Add support for rtemis.server

.Rbuildignore

@@ -10,7 +10,6 @@
 ^\.gemini$
 ^\.github$
 ^\.lintr$
-^\.rtms-instructions\.md$
 ^\.vscode$
 ^cran-comments\.md$
 ^LICENSE\.md$
@@ -23,3 +22,4 @@
 ^pkgdown$
 ^Makefile$
 ^SKILL\.md$
+^.*\.pdf$

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: rtemis
-Version: 1.2.0
+Version: 1.2.1
 Title: Machine Learning and Visualization
-Date: 2026-05-12
+Date: 2026-05-16
 Authors@R: person(given = "E.D.", family = "Gennatas", role = c("aut", "cre", "cph"), 
     email = "gennatas@gmail.com", comment = c(ORCID = "0000-0001-9280-3609"))
 Description: Machine learning and visualization package with an 'S7' backend 
@@ -28,7 +28,8 @@ Imports:
     data.table,
     future,
     htmltools,
-    cli
+    cli,
+    rtemis.core
 Suggests:
     arrow,
     bit64,
@@ -74,7 +75,6 @@ Suggests:
     parallelly,
     partykit,
     plotly,
-    plumber,
     pROC,
     progressr,
     psych,

Makefile

@@ -4,7 +4,7 @@ RSCRIPT ?= Rscript
 CHECK_DIR := $(PKG).Rcheck
 TARBALL_GLOB := $(PKG)_*.tar.gz
 
-msg = @printf '\033[38;2;108;163;160m[%s] %s\033[0m\n' "$$(date -u '+%Y-%m-%d %H:%M:%SZ')" "$(1)"
+msg = @printf '\033[38;2;108;163;160m[%s] %s\033[0m\n' "$$(date '+%Y-%m-%d %H:%M:%S')" "$(1)"
 
 .DEFAULT_GOAL := help
 

NAMESPACE

@@ -71,6 +71,7 @@ export(get_factor_names)
 export(get_mode)
 export(get_msg_sink)
 export(get_palette)
+export(get_varimp)
 export(getcharacternames)
 export(getdatenames)
 export(getfactornames)

R/00_S7init.R

@@ -36,6 +36,23 @@ class_tabnet_fit <- new_S3_class("tabnet_fit")
 repr <- new_generic("repr", "x")
 
 
+# %% get_varimp ----
+#' Get variable importance
+#'
+#' @param x `Supervised` or `SupervisedRes` object.
+#' @param ... Additional arguments passed to methods.
+#'
+#' @return `VariableImportance` object or list of `VariableImportance` objects.
+#'
+#' @author EDG
+#' @export
+#'
+#' @examples
+#' mod <- train(iris, algorithm = "LightRF")
+#' get_varimp(mod)
+get_varimp <- new_generic("get_varimp", "x")
+
+
 # %% inspect ----
 #' Inspect rtemis object
 #'

R/01_ExecutionConfig.R

@@ -12,6 +12,7 @@
 #' @noRd
 ExecutionConfig <- new_class(
   name = "ExecutionConfig",
+  package = "rtemis",
   properties = list(
     backend = class_character,
     n_workers = class_integer,

R/02_Hyperparameters.R

@@ -27,7 +27,7 @@ TUNED_STATUS_TUNED <- 1L
 # 1: Running on resampled training sets.
 
