fix: don't use delayedAssign

this simply was not necessary
mlr-org · Oct 16, 2023 · 47f8cda · 47f8cda
1 parent 3e43a28
commit 47f8cda
Show file tree

Hide file tree

Showing 36 changed files with 3,483 additions and 3,590 deletions.
diff --git a/R/learner_CoxBoost_surv_coxboost.R b/R/learner_CoxBoost_surv_coxboost.R
@@ -24,96 +24,93 @@
 #' @template seealso_learner
 #' @template example
 #' @export
-delayedAssign(
-  "LearnerSurvCoxboost",
-  R6Class("LearnerSurvCoxboost",
-    inherit = mlr3proba::LearnerSurv,
+LearnerSurvCoxboost = R6Class("LearnerSurvCoxboost",
+  inherit = mlr3proba::LearnerSurv,
 
-    public = list(
-      #' @description
-      #' Creates a new instance of this [R6][R6::R6Class] class.
-      initialize = function() {
-        ps = ps(
-          unpen.index = p_uty(tags = "train"),
-          standardize = p_lgl(default = TRUE, tags = "train"),
-          stepno = p_int(default = 100, lower = 0, tags = "train"),
-          penalty = p_dbl(tags = "train"),
-          criterion = p_fct(default = "pscore", levels = c("pscore", "score", "hpscore", "hscore"), tags = "train"),
-          stepsize.factor = p_dbl(default = 1, tags = "train"),
-          sf.scheme = p_fct(default = "sigmoid", levels = c("sigmoid", "linear"), tags = "train"),
-          pendistmat = p_uty(tags = "train"),
-          connected.index = p_uty(tags = "train"),
-          x.is.01 = p_lgl(default = FALSE, tags = "train"),
-          return.score = p_lgl(default = TRUE, tags = "train"),
-          trace = p_lgl(default = FALSE, tags = "train"),
-          at.step = p_uty(tags = "predict")
-        )
+  public = list(
+    #' @description
+    #' Creates a new instance of this [R6][R6::R6Class] class.
+    initialize = function() {
+      ps = ps(
+        unpen.index = p_uty(tags = "train"),
+        standardize = p_lgl(default = TRUE, tags = "train"),
+        stepno = p_int(default = 100, lower = 0, tags = "train"),
+        penalty = p_dbl(tags = "train"),
+        criterion = p_fct(default = "pscore", levels = c("pscore", "score", "hpscore", "hscore"), tags = "train"),
+        stepsize.factor = p_dbl(default = 1, tags = "train"),
+        sf.scheme = p_fct(default = "sigmoid", levels = c("sigmoid", "linear"), tags = "train"),
+        pendistmat = p_uty(tags = "train"),
+        connected.index = p_uty(tags = "train"),
+        x.is.01 = p_lgl(default = FALSE, tags = "train"),
+        return.score = p_lgl(default = TRUE, tags = "train"),
+        trace = p_lgl(default = FALSE, tags = "train"),
+        at.step = p_uty(tags = "predict")
+      )
 
-        super$initialize(
-          # see the mlr3book for a description: https://mlr3book.mlr-org.com/extending-mlr3.html
-          id = "surv.coxboost",
-          packages = c("mlr3extralearners", "CoxBoost", "pracma"),
-          feature_types = c("integer", "numeric"),
-          predict_types = c("distr", "crank", "lp"),
-          param_set = ps,
-          properties = "weights",
-          man = "mlr3extralearners::mlr_learners_surv.coxboost",
-          label = "Likelihood-based Boosting"
-        )
-      }
-    ),
+      super$initialize(
+        # see the mlr3book for a description: https://mlr3book.mlr-org.com/extending-mlr3.html
+        id = "surv.coxboost",
+        packages = c("mlr3extralearners", "CoxBoost", "pracma"),
+        feature_types = c("integer", "numeric"),
+        predict_types = c("distr", "crank", "lp"),
+        param_set = ps,
+        properties = "weights",
+        man = "mlr3extralearners::mlr_learners_surv.coxboost",
+        label = "Likelihood-based Boosting"
+      )
+    }
+  ),
 
-    private = list(
-      .train = function(task) {
-        pars = self$param_set$get_values(tags = "train")
+  private = list(
+    .train = function(task) {
+      pars = self$param_set$get_values(tags = "train")
 
-        if ("weights" %in% task$properties) {
-          pars$weights = as.numeric(task$weights$weight)
-        }
+      if ("weights" %in% task$properties) {
+        pars$weights = as.numeric(task$weights$weight)
+      }
 
-        data = task$data()
-        tn = task$target_names
-        time = data[[tn[1L]]]
-        status = data[[tn[2L]]]
-        data = as.matrix(data[, !tn, with = FALSE])
+      data = task$data()
+      tn = task$target_names
+      time = data[[tn[1L]]]
+      status = data[[tn[2L]]]
+      data = as.matrix(data[, !tn, with = FALSE])
 
-        with_package("CoxBoost", {
-          invoke(
-            CoxBoost::CoxBoost,
-            time = time,
-            status = status,
-            x = data,
-            .args = pars
-          )
-        })
-      },
+      with_package("CoxBoost", {
+        invoke(
+          CoxBoost::CoxBoost,
+          time = time,
+          status = status,
+          x = data,
+          .args = pars
+        )
+      })
+    },
 
-      .predict = function(task) {
+    .predict = function(task) {
 
-        pars = self$param_set$get_values(tags = "predict")
+      pars = self$param_set$get_values(tags = "predict")
 
-        # get newdata and ensure same ordering in train and predict
-        newdata = as.matrix(ordered_features(task, self))
+      # get newdata and ensure same ordering in train and predict
+      newdata = as.matrix(ordered_features(task, self))
 
-        lp = as.numeric(invoke(predict,
-          self$model,
-          newdata = newdata,
-          .args = pars,
-          type = "lp"))
+      lp = as.numeric(invoke(predict,
+        self$model,
+        newdata = newdata,
+        .args = pars,
+        type = "lp"))
 
-        surv = invoke(predict,
-          self$model,
-          newdata = newdata,
-          .args = pars,
-          type = "risk",
-          times = sort(unique(self$model$time)))
+      surv = invoke(predict,
+        self$model,
+        newdata = newdata,
+        .args = pars,
+        type = "risk",
+        times = sort(unique(self$model$time)))
 
-        mlr3proba::.surv_return(times = sort(unique(self$model$time)),
-          surv = surv,
-          lp = lp)
-      }
-    )
+      mlr3proba::.surv_return(times = sort(unique(self$model$time)),
+        surv = surv,
+        lp = lp)
+    }
   )
 )
 
-.extralrns_dict$add("surv.coxboost", function() LearnerSurvCoxboost$new())
+.extralrns_dict$add("surv.coxboost", LearnerSurvCoxboost)