Classification single learners (#14)

* feat: classif single learners

* test: classif single learners

* fix: remove extra_trees & lda

* test: rename

* test: extra_trees and glmnet

DESCRIPTION

@@ -58,6 +58,12 @@ RoxygenNote: 7.3.2
 Collate:
     'aaa.R'
     'LearnerClassifAuto.R'
+    'LearnerClassifAutoCatboost.R'
+    'LearnerClassifAutoGlmnet.R'
+    'LearnerClassifAutoKKNN.R'
+    'LearnerClassifAutoLightGBM.R'
+    'LearnerClassifAutoNnet.R'
+    'LearnerClassifAutoRanger.R'
     'LearnerClassifAutoSVM.R'
     'LearnerClassifAutoXgboost.R'
     'LearnerRegrAuto.R'

R/LearnerClassifAutoCatboost.R

@@ -0,0 +1,41 @@
+#' @title Classification Gradient Boosted Decision Trees Auto Learner
+#'
+#' @description
+#' Classification auto learner.
+#'
+#' @template param_id
+#'
+#' @export
+LearnerClassifAutoCatboost = R6Class("LearnerClassifAutoCatboost",
+  inherit = LearnerClassifAuto,
+  public = list(
+
+    #' @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")
+    }
+  )
+)
+
+#' @include aaa.R
+learners[["classif.auto_catboost"]] = LearnerClassifAutoCatboost

R/LearnerClassifAutoGlmnet.R

@@ -0,0 +1,40 @@
+#' @title Classification GLM with Elastic Net Regularization Auto Learner
+#'
+#' @description
+#' Classification auto learner.
+#'
+#' @template param_id
+#'
+#' @export
+LearnerClassifAutoGlmnet = R6Class("LearnerClassifAutoGlmnet",
+  inherit = LearnerClassifAuto,
+  public = list(
+
+    #' @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")
+    }
+  )
+)
+
+#' @include aaa.R
+learners[["classif.auto_glmnet"]] = LearnerClassifAutoGlmnet

R/LearnerClassifAutoKKNN.R

@@ -0,0 +1,40 @@
+#' @title Classification k-Nearest-Neighbor Auto Learner
+#'
+#' @description
+#' Classification auto learner.
+#'
+#' @template param_id
+#'
+#' @export
+LearnerClassifAutoKKNN = R6Class("LearnerClassifAutoKKNN",
+  inherit = LearnerClassifAuto,
+  public = list(
+
+    #' @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")
+    }
+  )
+)
+
+#' @include aaa.R
+learners[["classif.auto_kknn"]] = LearnerClassifAutoKKNN

R/LearnerClassifAutoLightGBM.R

@@ -0,0 +1,41 @@
+#' @title Classification LightGBM Auto Learner
+#'
+#' @description
+#' Classification auto learner.
+#'
+#' @template param_id
+#'
+#' @export
+LearnerClassifAutoLightGBM = R6Class("LearnerClassifAutoLightGBM",
+  inherit = LearnerClassifAuto,
+  public = list(
+
+    #' @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")
+    }
+  )
+)
+
+#' @include aaa.R
+learners[["classif.auto_lightgbm"]] = LearnerClassifAutoLightGBM

R/LearnerClassifAutoNnet.R

@@ -0,0 +1,40 @@
+#' @title Classification Neural Network Auto Learner
+#'
+#' @description
+#' Classification auto learner.
+#'
+#' @template param_id
+#'
+#' @export
+LearnerClassifAutoNnet = R6Class("LearnerClassifAutoNnet",
+  inherit = LearnerClassifAuto,
+  public = list(
+
+    #' @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")
+    }
+  )
+)
+
+#' @include aaa.R
+learners[["classif.auto_nnet"]] = LearnerClassifAutoNnet

R/LearnerClassifAutoRanger.R

@@ -0,0 +1,40 @@
+#' @title Classification Ranger Auto Learner
+#'
+#' @description
+#' Classification auto learner.
+#'
+#' @template param_id
+#'
+#' @export
+LearnerClassifAutoRanger = R6Class("LearnerClassifAutoRanger",
+  inherit = LearnerClassifAuto,
+  public = list(
+
+    #' @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")
+    }
+  )
+)
+
+#' @include aaa.R
+learners[["classif.auto_ranger"]] = LearnerClassifAutoRanger

