set standarzie = false by default and changes all the tests.

h2o-algos/src/main/java/hex/hglm/HGLMModel.java

@@ -88,7 +88,7 @@ private HGLMScore makeScoringTask(Frame adaptFrm, boolean makePredictions, Job j
   public static class HGLMParameters extends Model.Parameters {
     public long _seed = -1;
     public GLMModel.GLMParameters.Family _family;
-    public boolean _standardize = true;
+    public boolean _standardize = false;
     public int _max_iterations = -1;
     public double[] _initial_fixed_effects; // initial values of fixed coefficients   
     public Key _initial_random_effects; // frame key that contains the initial starting values of random coefficient effects

h2o-algos/src/test/java/hex/hglm/HGLMBasicTest.java

@@ -112,6 +112,7 @@ public void testLevel2enum1() {
       params._random_columns = new String[]{"enum6", "enum4", "enum2", "num1", "num3"};
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum1, params._group_column, params._standardize,
@@ -135,6 +136,7 @@ public void testLevel2enum1NoIntercept() {
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
       params._random_intercept = true;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum1, params._group_column, params._standardize,
@@ -252,6 +254,7 @@ public void testLevel2enum3() {
       params._random_columns = new String[]{"enum4", "enum1", "num1", "num2"};
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum3, params._group_column, params._standardize,
@@ -274,6 +277,7 @@ public void testLevel2enum3V2() {
       params._random_columns = new String[]{"enum4", "enum1", "num1", "num2"};
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum3, params._group_column, params._standardize,
@@ -297,6 +301,7 @@ public void testLevel2enum4() {
       params._random_columns = new String[]{"enum3", "enum1", "enum2", "enum6", "num1", "num2"};
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum4, params._group_column, params._standardize,
@@ -319,6 +324,7 @@ public void testLevel2enum4V2() {
       params._random_columns = new String[]{"enum3", "enum1", "enum2", "enum6", "num1", "num2"};
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum4, params._group_column, params._standardize,
@@ -341,6 +347,7 @@ public void testLevel2enum5() {
       params._random_columns = new String[]{"enum3", "enum1", "enum2", "enum6", "num1", "num3"};
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum5, params._group_column, params._standardize,
@@ -363,6 +370,7 @@ public void testLevel2enum5V2() {
       params._random_columns = new String[]{"enum3", "enum1", "enum2", "enum6", "num1", "num3"};
       params._showFixedMatVecs = true;
       params._max_iterations = 0;
+      params._standardize = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
       checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum5, params._group_column, params._standardize,
@@ -749,6 +757,7 @@ public void testCoeffDeNNormalizationWithRandomIntercept() {
       parms._initial_fixed_effects = initBetaStandardize;
       parms._initial_random_effects = ubetaFrameStandardize._key;
       parms._random_intercept = true;
+      parms._standardize = true;
 
       // just make sure it runs
       HGLMModel model1 = new HGLM(parms).trainModel().get();
@@ -906,6 +915,7 @@ public void testSemiconductor() {
       parms._random_intercept = true;
       parms._group_column = "Device";
       parms._max_iterations = 1;
+      parms._standardize = true;
 
       // just make sure it runs
       HGLMModel model = new HGLM(parms).trainModel().get();

h2o-py/h2o/estimators/hglm.py

@@ -41,7 +41,7 @@ def __init__(self,
                  plug_values=None,  # type: Optional[Union[None, str, H2OFrame]]
                  family="gaussian",  # type: Literal["gaussian"]
                  rand_family=None,  # type: Optional[Literal["gaussian"]]
-                 standardize=True,  # type: bool
+                 standardize=False,  # type: bool
                  max_iterations=-1,  # type: int
                  initial_fixed_effects=None,  # type: Optional[List[float]]
                  initial_random_effects=None,  # type: Optional[Union[None, str, H2OFrame]]
@@ -116,7 +116,7 @@ def __init__(self,
                Defaults to ``None``.
         :type rand_family: Literal["gaussian"], optional
         :param standardize: Standardize numeric columns to have zero mean and unit variance.
-               Defaults to ``True``.
+               Defaults to ``False``.
         :type standardize: bool
         :param max_iterations: Maximum number of iterations.  Value should >=1.  A value of 0 is only set when only the
                model coefficient names and model coefficient dimensions are needed.
@@ -432,7 +432,7 @@ def standardize(self):
         """
         Standardize numeric columns to have zero mean and unit variance.
 
