add --add-total-observation for behaviour model evaluation

2 files changed, 28 insertions, 7 deletions

diff --git a/lib/cli.py b/lib/cli.py
index dfe0e34..b94bdbb 100644
--- a/lib/cli.py
+++ b/lib/cli.py
@@ -293,7 +293,11 @@ def model_quality_table(
             buf = f"{key:>{key_len}s} {attr:>{attr_len}s}"
             for results, info in ((lut, None), (model, model_info), (static, None)):
                 buf += "   "
-                if results is not None and (
+
+                # special case for "TOTAL" (--add-total-observation)
+                if attr == "TOTAL" and attr not in results[key]:
+                    buf += f"""{"----":>7s} """
+                elif results is not None and (
                     info is None
                     or (
                         attr != "energy_Pt"
@@ -311,7 +315,7 @@ def model_quality_table(
                     buf += format_quality_measures(result, error_metric=error_metric)
                 else:
                     buf += f"""{"----":>7s} """
-            if type(model_info(key, attr)) is not df.StaticFunction:
+            if attr != "TOTAL" and type(model_info(key, attr)) is not df.StaticFunction:
                 if model[key][attr]["mae"] > static[key][attr]["mae"]:
                     buf += "  :-("
                 elif (
@@ -629,6 +633,11 @@ def add_standard_arguments(parser):
         help="Show model complexity score and details (e.g. regression tree height and node count)",
     )
     parser.add_argument(
+        "--add-total-observation",
+        action="store_true",
+        help="Add a TOTAL observation for each <key> that consists of the sums of its <attribute> entries. This allows for cross-validation of behaviour models vs. non-behaviour-aware models.",
+    )
+    parser.add_argument(
         "--cross-validate",
         metavar="<method>:<count>",
         type=str,
diff --git a/lib/validation.py b/lib/validation.py
index 958a9e0..bf6764d 100644
--- a/lib/validation.py
+++ b/lib/validation.py
@@ -109,7 +109,7 @@ class CrossValidator:
         self.args = args
         self.kwargs = kwargs
 
-    def kfold(self, model_getter, k=10, static=False):
+    def kfold(self, model_getter, k=10, static=False, with_sum=False):
         """
         Perform k-fold cross-validation and return average model quality.
 
@@ -161,10 +161,10 @@ class CrossValidator:
                 training_and_validation_sets[i][name] = subsets_by_name[name][i]
 
         return self._generic_xv(
-            model_getter, training_and_validation_sets, static=static
+            model_getter, training_and_validation_sets, static=static, with_sum=with_sum
         )
 
-    def montecarlo(self, model_getter, count=200, static=False):
+    def montecarlo(self, model_getter, count=200, static=False, with_sum=False):
         """
         Perform Monte Carlo cross-validation and return average model quality.
 
@@ -211,10 +211,12 @@ class CrossValidator:
                 training_and_validation_sets[i][name] = subsets_by_name[name][i]
 
         return self._generic_xv(
-            model_getter, training_and_validation_sets, static=static
+            model_getter, training_and_validation_sets, static=static, with_sum=with_sum
         )
 
-    def _generic_xv(self, model_getter, training_and_validation_sets, static=False):
+    def _generic_xv(
+        self, model_getter, training_and_validation_sets, static=False, with_sum=False
+    ):
         ret = dict()
         models = list()
 
@@ -268,6 +270,16 @@ class CrossValidator:
                     )
                 )
 
+        if with_sum:
+            for name in self.names:
+                attr_0 = self.by_name[name]["attributes"][0]
+                gt_sum = np.zeros(len(ret[name][attr_0]["groundTruth"]))
+                mo_sum = np.zeros(len(ret[name][attr_0]["modelOutput"]))
+                for attribute in self.by_name[name]["attributes"]:
+                    gt_sum += np.array(ret[name][attribute]["groundTruth"])
+                    mo_sum += np.array(ret[name][attribute]["modelOutput"])
+                ret[name]["TOTAL"] = regression_measures(mo_sum, gt_sum)
+
         return ret, models
 
     def _single_xv(self, model_getter, tv_set_dict, static=False):