chore (breaking): rename target_weights to case_weights

kapsner · Apr 15, 2024 · 58d1f66 · 58d1f66
1 parent 1674811
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: mllrnrs
 Title: R6-Based ML Learners for 'mlexperiments'
-Version: 0.0.3.9006
+Version: 0.0.3.9007
 Authors@R: 
     person("Lorenz A.", "Kapsner", , "[email protected]", role = c("cre", "aut", "cph"),
            comment = c(ORCID = "0000-0003-1866-860X"))
@@ -40,7 +40,7 @@ VignetteBuilder:
     knitr
 Config/testthat/edition: 3
 Config/testthat/parallel: false
-Date/Publication: 2024-04-12 10:43:41.959072 UTC
+Date/Publication: 2024-04-15 16:09:40.514639 UTC
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.1
diff --git a/NEWS.md b/NEWS.md
@@ -18,13 +18,15 @@
 
 #### Other changes
 
+-   add weight support also for glmnet
+    ([1674811](https://github.com/kapsner/mllrnrs/tree/1674811286f31ef4bfae5f351686396e5f1845ed))
 -   updated dev-version
     ([4b1625c](https://github.com/kapsner/mllrnrs/tree/4b1625cc389701ce114b44ee8565f0b147362483))
 -   updated news.md
     ([323f9cb](https://github.com/kapsner/mllrnrs/tree/323f9cb0dc0e38213154669dccff7a700f5071c2))
 
 Full set of changes:
-[`v0.0.3...935e0dc`](https://github.com/kapsner/mllrnrs/compare/v0.0.3...935e0dc)
+[`v0.0.3...1674811`](https://github.com/kapsner/mllrnrs/compare/v0.0.3...1674811)
 
 ## v0.0.3 (2024-03-07)
 

diff --git a/R/learner_glmnet.R b/R/learner_glmnet.R
@@ -246,14 +246,14 @@ glmnet_optimization <- function(
     )
   )
 
-  # rename mlexperiments "target_weights" to implementation specific (cv.glment)
+  # rename mlexperiments "case_weights" to implementation specific (cv.glment)
   # "weights"
-  if ("target_weights" %in% names(cv_args)) {
+  if ("case_weights" %in% names(cv_args)) {
     stopifnot(
-      "late fail: `target_weights` must be of same length as `y`" =
-        length(cv_args$target_weights) == length(y)
+      "late fail: `case_weights` must be of same length as `y`" =
+        length(cv_args$case_weights) == length(y)
     )
-    names(cv_args)[which(names(cv_args) == "target_weights")] <-
+    names(cv_args)[which(names(cv_args) == "case_weights")] <-
       "weights"
   }
 
@@ -292,14 +292,14 @@ glmnet_fit <- function(x, y, ncores, seed, ...) {
     kwargs
   )
 
-  # rename mlexperiments "target_weights" to implementation specific (cv.glment)
+  # rename mlexperiments "case_weights" to implementation specific (cv.glment)
   # "weights"
-  if ("target_weights" %in% names(fit_args)) {
+  if ("case_weights" %in% names(fit_args)) {
     stopifnot(
-      "late fail: `target_weights` must be of same length as `y`" =
-        length(fit_args$target_weights) == length(y)
+      "late fail: `case_weights` must be of same length as `y`" =
+        length(fit_args$case_weights) == length(y)
     )
-    names(fit_args)[which(names(fit_args) == "target_weights")] <-
+    names(fit_args)[which(names(fit_args) == "case_weights")] <-
       "weights"
   }
 

diff --git a/R/learner_lightgbm.R b/R/learner_lightgbm.R
@@ -253,17 +253,17 @@ lgb_dataset_wrapper <- function(x, y, params) {
     y = y,
     objective = params$objective
   )
-  if ("target_weights" %in% names(params)) {
+  if ("case_weights" %in% names(params)) {
     stopifnot(
-      "late fail: `target_weights` must be of same length as `y`" =
-        length(params$target_weights) == length(y)
+      "late fail: `case_weights` must be of same length as `y`" =
+        length(params$case_weights) == length(y)
     )
     dataset_args <- c(
       dataset_args,
-      list(target_weights = params$target_weights)
+      list(case_weights = params$case_weights)
     )
-    # remove target_weights-param from learner-args
-    params$target_weights <- NULL
+    # remove case_weights-param from learner-args
+    params$case_weights <- NULL
   }
   if ("cat_vars" %in% names(params)) {
     cat_vars <- params$cat_vars
@@ -294,8 +294,8 @@ setup_lgb_dataset <- function(x, y, objective, ...) {
     data = x,
     label = y
   )
-  if ("target_weights" %in% names(kwargs)) {
-    dataset_args <- c(dataset_args, list(weight = kwargs$target_weights))
+  if ("case_weights" %in% names(kwargs)) {
+    dataset_args <- c(dataset_args, list(weight = kwargs$case_weights))
   }
 
   if (!is.null(kwargs$cat_vars)) {

diff --git a/R/learner_ranger.R b/R/learner_ranger.R
@@ -179,9 +179,9 @@ ranger_cv <- function(
       params
     )
 
