def test_fix_test_data_bug(self):
# MOE does not interpolate the first training point
def myfunc(x):
return ((x * 6 - 2) ** 2) * np.sin((x * 6 - 2) * 2)
# limits of teh design space
xlimits = np.array([[0.0, 1.0]])
# LHS DOE with ndoe points
ndoe = 6
# Construction of the DOE
sampling = LHS(xlimits=xlimits, criterion="m", random_state=0)
x1D = sampling(ndoe)
x1D = np.sort(x1D, axis=0)
# Compute the output
y1D = myfunc(x1D)
# test data
num = 50
xv1D = sampling(num)
xv1D = np.sort(xv1D, axis=0)
yv1D = myfunc(xv1D)
moe1D = MOE(n_clusters=1, xtest=xv1D, ytest=yv1D, allow=["KRG"])
moe1D.set_training_values(x1D, y1D)
moe1D.train()
# Check that moe1D is interpolating all training values
ypred = moe1D.predict_values(x1D)
self.assertTrue(np.allclose(y1D, ypred))
def test_select_expert_types(self):
moe = MOE(variances_support=True)
expected = ["KPLS", "KPLSK", "KRG"]
self.assertEqual(expected, sorted(moe._select_expert_types().keys()))
moe = MOE(derivatives_support=True)
expected = ["IDW", "KPLS", "KPLSK", "KRG", "LS", "QP", "RBF", "RMTB", "RMTC"]
self.assertEqual(expected, sorted(moe._select_expert_types().keys()))
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, 0.0, 3e-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")
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_fix_moe_rmts_bug(self):
def myfunc(x):
return -0.5 * (
np.sin(40 * (x - 0.85) ** 4) * np.cos(2.5 * (x - 0.95))
+ 0.5 * (x - 0.9)
+ 1
)
nt1 = 11
nt2 = 15
ne = 101
# Training data
X1 = np.linspace(0.001, 0.3, nt1).reshape(nt1, 1)
X1 = np.concatenate((X1, np.array([[0.35]])), axis=0)
X2 = np.linspace(0.4, 1.0, nt2).reshape(nt2, 1)
xt = np.concatenate((X1, X2), axis=0)
yt = myfunc(xt)
moe = MOE(smooth_recombination=True, n_clusters=2, heaviside_optimization=True)
moe._surrogate_type = {"RMTB": RMTB, "RMTC": RMTC}
moe.set_training_values(xt, yt)
moe.train()
def test_enabled_expert_types(self):
moe = MOE(variances_support=True)
expected = ["KPLS", "KPLSK", "KRG"]
self.assertEqual(expected, sorted(moe.enabled_experts))
moe = MOE(derivatives_support=True)
expected = ["IDW", "KPLS", "KPLSK", "KRG", "LS", "QP", "RBF", "RMTB", "RMTC"]
self.assertEqual(expected, sorted(moe.enabled_experts))
moe = MOE(deny=["KRG", "RMTB"])
expected = ["IDW", "KPLS", "KPLSK", "LS", "QP", "RBF", "RMTC"]
self.assertEqual(expected, sorted(moe.enabled_experts))
moe = MOE(allow=["KRG", "RMTB"])
expected = ["KRG", "RMTB"]
self.assertEqual(expected, sorted(moe.enabled_experts))
moe = MOE(variances_support=True, allow=["KRG", "RMTB"])
expected = ["KRG"]
self.assertEqual(expected, sorted(moe.enabled_experts))
moe = MOE(derivatives_support=True, deny=["RBF", "IDW", "KPLSK"])
expected = ["KPLS", "KRG", "LS", "QP", "RMTB", "RMTC"]
self.assertEqual(expected, sorted(moe.enabled_experts))
def run_moe_example():
import numpy as np
from smt.applications import MOE
from smt.problems import LpNorm
from smt.sampling_methods import FullFactorial
import sklearn
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.mplot3d import Axes3D
ndim = 2
nt = 200
ne = 200
# Problem: L1 norm (dimension 2)
prob = LpNorm(ndim=ndim)
# Training data
sampling = FullFactorial(xlimits=prob.xlimits, clip=True)
np.random.seed(0)
xt = sampling(nt)
yt = prob(xt)
# Mixture of experts
moe = MOE(smooth_recombination=True, n_clusters=5)
moe.set_training_values(xt, yt)
moe.train()
# Validation data
np.random.seed(1)
xe = sampling(ne)
ye = prob(xe)
# Prediction
y = moe.predict_values(xe)
fig = plt.figure(1)
fig.set_size_inches(12, 11)
# Cluster display
colors_ = list(colors.cnames.items())
GMM = moe.cluster
weight = GMM.weights_
mean = GMM.means_
if sklearn.__version__ < "0.20.0":
cov = GMM.covars_
else:
cov = GMM.covariances_
prob_ = moe._proba_cluster(xt)
sort = np.apply_along_axis(np.argmax, 1, prob_)
xlim = prob.xlimits
x0 = np.linspace(xlim[0, 0], xlim[0, 1], 20)
x1 = np.linspace(xlim[1, 0], xlim[1, 1], 20)
xv, yv = np.meshgrid(x0, x1)
x = np.array(list(zip(xv.reshape((-1,)), yv.reshape((-1,)))))
prob = moe._proba_cluster(x)
plt.subplot(221, projection="3d")
ax = plt.gca()
for i in range(len(sort)):
color = colors_[int(((len(colors_) - 1) / sort.max()) * sort[i])][0]
ax.scatter(xt[i][0], xt[i][1], yt[i], c=color)
plt.title("Clustered Samples")
plt.subplot(222, projection="3d")
ax = plt.gca()
for i in range(len(weight)):
color = colors_[int(((len(colors_) - 1) / len(weight)) * i)][0]
ax.plot_trisurf(
x[:, 0], x[:, 1], prob[:, i], alpha=0.4, linewidth=0, color=color
)
plt.title("Membership Probabilities")
plt.subplot(223)
for i in range(len(weight)):
color = colors_[int(((len(colors_) - 1) / len(weight)) * i)][0]
plt.tricontour(x[:, 0], x[:, 1], prob[:, i], 1, colors=color, linewidths=3)
plt.title("Cluster Map")
plt.subplot(224)
plt.plot(ye, ye, "-.")
