GH-16484 Merge release branch manually (#16517)

* GH-16466 escape sequences (#16505) * GH-16466: Fixed invalid escape sequences in docstrings Allow h2o to be imported without triggering syntax errors. Closes #16466 * Adjust escape in the gen_xgboost.py --------- Co-authored-by: Justin Mahlik <[email protected]> * GH-16471 - replace hortonworks with private nexus repo (#16506) --------- Co-authored-by: Justin Mahlik <[email protected]>
h2oai · Feb 6, 2025 · 247686e · 247686e
1 parent 6740655
commit 247686e
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Showing 4 changed files with 5 additions and 7 deletions.
diff --git a/build.gradle b/build.gradle
@@ -251,10 +251,8 @@ subprojects {
                 url "https://repository.cloudera.com/artifactory/cloudera-repos/"
             }
             maven {
-                url "https://repo.hortonworks.com/content/repositories/releases/"
-            }
-            maven {
-                url "https://repo.hortonworks.com/content/repositories/jetty-hadoop/"
+                allowInsecureProtocol = true
+                url "http://nexus-private.hortonworks.com:8081/nexus/"
             }
             maven {
                 url "https://repository.mapr.com/maven/"

diff --git a/h2o-bindings/bin/custom/python/gen_xgboost.py b/h2o-bindings/bin/custom/python/gen_xgboost.py
@@ -49,7 +49,7 @@ def convert_H2OXGBoostParams_2_XGBoostParams(self):
 
          1. Train the H2OXGBoost model with H2OFrame trainFile and generate a prediction:
 
-          - h2oModelD = H2OXGBoostEstimator(\*\*h2oParamsD) # parameters specified as a dict()
+          - h2oModelD = H2OXGBoostEstimator(\\*\\*h2oParamsD) # parameters specified as a dict()
           - h2oModelD.train(x=myX, y=y, training_frame=trainFile) # train with H2OFrame trainFile
           - h2oPredict = h2oPredictD = h2oModelD.predict(trainFile)
 

diff --git a/h2o-py/h2o/estimators/xgboost.py b/h2o-py/h2o/estimators/xgboost.py
@@ -2568,7 +2568,7 @@ def convert_H2OXGBoostParams_2_XGBoostParams(self):
 
          1. Train the H2OXGBoost model with H2OFrame trainFile and generate a prediction:
 
-          - h2oModelD = H2OXGBoostEstimator(\*\*h2oParamsD) # parameters specified as a dict()
+          - h2oModelD = H2OXGBoostEstimator(\\*\\*h2oParamsD) # parameters specified as a dict()
           - h2oModelD.train(x=myX, y=y, training_frame=trainFile) # train with H2OFrame trainFile
           - h2oPredict = h2oPredictD = h2oModelD.predict(trainFile)
 

diff --git a/h2o-py/h2o/model/model_base.py b/h2o-py/h2o/model/model_base.py
@@ -915,7 +915,7 @@ def r2(self, train=False, valid=False, xval=False):
 
         Will return :math:`R^2` for GLM Models. 
 
-        The :math:`R^2` value is defined to be :math:`1 - MSE / var`, where var is computed as :math:`\sigma * \sigma`.
+        The :math:`R^2` value is defined to be :math:`1 - MSE / var`, where var is computed as :math:`\\sigma * \\sigma`.
 
         If all are ``False`` (default), then return the training metric value.
         If more than one option is set to ``True``, then return a dictionary of metrics where the keys are "train",