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willtebbuttgithub-actions[bot]st--theogf
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Multi-Output helper function (#345)
* Add helper function * Docs * Update src/mokernels/moinput.jl Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * Relax type constraint Co-authored-by: st-- <[email protected]> * Split into two methods * Update docs * Update src/mokernels/moinput.jl Co-authored-by: st-- <[email protected]> * Update src/mokernels/moinput.jl Co-authored-by: st-- <[email protected]> * Move consistency tests to test * Tweak wording * Apply suggestions from code review Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * Update runtests.jl * Apply suggestions from code review Co-authored-by: st-- <[email protected]> * Update test/mokernels/moinput.jl Co-authored-by: st-- <[email protected]> * Bump patch Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: st-- <[email protected]> Co-authored-by: Théo Galy-Fajou <[email protected]>
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‎Project.toml

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name = "KernelFunctions"
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uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
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version = "0.10.9"
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version = "0.10.10"
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[deps]
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ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

‎docs/src/api.md

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@@ -58,7 +58,13 @@ For an explanation of this design choice, see [the design notes on multi-output
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An input to a multi-output `Kernel` should be a `Tuple{T, Int}`, whose first element specifies a location in the domain of the multi-output GP, and whose second element specifies which output the inputs corresponds to.
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The type of collections of inputs for multi-output GPs is therefore `AbstractVector{<:Tuple{T, Int}}`.
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KernelFunctions.jl provides the following type or situations in which all outputs are observed all of the time:
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KernelFunctions.jl provides the following helper function for situations in which all outputs are observed all of the time:
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```@docs
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prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)
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prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)
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```
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The input types that it constructs can also be constructed manually:
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```@docs
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MOInput
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```
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To find out more about the background, read this [review of kernels for vector-valued functions](https://arxiv.org/pdf/1106.6251.pdf).
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## Utilities
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## Generic Utilities
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KernelFunctions also provides miscellaneous utility functions.
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```@docs

‎src/KernelFunctions.jl

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export ColVecs, RowVecs
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export MOInput
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export MOInput, prepare_isotopic_multi_output_data
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export IndependentMOKernel,
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LatentFactorMOKernel, IntrinsicCoregionMOKernel, LinearMixingModelKernel
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‎src/mokernels/moinput.jl

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and removed in version 0.12.
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"""
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const MOInput = MOInputIsotopicByOutputs
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"""
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prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)
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Utility functionality to convert a collection of `N = length(x)` inputs `x`, and a
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vector-of-vectors `y` (efficiently represented by a `ColVecs`) into a format suitable for
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use with multi-output kernels.
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`y[n]` is the vector-valued output corresponding to the input `x[n]`.
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Consequently, it is necessary that `length(x) == length(y)`.
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For example, if outputs are initially stored in a `num_outputs × N` matrix:
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```julia
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julia> x = [1.0, 2.0, 3.0];
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julia> Y = [1.1 2.1 3.1; 1.2 2.2 3.2]
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2×3 Matrix{Float64}:
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1.1 2.1 3.1
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1.2 2.2 3.2
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julia> inputs, outputs = prepare_isotopic_multi_output_data(x, ColVecs(Y));
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julia> inputs
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6-element KernelFunctions.MOInputIsotopicByFeatures{Float64, Vector{Float64}}:
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(1.0, 1)
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(1.0, 2)
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(2.0, 1)
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(2.0, 2)
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(3.0, 1)
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(3.0, 2)
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julia> outputs
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6-element Vector{Float64}:
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1.1
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1.2
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2.1
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2.2
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3.1
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3.2
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```
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"""
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function prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)
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length(x) == length(y) || throw(ArgumentError("length(x) not equal to length(y)."))
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return MOInputIsotopicByFeatures(x, size(y.X, 1)), vec(y.X)
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end
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"""
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prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)
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Utility functionality to convert a collection of `N = length(x)` inputs `x` and output
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vectors `y` (efficiently represented by a `RowVecs`) into a format suitable for
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use with multi-output kernels.
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`y[n]` is the vector-valued output corresponding to the input `x[n]`.
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Consequently, it is necessary that `length(x) == length(y)`.
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For example, if outputs are initial stored in an `N × num_outputs` matrix:
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```jldoctest
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julia> x = [1.0, 2.0, 3.0];
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julia> Y = [1.1 1.2; 2.1 2.2; 3.1 3.2]
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3×2 Matrix{Float64}:
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1.1 1.2
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2.1 2.2
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3.1 3.2
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julia> inputs, outputs = prepare_isotopic_multi_output_data(x, RowVecs(Y));
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julia> inputs
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6-element KernelFunctions.MOInputIsotopicByOutputs{Float64, Vector{Float64}}:
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(1.0, 1)
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(2.0, 1)
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(3.0, 1)
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(1.0, 2)
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(2.0, 2)
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(3.0, 2)
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julia> outputs
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6-element Vector{Float64}:
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1.1
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2.1
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3.1
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1.2
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2.2
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3.2
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```
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"""
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function prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)
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length(x) == length(y) || throw(ArgumentError("length(x) not equal to length(y)."))
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return MOInputIsotopicByOutputs(x, size(y.X, 2)), vec(y.X)
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end

‎test/mokernels/moinput.jl

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@test ibf[7] == (x[3], 1)
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@test all([(x_, i) for x_ in x for i in 1:3] .== ibf)
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end
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@testset "prepare_isotopic_multi_output_data" begin
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@testset "ColVecs" begin
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N = 5
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P = 3
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x = randn(N)
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y = ColVecs(randn(P, N))
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x_canon, y_canon = prepare_isotopic_multi_output_data(x, y)
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@test x_canon isa KernelFunctions.MOInputIsotopicByFeatures
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@test length(x_canon) == N * P
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@test y_canon isa AbstractVector{<:Real}
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@test length(y_canon) == length(x_canon)
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end
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@testset "RowVecs" begin
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N = 5
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P = 3
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x = randn(N)
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y = RowVecs(randn(N, P))
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x_canon, y_canon = prepare_isotopic_multi_output_data(x, y)
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@test x_canon isa KernelFunctions.MOInputIsotopicByOutputs
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@test length(x_canon) == N * P
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@test y_canon isa AbstractVector{<:Real}
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@test length(y_canon) == length(x_canon)
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end
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end
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end

‎test/runtests.jl

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KernelFunctions;
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doctestfilters=[
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r"{([a-zA-Z0-9]+,\s?)+[a-zA-Z0-9]+}",
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r"(Array{[a-zA-Z0-9]+,\s?1}|\s?Vector{[a-zA-Z0-9]+})",
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r"(Array{[a-zA-Z0-9]+,\s?2}|\s?Matrix{[a-zA-Z0-9]+})",
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r"(\s?Array{[a-zA-Z0-9]+,\s?1}|\s?Vector{[a-zA-Z0-9]+})",
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r"(\s?Array{[a-zA-Z0-9]+,\s?2}|\s?Matrix{[a-zA-Z0-9]+})",
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],
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)
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end

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