Updated auto_symbolic to include configurable threshold & weight

kan/MultKAN.py

@@ -2160,7 +2160,7 @@ def suggest_symbolic(self, l, i, j, a_range=(-10, 10), b_range=(-10, 10), lib=No
 
         return best_name, best_fun, best_r2, best_c;
 
-    def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=1):
+    def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=1, weight_simple = 0.8, r2_threshold=0.0):
         '''
         automatic symbolic regression for all edges
 
@@ -2174,7 +2174,10 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
                 library of candidate symbolic functions
             verbose : int
                 larger verbosity => more verbosity
-            
+            weight_simple : float
+                a weight that prioritizies simplicity (low complexity) over performance (high r2) - set to 0.0 to ignore complexity
+            r2_threshold : float
+                If r2 is below this threshold, the edge will not be fixed with any symbolic function - set to 0.0 to ignore this threshold
         Returns:
         --------
             None
@@ -2191,17 +2194,19 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
         for l in range(len(self.width_in) - 1):
             for i in range(self.width_in[l]):
                 for j in range(self.width_out[l + 1]):
-                    #if self.symbolic_fun[l].mask[j, i] > 0. and self.act_fun[l].mask[i][j] == 0.:
                     if self.symbolic_fun[l].mask[j, i] > 0. and self.act_fun[l].mask[i][j] == 0.:
                         print(f'skipping ({l},{i},{j}) since already symbolic')
                     elif self.symbolic_fun[l].mask[j, i] == 0. and self.act_fun[l].mask[i][j] == 0.:
                         self.fix_symbolic(l, i, j, '0', verbose=verbose > 1, log_history=False)
                         print(f'fixing ({l},{i},{j}) with 0')
                     else:
-                        name, fun, r2, c = self.suggest_symbolic(l, i, j, a_range=a_range, b_range=b_range, lib=lib, verbose=False)
-                        self.fix_symbolic(l, i, j, name, verbose=verbose > 1, log_history=False)
-                        if verbose >= 1:
-                            print(f'fixing ({l},{i},{j}) with {name}, r2={r2}, c={c}')
+                        name, fun, r2, c = self.suggest_symbolic(l, i, j, a_range=a_range, b_range=b_range, lib=lib, verbose=False, weight_simple=weight_simple)
+                        if r2 >= r2_threshold:
+                            self.fix_symbolic(l, i, j, name, verbose=verbose > 1, log_history=False)
+                            if verbose >= 1:
+                                print(f'fixing ({l},{i},{j}) with {name}, r2={r2}, c={c}')
+                        else:
+                            print(f'For ({l},{i},{j}) the best fit was {name}, but r^2 = {r2} and this is lower than {r2_threshold}. This edge was omitted, keep training or try a different threshold.')
 
         self.log_history('auto_symbolic')