chore: remove namespace prefix for mlr3misc, data.table, checkmate where applicable…

R/Graph.R

@@ -383,13 +383,13 @@ Graph = R6Class("Graph",
           extra_vertices = setdiff(ids, c(df$from, df$to))
 
           all_names = unique(unlist(df))
-          df = data.table::setDT(mlr3misc::map(df, function(x) match(x, all_names)))
+          df = setDT(map(df, function(x) match(x, all_names)))
           gr = paste0(map(seq_len(nrow(df)), function(x) {
             paste0(df[x, ][[1L]], " -> ", df[x, ][[2L]])
           }), collapse = ";\n")
 
           all_names = gsub("\\.", "_", all_names)
-          labels = paste0(unlist(mlr3misc::map(unique(unlist(df)), function(x) {
+          labels = paste0(unlist(map(unique(unlist(df)), function(x) {
             paste0(x, " [label=", '"', all_names[x], '"', ",fontsize=", fontsize, ']')
           })), collapse = ";\n")
           dot = paste0(gr, ";\n", labels)

R/PipeOp.R

@@ -536,7 +536,7 @@ check_types = function(self, data, direction, operation) {
     autoconverter = get_autoconverter(typereq)
     msg = ""
     if (!is.null(autoconverter)) {
-      mlr3misc::require_namespaces(autoconverter$packages,
+      require_namespaces(autoconverter$packages,
         sprintf("The following packages are required to convert object of class %s to class %s: %%s.", class(data_element)[1], typereq))
       msg = tryCatch({
         data_element = autoconverter$fun(data_element)

R/PipeOpNMF.R

@@ -166,7 +166,7 @@ PipeOpNMF = R6Class("PipeOpNMF",
       names(.args)[match("pbackend", names(.args), nomatch = 0L)] = ".pbackend"
       names(.args)[match("callback", names(.args), nomatch = 0L)] = ".callback"
 
-      nmf = mlr3misc::invoke(NMF::nmf,
+      nmf = invoke(NMF::nmf,
         x = x,
         rng = NULL,
         model = NULL,
@@ -178,9 +178,9 @@ PipeOpNMF = R6Class("PipeOpNMF",
       self$state = structure(list(nmf = nmf), class = "PipeOpNMFstate")
 
       # here we have two options? return directly h or do what we do during prediction
-      #h = t(mlr3misc::invoke(NMF::coef, object = nmf))
-      w = mlr3misc::invoke(NMF::basis, object = nmf)
-      h_ = t(mlr3misc::invoke(MASS::ginv, X = w) %*% x)
+      #h = t(invoke(NMF::coef, object = nmf))
+      w = invoke(NMF::basis, object = nmf)
+      h_ = t(invoke(MASS::ginv, X = w) %*% x)
       colnames(h_) = paste0("NMF", seq_len(self$param_set$values$rank))
       h_
     },
@@ -197,8 +197,8 @@ PipeOpNMF = R6Class("PipeOpNMF",
       }
 
       x = t(as.matrix(dt))
-      w = mlr3misc::invoke(NMF::basis, object = self$state$nmf)
-      h_ = t(mlr3misc::invoke(MASS::ginv, X = w) %*% x)
+      w = invoke(NMF::basis, object = self$state$nmf)
+      h_ = t(invoke(MASS::ginv, X = w) %*% x)
       colnames(h_) = paste0("NMF", seq_len(self$param_set$values$rank))
       h_
     },

R/PipeOpVtreat.R

@@ -134,7 +134,7 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
     initialize = function(id = "vtreat", param_vals = list()) {
       ps = ps(
         recommended = p_lgl(tags = c("train", "predict")),
-        cols_to_copy = p_uty(custom_check = checkmate::check_function, tags = c("train", "predict")),
+        cols_to_copy = p_uty(custom_check = check_function, tags = c("train", "predict")),
         # tags stand for: regression vtreat::regression_parameters() / classification vtreat::classification_parameters() / multinomial vtreat::multinomial_parameters()
         minFraction = p_dbl(lower = 0, upper = 1, default = 0.02, tags = c("train", "regression", "classification", "multinomial")),
         smFactor = p_dbl(lower = 0, upper = Inf, default = 0, tags = c("train", "regression", "classification", "multinomial")),
@@ -207,40 +207,40 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
       }
 
       if (length(self$param_set$values$imputation_map)) {
-        checkmate::assert_subset(names(self$param_set$values$imputation_map), choices = var_list, empty.ok = TRUE)
+        assert_subset(names(self$param_set$values$imputation_map), choices = var_list, empty.ok = TRUE)
       }
 
