def test_valid_get_bit_positions(number, result):
ga = BinaryGA(list(range(10)), fitness_test_func)
bin_repr = ga._get_bit_positions(number)
assert len(bin_repr) == len(result)
for bit in result:
assert bit in bin_repr
def test_valid_get_bit_positions(number, result):
ga = BinaryGA(list(range(10)), fitness_test_func)
bin_repr = ga._get_bit_positions(number)
assert len(bin_repr) == len(result)
for bit in result:
assert bit in bin_repr
def test_valid_replace_bits(start, stop, result):
ga = BinaryGA(list(range(21)), fitness_test_func, cross_prob=1)
source = [10, 5, 20]
mutant = ga._replace_bits(source, [], start, stop)
assert len(mutant) == len(result)
for bit in result:
assert bit in mutant
def test_valid_replace_bits(start, stop, result):
ga = BinaryGA(list(range(21)), fitness_test_func, cross_prob=1)
source = [10, 5, 20]
mutant = ga._replace_bits(source, [], start, stop)
assert len(mutant) == len(result)
for bit in result:
assert bit in mutant
def test_generate_random_population(size):
ga = BinaryGA(list(range(5)), fitness_test_func)
max_num = 2**5
population = ga._generate_random_population(max_num, size)
assert len(population) == size
assert len(population) == len(set(population))
for num in population:
assert max_num > num >= 1
def test_generate_random_population(size):
ga = BinaryGA(list(range(5)), fitness_test_func)
max_num = 2**5
population = ga._generate_random_population(max_num, size)
assert len(population) == size
assert len(population) == len(set(population))
for num in population:
assert max_num > num >= 1
def test_mutate(mut_type):
"""
*_mutate()* method is the same (but bit inversion process itself is not)
for both types of GA: RealGA and Binary GA and thus,
it is not necessary to test it twice.
"""
ga = BinaryGA(list(range(10)), fitness_test_func, mut_type=mut_type, mut_prob=1)
chromosome = []
mutant = ga._mutate(chromosome)
assert len(mutant) == mut_type
def test_invert_bit():
ga = BinaryGA(test_bin_data, fitness_test_func, mut_prob=1)
chromosome = [10, 5, 20]
bit_to_invert = [10, 7, 9]
mutant = ga._invert_bit(chromosome, bit_to_invert)
assert len(mutant) == 4
assert 10 not in mutant
assert 5 in mutant
assert 20 in mutant
assert 7 in mutant
assert 9 in mutant
def test_invert_bit():
ga = BinaryGA(test_bin_data, fitness_test_func, mut_prob=1)
chromosome = [10, 5, 20]
bit_to_invert = [10, 7, 9]
mutant = ga._invert_bit(chromosome, bit_to_invert)
assert len(mutant) == 4
assert 10 not in mutant
assert 5 in mutant
assert 20 in mutant
assert 7 in mutant
assert 9 in mutant
def test_mutate(mut_type):
"""
*_mutate()* method is the same (but bit inversion process itself is not)
for both types of GA: RealGA and Binary GA and thus,
it is not necessary to test it twice.
"""
ga = BinaryGA(list(range(10)),
fitness_test_func,
mut_type=mut_type,
mut_prob=1)
chromosome = []
mutant = ga._mutate(chromosome)
assert len(mutant) == mut_type
def test_init_random_population_real(optim, type):
size, dim, interval = 9, 1, (-5, 5)
if type == 'real':
ga = RealGA(fitness_test_linear_func, optim=optim)
dga = DiffusionGA(ga)
dga.init_random_population(size, dim, interval)
else:
ga = BinaryGA(test_bin_data, fitness_test_func, optim=optim)
dga = DiffusionGA(ga)
dga.init_random_population(size)
assert dga.population[0].size == size
assert dga.population[1].size == size
shape = dga.population[0].shape
if optim == 'max':
for row in range(shape[0]):
for column in range(shape[1]):
assert dga.population[1][row][column] <= dga.best_solution[1]
else:
for row in range(shape[0]):
for column in range(shape[1]):
assert dga.population[1][row][column] >= dga.best_solution[1]
def test_init_random_population(optim):
ga = BinaryGA(test_bin_data, fitness_test_func, optim=optim)
pop_size = 5
ga.init_random_population(pop_size)
assert len(ga.population) == pop_size
assert ga.population[pop_size - 1].fitness_val == ga.best_solution[1]
assert ga.population[pop_size - 1].chromosome == ga.best_solution[0]
if optim == 'max':
for i in range(pop_size - 1):
assert ga.population[i].fitness_val <= ga.best_solution[1]
assert ga.population[0].fitness_val <= ga.population[i].fitness_val
else:
for i in range(pop_size - 1):
assert ga.population[i].fitness_val >= ga.best_solution[1]
assert ga.population[0].fitness_val >= ga.population[i].fitness_val
def test_init_random_population(optim):
ga = BinaryGA(test_bin_data, fitness_test_func, optim=optim)
pop_size = 5
ga.init_random_population(pop_size)
assert len(ga.population) == pop_size
assert ga.population[pop_size - 1].fitness_val == ga.best_solution[1]
assert ga.population[pop_size - 1].chromosome == ga.best_solution[0]
if optim == 'max':
for i in range(pop_size - 1):
assert ga.population[i].fitness_val <= ga.best_solution[1]
assert ga.population[0].fitness_val <= ga.population[i].fitness_val
else:
for i in range(pop_size - 1):
assert ga.population[i].fitness_val >= ga.best_solution[1]
assert ga.population[0].fitness_val >= ga.population[i].fitness_val
def test_valid_init_population(optim):
chromosomes = list(test_bin_population)
expected_population = []
for chromosome in chromosomes:
expected_population.append(IndividualGA(chromosome, fitness_test_func(chromosome, test_bin_data)))
expected_population = sort_population(optim, expected_population)
ga = BinaryGA(test_bin_data, fitness_test_func, optim=optim)
ga.init_population(test_bin_population)
best_solution = ga.best_solution
if optim == 'min':
assert test_bin_population_best_min[0] == best_solution[0]
else:
assert test_bin_population_best_max[0] == best_solution[0]
for actual, expected in zip(ga.population, expected_population):
assert actual.chromosome == expected.chromosome
def test_cross(cross_type):
"""
*_cross()* method is the same (but bit replacement process itself is not)
for both types of GA: RealGA and Binary GA and thus,
it is not necessary to test it twice.
