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test_crossover_mutation.py
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import pygad
import random
import numpy
num_generations = 1
initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
def output_crossover_mutation(gene_space=None,
gene_type=float,
num_genes=10,
mutation_by_replacement=False,
random_mutation_min_val=-1,
random_mutation_max_val=1,
init_range_low=-4,
init_range_high=4,
initial_population=None,
crossover_probability=None,
mutation_probability=None,
crossover_type=None,
mutation_type=None,
parent_selection_type='sss',
multi_objective=False):
def fitness_func_no_batch_single(ga, solution, idx):
return random.random()
def fitness_func_no_batch_multi(ga, solution, idx):
return [random.random(), random.random()]
if multi_objective == True:
fitness_func = fitness_func_no_batch_multi
else:
fitness_func = fitness_func_no_batch_single
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=5,
fitness_func=fitness_func,
sol_per_pop=10,
num_genes=num_genes,
gene_space=gene_space,
gene_type=gene_type,
parent_selection_type=parent_selection_type,
initial_population=initial_population,
init_range_low=init_range_low,
init_range_high=init_range_high,
random_mutation_min_val=random_mutation_min_val,
random_mutation_max_val=random_mutation_max_val,
allow_duplicate_genes=True,
mutation_by_replacement=mutation_by_replacement,
save_solutions=True,
crossover_probability=crossover_probability,
mutation_probability=mutation_probability,
crossover_type=crossover_type,
mutation_type=mutation_type,
suppress_warnings=True,
random_seed=1)
ga_instance.run()
comparison_result = []
for solution_idx, solution in enumerate(ga_instance.population):
if list(solution) in ga_instance.initial_population.tolist():
comparison_result.append(True)
else:
comparison_result.append(False)
comparison_result = numpy.array(comparison_result)
result = numpy.all(comparison_result == True)
print(f"Comparison result is {result}")
return result, ga_instance
def test_no_crossover_no_mutation():
result, ga_instance = output_crossover_mutation()
assert result == True
def test_no_crossover_no_mutation_gene_space():
result, ga_instance = output_crossover_mutation(gene_space=range(10))
assert result == True
def test_no_crossover_no_mutation_int_gene_type():
result, ga_instance = output_crossover_mutation(gene_type=int)
assert result == True
def test_no_crossover_no_mutation_gene_space_gene_type():
result, ga_instance = output_crossover_mutation(gene_space={"low": 0, "high": 10},
gene_type=[float, 2])
assert result == True
def test_no_crossover_no_mutation_nested_gene_space():
result, ga_instance = output_crossover_mutation(gene_space=[[0, 1, 2, 3, 4],
numpy.arange(5, 10),
range(10, 15),
{"low": 15, "high": 20},
{"low": 20, "high": 30, "step": 2},
None,
numpy.arange(30, 35),
numpy.arange(35, 40),
numpy.arange(40, 45),
[45, 46, 47, 48, 49]])
assert result == True
def test_no_crossover_no_mutation_nested_gene_type():
result, ga_instance = output_crossover_mutation(gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert result == True
def test_no_crossover_no_mutation_nested_gene_space_nested_gene_type():
result, ga_instance = output_crossover_mutation(gene_space=[[0, 1, 2, 3, 4],
numpy.arange(5, 10),
range(10, 15),
{"low": 15, "high": 20},
{"low": 20, "high": 30, "step": 2},
None,
numpy.arange(30, 35),
numpy.arange(35, 40),
numpy.arange(40, 45),
[45, 46, 47, 48, 49]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert result == True
def test_no_crossover_no_mutation_initial_population():
global initial_population
result, ga_instance = output_crossover_mutation(initial_population=initial_population)
assert result == True
def test_no_crossover_no_mutation_initial_population_nested_gene_type():
global initial_population
result, ga_instance = output_crossover_mutation(initial_population=initial_population,
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert result == True
def test_crossover_no_mutation_zero_crossover_probability():
global initial_population
result, ga_instance = output_crossover_mutation(crossover_type="single_point",
crossover_probability=0.0)
assert result == True
def test_zero_crossover_probability_zero_mutation_probability():
global initial_population
result, ga_instance = output_crossover_mutation(crossover_type="single_point",
crossover_probability=0.0,
mutation_type="random",
mutation_probability=0.0)
assert result == True
def test_random_mutation_manual_call():
result, ga_instance = output_crossover_mutation(mutation_type="random",
random_mutation_min_val=888,
random_mutation_max_val=999)
ga_instance.mutation_num_genes = 9
temp_offspring = numpy.array(initial_population[0:1])
offspring = ga_instance.random_mutation(offspring=temp_offspring.copy())
comp = offspring - temp_offspring
comp_sorted = sorted(comp.copy())
comp_sorted = numpy.abs(numpy.unique(comp_sorted))
