def test_lexicase_shapes():
"""test_selection.py: lexicase selection returns correct shape"""
few = FEW(seed_with_ml=False, population_size=257)
few.term_set = [node('x', loc=0)]
pop = few.init_pop()
offspring, locs = few.lexicase(pop.individuals)
assert len(offspring) == 257
# smaller popsize than tournament size
few = FEW(seed_with_ml=False, population_size=2)
few.term_set = [node('x', loc=0)]
pop = few.init_pop()
offspring, locs = few.lexicase(pop.individuals)
assert len(offspring) == 2
def test_epsilon_lexicase_shapes():
"""test_selection.py: epsilon lexicase selection returns correct shape"""
np.random.seed(42)
few = FEW(seed_with_ml=False, population_size=257, lex_size=False)
few.term_set = [node('x', loc=0)]
pop = few.init_pop()
offspring = few.epsilon_lexicase(np.random.rand(257, 100), [])
assert len(offspring) == 257
# smaller popsize than tournament size
few = FEW(seed_with_ml=False, population_size=2, lex_size=False)
few.term_set = [node('x', loc=0)]
pop = few.init_pop()
offspring = few.epsilon_lexicase(np.random.rand(2, 100), [])
assert len(offspring) == 2
def test_out_shapes():
"""test_evaluation.py: program output is correct size """
# load test data set
boston = load_boston()
# boston.data = boston.data[::10]
# boston.target = boston.target[::10]
n_features = boston.data.shape[1]
# function set
# terminal set
term_set = []
# numbers represent column indices of features
for i in np.arange(n_features):
term_set.append(node('x', loc=i)) # features
# term_set.append(('k',0,np.random.rand())) # ephemeral random constants
# initialize population
pop_size = 5
few = FEW(population_size=pop_size, seed_with_ml=False)
few.term_set = term_set
few.n_features = n_features
pop = few.init_pop()
pop.X = np.asarray(
list(map(lambda I: few.out(I, boston.data), pop.individuals)))
#pop.X = out(pop.individuals[0],boston.data,boston.target)
print("pop.X.shape:", pop.X.shape)
print("boston.target.shape", boston.target.shape)
assert pop.X.shape == (pop_size, boston.target.shape[0])
def test_calc_fitness_shape():
"""test_evaluation.py: calc_fitness correct shapes """
# load test data set
boston = load_boston()
# boston.data = boston.data[::10]
# boston.target = boston.target[::10]
n_features = boston.data.shape[1]
# terminal set
term_set = []
# numbers represent column indices of features
for i in np.arange(n_features):
term_set.append(node('x', loc=i)) # features
# term_set.append(('k',0,np.random.rand())) # ephemeral random constants
# initialize population
pop_size = 5
few = FEW(population_size=pop_size, seed_with_ml=False)
few.term_set = term_set
few.n_features = n_features
pop = few.init_pop()
pop.X = np.asarray(
list(map(lambda I: few.out(I, boston.data), pop.individuals)))
fitnesses = few.calc_fitness(pop.X, boston.target, 'mse', 'tournament')
assert len(fitnesses) == len(pop.individuals)
# test vectorized fitnesses
vec_fitnesses = few.calc_fitness(pop.X, boston.target, 'mse', 'lexicase')
fitmat = np.asarray(vec_fitnesses)
print("fitmat.shape:", fitmat.shape)
assert fitmat.shape == (len(pop.individuals), boston.target.shape[0])
def test_lexicase_survival_shapes():
"""test_selection.py: lexicase survival returns correct shape"""
# func_set = [node('+'), node('-'), node('*'), node('/'), node('sin'),
# node('cos'), node('exp'),node('log'), node('^2'),
# node('^3'), node('sqrt')]
# terminal set
term_set = []
n_features = 3
# numbers represent column indices of features
# for i in np.arange(n_features):
# term_set.append(node('x',loc=i)) # features
term_set = [node('x', loc=i) for i in np.arange(n_features)]
# term_set.append(('erc',0,np.random.rand())) # ephemeral random constants
few = FEW(seed_with_ml=False, population_size=257)
few.term_set = term_set
pop = few.init_pop()
for i in pop.individuals:
i.fitness_vec = list(np.random.rand(10, 1))
offspring, locs = few.lexicase(pop.individuals,
num_selections=100,
survival=True)
assert len(offspring) == 100
# smaller popsize than tournament size
ew = FEW(seed_with_ml=False, population_size=2)
few.term_set = term_set
pop = few.init_pop()
for i in pop.individuals:
i.fitness_vec = np.random.rand(10, 1)
offspring, locs = few.lexicase(pop.individuals,
num_selections=1,
survival=True)
assert len(offspring) == 1
def test_pop_init():
"""test_population.py: population initialization makes valid trees """
# define function set
# function set
# func_set = [('+',2),('-',2),('*',2),('/',2),('sin',1),('cos',1),('exp',1),('log',1)]
# terminal set
term_set = []
n_features = 3
# numbers represent column indices of features
term_set = [node('x',loc=i) for i in np.arange(n_features)]
# term_set.append(('erc',0,np.random.rand())) # ephemeral random constants
few = FEW(seed_with_ml=False)
few.term_set = term_set
few.n_features=n_features
pop = few.init_pop()
for I in pop.individuals:
assert is_valid_program(I.stack)