feat: regression learners and estimate memory functions

DESCRIPTION

@@ -44,11 +44,9 @@ Suggests:
     xgboost
 Remotes:
     catboost/catboost/catboost/R-package,
-    mlr-org/mlr3@learner_size,
-    mlr-org/mlr3extralearners@mlr3automl,
-    mlr-org/mlr3learners@mlr3automl,
-    mlr-org/mlr3mbo@adbo,
-    mlr-org/mlr3pipelines
+    mlr-org/mlr3,
+    mlr-org/mlr3extralearners,
+    mlr-org/mlr3mbo@adbo
 Config/testthat/edition: 3
 Config/testthat/parallel: false
 Encoding: UTF-8
@@ -67,7 +65,17 @@ Collate:
     'LearnerClassifAutoSVM.R'
     'LearnerClassifAutoXgboost.R'
     'LearnerRegrAuto.R'
-    'helper.R'
+    'LearnerRegrAutoCatboost.R'
+    'LearnerRegrAutoGlmnet.R'
+    'LearnerRegrAutoKKNN.R'
+    'LearnerRegrAutoLightGBM.R'
+    'LearnerRegrAutoNnet.R'
+    'LearnerRegrAutoRanger.R'
+    'LearnerRegrAutoSVM.R'
+    'LearnerRegrAutoXgboost.R'
     'build_graph.R'
+    'estimate_memory.R'
+    'helper.R'
+    'internal_measure.R'
     'train_auto.R'
     'zzz.R'

NAMESPACE

@@ -1,9 +1,36 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(estimate_memory,Learner)
+S3method(estimate_memory,LearnerClassifCatboost)
+S3method(estimate_memory,LearnerClassifLightGBM)
+S3method(estimate_memory,LearnerClassifRanger)
+S3method(estimate_memory,LearnerClassifXgboost)
+S3method(estimate_memory,LearnerRegrCatboost)
+S3method(estimate_memory,LearnerRegrLightGBM)
+S3method(estimate_memory,LearnerRegrRanger)
+S3method(estimate_memory,LearnerRegrXgboost)
 export(LearnerClassifAuto)
+export(LearnerClassifAutoCatboost)
+export(LearnerClassifAutoGlmnet)
+export(LearnerClassifAutoKKNN)
+export(LearnerClassifAutoLightGBM)
+export(LearnerClassifAutoNnet)
+export(LearnerClassifAutoRanger)
 export(LearnerClassifAutoSVM)
 export(LearnerClassifAutoXgboost)
 export(LearnerRegrAuto)
+export(LearnerRegrAutoCatboost)
+export(LearnerRegrAutoGlmnet)
+export(LearnerRegrAutoKKNN)
+export(LearnerRegrAutoLightGBM)
+export(LearnerRegrAutoNnet)
+export(LearnerRegrAutoRanger)
+export(LearnerRegrAutoSVM)
+export(LearnerRegrAutoXgboost)
+export(catboost_internal_measure)
+export(estimate_memory)
+export(lightgbm_internal_measure)
+export(xgboost_internal_measure)
 import(R6)
 import(checkmate)
 import(data.table)

R/LearnerClassifAuto.R

@@ -4,6 +4,7 @@
 #' Classification auto learner.
 #'
 #' @template param_id
+#' @template param_learner_ids
 #'
 #' @export
 LearnerClassifAuto = R6Class("LearnerClassifAuto",
@@ -21,47 +22,52 @@ LearnerClassifAuto = R6Class("LearnerClassifAuto",
 