 # %% Hyperparameters ----
-#' @title Hyperparameters
+#' Hyperparameters
 #'
 #' @description
 #' Superclass for hyperparameters.
@@ -41,9 +41,11 @@ TUNED_STATUS_TUNED <- 1L
 #' @field n_workers Integer: Number of workers to use for tuning.
 #'
 #' @author EDG
+#' @keywords internal
 #' @noRd
 Hyperparameters <- new_class(
   name = "Hyperparameters",
+  package = "rtemis",
   properties = list(
     algorithm = class_character,
     hyperparameters = class_list,
@@ -542,19 +544,19 @@ setup_CART <- function(
   cost = NULL,
   ifw = FALSE
 ) {
-  check_inherits(cp, "numeric")
+  rtemis.core::check_numeric(cp)
   maxdepth <- clean_int(maxdepth)
   minsplit <- clean_int(minsplit)
   minbucket <- clean_int(minbucket)
-  check_inherits(prune_cp, "numeric")
+  rtemis.core::check_numeric(prune_cp)
   check_inherits(method, "character")
   check_inherits(model, "logical")
   maxcompete <- clean_int(maxcompete)
   maxsurrogate <- clean_int(maxsurrogate)
   usesurrogate <- clean_int(usesurrogate)
   surrogatestyle <- clean_int(surrogatestyle)
   xval <- clean_int(xval)
-  check_inherits(cost, "numeric")
+  rtemis.core::check_numeric(cost)
   CARTHyperparameters(
     cp = cp,
     maxdepth = maxdepth,
@@ -615,7 +617,7 @@ GLMNETHyperparameters <- new_class(
     check_float01inc(alpha)
     check_inherits(which_lambda_cv, "character")
     nlambda <- clean_posint(nlambda)
-    check_inherits(penalty_factor, "numeric")
+    rtemis.core::check_numeric(penalty_factor)
     check_inherits(standardize, "logical")
     new_object(
       Hyperparameters(
@@ -682,7 +684,7 @@ setup_GLMNET <- function(
   check_float01inc(alpha)
   check_inherits(which_lambda_cv, "character")
   nlambda <- clean_posint(nlambda)
-  check_inherits(penalty_factor, "numeric")
+  rtemis.core::check_numeric(penalty_factor)
   check_logical(standardize)
   check_logical(ifw)
   GLMNETHyperparameters(
@@ -1395,10 +1397,10 @@ setup_LightRuleFit <- function(
   check_floatpos1(learning_rate)
   check_floatpos1(subsample)
   subsample_freq <- clean_posint(subsample_freq)
-  check_inherits(lambda_l1, "numeric")
-  check_inherits(lambda_l2, "numeric")
+  rtemis.core::check_numeric(lambda_l1)
+  rtemis.core::check_numeric(lambda_l2)
   check_float01inc(alpha)
-  check_inherits(lambda, "numeric")
+  rtemis.core::check_numeric(lambda)
   check_logical(ifw_lightgbm)
   check_logical(ifw_glmnet)
   check_logical(ifw)
@@ -1563,7 +1565,7 @@ setup_LinearSVM <- function(
   cost = 1,
   ifw = FALSE
 ) {
-  check_inherits(cost, "numeric")
+  rtemis.core::check_numeric(cost)
   check_logical(ifw)
   LinearSVMHyperparameters(
     cost = cost,
@@ -1634,8 +1636,8 @@ setup_RadialSVM <- function(
   gamma = 0.01,
   ifw = FALSE
 ) {
-  check_inherits(cost, "numeric")
-  check_inherits(gamma, "numeric")
+  rtemis.core::check_numeric(cost)
+  rtemis.core::check_numeric(gamma)
   check_logical(ifw)
   RadialSVMHyperparameters(
     cost = cost,
@@ -2170,30 +2172,30 @@ setup_Ranger <- function(
   max_depth <- clean_posint(max_depth)
   check_inherits(replace, "logical")
   check_float01inc(sample_fraction)
-  check_inherits(case_weights, "numeric")
-  check_inherits(class_weights, "numeric")
+  rtemis.core::check_numeric(case_weights)
+  rtemis.core::check_numeric(class_weights)
   check_inherits(splitrule, "character")
   num_random_splits <- clean_posint(num_random_splits)
   check_float01inc(alpha)
   check_float01inc(minprop)
-  check_inherits(poisson_tau, "numeric")
-  check_inherits(split_select_weights, "numeric")
+  rtemis.core::check_numeric(poisson_tau)
+  rtemis.core::check_numeric(split_select_weights)
   check_inherits(always_split_variables, "character")
   check_inherits(respect_unordered_factors, "logical")
   check_inherits(scale_permutation_importance, "logical")
   check_inherits(local_importance, "logical")
-  check_inherits(regularization_factor, "numeric")
+  rtemis.core::check_numeric(regularization_factor)
   check_inherits(regularization_usedepth, "logical")
   check_inherits(keep_inbag, "logical")
   check_inherits(inbag, "list")
   check_inherits(holdout, "logical")
   check_inherits(quantreg, "logical")
-  check_inherits(time_interest, "numeric")
+  rtemis.core::check_numeric(time_interest)
   check_inherits(oob_error, "logical")
   check_inherits(save_memory, "logical")
   check_inherits(verbose, "logical")
   check_inherits(node_stats, "logical")
-  check_inherits(seed, "numeric")
+  rtemis.core::check_numeric(seed)
   check_inherits(na_action, "character")
   check_logical(ifw)
   RangerHyperparameters(

