def test_trace():
from EvoDAG import RootGP
from EvoDAG.node import Add
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
Add.nargs = 3
gp = RootGP(generations=np.inf,
tournament_size=2,
function_set=[Add],
classifier=False,
early_stopping_rounds=-1,
seed=0,
popsize=4)
gp.X = X[:-10]
gp.Xtest = X[-10:]
gp.y = y[:-10]
gp.create_population()
a = gp.random_offspring()
gp.population.replace(a)
print(a.position, a.variable, a._weight, gp.population.hist[0].variable)
s = gp.trace(a)
print(len(s), s)
assert a._weight.shape[0] + 1 == len(s)
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_features():
from EvoDAG import RootGP
gp = RootGP(generations=1)
gp.X = X
assert gp.nvar == 4
print(gp.X)
assert isinstance(gp.X[0], Variable)
assert isinstance(gp.X[0].hy, SparseArray)
gp.Xtest = X
assert gp.X[0].hy.SSE(gp.X[0].hy_test) == 0
def test_features():
from EvoDAG.sparse_array import SparseArray
from EvoDAG import RootGP
gp = RootGP(generations=1)
gp.X = X
assert gp.nvar == 4
print(gp.X)
assert isinstance(gp.X[0], Variable)
assert isinstance(gp.X[0].hy, SparseArray)
gp.Xtest = X
assert gp.X[0].hy.SSE(gp.X[0].hy_test) == 0
def create_problem_node(nargs=4, seed=0):
from EvoDAG import RootGP
gp = RootGP(generations=1, popsize=nargs, classifier=False, seed=seed)
gp.X = X
gp.Xtest = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
return gp, [gp.X[x] for x in range(nargs)]
def create_problem_node(nargs=4):
from EvoDAG import RootGP
gp = RootGP(generations=1, popsize=4)
gp.X = X
gp.Xtest = X
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
return gp, [gp.X[x] for x in range(nargs)]
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_variable():
from EvoDAG import RootGP
gp = RootGP(generations=1, popsize=4)
gp.X = X
Xtest = [x for x in X]
Xtest[0] = Xtest[0] + np.inf
gp.Xtest = Xtest
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock()
mock.side_effect = list(range(gp.popsize))
np.random.randint = mock
var = gp.random_leaf()
assert var.isfinite()
assert var.hy.isfinite()
np.random.randint = randint
def test_variable():
from EvoDAG import RootGP
gp = RootGP(generations=1, classifier=False, popsize=4)
gp.X = X
Xtest = [x for x in X]
Xtest[0] = Xtest[0] + np.inf
gp.Xtest = Xtest
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
gp.y = y
randint = np.random.randint
mock = MagicMock()
mock.side_effect = list(range(gp.popsize))
np.random.randint = mock
var = gp.random_leaf()
assert var.isfinite()
assert var.hy.isfinite()
np.random.randint = randint
def test_trace():
from EvoDAG import RootGP
from EvoDAG.node import Add
y = cl.copy()
mask = y == 0
y[mask] = 1
y[~mask] = -1
Add.nargs = 3
gp = RootGP(generations=np.inf,
tournament_size=2,
function_set=[Add],
early_stopping_rounds=-1,
seed=0,
popsize=4)
gp.X = X[:-10]
gp.Xtest = X[-10:]
gp.y = y[:-10]
gp.create_population()
a = gp.random_offspring()
gp.population.replace(a)
print(a.position, a.variable, a._weight, gp.population.hist[0].variable)
s = gp.trace(a)
assert len(s) == 4