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
-    initialize = function(id = "classif.auto") {
+    initialize = function(
+      id = "classif.auto",
+      learner_ids = c("glmnet", "kknn", "lda", "nnet", "ranger", "svm", "xgboost", "catboost", "extra_trees", "lightgbm")
+      ) {
+      assert_subset(learner_ids, c("glmnet", "kknn", "lda", "nnet", "ranger", "svm", "xgboost", "catboost", "extra_trees", "lightgbm"))
+      if (all(learner_ids %in% c("lda", "extra_trees"))) {
+        stop("Learner 'lda' and 'extra_trees' must be combined with other learners")
+      }
+
+      private$.learner_ids = learner_ids
+      self$tuning_space = tuning_space[private$.learner_ids]
+
       param_set = ps(
-        # learner
-        learner_ids = p_uty(default = c("glmnet", "kknn", "lda", "nnet", "ranger", "svm", "xgboost", "catboost", "extra_trees", "lightgbm"),
-          custom_check = function(x) {
-            if (all(x %in% c("lda", "extra_trees"))) {
-              return("Learner 'lda' and 'extra_trees' must be combined with other learners")
-            }
-            check_subset(x, c("glmnet", "kknn", "lda", "nnet", "ranger", "svm", "xgboost", "catboost", "extra_trees", "lightgbm"))
-        }),
-        learner_timeout = p_int(lower = 1L, default = 900L),
-        xgboost_eval_metric = p_uty(),
-        catboost_eval_metric = p_uty(),
-        lightgbm_eval_metric = p_uty(),
+        learner_timeout = p_int(lower = 1L, default = 900L, tags = c("train", "super")),
+        # internal eval metric
+        xgboost_eval_metric = p_uty(tags = c("train", "xgboost")),
+        catboost_eval_metric = p_uty(tags = c("train", "catboost")),
+        lightgbm_eval_metric = p_uty(tags = c("train", "lightgbm")),
         # system
-        max_nthread = p_int(lower = 1L, default = 1L),
-        max_memory = p_int(lower = 1L, default = 32000L),
+        max_nthread = p_int(lower = 1L, default = 1L, tags = c("train", "catboost", "lightgbm", "ranger", "xgboost")),
+        max_memory = p_int(lower = 1L, default = 32000L, tags = c("train", "catboost", "lightgbm", "ranger", "xgboost")),
         # large data
-        large_data_size = p_int(lower = 1L, default = 1e6),
-        large_data_learner_ids = p_uty(),
-        large_data_nthread = p_int(lower = 1L, default = 4L),
+        large_data_size = p_int(lower = 1L, default = 1e6, tags = c("train", "super")),
+        large_data_learner_ids = p_uty(tags = c("train", "super")),
+        large_data_nthread = p_int(lower = 1L, default = 4L, tags = c("train", "catboost", "lightgbm", "ranger", "xgboost")),
         # small data
-        small_data_size = p_int(lower = 1L, default = 5000L),
-        small_data_resampling = p_uty(),
-        max_cardinality = p_int(lower = 1L, default = 100L),
-        extra_trees_max_cardinality = p_int(lower = 1L, default = 40L),
+        small_data_size = p_int(lower = 1L, default = 5000L, tags = c("train", "super")),
+        small_data_resampling = p_uty(tags = c("train", "super")),
+        # cardinality
+        max_cardinality = p_int(lower = 1L, default = 100L, tags = c("train", "super")),
+        extra_trees_max_cardinality = p_int(lower = 1L, default = 40L, tags = c("train", "extra_trees")),
         # tuner
-        resampling = p_uty(),
-        terminator = p_uty(),
-        measure = p_uty(),
-        lhs_size = p_int(lower = 1L, default = 4L),
-        callbacks = p_uty(),
-        store_benchmark_result = p_lgl(default = FALSE))
+        resampling = p_uty(tags = c("train", "super")),
+        terminator = p_uty(tags = c("train", "super")),
+        measure = p_uty(tags = c("train", "super")),
+        lhs_size = p_int(lower = 1L, default = 4L, tags = c("train", "super")),
+        callbacks = p_uty(tags = c("train", "super")),
+        store_benchmark_result = p_lgl(default = FALSE, tags = c("train", "super")),
+        store_models = p_lgl(default = FALSE, tags = c("train", "super")))
 
