Add GBM to the weaklearners

h2oai · Sep 19, 2023 · 57d471a · 57d471a
1 parent b494106
commit 57d471a
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Showing 3 changed files with 8 additions and 8 deletions.
diff --git a/h2o-algos/src/main/java/hex/schemas/AdaBoostV3.java b/h2o-algos/src/main/java/hex/schemas/AdaBoostV3.java
@@ -29,7 +29,7 @@ public static final class AdaBoostParametersV3 extends ModelParametersSchemaV3<A
         @API(help = "Number of AdaBoost weak learners.", gridable = true)
         public int n_estimators;
 
-        @API(help = "Choose a weak learner type. Defaults to DRF.", gridable = true, values = {"AUTO", "DRF", "GLM"})
+        @API(help = "Choose a weak learner type. Defaults to DRF.", gridable = true, values = {"AUTO", "DRF", "GLM", "GBM"})
         public AdaBoostModel.Algorithm weak_learner;
 
         @API(help="Learning rate (from 0.0 to 1.0)", gridable = true)

diff --git a/h2o-py/h2o/estimators/adaboost.py b/h2o-py/h2o/estimators/adaboost.py
@@ -29,7 +29,7 @@ def __init__(self,
                  categorical_encoding="auto",  # type: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
                  weights_column=None,  # type: Optional[str]
                  n_estimators=50,  # type: int
-                 weak_learner="auto",  # type: Literal["auto", "drf", "glm"]
+                 weak_learner="auto",  # type: Literal["auto", "drf", "glm", "gbm"]
                  learn_rate=0.5,  # type: float
                  seed=-1,  # type: int
                  ):
@@ -64,7 +64,7 @@ def __init__(self,
         :type n_estimators: int
         :param weak_learner: Choose a weak learner type. Defaults to DRF.
                Defaults to ``"auto"``.
-        :type weak_learner: Literal["auto", "drf", "glm"]
+        :type weak_learner: Literal["auto", "drf", "glm", "gbm"]
         :param learn_rate: Learning rate (from 0.0 to 1.0)
                Defaults to ``0.5``.
         :type learn_rate: float
@@ -180,13 +180,13 @@ def weak_learner(self):
         """
         Choose a weak learner type. Defaults to DRF.
 
-        Type: ``Literal["auto", "drf", "glm"]``, defaults to ``"auto"``.
+        Type: ``Literal["auto", "drf", "glm", "gbm"]``, defaults to ``"auto"``.
         """
         return self._parms.get("weak_learner")
 
     @weak_learner.setter
     def weak_learner(self, weak_learner):
-        assert_is_type(weak_learner, None, Enum("auto", "drf", "glm"))
+        assert_is_type(weak_learner, None, Enum("auto", "drf", "glm", "gbm"))
         self._parms["weak_learner"] = weak_learner
 
     @property

diff --git a/h2o-r/h2o-package/R/adaboost.R b/h2o-r/h2o-package/R/adaboost.R
@@ -25,7 +25,7 @@
 #'        you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get
 #'        an accurate prediction, remove all rows with weight == 0.
 #' @param n_estimators Number of AdaBoost weak learners. Defaults to 50.
-#' @param weak_learner Choose a weak learner type. Defaults to DRF. Must be one of: "AUTO", "DRF", "GLM". Defaults to AUTO.
+#' @param weak_learner Choose a weak learner type. Defaults to DRF. Must be one of: "AUTO", "DRF", "GLM", "GBM". Defaults to AUTO.
 #' @param learn_rate Learning rate (from 0.0 to 1.0) Defaults to 0.5.
 #' @param seed Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default).
 #'        Defaults to -1 (time-based random number).
@@ -57,7 +57,7 @@ h2o.adaBoost <- function(x,
                          categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
                          weights_column = NULL,
                          n_estimators = 50,
-                         weak_learner = c("AUTO", "DRF", "GLM"),
+                         weak_learner = c("AUTO", "DRF", "GLM", "GBM"),
                          learn_rate = 0.5,
                          seed = -1)
 {
@@ -109,7 +109,7 @@ h2o.adaBoost <- function(x,
                                          categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
                                          weights_column = NULL,
                                          n_estimators = 50,
-                                         weak_learner = c("AUTO", "DRF", "GLM"),
+                                         weak_learner = c("AUTO", "DRF", "GLM", "GBM"),
                                          learn_rate = 0.5,
                                          seed = -1,
                                          segment_columns = NULL,