GH-15719: fix parameters checks and AIC warnings

h2oai · Sep 18, 2023 · f5fec9f · f5fec9f
1 parent 3e70494
commit f5fec9f
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Showing 2 changed files with 7 additions and 15 deletions.
diff --git a/h2o-py/h2o/model/model_base.py b/h2o-py/h2o/model/model_base.py
@@ -544,13 +544,13 @@ def negative_log_likelihood(self):
         
         :return: the negative likelihood function value
         """
-        if self.parms['calc_like']:
+        if self.parms['calc_like']['actual_value']:
             warning_message = "This is the simplified negative log likelihood function used during training for " \
-                              "speedup. To see the correct value call model.model_performance().loglikelihood()"
+                              "speedup. To see the correct value call model.model_performance().loglikelihood()."
         else:
             warning_message = "This is the simplified negative log likelihood function used during training for " \
                               "speedup. To see the correct value, set calc_like=True, retrain the model and call " \
-                              "model.model_performance().loglikelihood()"
+                              "model.model_performance().loglikelihood()."
         warnings.warn(warning_message, UserWarning, stacklevel=2)
         return self._extract_scoring_history("negative_log_likelihood")
 
@@ -1048,16 +1048,11 @@ def aic(self, train=False, valid=False, xval=False):
 
         :returns: The AIC.
         """
-        if self.parms['calc_like']:
-            warning_message = "This is the AIC function using the simplified negative log likelihood used during " \
-                              "training for speedup. To see the correct value call " \
-                              "model.model_performance().aic() again."
-        else:
-
+        if not self.parms['calc_like']['actual_value']:
             warning_message = "This is the AIC function using the simplified negative log likelihood used during " \
                               "training for speedup. To see the correct value, set calc_like=True, " \
                               "retrain the model and call model.model_performance().aic() again."
-        warnings.warn(warning_message, UserWarning, stacklevel=2)
+            warnings.warn(warning_message, UserWarning, stacklevel=2)
         tm = ModelBase._get_metrics(self, train, valid, xval)
         m = {}
         for k, v in tm.items(): m[k] = None if v is None else v.aic()

diff --git a/h2o-r/h2o-package/R/models.R b/h2o-r/h2o-package/R/models.R
@@ -2009,15 +2009,12 @@ h2o.mean_per_class_error <- function(object, train=FALSE, valid=FALSE, xval=FALS
 h2o.aic <- function(object, train=FALSE, valid=FALSE, xval=FALSE) {
   if( is(object, "H2OModelMetrics") ) return( object@metrics$AIC )
   if( is(object, "H2OModel") ) {
-      if (model@allparameters$calc_like) {
-          warning_message <- paste0("This is the AIC function using the simplified negative log likelihood used during ",
-                                   "training for speedup. To see the correct value call h2o.aic(model).")
-      } else {
+      if (!object@allparameters$calc_like) {
           warning_message <- paste0("This is the AIC function using the simplified negative log likelihood used during ",
                                    "training for speedup. To see the correct value, set calc_like=True, ",
                                    "retrain and call h2o.aic(model).")
+          warning(warning_message)
       }
-      warning(warning_message)
     model.parts <- .model.parts(object)
     if ( !train && !valid && !xval ) {
       metric <- model.parts$tm@metrics$AIC