       param_set$set_values(
-        learner_ids = c("glmnet", "kknn", "lda", "nnet", "ranger", "svm", "xgboost", "catboost", "extra_trees", "lightgbm"),
         learner_timeout = 900L,
         max_nthread = 1L,
         max_memory = 32000L,
         large_data_size = 1e6L,
-        large_data_learner_ids = c("lda", "ranger", "xgboost", "catboost", "extra_trees", "lightgbm"),
+        large_data_learner_ids = intersect(c("lda", "ranger", "xgboost", "catboost", "extra_trees", "lightgbm"), private$.learner_ids),
         large_data_nthread = 4L,
         small_data_size = 5000L,
         small_data_resampling = rsmp("cv", folds = 10L),
@@ -71,13 +77,19 @@ LearnerClassifAuto = R6Class("LearnerClassifAuto",
         terminator = trm("run_time", secs = 14400L),
         measure = msr("classif.ce"),
         lhs_size = 4L,
-        store_benchmark_result = FALSE)
+        store_benchmark_result = FALSE,
+        store_models = FALSE)
+
+      # subset to relevant parameters for selected learners
+      param_set = param_set$subset(ids = unique(param_set$ids(any_tags = c("super", learner_ids))))
+
+      self$graph = build_graph(private$.learner_ids, "classif")
 
       super$initialize(
         id = id,
         task_type = "classif",
         param_set = param_set,
-        packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "xgboost", "catboost", "lightgbm", "ranger", "nnet", "kknn", "glmnet", "MASS", "e1071"),
+        packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", self$graph$packages),
         feature_types = c("logical", "integer", "numeric", "character", "factor"),
         predict_types = c("response", "prob"),
         properties = c("missings", "weights", "twoclass", "multiclass"),
@@ -86,8 +98,10 @@ LearnerClassifAuto = R6Class("LearnerClassifAuto",
   ),
 
   private = list(
+    .learner_ids = NULL,
+
    .train = function(task) {
-      train_auto(self, task, task_type = "classif")
+      train_auto(self, private, task)
     },
 
     .predict = function(task) {

R/LearnerClassifAutoCatboost.R

@@ -13,26 +13,7 @@ LearnerClassifAutoCatboost = R6Class("LearnerClassifAutoCatboost",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id = "classif.auto_catboost") {
-      super$initialize(id = id)
-
-      # reduce parameter set to the relevant parameters
-      private$.param_set = private$.param_set$subset(
-        c("learner_ids",
-        "learner_timeout",
-        "catboost_eval_metric",
-        "small_data_size",
-        "small_data_resampling",
-        "max_cardinality",
-        "resampling",
-        "terminator",
-        "measure",
-        "lhs_size",
-        "callbacks",
-        "store_benchmark_result")
-      )
-
-      self$param_set$set_values(learner_ids = "catboost")
-      self$packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "catboost")
+      super$initialize(id = id, learner_ids = "catboost")
     }
   )
 )

R/LearnerClassifAutoGlmnet.R

@@ -13,25 +13,7 @@ LearnerClassifAutoGlmnet = R6Class("LearnerClassifAutoGlmnet",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id = "classif.auto_glmnet") {
-      super$initialize(id = id)
-
-      # reduce parameter set to the relevant parameters
-      private$.param_set = private$.param_set$subset(
-        c("learner_ids",
-        "learner_timeout",
-        "small_data_size",
-        "small_data_resampling",
-        "max_cardinality",
-        "resampling",
-        "terminator",
-        "measure",
-        "lhs_size",
-        "callbacks",
-        "store_benchmark_result")
-      )
-
-      self$param_set$set_values(learner_ids = "glmnet")
-      self$packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "glmnet")
+      super$initialize(id = id, learner_ids = "glmnet")
     }
   )
 )

