Re: [ot-users] duplicate with SobolIndicesExperiment

Thu, 07 Dec 2017 02:10:23 -0800

Hi,


The cache mecanism could be used here to spare these evaluations.


j

De : [email protected] <[email protected]> de la part de roy <[email protected]>
Envoyé : jeudi 7 décembre 2017 10:42:34
À : HADDAD Sofiane
Cc : users
Objet : Re: [ot-users] duplicate with SobolIndicesExperiment
 
Hi Sofiane,

Thanks for the update.

Do you plan to have it implemented on OT? Or should I handle this in my package?

Also, I have a last interrogation. When I create the sample with SobolIndicesExperiment,
I found that Sobol' indices are not always computed correctly. I am not able to provide a MCVE here as
from the sample generation to the computation of the indices, a lot happens. Sorry.
But from my trying, if I set OT’s seed, I get correct results.

So is there a way to ensure that the sample generated by SobolIndicesExperiment will correspond to
what is expected by the indices classes? Seems that there is a randomization effect here.

Thanks again for the support.

Sincerely,

Pamphile ROY


Le 7 déc. 2017 à 00:39, HADDAD Sofiane <[email protected]> a écrit :

Hi

You are right  only the case dim=2 has duplicates if compute second order is set tot True.
I miss it, sorry!

An enhancement is to generate samples of size N * (dim + 1) in case dim=2 whatever second order is True or False

Thanks for the report.

Regards,
Sofiane


Le samedi 2 décembre 2017 à 13:25:25 UTC+1, Pamphile ROY <[email protected]> a écrit :


Hi Sofiane,

I got it now.

But if in 2dim this behavior is to be expected, why not doing this internally?
The root of this was that I have an expensive numerical model. So having to compute twice a sample is not tractable.

Thanks for your support.

Sincerely,

Pamphile ROY
De: "HADDAD Sofiane" <[email protected]>
À: "users" <[email protected]>, "roy" <[email protected]>
Envoyé: Vendredi 1 Décembre 2017 13:31:14
Objet: Re: [ot-users] duplicate with SobolIndicesExperiment

Hi

There is no problem here

You can find here how the experiment is defined. As you set second order to True and your sub samples are of size 5, you have (2 * 2 + 2) blocks of size 5 Have a look at SobolIndicesAlgorithm — OpenTURNS documentation




Using this :
import openturns as ot
ot.RandomGenerator.SetSeed(0)
distribution = ot.ComposedDistribution([ot.Uniform(15, 60), ot.Normal(3000, 400)])
sample = np.array(ot.SobolIndicesAlgorithmImplementation.Generate(distribution, 5, True))
print(sample.reshape(-1,5,2))

you will see all matrices defines in link.
Hope this helps

Bien cordialement,
Sofiane HADDAD


Le mercredi 29 novembre 2017 à 19:30:39 UTC+1, roy <[email protected]> a écrit :


Hi,

I am using ot.SobolIndicesExperiment and if I set my input dimension to 2,
I get some repeated points. From my understanding of the method, this should not be the case.
Also going with a higher dimension does not do that.

Am I wrong?

Here is an example (same behaviour with the old method available in OT 1.9) where I highlighted some duplicate:

import openturns as ot
distribution = ot.ComposedDistribution([ot.Uniform(15, 60), ot.Normal(3000, 400)])
sample = np.array(ot.SobolIndicesAlgorithmImplementation.Generate(distribution, 5, True))

array([[   29.65970938,  2535.47991432],
       [   33.01991727,  2559.28624639],
       [   33.25474751,  2682.95080229],
       [   32.95380182,  2419.44678937],
       [   55.23575378,  3039.33121131],
       [   26.05095231,  3271.18330661],
       [   41.00594229,  3683.75154513],
       [   54.81729255,  3428.24812578],
       [   28.2423326 ,  2797.23010815],
       [   52.36310769,  2335.65441869],
       [   26.05095231,  2535.47991432],
       [   41.00594229,  2559.28624639],
       [   54.81729255,  2682.95080229],
       [   28.2423326 ,  2419.44678937],
       [   52.36310769,  3039.33121131],
       [   29.65970938,  3271.18330661],
       [   33.01991727,  3683.75154513],
       [   33.25474751,  3428.24812578],
       [   32.95380182,  2797.23010815],
       [   55.23575378,  2335.65441869],
       [   29.65970938,  3271.18330661],
       [   33.01991727,  3683.75154513],
       [   33.25474751,  3428.24812578],
       [   32.95380182,  2797.23010815],
       [   55.23575378,  2335.65441869],
       [   26.05095231,  2535.47991432],
       [   41.00594229,  2559.28624639],
       [   54.81729255,  2682.95080229],
       [   28.2423326 ,  2419.44678937],
       [   52.36310769,  3039.33121131]])

Thanks for your support.

Sincerely,


Pamphile ROY
PhD candidate in Uncertainty Quantification
CERFACS - Toulouse (31) - France
+33 (0) 5 61 19 31 57
+33 (0) 7 86 43 24 22

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