Fix type inference and performance problems of munge_data

Author: devmotion

Project.toml

@@ -32,6 +32,7 @@ EnumX = "1.0.4"
 FindFirstFunctions = "1.3"
 FiniteDifferences = "0.12.31"
 ForwardDiff = "0.10.36"
+JET = "0.9.17"
 LinearAlgebra = "1.10"
 Optim = "1.6"
 PrettyTables = "2"
@@ -53,6 +54,7 @@ BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
 RegularizationTools = "29dad682-9a27-4bc3-9c72-016788665182"
@@ -64,4 +66,4 @@ Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "BenchmarkTools", "SafeTestsets", "ChainRulesCore", "Optim", "RegularizationTools", "Test", "StableRNGs", "FiniteDifferences", "QuadGK", "ForwardDiff", "Symbolics", "Unitful", "Zygote"]
+test = ["Aqua", "BenchmarkTools", "JET", "SafeTestsets", "ChainRulesCore", "Optim", "RegularizationTools", "Test", "StableRNGs", "FiniteDifferences", "QuadGK", "ForwardDiff", "Symbolics", "Unitful", "Zygote"]

src/interpolation_utils.jl

@@ -104,51 +104,49 @@ function quadratic_spline_params(t::AbstractVector, sc::AbstractVector)
 end
 
 # helper function for data manipulation
-function munge_data(u::AbstractVector{<:Real}, t::AbstractVector{<:Real})
-    return u, t
-end
-
 function munge_data(u::AbstractVector, t::AbstractVector)
-    Tu = Base.nonmissingtype(eltype(u))
-    Tt = Base.nonmissingtype(eltype(t))
-    @assert length(t) == length(u)
-    non_missing_indices = collect(
-        i for i in 1:length(t)
-    if !ismissing(u[i]) && !ismissing(t[i])
-    )
+    Tu = nonmissingtype(eltype(u))
+    Tt = nonmissingtype(eltype(t))
+    if Tu === eltype(u) && Tt === eltype(t)
+        return u, t
+    end
 
-    u = Tu.([u[i] for i in non_missing_indices])
-    t = Tt.([t[i] for i in non_missing_indices])
+    @assert length(t) == length(u)
+    non_missing_mask = map((ui, ti) -> !ismissing(ui) && !ismissing(ti), u, t)
+    u = convert(AbstractVector{Tu}, u[non_missing_mask])
+    t = convert(AbstractVector{Tt}, t[non_missing_mask])
 
     return u, t
 end
 
-function munge_data(U::StridedMatrix, t::AbstractVector)
-    TU = Base.nonmissingtype(eltype(U))
-    Tt = Base.nonmissingtype(eltype(t))
-    @assert length(t) == size(U, 2)
-    non_missing_indices = collect(
-        i for i in 1:length(t)
-    if !any(ismissing, U[:, i]) && !ismissing(t[i])
-    )
+function munge_data(U::AbstractMatrix, t::AbstractVector)
+    TU = nonmissingtype(eltype(U))
+    Tt = nonmissingtype(eltype(t))
+    if TU === eltype(U) && Tt === eltype(t)
+        return U, t
+    end
 
-    U = hcat([TU.(U[:, i]) for i in non_missing_indices]...)
-    t = Tt.([t[i] for i in non_missing_indices])
+    @assert length(t) == size(U, 2)
+    non_missing_mask = map(
+        (uis, ti) -> !any(ismissing, uis) && !ismissing(ti), eachcol(U), t)
+    U = convert(AbstractMatrix{TU}, U[:, non_missing_mask])
+    t = convert(AbstractVector{Tt}, t[non_missing_mask])
 
     return U, t
 end
 
 function munge_data(U::AbstractArray{T, N}, t) where {T, N}
-    TU = Base.nonmissingtype(eltype(U))
-    Tt = Base.nonmissingtype(eltype(t))
-    @assert length(t) == size(U, ndims(U))
-    ax = axes(U)[1:(end - 1)]
-    non_missing_indices = collect(
-        i for i in 1:length(t)
-    if !any(ismissing, U[ax..., i]) && !ismissing(t[i])
-    )
-    U = cat([TU.(U[ax..., i]) for i in non_missing_indices]...; dims = ndims(U))
-    t = Tt.([t[i] for i in non_missing_indices])
+    TU = nonmissingtype(eltype(U))
+    Tt = nonmissingtype(eltype(t))
+    if TU === eltype(U) && Tt === eltype(t)
+        return U, t
+    end
+
+    @assert length(t) == size(U, N)
+    non_missing_mask = map(
+        (uis, ti) -> !any(ismissing, uis) && !ismissing(ti), eachslice(U; dims = N), t)
+    U = convert(AbstractArray{TU, N}, copy(selectdim(U, N, non_missing_mask)))
+    t = convert(AbstractVector{Tt}, t[non_missing_mask])
 
     return U, t
 end

test/interpolation_tests.jl

@@ -4,6 +4,7 @@ using StableRNGs
 using Optim, ForwardDiff
 using BenchmarkTools
 using Unitful
+using JET
 
 function test_interpolation_type(T)
     @test T <: DataInterpolations.AbstractInterpolation
@@ -920,3 +921,22 @@ f_cubic_spline = c -> square(CubicSpline, c)
 @test ForwardDiff.derivative(f_quadratic_spline, 4.0) ≈ 8.0
 @test ForwardDiff.derivative(f_cubic_spline, 2.0) ≈ 4.0
 @test ForwardDiff.derivative(f_cubic_spline, 4.0) ≈ 8.0
+
+@testset "munge_data" begin
+    t0 = [0.1, 0.2, 0.3]
+    u0 = ["A", "B", "C"]
+
+    for T in (String, Union{String, Missing}), dims in 1:3
+        _u0 = convert(Array{T}, reshape(u0, ntuple(i -> i == dims ? 3 : 1, dims)))
+
+        u, t = @inferred(DataInterpolations.munge_data(_u0, t0))
+        @test u isa Array{String, dims}
+        @test t isa Vector{Float64}
+        if T === String
+            @test u === _u0
+            @test t === t
+        end
+
+        @test_call DataInterpolations.munge_data(_u0, t0)
+    end
+end