def test_rmtc(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import RMTC
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])
xlimits = np.array([[0.0, 4.0]])
sm = RMTC(
xlimits=xlimits,
num_elements=20,
energy_weight=1e-15,
regularization_weight=0.0,
)
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction"])
plt.show()
def rMTCSimba(xt, yt, xtest, ytest, funXLimits):
t = RMTC(xlimits=funXLimits,
min_energy=True,
nonlinear_maxiter=20,
print_prediction=False)
t.set_training_values(xt, yt)
t.train()
# Prediction of the validation points
print('RMTC, err: ' + str(compute_rms_error(t, xtest, ytest)))
title = 'RMTC model'
return t, title, xtest, ytest
def get_prop_smt_model():
xt, yt, dyt_dxt, xlimits = get_b777_engine()
this_dir = os.path.split(__file__)[0]
interp = RMTC(
num_elements=6,
xlimits=xlimits,
nonlinear_maxiter=20,
approx_order=2,
energy_weight=0.,
regularization_weight=0.,
extrapolate=True,
print_global=False,
data_dir=os.path.join(this_dir, '_smt_cache'),
)
interp.set_training_values(xt, yt)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 0], 0)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 1], 1)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 2], 2)
interp.train()
return interp
from smt.surrogate_models import RMTC
from smt.examples.rans_crm_wing.rans_crm_wing import (
get_rans_crm_wing,
plot_rans_crm_wing,
)
xt, yt, xlimits = get_rans_crm_wing()
interp = RMTC(
num_elements=20, xlimits=xlimits, nonlinear_maxiter=100, energy_weight=1e-10
)
interp.set_training_values(xt, yt)
interp.train()
plot_rans_crm_wing(xt, yt, xlimits, interp)
class SurrogateModel:
"""`SurrogateModel` class sets up a surrogate model object defined in the Surrogate Modelling Toolbox (SMT) [2]_
package to compute and exploit data for different transfer trajectories.
The model inputs are the spacecraft specific impulse `Isp` and initial thrust/weight ratio `twr` while the model
output is the propellant fraction `m_prop`.
Parameters
----------
train_method : str
Training method among ``IDW``, ``KPLS``, ``KPLSK``, ``KRG``, ``LS``, ``QP``, ``RBF``, ``RMTB``, ``RMTC``
rec_file : str or ``None``, optional
Name of the file in `latom.data.smt` where the surrogate model is stored or ``None`` if a new model has to
be built. Default is ``None``
Attributes
----------
limits : ndarray
Sampling grid limits in terms of minimum and maximum `Isp` and `twr`
x_samp : ndarray
Sampling points as `Isp, twr` tuples
m_prop : ndarray
Propellant fraction on training points [-]
failures : ndarray
Matrix of boolean to verify each NLP solution has converged
d : dict
Dictionary that contain all the information to reconstruct a meta model
trained : IDW, KPLS, KPLSK, KRG, LS, QP, RBF, RMTB or RMTC
Surrogate model object defined by SMT
References
----------
.. [2] M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins.
A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.
"""
def __init__(self, train_method, rec_file=None):
"""Initializes `SurrogateModel` class. """
self.limits = self.x_samp = self.m_prop = self.failures = self.d = None
self.trained = None
if rec_file is not None:
self.load(rec_file)
self.train(train_method)
@staticmethod
def abs_path(rec_file):
"""Returns the absolute path of the file where the surrogate model is stored.
Parameters
----------
rec_file : str
Name of the file in `latom.data.smt` where the surrogate model is stored
Returns
-------
fid : str
Full path where the surrogate model is stored
"""
return '/'.join([dirname_smt, rec_file])
def load(self, rec_file):
"""Loads stored data to instantiate a surrogate model.
Parameters
----------
rec_file : str
Name of the file in `latom.data.smt` where the surrogate model is stored
"""
self.d = load(self.abs_path(rec_file))
self.limits = self.d['limits']
self.x_samp = self.d['x_samp']
self.m_prop = self.d['m_prop']
self.failures = self.d['failures']
def save(self, rec_file):
"""Saves data corresponding to a surrogate model instance.
Parameters
----------
rec_file : str
Name of the file in `latom.data.smt` where the surrogate model is stored
"""
d = {
'limits': self.limits,
'x_samp': self.x_samp,
'm_prop': self.m_prop,
'failures': self.failures
}
save(d, self.abs_path(rec_file))
def compute_grid(self,
isp_lim,
twr_lim,
nb_samp,
samp_method='full',
criterion='m'):
"""Compute the sampling grid fro given `Isp` and `twr` limits and sampling scheme.
