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test_gene_space.py
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1605 lines (1327 loc) · 93.5 KB
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"""
This script is identical to the test_gene_space_allow_duplicate_genes.py script except for:
Setting allow_duplicate_genes=True instead of False.
"""
import pygad
import random
import numpy
num_generations = 100
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]]
# Test single gene space with nested gene type.
def number_respect_gene_space(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,
mutation_type="random",
mutation_percent_genes="default",
mutation_probability=None,
initial_population=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,
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,
parent_selection_type=parent_selection_type,
save_solutions=True,
mutation_type=mutation_type,
mutation_percent_genes=mutation_percent_genes,
mutation_probability=mutation_probability,
suppress_warnings=True,
random_seed=2)
ga_instance.run()
ga_instance.solutions = numpy.array(ga_instance.solutions,
dtype=object)
# gene_space_unpacked = ga_instance.unpack_gene_space(num_values_from_inf_range=100)
num_outside = 0
if ga_instance.gene_space_nested == True:
for gene_idx in range(ga_instance.num_genes):
if type(ga_instance.init_range_low) in ga_instance.supported_int_float_types:
range_min_init = ga_instance.init_range_low
range_max_init = ga_instance.init_range_high
else:
range_min_init = ga_instance.init_range_low[gene_idx]
range_max_init = ga_instance.init_range_high[gene_idx]
if type(ga_instance.random_mutation_min_val) in ga_instance.supported_int_float_types:
range_min_mutation = ga_instance.random_mutation_min_val
range_max_mutation = ga_instance.random_mutation_max_val
else:
range_min_mutation = ga_instance.random_mutation_min_val[gene_idx]
range_max_mutation = ga_instance.random_mutation_max_val[gene_idx]
all_gene_values = ga_instance.solutions[:, gene_idx]
if type(ga_instance.gene_space[gene_idx]) in [list, tuple, range, numpy.ndarray]:
current_gene_space = list(ga_instance.gene_space[gene_idx])
# print("current_gene_space", current_gene_space)
for val_idx, val in enumerate(all_gene_values):
if None in current_gene_space:
if (val in current_gene_space) or (val >= range_min_init and val < range_max_init) or (val >= range_min_mutation and val < range_max_mutation):
pass
else:
# print("###########")
# print(gene_idx, val)
# print(current_gene_space)
# print(range_min_mutation, range_max_mutation)
# print("\n\n")
num_outside += 1
elif val in current_gene_space:
# print("val, current_gene_space", val, current_gene_space)
pass
else:
# print(gene_idx, val, current_gene_space)
num_outside += 1
elif type(ga_instance.gene_space[gene_idx]) is dict:
if not "step" in ga_instance.gene_space[gene_idx].keys():
for val in all_gene_values:
if val >= ga_instance.gene_space[gene_idx]["low"] and val < ga_instance.gene_space[gene_idx]["high"]:
pass
else:
print(gene_idx, val, current_gene_space)
num_outside += 1
else:
gene_space_values = numpy.arange(ga_instance.gene_space[gene_idx]["low"],
ga_instance.gene_space[gene_idx]["high"],
ga_instance.gene_space[gene_idx]["step"])
for val in all_gene_values:
if val in gene_space_values:
pass
else:
num_outside += 1
elif type(ga_instance.gene_space[gene_idx]) in ga_instance.supported_int_float_types:
for val in all_gene_values:
if val == ga_instance.gene_space[gene_idx]:
pass
else:
num_outside += 1
elif ga_instance.gene_space[gene_idx] is None:
for val in all_gene_values:
# print(val)
if (val >= range_min_init and val < range_max_init) or (val >= range_min_mutation and val < range_max_mutation):
pass
else:
# print("###########")
# print(gene_idx, val)
# print(ga_instance.gene_space[gene_idx])
# print(range_min_init, range_max_init)
# print(range_min_mutation, range_max_mutation)
# print("\n\n")
num_outside += 1
else:
for gene_idx in range(ga_instance.num_genes):
if type(ga_instance.init_range_low) in ga_instance.supported_int_float_types:
range_min_init = ga_instance.init_range_low
