Extension of #269: Use \circ and compose and deprecate transform

Project.toml

@@ -5,6 +5,7 @@ version = "0.9.0"
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
+CompositionsBase = "a33af91c-f02d-484b-be07-31d278c5ca2b"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -20,6 +21,7 @@ ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 [compat]
 ChainRulesCore = "0.9"
 Compat = "3.7"
+CompositionsBase = "0.1"
 Distances = "0.10"
 Functors = "0.1"
 Requires = "1.0.1"

src/KernelFunctions.jl

@@ -39,11 +39,12 @@ export MOInput
 export IndependentMOKernel, LatentFactorMOKernel
 
 # Reexports
-export tensor, ⊗
+export tensor, ⊗, compose
 
 using Compat
 using ChainRulesCore: ChainRulesCore, Composite, Zero, One, DoesNotExist, NO_FIELDS
 using ChainRulesCore: @thunk, InplaceableThunk
+using CompositionsBase
 using Requires
 using Distances, LinearAlgebra
 using Functors
@@ -106,6 +107,8 @@ include("zygoterules.jl")
 
 include("test_utils.jl")
 
+include("deprecations.jl")
+
 function __init__()
     @require Kronecker = "2c470bb0-bcc8-11e8-3dad-c9649493f05e" begin
         include(joinpath("matrix", "kernelkroneckermat.jl"))

src/deprecations.jl

@@ -0,0 +1,4 @@
+@deprecate transform(k::Kernel, t::Transform) ∘(k, t)
+@deprecate transform(k::TransformedKernel, t::Transform) ∘(k.kernel, t ∘ k.transform)
+@deprecate transform(k::Kernel, ρ::Real) ∘(k, ScaleTransform(ρ))
+@deprecate transform(k::Kernel, ρ::AbstractVector) ∘(k, ARDTransform(ρ))

src/kernels/transformedkernel.jl

@@ -3,9 +3,9 @@
 
 Kernel derived from `k` for which inputs are transformed via a [`Transform`](@ref) `t`.
 
-It is preferred to create kernels with input transformations with [`transform`](@ref)
-instead of  `TransformedKernel` directly since [`transform`](@ref) allows optimized
-implementations for specific kernels and transformations.
+It is preferred to create kernels with input transformations with [`∘`](@ref), or its
+alias [`compose`](@ref), instead of `TransformedKernel` directly since [`∘`](@ref)
+allows optimized implementations for specific kernels and transformations.
 
 # Definition
 
@@ -41,31 +41,8 @@ function _scale(t::ScaleTransform, metric::Union{SqEuclidean,DotProduct}, x, y)
 end
 _scale(t::ScaleTransform, metric, x, y) = evaluate(metric, t(x), t(y))
 
-"""
-    transform(k::Kernel, t::Transform)
-
-Create a [`TransformedKernel`](@ref) for kernel `k` and transform `t`.
-"""
-transform(k::Kernel, t::Transform) = TransformedKernel(k, t)
-function transform(k::TransformedKernel, t::Transform)
-    return TransformedKernel(k.kernel, t ∘ k.transform)
-end
-
-"""
-    transform(k::Kernel, ρ::Real)
-
-Create a [`TransformedKernel`](@ref) for kernel `k` and inverse lengthscale `ρ`.
-"""
-transform(k::Kernel, ρ::Real) = transform(k, ScaleTransform(ρ))
-
-"""
-    transform(k::Kernel, ρ::AbstractVector)
-
-Create a [`TransformedKernel`](@ref) for kernel `k` and inverse lengthscales `ρ`.
-"""
-transform(k::Kernel, ρ::AbstractVector) = transform(k, ARDTransform(ρ))
-
-kernel(κ) = κ.kernel
+Base.:∘(k::Kernel, t::Transform) = TransformedKernel(k, t)
+Base.:∘(k::TransformedKernel, t::Transform) = TransformedKernel(k.kernel, k.transform ∘ t)
 
 Base.show(io::IO, κ::TransformedKernel) = printshifted(io, κ, 0)
 
@@ -87,13 +64,13 @@ function kernelmatrix_diag!(
 end
 
 function kernelmatrix!(K::AbstractMatrix, κ::TransformedKernel, x::AbstractVector)
-    return kernelmatrix!(K, kernel(κ), _map(κ.transform, x))
+    return kernelmatrix!(K, κ.kernel, _map(κ.transform, x))
 end
 
 function kernelmatrix!(
     K::AbstractMatrix, κ::TransformedKernel, x::AbstractVector, y::AbstractVector
 )
-    return kernelmatrix!(K, kernel(κ), _map(κ.transform, x), _map(κ.transform, y))
+    return kernelmatrix!(K, κ.kernel, _map(κ.transform, x), _map(κ.transform, y))
 end
 
 function kernelmatrix_diag(κ::TransformedKernel, x::AbstractVector)
@@ -105,9 +82,9 @@ function kernelmatrix_diag(κ::TransformedKernel, x::AbstractVector, y::Abstract
 end
 
 function kernelmatrix(κ::TransformedKernel, x::AbstractVector)
-    return kernelmatrix(kernel(κ), _map(κ.transform, x))
+    return kernelmatrix(κ.kernel, _map(κ.transform, x))
 end
 
 function kernelmatrix(κ::TransformedKernel, x::AbstractVector, y::AbstractVector)
-    return kernelmatrix(kernel(κ), _map(κ.transform, x), _map(κ.transform, y))
+    return kernelmatrix(κ.kernel, _map(κ.transform, x), _map(κ.transform, y))
 end

test/kernels/transformedkernel.jl

@@ -12,8 +12,10 @@
     ktard = TransformedKernel(k, ARDTransform(v))
     @test kt(v1, v2) == transform(k, ScaleTransform(s))(v1, v2)
     @test kt(v1, v2) == transform(k, s)(v1, v2)
+    @test kt(v1, v2) == (k ∘ ScaleTransform(s))(v1, v2)
     @test kt(v1, v2) ≈ k(s * v1, s * v2) atol = 1e-5
     @test ktard(v1, v2) ≈ transform(k, ARDTransform(v))(v1, v2) atol = 1e-5
+    @test ktard(v1, v2) == (k ∘ ARDTransform(v))(v1, v2)
     @test ktard(v1, v2) == transform(k, v)(v1, v2)
     @test ktard(v1, v2) == k(v .* v1, v .* v2)
     @test transform(kt, s2)(v1, v2) ≈ kt(s2 * v1, s2 * v2)
@@ -54,4 +56,9 @@
     test_params(transform(k, LinearTransform(P)), (k, P))
     test_params(transform(k, LinearTransform(P) ∘ ScaleTransform(s)), (k, [s], P))
     test_params(transform(k, FunctionTransform(c)), (k, c))
+
+    @test (k ∘ (LinearTransform(P') ∘ ScaleTransform(s)))(v1, v2) == ((k ∘ LinearTransform(P')) ∘ ScaleTransform(s))(v1, v2)
+    test_params(k ∘ LinearTransform(P), (P, k))
+    test_params(k ∘ LinearTransform(P) ∘ ScaleTransform(s), ([s], P, k))
+    test_params(k ∘ FunctionTransform(c), (c, k))
 end