def test_model_graphviz():
from EvoDAG import RootGP
from EvoDAG.node import Function
import tempfile
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp = RootGP(generations=3,
tournament_size=2,
early_stopping_rounds=-1,
seed=0,
popsize=10).fit(X, y)
m = gp.model()
print(m._hist)
print(m._hist[-1].position, m._hist[-1]._variable)
with tempfile.TemporaryFile('w+') as io:
m.graphviz(io)
io.seek(0)
l = io.readlines()
cnt = len(m._hist)
for k in m._hist:
if isinstance(k, Function):
v = k._variable
if isinstance(v, list):
cnt += len(v)
else:
cnt += 1
print("".join(l))
print(cnt, len(l))
assert 2 + cnt == len(l)
def test_model_graphviz():
from EvoDAG import RootGP
from EvoDAG.node import Function
import tempfile
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp = RootGP(generations=3,
tournament_size=2,
early_stopping_rounds=-1,
classifier=False,
pr_variable=1,
seed=0,
popsize=10).fit(X, y)
m = gp.model()
print(m._hist)
print(m._hist[-1].position, m._hist[-1]._variable)
with tempfile.TemporaryFile('w+') as io:
m.graphviz(io)
io.seek(0)
l = io.readlines()
cnt = len(m._hist)
for k in m._hist:
if isinstance(k, Function):
v = k._variable
if isinstance(v, list):
cnt += len(v)
else:
cnt += 1
print("".join(l))
print(cnt, len(l))
assert 2 + cnt == len(l)
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_labels():
from EvoDAG import RootGP
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = 2
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 = m.predict(X=X[:-10])
print(np.unique(hy))
print(np.array([1, 2]))
assert np.all(np.unique(hy) == np.array([1, 2]))
def test_labels():
from EvoDAG import RootGP
y = cl.copy()
mask = y == 0
y[mask] = 1
gp = RootGP(generations=np.inf,
tournament_size=2,
early_stopping_rounds=-1,
multiple_outputs=True,
seed=0,
popsize=100).fit(X, y, test_set=X)
m = gp.model()
hy = m.predict(X=X)
print(np.unique(hy), np.unique(y))
print(np.array([1, 2]))
for k in np.unique(hy):
assert k in [1, 2]
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_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_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
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)