range_max_init = ga_instance.init_range_high
else:
range_min_init = ga_instance.init_range_low[gene_idx]
range_max_init = ga_instance.init_range_high[gene_idx]
if type(ga_instance.random_mutation_min_val) in ga_instance.supported_int_float_types:
range_min_mutation = ga_instance.random_mutation_min_val
range_max_mutation = ga_instance.random_mutation_max_val
else:
range_min_mutation = ga_instance.random_mutation_min_val[gene_idx]
range_max_mutation = ga_instance.random_mutation_max_val[gene_idx]
all_gene_values = ga_instance.solutions[:, gene_idx]
# print("all_gene_values", gene_idx, all_gene_values)
if type(ga_instance.gene_space) in [list, tuple, range, numpy.ndarray]:
current_gene_space = list(ga_instance.gene_space)
for val in all_gene_values:
if None in current_gene_space:
if (val in current_gene_space) or (val >= range_min_init and val < range_max_init) or (val >= range_min_mutation and val < range_max_mutation):
pass
else:
# print("###########")
# print(gene_idx, val)
# print(current_gene_space)
# print(range_min_mutation, range_max_mutation)
# print("\n\n")
num_outside += 1
elif val in current_gene_space:
pass
else:
num_outside += 1
elif type(ga_instance.gene_space) is dict:
if not "step" in ga_instance.gene_space.keys():
for val in all_gene_values:
if val >= ga_instance.gene_space["low"] and val < ga_instance.gene_space["high"]:
pass
else:
num_outside += 1
else:
gene_space_values = numpy.arange(ga_instance.gene_space["low"],
ga_instance.gene_space["high"],
ga_instance.gene_space["step"])
for val in all_gene_values:
if val in gene_space_values:
pass
else:
num_outside += 1
print(f"Number of outside range is {num_outside}.")
return num_outside, ga_instance
#### Single-Objective
def test_gene_space_range():
num_outside, _ = number_respect_gene_space(gene_space=range(10))
assert num_outside == 0
def test_gene_space_numpy_arange():
num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10))
assert num_outside == 0
def test_gene_space_list():
num_outside, _ = number_respect_gene_space(gene_space=list(range(10)))
assert num_outside == 0
def test_gene_space_list_None():
num_outside, _ = number_respect_gene_space(gene_space=[30, None, 40, 50, None, 60, 70, None, None, None])
assert num_outside == 0
def test_gene_space_numpy():
num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10))))
assert num_outside == 0
def test_gene_space_dict_without_step():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10})
assert num_outside == 0
def test_gene_space_dict_with_step():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2})
assert num_outside == 0
def test_gene_space_list_single_value():
num_outside, ga_instance = number_respect_gene_space(gene_space=[5])
assert num_outside == 0
def test_gene_space_range_nested_gene_type():
num_outside, _ = number_respect_gene_space(gene_space=range(10),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_gene_space_numpy_arange_nested_gene_type():
num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_gene_space_list_nested_gene_type():
num_outside, _ = number_respect_gene_space(gene_space=list(range(10)),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_gene_space_numpy_nested_gene_type():
num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10))),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_gene_space_dict_without_step_nested_gene_type():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10},
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_gene_space_dict_with_step_nested_gene_type():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2},
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_gene_space_list_single_value_nested_gene_type():
num_outside, ga_instance = number_respect_gene_space(gene_space=[5],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_nested_gene_space_range():
num_outside, ga_instance = number_respect_gene_space(gene_space=[range(0, 10),
range(10, 20),
range(20, 30),
range(30, 40),
range(40, 50),
range(50, 60),
range(60, 70),
range(70, 80),
range(80, 90),
range(90, 100)])
assert num_outside == 0
def test_nested_gene_space_dict_without_step():
