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test_lifecycle_callbacks_calls.py
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import pygad
num_generations = 100
def number_lifecycle_callback_functions_calls(stop_criteria=None,
on_generation_stop=None,
crossover_type="single_point",
mutation_type="random"):
actual_num_callbacks_calls = 0
def fitness_func(ga_instanse, solution, solution_idx):
return 1
def on_start(ga_instance):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_fitness(ga_instance, population_fitness):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_parents(ga_instance, selected_parents):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_crossover(ga_instance, offspring_crossover):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_mutation(ga_instance, offspring_mutation):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_generation(ga_instance):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
if on_generation_stop:
if ga_instance.generations_completed == on_generation_stop:
return "stop"
def on_stop(ga_instance, last_population_fitness):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=5,
fitness_func=fitness_func,
sol_per_pop=10,
num_genes=5,
crossover_type=crossover_type,
mutation_type=mutation_type,
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,
stop_criteria=stop_criteria,
suppress_warnings=True)
ga_instance.run()
# The total number is:
# 1 [for on_start()] +
# num_generations [for on_fitness()] +
# num_generations [for on_parents()] +
# num_generations [for on_crossover()] +
# num_generations [for on_mutation()] +
# num_generations [for on_generation()] +
# 1 [for on_stop()]
# = 1 + num_generations * 5 + 1
# Use 'generations_completed' instead of 'num_generations' because the evolution may stops in the on_generation() callback.
expected_num_callbacks_calls = 1 + ga_instance.generations_completed * 5 + 1
print("Expected {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls))
print("Actual {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls))
return actual_num_callbacks_calls, expected_num_callbacks_calls
def number_lifecycle_callback_methods_calls(stop_criteria=None,
on_generation_stop=None,
crossover_type="single_point",
mutation_type="random"):
actual_num_callbacks_calls = 0
class Callbacks:
def fitness_func(self, ga_instanse, solution, solution_idx):
return 1
def on_start(self, ga_instance):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_fitness(self, ga_instance, population_fitness):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_parents(self, ga_instance, selected_parents):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_crossover(self, ga_instance, offspring_crossover):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_mutation(self, ga_instance, offspring_mutation):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
def on_generation(self, ga_instance):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
if on_generation_stop:
if ga_instance.generations_completed == on_generation_stop:
return "stop"
def on_stop(self, ga_instance, last_population_fitness):
nonlocal actual_num_callbacks_calls
actual_num_callbacks_calls = actual_num_callbacks_calls + 1
Callbacks_obj = Callbacks()
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=5,
fitness_func=Callbacks_obj.fitness_func,
sol_per_pop=10,
num_genes=5,
crossover_type=crossover_type,
mutation_type=mutation_type,
on_start=Callbacks_obj.on_start,
on_fitness=Callbacks_obj.on_fitness,
on_parents=Callbacks_obj.on_parents,
on_crossover=Callbacks_obj.on_crossover,
on_mutation=Callbacks_obj.on_mutation,
on_generation=Callbacks_obj.on_generation,
on_stop=Callbacks_obj.on_stop,
stop_criteria=stop_criteria,
suppress_warnings=True)
ga_instance.run()
# The total number is:
# 1 [for on_start()] +
# num_generations [for on_fitness()] +
# num_generations [for on_parents()] +
# num_generations [for on_crossover()] +
# num_generations [for on_mutation()] +
# num_generations [for on_generation()] +
# 1 [for on_stop()]
# = 1 + num_generations * 5 + 1
# Use 'generations_completed' instead of 'num_generations' because the evolution may stops in the on_generation() callback.
expected_num_callbacks_calls = 1 + ga_instance.generations_completed * 5 + 1
print("Expected {expected_num_callbacks_calls}.".format(expected_num_callbacks_calls=expected_num_callbacks_calls))
print("Actual {actual_num_callbacks_calls}.".format(actual_num_callbacks_calls=actual_num_callbacks_calls))
return actual_num_callbacks_calls, expected_num_callbacks_calls
def test_number_lifecycle_callback_functions_calls():
actual, expected = number_lifecycle_callback_functions_calls()
assert actual == expected
def test_number_lifecycle_callback_functions_calls_stop_criteria():
actual, expected = number_lifecycle_callback_functions_calls(on_generation_stop=30)
assert actual == expected
def test_number_lifecycle_callback_methods_calls():
actual, expected = number_lifecycle_callback_methods_calls()
assert actual == expected
def test_number_lifecycle_callback_methods_calls_stop_criteria():
actual, expected = number_lifecycle_callback_methods_calls(on_generation_stop=30)
assert actual == expected
def test_number_lifecycle_callback_functions_calls_no_crossover():
actual, expected = number_lifecycle_callback_functions_calls(crossover_type=None)
assert actual == expected
def test_number_lifecycle_callback_functions_calls_no_mutation():
actual, expected = number_lifecycle_callback_functions_calls(mutation_type=None)
assert actual == expected
def test_number_lifecycle_callback_functions_calls_no_crossover_no_mutation():
actual, expected = number_lifecycle_callback_functions_calls(crossover_type=None,
mutation_type=None)
assert actual == expected
def test_number_lifecycle_callback_methods_calls_no_crossover():
actual, expected = number_lifecycle_callback_methods_calls(crossover_type=None)
assert actual == expected
def test_number_lifecycle_callback_methods_calls_no_mutation():
actual, expected = number_lifecycle_callback_methods_calls(mutation_type=None)
assert actual == expected
def test_number_lifecycle_callback_methods_calls_no_crossover_no_mutation():
actual, expected = number_lifecycle_callback_methods_calls(crossover_type=None,
mutation_type=None)
assert actual == expected
if __name__ == "__main__":
print()
test_number_lifecycle_callback_functions_calls()
print()
test_number_lifecycle_callback_functions_calls_stop_criteria()
print()
test_number_lifecycle_callback_methods_calls()
print()
test_number_lifecycle_callback_methods_calls_stop_criteria()
print()
test_number_lifecycle_callback_functions_calls_no_crossover()
print()
test_number_lifecycle_callback_functions_calls_no_crossover()
print()
test_number_lifecycle_callback_functions_calls_no_mutation()
print()
test_number_lifecycle_callback_functions_calls_no_crossover_no_mutation()
print()
test_number_lifecycle_callback_methods_calls_no_crossover()
print()
test_number_lifecycle_callback_methods_calls_no_mutation()
print()
test_number_lifecycle_callback_methods_calls_no_crossover_no_mutation()
print()