From 6e2fa5f36deb9882e5bddb212695434c219c3d94 Mon Sep 17 00:00:00 2001
From: Daniel Friesel <daniel.friesel@uos.de>
Date: Wed, 5 Jan 2022 15:56:24 +0100
Subject: store decision tree attributes of xv models in dataref export

---
 lib/functions.py  |  7 +++++++
 lib/model.py      | 18 +++++++++++++++++-
 lib/parameters.py |  3 ++-
 lib/validation.py |  2 +-
 4 files changed, 27 insertions(+), 3 deletions(-)

(limited to 'lib')

diff --git a/lib/functions.py b/lib/functions.py
index 320e8ed..5358d8e 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -339,6 +339,13 @@ class SplitFunction(ModelFunction):
                 ret += 1
         return ret
 
+    def get_max_depth(self):
+        ret = [0]
+        for v in self.child.values():
+            if type(v) is SplitFunction:
+                ret.append(v.get_max_depth())
+        return 1 + max(ret)
+
     @classmethod
     def from_json(cls, data):
         assert data["type"] == "split"
diff --git a/lib/model.py b/lib/model.py
index 4f5f60f..5a44c7b 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -449,7 +449,9 @@ class AnalyticModel:
             threshold=threshold,
         )
 
-    def to_dref(self, static_quality, lut_quality, model_quality) -> dict:
+    def to_dref(
+        self, static_quality, lut_quality, model_quality, xv_models=None
+    ) -> dict:
         ret = dict()
         for name in self.names:
             param_data = {
@@ -546,6 +548,20 @@ class AnalyticModel:
                     )
                 except KeyError:
                     logger.warning(f"{name} {attr_name} param model has no MAPE")
+
+                if xv_models is not None:
+                    keys = ("decision tree/nodes", "decision tree/max depth")
+                    entry = dict()
+                    for k in keys:
+                        entry[k] = list()
+                    for xv_model in xv_models:
+                        dref = xv_model.attr_by_name[name][attr_name].to_dref()
+                        for k in keys:
+                            if k in dref:
+                                entry[k].append(dref[k])
+                    for k in keys:
+                        if len(entry[k]):
+                            ret[k] = np.mean(entry[k])
         return ret
 
     def to_json(self, **kwargs) -> dict:
diff --git a/lib/parameters.py b/lib/parameters.py
index 9cfe145..38e36b2 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -652,7 +652,8 @@ class ModelAttribute:
         ret = {"mean": (self.mean, unit), "median": (self.median, unit)}
 
         if type(self.model_function) is df.SplitFunction:
-            ret["decision tree nodes"] = self.model_function.get_number_of_nodes()
+            ret["decision tree/nodes"] = self.model_function.get_number_of_nodes()
+            ret["decision tree/max depth"] = self.model_function.get_max_depth()
 
         return ret
 
diff --git a/lib/validation.py b/lib/validation.py
index 5c65fe3..89bc67c 100644
--- a/lib/validation.py
+++ b/lib/validation.py
@@ -280,4 +280,4 @@ class CrossValidator:
             validation, self.parameters, *self.args, **self.kwargs
         )
 
-        return training_model, validation_data.assess(training_model)
+        return training_data, validation_data.assess(training_model)
-- 
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