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test_number_fitness_function_calls.py
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import pygad
import random
import numpy
actual_num_fitness_calls_default_keep = 0
actual_num_fitness_calls_no_keep = 0
actual_num_fitness_calls_keep_elitism = 0
actual_num_fitness_calls_keep_parents = 0
num_generations = 100
sol_per_pop = 10
num_parents_mating = 5
# TODO: Calculate the number when fitness_batch_size is used.
def number_calls_fitness_function(keep_elitism=1,
keep_parents=-1,
mutation_type="random",
mutation_percent_genes="default",
parent_selection_type='sss',
multi_objective=False,
fitness_batch_size=None):
actual_num_fitness_calls = 0
def fitness_func_no_batch_single(ga, solution, idx):
nonlocal actual_num_fitness_calls
actual_num_fitness_calls = actual_num_fitness_calls + 1
return random.random()
def fitness_func_no_batch_multi(ga_instance, solution, solution_idx):
nonlocal actual_num_fitness_calls
actual_num_fitness_calls = actual_num_fitness_calls + 1
return [random.random(), random.random()]
def fitness_func_batch_single(ga_instance, solution, solution_idx):
nonlocal actual_num_fitness_calls
actual_num_fitness_calls = actual_num_fitness_calls + 1
f = []
for sol in solution:
f.append(random.random())
return f
def fitness_func_batch_multi(ga_instance, solution, solution_idx):
nonlocal actual_num_fitness_calls
actual_num_fitness_calls = actual_num_fitness_calls + 1
f = []
for sol in solution:
f.append([random.random(), random.random()])
return f
if fitness_batch_size is None or (type(fitness_batch_size) in pygad.GA.supported_int_types and fitness_batch_size == 1):
if multi_objective == True:
fitness_func = fitness_func_no_batch_multi
else:
fitness_func = fitness_func_no_batch_single
elif (type(fitness_batch_size) in pygad.GA.supported_int_types and fitness_batch_size > 1):
if multi_objective == True:
fitness_func = fitness_func_batch_multi
else:
fitness_func = fitness_func_batch_single
ga_optimizer = pygad.GA(num_generations=num_generations,
sol_per_pop=sol_per_pop,
num_genes=6,
num_parents_mating=num_parents_mating,
fitness_func=fitness_func,
mutation_type=mutation_type,
parent_selection_type=parent_selection_type,
mutation_percent_genes=mutation_percent_genes,
keep_elitism=keep_elitism,
keep_parents=keep_parents,
suppress_warnings=True,
fitness_batch_size=fitness_batch_size)
ga_optimizer.run()
if fitness_batch_size is None:
if keep_elitism == 0:
if keep_parents == 0:
# 10 (for initial population) + 100*10 (for other generations) = 1010
expected_num_fitness_calls = sol_per_pop + num_generations * sol_per_pop
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * sol_per_pop
elif keep_parents == -1:
# 10 (for initial population) + 100*num_parents_mating (for other generations)
expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - num_parents_mating)
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * (sol_per_pop - num_parents_mating)
else:
# 10 (for initial population) + 100*keep_parents (for other generations)
expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - keep_parents)
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * (sol_per_pop - keep_parents)
else:
# 10 (for initial population) + 100*keep_elitism (for other generations)
expected_num_fitness_calls = sol_per_pop + num_generations * (sol_per_pop - keep_elitism)
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * (sol_per_pop - keep_elitism)
else:
if keep_elitism == 0:
if keep_parents == 0:
# 10 (for initial population) + 100*10 (for other generations) = 1010
expected_num_fitness_calls = int(numpy.ceil(sol_per_pop/fitness_batch_size)) + num_generations * int(numpy.ceil(sol_per_pop/fitness_batch_size))
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * int(numpy.ceil(sol_per_pop/fitness_batch_size))
elif keep_parents == -1:
# 10 (for initial population) + 100*num_parents_mating (for other generations)
expected_num_fitness_calls = int(numpy.ceil(sol_per_pop/fitness_batch_size)) + num_generations * int(numpy.ceil((sol_per_pop - num_parents_mating)/fitness_batch_size))
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * int(numpy.ceil((sol_per_pop - num_parents_mating)/fitness_batch_size))
else:
# 10 (for initial population) + 100*keep_parents (for other generations)
expected_num_fitness_calls = int(numpy.ceil(sol_per_pop/fitness_batch_size)) + num_generations * int(numpy.ceil((sol_per_pop - keep_parents)/fitness_batch_size))
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * int(numpy.ceil((sol_per_pop - keep_parents)/fitness_batch_size))
else:
# 10 (for initial population) + 100*keep_elitism (for other generations)
expected_num_fitness_calls = int(numpy.ceil(sol_per_pop/fitness_batch_size)) + num_generations * int(numpy.ceil((sol_per_pop - keep_elitism)/fitness_batch_size))
if mutation_type == "adaptive":
expected_num_fitness_calls += num_generations * int(numpy.ceil((sol_per_pop - keep_elitism)/fitness_batch_size))
print(f"Expected number of fitness function calls is {expected_num_fitness_calls}.")
