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parallelisation + pictures
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@HeleneBlt
HeleneBlt committed Feb 3, 2025
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157 changes: 101 additions & 56 deletions R/MS_EnsembleForecasting.R
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Original file line number Diff line number Diff line change
Expand Up @@ -9,15 +9,17 @@
##' represent new areas, resolution or time scales for example}).
##'
##'
##' @param ms.mod a \code{\link{BIOMOD.models.out}} object returned by the
##' @param ms.em a \code{\link{BIOMOD.models.out}} object returned by the
##' \code{\link{BIOMOD_Modeling}} function
##' @param ms.proj a \code{\link{MS.projection.out}} object returned by the
##' \code{\link{MS_Projection}} function
##' @param proj.name a \code{character} corresponding to the name (ID) of the projection set
##' (\emph{a new folder will be created within the simulation folder with this name})
##' @param new.env A \code{matrix}, \code{data.frame} or
##' \code{\link[terra:rast]{SpatRaster}} object containing the new
##' explanatory variables (in columns or layers, with names matching the
##' variables names given to the \code{\link{BIOMOD_FormatingData}} function to build
##' \code{ms.mod}) that will be used to project the species distribution model(s)
##' \code{ms.em}) that will be used to project the species distribution model(s)
##' \cr \emph{Note that old format from \pkg{raster} are still supported such as
##' \code{RasterStack} objects. }
##'
Expand All @@ -33,14 +35,14 @@
##' A \code{vector} containing evaluation metric names to be used to transform prediction values
##' into binary values based on models evaluation scores obtained with the
##' \code{\link{BIOMOD_Modeling}} function. Must be among \code{all} (same evaluation metrics than
##' those of \code{ms.mod}) or \code{POD}, \code{FAR}, \code{POFD}, \code{SR}, \code{ACCURACY},
##' those of \code{ms.em}) or \code{POD}, \code{FAR}, \code{POFD}, \code{SR}, \code{ACCURACY},
##' \code{BIAS}, \code{ROC}, \code{TSS}, \code{KAPPA}, \code{OR}, \code{ORSS}, \code{CSI},
##' \code{ETS}, \code{BOYCE}, \code{MPA}
##' @param metric.filter (\emph{optional, default} \code{NULL}) \cr
##' A \code{vector} containing evaluation metric names to be used to transform prediction values
##' into filtered values based on models evaluation scores obtained with the
##' \code{\link{BIOMOD_Modeling}} function. Must be among \code{all} (same evaluation metrics than
##' those of \code{ms.mod}) or \code{POD}, \code{FAR}, \code{POFD}, \code{SR}, \code{ACCURACY},
##' those of \code{ms.em}) or \code{POD}, \code{FAR}, \code{POFD}, \code{SR}, \code{ACCURACY},
##' \code{BIAS}, \code{ROC}, \code{TSS}, \code{KAPPA}, \code{OR}, \code{ORSS}, \code{CSI},
##' \code{ETS}, \code{BOYCE}, \code{MPA}
##'
Expand Down Expand Up @@ -131,60 +133,79 @@
##'
###################################################################################################

MS_EnsembleForecasting <- function(ms.mod,
proj.name,
new.env,
new.env.xy = NULL,
models.chosen = 'all',
metric.binary = NULL,
metric.filter = NULL,
compress = TRUE,
digits = 0,
build.clamping.mask = TRUE,
nb.cpu = 1,
seed.val = NULL,
...) {
MS_EnsembleForecasting <- function(ms.em,
ms.proj = NULL,
proj.name,
new.env,
new.env.xy = NULL,
models.chosen = 'all',
metric.binary = NULL,
metric.filter = NULL,
compress = TRUE,
digits = 0,
build.clamping.mask = TRUE,
nb.cpu = 1,
seed.val = NULL,
...) {
.bm_cat("Do Ensemble Models Projection")

## 0. Check arguments ---------------------------------------------------------------------------
args <- .MS_EnsembleForecasting.check.args(ms.mod, proj.name, new.env, new.env.xy
args <- .MS_EnsembleForecasting.check.args(ms.em, ms.proj, proj.name, new.env, new.env.xy
, models.chosen, metric.binary, metric.filter, compress, seed.val, ...)
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)


if (nb.cpu > 1) {
if (.getOS() != "windows") {
if (!isNamespaceLoaded("doParallel")) {
if(!requireNamespace('doParallel', quietly = TRUE)) stop("Package 'doParallel' not found")
}
doParallel::registerDoParallel(cores = nb.cpu)
} else {
warning("Parallelisation with `foreach` is not available for Windows. Sorry.")
}
}

