def test_cache(self):
x = np.array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.],
[4., 7.5], [-5., 9.], [5.5, 10.5], [10., 12.],
[7., 13.5], [2.5, 15.]])
y = np.array([[branin(case)] for case in x])
surrogate_before = KrigingSurrogate(nugget=0.,
eval_rmse=True,
training_cache='test_cache.npz')
surrogate_before.train(x, y)
surrogate = KrigingSurrogate(nugget=0.,
eval_rmse=True,
training_cache='test_cache.npz')
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_near_equal(mu, [y0], 1e-9)
assert_near_equal(sigma, [[0]], 1e-4)
mu, sigma = surrogate.predict([5., 5.])
assert_near_equal(mu, [[16.72]], 1e-1)
assert_near_equal(sigma, [[15.27]], 1e-2)
os.unlink('test_cache.npz')
def test_vector_derivs(self):
surrogate = KrigingSurrogate()
x = array([[a, b] for a, b in
itertools.product(linspace(0, 1, 10), repeat=2)])
y = array([[a+b, a-b] for a, b in x])
surrogate.train(x, y)
jac = surrogate.linearize(array([[0.5, 0.5]]))
assert_rel_error(self, jac, array([[1, 1], [1, -1]]), 1e-5)
def test_scalar_derivs(self):
surrogate = KrigingSurrogate()
x = array([[0.], [1.], [2.], [3.]])
y = x.copy()
surrogate.train(x, y)
jac = surrogate.linearize(array([[0.]]))
assert_rel_error(self, jac[0][0], 1., 1e-3)
def test_1d_ill_conditioned(self):
# Test for least squares solver utilization when ill-conditioned
x = np.array([[case] for case in np.linspace(0., 1., 40)])
y = np.sin(x)
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = np.array([0.5])
mu, sigma = surrogate.predict(new_x)
self.assertTrue(sigma < 3.e-8)
assert_rel_error(self, mu, np.sin(0.5), 1e-6)
def test_one_pt(self):
surrogate = KrigingSurrogate()
x = [[0.]]
y = [[1.]]
with self.assertRaises(ValueError) as cm:
surrogate.train(x, y)
self.assertEqual(str(cm.exception), 'KrigingSurrogate require at least'
' 2 training points.')
def test_1d_ill_conditioned(self):
# Test for least squares solver utilization when ill-conditioned
x = np.array([[case] for case in np.linspace(0., 1., 40)])
y = np.sin(x)
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = np.array([0.5])
mu, sigma = surrogate.predict(new_x)
self.assertTrue(sigma < 3.e-8)
assert_rel_error(self, mu, np.sin(0.5), 1e-6)
def test_vector_derivs(self):
surrogate = KrigingSurrogate()
n = 15
x = np.array([[a, b] for a, b in
itertools.product(np.linspace(0, 1, n), repeat=2)])
y = np.array([[a+b, a-b, a+2*b] for a, b in x])
surrogate.train(x, y)
jac = surrogate.linearize(np.array([[0.5, 0.5]]))
assert_near_equal(jac, np.array([[1, 1], [1, -1], [1, 2]]), 5e-4)
def test_1d_ill_conditioned(self):
# Test for least squares solver utilization when ill-conditioned
x = np.array([[case] for case in np.linspace(0., 1., 40)])
y = np.sin(x)
surrogate = KrigingSurrogate(eval_rmse=True)
surrogate.train(x, y)
new_x = np.array([0.5])
mu, sigma = surrogate.predict(new_x)
self.assertTrue(sigma < 1.e-5)
assert_near_equal(mu, [[np.sin(0.5)]], 1e-5)
def test_vector_derivs(self):
surrogate = KrigingSurrogate()
n = 15
x = np.array([[a, b] for a, b in
itertools.product(np.linspace(0, 1, n), repeat=2)])
y = np.array([[a+b, a-b, a+2*b] for a, b in x])
surrogate.train(x, y)
jac = surrogate.linearize(np.array([[0.5, 0.5]]))
assert_rel_error(self, jac, np.array([[1, 1], [1, -1], [1, 2]]), 5e-4)
def test_one_pt(self):
surrogate = KrigingSurrogate()
x = [[0.]]
y = [[1.]]
