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_out_is_correct():
"""test_evaluation.py: output matches known function outputs """
boston = load_boston()
n_features = boston.data.shape[1]
X = boston.data
Y = boston.target
p1 = Ind()
p2 = Ind()
p3 = Ind()
p4 = Ind()
p5 = Ind()
p1.stack = [
node('x', loc=4),
node('x', loc=5),
node('-'),
node('k', value=0.175),
node('log'),
node('-')
]
p2.stack = [node('x', loc=7), node('x', loc=8), node('*')]
p3.stack = [
node('x', loc=0),
node('exp'),
node('x', loc=5),
node('x', loc=7),
node('*'),
node('/')
]
p4.stack = [node('x', loc=12), node('sin')]
p5.stack = [
node('k', value=178.3),
node('x', loc=8),
node('*'),
node('x', loc=7),
node('cos'),
node('+')
]
few = FEW()
y1 = few.safe(np.log(0.175) - (X[:, 5] - X[:, 4]))
y2 = few.safe(X[:, 7] * X[:, 8])
y3 = few.safe(divs(X[:, 5] * X[:, 7], np.exp(X[:, 0])))
y4 = few.safe(np.sin(X[:, 12]))
y5 = few.safe(178.3 * X[:, 8] + np.cos(X[:, 7]))
# y1,y2,y3,y4,y5 = safe(y1),safe(y2),safe(y3),safe(y4),safe(y5)
few = FEW()
assert np.array_equal(y1, few.out(p1, X))
print("y1 passed")
assert np.array_equal(y2, few.out(p2, X))
print("y2 passed")
assert np.array_equal(y3, few.out(p3, X))
print("y3 passed")
# print("y4:",y4,"y4hat:",few.out(p4,X,Y))
assert np.array_equal(y4, few.out(p4, X))
print("y4 passed")
assert np.array_equal(y5, few.out(p5, X))
