def test_logging():
from EvoDAG.model import EvoDAG as evodag
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
evodag(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
classifier=False,
seed=0,
popsize=10).fit(X, y, test_set=X)
def test_regression():
from EvoDAG.model import EvoDAG as evodag
x = np.linspace(-1, 1, 100)
y = 4.3 * x**2 + 3.2 * x - 3.2
gp = evodag(classifier=False, popsize=10,
generations=2).fit([SparseArray.fromlist(x)],
y,
test_set=[SparseArray.fromlist(x)])
model = gp.model
yh = gp.predict([SparseArray.fromlist(x)])
assert not model._classifier
yh1 = model.predict(X=[SparseArray.fromlist(x)])
spf = SparseArray.fromlist
assert spf(yh).SSE(spf(yh1)) == 0
def test_ensemble_model():
from EvoDAG.model import EvoDAG as evodag
from EvoDAG.model import Ensemble
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gps = [
evodag(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
classifier=False,
seed=seed,
popsize=10).fit(X[:-10], y[:-10], test_set=X)
for seed in range(3)
]
ens = Ensemble([gp.model for gp in gps])
res = [gp.decision_function(X) for gp in gps]
res = np.median([x.finite().full_array() for x in res], axis=0)
r2 = ens.decision_function(X)
print(res, r2)
assert np.fabs(res - r2).mean() == 0
def test_probability_calibration_ensemble():
class C(object):
def __init__(self):
pass
def fit(self, X, y):
self._X = X
return self
def predict_proba(self, X):
return np.array([[0, 1], [0.5, 0.5], [1, 0]])
from EvoDAG.model import EvoDAGE as evodag
model = evodag(time_limit=4,
early_stopping_rounds=10,
probability_calibration=C).fit(X, cl)
for m in model.models:
assert isinstance(m._probability_calibration, C)
assert model.probability_calibration
hy = model.predict_proba(X)
pr = np.array([[0, 1], [0.5, 0.5], [1, 0]])
assert np.fabs(hy - pr).sum() == 0
default_nargs()