with self.assertRaises(ValueError) as cm:
surrogate.train(x, y)
self.assertEqual(str(cm.exception), 'KrigingSurrogate requires at least'
' 2 training points.')
def test_scalar_derivs(self):
surrogate = KrigingSurrogate(nugget=0.)
x = np.array([[0.], [1.], [2.], [3.]])
y = x.copy()
surrogate.train(x, y)
jac = surrogate.linearize(np.array([[0.]]))
assert_rel_error(self, jac[0][0], 1., 1e-3)
def test_1d_training(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
assert_rel_error(self, sigma, [[0]], 1e-5)
def test_1d_training(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate(nugget=0.)
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
def test_1d_training(self):
x = array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
def test_1d_training(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_near_equal(mu, [y0], 1e-9)
assert_near_equal(sigma, [[0]], 1e-5)
def test_vector_output(self):
surrogate = KrigingSurrogate(nugget=0.)
y = np.array([[0., 0.], [1., 1.], [2., 0.]])
x = np.array([[0.], [2.], [4.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
def test_vector_output(self):
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
y = np.array([[0., 0.], [1., 1.], [2., 0.]])
x = np.array([[0.], [2.], [4.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
assert_rel_error(self, sigma, [[0, 0]], 1e-6)
def test_vector_input(self):
surrogate = KrigingSurrogate(nugget=0.)
x = np.array([[0., 0., 0.], [1., 1., 1.]])
y = np.array([[0.], [3.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
def test_1d_predictor(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate(eval_rmse=True)
surrogate.train(x, y)
new_x = np.array([3.5])
mu, sigma = surrogate.predict(new_x)
assert_near_equal(mu, [[branin_1d(new_x)]], 1e-1)
assert_near_equal(sigma, [[0.07101449]], 1e-2)
def test_vector_output(self):
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
y = np.array([[0., 0.], [1., 1.], [2., 0.]])
x = np.array([[0.], [2.], [4.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_near_equal(mu, [y0], 1e-9)
assert_near_equal(sigma, [[0, 0]], 1e-6)
def test_1d_predictor(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = np.array([3.5])
mu, sigma = surrogate.predict(new_x)
assert_rel_error(self, mu, branin_1d(new_x), 1e-1)
assert_rel_error(self, sigma, 0.07101449, 1e-2)
def test_1d_predictor(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = np.array([3.5])
mu, sigma = surrogate.predict(new_x)
assert_rel_error(self, mu, branin_1d(new_x), 1e-1)
assert_rel_error(self, sigma, 0.07101449, 1e-2)
def test_vector_input(self):
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
x = np.array([[0., 0., 0.], [1., 1., 1.]])
y = np.array([[0.], [3.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
assert_rel_error(self, sigma, [[0]], 1e-6)
def test_vector_input(self):
surrogate = KrigingSurrogate()
x = array([[0., 0., 0.], [1., 1., 1.]])
y = array([[0.], [3.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
def test_vector_output(self):
surrogate = KrigingSurrogate()
y = array([[0., 0.], [1., 1.], [2., 0.]])
x = array([[0.], [2.], [4.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
def test_1d_predictor(self):
x = array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = array([pi])
mu, sigma = surrogate.predict(new_x)
assert_rel_error(self, mu, 0.397887, 1e-1)
assert_rel_error(self, sigma, 0.0294172, 1e-2)
def test_2d(self):
x = array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.], [4., 7.5], [-5., 9.], [5.5, 10.5],
[10., 12.], [7., 13.5], [2.5, 15.]])
y = array([[branin(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
mu, sigma = surrogate.predict([5., 5.])
assert_rel_error(self, mu, branin([5., 5.]), 1e-1)
assert_rel_error(self, sigma, 5.79, 1e-2)
def test_2d(self):
x = np.array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.], [4., 7.5], [-5., 9.], [5.5, 10.5],
[10., 12.], [7., 13.5], [2.5, 15.]])
y = np.array([[branin(case)] for case in x])
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-4)
mu, sigma = surrogate.predict([5., 5.])
assert_rel_error(self, mu, 16.72, 1e-1)
assert_rel_error(self, sigma, 15.27, 1e-2)
def test_2d(self):
x = np.array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.],
[4., 7.5], [-5., 9.], [5.5, 10.5], [10., 12.],
[7., 13.5], [2.5, 15.]])