-    if ("target_weights" %in% names(args)) {
-      args$target_weights <- kdry::mlh_subset(
-        args$target_weights, ranger_train_idx
+    if ("case_weights" %in% names(args)) {
+      args$case_weights <- kdry::mlh_subset(
+        args$case_weights, ranger_train_idx
       )
     }
 
@@ -304,14 +304,14 @@ ranger_fit <- function(x, y, ncores, seed, ...) {
 
   x <- kdry::dtr_matrix2df(matrix = x, cat_vars = cat_vars)
 
-  # rename mlexperiments "target_weights" to implementation specific (ranger)
+  # rename mlexperiments "case_weights" to implementation specific (ranger)
   # "case.weights"
-  if ("target_weights" %in% names(ranger_params)) {
+  if ("case_weights" %in% names(ranger_params)) {
     stopifnot(
-      "late fail: `target_weights` must be of same length as `y`" =
-        length(ranger_params$target_weights) == length(y)
+      "late fail: `case_weights` must be of same length as `y`" =
+        length(ranger_params$case_weights) == length(y)
     )
-    names(ranger_params)[which(names(ranger_params) == "target_weights")] <-
+    names(ranger_params)[which(names(ranger_params) == "case_weights")] <-
       "case.weights"
   }
 

diff --git a/R/learner_xgboost.R b/R/learner_xgboost.R
@@ -228,17 +228,17 @@ xgboost_dataset_wrapper <- function(x, y, params) {
     y = y,
     objective = params$objective
   )
-  if ("target_weights" %in% names(params)) {
+  if ("case_weights" %in% names(params)) {
     stopifnot(
-      "late fail: `target_weights` must be of same length as `y`" =
-        length(params$target_weights) == length(y)
+      "late fail: `case_weights` must be of same length as `y`" =
+        length(params$case_weights) == length(y)
     )
     dataset_args <- c(
       dataset_args,
-      list(target_weights = params$target_weights)
+      list(case_weights = params$case_weights)
     )
-    # remove target_weights-param from learner-args
-    params$target_weights <- NULL
+    # remove case_weights-param from learner-args
+    params$case_weights <- NULL
   }
   dtrain <- do.call(setup_xgb_dataset, dataset_args)
 
@@ -292,8 +292,8 @@ setup_xgb_dataset <- function(x, y, objective, ...) {
     dtrain <- xgboost::xgb.DMatrix(x)
     label <- y
     xgboost::setinfo(dtrain, "label", label)
-    if ("target_weights" %in% names(kwargs)) {
-      xgboost::setinfo(dtrain, "weight", kwargs$target_weights)
+    if ("case_weights" %in% names(kwargs)) {
+      xgboost::setinfo(dtrain, "weight", kwargs$case_weights)
     }
     return(dtrain)
   }

diff --git a/data-raw/devstuffs.R b/data-raw/devstuffs.R
@@ -19,7 +19,7 @@ my_desc$set_authors(c(
 # Remove some author fields
 my_desc$del("Maintainer")
 # Set the version
-my_desc$set_version("0.0.3.9006")
+my_desc$set_version("0.0.3.9007")
 # The title of your package
 my_desc$set(Title = "R6-Based ML Learners for 'mlexperiments'")
 # The description of your package

diff --git a/tests/testthat/test-glmnet_multiclass.R b/tests/testthat/test-glmnet_multiclass.R
@@ -83,7 +83,7 @@ test_that(
       family = "multinomial",
       type.measure = "class",
       standardize = TRUE,
-      target_weights = y_weights
+      case_weights = y_weights
     )
     glmnet_optimizer$predict_args <- list(type = "response", reshape = TRUE)
     glmnet_optimizer$performance_metric <- mlexperiments::metric("bacc")

diff --git a/tests/testthat/test-lightgbm_multiclass.R b/tests/testthat/test-lightgbm_multiclass.R
@@ -95,7 +95,7 @@ test_that(
       objective = "multiclass",
       metric = "multi_logloss",
       num_class = 3,
-      target_weights = y_weights
+      case_weights = y_weights
     )
     lightgbm_optimizer$predict_args <- list(reshape = TRUE)
     lightgbm_optimizer$performance_metric <- mlexperiments::metric("bacc")

