def test_ensemble_model():
from EvoDAG import RootGP
from EvoDAG.model import Ensemble
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gps = [
RootGP(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() for gp in gps]
res = np.median([x.full_array() for x in res], axis=0)
res = SparseArray.fromlist(res)
print(res)
# res = Add.cumsum(res) * (1 / 3.)
r2 = ens.decision_function(None)
print(res.full_array()[:10], r2.full_array()[:10])
print(res.SSE(r2))
assert res.SSE(r2) == 0
def test_ensemble():
from EvoDAG import RootGP
from EvoDAG.model import Ensemble
y = cl.copy()
gps = [RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=seed,
multiple_outputs=True,
popsize=10).fit(X[:-10],
y[:-10],
test_set=X)
for seed in range(2, 5)]
ens = Ensemble([gp.model() for gp in gps])
res = [gp.model().decision_function(X) for gp in gps]
res = np.array([np.median([x[j].finite().full_array() for x in res], axis=0) for j in range(3)]).T
r2 = ens.decision_function(X)
print(res, r2)
assert np.fabs(res - r2).sum() == 0
def test_ensemble():
from EvoDAG import RootGP
from EvoDAG.model import Ensemble
from EvoDAG.node import Add
y = cl.copy()
gps = [RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=seed,
popsize=10).fit(X[:-10],
y[:-10],
test_set=X)
for seed in range(2, 5)]
ens = Ensemble([gp.model() for gp in gps])
res = [gp.decision_function() for gp in gps]
res = [Add.cumsum([x[j] for x in res]) for j in range(3)]
res = [x / 3. for x in res]
r2 = ens.decision_function(None)
for a, b in zip(res, r2):
assert a.SSE(b) == 0
r2 = ens.predict(None)
def test_ensemble():
from EvoDAG import RootGP
from EvoDAG.model import Ensemble
from EvoDAG.node import Add
y = cl.copy()
gps = [
RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=seed,
multiple_outputs=True,
popsize=10).fit(X[:-10], y[:-10], test_set=X)
for seed in range(2, 5)
]
ens = Ensemble([gp.model() for gp in gps])
res = [gp.decision_function() for gp in gps]
res = [Add.cumsum([x[j] for x in res]) for j in range(3)]
res = [x * (1 / 3.) for x in res]
r2 = ens.decision_function(None)
for a, b in zip(res, r2):
print(a.SSE(b), a.data, b.data, b.full_array())
assert a.SSE(b) == 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_ensemble_model():
from EvoDAG import RootGP
from EvoDAG.model import Ensemble
from EvoDAG.node import Add
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gps = [RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
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() for gp in gps]
res = Add.cumsum(res) / 3
r2 = ens.decision_function(None)
assert res.SSE(r2) == 0
a = SparseArray.fromlist(ens.predict(None))
assert r2.sign().SSE(a) == 0
