def test_node_add():
gp, args = create_problem_node()
n = Add(list(range(len(args))), ytr=gp._ytr, mask=gp._mask)
coef = n.compute_weight([x.hy for x in args])
assert n.eval(args)
# a = map(lambda (a, b): a.hy * b, zip(args, coef))
a = [a.hy * b for a, b in zip(args, coef)]
r = n.cumsum(a)
assert n.hy.SSE(r) == 0
assert n.hy_test.SSE(r) == 0
def test_node_add():
gp, args = create_problem_node()
n = Add(list(range(len(args))), ytr=gp._ytr, mask=gp._mask)
D = np.array([tonparray(i.hy) for i in args]).T
coef = n.compute_weight([x.hy for x in args])
assert n.eval(args)
# a = map(lambda (a, b): a.hy * b, zip(args, coef))
a = [_a.hy * b for _a, b in zip(args, coef)]
r = n.cumsum(a)
print((D * coef).sum(axis=1)[:4], "*")
print(tonparray(n.hy)[:4])
print(tonparray(r)[:4])
print(tonparray(gp._mask)[:4])
assert n.hy.SSE(r) == 0
assert n.hy_test.SSE(r) == 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 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
