def test_init_regression():
from EvoDAG.model import Ensemble
m = Ensemble.init(n_estimators=4, n_jobs=4, seed=10,
classifier=False).fit(X, cl)
hy = m.predict(X)
assert np.unique(hy).shape[0] > 3
default_nargs()
def test_init2():
from EvoDAG.model import Ensemble
m = Ensemble.init(n_estimators=4, n_jobs=1, seed=10, early_stopping_rounds=100).fit(X, cl)
hy = m.predict(X)
print((cl == hy).mean(), cl, hy)
assert (cl == hy).mean() > 0.9
default_nargs()
def test_init2():
from EvoDAG.model import Ensemble
m = Ensemble.init(n_estimators=4, n_jobs=1, seed=10).fit(X, cl)
hy = m.predict(X)
print([x.full_array() for x in m.decision_function(X)])
print((cl == hy).mean(), cl, hy)
assert (cl == hy).mean() > 0.9
default_nargs()
def test_init():
from EvoDAG.model import Ensemble
m = Ensemble.init(n_estimators=4, n_jobs=4, seed=10).fit(X, cl)
hy = m.predict(X)
assert (cl == hy).mean() > 0.9
default_nargs()
