Fix bug with original optimum using (#181)

UsovaMA · Marina Usova · web-flow · commit 7678b5f938c9 · 2024-02-20T11:35:44.000+03:00
* fix bug with original optimum using

* add var for number of constraints and fix objective function value

---------

Co-authored-by: Marina Usova &lt;usova@itmm.unn.com&gt;
diff --git a/iOpt/output_system/listeners/static_painters.py b/iOpt/output_system/listeners/static_painters.py
@@ -40,8 +40,10 @@ def __init__(self, file_name: str, path_for_saves="", mode='analysis', calc='obj
         self.search_dataSorted = []
         self.bestValueSorted = []
         self.number_of_parallel_points = 1
+        self.number_of_constraints = 0
 
     def before_method_start(self, method: Method):
+        self.number_of_constraints = method.task.problem.number_of_constraints
         if method.task.problem.number_of_float_variables > 2 and self.calc == 'interpolation':
             raise Exception(
                 "StaticDiscreteListener with calc 'interpolation' supported with dimension <= 2")
@@ -50,7 +52,7 @@ def before_method_start(self, method: Method):
     def on_end_iteration(self, new_points, solution: Solution):
         for newPoint in new_points:
             self.search_dataSorted.append(newPoint)
-            self.bestValueSorted.append(solution.best_trials[0].function_values[0].value)
+            self.bestValueSorted.append(solution.best_trials[0].function_values[self.number_of_constraints].value)
 
     def on_method_stop(self, search_data: SearchData,
                        solution: Solution, status: bool):
@@ -64,8 +66,8 @@ def on_method_stop(self, search_data: SearchData,
                                   solution.problem.lower_bound_of_float_variables,
                                   solution.problem.upper_bound_of_float_variables,
                                   self.file_name, self.path_for_saves, solution.problem.calculate,
-                                  solution.best_trials[0].function_values[0].value,
-                                  search_data, self.number_of_parallel_points)
+                                  solution.best_trials[0].function_values[self.number_of_constraints].value,
+                                  search_data, self.number_of_parallel_points, self.number_of_constraints)
         if self.mode == 'analysis':
             painter.paint_analisys(mrks=2)
         elif self.mode == 'bestcombination':
@@ -105,11 +107,15 @@ def __init__(self, file_name: str, path_for_saves="", indx=0, is_points_at_botto
         self.parameterInNDProblem = indx
         self.is_points_at_bottom = is_points_at_bottom
         self.mode = mode
+        self.number_of_constraints = 0
+
+    def before_method_start(self, method: Method):
+        self.number_of_constraints = method.task.problem.number_of_constraints
 
     def on_method_stop(self, search_data: SearchData,
                        solution: Solution, status: bool):
         painter = StaticPainter(search_data, solution, self.mode, self.is_points_at_bottom,
-                                self.parameterInNDProblem, self.path_for_saves, self.file_name)
+                                self.parameterInNDProblem, self.path_for_saves, self.file_name, self.number_of_constraints)
         painter.paint_objective_func()
         painter.paint_points()
         painter.paint_optimum()
@@ -149,11 +155,14 @@ def __init__(self, file_name: str, path_for_saves="", vars_indxs=[0, 1], mode='l
         self.parameters = vars_indxs
         self.mode = mode
         self.calc = calc
+        self.number_of_constraints = 0
+    def before_method_start(self, method: Method):
+        self.number_of_constraints = method.task.problem.number_of_constraints
 
     def on_method_stop(self, search_data: SearchData,
                        solution: Solution, status: bool, ):
         painter = StaticPainterND(search_data, solution, self.parameters, self.mode, self.calc,
-                                  self.file_name, self.path_for_saves)
+                                  self.file_name, self.path_for_saves, self.number_of_constraints)
         painter.paint_objective_func()
         painter.paint_points()
         painter.paint_optimum()
diff --git a/iOpt/output_system/outputers/console_outputer.py b/iOpt/output_system/outputers/console_outputer.py
@@ -13,6 +13,7 @@ def __init__(self, problem: Problem, parameters: SolverParameters):
         self.__functions = OutputFunctions()
         self.iterNum = 1
         self.ndv = self.problem.number_of_discrete_variables
+        self.number_of_constraints = self.problem.number_of_constraints
 
