merge classif.liblinear

NAMESPACE

@@ -14,6 +14,7 @@ export(LearnerClassifJ48)
 export(LearnerClassifJRip)
 export(LearnerClassifKSVM)
 export(LearnerClassifLMT)
+export(LearnerClassifLiblineaR)
 export(LearnerClassifLiblineaRL1L2SVC)
 export(LearnerClassifLiblineaRL1LogReg)
 export(LearnerClassifLiblineaRL2L1SVC)

R/learner_LiblineaR_classif_liblinear.R

@@ -0,0 +1,96 @@
+#' @title LiblineaR Classification Learner
+#' @author be-marc
+#' @name mlr_learners_classif.liblinear
+#'
+#' @template class_learner
+#' @templateVar id classif.liblinear
+#' @templateVar caller LiblineaR
+#'
+#' @details Type of SVC depends on `type` argument:
+#'
+#' * `0` – L2-regularized logistic regression (primal)
+#' * `1` - L2-regularized L2-loss support vector classification (dual)
+#' * `3` - L2-regularized L1-loss support vector classification (dual)
+#' * `2` – L2-regularized L2-loss support vector classification (primal)
+#' * `4` – Support vector classification by Crammer and Singer
+#' * `5` - L1-regularized L2-loss support vector classification
+#' * `6` - L1-regularized logistic regression
+#' * `7` - L2-regularized logistic regression (dual)
+#'
+#' If number of records > number of features, `type = 2` is faster than `type = 1`
+#' (Hsu et al. 2003).
+#'
+#' The default `epsilon` value depends on the `type` parameter, see [LiblineaR::LiblineaR].
+#'
+#' @export
+#' @template seealso_learner
+#' @template example
+LearnerClassifLiblineaR = R6Class("LearnerClassifLiblineaR", # nolint
+  inherit = LearnerClassif,
+  public = list(
+
+    #' @description
+    #' Creates a new instance of this [R6][R6::R6Class] class.
+    initialize = function() {
+
+      ps = ParamSet$new(
+        params = list(
+          ParamInt$new(id = "type", default = 0, lower = 0, upper = 7, tags = "train"),
+          ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),
+          ParamDbl$new(id = "epsilon", lower = 0, tags = "train"),
+          ParamDbl$new(id = "bias", default = 1, tags = "train"),
+          ParamInt$new(id = "cross", default = 0L, lower = 0L, tags = "train"),
+          ParamLgl$new(id = "verbose", default = FALSE, tags = "train"),
+          ParamUty$new(id = "wi", default = NULL, tags = "train"),
+          ParamLgl$new(id = "findC", default = FALSE, tags = "train"),
+          ParamLgl$new(id = "useInitC", default = TRUE, tags = "train")
+        )
+      )
+
+      # 50 is an arbitrary choice here
+      ps$add_dep("findC", "cross", CondAnyOf$new(seq(2:50)))
+      ps$add_dep("useInitC", "findC", CondEqual$new(TRUE))
+
+      super$initialize(
+        id = "classif.liblinear",
+        packages = "LiblineaR",
+        feature_types = "numeric",
+        predict_types = c("response", "prob"),
+        param_set = ps,
+        properties = c("twoclass", "multiclass"),
+        man = "mlr3extralearners::mlr_learners_classif.liblinear"
+      )
+    }
+  ),
+  private = list(
+    .train = function(task) {
+      pars = self$param_set$get_values(tags = "train")
+      data = task$data()
+      train = task$data(cols = task$feature_names)
+      target = task$truth()
+
+      type = ifelse(is.null(pars$type), 0, pars$type)
+      pars = pars[names(pars) != "type"]
+
+      invoke(LiblineaR::LiblineaR, data = train, target = target, type = type, .args = pars)
+    },
+
+    .predict = function(task) {
+      newdata = task$data(cols = task$feature_names)
+
+      type = ifelse(is.null(self$param_set$values$type), 0, self$param_set$values$type)
+
+      if (type %nin% c(0, 6, 7) && self$predict_type == "prob") {
+        stop("'prob' predict_type only possible if `type` is `0`, `6`, or `7`.")
+      }
+
+      if (self$predict_type == "prob") {
+        return(list(prob = invoke(predict, self$model, newx = newdata, proba = TRUE)$probabilities))
+      } else {
+        return(list(response = invoke(predict, self$model, newx = newdata)$predictions))
+      }
+    }
+  )
+)
+
+.extralrns_dict$add("classif.liblinear", LearnerClassifLiblineaR)

R/learner_LiblineaR_classif_liblinearl1l2svc.R

@@ -18,6 +18,9 @@ LearnerClassifLiblineaRL1L2SVC = R6Class("LearnerClassifLiblineaRL1L2SVC",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
+
+      warning("Deprecated. In the future please use `classif.liblinear` with `type = 5`.") # nolint
+
       ps = ParamSet$new(
         params = list(
           ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),

R/learner_LiblineaR_classif_liblinearl1logreg.R

@@ -18,6 +18,9 @@ LearnerClassifLiblineaRL1LogReg = R6Class("LearnerClassifLiblineaRL1LogReg", # n
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
+
+      warning("Deprecated. In the future please use `classif.liblinear` with `type = 6`.") # nolint
+
       ps = ParamSet$new(
         params = list(
           ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),

