From 9a08c9096647d6a8ae7d67db3003556b9239da04 Mon Sep 17 00:00:00 2001 From: Maximilian Muecke Date: Thu, 28 Aug 2025 23:39:39 +0200 Subject: [PATCH] chore: remove namespace prefix for mlr3misc, data.table, checkmate where applicable --- R/Graph.R | 4 ++-- R/PipeOp.R | 2 +- R/PipeOpNMF.R | 12 ++++++------ R/PipeOpVtreat.R | 26 +++++++++++++------------- R/TaskRegr_boston_housing.R | 4 ++-- R/mlr_graphs.R | 2 +- R/mlr_pipeops.R | 2 +- 7 files changed, 26 insertions(+), 26 deletions(-) diff --git a/R/Graph.R b/R/Graph.R index a358fa33b..c5b0057d4 100644 --- a/R/Graph.R +++ b/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) diff --git a/R/PipeOp.R b/R/PipeOp.R index 8cbc111ab..9f5bb38d7 100644 --- a/R/PipeOp.R +++ b/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$fun(data_element) diff --git a/R/PipeOpNMF.R b/R/PipeOpNMF.R index ae35f4ab6..42f7ef467 100644 --- a/R/PipeOpNMF.R +++ b/R/PipeOpNMF.R @@ -166,7 +166,7 @@ PipeOpNMF = R6Class("PipeOpNMF", names(.args)[match("pbackend", names(.args), nomatch = 0L)] = ".pbackend" names(.args)[match("callback", names(.args), nomatch = 0L)] = ".callback" - nmf = mlr3misc::invoke(NMF::nmf, + nmf = invoke(NMF::nmf, x = x, rng = NULL, model = NULL, @@ -178,9 +178,9 @@ PipeOpNMF = R6Class("PipeOpNMF", self$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_ }, diff --git a/R/PipeOpVtreat.R b/R/PipeOpVtreat.R index 3e723c631..1cb9861bd 100644 --- a/R/PipeOpVtreat.R +++ b/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 diff --git a/R/TaskRegr_boston_housing.R b/R/TaskRegr_boston_housing.R index b0a13b3e5..ef7843e3c 100644 --- a/R/TaskRegr_boston_housing.R +++ b/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" diff --git a/R/mlr_graphs.R b/R/mlr_graphs.R index 00d89c3c3..a7f7283a1 100644 --- a/R/mlr_graphs.R +++ b/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) { diff --git a/R/mlr_pipeops.R b/R/mlr_pipeops.R index 6121432bf..724bc03fe 100644 --- a/R/mlr_pipeops.R +++ b/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()),