[WIP] Use scalar parameters with ParameterHandling

Project.toml

@@ -12,6 +12,7 @@ Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 IrrationalConstants = "92d709cd-6900-40b7-9082-c6be49f344b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
+ParameterHandling = "2412ca09-6db7-441c-8e3a-88d5709968c5"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
@@ -29,6 +30,7 @@ FillArrays = "0.10, 0.11, 0.12"
 Functors = "0.1, 0.2"
 IrrationalConstants = "0.1"
 LogExpFunctions = "0.2.1, 0.3"
+ParameterHandling = "0.4"
 Requires = "1.0.1"
 SpecialFunctions = "0.8, 0.9, 0.10, 1"
 StatsBase = "0.32, 0.33"

src/KernelFunctions.jl

@@ -41,6 +41,8 @@ export MOInput, prepare_isotopic_multi_output_data, prepare_heterotopic_multi_ou
 export IndependentMOKernel,
     LatentFactorMOKernel, IntrinsicCoregionMOKernel, LinearMixingModelKernel
 
+export ParameterKernel
+
 # Reexports
 export tensor, ⊗, compose
 
@@ -51,6 +53,7 @@ using CompositionsBase
 using Distances
 using FillArrays
 using Functors
+using ParameterHandling
 using LinearAlgebra
 using Requires
 using SpecialFunctions: loggamma, besselk, polygamma
@@ -107,6 +110,7 @@ include("kernels/kernelproduct.jl")
 include("kernels/kerneltensorproduct.jl")
 include("kernels/overloads.jl")
 include("kernels/neuralkernelnetwork.jl")
+include("kernels/parameterkernel.jl")
 include("approximations/nystrom.jl")
 include("generic.jl")
 

src/basekernels/constant.jl

@@ -15,7 +15,9 @@ See also: [`ConstantKernel`](@ref)
 """
 struct ZeroKernel <: SimpleKernel end
 
-kappa(κ::ZeroKernel, d::T) where {T<:Real} = zero(T)
+@noparams ZeroKernel
+
+kappa(::ZeroKernel, d::Real) = zero(d)
 
 metric(::ZeroKernel) = Delta()
 
@@ -35,6 +37,8 @@ k(x, x') = \\delta(x, x').
 """
 struct WhiteKernel <: SimpleKernel end
 
+@noparams WhiteKernel
+
 """
     EyeKernel()
 
@@ -62,19 +66,26 @@ k(x, x') = c.
 
 See also: [`ZeroKernel`](@ref)
 """
-struct ConstantKernel{Tc<:Real} <: SimpleKernel
-    c::Vector{Tc}
+struct ConstantKernel{T<:Real} <: SimpleKernel
+    c::T
 
-    function ConstantKernel(; c::Real=1.0)
+    function ConstantKernel(c::Real)
         @check_args(ConstantKernel, c, c >= zero(c), "c ≥ 0")
-        return new{typeof(c)}([c])
+        return new{typeof(c)}(c)
     end
 end
 
-@functor ConstantKernel
+ConstantKernel(; c::Real=1.0) = ConstantKernel(c)
+
+function ParameterHandling.flatten(::Type{T}, k::ConstantKernel{S}) where {T<:Real,S}
+    function unflatten_to_constantkernel(v::Vector{T})
+        return ConstantKernel(; c=S(exp(only(v))))
+    end
+    return T[log(k.c)], unflatten_to_constantkernel
+end
 
-kappa(κ::ConstantKernel, x::Real) = first(κ.c) * one(x)
+kappa(κ::ConstantKernel, x::Real) = κ.c * one(x)
 
 metric(::ConstantKernel) = Delta()
 
-Base.show(io::IO, κ::ConstantKernel) = print(io, "Constant Kernel (c = ", first(κ.c), ")")
+Base.show(io::IO, κ::ConstantKernel) = print(io, "Constant Kernel (c = ", κ.c, ")")

src/basekernels/cosine.jl

@@ -17,6 +17,8 @@ end
 
 CosineKernel(; metric=Euclidean()) = CosineKernel(metric)
 
+@noparams CosineKernel
+
 kappa(::CosineKernel, d::Real) = cospi(d)
 
 metric(k::CosineKernel) = k.metric

src/basekernels/exponential.jl

@@ -20,6 +20,8 @@ end
 
 SqExponentialKernel(; metric=Euclidean()) = SqExponentialKernel(metric)
 
