def test_RSE():
from EvoDAG import RootGP
from EvoDAG.sparse_array import SparseArray
from EvoDAG.utils import RSE as rse
x = np.linspace(-1, 1, 100)
y = 4.3*x**2 + 3.2 * x - 3.2
y[10:12] = 0
gp = RootGP(classifier=False,
popsize=10,
generations=2).fit([SparseArray.fromlist(x)], y,
test_set=[SparseArray.fromlist(x)])
model = gp.model()
yh = gp.predict()
assert not model._classifier
model.predict(X=[SparseArray.fromlist(x)])
gp._mask = SparseArray.fromlist([2] * yh.shape[0])
gp.fitness_vs(model._hist[-1])
print(rse(y, yh), model._hist[-1].fitness_vs)
assert_almost_equals(rse(y, yh),
-model._hist[-1].fitness_vs)
def test_BER():
from EvoDAG.node import Add
from EvoDAG import RootGP
from EvoDAG.utils import BER
gp = RootGP(generations=1, popsize=4)
assert gp._classifier
gp.X = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
m = ~ gp._mask.tonparray().astype(np.bool)
v = gp.random_leaf()
v1 = gp.random_leaf()
v1 = gp.random_leaf()
a = Add([0, 1], ytr=gp._ytr, mask=gp._mask)
a.eval([v, v1])
hy = a.hy.sign()
b = BER(y[m], hy.tonparray()[m])
gp.fitness_vs(a)
print(b, a.fitness_vs * 100)
assert_almost_equals(b, -a.fitness_vs * 100)