objectives update

Author: Gromwud

epde/eq_mo_objectives.py

@@ -36,7 +36,8 @@ def equation_fitness(system, equation_key = None):
     else:
         for equation in system.vals:
             assert equation.fitness_value
-        res = np.mean([equation.fitness_value for equation in system.vals])
+        # res = np.mean([equation.fitness_value for equation in system.vals])
+        res = tuple([equation.fitness_value for equation in system.vals])
     return res
 
 
@@ -109,7 +110,8 @@ def equation_terms_stability(system, equation_key = None):
     else:
         for equation in system.vals:
             assert equation.stability_calculated
-        res = np.mean([equation.coefficients_stability for equation in system.vals])
+        # res = np.mean([equation.coefficients_stability for equation in system.vals])
+        res = tuple([equation.coefficients_stability for equation in system.vals])
     return res
 
 def equation_aic(system, equation_key):

epde/operators/multiobjective/moeadd_specific.py

@@ -62,9 +62,12 @@ def penalty_based_intersection(sol_obj, weight, ideal_obj,
     # print(f'Objective before normalization: {sol_obj.obj_fun} for normalizer {obj_normalizer}')
     solution_objective = sol_obj.obj_fun if obj_normalizer is None else obj_normalizer(sol_obj.obj_fun)
     # print(f'Objective after expected normalization: {solution_objective}')
-    
-    d_1 = np.dot((solution_objective - ideal_obj), weight) / np.linalg.norm(weight)
-    d_2 = np.linalg.norm(solution_objective - (ideal_obj + d_1 * weight/np.linalg.norm(weight)))
+
+    weight_full = [item for item in weight for _ in sol_obj.vals]
+    ideal_obj_full = [item for item in ideal_obj for _ in sol_obj.vals]
+
+    d_1 = np.dot((solution_objective - ideal_obj_full), weight_full) / np.linalg.norm(weight_full)
+    d_2 = np.linalg.norm(solution_objective - (ideal_obj_full + np.multiply(d_1, weight_full) / np.linalg.norm(weight_full)))
     return d_1 + penalty_factor * d_2
 
 

epde/optimizers/moeadd/moeadd.py

@@ -366,9 +366,7 @@ def __init__(self, population_instruct, pop_size, solution_params,
                 raise TypeError(f'Incorrect type of the population passed. Expected ParetoLevels object, instead got \
                                  {type(passed_population)}')
             self.pareto_levels = passed_population
-
-        self.weights = []
-        weights_size = len(population[0].obj_funs) #np.empty((pop_size, len(optimized_functionals)))
+        weights_size = len(best_sol_vals) #np.empty((pop_size, len(optimized_functionals)))
         # weights_size = len(set([fun.func for fun in population[0].obj_funs]))
         self.weights = np.array(self.weights_generation_new(weights_size, H))
 
@@ -379,7 +377,7 @@ def __init__(self, population_instruct, pop_size, solution_params,
                     list(zip(np.arange(weights_num), [np.linalg.norm(self.weights[weights_idx, :] - self.weights[weights_idx_inner, :]) for weights_idx_inner in np.arange(weights_num)])), 
                     key = lambda pair: pair[1])][:neighbors_number+1]) # срез листа - задаёт регион "близости"
 
-        self.best_obj = None
+        self.best_obj = best_sol_vals
         self._hist = []
 
     def abbreviated_search(self, population, sorting_method, update_method):

epde/optimizers/moeadd/solution_template.py

@@ -30,7 +30,7 @@ def get_domain_idx(solution, weights) -> int:
     if type(solution) == np.ndarray:
         return np.fromiter(map(lambda x: acute_angle(x, solution), weights), dtype=float).argmin()
     elif type(solution.obj_fun) == np.ndarray:
-        return np.fromiter(map(lambda x: acute_angle(x, solution.obj_fun), weights), dtype=float).argmin()
+        return np.fromiter(map(lambda x: acute_angle([item for item in x for _ in solution.vals], solution.obj_fun), weights), dtype=float).argmin()
     else:
         raise ValueError(
             'Can not detect the vector of objective function for solution')

epde/optimizers/moeadd/supplementary.py

@@ -52,11 +52,11 @@ def check_dominance(target, compared_with) -> bool:
     flag = False
 
     sdn = 5 # Number of significant digits
-    for obj_fun_idx in range(len(target.obj_fun)):
+    for obj_fun_idx in range(len(target.obj_fun.reshape(-1))):
         # if rts(target.obj_fun[obj_fun_idx], sdn) <= rts(compared_with.obj_fun[obj_fun_idx], sdn):
         #     if rts(target.obj_fun[obj_fun_idx], sdn) < rts(compared_with.obj_fun[obj_fun_idx], sdn):
-        if target.obj_fun[obj_fun_idx] <= compared_with.obj_fun[obj_fun_idx]:
-            if target.obj_fun[obj_fun_idx] < compared_with.obj_fun[obj_fun_idx]:
+        if target.obj_fun.reshape(-1)[obj_fun_idx] <= compared_with.obj_fun.reshape(-1)[obj_fun_idx]:
+            if target.obj_fun.reshape(-1)[obj_fun_idx] < compared_with.obj_fun.reshape(-1)[obj_fun_idx]:
                 flag = True
         else:
             return False