num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10},
{"low": 10, "high": 20},
{"low": 20, "high": 30},
{"low": 30, "high": 40},
{"low": 40, "high": 50},
{"low": 50, "high": 60},
{"low": 60, "high": 70},
{"low": 70, "high": 80},
{"low": 80, "high": 90},
{"low": 90, "high": 100}])
assert num_outside == 0
def test_nested_gene_space_dict_without_step_float_gene_type():
num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10},
{"low": 10, "high": 20},
{"low": 20, "high": 30},
{"low": 30, "high": 40},
{"low": 40, "high": 50},
{"low": 50, "high": 60},
{"low": 60, "high": 70},
{"low": 70, "high": 80},
{"low": 80, "high": 90},
{"low": 90, "high": 100}],
gene_type=[float, 3])
assert num_outside == 0
def test_nested_gene_space_dict_with_step():
num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10, "step": 1},
{"low": 10, "high": 20, "step": 1.5},
{"low": 20, "high": 30, "step": 2},
{"low": 30, "high": 40, "step": 2.5},
{"low": 40, "high": 50, "step": 3},
{"low": 50, "high": 60, "step": 3.5},
{"low": 60, "high": 70, "step": 4},
{"low": 70, "high": 80, "step": 4.5},
{"low": 80, "high": 90, "step": 5},
{"low": 90, "high": 100, "step": 5.5}])
assert num_outside == 0
def test_nested_gene_space_numpy_arange():
num_outside, ga_instance = number_respect_gene_space(gene_space=[numpy.arange(0, 10),
numpy.arange(10, 20),
numpy.arange(20, 30),
numpy.arange(30, 40),
numpy.arange(40, 50),
numpy.arange(50, 60),
numpy.arange(60, 70),
numpy.arange(70, 80),
numpy.arange(80, 90),
numpy.arange(90, 100)])
assert num_outside == 0
def test_nested_gene_space_list():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[-10, 10, 20, 30, 40, 50, 60, 70, 80, 90],
[-11, 11, 22, 33, 44, 55, 66, 77, 88, 99],
[-100, 100, 200, 300, 400, 500, 600, 700, 800, 900],
[-4.1, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
[-5.1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9],
[-10.5, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9],
[-15, 15, 25, 35, 45, 55, 65, 75, 85, 95],
[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])
assert num_outside == 0
def test_nested_gene_space_list2():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1],
[1, 2],
[2, 3],
[3, 4],
[4, 5],
[5, 6],
[6, 7],
[7, 8],
[8, 9],
[9, 10]])
assert num_outside == 0
def test_nested_gene_space_list3_None():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, None],
[1, 2],
[2, None],
[3, 4],
[None, 5],
None,
[None, 7],
[None, None],
[8, 9],
None],
mutation_by_replacement=True)
assert num_outside == 0
def test_nested_gene_space_list4_None_custom_mutation_range():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, None],
[1, 2],
[2, None],
[3, 4],
[None, 5],
None,
[None, 7],
[None, None],
[8, 9],
None],
random_mutation_min_val=20,
random_mutation_max_val=40,
mutation_by_replacement=True)
assert num_outside == 0
def test_nested_gene_space_mix():
num_outside, ga_instance = number_respect_gene_space(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,
mutation_by_replacement=True)
assert num_outside == 0
def test_nested_gene_space_mix_nested_gene_type():
num_outside, ga_instance = number_respect_gene_space(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], int, numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_nested_gene_space_mix_initial_population():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 00, "high": 10, "step": 1},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
assert num_outside == 0
def test_nested_gene_space_mix_initial_population_single_gene_type():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[float, 4])
assert num_outside == 0
def test_nested_gene_space_single_gene_type_adaptive_mutation():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
# Due to rounding the genes, a gene at index 4 will have a value of 10 (outside the dict range) if [float, 2] is used.
gene_type=[float, 4],
mutation_percent_genes=[70, 50],
mutation_type="adaptive")
assert num_outside == 0
def test_nested_gene_space_single_gene_type_adaptive_mutation_probability():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