     def print_init_info(self):
         self.__functions.print_init(
@@ -57,7 +58,7 @@ def print_best_point_info(self, solution, iters):
         else:
             best_trial_point = solution.best_trials[0].point.float_variables
             best_trial_d_point = solution.best_trials[0].point.discrete_variables
-            best_trial_value = solution.best_trials[0].function_values[self.problem.number_of_constraints].value
+            best_trial_value = solution.best_trials[0].function_values[self.number_of_constraints].value
             self.__functions.print_best(
                 solution.number_of_global_trials,
                 solution.number_of_local_trials,
@@ -72,7 +73,7 @@ def print_best_point_info(self, solution, iters):
     def print_final_result_info(self, solution: Solution, status: bool):
         best_trial_point = solution.best_trials[0].point.float_variables
         best_trial_d_point = solution.best_trials[0].point.discrete_variables
-        best_trial_value = solution.best_trials[0].function_values[self.problem.number_of_constraints].value
+        best_trial_value = solution.best_trials[0].function_values[self.number_of_constraints].value
         self.__functions.print_result(
             status,
             solution.number_of_global_trials,
diff --git a/iOpt/output_system/painters/animate_painters.py b/iOpt/output_system/painters/animate_painters.py
@@ -16,12 +16,14 @@ def __init__(self, is_points_at_bottom, parameter_in_nd_problem, path_for_saves,
         self.objFunc = None
         self.parameterInNDProblem = parameter_in_nd_problem
         self.section = []
+        self.number_of_constraints = 0
 
         # настройки графика
         self.plotter = AnimatePlotter2D(parameter_in_nd_problem, 0, 1)
 
     def set_problem(self, problem: Problem):
         self.objFunc = problem.calculate
+        self.number_of_constraints = self.problem.number_of_constraints
 
         for i in range(problem.number_of_float_variables):
             self.section.append(float(problem.upper_bound_of_float_variables[i]) - float(problem.lower_bound_of_float_variables[i]))
@@ -44,7 +46,7 @@ def paint_points(self, curr_points):
 
     def paint_optimum(self, solution: Solution):
         optimum = solution.best_trials[0].point.float_variables[self.parameterInNDProblem]
-        optimumVal = solution.best_trials[0].function_values[0].value
+        optimumVal = solution.best_trials[0].function_values[self.number_of_constraints].value
 
         value = optimumVal
 
@@ -79,6 +81,7 @@ def __init__(self, parameters_in_nd_problem, path_for_saves, file_name):
         self.objFunc = None
         self.parametersInNDProblem = parameters_in_nd_problem
         self.section = []
+        self.number_of_constraints = 0
 
         # настройки графика
         self.plotter = AnimatePlotter3D(parameters_in_nd_problem)
@@ -103,7 +106,7 @@ def paint_points(self, curr_points):
     def paint_optimum(self, solution: Solution):
         optimum = [solution.best_trials[0].point.float_variables[self.parametersInNDProblem[0]],
                    solution.best_trials[0].point.float_variables[self.parametersInNDProblem[1]]]
-        optimumVal = solution.best_trials[0].function_values[0].value
+        optimumVal = solution.best_trials[0].function_values[self.number_of_constraints].value
 
         self.plotter.plot_points(optimum, [], 'red', 'o', 4)
 
diff --git a/iOpt/output_system/painters/plotters/plotters.py b/iOpt/output_system/painters/plotters/plotters.py
@@ -303,12 +303,14 @@ def plot_by_grid(self, calculate, section, points_count=100):
         xv, yv = np.meshgrid(x1, x2)
         z = []
 
