RFC Added SkipMissing wrapper metric#261
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| """ | ||
| Exclude any missing indices from being included the wrappped distance metric. | ||
| """ | ||
| struct SkipMissing{D<:PreMetric} <: PreMetric |
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I'm not sure if there's a benefit to have specific subtypes here? All the metrics I tested seemed to work.
| # what the parameters mean. | ||
| # NTOE: We call disallowmissings to avoid downstream type promotion issues. | ||
| if dist.d isa UnionMetrics | ||
| params = parameters(dist.d) |
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AFAICT, parameters is only defined for UnionMetrics?
| isnothing(params) ? params : view(params, mask), | ||
| ) | ||
| else | ||
| return dist.d( |
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I'm not sure if there's a safer fallback?
| @test_throws MethodError pairwise(dist, A, dims=2) | ||
| end | ||
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| # Handle weights |
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We're just testing that skipmissing returns the same results as deleting indices missing in either vector.
| @test D(a, b) == dist([1, 5], [6, 10]) | ||
| end | ||
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| @test colwise(D, A, B)[3:4] ≈ colwise(dist, disallowmissing(A[:, 3:4]), disallowmissing(B[:, 3:4])) |
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Double check results are the same for non-missing columns.
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| M = pairwise(D, A, dims=2) | ||
| @test size(M) == (4, 4) | ||
| @test !any(ismissing, M) |
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Could manually check the calculations here, but I figured a few smoke tests that it doesn't error or return missings was good enough?
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Hmm, looks like some of the metrics throw different errors on different Julia versions. |
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A potential solution to #11
Pros:
skipmissingalready exists in base, so hopefully this approach is relatively intuitiveCons:
Base.skipmissingthat we shouldn't export and could cause confusion?ntotal / ncompletefactor inside thesqrtfor euclidean distancesAlternatives: