GH-16433 Fixed misc small errors in GAM, GLM, ModelSelection toolboxes [nocheck]

h2o-algos/src/main/java/hex/gam/GAM.java

@@ -68,7 +68,8 @@ public class GAM extends ModelBuilder<GAMModel, GAMModel.GAMParameters, GAMModel
 
   @Override
   public ModelCategory[] can_build() {
-    return new ModelCategory[]{ModelCategory.Regression};
+    return new ModelCategory[]{ModelCategory.Regression, ModelCategory.Binomial, ModelCategory.Multinomial, 
+            ModelCategory.Ordinal};
   }
 
   @Override

h2o-algos/src/main/java/hex/glm/GLM.java

@@ -120,8 +120,10 @@ public boolean isSupervised() {
   @Override
   public ModelCategory[] can_build() {
     return new ModelCategory[]{
-      ModelCategory.Regression,
-      ModelCategory.Binomial,
+            ModelCategory.Regression,
+            ModelCategory.Binomial,
+            ModelCategory.Multinomial,
+            ModelCategory.Ordinal
     };
   }
 

h2o-algos/src/main/java/hex/modelselection/ModelSelectionModel.java

@@ -48,7 +48,7 @@ protected double[] score0(double[] data, double[] preds) {
 
     @Override
     public Frame score(Frame fr, String destination_key, Job j, boolean computeMetrics, CFuncRef customMetricFunc) {
-        throw new UnsupportedOperationException("AnovaGLM does not support scoring on data.  It only provide " +
+        throw new UnsupportedOperationException("ModelSelection does not support scoring on data.  It only provide " +
                 "information on predictor relevance");
     }
 

h2o-algos/src/main/java/hex/schemas/ModelSelectionV3.java

@@ -186,7 +186,7 @@ public static final class ModelSelectionParametersV3 extends ModelParametersSche
                 "inverse", "tweedie", "ologit"}) //"oprobit", "ologlog": will be supported.
         public GLMModel.GLMParameters.Link link;
 
-        @API(help = "double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.",
+        @API(help = "Double array to initialize coefficients for GLM.",
                 gridable=true)
         public double[] startval;
 

h2o-bindings/bin/custom/python/gen_gam.py

@@ -15,12 +15,6 @@ def update_param(name, param):
 
 
 def class_extensions():
-    def _additional_used_columns(self, parms):
-        """
-        :return: Gam columns if specified.
-        """
-        return parms["gam_columns"]
-
     def _summary(self):
         """Return a detailed summary of the model."""
         model = self._model_json["output"]

h2o-py/h2o/estimators/gam.py

@@ -1595,12 +1595,6 @@ def gainslift_bins(self, gainslift_bins):
 
     Lambda = deprecated_property('Lambda', lambda_)
 
-    def _additional_used_columns(self, parms):
-        """
-        :return: Gam columns if specified.
-        """
-        return parms["gam_columns"]
-
     def _summary(self):
         """Return a detailed summary of the model."""
         model = self._model_json["output"]

h2o-py/h2o/estimators/model_selection.py

@@ -227,7 +227,7 @@ def __init__(self,
                lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively.
                Defaults to ``-1.0``.
         :type gradient_epsilon: float
-        :param startval: double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.
+        :param startval: Double array to initialize coefficients for GLM.
                Defaults to ``None``.
         :type startval: List[float], optional
         :param prior: Prior probability for y==1. To be used only for logistic regression iff the data has been sampled
@@ -900,7 +900,7 @@ def gradient_epsilon(self, gradient_epsilon):
     @property
     def startval(self):
         """
-        double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.
+        Double array to initialize coefficients for GLM.
 
         Type: ``List[float]``.
         """

h2o-py/tests/testdir_algos/glm/pyunit_benign_glm.py

@@ -7,7 +7,7 @@
 
 def test_benign():
     training_data = h2o.import_file(pyunit_utils.locate("smalldata/logreg/benign.csv"))
-
+    training_data[3] = training_data[3].asfactor()
     Y = 3
     X = [0, 1, 2, 4, 5, 6, 7, 8, 9, 10]
 

h2o-r/h2o-package/R/modelselection.R

@@ -77,7 +77,7 @@
 #'        (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of
 #'        gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True,
 #'        the conditional values above are 1E-8 and 1E-6 respectively. Defaults to -1.
-#' @param startval double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.
+#' @param startval Double array to initialize coefficients for GLM.
 #' @param prior Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean
 #'        of response does not reflect reality. Defaults to 0.
 #' @param cold_start \code{Logical}. Only applicable to multiple alpha/lambda values.  If false, build the next model for next set