def test_unfeasible_counter():
from EvoDAG import RGP
gp = RGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
popsize=100)
assert gp._unfeasible_counter == 0
assert gp.unfeasible_offspring() is None
assert gp._unfeasible_counter == 1
def test_unfeasible_counter():
from EvoDAG import RGP
gp = RGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
classifier=False,
seed=0,
popsize=100)
assert gp._unfeasible_counter == 0
assert gp.unfeasible_offspring() is None
assert gp._unfeasible_counter == 1
def test_add():
from EvoDAG import RGP
gp = RGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
popsize=3)
gp.X = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
gp.create_population()
gp.unfeasible_offspring()
es = gp.population.estopping
for i in range(10):
n = gp.random_offspring()
if n.fitness_vs > es.fitness_vs:
break
gp.add(n)
assert gp._unfeasible_counter == 0
def test_time_limit():
from EvoDAG import RGP
import time
y = cl.copy()
t = time.time()
gp = RGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=100,
multiple_outputs=True,
time_limit=0.9,
seed=0,
popsize=10000).fit(X, y, test_set=X)
t2 = time.time()
print(t2 - t)
assert t2 - t < 1
assert gp._time_limit == 0.9
def test_add():
from EvoDAG import RGP
gp = RGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
classifier=False,
seed=0,
popsize=3)
gp.X = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
gp.create_population()
gp.unfeasible_offspring()
es = gp.population.estopping
for i in range(20):
n = gp.random_offspring()
if n.fitness_vs > es.fitness_vs:
break
gp.population.add(n)
print(gp._unfeasible_counter)
assert gp._unfeasible_counter == 0
def test_model_len():
from EvoDAG import RGP
rgp = RGP(popsize=5).fit(X, cl)
m = rgp.model()
print(len(m))
def test_model_iter():
from EvoDAG import RGP
rgp = RGP(popsize=5).fit(X, cl)
m = rgp.model()
x = [x for x in m]
print(x)