+        fv = section.copy()
+
         for i in range(points_count):
             z_ = []
             for j in range(points_count):
-                section[self.indexes[0]] = xv[i, j]
-                section[self.indexes[1]] = yv[i, j]
-                z_.append(calculate(section))
+                fv[self.indexes[0]] = xv[i, j]
+                fv[self.indexes[1]] = yv[i, j]
+                z_.append(calculate(fv))
             z.append(z_)
 
         self.ax.contour(x1, x2, z, linewidths=1, levels=25, cmap=plt.cm.viridis)
diff --git a/iOpt/output_system/painters/static_painters.py b/iOpt/output_system/painters/static_painters.py
@@ -12,14 +12,15 @@
 class DiscretePainter(Painter):
     def __init__(self, search_data_sorted, bestsvalues, pcount, floatdim, optimumPoint, discreteValues,
                  discrete_name, mode, calc, subparameters, lb, rb, file_name, path_for_saves, calculate,
-                 optimum_value, search_data, number_of_parallel_points):
+                 optimum_value, search_data, number_of_parallel_points, number_of_constraints):
         self.path_for_saves = path_for_saves
         self.file_name = file_name
         self.calc = calc
         self.calculate = calculate
         self.optimum = optimumPoint
         self.optimumVal = optimum_value
         self.number_of_parallel_points = number_of_parallel_points
+        self.number_of_constraints = number_of_constraints
 
         self.values = []
         self.points = []
@@ -130,7 +131,8 @@ def __init__(self, search_data: SearchData,
                  is_points_at_bottom,
                  parameter_in_nd_problem,
                  path_for_saves,
-                 file_name
+                 file_name,
+                 number_of_constraints
                  ):
         self.path_for_saves = path_for_saves
         self.file_name = file_name
@@ -139,6 +141,7 @@ def __init__(self, search_data: SearchData,
         self.is_points_at_bottom = is_points_at_bottom
 
         self.objFunc = solution.problem.calculate
+        self.number_of_constraints = number_of_constraints
 
         # формируем массив точек итераций для графика
         self.points = []
@@ -153,7 +156,7 @@ def __init__(self, search_data: SearchData,
 
         self.optimum = solution.best_trials[0].point.float_variables
         self.optimumC = solution.best_trials[0].point.float_variables[parameter_in_nd_problem]
-        self.optimumValue = solution.best_trials[0].function_values[0].value
+        self.optimumValue = solution.best_trials[0].function_values[self.number_of_constraints].value
 
         # настройки графика
         self.plotter = Plotter2D(parameter_in_nd_problem,
@@ -202,14 +205,15 @@ def calculate_func(self, x):
         return fv.value
 
 class StaticPainterND(Painter):
-    def __init__(self, search_data, solution, parameters, mode, calc, file_name, path_for_saves):
+    def __init__(self, search_data, solution, parameters, mode, calc, file_name, path_for_saves, number_of_constraints):
         self.path_for_saves = path_for_saves
         self.file_name = file_name
 
         self.objectFunctionPainterType = mode
         self.objectFunctionCalculatorType = calc
 
         self.objFunc = solution.problem.calculate
+        self.number_of_constraints = number_of_constraints
 
         # формируем массив точек итераций для графика
         self.points = []
@@ -223,7 +227,10 @@ def __init__(self, search_data, solution, parameters, mode, calc, file_name, pat
         self.values = self.values[1:-1]
 
         self.optimum = solution.best_trials[0].point.float_variables
-        self.optimumValue = solution.best_trials[0].function_values[0].value
+        self.optimum_section = [solution.best_trials[0].point.float_variables[parameters[0]],
+                        solution.best_trials[0].point.float_variables[parameters[1]]]
+
+        self.optimumValue = solution.best_trials[0].function_values[self.number_of_constraints].value
 
         self.leftBounds = [float(solution.problem.lower_bound_of_float_variables[parameters[0]]),
                            float(solution.problem.lower_bound_of_float_variables[parameters[1]])]
@@ -258,7 +265,7 @@ def paint_points(self, curr_point: SearchDataItem = None):
         self.plotter.plot_points(self.points, self.values, 'blue', 'o', 4)
 
     def paint_optimum(self, solution: Solution = None):
-        self.plotter.plot_points([self.optimum], [self.optimumValue], 'red', 'o', 4)
+        self.plotter.plot_points([self.optimum_section], [self.optimumValue], 'red', 'o', 4)
 
     def save_image(self):
         if not os.path.isdir(self.path_for_saves):
