others. } @@ -52,3 +52,6 @@ pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1)) Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. "Aggregated hold-out". Journal of Machine Learning Research 22(20):1--55, 2021. } +\seealso{ +Function \code{\link{compareTo}} +}