# Due to rounding the genes, a gene at index 4 will have a value of 10 (outside the dict range) if [float, 2] is used.
gene_type=[float, 4],
mutation_probability=[0.7, 0.5],
mutation_type="adaptive")
assert num_outside == 0
def test_nested_gene_space_nested_gene_type_adaptive_mutation():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
mutation_percent_genes=[70, 50],
mutation_type="adaptive")
assert num_outside == 0
def test_nested_gene_space_nested_gene_type_adaptive_mutation_probability():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
mutation_probability=[0.7, 0.5],
mutation_type="adaptive")
assert num_outside == 0
#### Multi-Objective
def test_gene_space_range_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=range(10),
multi_objective=True)
assert num_outside == 0
def test_gene_space_numpy_arange_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10),
multi_objective=True)
assert num_outside == 0
def test_gene_space_list_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=list(range(10)),
multi_objective=True)
assert num_outside == 0
def test_gene_space_list_None_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=[30, None, 40, 50, None, 60, 70, None, None, None],
multi_objective=True)
assert num_outside == 0
def test_gene_space_numpy_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10))),
multi_objective=True)
assert num_outside == 0
def test_gene_space_dict_without_step_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10},
multi_objective=True)
assert num_outside == 0
def test_gene_space_dict_with_step_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2},
multi_objective=True)
assert num_outside == 0
def test_gene_space_list_single_value_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[5],
multi_objective=True)
assert num_outside == 0
def test_gene_space_range_nested_gene_type_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=range(10),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_gene_space_numpy_arange_nested_gene_type_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_gene_space_list_nested_gene_type_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=list(range(10)),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_gene_space_numpy_nested_gene_type_multi_objective():
num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10))),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_gene_space_dict_without_step_nested_gene_type_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10},
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_gene_space_dict_with_step_nested_gene_type_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2},
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_gene_space_list_single_value_nested_gene_type_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[5],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_range_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[range(0, 10),
range(10, 20),
range(20, 30),
range(30, 40),
range(40, 50),
range(50, 60),
range(60, 70),
range(70, 80),
range(80, 90),
range(90, 100)],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_dict_without_step_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10},
{"low": 10, "high": 20},
{"low": 20, "high": 30},
{"low": 30, "high": 40},
{"low": 40, "high": 50},
{"low": 50, "high": 60},
{"low": 60, "high": 70},
{"low": 70, "high": 80},
{"low": 80, "high": 90},
{"low": 90, "high": 100}],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_dict_without_step_float_gene_type_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10},
{"low": 10, "high": 20},
{"low": 20, "high": 30},
{"low": 30, "high": 40},
{"low": 40, "high": 50},
{"low": 50, "high": 60},
{"low": 60, "high": 70},
{"low": 70, "high": 80},
{"low": 80, "high": 90},
{"low": 90, "high": 100}],
gene_type=[float, 3],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_dict_with_step_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[{"low": 0, "high": 10, "step": 1},
{"low": 10, "high": 20, "step": 1.5},
{"low": 20, "high": 30, "step": 2},
{"low": 30, "high": 40, "step": 2.5},
{"low": 40, "high": 50, "step": 3},
{"low": 50, "high": 60, "step": 3.5},
{"low": 60, "high": 70, "step": 4},
{"low": 70, "high": 80, "step": 4.5},
{"low": 80, "high": 90, "step": 5},
{"low": 90, "high": 100, "step": 5.5}],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_numpy_arange_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[numpy.arange(0, 10),
numpy.arange(10, 20),
numpy.arange(20, 30),
numpy.arange(30, 40),
numpy.arange(40, 50),
numpy.arange(50, 60),
numpy.arange(60, 70),
numpy.arange(70, 80),
numpy.arange(80, 90),
numpy.arange(90, 100)],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_list_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[-10, 10, 20, 30, 40, 50, 60, 70, 80, 90],
[-11, 11, 22, 33, 44, 55, 66, 77, 88, 99],
[-100, 100, 200, 300, 400, 500, 600, 700, 800, 900],
[-4.1, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
