Print the sensitivity analysis problems

In this example, we show how to print all the sensitivity analysis problems.

import otbenchmark as otb
import pandas as pd

We import the list of problems.

benchmarkProblemList = otb.SensitivityBenchmarkProblemList()
numberOfProblems = len(benchmarkProblemList)
print(numberOfProblems)

11

For each problem in the benchmark, print the problem name and the exact Sobol’ indices.

for i in range(numberOfProblems):
    problem = benchmarkProblemList[i]
    name = problem.getName()
    first_order_indices = problem.getFirstOrderIndices()
    total_order_indices = problem.getTotalOrderIndices()
    dimension = problem.getInputDistribution().getDimension()
    print(
        "#",
        i,
        ":",
        name,
        " : S = ",
        first_order_indices,
        "T=",
        total_order_indices,
        ", dimension=",
        dimension,
    )

# 0 : GaussianSum  : S =  [0.5,0.5] T= [0.5,0.5] , dimension= 2
# 1 : GaussianProduct  : S =  [0,0] T= [1,1] , dimension= 2
# 2 : GSobol  : S =  [0.986712,0.00986712,9.86712e-05] T= [0.990034,0.0131566,0.000132] , dimension= 3
# 3 : Ishigami  : S =  [0.313905,0.442411,0] T= [0.557589,0.442411,0.243684] , dimension= 3
# 4 : Borehole  : S =  [0.66,0,0,0.09,0,0.09,0.09,0.02] T= [0.69,0,0,0.11,0,0.11,0.1,0.02] , dimension= 8
# 5 : Dirichlet  : S =  [0.525547,0.233577,0.131387] T= [0.620438,0.310219,0.182482] , dimension= 3
# 6 : Flooding  : S =  [0.38,0.13,0.25,0,0.19,0.02,0,0] T= [0.4,0.15,0.25,0.01,0.19,0.02,0,0] , dimension= 8
# 7 : Morris  : S =  [0.08,0.08,0.06,0.08,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20 T= [0.11,0.11,0.06,0.11,0.06,0.13,0.06,0.13,0.13,0.12,0,0,0,0,0,0,0,0,0,0]#20 , dimension= 20
# 8 : N.L. Oscillator  : S =  [0.4,0.03,0.09,0.18,0.12,0.05,0.05,0] T= [0.4,0.04,0.1,0.23,0.16,0.07,0.06,0.01] , dimension= 8
# 9 : Borgonovo  : S =  [0.157895,0.157895,0.631579] T= [0.210526,0.210526,0.631579] , dimension= 3
# 10 : Oakley-O'Hagan  : S =  [0,0,0,0,0,0.02,0.02,0.03,0.05,0.01,0.1,0.14,0.1,0.11,0.12]#15 T= [0.06,0.06,0.04,0.05,0.02,0.04,0.06,0.08,0.1,0.04,0.15,0.15,0.14,0.14,0.16]#15 , dimension= 15

problem_names = [
    benchmarkProblem.getName() for benchmarkProblem in benchmarkProblemList
]
columns = ["$d$"]
df_problems_list = pd.DataFrame(index=problem_names, columns=columns)
for problem in benchmarkProblemList:
    name = problem.getName()
    d = problem.getInputDistribution().getDimension()
    df_problems_list.loc[name] = [int(d)]
print(df_problems_list)

                $d$
GaussianSum       2
GaussianProduct   2
GSobol            3
Ishigami          3
Borehole          8
Dirichlet         3
Flooding          8
Morris           20
N.L. Oscillator   8
Borgonovo         3
Oakley-O'Hagan   15

latex_code = df_problems_list.to_latex()
# text_file = open("sensitivity_problems_list.tex", "w")
# text_file.write(latex_code)
# text_file.close()