PUBDEV-3071: Add RMSE to model metrics

h2o-core/src/main/java/hex/ModelMetrics.java

@@ -53,13 +53,15 @@ public ModelMetrics(Model model, Frame frame, long nobs, double MSE, String desc
     sb.append(" model id: " + _modelKey + "\n");
     sb.append(" frame id: " + _frameKey + "\n");
     sb.append(" MSE: " + (float)_MSE + "\n");
+    sb.append(" RMSE: " + (float)rmse() + "\n");
     return sb.toString();
   }
 
   public Model model() { return _model==null ? (_model=DKV.getGet(_modelKey)) : _model; }
   public Frame frame() { return _frame==null ? (_frame=DKV.getGet(_frameKey)) : _frame; }
 
   public double mse() { return _MSE; }
+  public double rmse() { return Math.sqrt(_MSE);}
   public ConfusionMatrix cm() { return null; }
   public float[] hr() { return null; }
   public AUC2 auc_obj() { return null; }

h2o-core/src/main/java/hex/schemas/GridSchemaV99.java

@@ -27,7 +27,7 @@ public class GridSchemaV99 extends SchemaV3<Grid, GridSchemaV99> {
   @API(help = "Grid id")
   public KeyV3.GridKeyV3 grid_id;
 
-  @API(help = "Model performance metric to sort by. Examples: logloss, residual_deviance, mse, auc, r2, f1, recall, precision, accuracy, mcc, err, err_count, lift_top_group, max_per_class_error", required = false, direction = API.Direction.INOUT)
+  @API(help = "Model performance metric to sort by. Examples: logloss, residual_deviance, mse, rmse, auc, r2, f1, recall, precision, accuracy, mcc, err, err_count, lift_top_group, max_per_class_error", required = false, direction = API.Direction.INOUT)
   public String sort_by;
 
   @API(help = "Specify whether sort order should be decreasing.", required = false, direction = API.Direction.INOUT)

h2o-core/src/main/java/water/api/schemas3/ModelMetricsBaseV3.java

@@ -46,6 +46,9 @@ public class ModelMetricsBaseV3<I extends ModelMetrics, S extends ModelMetricsBa
   @API(help = "The Mean Squared Error of the prediction for this scoring run.", direction = API.Direction.OUTPUT)
   public double MSE;
 
+  @API(help = "The Root Mean Squared Error of the prediction for this scoring run.", direction = API.Direction.OUTPUT)
+  public double RMSE;
+
   @API(help="Number of observations.")
   public long nobs;
 
@@ -70,7 +73,7 @@ public ModelMetricsBaseV3() {}
 
     PojoUtils.copyProperties(this, modelMetrics, PojoUtils.FieldNaming.ORIGIN_HAS_UNDERSCORES,
             new String[]{"model", "model_category", "model_checksum", "frame", "frame_checksum"});
-
+    RMSE=modelMetrics.rmse();
 
     return (S) this;
   }

h2o-py/h2o/model/metrics_base.py

@@ -59,6 +59,7 @@ def show(self):
       print(reported_on.format("test"))
     print()
     print("MSE: "                                           + str(self.mse()))
+    print("RMSE: "                                           + str(self.rmse()))
     if metric_type in types_w_r2:
       print("R^2: "                                           + str(self.r2()))
     if metric_type in types_w_mean_residual_deviance:
@@ -144,6 +145,12 @@ def mse(self):
     """
     return self._metric_json['MSE']
 
+  def rmse(self):
+    """
+    :return: Retrieve the RMSE for this set of metrics
+    """
+    return self._metric_json['RMSE']
+
   def residual_deviance(self):
     """
     :return: the residual deviance if the model has residual deviance, or None if no residual deviance.

h2o-py/h2o/model/model_base.py

@@ -473,6 +473,31 @@ def mse(self, train=False, valid=False, xval=False):
     for k,v in zip(list(tm.keys()),list(tm.values())): m[k] = None if v is None else v.mse()
     return list(m.values())[0] if len(m) == 1 else m
 
