Decorators cleanly preserve both function names and doc-strings.

src/EasyGA.py

@@ -1,3 +1,21 @@
+def function_info(decorator):
+    """Recovers the name and doc-string for decorators."""
+
+    def new_decorator(method):
+
+        # Apply old decorator
+        new_method = decorator(method)
+
+        # Recover name and doc-string
+        new_method.__name__ = method.__name__
+        new_method.__doc__  = method.__doc__
+
+        # Return new method with proper name and doc-string
+        return new_method
+
+    return new_decorator
+
+
 # Import math for square root (ga.dist()) and ceil (crossover methods)
 import math
 
@@ -47,16 +65,16 @@ class GA(Attributes):
         """Evolves the ga the specified number of generations
         or until the ga is no longer active if consider_termination is True."""
 
+        # Create the initial population if necessary.
+        if self.population is None:
+            self.initialize_population()
+
         cond1 = lambda: number_of_generations > 0  # Evolve the specified number of generations.
         cond2 = lambda: not consider_termination   # If consider_termination flag is set:
         cond3 = lambda: cond2() or self.active()   #     check termination conditions.
 
         while cond1() and cond3():
 
-            # Create the initial population if necessary.
-            if self.population is None:
-                self.initialize_population()
-
             # If its the first generation, setup the database.
             if self.current_generation == 0:
 
@@ -70,15 +88,15 @@ class GA(Attributes):
             # Otherwise evolve the population.
             else:
 
-                self.parent_selection_impl(self)
+                self.parent_selection_impl()
-                self.crossover_population_impl(self)
+                self.crossover_population_impl()
-                self.survivor_selection_impl(self)
+                self.survivor_selection_impl()
                 self.population.update()
-                self.mutation_population_impl(self)
+                self.mutation_population_impl()
 
             # Update and sort fitnesses
             self.set_all_fitness()
-            self.population.sort_by_best_fitness(self)
+            self.sort_by_best_fitness()
 
             # Save the population to the database
             self.save_population()
@@ -96,7 +114,7 @@ class GA(Attributes):
     def active(self):
         """Returns if the ga should terminate based on the termination implimented."""
 
-        return self.termination_impl(self)
+        return self.termination_impl()
 
 
     def adapt(self):
@@ -107,7 +125,7 @@ class GA(Attributes):
 
         # Update and sort fitnesses
         self.set_all_fitness()
-        self.population.sort_by_best_fitness(self)
+        self.sort_by_best_fitness()
 
 
     def adapt_probabilities(self):
@@ -204,7 +222,7 @@ class GA(Attributes):
                     self,
                     self.population[n],
                     best_chromosome,
-                    min(0.25, 2 * tol_j / (tol(n) - tol_j))
+                    weight = min(0.25, 2 * tol_j / (tol(n) - tol_j))
                 )
 
             # If negative weights can't be used,
@@ -214,7 +232,7 @@ class GA(Attributes):
                     self,
                     self.population[n],
                     self.population[j],
-                    0.75
+                    weight = 0.75
                 )
 
             # Update fitnesses
@@ -275,7 +293,7 @@ class GA(Attributes):
                 chromosome.fitness = self.fitness_function_impl(chromosome)
 
 
-    def sort_by_best_fitness(self, chromosome_list, in_place = False):
+    def sort_by_best_fitness(self, chromosome_list = None, in_place = False):
         """Sorts the chromosome list by fitness based on fitness type.
         1st element has best fitness.
         2nd element has second best fitness.
@@ -285,6 +303,10 @@ class GA(Attributes):
         if self.target_fitness_type not in ('max', 'min'):
             raise ValueError("Unknown target fitness type")
 
+        # Sort the population if no chromosome list is given
+        if chromosome_list is None:
+            chromosome_list = self.population
+
         if in_place:
             chromosome_list.sort(                              # list to be sorted
                 key = lambda chromosome: chromosome.fitness,   # by fitness

src/crossover/crossover_methods.py

@@ -1,9 +1,12 @@
+from EasyGA import function_info
 import random
 
 # Round to an integer near x with higher probability
 # the closer it is to that integer.
 randround = lambda x: int(x + random.random())
 
+
+@function_info
 def _append_to_next_population(population_method):
     """Appends the new chromosomes to the next population.
     Also modifies the input to include the mating pool.
@@ -14,25 +17,25 @@ def _append_to_next_population(population_method):
             population_method(ga, ga.population.mating_pool)
         )
 