+@noparams SqExponentialKernel
+
 kappa(::SqExponentialKernel, d::Real) = exp(-d^2 / 2)
 kappa(::SqExponentialKernel{<:Euclidean}, d²::Real) = exp(-d² / 2)
 
@@ -76,6 +78,8 @@ end
 
 ExponentialKernel(; metric=Euclidean()) = ExponentialKernel(metric)
 
+@noparams ExponentialKernel
+
 kappa(::ExponentialKernel, d::Real) = exp(-d)
 
 metric(k::ExponentialKernel) = k.metric
@@ -121,30 +125,37 @@ See also: [`ExponentialKernel`](@ref), [`SqExponentialKernel`](@ref)
 
 [^RW]: C. E. Rasmussen & C. K. I. Williams (2006). Gaussian Processes for Machine Learning.
 """
-struct GammaExponentialKernel{Tγ<:Real,M} <: SimpleKernel
-    γ::Vector{Tγ}
+struct GammaExponentialKernel{T<:Real,M} <: SimpleKernel
+    γ::T
     metric::M
 
     function GammaExponentialKernel(γ::Real, metric)
         @check_args(GammaExponentialKernel, γ, zero(γ) < γ ≤ 2, "γ ∈ (0, 2]")
-        return new{typeof(γ),typeof(metric)}([γ], metric)
+        return new{typeof(γ),typeof(metric)}(γ, metric)
     end
 end
 
 function GammaExponentialKernel(; gamma::Real=1.0, γ::Real=gamma, metric=Euclidean())
     return GammaExponentialKernel(γ, metric)
 end
 
-@functor GammaExponentialKernel
+function ParameterHandling.flatten(
+    ::Type{T}, k::GammaExponentialKernel{S}
+) where {T<:Real,S<:Real}
+    metric = k.metric
+    function unflatten_to_gammaexponentialkernel(v::Vector{T})
+        γ = S(2 * logistic(only(v)))
+        return GammaExponentialKernel(; γ=γ, metric=metric)
+    end
+    return T[logit(k.γ / 2)], unflatten_to_gammaexponentialkernel
+end
 
-kappa(κ::GammaExponentialKernel, d::Real) = exp(-d^first(κ.γ))
+kappa(κ::GammaExponentialKernel, d::Real) = exp(-d^κ.γ)
 
 metric(k::GammaExponentialKernel) = k.metric
 
 iskroncompatible(::GammaExponentialKernel) = true
 
 function Base.show(io::IO, κ::GammaExponentialKernel)
-    return print(
-        io, "Gamma Exponential Kernel (γ = ", first(κ.γ), ", metric = ", κ.metric, ")"
-    )
+    return print(io, "Gamma Exponential Kernel (γ = ", κ.γ, ", metric = ", κ.metric, ")")
 end

src/basekernels/exponentiated.jl

@@ -12,6 +12,8 @@ k(x, x') = \\exp(x^\\top x').
 """
 struct ExponentiatedKernel <: SimpleKernel end
 
+@noparams ExponentiatedKernel
+
 kappa(::ExponentiatedKernel, xᵀy::Real) = exp(xᵀy)
 
 metric(::ExponentiatedKernel) = DotProduct()

