def create_problem_node2(nargs=4, seed=0):
from EvoDAG import RootGP
gp = RootGP(generations=1, popsize=nargs, multiple_outputs=True, seed=seed)
gp.X = X
gp.Xtest = X
y = cl.copy()
gp.nclasses(y)
gp.y = y
return gp, [gp.X[x] for x in range(nargs)]
def test_classification_y():
from EvoDAG import RootGP
gp = RootGP(generations=1, multiple_outputs=True, popsize=4)
assert gp._classifier
gp.X = X
y = cl.copy()
gp.nclasses(y)
gp.y = y
print(gp._ytr, gp.y)
for a, b in zip(gp._ytr, gp.y):
assert a.SSE(b) > 0
assert a.sum() == 0
assert b.sum() < 0
def create_problem_node2(nargs=4, seed=0):
from EvoDAG import RootGP
from test_root import X, cl
import numpy as np
gp = RootGP(generations=1, popsize=nargs, multiple_outputs=True, seed=seed)
X1 = np.concatenate((X, np.atleast_2d(np.zeros(X.shape[0])).T), axis=1)
for i in range(10, 20):
X1[i, -1] = 1
gp.X = X1
gp.Xtest = X1
y = cl.copy()
gp.nclasses(y)
gp.y = y
return gp, [gp.X[x] for x in range(nargs)]
def test_BER():
from EvoDAG.node import Add
from EvoDAG import RootGP
from EvoDAG.utils import BER
gp = RootGP(generations=1, popsize=4, multiple_outputs=True)
assert gp._classifier
gp.X = X
y = cl.copy()
gp.nclasses(y)
gp.y = y
m = np.sign(tonparray(gp._mask_ts)).astype(np.bool)
v = gp.random_leaf()
v1 = gp.random_leaf()
v1 = gp.random_leaf()
a = Add([0, 1], ytr=gp._ytr, mask=gp._mask)
a.eval([v, v1])
hy = SparseArray.argmax(a.hy)
b = BER(y[m], tonparray(hy)[m])
gp._bagging_fitness.fitness(a)
print(b, a.fitness * 100)
assert_almost_equals(b, -a.fitness * 100)