Always build model in get_fitted; never in constructor

2 files changed, 23 insertions, 40 deletions

diff --git a/lib/model.py b/lib/model.py
index e2a455d..cb08fce 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -154,6 +154,7 @@ class AnalyticModel:
             self.fit_done = True
             return
 
+        self.force_tree = force_tree
         self.fit_done = False
 
         if compute_stats:
@@ -177,23 +178,6 @@ class AnalyticModel:
                             (name, attr)
                         ]
 
-        if force_tree:
-            for name in self.names:
-                for attr in self.by_name[name]["attributes"]:
-                    if max_std and name in max_std and attr in max_std[name]:
-                        threshold = max_std[name][attr]
-                    elif compute_stats:
-                        threshold = self.attr_by_name[name][attr].stats.std_param_lut
-                    else:
-                        threshold = 0
-                    logger.debug(f"build_dtree({name}, {attr}, threshold={threshold})")
-                    self.build_dtree(
-                        name,
-                        attr,
-                        threshold=threshold,
-                    )
-            self.fit_done = True
-
     def get_by_param(self):
         if not "by_param" in self.cache:
             self.cache["by_param"] = by_name_to_by_param(self.by_name)
@@ -318,6 +302,27 @@ class AnalyticModel:
         model_info(name, attribute) -> {'fit_result' : ..., 'function' : ... } or None
         """
 
+        if self.force_tree and not self.fit_done:
+            for name in self.names:
+                for attr in self.by_name[name]["attributes"]:
+                    if (
+                        self.dtree_max_std
+                        and name in self.dtree_max_std
+                        and attr in self.dtree_max_std[name]
+                    ):
+                        threshold = self.dtree_max_std[name][attr]
+                    elif self.attr_by_name[name][attr].stats:
+                        threshold = self.attr_by_name[name][attr].stats.std_param_lut
+                    else:
+                        threshold = 0
+                    logger.debug(f"build_dtree({name}, {attr}, threshold={threshold})")
+                    self.build_dtree(
+                        name,
+                        attr,
+                        threshold=threshold,
+                    )
+            self.fit_done = True
+
         if not self.fit_done:
             paramfit = ParamFit()
             tree_allowed = bool(int(os.getenv("DFATOOL_DTREE_ENABLED", "1")))
@@ -772,26 +777,7 @@ class PTAModel(AnalyticModel):
         if compute_stats:
             self._compute_stats(by_name)
 
-        if force_tree:
-            for name in self.names:
-                for attr in self.by_name[name]["attributes"]:
-                    if (
-                        dtree_max_std
-                        and name in dtree_max_std
-                        and attr in dtree_max_std[name]
-                    ):
-                        threshold = dtree_max_std[name][attr]
-                    elif compute_stats:
-                        threshold = (self.attr_by_name[name][attr].stats.std_param_lut,)
-                    else:
-                        threshold = 0
-                    logger.debug(f"build_dtree({name}, {attr}, threshold={threshold})")
-                    self.build_dtree(
-                        name,
-                        attr,
-                        threshold=threshold,
-                    )
-            self.fit_done = True
+        self.force_tree = force_tree
 
         if self.pelt is not None:
             # cluster_substates uses self.attr_by_name[*]["power"].param_values, which is set by _compute_stats
diff --git a/lib/validation.py b/lib/validation.py
index 6203003..958a9e0 100644
--- a/lib/validation.py
+++ b/lib/validation.py
@@ -302,8 +302,6 @@ class CrossValidator:
 
         logger.debug("Creating training model instance")
         kwargs = self.kwargs.copy()
-        if static:
-            kwargs["force_tree"] = False
         training_data = self.model_class(
             training, self.parameters, *self.args, **kwargs
         )
@@ -311,7 +309,6 @@ class CrossValidator:
         training_model = model_getter(training_data)
         kwargs = self.kwargs.copy()
         kwargs["compute_stats"] = False
-        kwargs["force_tree"] = False
         logger.debug("Creating validation model instance")
         validation_data = self.model_class(
             validation, self.parameters, *self.args, **kwargs