def test_proportional_selection_offset():
''' Test of proportional selection with a non-default offset '''
pop = [
Individual(np.array([0, 0, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
# evaluate population and negate fitness of second individual
pop = Individual.evaluate_population(pop)
pop[1].fitness = -pop[1].fitness
# now we try to evaluate normally (this should throw a ValueError)
# due to the negative fitness
with pytest.raises(ValueError):
selector = ops.proportional_selection(pop)
selected = next(selector)
# it should work by setting the offset to +3
# this adds 3 to each fitness value, making the second
# individual's fitness 0.
selector = ops.proportional_selection(pop, offset=3)
# we expect the first individual to always be selected
# since the new zero point is now -3.
selected = next(selector)
assert np.all(selected.genome == [0, 0, 0])
selected = next(selector)
assert np.all(selected.genome == [0, 0, 0])
def test_proportional_selection2():
''' Test of a stochastic proportional selection '''
# Make a population where fitness proportional selection has an obvious
# reproducible choice
# Proportions here should be 1/4 and 3/4, respectively
pop = [
Individual(np.array([0, 1, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
# Assign a unique identifier to each individual
pop[0].id = 0
pop[1].id = 1
# We first need to evaluate all the individuals so that
# selection has fitnesses to compare
pop = Individual.evaluate_population(pop)
selected = ops.proportional_selection(pop)
N = 1000
p_thresh = 0.1
observed_dist = statistical_helpers.collect_distribution(
lambda: next(selected).id, samples=N)
expected_dist = {pop[0].id: 0.25 * N, pop[1].id: 0.75 * N}
print(f"Observed: {observed_dist}")
print(f"Expected: {expected_dist}")
assert (statistical_helpers.stochastic_equals(expected_dist,
observed_dist,
p=p_thresh))
def test_proportional_selection1():
''' Test of a deterministic case of proportional selection '''
# Make a population where proportional_selection has an obvious
# reproducible choice
pop = [
Individual(np.array([0, 0, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
parents = Individual.evaluate_population(pop)
# This selection operator will always select the [1, 1, 1] individual since
# [0, 0, 0] has zero fitness
selector = ops.proportional_selection(parents)
selected = next(selector)
assert np.all(selected.genome == [1, 1, 1])
selected = next(selector)
assert np.all(selected.genome == [1, 1, 1])
def test_proportional_selection_custom_key():
''' Test of proportional selection with custom evaluation '''
pop = [
Individual(np.array([0, 0, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
def custom_key(individual):
''' Returns fitness based on MaxZeros '''
return np.count_nonzero(individual.genome == 0)
pop = Individual.evaluate_population(pop)
selector = ops.proportional_selection(pop, key=custom_key)
# we expect the first individual to always be selected
# since its genome is all 0s
selected = next(selector)
assert np.all(selected.genome == [0, 0, 0])
selected = next(selector)
assert np.all(selected.genome == [0, 0, 0])
def test_proportional_selection_pop_min():
''' Test of proportional selection with pop-min offset '''
# Create a population of positive fitness individuals
# scaling the fitness by the population minimum makes it so the
# least fit member never gets selected.
pop = [
Individual(np.array([0, 1, 0]), problem=MaxOnes()),
Individual(np.array([1, 1, 1]), problem=MaxOnes())
]
pop = Individual.evaluate_population(pop)
selector = ops.proportional_selection(pop, offset='pop-min')
# we expect that the second individual is always selected
# since the new zero point will be at the minimum fitness
# of the population
selected = next(selector)
assert np.all(selected.genome == [1, 1, 1])
selected = next(selector)
assert np.all(selected.genome == [1, 1, 1])