src/basekernels/fbm.jl

@@ -13,16 +13,23 @@ k(x, x'; h) =  \\frac{\\|x\\|_2^{2h} + \\|x'\\|_2^{2h} - \\|x - x'\\|^{2h}}{2}.
 ```
 """
 struct FBMKernel{T<:Real} <: Kernel
-    h::Vector{T}
+    h::T
+
     function FBMKernel(h::Real)
         @check_args(FBMKernel, h, zero(h) ≤ h ≤ one(h), "h ∈ [0, 1]")
-        return new{typeof(h)}([h])
+        return new{typeof(h)}(h)
     end
 end
 
 FBMKernel(; h::Real=0.5) = FBMKernel(h)
 
-@functor FBMKernel
+function ParameterHandling.flatten(::Type{T}, k::FBMKernel{S}) where {T<:Real,S<:Real}
+    function unflatten_to_fbmkernel(v::Vector{T})
+        h = S(logistic(only(v)))
+        return FBMKernel(h)
+    end
+    return T[logit(k.h)], unflatten_to_fbmkernel
+end
 
 function (κ::FBMKernel)(x::AbstractVector{<:Real}, y::AbstractVector{<:Real})
     modX = sum(abs2, x)

src/basekernels/matern.jl

@@ -19,19 +19,23 @@ differentiable in the mean-square sense.
 
 See also: [`Matern12Kernel`](@ref), [`Matern32Kernel`](@ref), [`Matern52Kernel`](@ref)
 """
-struct MaternKernel{Tν<:Real,M} <: SimpleKernel
-    ν::Vector{Tν}
+struct MaternKernel{T<:Real,M} <: SimpleKernel
+    ν::T
     metric::M
 
     function MaternKernel(ν::Real, metric)
         @check_args(MaternKernel, ν, ν > zero(ν), "ν > 0")
-        return new{typeof(ν),typeof(metric)}([ν], metric)
+        return new{typeof(ν),typeof(metric)}(ν, metric)
     end
 end
 
 MaternKernel(; nu::Real=1.5, ν::Real=nu, metric=Euclidean()) = MaternKernel(ν, metric)
 
-@functor MaternKernel
+function ParameterHandling.flatten(::Type{T}, k::MaternKernel{S}) where {T<:Real,S<:Real}
+    metric = k.metric
+    unflatten_to_maternkernel(v::Vector{T}) = MaternKernel(S(exp(first(v))), metric)
+    return T[log(k.ν)], unflatten_to_maternkernel
+end
 
 @inline function kappa(κ::MaternKernel, d::Real)
     result = _matern(first(κ.ν), d)
@@ -73,6 +77,8 @@ end
 
 Matern32Kernel(; metric=Euclidean()) = Matern32Kernel(metric)
 
+@noparams Matern32Kernel
+
 kappa(::Matern32Kernel, d::Real) = (1 + sqrt(3) * d) * exp(-sqrt(3) * d)
 
 metric(k::Matern32Kernel) = k.metric
@@ -104,6 +110,8 @@ end
 
 Matern52Kernel(; metric=Euclidean()) = Matern52Kernel(metric)
 
+@noparams Matern52Kernel
+
 kappa(::Matern52Kernel, d::Real) = (1 + sqrt(5) * d + 5 * d^2 / 3) * exp(-sqrt(5) * d)
 
 metric(k::Matern52Kernel) = k.metric

src/basekernels/nn.jl

@@ -33,6 +33,8 @@ for inputs ``x, x' \\in \\mathbb{R}^d``.[^CW]
 """
 struct NeuralNetworkKernel <: Kernel end
 
+@noparams NeuralNetworkKernel
+
 function (κ::NeuralNetworkKernel)(x, y)
     return asin(dot(x, y) / sqrt((1 + sum(abs2, x)) * (1 + sum(abs2, y))))
 end

src/basekernels/periodic.jl

@@ -21,16 +21,25 @@ struct PeriodicKernel{T} <: SimpleKernel
     end
 end
 
+"""
+    PeriodicKernel(dims::Int)
+
+Create a [`PeriodicKernel`](@ref) with parameter `r=ones(Float64, dims)`.
+"""
 PeriodicKernel(dims::Int) = PeriodicKernel(Float64, dims)
 
 """
-    PeriodicKernel([T=Float64, dims::Int=1])
+    PeriodicKernel(T, dims::Int=1)
 
 Create a [`PeriodicKernel`](@ref) with parameter `r=ones(T, dims)`.
 """
-PeriodicKernel(T::DataType, dims::Int=1) = PeriodicKernel(; r=ones(T, dims))
+PeriodicKernel(::Type{T}, dims::Int=1) where {T} = PeriodicKernel(; r=ones(T, dims))
 
-@functor PeriodicKernel
+function ParameterHandling.flatten(::Type{T}, k::PeriodicKernel{S}) where {T<:Real,S}
+    vec = T[log(ri) for ri in k.r]
+    unflatten_to_periodickernel(v::Vector{T}) = PeriodicKernel(; r=S[exp(vi) for vi in v])
