def train(self, X_train, y_train):
if self.flavour == 'plain':
self.smt_model = KRG(poly=self.poly,
corr=self.corr,
theta0=self.theta0)
elif self.flavour == 'pls':
self.smt_model = KPLS(poly=self.poly,
corr=self.corr,
theta0=self.theta0,
n_comp=self.n_comp)
elif self.flavour == 'plsk':
self.smt_model = KPLSK(poly=self.poly,
corr=self.corr,
theta0=self.theta0,
n_comp=self.n_comp)
elif self.flavour == 'gepls':
self.smt_model = GEKPLS(poly=self.poly,
corr=self.corr,
theta0=self.theta0,
n_comp=self.n_comp,
xlimits=self.xlimits,
delta_x=self.delta_x,
extra_points=self.extra_points)
super(KrigingModel, self).train(X_train, y_train)
def setUp(self):
ndim = 3
nt = 100
ne = 100
ncomp = 1
problems = OrderedDict()
problems['exp'] = TensorProduct(ndim=ndim, func='exp')
problems['tanh'] = TensorProduct(ndim=ndim, func='tanh')
problems['cos'] = TensorProduct(ndim=ndim, func='cos')
sms = OrderedDict()
sms['LS'] = LS()
sms['QP'] = QP()
sms['KRG'] = KRG(theta0=[1e-2] * ndim)
sms['KPLS'] = KPLS(theta0=[1e-2] * ncomp, n_comp=ncomp)
sms['KPLSK'] = KPLSK(theta0=[1] * ncomp, n_comp=ncomp)
sms['GEKPLS'] = GEKPLS(theta0=[1e-2] * ncomp,
n_comp=ncomp,
delta_x=1e-1)
if compiled_available:
sms['IDW'] = IDW()
sms['RBF'] = RBF()
sms['RMTC'] = RMTC()
sms['RMTB'] = RMTB()
t_errors = {}
t_errors['LS'] = 1.0
t_errors['QP'] = 1.0
t_errors['KRG'] = 1e-5
t_errors['KPLS'] = 1e-5
t_errors['KPLSK'] = 1e-5
t_errors['GEKPLS'] = 1e-5
if compiled_available:
t_errors['IDW'] = 1e-15
t_errors['RBF'] = 1e-2
t_errors['RMTC'] = 1e-1
t_errors['RMTB'] = 1e-1
e_errors = {}
e_errors['LS'] = 1.5
e_errors['QP'] = 1.5
e_errors['KRG'] = 1e-2
e_errors['KPLS'] = 1e-2
e_errors['KPLSK'] = 1e-2
e_errors['GEKPLS'] = 1e-2
if compiled_available:
e_errors['IDW'] = 1e0
e_errors['RBF'] = 1e0
e_errors['RMTC'] = 2e-1
e_errors['RMTB'] = 2e-1
self.nt = nt
self.ne = ne
self.ndim = ndim
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
def kPLSK(xt, yt, xtest, ytest):
########### The KPLSK model
# 'n_comp' and 'theta0' must be an integer in [1,ndim[ and a list of length n_comp, respectively.
t = KPLSK(n_comp=2, theta0=[1e-2, 1e-2], print_prediction=False)
t.set_training_values(xt, yt)
t.train()
print('KPLSK, err: ' + str(compute_rms_error(t, xtest, ytest)))
title = 'KPLSK model: validation of the prediction model'
return t, title, xtest, ytest
def test_kplsk(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import KPLSK
xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
yt = np.array([0.0, 1.0, 1.5, 0.9, 1.0])
sm = KPLSK(theta0=[1e-2])
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
# estimated variance
s2 = sm.predict_variances(x)
# derivative according to the first variable
dydx = sm.predict_derivatives(xt, 0)
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction"])
plt.show()
# add a plot with variance
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.fill_between(
np.ravel(x),
np.ravel(y - 3 * np.sqrt(s2)),
np.ravel(y + 3 * np.sqrt(s2)),
color="lightgrey",
)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction", "Confidence Interval 99%"])
plt.show()
def test_kplsk(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import KPLSK
xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
yt = np.array([0.0, 1.0, 1.5, 0.5, 1.0])
sm = KPLSK(theta0=[1e-2])
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
yy = sm.predict_derivatives(xt, 0)
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction"])
plt.show()
def test_kplsk(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import KPLSK
xt = np.array([0., 1., 2., 3., 4.])
yt = np.array([0., 1., 1.5, 0.5, 1.0])
sm = KPLSK(theta0=[1e-2])
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0., 4., num)
y = sm.predict_values(x)
yy = sm.predict_derivatives(xt, 0)
plt.plot(xt, yt, 'o')
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.legend(['Training data', 'Prediction'])
plt.show()
def train(self, train_method, **kwargs):
"""Trains the surrogate model with given training data.
