From 24c645137264c2f9d9146cda73677a67ec815ca3 Mon Sep 17 00:00:00 2001
From: Daniel Friesel <daniel.friesel@uos.de>
Date: Tue, 26 Oct 2021 15:14:37 +0200
Subject: allow custom standard deviation thresholds for decision tree
 compilation

---
 lib/model.py | 30 ++++++++++++++++++++++++++----
 1 file changed, 26 insertions(+), 4 deletions(-)

(limited to 'lib')

diff --git a/lib/model.py b/lib/model.py
index 749eebb..b6318d7 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -76,6 +76,7 @@ class AnalyticModel:
         use_corrcoef=False,
         compute_stats=True,
         force_tree=False,
+        max_std=None,
     ):
         """
         Create a new AnalyticModel and compute parameter statistics.
@@ -119,6 +120,7 @@ class AnalyticModel:
         self.names = sorted(by_name.keys())
         self.parameters = sorted(parameters)
         self.function_override = function_override.copy()
+        self.dtree_max_std = max_std
         self._use_corrcoef = use_corrcoef
         self._num_args = arg_count
         if self._num_args is None:
@@ -138,8 +140,19 @@ class AnalyticModel:
         if force_tree:
             for name in self.names:
                 for attr in self.by_name[name]["attributes"]:
-                    # TODO specify correct threshold
-                    self.build_dtree(name, attr, 0)
+                    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
+                    with_function_leaves = bool(
+                        int(os.getenv("DFATOOL_DTREE_FUNCTION_LEAVES", "1"))
+                    )
+                    logger.debug(
+                        f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves})"
+                    )
+                    self.build_dtree(name, attr, threshold, with_function_leaves)
             self.fit_done = True
 
     def __repr__(self):
@@ -278,13 +291,20 @@ class AnalyticModel:
                         with_function_leaves = bool(
                             int(os.getenv("DFATOOL_DTREE_FUNCTION_LEAVES", "1"))
                         )
+                        threshold = self.attr_by_name[name][attr].stats.std_param_lut
+                        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]
                         logger.debug(
-                            f"build_dtree({name}, {attr}, threshold={self.attr_by_name[name][attr].stats.std_param_lut}, with_function_leaves={with_function_leaves})"
+                            f"build_dtree({name}, {attr}, threshold={threshold}, with_function_leaves={with_function_leaves})"
                         )
                         self.build_dtree(
                             name,
                             attr,
-                            self.attr_by_name[name][attr].stats.std_param_lut,
+                            threshold,
                             with_function_leaves=with_function_leaves,
                         )
                     else:
@@ -513,6 +533,7 @@ class PTAModel(AnalyticModel):
         pta=None,
         pelt=None,
         compute_stats=True,
+        dtree_max_std=None,
     ):
         """
         Prepare a new PTA energy model.
@@ -556,6 +577,7 @@ class PTAModel(AnalyticModel):
             )
         )
         self.states_and_transitions = self.states + self.transitions
+        self.dtree_max_std = dtree_max_std
 
         self._parameter_names = sorted(parameters)
         self.parameters = sorted(parameters)
-- 
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