+  def rmse(self, train=False, valid=False, xval=False):
+    """
+    Get the RMSE(s).
+    If all are False (default), then return the training metric value.
+    If more than one options is set to True, then return a dictionary of metrics where the keys are "train", "valid",
+    and "xval"
+
+    Parameters
+    ----------
+    train : bool, default=True
+      If train is True, then return the RMSE value for the training data.
+    valid : bool, default=True
+      If valid is True, then return the RMSE value for the validation data.
+    xval : bool, default=True
+      If xval is True, then return the RMSE value for the cross validation data.
+
+    Returns
+    -------
+      The RMSE for this regression model.
+    """
+    tm = ModelBase._get_metrics(self, train, valid, xval)
+    m = {}
+    for k,v in zip(list(tm.keys()),list(tm.values())): m[k] = None if v is None else v.rmse()
+    return list(m.values())[0] if len(m) == 1 else m
+
   def logloss(self, train=False, valid=False, xval=False):
     """
     Get the Log Loss(s).

h2o-r/h2o-package/R/models.R

@@ -1024,6 +1024,79 @@ h2o.mse <- function(object, train=FALSE, valid=FALSE, xval=FALSE) {
   invisible(NULL)
 }
 
+#' Retrieves Root Mean Squared Error Value
+#'
+#' Retrieves the root mean squared error value from an \linkS4class{H2OModelMetrics}
+#' object.
+#' If "train", "valid", and "xval" parameters are FALSE (default), then the training RMSEvalue is returned. If more
+#' than one parameter is set to TRUE, then a named vector of RMSEs are returned, where the names are "train", "valid"
+#' or "xval".
+#'
+#' This function only supports \linkS4class{H2OBinomialMetrics},
+#' \linkS4class{H2OMultinomialMetrics}, and \linkS4class{H2ORegressionMetrics} objects.
+#'
+#' @param object An \linkS4class{H2OModelMetrics} object of the correct type.
+#' @param train Retrieve the training RMSE
+#' @param valid Retrieve the validation RMSE
+#' @param xval Retrieve the cross-validation RMSE
+#' @seealso \code{\link{h2o.auc}} for AUC, \code{\link{h2o.mse}} for RMSE, and
+#'          \code{\link{h2o.metric}} for the various threshold metrics. See
+#'          \code{\link{h2o.performance}} for creating H2OModelMetrics objects.
+#' @examples
+#' \donttest{
+#' library(h2o)
+#' h2o.init()
+#'
+#' prosPath <- system.file("extdata", "prostate.csv", package="h2o")
+#' hex <- h2o.uploadFile(prosPath)
+#'
+#' hex[,2] <- as.factor(hex[,2])
+#' model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
+#' perf <- h2o.performance(model, hex)
+#' h2o.rmse(perf)
+#' }
+#' @export
+h2o.rmse <- function(object, train=FALSE, valid=FALSE, xval=FALSE) {
+  if( is(object, "H2OModelMetrics") ) return( object@metrics$RMSE )
+  if( is(object, "H2OModel") ) {
+    metrics <- NULL # break out special for clustering vs the rest
+    model.parts <- .model.parts(object)
+    if ( !train && !valid && !xval ) {
+      metric <- model.parts$tm@metrics$RMSE
+      if ( !is.null(metric) ) return(metric)
+    }
+    v <- c()
+    v_names <- c()
+    if ( train ) {
+      if( is(object, "H2OClusteringModel") ) v <- model.parts$tm@metrics$centroid_stats$within_cluster_sum_of_squares
+      else v <- c(v,model.parts$tm@metrics$RMSE)
+      v_names <- c(v_names,"train")
+    }
+    if ( valid ) {
+      if( is.null(model.parts$vm) ) return(invisible(.warn.no.validation()))
+      else {
+        if( is(object, "H2OClusteringModel") ) v <- model.parts$vm@metrics$centroid_stats$within_cluster_sum_of_squares
+        else v <- c(v,model.parts$vm@metrics$MSE)
+        v_names <- c(v_names,"valid")
+      }
+    }
+    if ( xval ) {
+      if( is.null(model.parts$xm) ) return(invisible(.warn.no.cross.validation()))
+      else {
+        if( is(object, "H2OClusteringModel") ) v <- model.parts$xm@metrics$centroid_stats$within_cluster_sum_of_squares
+        else v <- c(v,model.parts$xm@metrics$MSE)
+        v_names <- c(v_names,"xval")
+      }
+    }
+    if ( !is.null(v) ) {
+      names(v) <- v_names
+      if ( length(v)==1 ) { return( v[[1]] ) } else { return( v ) }
+    }
+  }
+  warning(paste0("No MSE for ",class(object)))
+  invisible(NULL)
+}
+
 #' Retrieve the Log Loss Value
 #'
 #' Retrieves the log loss output for a \linkS4class{H2OBinomialMetrics} or