def test_fitness():
from EvoDAG import RootGP
gp = RootGP(generations=1, classifier=False, popsize=4)
gp.X = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
l = gp.random_leaf()
assert l.fitness < 0
def test_fitness():
from EvoDAG import RootGP
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
l = gp.random_leaf()
assert l.fitness < 0
def test_BER():
from EvoDAG.node import Add
from EvoDAG import RootGP
from EvoDAG.utils import BER
gp = RootGP(generations=1, popsize=4, multiple_outputs=True)
assert gp._classifier
gp.X = X
y = cl.copy()
gp.nclasses(y)
gp.y = y
m = np.sign(tonparray(gp._mask_ts)).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 = SparseArray.argmax(a.hy)
b = BER(y[m], tonparray(hy)[m])
gp._bagging_fitness.fitness(a)
print(b, a.fitness * 100)
assert_almost_equals(b, -a.fitness * 100)
def test_random_leaf_inf():
from EvoDAG import RootGP
gp = RootGP(generations=1, classifier=False, popsize=4, tr_fraction=1)
Xc = [x for x in X]
Xc[0] = Xc[0] + np.inf
gp.X = Xc
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock()
mock.side_effect = list(range(2))
np.random.randint = mock
gp.random_leaf()
try:
assert mock()
assert False
except Exception:
pass
np.random.randint = randint
def test_random_leaf_inf():
from EvoDAG import RootGP
gp = RootGP(generations=1, classifier=False, popsize=4, tr_fraction=1)
Xc = [x for x in X]
Xc[0] = Xc[0] + np.inf
gp.X = Xc
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock()
mock.side_effect = list(range(2))
np.random.randint = mock
gp.random_leaf()
try:
assert mock()
assert False
except Exception:
pass
np.random.randint = randint
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)
def test_random_leaf():
from EvoDAG import RootGP
gp = RootGP(generations=1, popsize=4, tr_fraction=1)
gp.X = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock(return_value=0)
np.random.randint = mock
mask = gp._mask.tonparray().astype(np.bool)
weight = np.linalg.lstsq(X[mask, 0][:, np.newaxis], y[mask])[0][0]
var = gp.random_leaf()
assert isinstance(var, Variable)
print(weight, var.weight)
assert_almost_equals(weight, var.weight[0])
np.random.randint = randint
def test_random_leaf():
from EvoDAG import RootGP
gp = RootGP(generations=1, classifier=False, popsize=4)
gp.X = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock(return_value=0)
np.random.randint = mock
mask = tonparray(gp._mask).astype(np.bool)
weight = np.linalg.lstsq(X[mask, 0][:, np.newaxis], y[mask])[0][0]
var = gp.random_leaf()
assert isinstance(var, Variable)
print(weight, var.weight)
assert_almost_equals(weight, var.weight)
np.random.randint = randint
def test_variable():
from EvoDAG import RootGP
gp = RootGP(generations=1, popsize=4)
gp.X = X
Xtest = [x for x in X]
Xtest[0] = Xtest[0] + np.inf
gp.Xtest = Xtest
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock()
mock.side_effect = list(range(gp.popsize))
np.random.randint = mock
var = gp.random_leaf()
assert var.isfinite()
assert var.hy.isfinite()
np.random.randint = randint
def test_variable():
from EvoDAG import RootGP
gp = RootGP(generations=1, classifier=False, popsize=4)
gp.X = X
Xtest = [x for x in X]
Xtest[0] = Xtest[0] + np.inf
gp.Xtest = Xtest
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock()
mock.side_effect = list(range(gp.popsize))
np.random.randint = mock
var = gp.random_leaf()
assert var.isfinite()
assert var.hy.isfinite()
np.random.randint = randint