+    return vec, unflatten_to_periodickernel
+end
 
 metric(κ::PeriodicKernel) = Sinus(κ.r)
 

src/basekernels/piecewisepolynomial.jl

@@ -46,6 +46,8 @@ function PiecewisePolynomialKernel(; degree::Int=0, kwargs...)
     return PiecewisePolynomialKernel{degree}(; kwargs...)
 end
 
+@noparams PiecewisePolynomialKernel
+
 piecewise_polynomial_coefficients(::Val{0}, ::Int) = (1,)
 piecewise_polynomial_coefficients(::Val{1}, j::Int) = (1, j + 1)
 piecewise_polynomial_coefficients(::Val{2}, j::Int) = (1, j + 2, (j^2 + 4 * j)//3 + 1)

src/basekernels/polynomial.jl

@@ -13,24 +13,29 @@ k(x, x'; c) = x^\\top x' + c.
 
 See also: [`PolynomialKernel`](@ref)
 """
-struct LinearKernel{Tc<:Real} <: SimpleKernel
-    c::Vector{Tc}
+struct LinearKernel{T<:Real} <: SimpleKernel
+    c::T
 
     function LinearKernel(c::Real)
         @check_args(LinearKernel, c, c >= zero(c), "c ≥ 0")
-        return new{typeof(c)}([c])
+        return new{typeof(c)}(c)
     end
 end
 
 LinearKernel(; c::Real=0.0) = LinearKernel(c)
 
-@functor LinearKernel
+function ParameterHandling.flatten(::Type{T}, k::LinearKernel{S}) where {T<:Real,S<:Real}
+    function unflatten_to_linearkernel(v::Vector{T})
+        return LinearKernel(S(exp(only(v))))
+    end
+    return T[log(k.c)], unflatten_to_linearkernel
+end
 
-kappa(κ::LinearKernel, xᵀy::Real) = xᵀy + first(κ.c)
+kappa(κ::LinearKernel, xᵀy::Real) = xᵀy + κ.c
 
 metric(::LinearKernel) = DotProduct()
 
-Base.show(io::IO, κ::LinearKernel) = print(io, "Linear Kernel (c = ", first(κ.c), ")")
+Base.show(io::IO, κ::LinearKernel) = print(io, "Linear Kernel (c = ", κ.c, ")")
 
 """
     PolynomialKernel(; degree::Int=2, c::Real=0.0)
@@ -47,31 +52,35 @@ k(x, x'; c, \\nu) = (x^\\top x' + c)^\\nu.
 
 See also: [`LinearKernel`](@ref)
 """
-struct PolynomialKernel{Tc<:Real} <: SimpleKernel
+struct PolynomialKernel{T<:Real} <: SimpleKernel
     degree::Int
-    c::Vector{Tc}
+    c::T
 
-    function PolynomialKernel{Tc}(degree::Int, c::Vector{Tc}) where {Tc}
+    function PolynomialKernel(degree::Int, c::Real)
         @check_args(PolynomialKernel, degree, degree >= one(degree), "degree ≥ 1")
-        @check_args(PolynomialKernel, c, first(c) >= zero(Tc), "c ≥ 0")
-        return new{Tc}(degree, c)
+        @check_args(PolynomialKernel, c, c >= zero(c), "c ≥ 0")
+        return new{typeof(c)}(degree, c)
     end
 end
 
 function PolynomialKernel(; degree::Int=2, c::Real=0.0)
     return PolynomialKernel{typeof(c)}(degree, [c])
 end
 
-# The degree of the polynomial kernel is a fixed discrete parameter
-function Functors.functor(::Type{<:PolynomialKernel}, x)
-    reconstruct_polynomialkernel(xs) = PolynomialKernel{typeof(xs.c)}(x.degree, xs.c)
-    return (c=x.c,), reconstruct_polynomialkernel
+function ParameterHandling.flatten(
+    ::Type{T}, k::PolynomialKernel{S}
+) where {T<:Real,S<:Real}
+    degree = k.degree
+    function unflatten_to_polynomialkernel(v::Vector{T})
+        return PolynomialKernel(degree, S(exp(only(v))))
+    end
+    return T[log(k.c)], unflatten_to_polynomialkernel
 end
 
-kappa(κ::PolynomialKernel, xᵀy::Real) = (xᵀy + first(κ.c))^κ.degree
+kappa(κ::PolynomialKernel, xᵀy::Real) = (xᵀy + κ.c)^κ.degree
 
 metric(::PolynomialKernel) = DotProduct()
 
 function Base.show(io::IO, κ::PolynomialKernel)
-    return print(io, "Polynomial Kernel (c = ", first(κ.c), ", degree = ", κ.degree, ")")
+    return print(io, "Polynomial Kernel (c = ", κ.c, ", degree = ", κ.degree, ")")
 end