fix: add missing predict parameters to regr.lm (#272)

* fix: add missing predict parameters to regr.lm * fix: param set * fix: xgboost parameter
mlr-org · Jul 26, 2023 · 66f2fc4 · 66f2fc4
1 parent 2c278e7
commit 66f2fc4
Show file tree

Hide file tree

Showing 3 changed files with 16 additions and 13 deletions.
diff --git a/R/LearnerClassifXgboost.R b/R/LearnerClassifXgboost.R
@@ -84,6 +84,7 @@ LearnerClassifXgboost = R6Class("LearnerClassifXgboost",
         colsample_bylevel           = p_dbl(0, 1, default = 1, tags = "train"),
         colsample_bynode            = p_dbl(0, 1, default = 1, tags = "train"),
         colsample_bytree            = p_dbl(0, 1, default = 1, tags = c("train", "control")),
+        device                      = p_uty(default = "cpu", tags = "train"),
         disable_default_eval_metric = p_lgl(default = FALSE, tags = "train"),
         early_stopping_rounds       = p_int(1L, default = NULL, special_vals = list(NULL), tags = "train"),
         early_stopping_set          = p_fct(c("none", "train", "test"), default = "none", tags = "train"),
@@ -114,7 +115,6 @@ LearnerClassifXgboost = R6Class("LearnerClassifXgboost",
         one_drop                    = p_lgl(default = FALSE, tags = "train"),
         outputmargin                = p_lgl(default = FALSE, tags = "predict"),
         predcontrib                 = p_lgl(default = FALSE, tags = "predict"),
-        predictor                   = p_fct(c("cpu_predictor", "gpu_predictor"), default = "cpu_predictor", tags = "train"),
         predinteraction             = p_lgl(default = FALSE, tags = "predict"),
         predleaf                    = p_lgl(default = FALSE, tags = "predict"),
         print_every_n               = p_int(1L, default = 1L, tags = "train"),

diff --git a/R/LearnerRegrLM.R b/R/LearnerRegrLM.R
@@ -23,17 +23,20 @@ LearnerRegrLM = R6Class("LearnerRegrLM",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       ps = ps(
-        df          = p_dbl(default = Inf, tags = "predict"),
-        interval    = p_fct(c("none", "confidence", "prediction"), tags = "predict"),
-        level       = p_dbl(default = 0.95, tags = "predict"),
-        model       = p_lgl(default = TRUE, tags = "train"),
-        offset      = p_lgl(tags = "train"),
-        pred.var    = p_uty(tags = "predict"),
-        qr          = p_lgl(default = TRUE, tags = "train"),
-        scale       = p_dbl(default = NULL, special_vals = list(NULL), tags = "predict"),
-        singular.ok = p_lgl(default = TRUE, tags = "train"),
-        x           = p_lgl(default = FALSE, tags = "train"),
-        y           = p_lgl(default = FALSE, tags = "train")
+        df            = p_dbl(default = Inf, tags = "predict"),
+        interval      = p_fct(c("none", "confidence", "prediction"), tags = "predict"),
+        level         = p_dbl(default = 0.95, tags = "predict"),
+        model         = p_lgl(default = TRUE, tags = "train"),
+        offset        = p_lgl(tags = "train"),
+        pred.var      = p_uty(tags = "predict"),
+        qr            = p_lgl(default = TRUE, tags = "train"),
+        scale         = p_dbl(default = NULL, special_vals = list(NULL), tags = "predict"),
+        singular.ok   = p_lgl(default = TRUE, tags = "train"),
+        x             = p_lgl(default = FALSE, tags = "train"),
+        y             = p_lgl(default = FALSE, tags = "train"),
+        rankdeficient = p_fct(c("warnif", "simple", "non-estim", "NA", "NAwarn"), tags = "predict"),
+        tol           = p_dbl(default = 1e-07, tags = "predict"),
+        verbose       = p_lgl(default = FALSE, tags = "predict")
       )
 
       super$initialize(

diff --git a/R/LearnerRegrXgboost.R b/R/LearnerRegrXgboost.R
@@ -59,6 +59,7 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
         colsample_bylevel           = p_dbl(0, 1, default = 1, tags = "train"),
         colsample_bynode            = p_dbl(0, 1, default = 1, tags = "train"),
         colsample_bytree            = p_dbl(0, 1, default = 1, tags = "train"),
+        device                      = p_uty(default = "cpu", tags = "train"),
         disable_default_eval_metric = p_lgl(default = FALSE, tags = "train"),
         early_stopping_rounds       = p_int(1L, default = NULL, special_vals = list(NULL), tags = "train"),
         early_stopping_set          = p_fct(c("none", "train", "test"), default = "none", tags = "train"),
@@ -89,7 +90,6 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
         one_drop                    = p_lgl(default = FALSE, tags = "train"),
         outputmargin                = p_lgl(default = FALSE, tags = "predict"),
         predcontrib                 = p_lgl(default = FALSE, tags = "predict"),
-        predictor                   = p_fct(c("cpu_predictor", "gpu_predictor"), default = "cpu_predictor", tags = "train"),
         predinteraction             = p_lgl(default = FALSE, tags = "predict"),
         predleaf                    = p_lgl(default = FALSE, tags = "predict"),
         print_every_n               = p_int(1L, default = 1L, tags = "train"),