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Test adaptive mutation
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‎tests/test_adaptive_mutation.py

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import pygad
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import random
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import numpy
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num_generations = 1
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initial_population = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
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def output_adaptive_mutation(gene_space=None,
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gene_type=float,
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num_genes=10,
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mutation_by_replacement=False,
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random_mutation_min_val=-1,
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random_mutation_max_val=1,
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init_range_low=-4,
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init_range_high=4,
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initial_population=None,
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mutation_probability=[0.2, 0.1],
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fitness_batch_size=None,
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mutation_type="adaptive"):
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def fitness_func_single(ga, solution, idx):
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return random.random()
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def fitness_func_batch(ga, soluions, idxs):
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return numpy.random.uniform(size=len(soluions))
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if fitness_batch_size in [1, None]:
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fitness_func = fitness_func_single
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else:
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fitness_func = fitness_func_batch
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ga_instance = pygad.GA(num_generations=num_generations,
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num_parents_mating=5,
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fitness_func=fitness_func,
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sol_per_pop=10,
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num_genes=num_genes,
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gene_space=gene_space,
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gene_type=gene_type,
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initial_population=initial_population,
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init_range_low=init_range_low,
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init_range_high=init_range_high,
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random_mutation_min_val=random_mutation_min_val,
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random_mutation_max_val=random_mutation_max_val,
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allow_duplicate_genes=True,
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mutation_by_replacement=mutation_by_replacement,
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save_solutions=True,
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mutation_probability=mutation_probability,
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mutation_type=mutation_type,
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suppress_warnings=True,
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fitness_batch_size=fitness_batch_size,
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random_seed=1)
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ga_instance.run()
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return None, ga_instance
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def test_adaptive_mutation():
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result, ga_instance = output_adaptive_mutation()
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# assert result == True
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def test_adaptive_mutation_gene_space():
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result, ga_instance = output_adaptive_mutation(gene_space=range(10))
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# assert result == True
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def test_adaptive_mutation_int_gene_type():
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result, ga_instance = output_adaptive_mutation(gene_type=int)
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# assert result == True
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def test_adaptive_mutation_gene_space_gene_type():
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result, ga_instance = output_adaptive_mutation(gene_space={"low": 0, "high": 10},
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gene_type=[float, 2])
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# assert result == True
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def test_adaptive_mutation_nested_gene_space():
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result, ga_instance = output_adaptive_mutation(gene_space=[[0, 1, 2, 3, 4],
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numpy.arange(5, 10),
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range(10, 15),
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{"low": 15, "high": 20},
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{"low": 20, "high": 30, "step": 2},
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None,
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numpy.arange(30, 35),
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numpy.arange(35, 40),
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numpy.arange(40, 45),
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[45, 46, 47, 48, 49]])
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# assert result == True
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def test_adaptive_mutation_nested_gene_type():
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result, ga_instance = output_adaptive_mutation(gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
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# assert result == True
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def test_adaptive_mutation_nested_gene_space_nested_gene_type():
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result, ga_instance = output_adaptive_mutation(gene_space=[[0, 1, 2, 3, 4],
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numpy.arange(5, 10),
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range(10, 15),
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{"low": 15, "high": 20},
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{"low": 20, "high": 30, "step": 2},
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None,
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numpy.arange(30, 35),
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numpy.arange(35, 40),
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numpy.arange(40, 45),
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[45, 46, 47, 48, 49]],
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gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
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# assert result == True
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def test_adaptive_mutation_initial_population():
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global initial_population
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result, ga_instance = output_adaptive_mutation(initial_population=initial_population)
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# assert result == True
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def test_adaptive_mutation_initial_population_nested_gene_type():
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global initial_population
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result, ga_instance = output_adaptive_mutation(initial_population=initial_population,
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gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]])
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# assert result == True
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def test_adaptive_mutation_fitness_batch_size_1():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=1)
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def test_adaptive_mutation_fitness_batch_size_2():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=2)
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def test_adaptive_mutation_fitness_batch_size_3():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=3)
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def test_adaptive_mutation_fitness_batch_size_4():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=4)
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def test_adaptive_mutation_fitness_batch_size_5():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=5)
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def test_adaptive_mutation_fitness_batch_size_6():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=6)
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def test_adaptive_mutation_fitness_batch_size_7():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=7)
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def test_adaptive_mutation_fitness_batch_size_8():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=8)
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def test_adaptive_mutation_fitness_batch_size_9():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=9)
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def test_adaptive_mutation_fitness_batch_size_10():
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result, ga_instance = output_adaptive_mutation(fitness_batch_size=10)
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if __name__ == "__main__":
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print()
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test_adaptive_mutation()
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print()
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test_adaptive_mutation_int_gene_type()
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print()
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test_adaptive_mutation_gene_space()
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print()
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test_adaptive_mutation_gene_space_gene_type()
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print()
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test_adaptive_mutation_nested_gene_space()
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print()
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test_adaptive_mutation_nested_gene_type()
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print()
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test_adaptive_mutation_initial_population()
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print()
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test_adaptive_mutation_initial_population_nested_gene_type()
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print()
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test_adaptive_mutation_fitness_batch_size_1()
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print()
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test_adaptive_mutation_fitness_batch_size_1()
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print()
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test_adaptive_mutation_fitness_batch_size_2()
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print()
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test_adaptive_mutation_fitness_batch_size_3()
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print()
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test_adaptive_mutation_fitness_batch_size_4()
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print()
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test_adaptive_mutation_fitness_batch_size_5()
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print()
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test_adaptive_mutation_fitness_batch_size_6()
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print()
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test_adaptive_mutation_fitness_batch_size_7()
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print()
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test_adaptive_mutation_fitness_batch_size_8()
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print()
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test_adaptive_mutation_fitness_batch_size_9()
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print()
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test_adaptive_mutation_fitness_batch_size_10()
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print()
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‎tests/test_gene_space.py

