def test_normal_logEI():
#rng = np.random.RandomState(123)
N = 2000
thresh = np.linspace(-10, 50, N)
#N = 100
#thresh = np.linspace(37, 38, N)
mean = thresh * 0
var = thresh * 0 + 1
s_t, s_m, s_v = theano.tensor.dvectors('tmv')
fn = theano.function([s_t, s_m, s_v],
gpr_math.s_normal_logEI(s_t, s_m, s_v))
if 0:
#print zip(thresh, fn(thresh, mean, var))
#print
a = theano.tensor.dvector()
y = s_t ** 2 * a[2] + s_t * a[1] + a[0]
cost = ((y - gpr_math.s_normal_logEI(s_t, s_m, s_v)) ** 2).sum()
da = theano.grad(cost, a)
foo = theano.function([a, s_t, s_m, s_v], [cost, da])
res = scipy.optimize.minimize(foo, [0, -1, -1], jac=True,
args=(thresh, mean, var),
method='L-BFGS-B')
print res.x
from hyperopt.criteria import logEI_gaussian
if 0:
import matplotlib.pyplot as plt
y = fn(thresh, mean, var)
z = logEI_gaussian(mean, var, thresh)
plt.plot(thresh, y)
plt.plot(thresh, z)
plt.show()
# -- the gpr_math logEI uses a quadratic approximation for very
# hopeless points, which gives the right derivative, but the
# slightly wrong value
assert np.allclose(logEI_gaussian(mean, var, thresh),
fn(thresh, mean, var),
atol=1e-3, rtol=1e-4)
if 0:
d_t = theano.grad(gpr_math.s_normal_logEI(s_t, s_m, s_v).sum(), s_t)
d_fn = theano.function([s_t, s_m, s_v], d_t)
import matplotlib.pyplot as plt
plt.plot(thresh, d_fn(thresh, mean, var))
plt.show()
def test_normal_logEI_elemwise():
rng = np.random.RandomState(123)
N = 2000
thresh = np.linspace(-50, 500, N)
#N = 100
#thresh = np.linspace(37, 38, N)
mean = thresh * 0
var = 1e-1 + rng.rand(N)
sigma = np.sqrt(var)
s_t, s_m, s_v = theano.tensor.dvectors('tmv')
fn = theano.function([s_t, s_m, s_v],
normal_logEI_diff_sigma_elemwise(
s_t - s_m,
theano.tensor.sqrt(s_v)))
my = fn(thresh, mean, var)
ref = logEI_gaussian(mean, var, thresh)
for xi, myv, spv in zip(thresh, my, ref):
print xi, 'my', myv, 'sp', spv, 'diff', (myv - spv)
assert np.any(thresh / sigma > 34)
assert np.all(np.isfinite(my))
assert np.allclose(my[thresh/sigma < 34], ref[thresh/sigma < 34])
assert np.allclose(my, ref, rtol=.1)
def test_log_ei():
for mean, var in [(0, 1), (-4, 9)]:
thresholds = np.arange(-5, 30, 0.25) * np.sqrt(var) + mean
ei = np.asarray(
[crit.EI_gaussian(mean, var, thresh) for thresh in thresholds])
nlei = np.asarray(
[crit.logEI_gaussian(mean, var, thresh) for thresh in thresholds])
naive = np.log(ei)
# import matplotlib.pyplot as plt
# plt.plot(thresholds, ei, label='ei')
# plt.plot(thresholds, nlei, label='nlei')
# plt.plot(thresholds, naive, label='naive')
# plt.legend()
# plt.show()
# -- assert that they match when the threshold isn't too high
assert np.allclose(nlei, naive)
def test_log_ei():
for mean, var in [(0, 1), (-4, 9)]:
thresholds = np.arange(-5, 30, .25) * np.sqrt(var) + mean
ei = np.asarray(
[crit.EI_gaussian(mean, var, thresh)
for thresh in thresholds])
nlei = np.asarray(
[crit.logEI_gaussian(mean, var, thresh)
for thresh in thresholds])
naive = np.log(ei)
#import matplotlib.pyplot as plt
#plt.plot(thresholds, ei, label='ei')
#plt.plot(thresholds, nlei, label='nlei')
#plt.plot(thresholds, naive, label='naive')
#plt.legend()
#plt.show()
# -- assert that they match when the threshold isn't too high
assert np.allclose(nlei, naive)
def test_log_ei_range():
assert np.all(
np.isfinite([
crit.logEI_gaussian(0, 1, thresh)
for thresh in [-500, 0, 50, 100, 500, 5000]
]))
def test_log_ei_range():
assert np.all(
np.isfinite(
[crit.logEI_gaussian(0, 1, thresh)
for thresh in [-500, 0, 50, 100, 500, 5000]]))
