#' @title Classification Support Vector Machine Learner
#' @author damirpolat
#' @name mlr_learners_classif.smo
#'
#' @description
#' Support Vector classifier trained with John Platt's sequential minimal optimization algorithm.
#' Calls [RWeka::SMO()] from \CRANpkg{RWeka}.
#'
#' @section Custom mlr3 parameters:
#' - `output_debug_info`:
#' - original id: output-debug-info
#'
#' - `do_not_check_capabilities`:
#' - original id: do-not-check-capabilities
#'
#' - `num_decimal_places`:
#' - original id: num-decimal-places
#'
#' - `batch_size`:
#' - original id: batch-size
#'
#' - `E_poly`:
#' - original id: E
#'
#' - `L_poly`:
#' - original id: L
#'
#' - `C_poly`:
#' - original id: C
#'
#' - `C_logistic`:
#' - original id: C
#'
#' - `R_logistic`:
#' - original id: R
#'
#' - `M_logistic`:
#' - original id: M
#'
#' - Reason for change: This learner contains changed ids of the following control arguments
#' since their ids contain irregular pattern
#'
#'
#' @templateVar id classif.smo
#' @template learner
#'
#' @references
#' `r format_bib("Platt1998", "Keerthi2001", "Hastie1998")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerClassifSMO = R6Class("LearnerClassifSMO",
inherit = LearnerClassif,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
param_set = ps(
subset = p_uty(tags = "train"),
na.action = p_uty(tags = "train"),
no_checks = p_lgl(default = FALSE, tags = "train"),
C = p_dbl(default = 1L, tags = "train"),
N = p_fct(default = "0", levels = c("0", "1", "2"), tags = "train"),
L = p_dbl(default = 1.0e-3, tags = "train"),
P = p_dbl(default = 1.0e-12, tags = "train"),
M = p_lgl(default = FALSE, tags = "train"),
V = p_int(default = -1L, tags = "train"),
W = p_int(default = 1L, tags = "train"),
K = p_fct(default = "PolyKernel", c("NormalizedPolyKernel", "PolyKernel", "Puk", "RBFKernel", "StringKernel"),
tags = "train"),
calibrator = p_uty(default = "weka.classifiers.functions.Logistic", tags = "train"),
E_poly = p_dbl(default = 1L, depends = (K == "PolyKernel"), tags = "train"),
L_poly = p_lgl(default = FALSE, depends = (K == "PolyKernel"), tags = "train"),
C_poly = p_int(default = 25007, depends = (K == "PolyKernel"), tags = "train"),
C_logistic = p_lgl(default = FALSE, depends = (calibrator == "weka.classifiers.functions.Logistic"),
tags = "train"),
R_logistic = p_dbl(depends = (calibrator == "weka.classifiers.functions.Logistic"), tags = "train"),
M_logistic = p_int(default = -1L, depends = (calibrator == "weka.classifiers.functions.Logistic"),
tags = "train"),
output_debug_info = p_lgl(default = FALSE, tags = "train"),
do_not_check_capabilities = p_lgl(default = FALSE,
tags = "train"),
num_decimal_places = p_int(default = 2L, lower = 1L,
tags = "train"),
batch_size = p_int(default = 100L, lower = 1L, tags = "train"),
options = p_uty(default = NULL, tags = "train")
)
super$initialize(
id = "classif.smo",
packages = "RWeka",
feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
predict_types = c("response", "prob"),
param_set = param_set,
properties = c("missings", "multiclass", "twoclass"),
man = "mlr3extralearners::mlr_learners_classif.smo",
label = "Support Vector Machine"
)
}
),
private = list(
.train = function(task) {
weka_learner = RWeka::SMO
pars = self$param_set$get_values(tags = "train")
nested_pars = list(K = "_poly", calibrator = "_logistic")
rweka_train(task$data(), task$formula(), pars, weka_learner, nested_pars)
},
.predict = function(task) {
pars = self$param_set$get_values(tags = "predict")
newdata = ordered_features(task, self)
rweka_predict(newdata, pars, self$predict_type, self$model)
}
)
)
.extralrns_dict$add("classif.smo", LearnerClassifSMO)
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