R/LearnerClassifAutoKKNN.R

@@ -13,25 +13,7 @@ LearnerClassifAutoKKNN = R6Class("LearnerClassifAutoKKNN",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id = "classif.auto_kknn") {
-      super$initialize(id = id)
-
-      # reduce parameter set to the relevant parameters
-      private$.param_set = private$.param_set$subset(
-        c("learner_ids",
-        "learner_timeout",
-        "small_data_size",
-        "small_data_resampling",
-        "max_cardinality",
-        "resampling",
-        "terminator",
-        "measure",
-        "lhs_size",
-        "callbacks",
-        "store_benchmark_result")
-      )
-
-      self$param_set$set_values(learner_ids = "kknn")
-      self$packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "kknn")
+      super$initialize(id = id, learner_ids = "kknn")
     }
   )
 )

R/LearnerClassifAutoLightGBM.R

@@ -13,26 +13,7 @@ LearnerClassifAutoLightGBM = R6Class("LearnerClassifAutoLightGBM",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id = "classif.auto_lightgbm") {
-      super$initialize(id = id)
-
-      # reduce parameter set to the relevant parameters
-      private$.param_set = private$.param_set$subset(
-        c("learner_ids",
-        "learner_timeout",
-        "lightgbm_eval_metric",
-        "small_data_size",
-        "small_data_resampling",
-        "max_cardinality",
-        "resampling",
-        "terminator",
-        "measure",
-        "lhs_size",
-        "callbacks",
-        "store_benchmark_result")
-      )
-
-      self$param_set$set_values(learner_ids = "lightgbm")
-      self$packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "lightgbm")
+      super$initialize(id = id, learner_ids = "lightgbm")
     }
   )
 )

R/LearnerClassifAutoNnet.R

@@ -13,25 +13,7 @@ LearnerClassifAutoNnet = R6Class("LearnerClassifAutoNnet",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id = "classif.auto_nnet") {
-      super$initialize(id = id)
-
-      # reduce parameter set to the relevant parameters
-      private$.param_set = private$.param_set$subset(
-        c("learner_ids",
-        "learner_timeout",
-        "small_data_size",
-        "small_data_resampling",
-        "max_cardinality",
-        "resampling",
-        "terminator",
-        "measure",
-        "lhs_size",
-        "callbacks",
-        "store_benchmark_result")
-      )
-
-      self$param_set$set_values(learner_ids = "nnet")
-      self$packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "nnet")
+      super$initialize(id = id, learner_ids = "nnet")
     }
   )
 )

R/LearnerClassifAutoRanger.R

@@ -13,25 +13,7 @@ LearnerClassifAutoRanger = R6Class("LearnerClassifAutoRanger",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id = "classif.auto_ranger") {
-      super$initialize(id = id)
-
-      # reduce parameter set to the relevant parameters
-      private$.param_set = private$.param_set$subset(
-        c("learner_ids",
-        "learner_timeout",
-        "small_data_size",
-        "small_data_resampling",
-        "max_cardinality",
-        "resampling",
-        "terminator",
-        "measure",
-        "lhs_size",
-        "callbacks",
-        "store_benchmark_result")
-      )
-
-      self$param_set$set_values(learner_ids = "ranger")
-      self$packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "ranger")
+      super$initialize(id = id, learner_ids = "ranger")
     }
   )
 )

R/LearnerClassifAutoSVM.R

@@ -13,25 +13,7 @@ LearnerClassifAutoSVM = R6Class("LearnerClassifAutoSVM",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id = "classif.auto_svm") {
-      super$initialize(id = id)
-
-      # reduce parameter set to the relevant parameters
-      private$.param_set = private$.param_set$subset(
-        c("learner_ids",
-        "learner_timeout",
-        "small_data_size",
-        "small_data_resampling",
-        "max_cardinality",
-        "resampling",
-        "terminator",
-        "measure",
-        "lhs_size",
-        "callbacks",
-        "store_benchmark_result")
-      )
-
-      self$param_set$set_values(learner_ids = "svm")
-      self$packages = c("mlr3tuning", "mlr3learners", "mlr3pipelines", "mlr3mbo", "mlr3automl", "e1071")
+      super$initialize(id = id, learner_ids = "svm")
     }
   )
 )