"""
ga = BinaryGA(test_bin_data, fitness_test_func, cross_type=cross_type, cross_prob=1)
source_chrom = list(range(len(test_bin_data)))
child = ga._cross([], source_chrom)
print(child)
if cross_type > 2:
assert len(child) == cross_type
else:
# single- and two-point crossover differs from other types
# we don't know the exact replacement interval as its end points are random numbers
# but this interval must be at least 1
assert len(child) >= 1
def test_valid_init_population(optim):
chromosomes = list(test_bin_population)
expected_population = []
for chromosome in chromosomes:
expected_population.append(
IndividualGA(chromosome,
fitness_test_func(chromosome, test_bin_data)))
expected_population = sort_population(optim, expected_population)
ga = BinaryGA(test_bin_data, fitness_test_func, optim=optim)
ga.init_population(test_bin_population)
best_solution = ga.best_solution
if optim == 'min':
assert test_bin_population_best_min[0] == best_solution[0]
else:
assert test_bin_population_best_max[0] == best_solution[0]
for actual, expected in zip(ga.population, expected_population):
assert actual.chromosome == expected.chromosome
def test_cross(cross_type):
"""
*_cross()* method is the same (but bit replacement process itself is not)
for both types of GA: RealGA and Binary GA and thus,
it is not necessary to test it twice.
"""
ga = BinaryGA(test_bin_data,
fitness_test_func,
cross_type=cross_type,
cross_prob=1)
source_chrom = list(range(len(test_bin_data)))
child = ga._cross([], source_chrom)
print(child)
if cross_type > 2:
assert len(child) == cross_type
else:
# single- and two-point crossover differs from other types
# we don't know the exact replacement interval as its end points are random numbers
# but this interval must be at least 1
assert len(child) >= 1
def test_invalid_get_bit_positions():
ga = BinaryGA(list(range(10)), fitness_test_func)
with pytest.raises(ValueError):
ga._get_bit_positions(-1)
def test_valid_check_init_random_population():
ga = BinaryGA(list(range(5)), fitness_test_func)
max_num = ga._check_init_random_population(15)
assert max_num == 2**5
def test_invalid_init_data(data):
with pytest.raises(ValueError):
BinaryGA(data, fitness_test_func)
def test_invalid_replace_bits(start, stop):
ga = BinaryGA(list(range(10)), fitness_test_func)
with pytest.raises(ValueError):
ga._replace_bits([], [], start, stop)
def test_invalid_replace_bits(start, stop):
ga = BinaryGA(list(range(10)), fitness_test_func)
with pytest.raises(ValueError):
ga._replace_bits([], [], start, stop)
def test_compute_fitness(chromosome, expected):
ga = BinaryGA(test_bin_data, fitness_test_func)
assert ga._compute_fitness(chromosome) == expected
def test_compute_fitness(chromosome, expected):
ga = BinaryGA(test_bin_data, fitness_test_func)
assert ga._compute_fitness(chromosome) == expected
def test_invalid_get_bit_positions():
ga = BinaryGA(list(range(10)), fitness_test_func)
with pytest.raises(ValueError):
ga._get_bit_positions(-1)
def test_invalid_cross_type():
with pytest.raises(ValueError):
BinaryGA(list(range(5)), fitness_test_func, cross_type=10)
assert elem in dga._chrom_arr
assert fit in dga._fitness_arr
if optim == 'min':
assert dga.best_solution[0] == min(population)
assert dga.best_solution[1] == fitness_test_linear_func(min(population))
else:
assert dga.best_solution[0] == max(population)
assert dga.best_solution[1] == fitness_test_linear_func(max(population))
@pytest.mark.parametrize(['size', 'dim', 'interval', 'ga_inst'],
[(10, None, None, RealGA(fitness_test_linear_func)),
(None, None, None, RealGA(fitness_test_linear_func)),
(10, 3, None, RealGA(fitness_test_linear_func)),
(None, None, None, BinaryGA([1,2,3,4], fitness_test_linear_func))
])
def test_invalid_init_random_population(size, dim, interval, ga_inst):
with pytest.raises(ValueError):
DiffusionGA(ga_inst).init_random_population(size, dim, interval)
@pytest.mark.parametrize('optim', ('min', 'max'))
@pytest.mark.parametrize('type', ('real', 'binary'))
def test_init_random_population_real(optim, type):
size, dim, interval = 9, 1, (-5, 5)
if type == 'real':
ga = RealGA(fitness_test_linear_func, optim=optim)
dga = DiffusionGA(ga)
def test_invalid_check_init_random_population(size):
ga = BinaryGA(list(range(5)), fitness_test_func)
with pytest.raises(ValueError):
ga._check_init_random_population(size)
def test_invalid_check_init_random_population(size):
ga = BinaryGA(list(range(5)), fitness_test_func)
with pytest.raises(ValueError):
ga._check_init_random_population(size)
def test_valid_check_init_random_population():
ga = BinaryGA(list(range(5)), fitness_test_func)
max_num = ga._check_init_random_population(15)
assert max_num == 2**5
