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… get calculated twice
…named kucherenkoindices_bin
…jointdistribution.jl
Updated local package activation and added print statements/comments for clarity.
…_conditional_copula var values intput to tuples
FriesischScott
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enhancement
FriesischScott
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FriesischScott
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Please move both the conditional sampling and the corresponding tests into joint distribution files.
Overall we need to be able to pass both dependent and independent inputs to the kucherenkoindices function at the same time so we can for example compute the sensitivity for the cantilever example.
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| function _compute_first_order_kucherenko( |
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I think this can be moved inside the higher up function.
| return S_i | ||
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| function _compute_total_effect_kucherenko( |
| joint_dist::JointDistribution, | ||
| var_names::Vector{Symbol} | ||
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| input_var_names = [marginal.name for marginal in joint_dist.m] |
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This is just names(joint_dist).
| return DataFrame(permutedims(rand(jd.d, n)), jd.m) | ||
| end | ||
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| function sample_conditional_copula(joint::JointDistribution{<:GaussianCopula,<:RandomVariable}, var_values::Vector{Tuple{Symbol,Float64}}, N::Int) |
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I think it would be good to have a function that takes a JointDistribution and a DataFrame with less columns than the joint distribution requires and returns the data frame with the remaining columns samples conditionally based on the existing samples.
| - `min_bin_sample::Int=25`: Minimum samples per bin for 1 dimension for conditioning; Recommended amount is at least 25 samples per bin with bin amount around 100 | ||
| - `min_bin_sample_multi_dims::Int=25`: Minimum samples per bin-dimension in multiple dimensions for conditioning; Recommended amount is about 10-25 samples per bin | ||
| """ | ||
| function kucherenkoindices_bin(X::Matrix, Y::Vector; min_bin_sample=nothing, min_bin_sample_multi_dims::Int=25) |
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This should dispatch on a DataFrame with all the data and an output Symbol for which to compute the sensitivity indices. This way all the variable names are available as well.
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And keep the name kucherenkoindices.
| - `min_bin_sample::Int=25`: Minimum samples per bin for 1 dimension for conditioning; Recommended amount is at least 25 samples per bin but bin amount not higher than 100 | ||
| - `min_bin_sample_multi_dims::Int=25`: Minimum samples per bin-dimension in multiple dimensions for conditioning; Recommended amount is about 10-25 samples per bin | ||
| """ | ||
| function kucherenkoindices_bin( |
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By changing the first method to a DataFrame you can get rid of all of these and it will be up to the user if they want to use the conditional sampling or the binning.
| @testset "sample_conditional_copula" begin | ||
| marginals = [RandomVariable(Normal(0,1), :x1), RandomVariable(Normal(0,1), :x2), RandomVariable(Normal(0,1), :x3)] | ||
| joint_dist = JointDistribution(GaussianCopula([1 0.7071 0.5; 0.7071 1 0.3; 0.5 0.3 1]), marginals) | ||
| unconditional = sample(joint_dist, 500000) |
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I think we need a few more tests. We have to make sure that the conditional samples have the correct strength of dependence but also that they are resulting from a Gaussian copula.
2 participants
New pull req from inside the repo:
Original text:
Implementation of 2 methods to calculate Kucherenko Indices and ability to create conditional samples from a gaussian copula
New functions:
Standard Kucherenko Indices using conditional sampling
Sample based Kucherenko Indices using binning
create conditional samples from a gaussian copula