Parameters
----------
isp_lim : iterable
Specific impulse lower and upper bounds [s]
twr_lim : iterable
Thrust/weight ratio lower and upper bounds [-]
nb_samp : int
Total number of samples. Must be a perfect square if ``full`` is chosen as `samp_method`
samp_method : str, optional
Sampling scheme, ``lhs`` for Latin Hypercube Sampling or ``full`` for Full-Factorial Sampling.
Default is ``full``
criterion : str, optional
Criterion used to construct the LHS design among ``center``, ``maximin``, ``centermaximin``,
``correlation``, ``c``, ``m``, ``cm``, ``corr``, ``ese``. ``c``, ``m``, ``cm`` and ``corr`` are
abbreviations of ``center``, ``maximin``, ``centermaximin`` and ``correlation``, ``respectively``
Default is ``m``
"""
self.limits = np.vstack((np.asarray(isp_lim), np.asarray(twr_lim)))
if samp_method == 'lhs':
samp = LHS(xlimits=self.limits, criterion=criterion)
elif samp_method == 'full':
samp = FullFactorial(xlimits=self.limits)
else:
raise ValueError('samp_method must be either lhs or full')
self.x_samp = samp(nb_samp)
self.m_prop = np.zeros((nb_samp, 1))
self.failures = np.zeros((nb_samp, 1))
@staticmethod
def solve(nlp, i):
"""Solve the i-th NLP problem.
Parameters
----------
nlp : NLP
NLP object
i : int
Current iteration
Returns
-------
m_prop : float
Propellant fraction [-]
f : bool
Failure status
"""
print(
f"\nIteration {i}\nIsp: {nlp.sc.Isp:.6f} s\ttwr: {nlp.sc.twr:.6f}")
f = nlp.p.run_driver()
print("\nFailure: {0}".format(f))
if isinstance(nlp.phase_name, str):
phase_name = nlp.phase_name
else:
phase_name = nlp.phase_name[-1]
m_prop = 1.0 - nlp.p.get_val(phase_name + '.timeseries.states:m')[-1,
-1]
nlp.cleanup()
return m_prop, f
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 evaluate(self, isp, twr):
"""Evaluate the surrogate model in the given set of points.
Parameters
----------
isp : float or iterable
Specific impulse on evaluation points [s]
twr : float or iterable
Thrust/weight ratio on evaluation points [-]
Returns
-------
m_eval : float or iterable
Propellant fraction on evaluation points [-]
"""
if isinstance(isp, float):
isp = [isp]
if isinstance(twr, float):
twr = [twr]
x_eval = np.hstack(
(np.reshape(isp, (len(isp), 1)), np.reshape(twr, (len(twr), 1))))
m_eval = self.trained.predict_values(x_eval)
return m_eval
def compute_matrix(self, nb_eval=None):
"""Compute structured matrices for `Isp`, `twr` and `m_prop` to display the training data on a response surface.
Parameters
----------
nb_eval : int or ``None``
Number of points included in the matrix if Latin Hypercube Sampling has been used or ``None``.
Default is ``None``
Returns
-------
isp : ndarray
Matrix of specific impulses [s]
twr : ndarray
Matrix of thrust/weight ratios [-]
m_mat : ndarray
Matrix of propellant fractions [-]
"""
if nb_eval is not None: # LHS
samp_eval = FullFactorial(xlimits=self.limits)
x_eval = samp_eval(nb_eval)
m_prop_eval = self.trained.predict_values(x_eval)
else: # Full-Factorial
nb_eval = np.size(self.m_prop)
x_eval = deepcopy(self.x_samp)
m_prop_eval = deepcopy(self.m_prop)
isp = np.unique(x_eval[:, 0])
twr = np.unique(x_eval[:, 1])
n = int(np.sqrt(nb_eval))
m_mat = np.reshape(m_prop_eval, (n, n))
return isp, twr, m_mat
def plot(self, nb_eval=None, nb_lines=50, kind='prop'):
"""Plot the response surface corresponding to the loaded surrogate model.
Parameters
----------
nb_eval : int or ``None``
Number of points included in the matrix if Latin Hypercube Sampling has been used or ``None``.
Default is ``None``
nb_lines : int, optional
Number of contour lines. Default is ``50``
kind : str
``prop`` to display the propellant fraction `m_prop`, ``final`` to display the final spacecraft mass
`1 - m_prop`
"""
isp, twr, m_mat = self.compute_matrix(nb_eval=nb_eval)
if kind == 'prop':
surf_plot = RespSurf(isp,
twr,
m_mat,
'Propellant fraction',
nb_lines=nb_lines)
elif kind == 'final':
surf_plot = RespSurf(isp,
twr, (1 - m_mat),
'Final/initial mass ratio',
nb_lines=nb_lines)
else:
raise ValueError('kind must be either prop or final')
surf_plot.plot()
plt.show()