print(f"Actual number of fitness function calls is {actual_num_fitness_calls}.")
return actual_num_fitness_calls, expected_num_fitness_calls
def test_number_calls_fitness_function_default_keep():
actual, expected = number_calls_fitness_function()
assert actual == expected
def test_number_calls_fitness_function_no_keep():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0)
assert actual == expected
def test_number_calls_fitness_function_keep_elitism():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=0)
assert actual == expected
def test_number_calls_fitness_function_keep_parents():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=4)
assert actual == expected
def test_number_calls_fitness_function_both_keep():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4)
assert actual == expected
def test_number_calls_fitness_function_no_keep_adaptive_mutation():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
mutation_type="adaptive",
mutation_percent_genes=[10, 5])
assert actual == expected
def test_number_calls_fitness_function_default_adaptive_mutation():
actual, expected = number_calls_fitness_function(mutation_type="adaptive",
mutation_percent_genes=[10, 5])
assert actual == expected
def test_number_calls_fitness_function_both_keep_adaptive_mutation():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
mutation_type="adaptive",
mutation_percent_genes=[10, 5])
assert actual == expected
#### Multi Objective
def test_number_calls_fitness_function_no_keep_multi_objective():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_keep_elitism_multi_objective():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=0,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_keep_parents_multi_objective():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=4,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_both_keep_multi_objective():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_default_adaptive_mutation_multi_objective():
actual, expected = number_calls_fitness_function(mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True)
assert actual == expected
#### Multi Objective NSGA-II Parent Selection
def test_number_calls_fitness_function_no_keep_multi_objective_nsga2():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_keep_elitism_multi_objective_nsga2():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=0,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_keep_parents_multi_objective_nsga2():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=4,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_both_keep_multi_objective_nsga2():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective_nsga2():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_default_adaptive_mutation_multi_objective_nsga2():
actual, expected = number_calls_fitness_function(mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True)
assert actual == expected
def test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective_nsga2():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True)
assert actual == expected
######## Batch Fitness Calculation
#### Single Objective
def test_number_calls_fitness_function_no_keep_batch_1():
actual, expected = number_calls_fitness_function(fitness_batch_size=1)
assert actual == expected
def test_number_calls_fitness_function_no_keep_batch_4():
actual, expected = number_calls_fitness_function(fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_no_keep_batch_9():
actual, expected = number_calls_fitness_function(fitness_batch_size=9)
assert actual == expected
def test_number_calls_fitness_function_no_keep_batch_10():
actual, expected = number_calls_fitness_function(fitness_batch_size=10)
assert actual == expected
def test_number_calls_fitness_function_keep_elitism_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=0,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_keep_parents_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=4,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_both_keep_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_no_keep_adaptive_mutation_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_default_adaptive_mutation_batch_4():
actual, expected = number_calls_fitness_function(mutation_type="adaptive",
mutation_percent_genes=[10, 5],
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_both_keep_adaptive_mutation_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
fitness_batch_size=4)
assert actual == expected
#### Multi Objective
def test_number_calls_fitness_function_no_keep_multi_objective_batch_1():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=1)
assert actual == expected
def test_number_calls_fitness_function_no_keep_multi_objective_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_no_keep_multi_objective_batch_9():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=9)
assert actual == expected
def test_number_calls_fitness_function_no_keep_multi_objective_batch_10():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=10)
assert actual == expected
def test_number_calls_fitness_function_keep_elitism_multi_objective_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=0,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_keep_parents_multi_objective_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=4,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_both_keep_multi_objective_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_default_adaptive_mutation_multi_objective_batch_4():
actual, expected = number_calls_fitness_function(mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