-        Type: ``bool``, defaults to ``True``.
+        Type: ``bool``, defaults to ``False``.
         """
         return self._parms.get("standardize")
 

h2o-py/tests/testdir_algos/hglm/pyunit_GH_8487_1p5_noise_var_scoring_history_summary.py

@@ -73,7 +73,7 @@ def test_scoring_history_model_summary():
     # check and make sure the llg from training and validation frame should be increasing in values
     # this is only true when the true residual variance is high.  For low true residual variance, it is only
     # true for the last few iterations when the residual variance estimate is close to the true residual variance
-    if (residual_var_true >= 2):
+    if (residual_var_true > 2):
         for ind in list(range(1, model_iterations)):
             p_ind = ind-1
             assert scoring_history.cell_values[p_ind][3] <= scoring_history.cell_values[ind][3], \

h2o-py/tests/testdir_algos/hglm/pyunit_GH_8487_coefficients_check.py

@@ -17,7 +17,7 @@ def test_scoring_history_model_summary():
     x.remove("C1")
     random_columns = ["C2", "C3", "C10", "C20"]
     hglm_model = hglm(random_columns=random_columns, group_column = "C1", score_each_iteration=True, seed=12345, 
-                      max_iterations=10)
+                      max_iterations=10, standardize=True)
     hglm_model.train(x=x, y=y, training_frame=train, validation_frame=valid)
     # grab various metrics
     coef = hglm_model.coef()

h2o-py/tests/testdir_algos/hglm/pyunit_GH_8487_p5_noise_var_scoring_history_summary.py

@@ -73,7 +73,7 @@ def test_scoring_history_model_summary():
     # check and make sure the llg from training and validation frame should be increasing in values
     # this is only true when the true residual variance is high.  For low true residual variance, it is only
     # true for the last few iterations when the residual variance estimate is close to the true residual variance
-    if (residual_var_true >= 2):
+    if (residual_var_true > 2):
         for ind in list(range(1, model_iterations)):
             p_ind = ind-1
             assert scoring_history.cell_values[p_ind][3] <= scoring_history.cell_values[ind][3], \

h2o-r/h2o-package/R/hglm.R

@@ -40,7 +40,7 @@
 #' @param family Family. Only gaussian is supported now. Must be one of: "gaussian". Defaults to gaussian.
 #' @param rand_family rand_family. Set distribution of random effects.  Only Gaussian is implemented now. Must be one of:
 #'        "gaussian".
-#' @param standardize \code{Logical}. Standardize numeric columns to have zero mean and unit variance. Defaults to TRUE.
+#' @param standardize \code{Logical}. Standardize numeric columns to have zero mean and unit variance. Defaults to FALSE.
 #' @param max_iterations Maximum number of iterations.  Value should >=1.  A value of 0 is only set when only the model coefficient
 #'        names and model coefficient dimensions are needed. Defaults to -1.
 #' @param initial_fixed_effects An array that contains initial values of the fixed effects coefficient.
@@ -94,7 +94,7 @@ h2o.hglm <- function(x,
                      plug_values = NULL,
                      family = c("gaussian"),
                      rand_family = c("gaussian"),
-                     standardize = TRUE,
+                     standardize = FALSE,
                      max_iterations = -1,
                      initial_fixed_effects = NULL,
                      initial_random_effects = NULL,
@@ -214,7 +214,7 @@ h2o.hglm <- function(x,
                                      plug_values = NULL,
                                      family = c("gaussian"),
                                      rand_family = c("gaussian"),
-                                     standardize = TRUE,
+                                     standardize = FALSE,
                                      max_iterations = -1,
                                      initial_fixed_effects = NULL,
                                      initial_random_effects = NULL,