y = np.array([[branin(case)] for case in x])
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
assert_rel_error(self, sigma, [[0]], 1e-4)
mu, sigma = surrogate.predict([5., 5.])
assert_rel_error(self, mu, [[16.72]], 1e-1)
assert_rel_error(self, sigma, [[15.27]], 1e-2)
num_pt, num_xI)
ModelInfo_g[mm].y = np.append(ModelInfo_g[mm].y,
g[nonNAN,
mm]).reshape(num_pt, 1)
if eflag[nonNAN] >= 1:
fea_pt += 1
FEA_obj = np.append(FEA_obj, obj[nonNAN]).reshape(fea_pt, 1)
FEA_xopt = np.append(
FEA_xopt, np.concatenate(
(x0I[nonNAN, :],
xC_opt[nonNAN, :]))).reshape(fea_pt, num_xI + num_xC)
# Call the surrogate building function
surrogate = KrigingSurrogate() #Use ModelInfo_obj in the future release
# surrogate.train(ModelInfo_obj.X_org, ModelInfo_obj.y)
surrogate.train(ModelInfo_obj.X_hat, ModelInfo_obj.y, False)
ModelInfo_obj.X = surrogate.X
# ModelInfo_obj.ynorm = surrogate.Y
ModelInfo_obj.thetas = surrogate.thetas
ModelInfo_obj.mu = surrogate.mu #np.mean(surrogate.Y) #This value should always be 0.0
ModelInfo_obj.SigmaSqr = surrogate.SigmaSqr #surrogate.sigma2/np.square(surrogate.Y_std) #This value should always be 1.0
ModelInfo_obj.c_r = surrogate.c_r #surrogate.alpha
ModelInfo_obj.R_inv = surrogate.R_inv #surrogate.Vh.T.dot(np.einsum('i,ij->ij', surrogate.S_inv, surrogate.U.T))
surrogate.predict(ModelInfo_obj.X_hat[ii], False)
exit()
# ModelInfo_obj.Y_mean = surrogate.Y_mean
# ModelInfo_obj.Y_std = surrogate.Y_std
# ModelInfo_obj.X_std = surrogate.X_std.reshape(num_xI,1)
# ModelInfo_obj.X_mean = surrogate.X_mean.reshape(num_xI,1)
print "Surrogate building of the objective is complete..."
obj[nonNAN]).reshape(num_pt, 1)
ModelInfo_obj.eflag = np.append(ModelInfo_obj.eflag,
eflag[nonNAN]).reshape(num_pt, 1)
# Surrogate data for the constraint functions
# Goes here ....
if eflag[nonNAN] >= 1:
fea_pt += 1
FEA_obj = np.append(FEA_obj, obj[nonNAN]).reshape(fea_pt, 1)
FEA_xopt = np.append(FEA_xopt,
[x0I[nonNAN], xC_opt[nonNAN]]).reshape(
fea_pt, num_xI + num_xC)
# Call the surrogate building function
surrogate = KrigingSurrogate() #Use ModelInfo_obj in the future release
surrogate.train(ModelInfo_obj.X_org, ModelInfo_obj.y)
ModelInfo_obj.X = surrogate.X
ModelInfo_obj.ynorm = surrogate.Y
ModelInfo_obj.thetas = surrogate.thetas
ModelInfo_obj.mu = np.mean(surrogate.Y) #This value should always be 0.0
ModelInfo_obj.SigmaSqr = surrogate.sigma2 / np.square(
surrogate.Y_std) #This value should always be 1.0
ModelInfo_obj.c_r = surrogate.alpha
ModelInfo_obj.R_inv = surrogate.Vh.T.dot(
np.einsum('i,ij->ij', surrogate.S_inv, surrogate.U.T))
ModelInfo_obj.Y_mean = surrogate.Y_mean
ModelInfo_obj.Y_std = surrogate.Y_std
ModelInfo_obj.X_std = surrogate.X_std.reshape(num_xI, 1)
ModelInfo_obj.X_mean = surrogate.X_mean.reshape(num_xI, 1)
print "\nSurrogate building of the objective is complete..."
