13.7.9. Macro “modelerbuildSimpleGP.py”

13.7.9.1. Objective

This macro is the one described in Example: construction of a simple Kriging model, that creates a simple gaussian process, whose training (utf_4D_train.dat) and testing (utf_4D_test.dat) database can both be found in the document folder of the Uranie installation (${URANIESYS}/share/uranie/docUMENTS).

13.7.9.2. Macro Uranie

"""
Example of Gaussian Process building
"""
from URANIE import DataServer, Modeler

# Load observations
tdsObs = DataServer.TDataServer("tdsObs", "observations")
tdsObs.fileDataRead("utf_4D_train.dat")

# Construct the GPBuilder
gpb = Modeler.TGPBuilder(tdsObs,         # observations data
                         "x1:x2:x3:x4",  # list of input variables
                         "y",            # output variable
                         "matern3/2")    # name of the correlation function

# Search for the optimal hyper-parameters
gpb.findOptimalParameters("ML",          # optimisation criterion
                          100,           # screening design size
                          "neldermead",  # optimisation algorithm
                          500)           # max. number of optim iterations

# Construct the kriging model
krig = gpb.buildGP()

# Display model information
krig.printLog()

13.7.9.3. Console

This is the result of the last command:

.*******************************
** TKriging::printLog[]
*******************************
 Input Variables:      x1:x2:x3:x4
 Output Variable:      y
 Deterministic trend:  
 Correlation function: URANIE::Modeler::TMatern32CorrFunction
 Correlation length:   normalised   (not normalised)
                       1.6181e+00 (1.6172e+00 )
                       1.4372e+00 (1.4370e+00 )
                       1.5026e+00 (1.5009e+00 )
                       6.7884e+00 (6.7944e+00 )

 Variance of the gaussian process:      70.8755
 RMSE (by Leave One Out):               0.499108
 Q2:                                    0.849843
*******************************