model.assess: remove superfluous "by_name" indirection

2 files changed, 15 insertions, 18 deletions

diff --git a/lib/model.py b/lib/model.py
index 85cda71..829ca37 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -309,15 +309,15 @@ class AnalyticModel:
                 measures = regression_measures(predicted_data, elem[attribute])
                 detailed_results[name][attribute] = measures
 
-        return {"by_name": detailed_results}
+        return detailed_results
 
     def to_dref(self, static_quality, lut_quality, model_quality) -> dict:
         ret = dict()
         for name in self.names:
             for attr_name, attr in self.attr_by_name[name].items():
-                e_static = static_quality["by_name"][name][attr_name]
-                e_lut = lut_quality["by_name"][name][attr_name]
-                e_model = model_quality["by_name"][name][attr_name]
+                e_static = static_quality[name][attr_name]
+                e_lut = lut_quality[name][attr_name]
+                e_model = model_quality[name][attr_name]
                 unit = None
                 if "power" in attr.attr:
                     unit = r"\micro\watt"
@@ -791,9 +791,7 @@ class PTAModel(AnalyticModel):
         if pta is None:
             pta = PTA(self.states, parameters=self._parameter_names)
         pta.update(
-            param_info,
-            static_error=static_quality["by_name"],
-            function_error=analytic_quality["by_name"],
+            param_info, static_error=static_quality, function_error=analytic_quality
         )
         return pta.to_json()
 
@@ -812,7 +810,7 @@ class PTAModel(AnalyticModel):
         """
         if ref is None:
             ref = self.by_name
-        detailed_results = super().assess(model_function, ref=ref)["by_name"]
+        detailed_results = super().assess(model_function, ref=ref)
         for name, elem in sorted(ref.items()):
             if elem["isa"] == "transition":
                 predicted_data = np.array(
@@ -831,7 +829,7 @@ class PTAModel(AnalyticModel):
                 )
                 detailed_results[name]["energy_Pt"] = measures
 
-        return {"by_name": detailed_results}
+        return detailed_results
 
     def assess_states(
         self, model_function, model_attribute="power", distribution: dict = None
@@ -861,8 +859,7 @@ class PTAModel(AnalyticModel):
             sum(
                 map(
                     lambda x: np.square(
-                        model_quality["by_name"][x][model_attribute]["mae"]
-                        * distribution[x]
+                        model_quality[x][model_attribute]["mae"] * distribution[x]
                     ),
                     self.states,
                 )
diff --git a/lib/validation.py b/lib/validation.py
index ee147fe..cfd4deb 100644
--- a/lib/validation.py
+++ b/lib/validation.py
@@ -172,12 +172,12 @@ class CrossValidator:
         return self._generic_xv(model_getter, training_and_validation_sets)
 
     def _generic_xv(self, model_getter, training_and_validation_sets):
-        ret = {"by_name": dict()}
+        ret = dict()
 
         for name in self.names:
-            ret["by_name"][name] = dict()
+            ret[name] = dict()
             for attribute in self.by_name[name]["attributes"]:
-                ret["by_name"][name][attribute] = {
+                ret[name][attribute] = {
                     "mae_list": list(),
                     "rmsd_list": list(),
                     "smape_list": list(),
@@ -188,15 +188,15 @@ class CrossValidator:
             for name in self.names:
                 for attribute in self.by_name[name]["attributes"]:
                     for measure in ("mae", "rmsd", "smape"):
-                        ret["by_name"][name][attribute][f"{measure}_list"].append(
-                            res["by_name"][name][attribute][measure]
+                        ret[name][attribute][f"{measure}_list"].append(
+                            res[name][attribute][measure]
                         )
 
         for name in self.names:
             for attribute in self.by_name[name]["attributes"]:
                 for measure in ("mae", "rmsd", "smape"):
-                    ret["by_name"][name][attribute][measure] = np.mean(
-                        ret["by_name"][name][attribute][f"{measure}_list"]
+                    ret[name][attribute][measure] = np.mean(
+                        ret[name][attribute][f"{measure}_list"]
                     )
 
         return ret