implement review suggestions

Author: maurever

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

@@ -3416,7 +3416,8 @@ private void scoreAndUpdateModel() {
           _model._useRemoveOffsetEffects = false;
         }
       }
-      _model.update(_job._key);
+        _model.update(_job._key);
+        _model.generateSummary(_parms._train, _state._iter);
     }
 
     private void scorePostProcessingRestrictedModel(Frame train, long t1) {
@@ -3467,7 +3468,6 @@ private void scorePostProcessingRestrictedModel(Frame train, long t1) {
                         _state.alpha());
             }
         }
-        _job.update(_workPerIteration, _state.toString());
       }
       _model._output._scoring_history = _scoringHistory != null ? _scoringHistory.to2dTable(_parms, null, null) : null;
     }
@@ -3766,7 +3766,8 @@ public void computeImpl() {
             if (_parms._keepBetaDiffVar)
               keepFrameKeys(keep, _model._output._betadiff_var);
             Scope.untrack(keep.toArray(new Key[keep.size()]));
-          }
+          }_model.update(_job._key);
+          _model.generateSummary(_parms._train, _state._iter);
           _model.unlock(_job);
         }
       }

h2o-docs/src/product/data-science/algo-params/control_variables.rst

@@ -21,6 +21,9 @@ To get the unrestricted model with its own metrics use ``glm.make_unrestricted_g
 
 The control variables' coefficients are set to zero in the variable importance table. Use the unrestricted model to get the variable importance table with all variables included. 
 
+If you set up ``control_variables`` together with ``remove_offset_effects`` feature, model metrics and scoring history is calculated with both effects enabled. 
+If you need to get model only with one feature enabled, you can get it using ``glm.make_unrestricted_glm_model(control_variables_enabled=True)`` or ``glm.make_unrestricted_glm_model(remove_offset_effects_enabled=True)``
+
 
 **Notes**:
 

h2o-docs/src/product/data-science/algo-params/remove_offset_effects.rst

@@ -13,6 +13,8 @@ Model metrics and scoring history are calculated for both the restricted model (
 
 To get the unrestricted model with its own metrics use ``glm.make_unrestricted_glm_model()`` / ``h2o.make_unrestricted_glm_model(glm)``.
 
+If you set up ``remove_offset_effects`` together with ``control_variables`` feature, model metrics and scoring history is calculated with both effects enabled. 
+If you need to get model only with one feature enabled, you can get it using ``glm.make_unrestricted_glm_model(control_variables_enabled=True)`` or ``glm.make_unrestricted_glm_model(remove_offset_effects_enabled=True)``
 
 **Notes**:
 
@@ -48,7 +50,7 @@ Example
 		airlines['FlightNum'] <- as.factor(airlines['FlightNum'])
 
 		# set the predictor names and the response column name
-		predictors <- c("Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum")
+		predictors <- c("Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "FlightNum")
 		response <- "IsDepDelayed"
 
 		# split into train and validation
@@ -94,7 +96,7 @@ Example
 		airlines['FlightNum'] = airlines['FlightNum'].asfactor()
 
 		# set the predictor names and the response column name
-		predictors = ["Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum"]
+		predictors = ["Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "FlightNum"]
 		response = "IsDepDelayed"
 
 		# split into train and validation sets