Nothing
#' @title GLM with Elastic Net Regularization Regression Learner
#'
#' @name mlr_learners_regr.cv_glmnet
#'
#' @description
#' Generalized linear models with elastic net regularization.
#' Calls [glmnet::cv.glmnet()] from package \CRANpkg{glmnet}.
#'
#' The default for hyperparameter `family` is set to `"gaussian"`.
#'
#' @templateVar id regr.cv_glmnet
#' @template learner
#'
#' @references
#' `r format_bib("friedman_2010")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerRegrCVGlmnet = R6Class("LearnerRegrCVGlmnet",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
alignment = p_fct(c("lambda", "fraction"), default = "lambda", tags = "train"),
alpha = p_dbl(0, 1, default = 1, tags = "train"),
big = p_dbl(default = 9.9e35, tags = "train"),
devmax = p_dbl(0, 1, default = 0.999, tags = "train"),
dfmax = p_int(0L, tags = "train"),
eps = p_dbl(0, 1, default = 1.0e-6, tags = "train"),
epsnr = p_dbl(0, 1, default = 1.0e-8, tags = "train"),
exclude = p_int(1L, tags = "train"),
exmx = p_dbl(default = 250.0, tags = "train"),
family = p_fct(c("gaussian", "poisson"), default = "gaussian", tags = "train"),
fdev = p_dbl(0, 1, default = 1.0e-5, tags = "train"),
foldid = p_uty(default = NULL, tags = "train"),
gamma = p_uty(tags = "train"),
grouped = p_lgl(default = TRUE, tags = "train"),
intercept = p_lgl(default = TRUE, tags = "train"),
keep = p_lgl(default = FALSE, tags = "train"),
lambda = p_uty(tags = "train"),
lambda.min.ratio = p_dbl(0, 1, tags = "train"),
lower.limits = p_uty(tags = "train"),
maxit = p_int(1L, default = 100000L, tags = "train"),
mnlam = p_int(1L, default = 5L, tags = "train"),
mxit = p_int(1L, default = 100L, tags = "train"),
mxitnr = p_int(1L, default = 25L, tags = "train"),
nfolds = p_int(3L, default = 10L, tags = "train"),
nlambda = p_int(1L, default = 100L, tags = "train"),
offset = p_uty(default = NULL, tags = "train"),
parallel = p_lgl(default = FALSE, tags = "train"),
penalty.factor = p_uty(tags = "train"),
pmax = p_int(0L, tags = "train"),
pmin = p_dbl(0, 1, default = 1.0e-9, tags = "train"),
prec = p_dbl(default = 1e-10, tags = "train"),
predict.gamma = p_dbl(default = "gamma.1se", special_vals = list("gamma.1se", "gamma.min"), tags = "predict"),
relax = p_lgl(default = FALSE, tags = "train"),
s = p_dbl(0, special_vals = list("lambda.1se", "lambda.min"), default = "lambda.1se", tags = "predict"),
standardize = p_lgl(default = TRUE, tags = "train"),
standardize.response = p_lgl(default = FALSE, tags = "train"),
thresh = p_dbl(0, default = 1e-07, tags = "train"),
trace.it = p_int(0, 1, default = 0, tags = "train"),
type.gaussian = p_fct(c("covariance", "naive"), tags = "train"),
type.logistic = p_fct(c("Newton", "modified.Newton"), tags = "train"),
type.measure = p_fct(c("deviance", "class", "auc", "mse", "mae"), default = "deviance", tags = "train"),
type.multinomial = p_fct(c("ungrouped", "grouped"), tags = "train"),
upper.limits = p_uty(tags = "train")
)
ps$add_dep("gamma", "relax", CondEqual$new(TRUE))
ps$add_dep("type.gaussian", "family", CondEqual$new("gaussian"))
ps$values = list(family = "gaussian")
super$initialize(
id = "regr.cv_glmnet",
param_set = ps,
feature_types = c("logical", "integer", "numeric"),
properties = c("weights", "selected_features"),
packages = c("mlr3learners", "glmnet"),
label = "GLM with Elastic Net Regularization",
man = "mlr3learners::mlr_learners_regr.cv_glmnet"
)
},
#' @description
#' Returns the set of selected features as reported by [glmnet::predict.glmnet()]
#' with `type` set to `"nonzero"`.
#'
#' @param lambda (`numeric(1)`)\cr
#' Custom `lambda`, defaults to the active lambda depending on parameter set.
#'
#' @return (`character()`) of feature names.
selected_features = function(lambda = NULL) {
glmnet_selected_features(self, lambda)
}
),
private = list(
.train = function(task) {
data = as_numeric_matrix(task$data(cols = task$feature_names))
target = as_numeric_matrix(task$data(cols = task$target_names))
pv = self$param_set$get_values(tags = "train")
if ("weights" %in% task$properties) {
pv$weights = task$weights$weight
}
glmnet_invoke(data, target, pv, cv = TRUE)
},
.predict = function(task) {
newdata = as_numeric_matrix(ordered_features(task, self))
pv = self$param_set$get_values(tags = "predict")
pv = rename(pv, "predict.gamma", "gamma")
response = invoke(predict, self$model, newx = newdata,
type = "response", .args = pv)
list(response = drop(response))
}
)
)
#' @include aaa.R
learners[["regr.cv_glmnet"]] = LearnerRegrCVGlmnet
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.