plt.plot(ye, y, ".")
plt.xlabel("actual")
plt.ylabel("prediction")
plt.title("Predicted vs Actual")
plt.show()
limits = np.array([[0.2, 0.8], [0.05, 1.0], [0.0, 3.5]])
sm = RMTB(print_global=False,
order=3,
xlimits=limits,
nonlinear_maxiter=100)
# sm1 = KRG(hyper_opt='TNC', corr='abs_exp')
# sm2 = KPLS(n_comp=3, corr='abs_exp', hyper_opt='TNC')
sm3 = KPLSK(print_global=False,
n_comp=3,
theta0=np.ones(3),
corr='squar_exp')
sm4 = QP(print_global=False, )
sm5 = LS(print_global=False, )
sm1 = KPLS(print_global=False, n_comp=3, theta0=np.ones(3), corr='abs_exp')
sm2 = KRG(print_global=False, theta0=np.ones(3), corr='abs_exp')
sm6 = MOE(smooth_recombination=False, n_clusters=2)
experts_list = dict()
experts_list['KRG'] = (KRG, {'theta0': np.ones(3), 'corr': 'abs_exp'})
experts_list['RBF'] = (RBF, dict())
experts_list['KPLS'] = (KPLS, {
'n_comp': 3,
'theta0': np.ones(3),
'corr': 'abs_exp'
})
experts_list['KPLSK'] = (KPLSK, {
'n_comp': 3,
'theta0': np.ones(3),
'corr': 'squar_exp'
})
experts_list['QP'] = (QP, dict())
experts_list['LS'] = (LS, dict())
def run_moe_example_1d():
import numpy as np
from smt.applications import MOE
from smt.sampling_methods import FullFactorial
import matplotlib.pyplot as plt
ndim = 1
nt = 35
def function_test_1d(x):
import numpy as np # Note: only required by SMT doc testing toolchain
x = np.reshape(x, (-1,))
y = np.zeros(x.shape)
y[x < 0.4] = x[x < 0.4] ** 2
y[(x >= 0.4) & (x < 0.8)] = 3 * x[(x >= 0.4) & (x < 0.8)] + 1
y[x >= 0.8] = np.sin(10 * x[x >= 0.8])
return y.reshape((-1, 1))
x = np.linspace(0, 1, 100)
ytrue = function_test_1d(x)
# Training data
sampling = FullFactorial(xlimits=np.array([[0, 1]]), clip=True)
np.random.seed(0)
xt = sampling(nt)
yt = function_test_1d(xt)
# Mixture of experts
print("MOE Experts: ", MOE.AVAILABLE_EXPERTS)
# MOE1: Find the best surrogate model on the whole domain
moe1 = MOE(n_clusters=1)
print("MOE1 enabled experts: ", moe1.enabled_experts)
moe1.set_training_values(xt, yt)
moe1.train()
y_moe1 = moe1.predict_values(x)
# MOE2: Set nb of cluster with just KRG, LS and IDW surrogate models
moe2 = MOE(smooth_recombination=False, n_clusters=3, allow=["KRG", "LS", "IDW"])
print("MOE2 enabled experts: ", moe2.enabled_experts)
moe2.set_training_values(xt, yt)
moe2.train()
y_moe2 = moe2.predict_values(x)
fig, axs = plt.subplots(1)
axs.plot(x, ytrue, ".", color="black")
axs.plot(x, y_moe1)
axs.plot(x, y_moe2)
axs.set_xlabel("x")
axs.set_ylabel("y")
axs.legend(["Training data", "MOE 1 Prediction", "MOE 2 Prediction"])
plt.show()