[-5.1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9],
[-10.5, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9],
[-15, 15, 25, 35, 45, 55, 65, 75, 85, 95],
[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47, 48, 49]],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_list2_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1],
[1, 2],
[2, 3],
[3, 4],
[4, 5],
[5, 6],
[6, 7],
[7, 8],
[8, 9],
[9, 10]],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_list3_None_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, None],
[1, 2],
[2, None],
[3, 4],
[None, 5],
None,
[None, 7],
[None, None],
[8, 9],
None],
mutation_by_replacement=True,
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_list4_None_custom_mutation_range_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, None],
[1, 2],
[2, None],
[3, 4],
[None, 5],
None,
[None, 7],
[None, None],
[8, 9],
None],
random_mutation_min_val=20,
random_mutation_max_val=40,
mutation_by_replacement=True,
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_mix_multi_objective():
num_outside, ga_instance = number_respect_gene_space(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,
mutation_by_replacement=True,
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_mix_nested_gene_type_multi_objective():
num_outside, ga_instance = number_respect_gene_space(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], int, numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_mix_initial_population_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 00, "high": 10, "step": 1},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_mix_initial_population_single_gene_type_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[float, 4],
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_single_gene_type_adaptive_mutation_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
# Due to rounding the genes, a gene at index 4 will have a value of 10 (outside the dict range) if [float, 2] is used.
gene_type=[float, 4],
mutation_percent_genes=[70, 50],
mutation_type="adaptive",
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_single_gene_type_adaptive_mutation_probability_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
# Due to rounding the genes, a gene at index 4 will have a value of 10 (outside the dict range) if [float, 2] is used.
gene_type=[float, 4],
mutation_probability=[0.7, 0.5],
mutation_type="adaptive",
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_nested_gene_type_adaptive_mutation_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
mutation_percent_genes=[70, 50],
mutation_type="adaptive",
multi_objective=True)
assert num_outside == 0
def test_nested_gene_space_nested_gene_type_adaptive_mutation_probability_multi_objective():
num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
numpy.arange(0, 10),
range(0, 10),
{"low": 0, "high": 10},
{"low": 0, "high": 10},
range(0, 10),
numpy.arange(0, 10),
numpy.arange(0, 10),
{"low": 0, "high": 10},
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
mutation_probability=[0.7, 0.5],
mutation_type="adaptive",
multi_objective=True)
assert num_outside == 0
#### Multi-Objective NSGA-II Parent Selection
def test_gene_space_range_multi_objective_nsga2():
num_outside, _ = number_respect_gene_space(gene_space=range(10),
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_numpy_arange_multi_objective_nsga2():
num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10),
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_list_multi_objective_nsga2():
num_outside, _ = number_respect_gene_space(gene_space=list(range(10)),
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_list_None_multi_objective_nsga2():
num_outside, _ = number_respect_gene_space(gene_space=[30, None, 40, 50, None, 60, 70, None, None, None],
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_numpy_multi_objective_nsga2():
num_outside, _ = number_respect_gene_space(gene_space=numpy.array(list(range(10))),
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_dict_without_step_multi_objective_nsga2():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10},
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_dict_with_step_multi_objective_nsga2():
num_outside, ga_instance = number_respect_gene_space(gene_space={"low": 0, "high": 10, "step": 2},
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_list_single_value_multi_objective_nsga2():
num_outside, ga_instance = number_respect_gene_space(gene_space=[5],
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_range_nested_gene_type_multi_objective_nsga2():
num_outside, _ = number_respect_gene_space(gene_space=range(10),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0
def test_gene_space_numpy_arange_nested_gene_type_multi_objective_nsga2():
num_outside, _ = number_respect_gene_space(gene_space=numpy.arange(10),
gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
multi_objective=True,
parent_selection_type='nsga2')
assert num_outside == 0