1 files changed, 27 insertions, 23 deletions
diff --git a/lib/parameters.py b/lib/parameters.py
index 1ae4e4c..bafc2a5 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -830,29 +830,6 @@ class ModelAttribute:
                     return False
         return True
 
-    def build_symreg_model(self):
-        ignore_irrelevant = bool(
-            int(os.getenv("DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS", "0"))
-        )
-        ignore_param_indexes = list()
-        if ignore_irrelevant:
-            for param_index, param in enumerate(self.param_names):
-                if not self.stats.depends_on_param(param):
-                    ignore_param_indexes.append(param_index)
-        x = df.SymbolicRegressionFunction(
-            self.median,
-            self.param_names,
-            n_samples=self.data.shape[0],
-            num_args=self.arg_count,
-        )
-        x.fit(self.param_values, self.data, ignore_param_indexes=ignore_param_indexes)
-        if x.fit_success:
-            self.model_function = x
-        else:
-            logger.debug(
-                f"Symbolic Regression model generation for {self.name} {self.attr} failed."
-            )
-
     def fit_override_function(self):
         function_str = self.function_override
         x = df.AnalyticFunction(
@@ -986,6 +963,33 @@ class ModelAttribute:
             )
             return False
 
+    def build_symreg(self):
+        ignore_irrelevant = bool(
+            int(os.getenv("DFATOOL_RMT_IGNORE_IRRELEVANT_PARAMS", "0"))
+        )
+        ignore_param_indexes = list()
+        if ignore_irrelevant:
+            for param_index, param in enumerate(self.param_names):
+                if not self.stats.depends_on_param(param):
+                    ignore_param_indexes.append(param_index)
+        x = df.SymbolicRegressionFunction(
+            np.mean(self.data),
+            n_samples=self.data.shape[0],
+            param_names=self.param_names,
+            arg_count=self.arg_count,
+        ).fit(self.param_values, self.data, ignore_param_indexes=ignore_param_indexes)
+        if x.fit_success:
+            self.model_function = x
+            return True
+        else:
+            logger.debug(
+                f"Symbolic Regression model generation for {self.name} {self.attr} failed."
+            )
+            self.model_function = df.StaticFunction(
+                np.mean(self.data), n_samples=len(self.data)
+            )
+            return False
+
     def build_xgb(self):
         mf = df.XGBoostFunction(
             np.mean(self.data),