Lines changed: 49 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -30,6 +30,8 @@ def number_respect_gene_space(gene_space=None,
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random_mutation_max_val=1,
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init_range_low=-4,
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init_range_high=4,
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mutation_type="random",
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mutation_percent_genes="default",
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initial_population=None):
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def fitness_func(ga, solution, idx):
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allow_duplicate_genes=True,
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mutation_by_replacement=mutation_by_replacement,
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save_solutions=True,
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mutation_type=mutation_type,
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mutation_percent_genes=mutation_percent_genes,
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suppress_warnings=True,
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random_seed=2)
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if val >= ga_instance.gene_space[gene_idx]["low"] and val < ga_instance.gene_space[gene_idx]["high"]:
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pass
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else:
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print(gene_idx, val, current_gene_space)
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num_outside += 1
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else:
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gene_space_values = numpy.arange(ga_instance.gene_space[gene_idx]["low"],
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assert num_outside == 0
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def test_nested_gene_space_single_gene_type_adaptive_mutation():
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num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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numpy.arange(0, 10),
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range(0, 10),
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{"low": 0, "high": 10},
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{"low": 0, "high": 10},
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range(0, 10),
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numpy.arange(0, 10),
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numpy.arange(0, 10),
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{"low": 0, "high": 10},
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
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# 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.
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gene_type=[float, 4],
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mutation_percent_genes=[70, 50],
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mutation_type="adaptive")
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# print(ga_instance.population)
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assert num_outside == 0
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def test_nested_gene_space_nested_gene_type_adaptive_mutation():
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num_outside, ga_instance = number_respect_gene_space(gene_space=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
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numpy.arange(0, 10),
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range(0, 10),
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{"low": 0, "high": 10},
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{"low": 0, "high": 10},
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range(0, 10),
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numpy.arange(0, 10),
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numpy.arange(0, 10),
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{"low": 0, "high": 10},
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]],
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gene_type=[int, float, numpy.float64, [float, 3], [float, 4], numpy.int16, [numpy.float32, 1], int, float, [float, 3]],
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mutation_percent_genes=[70, 50],
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mutation_type="adaptive")
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# print(ga_instance.population)
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assert num_outside == 0
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if __name__ == "__main__":
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print()
383425
test_gene_space_range()
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446488
print()
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448490
test_nested_gene_space_mix_initial_population_single_gene_type()
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print()
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print()
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test_nested_gene_space_single_gene_type_adaptive_mutation()
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print()
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test_nested_gene_space_nested_gene_type_adaptive_mutation()
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print()

‎tests/test_number_fitness_function_calls.py

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sol_per_pop = 10
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num_parents_mating = 5
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# TODO: Calculate the number when fitness_batch_size is used.
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def number_calls_fitness_function(keep_elitism=1,
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keep_parents=-1,
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mutation_type="random",

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