Merge pull request #58 from Gromwud/main

Bugfixes

Author: Gromwud

epde/operators/common/fitness.py

@@ -202,11 +202,14 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
                 eq_window_weights.append(valuable_weights)
             std = np.array(eq_window_weights).std(axis=0, ddof=1)
             mu = np.array(eq_window_weights).mean(axis=0)
-            scale = []
-            for feature in range(features.shape[-1]):
-                scale.append(feature_window[:, feature] ** 2 / (feature_window[:, feature] ** 2 + target_window ** 2))
-            scale = np.array(scale).mean(axis=1)
-            eq_cv = np.sqrt(std ** 2 / (std ** 2 + mu ** 2) * scale)
+            # scale = []
+            # for feature in range(features.shape[-1]):
+            #     # scale.append(feature_window[:, feature] ** 2 / (feature_window[:, feature] ** 2 + target_window ** 2))
+            #     scale.append(np.linalg.norm(feature_window[:, feature] * mu[feature], ord=2) / np.linalg.norm(target_window, ord=2))
+            # scale = np.array(scale) / sum(scale)
+            # eq_cv = np.sqrt(std ** 2 / (std ** 2 + mu ** 2) * scale)
+            # eq_cv = np.sqrt(std ** 2 / (std ** 2 + mu ** 2)) * scale
+            eq_cv = std ** 2 / (mu ** 2)
             lr += np.nan_to_num(eq_cv).sum()
 
         lr = lr / (len(objective.structure) - 1) / target_vals.ndim

epde/operators/common/sparsity.py

@@ -31,7 +31,8 @@ def get_cv(self, weights):
         std = np.array(weights).std(axis=0, ddof=1)
         mu = np.array(weights).mean(axis=0)
         # cv = std ** 2 / (std ** 2 + mu ** 2)
-        cv = np.sqrt(std ** 2 / (std ** 2 + mu ** 2))
+        # cv = np.sqrt(std ** 2 / (std ** 2 + mu ** 2))
+        cv = std ** 2 / (mu ** 2)
         return cv
 
     def calculate_weights(self, X, y):

epde/operators/multiobjective/moeadd_specific.py

@@ -168,7 +168,7 @@ def apply_deprecated(self, objective : Tuple[Union[SoEq, ParetoLevels]], argumen
 
         # TODO: Init normalizer here!
         # print('objective is ', objective)
-        # objective[1].set_normalizer()
+        objective[1].set_normalizer()
 
         objective[1].update(objective[0])  #levels_updated = ndl_update(offspring, levels)
         if len(objective[1].levels) == 1:
@@ -434,9 +434,9 @@ def apply(self, objective: ParetoLevels, arguments: dict):
         while objective.unplaced_candidates:
             offspring = objective.unplaced_candidates.pop()
             attempt = 0
-            replaced = 0
+            # replaced = 0
             mutation_attempt_limit = self.params['mutation_attempt_limit']
-            offspring_attempt_limit = self.params['offspring_attempt_limit']
+            # offspring_attempt_limit = self.params['offspring_attempt_limit']
             # self.suboperators['sparsity'].apply(objective=offspring,
             #                                     arguments=subop_args['sparsity'])
             temp_offspring = deepcopy(offspring)
@@ -464,13 +464,13 @@ def apply(self, objective: ParetoLevels, arguments: dict):
                     objective.history.add(system)
                     print(temp_offspring.obj_fun)
                     break
-                elif replaced == offspring_attempt_limit:
+                elif attempt == mutation_attempt_limit:
                     print("Could not generate unique offspring")
                     break
-                elif attempt == mutation_attempt_limit:
-                    temp_offspring = deepcopy(offspring)
-                    replaced += 1
-                    attempt = 0
+                # elif attempt == mutation_attempt_limit:
+                #     temp_offspring = deepcopy(offspring)
+                #     replaced += 1
+                #     attempt = 0
                 attempt += 1
         return objective
 

epde/operators/multiobjective/mutations.py

@@ -34,12 +34,12 @@ def apply(self, objective : SoEq, arguments : dict): # TODO: add setter for best
         # altered_eq = self.suboperators['equation_mutation'].apply(altered_objective.vals[eq_key],
         #                                                           subop_args['equation_mutation'])
         for eq_key in eqs_keys:
-            affected_by_mutation = np.random.random() < (self.params['indiv_mutation_prob'] / len(eq_key))
+            affected_by_mutation = np.random.random() < (self.params['indiv_mutation_prob'] / len(eqs_keys))
             if affected_by_mutation:
                 altered_eq = self.suboperators['equation_mutation'].apply(altered_objective.vals[eq_key],
                                                                           subop_args['equation_mutation'])
 
-            altered_objective.vals.replace_gene(gene_key = eq_key, value = altered_eq)
+                altered_objective.vals.replace_gene(gene_key = eq_key, value = altered_eq)
 
         for param_key in params_keys:
             altered_param = self.suboperators['param_mutation'].apply(altered_objective.vals[param_key],

epde/operators/multiobjective/variation.py

@@ -83,15 +83,14 @@ def apply(self, objective : ParetoLevels, arguments : dict):
             new_system_2 = deepcopy(crossover_pool[pair_idx, 1])
             # new_system_1.reset_state(False); new_system_2.reset_state()
 
-            new_system_1, new_system_2 = self.suboperators['chromosome_crossover'].apply(objective = (new_system_1, new_system_2), 
-
+            new_system_1, new_system_2 = self.suboperators['chromosome_crossover'].apply(objective = (new_system_1, new_system_2),
                                                                                          arguments = subop_args['chromosome_crossover'])
 
-            if len(new_system_1.vars_to_describe) > 1 and np.random.random() < 0.2:
-                key = np.random.choice(new_system_1.vars_to_describe)
-                temp = deepcopy(new_system_1.vals.chromosome[key])
-                new_system_1.vals.chromosome[key] = new_system_2.vals.chromosome[key]
-                new_system_2.vals.chromosome[key] = temp
+            # if len(new_system_1.vars_to_describe) > 1 and np.random.random() < 0.2:
+            #     key = np.random.choice(new_system_1.vars_to_describe)
+            #     temp = deepcopy(new_system_1.vals.chromosome[key])
+            #     new_system_1.vals.chromosome[key] = new_system_2.vals.chromosome[key]
+            #     new_system_2.vals.chromosome[key] = temp
 
             offsprings.extend([new_system_1, new_system_2])