corrected accidental changes to test/rankcorr.jl

test/rankcorr.jl

@@ -187,117 +187,16 @@ include("compare_implementations.jl")
         missing missing
         5 6]
 
-    c11 = f(x1, x1)
-    c12 = f(x1, x2)
-    c22 = f(x2, x2)
-
-    # AbstractMatrix{<:Real}, AbstractMatrix{<:Real}
-    @test f(x, x) ≈ [c11 c12; c12 c22]
-    # AbstractMatrix{<:Real}
-    @test f(x) ≈ [c11 c12; c12 c22]
-
-    @test c11 == 1.0
-    @test c22 == 1.0
-    @test c12 == 3 / sqrt(20)
-    # Finished testing for overflow, so redefine n for speedier tests
-    n = 100
-
-    @test f(repeat(x, n), repeat(x, n)) ≈ [c11 c12; c12 c22]
-    @test f(repeat(x, n)) ≈ [c11 c12; c12 c22]
-
-    # All eight three-element permutations
-    z = [1 1 1
-        1 1 2
-        1 2 2
-        1 2 2
-        1 2 1
-        2 1 2
-        1 1 2
-        2 2 2]
-
-    @test f(z) == [1 0 1/3; 0 1 0; 1/3 0 1]
-    @test f(z, z) == [1 0 1/3; 0 1 0; 1/3 0 1]
-    @test f(z[:, 1], z) == [1 0 1 / 3]
-    @test f(z, z[:, 1]) == [1; 0; 1 / 3]
-
-    z = float(z)
-    @test f(z) == [1 0 1/3; 0 1 0; 1/3 0 1]
-    @test f(z, z) == [1 0 1/3; 0 1 0; 1/3 0 1]
-    @test f(z[:, 1], z) == [1 0 1 / 3]
-    @test f(z, z[:, 1]) == [1; 0; 1 / 3]
-
-    w = repeat(z, n)
-    @test f(w) == [1 0 1/3; 0 1 0; 1/3 0 1]
-    @test f(w, w) == [1 0 1/3; 0 1 0; 1/3 0 1]
-    @test f(w[:, 1], w) == [1 0 1 / 3]
-    @test f(w, w[:, 1]) == [1; 0; 1 / 3]
-
-    KendallTau.midpoint(1, 10) == 5
-    KendallTau.midpoint(1, widen(10)) == 5
-
-    # NaN handling
-
-    Xnan = copy(x)
-    Xnan[1, 1] = NaN
-    Ynan = copy(Y)
-    Ynan[2, 1] = NaN
-
-    @test isnan(f([1.0, NaN, 2.0], [2.0, 1.0, 3.4]))
-    @test all(isnan, f([1.0, NaN], [1 2; 3 4]))
-    @test all(isnan, f([1 2; 3 4], [1.0, NaN]))
-    @test isequal(f([1 NaN; NaN 4]), [1 NaN; NaN 1])
-    @test all(isnan, f([1 NaN; NaN 4], [1 NaN; NaN 4]))
-    @test all(isnan, f([1 NaN; NaN 4], [NaN 1; NaN 4]))
-
-    @test isequal(f(Xnan, Ynan),
-        [f(Xnan[:, i], Ynan[:, j]) for i in axes(Xnan, 2), j in axes(Ynan, 2)])
-    @test isequal(f(Xnan),
-        [i == j ? 1.0 : f(Xnan[:, i], Xnan[:, j])
-         for i in axes(Xnan, 2), j in axes(Xnan, 2)])
-    for k in 1:2
-        @test isequal(f(Xnan[:, k], Ynan),
-            [f(Xnan[:, k], Ynan[:, j]) for i in 1:1, j in axes(Ynan, 2)])
-        @test isequal(f(Xnan, Ynan[:, k]),
-            [f(Xnan[:, i], Ynan[:, k]) for i in axes(Xnan, 2)])
-    end
-
-    # Wrong dimensions
-    @test_throws DimensionMismatch f([1], [1, 2])
-    @test_throws DimensionMismatch f([1], [1 2; 3 4])
-    @test_throws DimensionMismatch f([1 2; 3 4], [1])
-    @test_throws DimensionMismatch f([1 2; 3 4; 5 6], [1 2; 3 4])
-
-    # x has sufficient columns to ensure that variable use_atomic evaluates to false
-    n_reps = Threads.nthreads()
-    @test f(repeat(hcat(a, b), outer=[1, n_reps])) == repeat(f(hcat(a, b)), outer=[n_reps, n_reps])
-
-    #= Test functions against corkendall_naive, a "reference implementation" that has the 
-    advantage of simplicity.
-    =#
-    if f !== corkendall_naive
-        @test compare_implementations(f, corkendall_naive, abstol=0.0, maxcols=10, maxrows=10, numtests=200, fns_handle_missings=true) == true
-        @test compare_implementations(f, corkendall_naive, abstol=0.0, maxcols=10, maxrows=100, numtests=200, fns_handle_missings=true) == true
-        @test compare_implementations(f, corkendall_naive, abstol=1e14, maxcols=2, maxrows=20000, numtests=5, fns_handle_missings=true) == true
-    end
-end
-
-#Auxiliary functions for corkendall
-x = [1, 2, 3, missing, 4]
-y = [missing, 1, 2, 3, 4]
-u = [missing, missing, 1, 2]
-v = [3, 4, missing, missing]
-
-mx = [1 2
-    missing 3
-    4 missing
-    missing missing
-    5 6]
+    @test KendallTau.handle_pairwise!(x, y, similar(x), similar(y)) == ([2, 3, 4], [1, 2, 4])
+    @test KendallTau.handle_pairwise!(float.(x), y, similar(float.(x)), similar(y)) == ([2.0, 3.0, 4.0], [1, 2, 4])
+    @test KendallTau.handle_pairwise!(x, float.(y), similar(x), similar(float.(y))) == ([2, 3, 4], [1.0, 2.0, 4.0])
+    @test KendallTau.handle_pairwise!(u, v, similar(u), similar(v)) == (Int64[], Int64[])
+    @test KendallTau.handle_listwise!(mx, mx) == ([1 2; 5 6], [1 2; 5 6])
 
-@test KendallTau.handlepairwise!(x, y, similar(x), similar(y)) == ([2, 3, 4], [1, 2, 4])
-@test KendallTau.handlepairwise!(float.(x), y, similar(float.(x)), similar(y)) == ([2.0, 3.0, 4.0], [1, 2, 4])
-@test KendallTau.handlepairwise!(x, float.(y), similar(x), similar(float.(y))) == ([2, 3, 4], [1.0, 2.0, 4.0])
-@test KendallTau.handlepairwise!(u, v, similar(u), similar(v)) == (Int64[], Int64[])
-@test KendallTau.handlelistwise(mx, mx) == ([1 2; 5 6], [1 2; 5 6])
+    smallx = randn(MersenneTwister(123), 1000, 3)
+    indicators = rand(MersenneTwister(456), 1000, 3) .< 0.05
+    smallx = ifelse.(indicators, missing, smallx)
+    @test corkendall_naive(smallx, skipmissing=:pairwise) == KendallTau.corkendall(smallx, skipmissing=:pairwise)
 
 
-end #testset
+end