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_regression():
from EvoDAG import RootGP
from EvoDAG.sparse_array import SparseArray
from EvoDAG.model import Ensemble
x = np.linspace(-1, 1, 100)
y = 4.3*x**2 + 3.2 * x - 3.2
gps = [RootGP(classifier=False,
seed=seed,
popsize=10,
generations=2).fit([SparseArray.fromlist(x)], y,
test_set=[SparseArray.fromlist(x)])
for seed in range(3)]
ens = Ensemble([gp.model() for gp in gps])
hy = np.median([gp.predict() for gp in gps], axis=0)
hy1 = ens.predict(X=[SparseArray.fromlist(x)])
assert np.all(hy == hy1)
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_regression():
from EvoDAG import RootGP
from EvoDAG.model import Ensemble
x = np.linspace(-1, 1, 100)
y = 4.3 * x**2 + 3.2 * x - 3.2
gps = [
RootGP(classifier=False, seed=seed, popsize=10,
generations=2).fit([SparseArray.fromlist(x)],
y,
test_set=[SparseArray.fromlist(x)])
for seed in range(3)
]
ens = Ensemble([gp.model() for gp in gps])
hy = np.median([gp.predict() for gp in gps], axis=0)
hy1 = ens.predict(X=[SparseArray.fromlist(x)])
print(hy, hy1)
assert np.all(hy == hy1)
def test_multiple_outputs_ensemble():
from EvoDAG import EvoDAG
from EvoDAG.model import Ensemble
y = cl.copy()
gp = [EvoDAG(generations=np.inf,
tournament_size=2,
multiple_outputs=True,
early_stopping_rounds=-1,
seed=x,
popsize=10).fit(X[:-10], y[:-10], test_set=X[-10:])
for x in range(2)]
ens = Ensemble([x.model() for x in gp])
assert ens.multiple_outputs
hy = ens.predict(X)
u = np.unique(y)
for i in np.unique(hy):
assert i in u
def test_regression():
from EvoDAG import EvoDAG as evodag
from EvoDAG.model import Ensemble
x = np.linspace(-1, 1, 100)
y = 4.3*x**2 + 3.2 * x - 3.2
gps = [evodag.init(classifier=False,
seed=seed,
popsize=10,
generations=2).fit([SparseArray.fromlist(x)], y,
test_set=[SparseArray.fromlist(x)])
for seed in range(3)]
model = [_.model() for _ in gps]
ens = Ensemble(model)
X = [SparseArray.fromlist(x)]
hy = np.median([m.decision_function(X).finite().full_array() for m in model], axis=0)
hy1 = ens.predict(X)
print(hy, hy1)
assert np.all(hy == hy1)
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
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()
