Merge branch 'master' into dw/fix_matern

Author: devmotion

src/KernelFunctions.jl

@@ -124,7 +124,7 @@ include("mokernels/lmm.jl")
 include("chainrules.jl")
 include("zygoterules.jl")
 
-include("test_utils.jl")
+include("TestUtils.jl")
 
 function __init__()
     @require Kronecker = "2c470bb0-bcc8-11e8-3dad-c9649493f05e" begin

src/test_utils.jl renamed to src/TestUtils.jl

@@ -1,33 +1,42 @@
 module TestUtils
 
-const __ATOL = 1e-9
-const __RTOL = 1e-9
-
 using Distances
 using LinearAlgebra
 using KernelFunctions
 using Random
 using Test
 
+# default tolerance values for test_interface:
+const __ATOL = sqrt(eps(Float64))
+const __RTOL = sqrt(eps(Float64))
+# ≈ 1.5e-8; chosen for no particular reason other than because it seems to
+# satisfy our own test cases within KernelFunctions.jl
+
 """
     test_interface(
         k::Kernel,
         x0::AbstractVector,
         x1::AbstractVector,
         x2::AbstractVector;
         atol=__ATOL,
+        rtol=__RTOL,
     )
 
 Run various consistency checks on `k` at the inputs `x0`, `x1`, and `x2`.
 `x0` and `x1` should be of the same length with different values, while `x0` and `x2` should
 be of different lengths.
 
-    test_interface([rng::AbstractRNG], k::Kernel, T::Type{<:AbstractVector}; atol=__ATOL)
+These tests are intended to pick up on really substantial issues with a kernel implementation
+(e.g. substantial asymmetry in the kernel matrix, large negative eigenvalues), rather than to
+test the numerics in detail, which can be kernel-specific.
+The default value of `__ATOL` and `__RTOL` is `sqrt(eps(Float64)) ≈ 1.5e-8`, which satisfied
+this intention in the cases tested within KernelFunctions.jl itself.
+
+    test_interface([rng::AbstractRNG], k::Kernel, T::Type{<:Real}; atol=__ATOL, rtol=__RTOL)
 
-`test_interface` offers certain types of test data generation to make running these tests
-require less code for common input types. For example, `Vector{<:Real}`, `ColVecs{<:Real}`,
-and `RowVecs{<:Real}` are supported. For other input vector types, please provide the data
-manually. 
+`test_interface` offers automated test data generation for kernels whose inputs are reals.
+This will run the tests for `Vector{T}`, `Vector{Vector{T}}`, `ColVecs{T}`, and `RowVecs{T}`.
+For other input vector types, please provide the data manually.
 """
 function test_interface(
     k::Kernel,
@@ -50,11 +59,12 @@ function test_interface(
     @test size(kernelmatrix(k, x0, x2)) == (length(x0), length(x2))
 
     # Check that elementwise is consistent with pairwise.
-    @test kernelmatrix_diag(k, x0, x1) ≈ diag(kernelmatrix(k, x0, x1)) atol = atol
+    @test kernelmatrix_diag(k, x0, x1) ≈ diag(kernelmatrix(k, x0, x1)) atol = atol rtol =
+        rtol
 
     # Check additional binary elementwise properties for kernels.
     @test kernelmatrix_diag(k, x0, x1) ≈ kernelmatrix_diag(k, x1, x0)
-    @test kernelmatrix(k, x0, x2) ≈ kernelmatrix(k, x2, x0)' atol = atol
+    @test kernelmatrix(k, x0, x2) ≈ kernelmatrix(k, x2, x0)' atol = atol rtol = rtol
 
     # Check that unary elementwise basically works.
     @test kernelmatrix_diag(k, x0) isa AbstractVector
@@ -63,10 +73,10 @@ function test_interface(
     # Check that unary pairwise basically works.
     @test kernelmatrix(k, x0) isa AbstractMatrix
     @test size(kernelmatrix(k, x0)) == (length(x0), length(x0))
-    @test kernelmatrix(k, x0) ≈ kernelmatrix(k, x0)' atol = atol
+    @test kernelmatrix(k, x0) ≈ kernelmatrix(k, x0)' atol = atol rtol = rtol
 
     # Check that unary elementwise is consistent with unary pairwise.
-    @test kernelmatrix_diag(k, x0) ≈ diag(kernelmatrix(k, x0)) atol = atol
+    @test kernelmatrix_diag(k, x0) ≈ diag(kernelmatrix(k, x0)) atol = atol rtol = rtol
 
     # Check that unary pairwise produces a positive definite matrix (approximately).
     @test eigmin(Matrix(kernelmatrix(k, x0))) > -atol

src/transform/transform.jl

@@ -5,6 +5,9 @@ Abstract type defining a transformation of the input.
 """
 abstract type Transform end
 
+# We introduce our own _map for Transform so that we can work around
+# https://github.com/FluxML/Zygote.jl/issues/646 and define our own pullback
+# (see zygoterules.jl)
 Base.map(t::Transform, x::AbstractVector) = _map(t, x)
 _map(t::Transform, x::AbstractVector) = t.(x)