R/03_Metrics.R

@@ -3,7 +3,7 @@
 # 2025- EDG rtemis.org
 
 # %% Metrics ----
-#' @title Metrics
+#' Metrics
 #'
 #' @description
 #' Superclass for Metrics metrics.
@@ -12,9 +12,11 @@
 #' @field metrics List or data.frame: Metrics.
 #'
 #' @author EDG
+#' @keywords internal
 #' @noRd
 Metrics <- new_class(
   name = "Metrics",
+  package = "rtemis",
   properties = list(
     sample = class_character | NULL,
     metrics = class_list | class_data.frame

R/04_Preprocessor.R

@@ -7,7 +7,7 @@
 # https://rconsortium.github.io/S7
 
 # %% PreprocessorConfig ----
-#' @title PreprocessorConfig
+#' PreprocessorConfig
 #'
 #' @description
 #' PreprocessorConfig class.
@@ -16,6 +16,7 @@
 #' @noRd
 PreprocessorConfig <- new_class(
   name = "PreprocessorConfig",
+  package = "rtemis",
   properties = list(
     complete_cases = class_logical,
     remove_features_thres = class_numeric | NULL,
@@ -335,7 +336,7 @@ setup_Preprocessor <- function(
 # )
 
 # %% Preprocessor ----
-#' @title Preprocessor
+#' Preprocessor
 #'
 #' @description
 #' Class to hold output of preprocessing values after applying `PreprocessorConfig` to
@@ -353,6 +354,7 @@ setup_Preprocessor <- function(
 #' @noRd
 Preprocessor <- new_class(
   name = "Preprocessor",
+  package = "rtemis",
   properties = list(
     config = PreprocessorConfig,
     preprocessed = class_data.frame | class_list,

R/05_Resampler.R

@@ -16,7 +16,7 @@
 # Note: `id_strat` is used by `resample()`, not individual resamplers
 
 # %% ResamplerConfig ----
-#' @title ResamplerConfig
+#' ResamplerConfig
 #'
 #' @description
 #' Superclass for resampler configuration.
@@ -28,6 +28,7 @@
 #' @noRd
 ResamplerConfig <- new_class(
   name = "ResamplerConfig",
+  package = "rtemis",
   properties = list(
     type = class_character,
     n = class_integer # scalar_int_pos
@@ -401,7 +402,7 @@ setup_Resampler <- function(
 
 
 # %% Resampler ----
-#' @title Resampler
+#' Resampler
 #'
 #' @description
 #' Class for resampling objects.
@@ -410,6 +411,7 @@ setup_Resampler <- function(
 #' @noRd
 Resampler <- new_class(
   name = "Resampler",
+  package = "rtemis",
   properties = list(
     type = class_character,
     resamples = class_list,

R/06_Tuner.R

@@ -29,9 +29,11 @@
 #' @field config Named list of tuner config.
 #'
 #' @author EDG
+#' @keywords internal
 #' @noRd
 TunerConfig <- new_class(
   name = "TunerConfig",
+  package = "rtemis",
   properties = list(
     type = class_character,
     config = class_list
@@ -94,7 +96,7 @@ method(`[[`, TunerConfig) <- function(x, name) {
 
 
 # %% GridSearchConfig ----
-#' @title GridSearchConfig
+#' GridSearchConfig
 #'
 #' @description
 #' TunerConfig subclass for grid search config.
@@ -104,6 +106,7 @@ method(`[[`, TunerConfig) <- function(x, name) {
 GridSearchConfig <- new_class(
   name = "GridSearchConfig",
   parent = TunerConfig,
+  package = "rtemis",
   constructor = function(
     resampler_config = NULL,
     search_type = NULL,
@@ -209,6 +212,7 @@ setup_GridSearch <- function(
 #' @noRd
 Tuner <- new_class(
   name = "Tuner",
+  package = "rtemis",
   properties = list(
     type = class_character,
     hyperparameters = Hyperparameters,
@@ -237,6 +241,7 @@ method(desc, Tuner) <- function(x) {
 GridSearch <- new_class(
   name = "GridSearch",
   parent = Tuner,
+  package = "rtemis",
   constructor = function(
     hyperparameters,
     tuner_config,