From 164c9e5a7e1015578ee6005c35dc1cbc39125550 Mon Sep 17 00:00:00 2001
From: Birte Kristina Friesel <birte.friesel@uos.de>
Date: Fri, 8 Mar 2024 10:55:46 +0100
Subject: make FOLFunction inherit from SKLearnRegressionFunction

---
 lib/functions.py | 31 +++++++++++++++----------------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/lib/functions.py b/lib/functions.py
index 212e47a..0f19668 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -1555,14 +1555,9 @@ class SymbolicRegressionFunction(SKLearnRegressionFunction):
 
 
 # first-order linear function (no feature interaction)
-class FOLFunction(ModelFunction):
+class FOLFunction(SKLearnRegressionFunction):
     always_predictable = True
-
-    def __init__(self, value, parameters, num_args=0, **kwargs):
-        super().__init__(value, **kwargs)
-        self.parameter_names = parameters
-        self._num_args = num_args
-        self.fit_success = False
+    has_eval_arr = False
 
     def fit(self, param_values, data, ignore_param_indexes=None):
         self.categorical_to_scalar = bool(
@@ -1599,7 +1594,7 @@ class FOLFunction(ModelFunction):
                     num_vars += 1
             funbuf = "regression_arg(0)"
             num_vars = 1
-            for j, param_name in enumerate(self.parameter_names):
+            for j, param_name in enumerate(self.param_names):
                 if self.ignore_index[j]:
                     continue
                 else:
@@ -1608,10 +1603,10 @@ class FOLFunction(ModelFunction):
                             f" + regression_arg({num_vars}) * parameter({param_name})"
                         )
                         num_vars += 1
-                    for k in range(j + 1, len(self.parameter_names)):
+                    for k in range(j + 1, len(self.param_names)):
                         if self.ignore_index[j]:
                             continue
-                        funbuf += f" + regression_arg({num_vars}) * parameter({param_name}) * parameter({self.parameter_names[k]})"
+                        funbuf += f" + regression_arg({num_vars}) * parameter({param_name}) * parameter({self.param_names[k]})"
                         num_vars += 1
         else:
             num_vars = fit_parameters.shape[0] + 1
@@ -1620,7 +1615,7 @@ class FOLFunction(ModelFunction):
                 rawbuf += f" + reg_param[{i}] * model_param[{i-1}]"
             funbuf = "regression_arg(0)"
             i = 1
-            for j, param_name in enumerate(self.parameter_names):
+            for j, param_name in enumerate(self.param_names):
                 if self.ignore_index[j]:
                     continue
                 else:
@@ -1711,17 +1706,21 @@ class FOLFunction(ModelFunction):
             {
                 "type": "analytic",
                 "functionStr": self.model_function,
-                "argCount": self._num_args,
-                "parameterNames": self.parameter_names,
+                "argCount": self.arg_count,
+                "parameterNames": self.param_names,
                 "regressionModel": list(self.model_args),
             }
         )
         return ret
 
     def hyper_to_dref(self):
-        return {
-            "fol/categorical to scalar": int(self.categorical_to_scalar),
-        }
+        hyper = super().hyper_to_dref()
+        hyper.update(
+            {
+                "fol/categorical to scalar": int(self.categorical_to_scalar),
+            }
+        )
+        return hyper
 
 
 class AnalyticFunction(ModelFunction):
-- 
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