...

NAMESPACE

@@ -143,7 +143,9 @@ importFrom(mlr3,LearnerClassif)
 importFrom(mlr3,LearnerRegr)
 importFrom(mlr3,lrn)
 importFrom(mlr3,lrns)
+importFrom(mlr3,marshal_model)
 importFrom(mlr3,mlr_learners)
+importFrom(mlr3,unmarshal_model)
 importFrom(stats,as.formula)
 importFrom(stats,formula)
 importFrom(stats,na.omit)

R/helpers_rweka.R

@@ -58,7 +58,6 @@ rweka_predict = function(newdata, pars, predict_type, model) {
 }
 
 #' @export
-#' @method marshal_model.Weka_classifier
 marshal_model.Weka_classifier = function(model, inplace = FALSE, ...) {
   rJava::.jcache(model$classifier)
   structure(list(
@@ -68,7 +67,6 @@ marshal_model.Weka_classifier = function(model, inplace = FALSE, ...) {
 }
 
 #' @export
-#' @method unmarshal_model.Weka_classifier_marshaled
 unmarshal_model.Weka_classifier_marshaled = function(model, inplace = FALSE, ...) {
   model$marshaled
 }

R/learner_RWeka_classif_IBk.R

@@ -72,10 +72,33 @@ LearnerClassifIBk = R6Class("LearnerClassifIBk",
         feature_types = c("numeric", "factor", "ordered", "integer"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass"),
+        properties = c("twoclass", "multiclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.IBk",
         label = "Nearest Neighbour"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_J48.R

@@ -76,10 +76,33 @@ LearnerClassifJ48 = R6Class("LearnerClassifJ48",
         feature_types = c("numeric", "factor", "ordered", "integer"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass", "missings"),
+        properties = c("twoclass", "multiclass", "missings", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.J48",
         label = "Tree-based Model"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_JRip.R

@@ -62,10 +62,33 @@ LearnerClassifJRip = R6Class("LearnerClassifJRip",
         feature_types = c("numeric", "factor", "ordered", "integer"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass"),
+        properties = c("twoclass", "multiclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.JRip",
         label = "Propositional Rule Learner."
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_LMT.R

@@ -65,10 +65,33 @@ LearnerClassifLMT = R6Class("LearnerClassifLMT",
         feature_types = c("numeric", "factor", "ordered", "integer"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass"),
+        properties = c("twoclass", "multiclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.LMT",
         label = "Tree-based Model"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_OneR.R

@@ -56,10 +56,33 @@ LearnerClassifOneR = R6Class("LearnerClassifOneR",
         feature_types = c("numeric", "factor", "ordered", "integer"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass"),
+        properties = c("twoclass", "multiclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.OneR",
         label = "One Rule"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_PART.R

@@ -68,10 +68,33 @@ LearnerClassifPART = R6Class("LearnerClassifPART",
         feature_types = c("numeric", "factor", "ordered", "integer"),
         predict_types = c("response", "prob"),
         param_set = ps,
-        properties = c("twoclass", "multiclass", "missings"),
+        properties = c("twoclass", "multiclass", "missings", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.PART",
         label = "Tree-based Model"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_bayes_net.R

@@ -82,10 +82,33 @@ LearnerClassifBayesNet = R6Class("LearnerClassifBayesNet",
         feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
         predict_types = c("response", "prob"),
         param_set = param_set,
-        properties = c("missings", "multiclass", "twoclass"),
+        properties = c("missings", "multiclass", "twoclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.bayes_net",
         label = "Bayes Network"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_decision_stump.R

@@ -52,10 +52,33 @@ LearnerClassifDecisionStump = R6Class("LearnerClassifDecisionStump",
         feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
         predict_types = c("response", "prob"),
         param_set = param_set,
-        properties = c("missings", "multiclass", "twoclass"),
+        properties = c("missings", "multiclass", "twoclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.decision_stump",
         label = "Decision Stump"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_decision_table.R

@@ -83,10 +83,33 @@ LearnerClassifDecisionTable = R6Class("LearnerClassifDecisionTable",
         feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
         predict_types = c("response", "prob"),
         param_set = param_set,
-        properties = c("missings", "multiclass", "twoclass"),
+        properties = c("missings", "multiclass", "twoclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.decision_table",
         label = "Decision Table"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),
 

R/learner_RWeka_classif_kstar.R

@@ -58,10 +58,33 @@ LearnerClassifKStar = R6Class("LearnerClassifKStar",
         feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
         predict_types = c("response", "prob"),
         param_set = param_set,
-        properties = c("missings", "multiclass", "twoclass"),
+        properties = c("missings", "multiclass", "twoclass", "marshal"),
         man = "mlr3extralearners::mlr_learners_classif.kstar",
         label = "KStar"
       )
+    },
+
+    #' @description
+    #' Marshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`marshal_model()`].
+    marshal = function(...) {
+      learner_marshal(.learner = self, ...)
+    },
+    #' @description
+    #' Unmarshal the learner's model.
+    #' @param ... (any)\cr
+    #'   Additional arguments passed to [`unmarshal_model()`].
+    unmarshal = function(...) {
+      learner_unmarshal(.learner = self, ...)
+    }
+  ),
+
+  active = list(
+    #' @field marshaled (`logical(1)`)\cr
+    #' Whether the learner has been marshaled.
+    marshaled = function() {
+      learner_marshaled(self)
     }
   ),