cleanup

mlr-org · Aug 16, 2024 · 0b19ba1 · 0b19ba1
1 parent e7db2c0
commit 0b19ba1
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Showing 13 changed files with 24 additions and 38 deletions.
diff --git a/R/LearnerClassifCVGlmnet.R b/R/LearnerClassifCVGlmnet.R
@@ -105,8 +105,7 @@ LearnerClassifCVGlmnet = R6Class("LearnerClassifCVGlmnet",
       target = swap_levels(task$truth())
       pv = self$param_set$get_values(tags = "train")
       pv$family = ifelse(length(task$class_names) == 2L, "binomial", "multinomial")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       glmnet_invoke(data, target, pv, cv = TRUE)
     },

diff --git a/R/LearnerClassifGlmnet.R b/R/LearnerClassifGlmnet.R
@@ -115,8 +115,7 @@ LearnerClassifGlmnet = R6Class("LearnerClassifGlmnet",
       target = swap_levels(task$truth())
       pv = self$param_set$get_values(tags = "train")
       pv$family = ifelse(length(task$class_names) == 2L, "binomial", "multinomial")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       glmnet_invoke(data, target, pv)
     },

diff --git a/R/LearnerClassifMultinom.R b/R/LearnerClassifMultinom.R
@@ -64,8 +64,7 @@ LearnerClassifMultinom = R6Class("LearnerClassifMultinom",
   private = list(
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       if (!is.null(pv$summ)) {
         pv$summ = as.integer(pv$summ)

diff --git a/R/LearnerClassifNnet.R b/R/LearnerClassifNnet.R
@@ -72,8 +72,7 @@ LearnerClassifNnet = R6Class("LearnerClassifNnet",
   private = list(
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       if (is.null(pv$formula)) {
         pv$formula = task$formula()

diff --git a/R/LearnerClassifRanger.R b/R/LearnerClassifRanger.R
@@ -117,8 +117,7 @@ LearnerClassifRanger = R6Class("LearnerClassifRanger",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       pv = convert_ratio(pv, "mtry", "mtry.ratio", length(task$feature_names))
-      pv$case.weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv, "case.weights")
 
       invoke(ranger::ranger,
         dependent.variable.name = task$target_names,

diff --git a/R/LearnerClassifXgboost.R b/R/LearnerClassifXgboost.R
@@ -246,11 +246,11 @@ LearnerClassifXgboost = R6Class("LearnerClassifXgboost",
       label = nlvls - as.integer(task$truth())
       data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = label)
 
-      weights = get_weights(task, pv)
-      if (!is.null(weights)) {
-        xgboost::setinfo(data, "weight", weights)
+      pv = get_weights(task, pv)
+      if (!is.null(pv$weights)) {
+        xgboost::setinfo(data, "weight", pv$weights)
+        pv$weights = NULL
       }
-      pv = remove_named(pv, "use_weights")
 
       # the last element in the watchlist is used as the early stopping set
 

diff --git a/R/LearnerRegrCVGlmnet.R b/R/LearnerRegrCVGlmnet.R
@@ -103,8 +103,7 @@ LearnerRegrCVGlmnet = R6Class("LearnerRegrCVGlmnet",
       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")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       glmnet_invoke(data, target, pv, cv = TRUE)
     },

diff --git a/R/LearnerRegrGlmnet.R b/R/LearnerRegrGlmnet.R
@@ -70,7 +70,6 @@ LearnerRegrGlmnet = R6Class("LearnerRegrGlmnet",
         type.multinomial      = p_fct(c("ungrouped", "grouped"), tags = "train"),
         upper.limits          = p_uty(tags = "train"),
         use_weights           = p_lgl(default = FALSE, tags = "train")
-
       )
 
       ps$values = list(family = "gaussian")
@@ -104,8 +103,7 @@ LearnerRegrGlmnet = R6Class("LearnerRegrGlmnet",
       data = as_numeric_matrix(task$data(cols = task$feature_names))
       target = as_numeric_matrix(task$data(cols = task$target_names))
       pv = self$param_set$get_values(tags = "train")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       glmnet_invoke(data, target, pv)
     },

diff --git a/R/LearnerRegrLM.R b/R/LearnerRegrLM.R
@@ -62,8 +62,7 @@ LearnerRegrLM = R6Class("LearnerRegrLM",
   private = list(
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       invoke(stats::lm,
         formula = task$formula(), data = task$data(),

diff --git a/R/LearnerRegrNnet.R b/R/LearnerRegrNnet.R
@@ -71,8 +71,7 @@ LearnerRegrNnet = R6Class("LearnerRegrNnet",
   private = list(
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
-      pv$weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv)
 
       if (is.null(pv$formula)) {
         pv$formula = task$formula()

diff --git a/R/LearnerRegrRanger.R b/R/LearnerRegrRanger.R
@@ -108,8 +108,7 @@ LearnerRegrRanger = R6Class("LearnerRegrRanger",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       pv = convert_ratio(pv, "mtry", "mtry.ratio", length(task$feature_names))
-      pv$case.weights = get_weights(task, pv)
-      pv = remove_named(pv, "use_weights")
+      pv = get_weights(task, pv, "case.weights")
 
       if (self$predict_type == "se") {
         pv$keep.inbag = TRUE # nolint

diff --git a/R/LearnerRegrXgboost.R b/R/LearnerRegrXgboost.R
@@ -129,7 +129,6 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
         watchlist                   = p_uty(default = NULL, tags = "train"),
         xgb_model                   = p_uty(default = NULL, tags = "train"),
         use_weights                 = p_lgl(default = FALSE, tags = "train")
-
       )
       # param deps
 
@@ -202,9 +201,10 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
       target = task$data(cols = task$target_names)
       data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = data.matrix(target))
 
-      weights = get_weights(task, pv)
-      if (!is.null(weights)) {
-        xgboost::setinfo(data, "weight", weights)
+      pv = get_weights(task, pv)
+      if (!is.null(pv$weights)) {
+        xgboost::setinfo(data, "weight", pv$weights)
+        pv$weights = NULL
       }
 
       # the last element in the watchlist is used as the early stopping set

diff --git a/R/helpers.R b/R/helpers.R
@@ -44,16 +44,13 @@ extract_loglik = function(self) {
   stats::logLik(self$model)
 }
 
-get_weights = function(task, pv) {
-  tmp = c("weights", "weights_learner") %in% task$properties
-
-  if (tmp[1L]) { # old mlr3 version, deprecated weights
-    task$weights$weight
-  } else if (tmp[2L] && isTRUE(pv$use_weights)) {
-    task$weights_learner$weight
-  } else {
-    NULL
+get_weights = function(task, pv, name) {
+  if (isTRUE(pv$use_weights)) {
+    pv[[name]] = task$weights$weight
   }
+  pv[["use_weights"]] = NULL
+
+  return(pv)
 }
 
 opts_default_contrasts = list(contrasts = c("contr.treatment", "contr.poly"))