cross validation: return intermediate models used for XV

These are interesting for statistics, e.g. to determine the average dtree size

1 files changed, 5 insertions, 3 deletions

diff --git a/lib/validation.py b/lib/validation.py
index 47395dc..5c65fe3 100644
--- a/lib/validation.py
+++ b/lib/validation.py
@@ -211,6 +211,7 @@ class CrossValidator:
 
     def _generic_xv(self, model_getter, training_and_validation_sets):
         ret = dict()
+        models = list()
 
         for name in self.names:
             ret[name] = dict()
@@ -223,7 +224,8 @@ class CrossValidator:
                 }
 
         for training_and_validation_by_name in training_and_validation_sets:
-            res = self._single_xv(model_getter, training_and_validation_by_name)
+            model, res = self._single_xv(model_getter, training_and_validation_by_name)
+            models.append(model)
             for name in self.names:
                 for attribute in self.by_name[name]["attributes"]:
                     for measure in ("mae", "rmsd", "mape", "smape"):
@@ -238,7 +240,7 @@ class CrossValidator:
                         ret[name][attribute][f"{measure}_list"]
                     )
 
-        return ret
+        return ret, models
 
     def _single_xv(self, model_getter, tv_set_dict):
         training = dict()
@@ -278,4 +280,4 @@ class CrossValidator:
             validation, self.parameters, *self.args, **self.kwargs
         )
 
-        return validation_data.assess(training_model)
+        return training_model, validation_data.assess(training_model)