tests/testthat/test_LearnerClassifAutoCatboost.R

@@ -0,0 +1,25 @@
+test_that("LearnerClassifAutoCatboost is initialized", {
+  learner = lrn("classif.auto_catboost",
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 10))
+
+  expect_null(learner$graph)
+  expect_null(learner$tuning_space)
+})
+
+test_that("LearnerClassifAutoCatboost is trained", {
+  rush_plan(n_workers = 2)
+  lgr::get_logger("mlr3automl")$set_threshold("debug")
+
+  task = tsk("penguins")
+  learner = lrn("classif.auto_catboost",
+    small_data_size = 1,
+    resampling = rsmp("holdout"),
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 6)
+  )
+
+  expect_class(learner$train(task), "LearnerClassifAutoCatboost")
+  expect_equal(learner$graph$param_set$values$branch.selection, "catboost")
+  expect_equal(learner$model$instance$result$branch.selection, "catboost")
+})

tests/testthat/test_LearnerClassifAutoGlmnet.R

@@ -0,0 +1,25 @@
+test_that("LearnerClassifAutoGlmnet is initialized", {
+  learner = lrn("classif.auto_glmnet",
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 10))
+
+  expect_null(learner$graph)
+  expect_null(learner$tuning_space)
+})
+
+test_that("LearnerClassifAutoGlmnet is trained", {
+  rush_plan(n_workers = 2)
+  lgr::get_logger("mlr3automl")$set_threshold("debug")
+
+  task = tsk("penguins")
+  learner = lrn("classif.auto_glmnet",
+    small_data_size = 1,
+    resampling = rsmp("holdout"),
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 6)
+  )
+
+  expect_class(learner$train(task), "LearnerClassifAutoGlmnet")
+  expect_equal(learner$graph$param_set$values$branch.selection, "glmnet")
+  expect_equal(learner$model$instance$result$branch.selection, "glmnet")
+})

tests/testthat/test_LearnerClassifAutoKKNN.R

@@ -0,0 +1,25 @@
+test_that("LearnerClassifAutoKKNN is initialized", {
+  learner = lrn("classif.auto_kknn",
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 10))
+
+  expect_null(learner$graph)
+  expect_null(learner$tuning_space)
+})
+
+test_that("LearnerClassifAutoKKNN is trained", {
+  rush_plan(n_workers = 2)
+  lgr::get_logger("mlr3automl")$set_threshold("debug")
+
+  task = tsk("penguins")
+  learner = lrn("classif.auto_kknn",
+    small_data_size = 1,
+    resampling = rsmp("holdout"),
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 6)
+  )
+
+  expect_class(learner$train(task), "LearnerClassifAutoKKNN")
+  expect_equal(learner$graph$param_set$values$branch.selection, "kknn")
+  expect_equal(learner$model$instance$result$branch.selection, "kknn")
+})

tests/testthat/test_LearnerClassifAutoLightGBM.R

@@ -0,0 +1,25 @@
+test_that("LearnerClassifAutoLightGBM is initialized", {
+  learner = lrn("classif.auto_lightgbm",
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 10))
+
+  expect_null(learner$graph)
+  expect_null(learner$tuning_space)
+})
+
+test_that("LearnerClassifAutoLightGBM is trained", {
+  rush_plan(n_workers = 2)
+  lgr::get_logger("mlr3automl")$set_threshold("debug")
+
+  task = tsk("penguins")
+  learner = lrn("classif.auto_lightgbm",
+    small_data_size = 1,
+    resampling = rsmp("holdout"),
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 6)
+  )
+
+  expect_class(learner$train(task), "LearnerClassifAutoLightGBM")
+  expect_equal(learner$graph$param_set$values$branch.selection, "lightgbm")
+  expect_equal(learner$model$instance$result$branch.selection, "lightgbm")
+})

tests/testthat/test_LearnerClassifAutoNnet.R

@@ -0,0 +1,25 @@
+test_that("LearnerClassifAutoNnet is initialized", {
+  learner = lrn("classif.auto_nnet",
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 10))
+
+  expect_null(learner$graph)
+  expect_null(learner$tuning_space)
+})
+
+test_that("LearnerClassifAutoNnet is trained", {
+  rush_plan(n_workers = 2)
+  lgr::get_logger("mlr3automl")$set_threshold("debug")
+
+  task = tsk("penguins")
+  learner = lrn("classif.auto_nnet",
+    small_data_size = 1,
+    resampling = rsmp("holdout"),
+    measure = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 6)
+  )
+
+  expect_class(learner$train(task), "LearnerClassifAutoNnet")
+  expect_equal(learner$graph$param_set$values$branch.selection, "nnet")
+  expect_equal(learner$model$instance$result$branch.selection, "nnet")
+})