def test_predict():
from EvoDAG import RootGP
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
function_set = [
x for x in RootGP()._function_set if x.regression and x.nargs
]
gp = RootGP(generations=np.inf,
tournament_size=2,
function_set=function_set,
early_stopping_rounds=-1,
classifier=False,
remove_raw_inputs=False,
seed=0,
popsize=10).fit(X[:-10], y[:-10], test_set=X[-10:])
es = gp.population.estopping
assert gp.decision_function().SSE(es.hy_test) == 0
hy_test = es.hy_test
assert gp.decision_function(X=X[-10:]).SSE(hy_test) == 0
hy = gp.decision_function(X=X[-10:])
_ = gp.predict(X=X[-10:])
assert SparseArray.fromlist(_).SSE(hy) == 0
assert len(gp.Xtest)
def test_multiclass_decision_function():
from EvoDAG import RootGP
y = cl.copy()
gp = RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
popsize=100).fit(X, y, test_set=X)
d = gp.decision_function()
assert len(d) == np.unique(y).shape[0]
def test_predict():
from EvoDAG import RootGP
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp = RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
popsize=10).fit(X[:-10], y[:-10], test_set=X[-10:])
es = gp.population.estopping
assert gp.decision_function().SSE(es.hy_test.boundaries()) == 0
hy_test = es.hy_test.boundaries()
assert gp.decision_function(X=X[-10:]).SSE(hy_test) == 0
hy = gp.decision_function(X=X[-10:])
_ = gp.predict(X=X[-10:])
assert SparseArray.fromlist(_).SSE(hy.sign()) == 0
assert len(gp.Xtest)
def test_pickle_model():
from EvoDAG import RootGP
import pickle
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp = RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
popsize=10).fit(X[:-10], y[:-10], test_set=X[-10:])
m = gp.model()
hy = gp.decision_function(X=X[-10:])
m1 = pickle.loads(pickle.dumps(m))
hy1 = m1.decision_function(X=X[-10:])
assert hy.SSE(hy1) == 0
def test_pickle_model():
from EvoDAG import RootGP
import pickle
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp = RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
multiple_outputs=True,
popsize=10).fit(X[:-10], y[:-10], test_set=X[-10:])
m = gp.model()
hy = gp.decision_function(X=X[-10:])
m1 = pickle.loads(pickle.dumps(m))
hy1 = m1.decision_function(X=X[-10:])
for a, b in zip(hy, hy1):
assert a.SSE(b) == 0