diff --git a/tests/testthat/test-ranger_multiclass.R b/tests/testthat/test-ranger_multiclass.R
@@ -87,7 +87,7 @@ test_that(
     y_weights <- ifelse(train_y == 1, 0.8, ifelse(train_y == 2, 1.2, 1))
     ranger_optimizer$learner_args <- list(
       classification = TRUE,
-      target_weights = y_weights
+      case_weights = y_weights
     )
 
     ranger_optimizer$performance_metric <- mlexperiments::metric("bacc")

diff --git a/tests/testthat/test-xgboost_multiclass.R b/tests/testthat/test-xgboost_multiclass.R
@@ -93,7 +93,7 @@ test_that(
       objective = "multi:softprob",
       eval_metric = "mlogloss",
       num_class = 3,
-      target_weights = y_weights
+      case_weights = y_weights
     )
     xgboost_optimizer$predict_args <- list(reshape = TRUE)
     xgboost_optimizer$performance_metric <- mlexperiments::metric("bacc")

diff --git a/vignettes/mllrnrs_glmnet_multiclass.Rmd b/vignettes/mllrnrs_glmnet_multiclass.Rmd
@@ -453,7 +453,7 @@ tuner_w_weights <- mlexperiments::MLTuneParameters$new(
 tuner_w_weights$parameter_grid <- parameter_grid
 tuner_w_weights$learner_args <- c(
   learner_args,
-  list(target_weights = y_weights)
+  list(case_weights = y_weights)
 )
 tuner_w_weights$split_type <- "stratified"
 
@@ -499,7 +499,7 @@ validator <- mlexperiments::MLCrossValidation$new(
 # append the optimized setting from above with the newly created weights
 validator$learner_args <- c(
   tuner$results$best.setting[-1],
-  list("target_weights" = y_weights)
+  list("case_weights" = y_weights)
 )
 
 validator$predict_args <- predict_args

diff --git a/vignettes/mllrnrs_lightgbm_multiclass.Rmd b/vignettes/mllrnrs_lightgbm_multiclass.Rmd
@@ -876,7 +876,7 @@ tuner_w_weights <- mlexperiments::MLTuneParameters$new(
 tuner_w_weights$parameter_grid <- parameter_grid
 tuner_w_weights$learner_args <- c(
   learner_args,
-  list(target_weights = y_weights)
+  list(case_weights = y_weights)
 )
 tuner_w_weights$split_type <- "stratified"
 
@@ -931,7 +931,7 @@ validator <- mlexperiments::MLCrossValidation$new(
 # append the optimized setting from above with the newly created weights
 validator$learner_args <- c(
   tuner$results$best.setting[-1],
-  list("target_weights" = y_weights)
+  list("case_weights" = y_weights)
 )
 
 validator$predict_args <- predict_args

diff --git a/vignettes/mllrnrs_ranger_multiclass.Rmd b/vignettes/mllrnrs_ranger_multiclass.Rmd
@@ -522,7 +522,7 @@ tuner_w_weights <- mlexperiments::MLTuneParameters$new(
 tuner_w_weights$parameter_grid <- parameter_grid
 tuner_w_weights$learner_args <- c(
   learner_args,
-  list(target_weights = y_weights)
+  list(case_weights = y_weights)
 )
 tuner_w_weights$split_type <- "stratified"
 
@@ -567,7 +567,7 @@ validator <- mlexperiments::MLCrossValidation$new(
 # append the optimized setting from above with the newly created weights
 validator$learner_args <- c(
   tuner$results$best.setting[-1],
-  list("target_weights" = y_weights)
+  list("case_weights" = y_weights)
 )
 
 validator$predict_args <- predict_args

diff --git a/vignettes/mllrnrs_xgboost_multiclass.Rmd b/vignettes/mllrnrs_xgboost_multiclass.Rmd
@@ -481,7 +481,7 @@ tuner_w_weights <- mlexperiments::MLTuneParameters$new(
 tuner_w_weights$parameter_grid <- parameter_grid
 tuner_w_weights$learner_args <- c(
   learner_args,
-  list(target_weights = y_weights)
+  list(case_weights = y_weights)
 )
 tuner_w_weights$split_type <- "stratified"
 
@@ -535,7 +535,7 @@ validator <- mlexperiments::MLCrossValidation$new(
 # append the optimized setting from above with the newly created weights
 validator$learner_args <- c(
   tuner$results$best.setting[-1],
-  list("target_weights" = y_weights)
+  list("case_weights" = y_weights)
 )
 
 validator$predict_args <- predict_args