Refactor n_estimators to nlearners in python and R api

h2o-algos/src/main/java/hex/schemas/AdaBoostV3.java

@@ -20,14 +20,14 @@ public static final class AdaBoostParametersV3 extends ModelParametersSchemaV3<A
                 "weights_column",
 
                 // AdaBoost specific
-                "n_estimators",
+                "nlearners",
                 "weak_learner",
                 "learn_rate",
                 "seed",
         };
 
         @API(help = "Number of AdaBoost weak learners.", gridable = true)
-        public int n_estimators;
+        public int nlearners;
 
         @API(help = "Choose a weak learner type. Defaults to DRF.", gridable = true, values = {"AUTO", "DRF", "GLM", "GBM"})
         public AdaBoostModel.Algorithm weak_learner;

h2o-py/h2o/estimators/adaboost.py

@@ -28,7 +28,7 @@ def __init__(self,
                  ignore_const_cols=True,  # type: bool
                  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
+                 nlearners=50,  # type: int
                  weak_learner="auto",  # type: Literal["auto", "drf", "glm", "gbm"]
                  learn_rate=0.5,  # type: float
                  seed=-1,  # type: int
@@ -59,9 +59,9 @@ def __init__(self,
                that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
                Defaults to ``None``.
         :type weights_column: str, optional
-        :param n_estimators: Number of AdaBoost weak learners.
+        :param nlearners: Number of AdaBoost weak learners.
                Defaults to ``50``.
-        :type n_estimators: int
+        :type nlearners: int
         :param weak_learner: Choose a weak learner type. Defaults to DRF.
                Defaults to ``"auto"``.
         :type weak_learner: Literal["auto", "drf", "glm", "gbm"]
@@ -80,7 +80,7 @@ def __init__(self,
         self.ignore_const_cols = ignore_const_cols
         self.categorical_encoding = categorical_encoding
         self.weights_column = weights_column
-        self.n_estimators = n_estimators
+        self.nlearners = nlearners
         self.weak_learner = weak_learner
         self.learn_rate = learn_rate
         self.seed = seed
@@ -162,18 +162,18 @@ def weights_column(self, weights_column):
         self._parms["weights_column"] = weights_column
 
     @property
-    def n_estimators(self):
+    def nlearners(self):
         """
         Number of AdaBoost weak learners.
 
         Type: ``int``, defaults to ``50``.
         """
-        return self._parms.get("n_estimators")
+        return self._parms.get("nlearners")
 
-    @n_estimators.setter
-    def n_estimators(self, n_estimators):
-        assert_is_type(n_estimators, None, int)
-        self._parms["n_estimators"] = n_estimators
+    @nlearners.setter
+    def nlearners(self, nlearners):
+        assert_is_type(nlearners, None, int)
+        self._parms["nlearners"] = nlearners
 
     @property
     def weak_learner(self):

h2o-py/tests/testdir_algos/adaboost/pyunit_adaboost_saveload.py

@@ -11,7 +11,7 @@ def adaBoost_save_and_load():
     train = h2o.import_file(pyunit_utils.locate("smalldata/prostate/prostate.csv"))
     train["CAPSULE"] = train["CAPSULE"].asfactor()
 
-    adaboost_model = H2OAdaBoostEstimator(n_estimators=7, seed=12)
+    adaboost_model = H2OAdaBoostEstimator(nlearners=7, seed=12)
     adaboost_model.train(training_frame=train, y="CAPSULE")
     predict = adaboost_model.predict(train)
 

h2o-py/tests/testdir_algos/adaboost/pyunit_adaboost_smoke.py

@@ -11,7 +11,7 @@ def adaboost():
     train = h2o.import_file(pyunit_utils.locate("smalldata/prostate/prostate.csv"))
     train["CAPSULE"] = train["CAPSULE"].asfactor()
 
-    adaboost_model = H2OAdaBoostEstimator(n_estimators=50, seed=0xBEEF, weak_learner="DRF", learn_rate=0.5)
+    adaboost_model = H2OAdaBoostEstimator(nlearners=50, seed=0xBEEF, weak_learner="DRF", learn_rate=0.5)
     adaboost_model.train(training_frame=train, y="CAPSULE")
     predict = adaboost_model.predict(train)
 

h2o-r/h2o-package/R/adaboost.R

@@ -24,7 +24,7 @@
 #'        well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If
 #'        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 nlearners 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", "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).
@@ -56,7 +56,7 @@ h2o.adaBoost <- function(x,
                          ignore_const_cols = TRUE,
                          categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
                          weights_column = NULL,
-                         n_estimators = 50,
+                         nlearners = 50,
                          weak_learner = c("AUTO", "DRF", "GLM", "GBM"),
                          learn_rate = 0.5,
                          seed = -1)
@@ -89,8 +89,8 @@ h2o.adaBoost <- function(x,
     parms$categorical_encoding <- categorical_encoding
   if (!missing(weights_column))
     parms$weights_column <- weights_column
-  if (!missing(n_estimators))
-    parms$n_estimators <- n_estimators
+  if (!missing(nlearners))
+    parms$nlearners <- nlearners
   if (!missing(weak_learner))
     parms$weak_learner <- weak_learner
   if (!missing(learn_rate))
@@ -108,7 +108,7 @@ h2o.adaBoost <- function(x,
                                          ignore_const_cols = TRUE,
                                          categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
                                          weights_column = NULL,
-                                         n_estimators = 50,
+                                         nlearners = 50,
                                          weak_learner = c("AUTO", "DRF", "GLM", "GBM"),
                                          learn_rate = 0.5,
                                          seed = -1,
@@ -146,8 +146,8 @@ h2o.adaBoost <- function(x,
     parms$categorical_encoding <- categorical_encoding
   if (!missing(weights_column))
     parms$weights_column <- weights_column
-  if (!missing(n_estimators))
-    parms$n_estimators <- n_estimators
+  if (!missing(nlearners))
+    parms$nlearners <- nlearners
   if (!missing(weak_learner))
     parms$weak_learner <- weak_learner
   if (!missing(learn_rate))