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_predict():
from EvoDAG import RootGP
y = cl.copy()
y[y == 0] = 3
gp = RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
popsize=100).fit(X, y, test_set=X)
d = gp.predict()
assert np.unique(d).shape[0] == np.unique(y).shape[0]
assert np.all(np.unique(d) == np.unique(y))
def test_regression():
from EvoDAG import RootGP
x = np.linspace(-1, 1, 100)
y = 4.3 * x**2 + 3.2 * x - 3.2
gp = RootGP(classifier=False, popsize=10,
generations=2).fit([SparseArray.fromlist(x)],
y,
test_set=[SparseArray.fromlist(x)])
model = gp.model()
yh = gp.predict()
assert not model._classifier
yh1 = model.predict(X=[SparseArray.fromlist(x)])
spf = SparseArray.fromlist
assert spf(yh).SSE(spf(yh1)) == 0
def test_regression():
from EvoDAG import RootGP
from EvoDAG.sparse_array import SparseArray
x = np.linspace(-1, 1, 100)
y = 4.3*x**2 + 3.2 * x - 3.2
gp = RootGP(classifier=False,
popsize=10,
generations=2).fit([SparseArray.fromlist(x)], y,
test_set=[SparseArray.fromlist(x)])
model = gp.model()
yh = gp.predict()
assert not model._classifier
yh1 = model.predict(X=[SparseArray.fromlist(x)])
spf = SparseArray.fromlist
assert spf(yh).SSE(spf(yh1)) == 0
def test_RSE():
from EvoDAG import RootGP
from EvoDAG.utils import RSE as rse
x = np.linspace(-1, 1, 100)
y = 4.3 * x**2 + 3.2 * x - 3.2
y[10:12] = 0
gp = RootGP(classifier=False, popsize=10,
generations=2).fit([SparseArray.fromlist(x)],
y,
test_set=[SparseArray.fromlist(x)])
model = gp.model()
yh = gp.predict()
assert not model._classifier
model.predict(X=[SparseArray.fromlist(x)])
gp._mask = SparseArray.fromlist([2] * yh.shape[0])
gp._bagging_fitness.fitness_vs(model._hist[-1])
print(rse(y, yh), model._hist[-1].fitness_vs)
assert_almost_equals(rse(y, yh), -model._hist[-1].fitness_vs)
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_RSE():
from EvoDAG import RootGP
from EvoDAG.sparse_array import SparseArray
from EvoDAG.utils import RSE as rse
x = np.linspace(-1, 1, 100)
y = 4.3*x**2 + 3.2 * x - 3.2
y[10:12] = 0
gp = RootGP(classifier=False,
popsize=10,
generations=2).fit([SparseArray.fromlist(x)], y,
test_set=[SparseArray.fromlist(x)])
model = gp.model()
yh = gp.predict()
assert not model._classifier
model.predict(X=[SparseArray.fromlist(x)])
gp._mask = SparseArray.fromlist([2] * yh.shape[0])
gp.fitness_vs(model._hist[-1])
print(rse(y, yh), model._hist[-1].fitness_vs)
assert_almost_equals(rse(y, yh),
-model._hist[-1].fitness_vs)