-    new_method.__name__ = population_method.__name__
     return new_method
 
 
+@function_info
 def _check_weight(individual_method):
     """Checks if the weight is between 0 and 1 before running.
     Exception may occur when using ga.adapt, which will catch
     the error and try again with valid weight.
     """
 
-    def new_method(ga, parent_1, parent_2, weight = 0.5):
+    def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', None)):
 
-        if 0 < weight < 1:
+        if weight is None:
-            return individual_method(ga, parent_1, parent_2, weight)
+            return individual_method(ga, parent_1, parent_2)
+        elif 0 < weight < 1:
+            return individual_method(ga, parent_1, parent_2, weight = weight)
         else:
-            raise ValueError("""Weight must be between 0 and 1 when using
+            raise ValueError(f"Weight must be between 0 and 1 when using {individual_method.__name__}.")
-             the given crossover method.""")
 
-    new_method.__name__ = individual_method.__name__
     return new_method
 
 
@@ -53,10 +56,9 @@ class Crossover_Methods:
             Every parent is paired with the previous parent.
             The first parent is paired with the last parent.
             """
-
+            
             for index in range(len(mating_pool)):    # for each parent in the mating pool
                 yield ga.crossover_individual_impl(  #     apply crossover to
-                    ga,                              #
                     mating_pool[index],              #         the parent and
                     mating_pool[index-1],            #         the previous parent
                 )
@@ -70,7 +72,6 @@ class Crossover_Methods:
 
             for parent in mating_pool:               # for each parent in the mating pool
                 yield ga.crossover_individual_impl(  #     apply crossover to
-                    ga,                              #
                     parent,                          #         the parent and
                     random.choice(mating_pool),      #         a random parent
                 )
@@ -81,7 +82,7 @@ class Crossover_Methods:
 
 
         @_check_weight
-        def single_point(ga, parent_1, parent_2, weight = 0.5):
+        def single_point(ga, parent_1, parent_2, *, weight = 0.5):
             """Cross two parents by swapping genes at one random point."""
 
             minimum_parent_length = min(len(parent_1), len(parent_2))
@@ -107,13 +108,13 @@ class Crossover_Methods:
 
 
         @_check_weight
-        def multi_point(ga, parent_1, parent_2, weight = 0.5):
+        def multi_point(ga, parent_1, parent_2, *, weight = 0.5):
             """Cross two parents by swapping genes at multiple points."""
             pass
 
 
         @_check_weight
-        def uniform(ga, parent_1, parent_2, weight = 0.5):
+        def uniform(ga, parent_1, parent_2, *, weight = 0.5):
             """Cross two parents by swapping all genes randomly."""
 
             for gene_pair in zip(parent_1, parent_2):
@@ -123,7 +124,7 @@ class Crossover_Methods:
         class Arithmetic:
             """Crossover methods for numerical genes."""
 
-            def average(ga, parent_1, parent_2, weight = 0.5):
+            def average(ga, parent_1, parent_2, *, weight = 0.5):
                 """Cross two parents by taking the average of the genes."""
 
                 values_1 = parent_1.gene_value_iter
@@ -139,7 +140,7 @@ class Crossover_Methods:
                     yield value
 
 
-            def extrapolate(ga, parent_1, parent_2, weight = 0.5):
+            def extrapolate(ga, parent_1, parent_2, *, weight = 0.5):
 
                 """Cross two parents by extrapolating towards the first parent.
                 May result in gene values outside the expected domain.
@@ -159,7 +160,7 @@ class Crossover_Methods:
 
 
             @_check_weight
-            def random(ga, parent_1, parent_2, weight = 0.5):
+            def random(ga, parent_1, parent_2, *, weight = 0.5):
                 """Cross two parents by taking a random integer or float value between each of the genes."""
 
                 values_1 = parent_1.gene_value_iter
@@ -189,7 +190,7 @@ class Crossover_Methods:
             """Crossover methods for permutation based chromosomes."""
 
             @_check_weight
-            def ox1(ga, parent_1, parent_2, weight = 0.5):
+            def ox1(ga, parent_1, parent_2, *, weight = 0.5):
                 """Cross two parents by slicing out a random part of one parent
                 and then filling in the rest of the genes from the second parent."""
 