## 1. Create output object ----------------------------------------------------------------------
proj_out <- new('MS.projection.out',
proj.name = proj.name,
dir.name = ms.mod@dir.name,
sp.name = ms.mod@sp.name,
expl.var.names = ms.mod@expl.var.names,
dir.name = ms.em@dir.name,
sp.name = ms.em@sp.name,
expl.var.names = ms.em@expl.var.names,
models.projected = models.chosen,
coord = new.env.xy,
data.type = ms.mod@data.type,
modeling.id = ms.mod@modeling.id,
data.type = ms.em@data.type,
modeling.id = ms.em@modeling.id,
type = "em")


cat("\n")
workflow <- foreach(sp = ms.mod@sp.name) %do% {
workflow <- foreach(sp = ms.em@sp.name) %dopar% {
cat("\n\t Projection of", sp)
# 1. Récupération ms.mod
bm.em <- get(load(file.path(ms.mod@dir.name, sp, paste0(sp, ".", ms.mod@modeling.id,".ensemble.models.out"))))
# 1. Récupération ms.em

bm.em <- get(load(file.path(ms.em@dir.name, sp, paste0(sp, ".", ms.em@modeling.id,".ensemble.models.out"))))
if (!is.null(ms.proj)){
bm.proj = get(load(file.path(ms.proj@dir.name, sp, paste0("proj_", ms.proj@proj.name), paste0(sp, ".", ms.proj@proj.name,".projection.out"))))
} else {
bm.proj = NULL
}

models.chosen.sp <- models.chosen[[sp]]
# 2. Run MS_EnsembleModeling
output <- capture.output(proj_sp <- BIOMOD_EnsembleForecasting(bm.em,
proj.name = proj.name,
new.env = new.env,
new.env.xy = new.env.xy,
models.chosen = models.chosen.sp,
metric.binary = metric.binary,
metric.filter = metric.filter,
compress = TRUE,
build.clamping.mask = TRUE,
nb.cpu = 1,
seed.val = NULL))
bm.proj = bm.proj,
proj.name = proj.name,
new.env = new.env,
new.env.xy = new.env.xy,
models.chosen = models.chosen.sp,
metric.binary = metric.binary,
metric.filter = metric.filter,
compress = TRUE,
build.clamping.mask = TRUE,
nb.cpu = 1,
seed.val = NULL))
models.chosen[[sp]] <- proj_sp@models.projected

}
Expand All @@ -196,24 +217,37 @@ MS_EnsembleForecasting <- function(ms.mod,

# .MS_EnsembleForecasting.check.args---------------------------------------------

.MS_EnsembleForecasting.check.args <- function(ms.mod, proj.name, new.env, new.env.xy,
.MS_EnsembleForecasting.check.args <- function(ms.em, ms.proj, proj.name, new.env, new.env.xy,
models.chosen, metric.binary, metric.filter, compress, seed.val, ...)
{
args <- list(...)

## 1. Check ms.mod ----------------------------------------------------------
.fun_testIfInherits(TRUE, "ms.mod", ms.mod, "MS.ensemble.models.out")
## 1. Check ms.em ----------------------------------------------------------
.fun_testIfInherits(TRUE, "ms.em", ms.em, "MS.ensemble.models.out")

## 2. Check proj.name -------------------------------------------------------
if (is.null(proj.name)) {
stop("\nYou must define a name for Projection Outputs")
}
if (!length(proj.name) && !length(ms.proj)) {
stop("You have to give a valid 'proj.name' if you don't work with ms.proj")
} else if (!length(proj.name)) {
proj.name <- ms.proj@proj.name
}


## 3. Check needed data and predictions -------------------------------------
if ((is.null(ms.proj) && is.null(new.env)) ||
(!is.null(ms.proj) & !is.null(new.env))) {
stop("You have to refer at one of 'ms.proj' or 'new.env' argument")
}

if (!is.null(ms.proj)) {
.fun_testIfInherits(TRUE, "ms.proj", ms.proj, "MS.projection.out")
}

## 3. Check new.env ---------------------------------------------------------
## 3.bis Check new.env ---------------------------------------------------------
.fun_testIfInherits(TRUE, "new.env", new.env, c('matrix', 'data.frame', 'SpatRaster','Raster'))

if (inherits(new.env, 'matrix')) {
if (any(sapply(get_formal_data(ms.mod, sp = ms.mod@sp.name[1], "expl.var"), is.factor))) {
if (any(sapply(get_formal_data(ms.em, sp = ms.em@sp.name[1], "expl.var"), is.factor))) {
stop("new.env cannot be given as matrix when model involves categorical variables")
}
new.env <- data.frame(new.env)
Expand All @@ -226,27 +260,37 @@ MS_EnsembleForecasting <- function(ms.mod,
# conversion into SpatRaster
if (any(raster::is.factor(new.env))) {
new.env <- .categorical_stack_to_terra(raster::stack(new.env),
expected_levels = head(get_formal_data(ms.mod, sp = ms.mod@sp.name[1], subinfo = "expl.var"))
expected_levels = head(get_formal_data(ms.em, sp = ms.em@sp.name[1], subinfo = "expl.var"))
)
} else {
new.env <- rast(new.env)
}
}

