Refactor the pygad.py script

.github/workflows/scorecard.yml

@@ -4,6 +4,9 @@
 
 name: Scorecard supply-chain security
 on:
+  # This allows you to run the workflow manually from the Actions tab
+  workflow_dispatch:
+
   # For Branch-Protection check. Only the default branch is supported. See
   # https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection
   branch_protection_rule:
@@ -61,7 +64,7 @@ jobs:
       - name: "Upload artifact"
         uses: actions/upload-artifact@97a0fba1372883ab732affbe8f94b823f91727db # v3.pre.node20
         with:
-          name: SARIF file
+          name: SARIF-file
           path: results.sarif
           retention-days: 5
 

examples/example_lifecycle_classes.py

@@ -0,0 +1,78 @@
+import pygad
+import numpy
+
+"""
+Use a method to build the lifecycle.
+"""
+
+class Fitness:
+    def __call__(self, ga_instance, solution, solution_idx):
+        fitness = numpy.sum(solution)
+        return fitness
+
+class Crossover:
+    def __call__(self, parents, offspring_size, ga_instance):
+        return numpy.random.rand(offspring_size[0], offspring_size[1])
+
+class Mutation:
+    def __call__(self, offspring, ga_instance):
+        return offspring
+
+class OnStart:
+    def __call__(self, ga_instance):
+        print("on_start")
+
+class OnFitness:
+    def __call__(self, ga_instance, fitness):
+        print("on_fitness")
+
+class OnCrossover:
+    def __call__(self, ga_instance, offspring):
+        print("on_crossover")
+
+class OnMutation:
+    def __call__(self, ga_instance, offspring):
+        print("on_mutation")
+
+class OnParents:
+    def __call__(self, ga_instance, parents):
+        print("on_parents")
+
+class OnGeneration:
+    def __call__(self, ga_instance):
+        print("on_generation")
+
+class OnStop:
+    def __call__(self, ga_instance, fitness):
+        print("on_stop")
+
+num_generations = 10 # Number of generations.
+num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
+
+sol_per_pop = 10 # Number of solutions in the population.
+num_genes = 5
+
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
+                       sol_per_pop=sol_per_pop,
+                       num_genes=num_genes,
+
+                       fitness_func=Fitness(),
+
+                       crossover_type=Crossover(),
+                       mutation_type=Mutation(),
+
+                       on_start=OnStart(),
+                       on_fitness=OnFitness(),
+                       on_crossover=OnCrossover(),
+                       on_mutation=OnMutation(),
+                       on_parents=OnParents(),
+                       on_generation=OnGeneration(),
+                       on_stop=OnStop(),
+
+                       suppress_warnings=True)
+
+# Running the GA to optimize the parameters of the function.
+ga_instance.run()
+
+ga_instance.plot_fitness()

examples/example_lifecycle_methods.py

@@ -0,0 +1,73 @@
+import pygad
+import numpy
+
+"""
+Use a method to build the lifecycle.
+"""
+
+class GAOperations:
+    def fitness_func(self, ga_instance, solution, solution_idx):
+        fitness = numpy.sum(solution)
+        return fitness
+
+    def crossover(self, parents, offspring_size, ga_instance):
+        return numpy.random.rand(offspring_size[0], offspring_size[1])
+
+    def mutation(self, offspring, ga_instance):
+        return offspring
+
+class Lifecycle:
+    def on_start(self, ga_instance):
+        print("on_start")
+
+    def on_fitness(self, ga_instance, fitness):
+        print("on_fitness")
+
+    def on_crossover(self, ga_instance, offspring):
+        print("on_crossover")
+
+    def on_mutation(self, ga_instance, offspring):
+        print("on_mutation")
+
+    def on_parents(self, ga_instance, parents):
+        print("on_parents")
+
+    def on_generation(self, ga_instance):
+        print("on_generation")
+
+    def on_stop(self, ga_instance, fitness):
+        print("on_stop")
+
+ga_obj = GAOperations()
+lifecycle_obj = Lifecycle()
+
+num_generations = 10 # Number of generations.
+num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
+
+sol_per_pop = 10 # Number of solutions in the population.
+num_genes = 5
+
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
+                       sol_per_pop=sol_per_pop,
+                       num_genes=num_genes,
+
+                       fitness_func=ga_obj.fitness_func,
+
+                       crossover_type=ga_obj.crossover,
+                       mutation_type=ga_obj.mutation,
+
+                       on_start=lifecycle_obj.on_start,
+                       on_fitness=lifecycle_obj.on_fitness,
+                       on_crossover=lifecycle_obj.on_crossover,
+                       on_mutation=lifecycle_obj.on_mutation,
+                       on_parents=lifecycle_obj.on_parents,
+                       on_generation=lifecycle_obj.on_generation,
+                       on_stop=lifecycle_obj.on_stop,
+
+                       suppress_warnings=True)
+
+# Running the GA to optimize the parameters of the function.
+ga_instance.run()
+
+ga_instance.plot_fitness()

examples/example_summary.py

@@ -0,0 +1,30 @@
+import pygad
+import numpy
+
+function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
+desired_output = 44 # Function output.
+
+def fitness_func(ga_instance, solution, solution_idx):
+    output = numpy.sum(solution*function_inputs)
+    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
+    return fitness
+
+num_generations = 100 # Number of generations.
+num_parents_mating = 10 # Number of solutions to be selected as parents in the mating pool.
+
+sol_per_pop = 20 # Number of solutions in the population.
+num_genes = len(function_inputs)
+
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
+                       sol_per_pop=sol_per_pop,
+                       num_genes=num_genes,
+                       fitness_func=fitness_func)
+
+# Running the GA to optimize the parameters of the function.
+ga_instance.run()
+
+ga_instance.plot_fitness()
+
+ga_instance.summary()
+

examples/pygad_lifecycle.py

@@ -37,12 +37,15 @@ def on_stop(ga_instance, last_population_fitness):
                        fitness_func=fitness_function,
                        sol_per_pop=10,
                        num_genes=len(function_inputs),
+
                        on_start=on_start,
                        on_fitness=on_fitness,
                        on_parents=on_parents,
                        on_crossover=on_crossover,
                        on_mutation=on_mutation,
                        on_generation=on_generation,
-                       on_stop=on_stop)
+                       on_stop=on_stop,
+
+                       suppress_warnings=True)
 
 ga_instance.run()

pygad/__init__.py

@@ -1,3 +1,3 @@
 from .pygad import * # Relative import.
 
-__version__ = "3.5.0"
+__version__ = "3.6.0"

pygad/helper/__init__.py

@@ -1,4 +1,4 @@
 from pygad.helper import unique
 from pygad.helper import misc
 
-__version__ = "1.2.0"
+__version__ = "1.3.0"