#### Multi Objective NSGA-II Parent Selection
def test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_1():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=1)
assert actual == expected
def test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_9():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=9)
assert actual == expected
def test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_10():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
multi_objective=True,
fitness_batch_size=10)
assert actual == expected
def test_number_calls_fitness_function_keep_elitism_multi_objective_nsga2_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=0,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_keep_parents_multi_objective_nsga2_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=4,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_both_keep_multi_objective_nsga2_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective_nsga2_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=0,
keep_parents=0,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_default_adaptive_mutation_multi_objective_nsga2_batch_4():
actual, expected = number_calls_fitness_function(mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
def test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective_nsga2_batch_4():
actual, expected = number_calls_fitness_function(keep_elitism=3,
keep_parents=4,
mutation_type="adaptive",
mutation_percent_genes=[10, 5],
multi_objective=True,
fitness_batch_size=4)
assert actual == expected
if __name__ == "__main__":
#### Single-objective
print()
test_number_calls_fitness_function_default_keep()
print()
test_number_calls_fitness_function_no_keep()
print()
test_number_calls_fitness_function_keep_elitism()
print()
test_number_calls_fitness_function_keep_parents()
print()
test_number_calls_fitness_function_both_keep()
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation()
print()
test_number_calls_fitness_function_default_adaptive_mutation()
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation()
print()
#### Multi-Objective
print()
test_number_calls_fitness_function_no_keep_multi_objective()
print()
test_number_calls_fitness_function_keep_elitism_multi_objective()
print()
test_number_calls_fitness_function_keep_parents_multi_objective()
print()
test_number_calls_fitness_function_both_keep_multi_objective()
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective()
print()
test_number_calls_fitness_function_default_adaptive_mutation_multi_objective()
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective()
print()
#### Multi-Objective NSGA-II Parent Selection
print()
test_number_calls_fitness_function_no_keep_multi_objective_nsga2()
print()
test_number_calls_fitness_function_keep_elitism_multi_objective_nsga2()
print()
test_number_calls_fitness_function_keep_parents_multi_objective_nsga2()
print()
test_number_calls_fitness_function_both_keep_multi_objective_nsga2()
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective_nsga2()
print()
test_number_calls_fitness_function_default_adaptive_mutation_multi_objective_nsga2()
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective_nsga2()
print()
######## Batch Fitness
#### Single-objective
print()
test_number_calls_fitness_function_no_keep_batch_1()
print()
test_number_calls_fitness_function_no_keep_batch_4()
print()
test_number_calls_fitness_function_no_keep_batch_9()
print()
test_number_calls_fitness_function_no_keep_batch_10()
print()
test_number_calls_fitness_function_keep_elitism_batch_4()
print()
test_number_calls_fitness_function_keep_parents_batch_4()
print()
test_number_calls_fitness_function_both_keep_batch_4()
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation_batch_4()
print()
test_number_calls_fitness_function_default_adaptive_mutation_batch_4()
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation_batch_4()
print()
#### Multi-Objective
print()
test_number_calls_fitness_function_no_keep_multi_objective_batch_1()
print()
test_number_calls_fitness_function_no_keep_multi_objective_batch_4()
print()
test_number_calls_fitness_function_no_keep_multi_objective_batch_9()
print()
test_number_calls_fitness_function_no_keep_multi_objective_batch_10()
print()
test_number_calls_fitness_function_keep_elitism_multi_objective_batch_4()
print()
test_number_calls_fitness_function_keep_parents_multi_objective_batch_4()
print()
test_number_calls_fitness_function_both_keep_multi_objective_batch_4()
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective_batch_4()
print()
test_number_calls_fitness_function_default_adaptive_mutation_multi_objective_batch_4()
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective_batch_4()
print()
#### Multi-Objective NSGA-II Parent Selection
print()
test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_1()
print()
test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_4()
print()
test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_9()
print()
test_number_calls_fitness_function_no_keep_multi_objective_nsga2_batch_10()
print()
test_number_calls_fitness_function_keep_elitism_multi_objective_nsga2_batch_4()
print()
test_number_calls_fitness_function_keep_parents_multi_objective_nsga2_batch_4()
print()
test_number_calls_fitness_function_both_keep_multi_objective_nsga2_batch_4()
print()
test_number_calls_fitness_function_no_keep_adaptive_mutation_multi_objective_nsga2_batch_4()
print()
test_number_calls_fitness_function_default_adaptive_mutation_multi_objective_nsga2_batch_4()
print()
test_number_calls_fitness_function_both_keep_adaptive_mutation_multi_objective_nsga2_batch_4()
print()