# The other 1 added to include the last value in the range.
assert len(comp_sorted) in range(1, 1 + 1 + ga_instance.mutation_num_genes)
assert comp_sorted[0] == 0
def test_random_mutation_manual_call2():
result, ga_instance = output_crossover_mutation(mutation_type="random",
random_mutation_min_val=888,
random_mutation_max_val=999)
ga_instance.mutation_num_genes = 10
temp_offspring = numpy.array(initial_population[0:1])
offspring = ga_instance.random_mutation(offspring=temp_offspring.copy())
comp = offspring - temp_offspring
comp_sorted = sorted(comp.copy())
comp_sorted = numpy.abs(numpy.unique(comp_sorted))
# The other 1 added to include the last value in the range.
assert len(comp_sorted) in range(1, 1 + 1 + ga_instance.mutation_num_genes)
# assert comp_sorted[0] == 0
def test_random_mutation_manual_call3():
# Use random_mutation_min_val & random_mutation_max_val as numbers.
random_mutation_min_val = 888
random_mutation_max_val = 999
result, ga_instance = output_crossover_mutation(mutation_type="random",
random_mutation_min_val=random_mutation_min_val,
random_mutation_max_val=random_mutation_max_val,
mutation_by_replacement=True)
ga_instance.mutation_num_genes = 10
temp_offspring = numpy.array(initial_population[0:1])
offspring = ga_instance.random_mutation(offspring=temp_offspring.copy())
comp = offspring
comp_sorted = sorted(comp.copy())
comp_sorted = numpy.abs(numpy.unique(comp))
value_space = list(range(random_mutation_min_val, random_mutation_max_val))
for value in comp_sorted:
assert value in value_space
def test_random_mutation_manual_call4():
# Use random_mutation_min_val & random_mutation_max_val as lists.
random_mutation_min_val = [888]*10
random_mutation_max_val = [999]*10
result, ga_instance = output_crossover_mutation(mutation_type="random",
random_mutation_min_val=random_mutation_min_val,
random_mutation_max_val=random_mutation_max_val,
mutation_by_replacement=True)
ga_instance.mutation_num_genes = 10
temp_offspring = numpy.array(initial_population[0:1])
offspring = ga_instance.random_mutation(offspring=temp_offspring.copy())
comp = offspring
comp_sorted = sorted(comp.copy())
comp_sorted = numpy.abs(numpy.unique(comp))
value_space = list(range(random_mutation_min_val[0], random_mutation_max_val[0]))
for value in comp_sorted:
assert value in value_space
if __name__ == "__main__":
#### Single-objective
print()
test_no_crossover_no_mutation()
print()
test_no_crossover_no_mutation_int_gene_type()
print()
test_no_crossover_no_mutation_gene_space()
print()
test_no_crossover_no_mutation_gene_space_gene_type()
print()
test_no_crossover_no_mutation_nested_gene_space()
print()
test_no_crossover_no_mutation_nested_gene_type()
print()
test_no_crossover_no_mutation_initial_population()
print()
test_no_crossover_no_mutation_initial_population_nested_gene_type()
print()
test_crossover_no_mutation_zero_crossover_probability()
print()
test_zero_crossover_probability_zero_mutation_probability()
print()
test_random_mutation_manual_call()
print()
test_random_mutation_manual_call2()
print()
test_random_mutation_manual_call3()
print()
test_random_mutation_manual_call4()
print()