@@ -234,7 +235,7 @@ class Crossover_Methods:
 
 
             @_check_weight
-            def partially_mapped(ga, parent_1, parent_2, weight = 0.5):
+            def partially_mapped(ga, parent_1, parent_2, *, weight = 0.5):
                 """Cross two parents by slicing out a random part of one parent
                 and then filling in the rest of the genes from the second parent,
                 preserving the ordering of genes wherever possible.

src/mutation/mutation_methods.py

@@ -1,6 +1,9 @@
+from EasyGA import function_info
 import random
 from math import ceil
 
+
+@function_info
 def _check_chromosome_mutation_rate(population_method):
     """Checks if the chromosome mutation rate is a float between 0 and 1 before running."""
 
@@ -15,10 +18,10 @@ def _check_chromosome_mutation_rate(population_method):
         else:
             raise ValueError("Chromosome mutation rate must be between 0 and 1.")
 
-    new_method.__name__ = population_method.__name__
     return new_method
 
 
+@function_info
 def _check_gene_mutation_rate(individual_method):
     """Checks if the gene mutation rate is a float between 0 and 1 before running."""
 
@@ -33,10 +36,10 @@ def _check_gene_mutation_rate(individual_method):
         else:
             raise ValueError("Gene mutation rate must be between 0 and 1.")
 
-    new_method.__name__ = individual_method.__name__
     return new_method
 
 
+@function_info
 def _reset_fitness(individual_method):
     """Resets the fitness value of the chromosome."""
 
@@ -44,10 +47,10 @@ def _reset_fitness(individual_method):
         chromosome.fitness = None
         individual_method(ga, chromosome)
 
-    new_method.__name__ = individual_method.__name__
     return new_method
 
 
+@function_info
 def _loop_random_mutations(individual_method):
     """Runs the individual method until enough
     genes are mutated on the indexed chromosome.
@@ -63,7 +66,6 @@ def _loop_random_mutations(individual_method):
         for index in random.sample(sample_space, sample_size):
             individual_method(ga, chromosome, index)
 
-    new_method.__name__ = individual_method.__name__
     return new_method
 
 
@@ -87,7 +89,7 @@ class Mutation_Methods:
 
             # Loop the individual method until enough genes are mutated.
             for index in random.sample(sample_space, sample_size):
-                ga.mutation_individual_impl(ga, ga.population[index])
+                ga.mutation_individual_impl(ga.population[index])
 
 
         @_check_chromosome_mutation_rate
@@ -98,7 +100,7 @@ class Mutation_Methods:
             sample_size  = ceil(ga.chromosome_mutation_rate*len(ga.population))
 
             for index in random.sample(sample_space, sample_size):
-                ga.mutation_individual_impl(ga, ga.population[index])
+                ga.mutation_individual_impl(ga.population[index])
 
 
         @_check_chromosome_mutation_rate
@@ -109,7 +111,7 @@ class Mutation_Methods:
 
             for i in range(mutation_amount):
                 ga.population[-i-1] = ga.make_chromosome(ga.population[i])
-                ga.mutation_individual_impl(ga, ga.population[-i-1])
+                ga.mutation_individual_impl(ga.population[-i-1])
 
 
     class Individual:

src/parent_selection/parent_selection_methods.py

@@ -1,5 +1,8 @@
+from EasyGA import function_info
 import random
 
+
+@function_info
 def _check_selection_probability(selection_method):
     """Raises an exception if the selection_probability
     is not between 0 and 1 inclusively. Otherwise runs
@@ -16,6 +19,7 @@ def _check_selection_probability(selection_method):
     return new_method
 
 
+@function_info
 def _check_positive_fitness(selection_method):
     """Raises an exception if the population contains a
     chromosome with negative fitness. Otherwise runs
@@ -32,6 +36,7 @@ def _check_positive_fitness(selection_method):
     return new_method
 
 
+@function_info
 def _ensure_sorted(selection_method):
     """Sorts the population by fitness
     and then runs the selection method.
@@ -45,6 +50,7 @@ def _ensure_sorted(selection_method):
     return new_method
 
 
+@function_info
 def _compute_parent_amount(selection_method):
     """Computes the amount of parents
     needed to be selected, and passes it

src/survivor_selection/survivor_selection_methods.py

@@ -1,5 +1,8 @@
+from EasyGA import function_info
 import random
 
+
+@function_info
 def _append_to_next_population(survivor_method):
     """Appends the selected chromosomes to the next population."""