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Cover Problem

OptEnrichedSetCover.CoverParams — Type
CoverParams(; [sel_tax=0.0], [set_shape=1.0],
              [min_weight=1E-2], [mask_discount=0.9], [setXset_factor=1.0],
              [uncovered_factor=0.1], [covered_factor=0.001],
              [set_relevance_shape=0.5], [set_relevance_min=0.5]) -> CoverParams

Specify parameters for the AbstractCoverProblem. One can specify a non-default value for a particular parameter.

See cover quality score for the detailed description of the parameters.

Arguments

  • sel_tax: $\epsilon$, the constant added to the set score of each selected set to penalize the number of sets in the cover
  • set_shape: $\beta$, applied to set or set×set scores
  • min_weight: minimal non-zero set probability ???
  • mask_discount: $\alpha$, how much the overlap score of each subsequent mask (from most to less enriched) is discounted
  • setXset_factor: $w_r$, the weight of redundancy cover quality component (setXset_score scale), 0 = no redunancy penalty
  • uncovered_factor: $w_u$, the weight of uncovered hits component in cover quality
  • covered_factor: $w_c$, the weight of covered non-hits component in cover quality
  • set_relevance_shape: $\beta_L$, how much set relevance affects set score, 0 = no effect
  • set_relevance_min: $L_{\min$, if shaped relevance is below, it's set to set_relevance_min
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OptEnrichedSetCover.AbstractCoverProblem — Type
AbstractCoverProblem{T}

Optimal Enriched-Set Cover problem – choose the sets from the collection 𝒞 to cover the masked(selected) elements M. The optimal sets cover C = {c₁, c₂, ..., cₙ} ⊂ 𝒞 has to deliver 3 goals:

  • be relevant (i.e. minimize the P-values of M and cᵢ sets overlap)
  • be minimal (i.e. minimize the number of sets in C)
  • be non-redundant (i.e. minimize the P-values of the pairwise non-overlap of C sets with each other).

Fuzzy set selection is possible – each set is assigned a weight from [0, 1] range.

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OptEnrichedSetCover.optimize — Function
optimize(problem::MultiobjCoverProblem,
         [opt_params::MultiobjOptimizerParams]) -> MultiobjCoverProblemResult

Optimize MultiobjCoverProblem and return the result. Uses Borf multi-objective optimization method from BlackBoxOptim.jl package.

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OptEnrichedSetCover.score — Function
score(w::AbstractVector{Float64}, problem) -> NTuple{4, Float64}

Unfolded multiobjective score (fitness) of the OESC coverage.

  • w: probabilities of the sets being covered

See "Cover quality".

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