       # FIXME: Handle non-Regr / non-Classif Tasks that inherit from TaskSupervised, #913
       task_type = task$task_type
       transform_design = if (task_type == "regr") {
-        mlr3misc::invoke(vtreat::NumericOutcomeTreatment,
+        invoke(vtreat::NumericOutcomeTreatment,
           var_list = var_list,
           outcome_name = task$target_names,
           cols_to_copy = self$param_set$values$cols_to_copy(task),
-          params = vtreat::regression_parameters(mlr3misc::insert_named(self$param_set$get_values(tags = "regression"), list(check_for_duplicate_frames = FALSE))),
+          params = vtreat::regression_parameters(insert_named(self$param_set$get_values(tags = "regression"), list(check_for_duplicate_frames = FALSE))),
           imputation_map = self$param_set$values$imputation_map)
       } else if (task_type == "classif") {
         if (length(task$class_names) > 2L) {
-          mlr3misc::invoke(vtreat::MultinomialOutcomeTreatment,
+          invoke(vtreat::MultinomialOutcomeTreatment,
             var_list = var_list,
             outcome_name = task$target_names,
             cols_to_copy = self$param_set$values$cols_to_copy(task),
-            params = vtreat::multinomial_parameters(mlr3misc::insert_named(self$param_set$get_values(tags = "multinomial"), list(check_for_duplicate_frames = FALSE))),
+            params = vtreat::multinomial_parameters(insert_named(self$param_set$get_values(tags = "multinomial"), list(check_for_duplicate_frames = FALSE))),
             imputation_map = self$param_set$values$imputation_map)
         } else {
-          mlr3misc::invoke(vtreat::BinomialOutcomeTreatment,
+          invoke(vtreat::BinomialOutcomeTreatment,
             var_list = var_list,
             outcome_name = task$target_names,
             outcome_target = task$positive,
             cols_to_copy = self$param_set$values$cols_to_copy(task),
-            params = vtreat::classification_parameters(mlr3misc::insert_named(self$param_set$get_values(tags = "classification"), list(check_for_duplicate_frames = FALSE))),
+            params = vtreat::classification_parameters(insert_named(self$param_set$get_values(tags = "classification"), list(check_for_duplicate_frames = FALSE))),
             imputation_map = self$param_set$values$imputation_map)
         }
       }
 
       # the following exception handling is necessary because vtreat sometimes fails with "no usable vars" if the data is already "clean" enough
       vtreat_res = tryCatch(
-        mlr3misc::invoke(vtreat::fit_prepare,
+        invoke(vtreat::fit_prepare,
           vps = transform_design,
           dframe = task$data(),
           weights = if ("weights_learner" %in% names(task)) task$weights_learner$weight else task$weights$weight,
@@ -261,11 +261,11 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
 
       self$state$treatment_plan = vtreat_res$treatments
 
-      d_prepared = data.table::setDT(vtreat_res$cross_frame)
+      d_prepared = setDT(vtreat_res$cross_frame)
 
       feature_subset = self$state$treatment_plan$get_feature_names()  # subset to vtreat features
       if (self$param_set$values$recommended) {
-        score_frame = mlr3misc::invoke(vtreat::get_score_frame, vps = self$state$treatment_plan)
+        score_frame = invoke(vtreat::get_score_frame, vps = self$state$treatment_plan)
         feature_subset = feature_subset[feature_subset %in% score_frame$varName[score_frame$recommended]]  # subset to only recommended
       }
       feature_subset = c(feature_subset, self$param_set$values$cols_to_copy(task))  # respect cols_to_copy
@@ -283,7 +283,7 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
 
       # the following exception handling is necessary because vtreat sometimes fails with "no usable vars" if the data is already "clean" enough
       d_prepared = tryCatch(
-        data.table::setDT(mlr3misc::invoke(vtreat::prepare,
+        setDT(invoke(vtreat::prepare,
           treatmentplan = self$state$treatment_plan,
           dframe = task$data())),
         error = function(error_condition) {
@@ -297,7 +297,7 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
 
       feature_subset = self$state$treatment_plan$get_feature_names()  # subset to vtreat features
       if (self$param_set$values$recommended) {
-        score_frame = mlr3misc::invoke(vtreat::get_score_frame, vps = self$state$treatment_plan)
+        score_frame = invoke(vtreat::get_score_frame, vps = self$state$treatment_plan)
         feature_subset = feature_subset[feature_subset %in% score_frame$varName[score_frame$recommended]]  # subset to only recommended
       }
       feature_subset = c(feature_subset, self$param_set$values$cols_to_copy(task))  # respect cols_to_copy

R/TaskRegr_boston_housing.R

@@ -4,14 +4,14 @@
 #' @name mlr_tasks_boston_housing
 #' @format [`R6Class`][R6::R6Class] object inheriting from [`TaskRegr`][mlr3::TaskRegr].
 #'
-#' The [`BostonHousing2`][mlbench::BostonHousing2] dataset 
+#' The [`BostonHousing2`][mlbench::BostonHousing2] dataset
 #' containing the corrected data from `r format_bib("freeman_1979")`
 #' as provided by the `mlbench` package. See data description there.
 #'
 NULL
 
 load_boston_housing = function(id = "boston_housing") {
-  bh = mlr3misc::load_dataset("BostonHousing2", "mlbench")
+  bh = load_dataset("BostonHousing2", "mlbench")
   bh$medv = NULL
   bht = as_task_regr(bh, target = "cmedv", id = id, label = "Boston Housing Prices")
   bht$man = "mlr3pipelines::mlr_tasks_boston_housing"

R/mlr_graphs.R

@@ -39,7 +39,7 @@
 #'
 #' # all Graphs currently in the dictionary:
 #' as.data.table(mlr_graphs)
-mlr_graphs = R6Class("DictionaryGraph", inherit = mlr3misc::Dictionary,
+mlr_graphs = R6Class("DictionaryGraph", inherit = Dictionary,
   cloneable = FALSE,
   public = list(
     add = function(key, value) {

R/mlr_pipeops.R

@@ -62,7 +62,7 @@
 #'
 #' # all PipeOps currently in the dictionary:
 #' as.data.table(mlr_pipeops)[, c("key", "input.num", "output.num", "packages")]
-mlr_pipeops = R6Class("DictionaryPipeOp", inherit = mlr3misc::Dictionary,
+mlr_pipeops = R6Class("DictionaryPipeOp", inherit = Dictionary,
   cloneable = FALSE,
   public = list(
     metainf = new.env(parent = emptyenv()),