analysis.py

# from PyGMO import *

#
# prob=earthToMars()
# inspector=util.analysis(prob, 500, method='sobol', output_to_file=True)
# algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
# algo2 = algorithm.mbh(algo, n_restarts, 0.005)
# pop = population(prob)

from PyGMO import *
from blackBoxHigh import earthToMars

import numpy
import math
import matplotlib
import pandas

#We instantiate the optimization problem as usual. We will choose a 4-dimensional 2-objective
#problem.
prob=earthToMars()

#Now we instantiate the analysis class. In this case we will sample 500 points via sobol method
#and choose output to file.
inspector=util.analysis(prob, 500, method='sobol', output_to_file=True)


inspector.f_distribution()

inspector.f_linearity_convexity()

#We will compute all regression properties for linear, quadratic and cubic regression, with
#and without interaction.
inspector.f_regression(degree=[1,1,2,2,3,3],interaction=[False,True,False,True,False,True])

inspector.f_sensitivity()

#The levelset test is very slow, we do not recommend launching it out of development purposes.
#Anyway this is an example so here it is. We will launch it using percentile 45 as threshold
#instead of the default 50, and only with 2-fold crossvalidation for tuning of svm.
inspector.levelset(threshold=45, k_tune=2)

#For the local searches, we will choose Nelder-Mead simplex algorithm. As it is a multi-objective
#problem, we will also choose a decomposition method, for instance weighted with uniform weights.
#To keep it short, we will only show properties of the best 3 clusters instead of the default 10.
#We will also generate a scatter plot for dimensions x1 and x2.
inspector.local_search(clusters_to_show=3, scatter_plot_dimensions=[1,2],\
algo=algorithm.gsl_nm(), decomposition_method='weighted', weights='uniform')

#Being only a 2-objective problem, we probably will not obtain dimensionality reduction, but we
#will launch this test all the same to look into the f-correlation matrix.
inspector.f_correlation()