if (inherits(new.env, 'SpatRaster')) {
.fun_testIfIn(TRUE, "names(new.env)", names(new.env), ms.mod@expl.var.names, exact = TRUE)
new.env <- new.env[[ms.mod@expl.var.names]]
.fun_testIfIn(TRUE, "names(new.env)", names(new.env), ms.em@expl.var.names, exact = TRUE)
new.env <- new.env[[ms.em@expl.var.names]]
new.env.mask <- .get_data_mask(new.env, value.out = 1)
new.env <- mask(new.env, new.env.mask)
} else {
.fun_testIfIn(TRUE, "colnames(new.env)", colnames(new.env), ms.mod@expl.var.names, exact = TRUE)
new.env <- new.env[ , ms.mod@expl.var.names, drop = FALSE]
.fun_testIfIn(TRUE, "colnames(new.env)", colnames(new.env), ms.em@expl.var.names, exact = TRUE)
new.env <- new.env[ , ms.em@expl.var.names, drop = FALSE]
}

which.factor <- which(sapply(new.env, is.factor))
if (length(which.factor) > 0) {
new.env <- .check_env_levels(new.env,
expected_levels = head(get_formal_data(ms.mod, sp = ms.mod@sp.name[1], subinfo = "expl.var")))
expected_levels = head(get_formal_data(ms.em, sp = ms.em@sp.name[1], subinfo = "expl.var")))
}

## 4. Check models.chosen ---------------------------------------------------
if (models.chosen[1] == 'all') {
models.chosen <- get_built_models(ms.em)
} else {
models.chosen <- intersect(models.chosen, get_built_models(ms.em))
}
if (length(models.chosen) < 1) {
stop('No models selected')
}

## 4. Check new.env.xy ------------------------------------------------------
Expand All @@ -258,10 +302,11 @@ MS_EnsembleForecasting <- function(ms.mod,
} else {
new.env.xy = data.frame()
}

## 5. Check models.chosen ---------------------------------------------------
if ( is.null(models.chosen) | (length(models.chosen) == 1 && models.chosen[1] == 'all')) {
models.chosen <- as.list(rep("all", length(ms.mod@sp.name)))
names(models.chosen) <- ms.mod@sp.name
models.chosen <- as.list(rep("all", length(ms.em@sp.name)))
names(models.chosen) <- ms.em@sp.name
} else {
.fun_testIfInherits(TRUE, "models.chosen", models.chosen, "list")
}
Expand All @@ -272,12 +317,12 @@ MS_EnsembleForecasting <- function(ms.mod,

## 6. Check metric.binary & metric.filter -----------------------------------
if (!is.null(metric.binary) | !is.null(metric.filter)) {
if ( ms.mod@data.type != "binary"){
cat ("No metric.binary or metric.filter are needed with",ms.mod@data.type, "data")
if ( ms.em@data.type != "binary"){
cat ("No metric.binary or metric.filter are needed with",ms.em@data.type, "data")
metric.binary <- NULL
metric.filter <- NULL
} else {
models.evaluation <- get_evaluations(ms.mod, sp = ms.mod@sp.name[1])
models.evaluation <- get_evaluations(ms.em, sp = ms.em@sp.name[1])
if (is.null(models.evaluation)) {
warning("Binary and/or Filtered transformations of projection not ran because of models evaluation information missing")
} else {
Expand Down Expand Up @@ -347,7 +392,7 @@ MS_EnsembleForecasting <- function(ms.mod,
## 10.on_0_1000 --------------------------------

on_0_1000 <- ifelse(is.null(args$on_0_1000), TRUE, args$on_0_1000)
if (ms.mod@data.type %in% c("count","abundance","ordinal")) {on_0_1000 <- FALSE}
if (ms.em@data.type %in% c("count","abundance","ordinal")) {on_0_1000 <- FALSE}

## 11.Check overwrite
overwrite <- ifelse(is.null(args$overwrite), ifelse(do.stack, TRUE, FALSE), args$overwrite)
Expand Down
16 changes: 13 additions & 3 deletions R/MS_EnsembleModeling.R
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Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,7 @@
##' \code{\link{bm_PlotVarImpBoxplot}}, \code{\link{bm_PlotResponseCurves}}
##' @family Main functions
##'
##' @importFrom foreach foreach %do%
##' @importFrom foreach foreach %do% %dopar%
##' @importFrom biomod2 BIOMOD_EnsembleModeling
##'
##' @export
Expand Down Expand Up @@ -119,7 +119,17 @@ MS_EnsembleModeling <- function(ms.mod,
)
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)


if (nb.cpu > 1) {
if (.getOS() != "windows") {
if (!isNamespaceLoaded("doParallel")) {
if(!requireNamespace('doParallel', quietly = TRUE)) stop("Package 'doParallel' not found")
}
doParallel::registerDoParallel(cores = nb.cpu)
} else {
warning("Parallelisation with `foreach` is not available for Windows. Sorry.")
}
}

