Added the ability to calculate all criteria using one model

aimclub · May 8, 2024 · 391a42f · 391a42f
1 parent a12ad34
commit 391a42f
Show file tree

Hide file tree

Showing 5 changed files with 67 additions and 5 deletions.
diff --git a/examples/Machine_learning/SVC/_2D/Problems/mco_breast_cancer.py b/examples/Machine_learning/SVC/_2D/Problems/mco_breast_cancer.py
@@ -65,3 +65,29 @@ def calculate(self, point: Point, function_value: FunctionValue) -> FunctionValu
 
         function_value.value = result
         return function_value
+
+    def calculateAllFunction(self, point: Point, function_values: np.ndarray(shape=(1), dtype=FunctionValue)) -> \
+            np.ndarray(shape=(1), dtype=FunctionValue):
+        """
+        Вычисление значения выбранной функции в заданной точке.
+
+        :param point: координаты точки испытания, в которой будет вычислено значение функции
+        :param function_values: массив объектов, определяющих номера функций в задаче и хранящий значения функций
+        :return: массив вычисленных значений функций в точке point
+        """
+        x = point.float_variables
+
+        svc_c = 10 ** x[0]
+        gamma = 10 ** x[1]
+
+        classifier_obj = SVC(C=svc_c, gamma=gamma)
+        classifier_obj.fit(self.X_train, self.y_train)
+
+        # OBJECTIV 1
+        function_values[0].value = - cross_val_score(classifier_obj, self.X, self.y, n_jobs=4,
+                                                     scoring='precision').mean()
+        # OBJECTIV 2
+        function_values[1].value = - cross_val_score(classifier_obj, self.X, self.y, n_jobs=4,
+                                                     scoring='recall').mean()
+
+        return function_values
diff --git a/iOpt/method/mco_method_evaluate.py b/iOpt/method/mco_method_evaluate.py
@@ -31,7 +31,7 @@ def calculate_functionals(self, point: SearchDataItem) -> SearchDataItem:
 
             for i in range(self.task.problem.number_of_objectives):
                 point.function_values[number_of_constraints + i] = FunctionValue(FunctionType.OBJECTIV, i)
-                point = self.task.calculate(point, number_of_constraints + i)
+            point = self.task.calculate(point, -1)
 
             point = self.task.calculate(point, -1, TypeOfCalculation.CONVOLUTION)
             point.set_index(number_of_constraints)

diff --git a/iOpt/method/mco_optim_task.py b/iOpt/method/mco_optim_task.py
@@ -92,10 +92,18 @@ def calculate(self,
                   ) -> SearchDataItem:
         """Compute selected function by number."""
         if calculation_type == TypeOfCalculation.FUNCTION:
-            data_item.function_values[self.perm[function_index]] = \
-                self.problem.calculate(data_item.point, data_item.function_values[self.perm[function_index]])
-            if not np.isfinite(data_item.function_values[self.perm[function_index]].value):
-                raise Exception("Infinity values")
+            if function_index == -1:  # Calculate all criteria
+                data_item.function_values = self.problem.calculateAllFunction(data_item.point,
+                                                                              data_item.function_values)
+                for i in range(self.problem.number_of_objectives):
+                    if not np.isfinite(data_item.function_values[self.perm[self.problem.number_of_constraints +
+                                                                           i]].value):
+                        raise Exception("Infinity values")
+            else:
+                data_item.function_values[self.perm[function_index]] = \
+                    self.problem.calculate(data_item.point, data_item.function_values[self.perm[function_index]])
+                if not np.isfinite(data_item.function_values[self.perm[function_index]].value):
+                    raise Exception("Infinity values")
 
         elif calculation_type == TypeOfCalculation.CONVOLUTION:
             data_item = self.convolution.calculate_convolution(data_item, self.min_value, self.max_value)

diff --git a/iOpt/problem.py b/iOpt/problem.py
@@ -34,6 +34,19 @@ def calculate(self, point: Point, function_value: FunctionValue) -> FunctionValu
         :return: Calculated value of the function."""
         pass
 
+    def calculateAllFunction(self, point: Point, function_values: np.ndarray(shape=(1), dtype=FunctionValue)) -> \
+            np.ndarray(shape=(1), dtype=FunctionValue):
+        """
+        Calculate all functions at a given point.
+          For any new problem statement that inherits from :class:`Problem`, this method should be overloaded
+
+        :return: Calculated values of the functions."""
+        for i in range(self.number_of_objectives):
+            function_values[i].value = self.calculate(point, function_values[i])
+
+        return function_values
+
+
     # @abstractmethod
     def get_name(self):
         """

diff --git a/problems/mco_test1.py b/problems/mco_test1.py
@@ -57,4 +57,19 @@ def calculate(self, point: Point, function_value: FunctionValue) -> FunctionValu
         function_value.value = result
         return function_value
 
+    def calculateAllFunction(self, point: Point, function_values: np.ndarray(shape=(1), dtype=FunctionValue)) -> \
+            np.ndarray(shape=(1), dtype=FunctionValue):
+        """
+        Calculate all function at a given point.
+          For any new problem statement that inherits from :class:`Problem`, this method should be overloaded
+
+        :return: Calculated values of the functions."""
+        x = point.float_variables
+
+        # OBJECTIVE 1
+        function_values[0].value = np.double((x[0]-1)*x[1]*x[1]+1)
+        # OBJECTIVE 2
+        function_values[1].value = np.double(x[1])
+
+        return function_values