R/learner_LiblineaR_classif_liblinearl2l1svc.R

@@ -26,6 +26,9 @@ LearnerClassifLiblineaRL2L1SVC = R6Class("LearnerClassifLiblineaRL2L1SVC",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
+
+      warning("Deprecated. In the future please use `classif.liblinear` with `type = 3`.") # nolint
+
       ps = ParamSet$new(
         params = list(
           ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),

R/learner_LiblineaR_classif_liblinearl2l2svc.R

@@ -26,6 +26,9 @@ LearnerClassifLiblineaRL2L2SVC = R6Class("LearnerClassifLiblineaRL2L2SVC", # nol
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
+
+      warning("Deprecated. In the future please use `classif.liblinear` with `type = 1` or `2`.") # nolint
+
       ps = ParamSet$new(
         params = list(
           ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),

R/learner_LiblineaR_classif_liblinearl2logreg.R

@@ -18,6 +18,9 @@ LearnerClassifLiblineaRL2LogReg = R6Class("LearnerClassifLiblineaRL2LogReg", # n
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
+
+      warning("Deprecated. In the future please use `classif.liblinear` with `type = 0`.") # nolint
+
       ps = ParamSet$new(
         params = list(
           ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),

R/learner_LiblineaR_classif_liblinearmulticlasssvc.R

@@ -27,6 +27,8 @@ LearnerClassifLiblineaRMultiClassSVC = R6Class("LearnerClassifLiblineaRMultiClas
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
 
+      warning("Deprecated. In the future please use `classif.liblinear` with `type = 4`.") # nolint
+
       ps = ParamSet$new(
         params = list(
           ParamDbl$new(id = "cost", default = 1, lower = 0, tags = "train"),

R/learner_LiblineaR_regr_liblinear.R

@@ -73,11 +73,7 @@ LearnerRegrLiblineaR = R6Class("LearnerRegrLiblineaR",
       train = task$data(cols = task$feature_names)
       target = task$truth()
 
-      if (is.null(pars$type)) {
-        type = 11
-      } else {
-        type = as.numeric(pars$type)
-      }
+      type = ifelse(is.null(pars$type), 11, pars$type)
       pars = pars[names(pars) != "type"]
 
       invoke(LiblineaR::LiblineaR, data = train, target = target, type = type, .args = pars)

R/learner_LiblineaR_regr_liblinearl2l1svr.R

@@ -32,7 +32,7 @@ LearnerRegrLiblineaRL2L1SVR = R6Class("LearnerRegrLiblineaRL2L1SVR",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
 
-      warning("Deprecated. In the future please use `regr.liblinear` with `type = 11` or `type = 12`.") # nolint
+      warning("Deprecated. In the future please use `regr.liblinear` with `type = 13`.") # nolint
 
       ps = ParamSet$new(
         params = list(

inst/paramtest/test_paramtest_LiblineaR_classif_liblinear.R

@@ -0,0 +1,33 @@
+library(mlr3extralearners)
+install_learners("classif.liblinear")
+
+test_that("classif.liblinear", {
+  learner = lrn("classif.liblinear")
+  fun = LiblineaR::LiblineaR
+  exclude = c(
+    "data", # handled by mlr3
+    "target", # handled by mlr3
+    "svr_eps" # only for regression
+  )
+
+  ParamTest = run_paramtest(learner, fun, exclude)
+  expect_true(ParamTest, info = paste0(
+    "Missing parameters:",
+    paste0("- '", ParamTest$missing, "'", collapse = "")))
+})
+
+test_that("classif.liblinear predict", {
+  learner = lrn("classif.liblinear")
+  fun = LiblineaR:::predict.LiblineaR
+  exclude = c(
+    "object", # handled internally
+    "newx", # handled internally
+    "proba", # handled internally
+    "decisionValues" # handled internally
+  )
+
+  ParamTest = run_paramtest(learner, fun, exclude)
+  expect_true(ParamTest, info = paste0(
+    "Missing parameters:",
+    paste0("- '", ParamTest$missing, "'", collapse = "")))
+})

inst/paramtest/test_paramtest_LiblineaR_regr_liblinear.R

@@ -13,9 +13,22 @@ test_that("regr.liblinear", {
 
   ParamTest = run_paramtest(learner, fun, exclude)
   expect_true(ParamTest, info = paste0(
-    "
-Missing parameters:
-",
-    paste0("- '", ParamTest$missing, "'", collapse = "
-")))
+    "Missing parameters:",
+    paste0("- '", ParamTest$missing, "'", collapse = "")))
+})
+
+test_that("regr.liblinear predict", {
+  learner = lrn("regr.liblinear")
+  fun = LiblineaR:::predict.LiblineaR
+  exclude = c(
+    "object", # handled internally
+    "newx", # handled internally
+    "proba", # classif only
+    "decisionValues" # classif only
+  )
+
+  ParamTest = run_paramtest(learner, fun, exclude)
+  expect_true(ParamTest, info = paste0(
+    "Missing parameters:",
+    paste0("- '", ParamTest$missing, "'", collapse = "")))
 })

tests/testthat/test_LiblineaR_classif_liblinear.R

@@ -0,0 +1,8 @@
+install_learners("classif.liblinear")
+
+test_that("autotest", {
+  learner = LearnerClassifLiblineaR$new()
+  expect_learner(learner)
+  result = run_autotest(learner)
+  expect_true(result, info = result$error)
+})