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Lrnr_glm_fast.R
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Lrnr_glm_fast.R
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#' Computationally Efficient Generalized Linear Model (GLM) Fitting
#'
#' This learner provides faster procedures for fitting linear and generalized
#' linear models than \code{\link{Lrnr_glm}} with a minimal memory footprint.
#' This learner uses the internal fitting function provided by \pkg{speedglm}
#' package, \code{\link[speedglm]{speedglm.wfit}}. See
#' \insertCite{speedglm;textual}{sl3} for more detail. The
#' \code{\link[stats]{glm.fit}} function is used as a fallback, if
#' \code{\link[speedglm]{speedglm.wfit}} fails.
#'
#' @docType class
#'
#' @importFrom R6 R6Class
#' @importFrom stats glm predict family
#'
#' @export
#'
#' @keywords data
#'
#' @return A learner object inheriting from \code{\link{Lrnr_base}} with
#' methods for training and prediction. For a full list of learner
#' functionality, see the complete documentation of \code{\link{Lrnr_base}}.
#'
#' @format An \code{\link[R6]{R6Class}} object inheriting from
#' \code{\link{Lrnr_base}}.
#'
#' @family Learners
#'
#' @section Parameters:
#' - \code{intercept = TRUE}: Should an intercept be included in the model?
#' - \code{method = "Cholesky"}: The method to check for singularity.
#' - \code{...}: Other parameters to be passed to
#' \code{\link[speedglm]{speedglm.wfit}}.
#'
#' @references
#' \insertAllCited{}
#'
#' @examples
#' data(cpp_imputed)
#' covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
#' task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
#'
#' # simple, main-terms GLM
#' lrnr_glm_fast <- Lrnr_glm_fast$new(method = "eigen")
#' glm_fast_fit <- lrnr_glm_fast$train(task)
#' glm_fast_preds <- glm_fast_fit$predict()
Lrnr_glm_fast <- R6Class(
classname = "Lrnr_glm_fast",
inherit = Lrnr_base, portable = TRUE, class = TRUE,
public = list(
initialize = function(intercept = TRUE, method = "Cholesky", ...) {
super$initialize(params = args_to_list(), ...)
}
),
private = list(
.default_params = list(method = "Cholesky"),
.properties = c("continuous", "binomial", "weights", "offset"),
.train = function(task) {
verbose <- getOption("sl3.verbose")
args <- self$params
outcome_type <- self$get_outcome_type(task)
args$y <- outcome_type$format(task$Y)
if (is.null(args$family)) {
args$family <- outcome_type$glm_family(return_object = TRUE)
}
family_name <- args$family$family
linkinv_fun <- args$family$linkinv
link_fun <- args$family$linkfun
# specify data
if (args$intercept) {
args$X <- as.matrix(task$X_intercept)
} else {
args$X <- as.matrix(task$X)
}
if (task$has_node("weights")) {
args$weights <- task$weights
}
if (task$has_node("offset")) {
args$offset <- task$offset_transformed(link_fun)
}
SuppressGivenWarnings(
{
fit_object <- try(
call_with_args(speedglm::speedglm.wfit, args),
silent = TRUE
)
},
GetWarningsToSuppress()
)
if (inherits(fit_object, "try-error")) {
# if failed, fall back on stats::glm
if (verbose) {
message(paste(
"speedglm::speedglm.wfit failed, falling back on",
"stats::glm.fit;", fit_object
))
}
args$ctrl <- glm.control(trace = FALSE)
args$x <- args$X
SuppressGivenWarnings(
{
fit_object <- call_with_args(stats::glm.fit, args)
},
GetWarningsToSuppress()
)
fit_object$linear.predictors <- NULL
fit_object$weights <- NULL
fit_object$prior.weights <- NULL
fit_object$y <- NULL
fit_object$residuals <- NULL
fit_object$fitted.values <- NULL
fit_object$effects <- NULL
fit_object$qr <- NULL
}
fit_object$linkinv_fun <- linkinv_fun
fit_object$link_fun <- link_fun
fit_object$training_offset <- task$has_node("offset")
return(fit_object)
},
.predict = function(task = NULL) {
verbose <- getOption("sl3.verbose")
if (self$params$intercept) {
X <- task$X_intercept
} else {
X <- task$X
}
predictions <- rep.int(NA, nrow(X))
if (nrow(X) > 0) {
coef <- self$fit_object$coef
if (!all(is.na(coef))) {
eta <- as.matrix(X
[, which(!is.na(coef)),
drop = FALSE,
with = FALSE
]) %*% coef[!is.na(coef)]
if (self$fit_object$training_offset) {
offset <- task$offset_transformed(self$fit_object$link_fun, for_prediction = TRUE)
eta <- eta + offset
}
predictions <- as.vector(self$fit_object$linkinv_fun(eta))
}
}
return(predictions)
},
.required_packages = c("speedglm")
)
)