MSEM<- new(
'MS.ensemble.models.out',
Expand All @@ -135,7 +145,7 @@ MS_EnsembleModeling <- function(ms.mod,
)

cat("\n")
workflow <- foreach(sp = ms.mod@sp.name) %do% {
workflow <- foreach(sp = ms.mod@sp.name) %dopar% {
cat("\n\t Ensemble Modeling of", sp)
# 1. Récupération ms.mod
bm.mod <- get(load(file.path(ms.mod@dir.name, sp, paste0(sp, ".", ms.mod@modeling.id,".models.out"))))
Expand Down
4 changes: 1 addition & 3 deletions R/MS_FormatingData.R
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Original file line number Diff line number Diff line change
Expand Up @@ -118,9 +118,7 @@ MS_FormatingData <- function(ms.project.name,
nameFolder <- file.path(dir.name, ms.project.name, ".BIOMOD_DATA", "single.formated.data")

## 3.Create MS.formated.data with the data frame
# Est ce que je fais un truc dataframe -> direct MS ou
# est ce que je complete
new.formated.data <- foreach(sp = resp.name) %do% {
new.formated.data <- foreach(sp = resp.name) %do% { ## No parallelisation as it is really quick but can be add if necessary ?

parameters <- params.PA[[sp]]

Expand Down
15 changes: 13 additions & 2 deletions R/MS_Modeling.R
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@
##'
##' A \code{\link{MS.models.out}} object acting as a proxi for the created \code{BIOMOD.models.out}.
##'
##' @importFrom foreach foreach %do%
##' @importFrom foreach foreach %do% %dopar%
##' @importFrom biomod2 BIOMOD_Modeling
##'
##' @export
Expand Down Expand Up @@ -89,6 +89,17 @@ MS_Modeling <- function(ms.format,
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)

if (nb.cpu > 1) {
if (.getOS() != "windows") {
if (!isNamespaceLoaded("doParallel")) {
if(!requireNamespace('doParallel', quietly = TRUE)) stop("Package 'doParallel' not found")
}
doParallel::registerDoParallel(cores = nb.cpu)
} else {
warning("Parallelisation with `foreach` is not available for Windows. Sorry.")
}
}

MSmodels<- new(
'MS.models.out',
ms.project = ms.format@ms.project,
Expand All @@ -106,7 +117,7 @@ MS_Modeling <- function(ms.format,

dir.name <- file.path(ms.format@dir.name, ms.format@ms.project)

workflow <- foreach(sp = ms.format@sp.name) %do% {
workflow <- foreach(sp = ms.format@sp.name) %dopar% {
cat("\n\t Modeling of", sp)
# 1. Récupération ms.format
sfd <- get(load(file.path(nameFolder, paste0(sp,".sfd"))))
Expand Down
13 changes: 12 additions & 1 deletion R/MS_Projection.R
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Original file line number Diff line number Diff line change
Expand Up @@ -152,6 +152,17 @@ MS_Projection <- function(ms.mod,
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)

if (nb.cpu > 1) {
if (.getOS() != "windows") {
if (!isNamespaceLoaded("doParallel")) {
if(!requireNamespace('doParallel', quietly = TRUE)) stop("Package 'doParallel' not found")
}
doParallel::registerDoParallel(cores = nb.cpu)
} else {
warning("Parallelisation with `foreach` is not available for Windows. Sorry.")
}
}


## 1. Create output object ----------------------------------------------------------------------
proj_out <- new('MS.projection.out',
Expand All @@ -168,7 +179,7 @@ MS_Projection <- function(ms.mod,


cat("\n")
workflow <- foreach(sp = ms.mod@sp.name) %do% {
workflow <- foreach(sp = ms.mod@sp.name) %dopar% {
cat("\n\t Projection of", sp)
# 1. Récupération ms.mod
bm.mod <- get(load(file.path(ms.mod@dir.name, sp, paste0(sp, ".", ms.mod@modeling.id,".models.out"))))
Expand Down
4 changes: 4 additions & 0 deletions R/biomod3_class1_Fd.R
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Original file line number Diff line number Diff line change
Expand Up @@ -464,6 +464,10 @@ setMethod('show', signature('MS.formated.data'),
)


##'
##' @importMethodsFrom biomod2 summary
##'

setMethod('summary', signature(object = 'MS.formated.data'),
function(object, calib.lines = NULL) {
nameFolder <- file.path(object@dir.name, object@ms.project, ".BIOMOD_DATA", "single.formated.data")
Expand Down
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