Corrected criteria in the problem with transformers

aimclub · Dec 3, 2024 · 537bf05 · 537bf05
1 parent 602c7a2
commit 537bf05
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/examples/Machine_learning/SVC/_2D/Problems/MCO_SVC_2D_Transformators_State.py b/examples/Machine_learning/SVC/_2D/Problems/MCO_SVC_2D_Transformators_State.py
@@ -42,7 +42,7 @@ def __init__(self, x_dataset: np.ndarray, y_dataset: np.ndarray,
         self.upper_bound_of_float_variables = np.array([regularization_bound['up'], kernel_coefficient_bound['up']],
                                                    dtype=np.double)
 
-        self.cv = StratifiedKFold(shuffle=True, random_state=42)
+        #self.cv = StratifiedKFold(shuffle=True, random_state=42)
 
 
 
@@ -59,15 +59,15 @@ def calculateAllFunction(self, point: Point, function_values: np.ndarray(shape=(
 
 
         cs, gammas = point.float_variables[0], point.float_variables[1]
-        clf = SVC(C=10 ** cs, gamma=10 ** gammas)
+        clf = SVC(C=10 ** cs, gamma=10 ** gammas, probability=True)
         clf.fit(self.x, self.y)
 
         # OBJECTIV 1
-        function_values[0].value = - cross_val_score(clf, self.x, self.y, cv=self.cv,
-                                                     scoring='f1_macro').mean()
+        function_values[0].value = - cross_val_score(clf, self.x, self.y, n_jobs=4,
+                                                     scoring='neg_log_loss').mean()
         # OBJECTIV 2
-        function_values[1].value = - cross_val_score(clf, self.x, self.y, cv=self.cv,
-                                                     scoring='f1_weighted').mean()
+        function_values[1].value = - cross_val_score(clf, self.x, self.y, n_jobs=4,
+                                                     scoring='f1_macro').mean()
 
 
         return function_values