def test_1d_50_var(self):
self.ndim = 1
self.nt = 50
self.ne = 50
np.random.seed(0)
xt = np.random.sample(self.nt).reshape((-1, 1))
yt = self.function_test_1d(xt)
moe = MOE(
smooth_recombination=True,
heaviside_optimization=True,
n_clusters=3,
xt=xt,
yt=yt,
variances_support=True,
)
moe.train()
# validation data
np.random.seed(1)
xe = np.random.sample(self.ne)
ye = self.function_test_1d(xe)
rms_error = compute_rms_error(moe, xe, ye)
self.assert_error(rms_error, 0.0, 3e-1)
moe.predict_variances(xe)
moe_hard = MOE(
smooth_recombination=False,
heaviside_optimization=True,
n_clusters=3,
xt=xt,
yt=yt,
variances_support=True,
)
moe_hard.train()
moe_hard.predict_variances(xe)
if TestMOE.plot:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
y = moe.predict_values(xe)
plt.figure(1)
plt.plot(ye, ye, "-.")
plt.plot(ye, y, ".")
plt.xlabel(r"$y$ actual")
plt.ylabel(r"$y$ prediction")
plt.figure(2)
xv = np.linspace(0, 1, 100)
yv = self.function_test_1d(xv)
y = moe.predict_values(xv)
y_std = np.sqrt(moe.predict_variances(xv))
plt.plot(xv, yv, "--k", linewidth=1)
plt.plot(xv, y, "-b", linewidth=1)
plt.plot(xv, y+y_std, "--b", linewidth=1)
plt.plot(xv, y-y_std, "--b", linewidth=1)
plt.show()
def test_1d_50(self):
self.ndim = 1
self.nt = 50
self.ne = 50
np.random.seed(0)
xt = np.random.sample(self.nt).reshape((-1, 1))
yt = self.function_test_1d(xt)
moe = MOE(smooth_recombination=True,
heaviside_optimization=True,
n_clusters=3,
xt=xt,
yt=yt)
moe.train()
# validation data
np.random.seed(1)
xe = np.random.sample(self.ne)
ye = self.function_test_1d(xe)
rms_error = compute_rms_error(moe, xe, ye)
self.assert_error(rms_error, 0., 3e-1)
if TestMOE.plot:
y = moe.predict_values(xe)
plt.figure(1)
plt.plot(ye, ye, '-.')
plt.plot(ye, y, '.')
plt.xlabel(r'$y$ actual')
plt.ylabel(r'$y$ prediction')
plt.figure(2)
xv = np.linspace(0, 1, 100)
yv = self.function_test_1d(xv)
plt.plot(xv, yv, '-.')
plt.plot(xe, y, 'o')
plt.show()
def test_1d_50_surrogate_model(self):
self.ndim = 1
self.nt = 50
self.ne = 50
np.random.seed(0)
xt = np.random.sample(self.nt).reshape((-1, 1))
yt = self.function_test_1d(xt)
moe = MOESurrogateModel(
smooth_recombination=True,
heaviside_optimization=True,
n_clusters=3,
xt=xt,
yt=yt,
)
self.assertIsInstance(moe.moe, MOE)
moe.train()
self.assertFalse(moe.supports["variances"])
# validation data
np.random.seed(1)
xe = np.random.sample(self.ne)
ye = self.function_test_1d(xe)
rms_error = compute_rms_error(moe, xe, ye)
self.assert_error(rms_error, 0.0, 3e-1)
self.assertRaises(RuntimeError, lambda: moe.predict_variances(xe))
moe_var = MOESurrogateModel(
smooth_recombination=True,
heaviside_optimization=True,
n_clusters=3,
xt=xt,
yt=yt,
variances_support=True,
)
moe_var.train()
self.assertTrue(moe_var.supports["variances"])
moe_var.predict_variances(xe)
if TestMOE.plot:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
y = moe.predict_values(xe)
plt.figure(1)
plt.plot(ye, ye, "-.")
plt.plot(ye, y, ".")
plt.xlabel(r"$y$ actual")
plt.ylabel(r"$y$ prediction")
plt.figure(2)
xv = np.linspace(0, 1, 100)
yv = self.function_test_1d(xv)
plt.plot(xv, yv, "-.")
plt.plot(xe, y, "o")
plt.show()
def test_branin_2d_200(self):
self.ndim = 2
self.nt = 200
self.ne = 200
prob = Branin(ndim=self.ndim)
# training data
sampling = FullFactorial(xlimits=prob.xlimits, clip=True)
np.random.seed(0)
xt = sampling(self.nt)
yt = prob(xt)
# mixture of experts
moe = MOE(n_clusters=5)
moe.set_training_values(xt, yt)
moe.options["heaviside_optimization"] = True
moe.train()
# validation data
np.random.seed(1)
xe = sampling(self.ne)
ye = prob(xe)
rms_error = compute_rms_error(moe, xe, ye)
self.assert_error(rms_error, 0.0, 1e-1)
if TestMOE.plot:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
y = moe.analyse_results(x=xe, operation="predict_values")
plt.figure(1)
plt.plot(ye, ye, "-.")
plt.plot(ye, y, ".")
plt.xlabel(r"$y$ actual")
plt.ylabel(r"$y$ prediction")
fig = plt.figure(2)
ax = fig.add_subplot(111, projection="3d")
ax.scatter(xt[:, 0], xt[:, 1], yt)
plt.title("Branin function")
plt.show()
def test_norm1_2d_200(self):
self.ndim = 2
self.nt = 200
self.ne = 200
prob = LpNorm(ndim=self.ndim)
# training data
sampling = FullFactorial(xlimits=prob.xlimits, clip=True)
np.random.seed(0)
xt = sampling(self.nt)
yt = prob(xt)
# mixture of experts
moe = MOE(smooth_recombination=False, n_clusters=5)
moe.set_training_values(xt, yt)
moe.train()
# validation data
np.random.seed(1)
xe = sampling(self.ne)
ye = prob(xe)
rms_error = compute_rms_error(moe, xe, ye)
self.assert_error(rms_error, 0., 1e-1)
if TestMOE.plot:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
y = moe.predict_values(xe)
plt.figure(1)
plt.plot(ye, ye, '-.')
plt.plot(ye, y, '.')
plt.xlabel(r'$y$ actual')
plt.ylabel(r'$y$ prediction')
fig = plt.figure(2)
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xt[:, 0], xt[:, 1], yt)
plt.title('L1 Norm')
plt.show()
def test_branin_2d_200(self):
self.ndim = 2
self.nt = 200
self.ne = 200
prob = Branin(ndim=self.ndim)
# training data
sampling = FullFactorial(xlimits=prob.xlimits, clip=True)
np.random.seed(0)
xt = sampling(self.nt)
yt = prob(xt)
# mixture of experts
moe = MOE(n_clusters=5)
moe.set_training_values(xt, yt)
moe.options['heaviside_optimization'] = True
moe.train()
# validation data
np.random.seed(1)
xe = sampling(self.ne)
ye = prob(xe)
rms_error = compute_rms_error(moe, xe, ye)
self.assert_error(rms_error, 0., 1e-1)
if TestMOE.plot:
y = moe.analyse_results(x=xe, operation='predict_values')
plt.figure(1)
plt.plot(ye, ye, '-.')
plt.plot(ye, y, '.')
plt.xlabel(r'$y$ actual')
plt.ylabel(r'$y$ prediction')
fig = plt.figure(2)
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xt[:, 0], xt[:, 1], yt)
plt.title('Branin function')
plt.show()
