do not build dtree in static and LUT cross-validation runs

2 files changed, 23 insertions, 12 deletions

diff --git a/bin/analyze-kconfig.py b/bin/analyze-kconfig.py
index cea1c9e..c33292e 100755
--- a/bin/analyze-kconfig.py
+++ b/bin/analyze-kconfig.py
@@ -421,10 +421,12 @@ def main():
     logging.debug(f"model.get_fitted(...) took {fit_duration : 7.1f} seconds")
 
     if xv_method == "montecarlo":
-        static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count)
+        static_quality, _ = xv.montecarlo(
+            lambda m: m.get_static(), xv_count, static=True
+        )
         if lut_model:
             lut_quality, _ = xv.montecarlo(
-                lambda m: m.get_param_lut(fallback=True), xv_count
+                lambda m: m.get_param_lut(fallback=True), xv_count, static=True
             )
         else:
             lut_quality = None
@@ -433,10 +435,10 @@ def main():
             lambda m: m.get_fitted()[0], xv_count
         )
     elif xv_method == "kfold":
-        static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count)
+        static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count, static=True)
         if lut_model:
             lut_quality, _ = xv.kfold(
-                lambda m: m.get_param_lut(fallback=True), xv_count
+                lambda m: m.get_param_lut(fallback=True), xv_count, static=True
             )
         else:
             lut_quality = None
diff --git a/lib/validation.py b/lib/validation.py
index 0e735a0..95815ac 100644
--- a/lib/validation.py
+++ b/lib/validation.py
@@ -107,7 +107,7 @@ class CrossValidator:
         self.args = args
         self.kwargs = kwargs
 
-    def kfold(self, model_getter, k=10):
+    def kfold(self, model_getter, k=10, static=False):
         """
         Perform k-fold cross-validation and return average model quality.
 
@@ -159,9 +159,11 @@ class CrossValidator:
             for name in self.names:
                 training_and_validation_sets[i][name] = subsets_by_name[name][i]
 
-        return self._generic_xv(model_getter, training_and_validation_sets)
+        return self._generic_xv(
+            model_getter, training_and_validation_sets, static=static
+        )
 
-    def montecarlo(self, model_getter, count=200):
+    def montecarlo(self, model_getter, count=200, static=False):
         """
         Perform Monte Carlo cross-validation and return average model quality.
 
@@ -208,9 +210,11 @@ class CrossValidator:
             for name in self.names:
                 training_and_validation_sets[i][name] = subsets_by_name[name][i]
 
-        return self._generic_xv(model_getter, training_and_validation_sets)
+        return self._generic_xv(
+            model_getter, training_and_validation_sets, static=static
+        )
 
-    def _generic_xv(self, model_getter, training_and_validation_sets):
+    def _generic_xv(self, model_getter, training_and_validation_sets, static=False):
         ret = dict()
         models = list()
 
@@ -225,7 +229,9 @@ class CrossValidator:
                 }
 
         for training_and_validation_by_name in training_and_validation_sets:
-            model, res = self._single_xv(model_getter, training_and_validation_by_name)
+            model, res = self._single_xv(
+                model_getter, training_and_validation_by_name, static=static
+            )
             models.append(model)
             for name in self.names:
                 for attribute in self.by_name[name]["attributes"]:
@@ -249,7 +255,7 @@ class CrossValidator:
 
         return ret, models
 
-    def _single_xv(self, model_getter, tv_set_dict):
+    def _single_xv(self, model_getter, tv_set_dict, static=False):
         training = dict()
         validation = dict()
         for name in self.names:
@@ -279,8 +285,11 @@ class CrossValidator:
             for idx in validation_subset:
                 validation[name]["param"].append(self.by_name[name]["param"][idx])
 
+        kwargs = self.kwargs.copy()
+        if static:
+            kwargs["force_tree"] = False
         training_data = self.model_class(
-            training, self.parameters, *self.args, **self.kwargs
+            training, self.parameters, *self.args, **kwargs
         )
         training_model = model_getter(training_data)
         kwargs = self.kwargs.copy()