Parameters
----------
train_method : str
Training method among ``IDW``, ``KPLS``, ``KPLSK``, ``KRG``, ``LS``, ``QP``, ``RBF``, ``RMTB``, ``RMTC``
kwargs : dict
Additional keyword arguments supported by SMT objects
"""
if train_method == 'IDW':
self.trained = IDW(**kwargs)
elif train_method == 'KPLS':
self.trained = KPLS(**kwargs)
elif train_method == 'KPLSK':
self.trained = KPLSK(**kwargs)
elif train_method == 'KRG':
self.trained = KRG(**kwargs)
elif train_method == 'LS':
self.trained = LS(**kwargs)
elif train_method == 'QP':
self.trained = QP(**kwargs)
elif train_method == 'RBF':
self.trained = RBF(**kwargs)
elif train_method == 'RMTB':
self.trained = RMTB(xlimits=self.limits, **kwargs)
elif train_method == 'RMTC':
self.trained = RMTC(xlimits=self.limits, **kwargs)
else:
raise ValueError(
'train_method must be one between IDW, KPLS, KPLSK, KRG, LS, QP, RBF, RMTB, RMTC'
)
self.trained.set_training_values(self.x_samp, self.m_prop)
self.trained.train()
def setUp(self):
ndim = 3
nt = 100
ne = 100
ncomp = 1
problems = OrderedDict()
problems["exp"] = TensorProduct(ndim=ndim, func="exp")
problems["tanh"] = TensorProduct(ndim=ndim, func="tanh")
problems["cos"] = TensorProduct(ndim=ndim, func="cos")
sms = OrderedDict()
sms["LS"] = LS()
sms["QP"] = QP()
sms["KRG"] = KRG(theta0=[1e-2] * ndim)
sms["MFK"] = MFK(theta0=[1e-2] * ndim)
sms["KPLS"] = KPLS(theta0=[1e-2] * ncomp, n_comp=ncomp)
sms["KPLSK"] = KPLSK(theta0=[1] * ncomp, n_comp=ncomp)
sms["GEKPLS"] = GEKPLS(theta0=[1e-2] * ncomp,
n_comp=ncomp,
delta_x=1e-1)
sms["GENN"] = genn()
if compiled_available:
sms["IDW"] = IDW()
sms["RBF"] = RBF()
sms["RMTC"] = RMTC()
sms["RMTB"] = RMTB()
t_errors = {}
t_errors["LS"] = 1.0
t_errors["QP"] = 1.0
t_errors["KRG"] = 1e0
t_errors["MFK"] = 1e0
t_errors["KPLS"] = 1e0
t_errors["KPLSK"] = 1e0
t_errors["GEKPLS"] = 1e0
t_errors["GENN"] = 1e0
if compiled_available:
t_errors["IDW"] = 1e0
t_errors["RBF"] = 1e-2
t_errors["RMTC"] = 1e-1
t_errors["RMTB"] = 1e-1
e_errors = {}
e_errors["LS"] = 1.5
e_errors["QP"] = 1.5
e_errors["KRG"] = 1e-2
e_errors["MFK"] = 1e-2
e_errors["KPLS"] = 1e-2
e_errors["KPLSK"] = 1e-2
e_errors["GEKPLS"] = 1e-2
e_errors["GENN"] = 1e-2
if compiled_available:
e_errors["IDW"] = 1e0
e_errors["RBF"] = 1e0
e_errors["RMTC"] = 2e-1
e_errors["RMTB"] = 2e-1
self.nt = nt
self.ne = ne
self.ndim = ndim
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
# an example using 2 principal components.
t = KPLS(n_comp=2, theta0=[1e-2, 1e-2], print_prediction=False, corr='abs_exp')
t.set_training_values(xt, yt[:, 0])
t.train()
# Prediction of the validation points
y = t.predict_values(xtest)
print('KPLS + abs exp, err: ' + str(compute_rms_error(t, xtest, ytest)))
########### The KPLSK model
# 'n_comp' and 'theta0' must be an integer in [1,ndim[ and a list of length n_comp, respectively.
t = KPLSK(n_comp=2, theta0=[1e-2, 1e-2], print_prediction=False)
t.set_training_values(xt, yt[:, 0])
t.train()
# Prediction of the validation points
y = t.predict_values(xtest)
print("KPLSK, err: " + str(compute_rms_error(t, xtest, ytest)))
if plot_status:
k, l = 0, 0
f, axarr = plt.subplots(4, 3)
axarr[k, l].plot(ytest, ytest, "-.")
axarr[k, l].plot(ytest, y, ".")
l += 1
axarr[3, 2].arrow(0.3, 0.3, 0.2, 0)
axarr[3, 2].arrow(0.3, 0.3, 0.0, 0.4)
def test_smt_kplsk(self):
self._check_smt(KPLSK(theta0=[1e-2]))
xt = training_set[:, 0:3]
yt = training_set[:, 5]
# train the model
# define a RMTS spline interpolant
# TODO replace with a different SMT surrogate
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, {