Fix @test_broken problems (#546)

* Fix `@test_broken` problems

* Correct remaining tests

* Bump version

Project.toml

@@ -1,6 +1,6 @@
 name = "KernelFunctions"
 uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
-version = "0.10.60"
+version = "0.10.61"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

test/basekernels/periodic.jl

@@ -16,6 +16,7 @@
     TestUtils.test_interface(PeriodicKernel(; r=[0.8, 0.7]), RowVecs{Float64})
 
     # test_ADs(r->PeriodicKernel(r =exp.(r)), log.(r), ADs = [:ForwardDiff, :ReverseDiff])
-    @test_broken "Undefined adjoint for Sinus metric, and failing randomly for ForwardDiff and ReverseDiff"
+    # Undefined adjoint for Sinus metric, and failing randomly for ForwardDiff and ReverseDiff
+    @test_broken false
     test_params(k, (r,))
 end

test/basekernels/sm.jl

@@ -57,5 +57,6 @@
     end
 
     # test_ADs(x->spectral_mixture_kernel(exp.(x[1:3]), reshape(x[4:18], 5, 3), reshape(x[19:end], 5, 3)), vcat(log.(αs₁), γs[:], ωs[:]), dims = [5,5])
-    @test_broken "No tests passing (BaseKernel)"
+    # No tests passing (BaseKernel)
+    @test_broken false
 end

test/basekernels/wiener.jl

@@ -44,5 +44,6 @@
     TestUtils.test_interface(k2, x0, x1, x2)
     TestUtils.test_interface(k3, x0, x1, x2)
     # test_ADs(()->WienerKernel(i=1))
-    @test_broken "No tests passing"
+    # No tests passing
+    @test_broken false
 end

test/transform/selecttransform.jl

@@ -104,17 +104,41 @@
     end
 
     @testset "$(AD)" for AD in [:ReverseDiff]
-        @test_broken ga = gradient(AD, A) do a
-            testfunction(ta_row, a, 2)
+        @test_broken let
+            gx = gradient(AD, X) do x
+                testfunction(tx_row, x, 2)
+            end
+            ga = gradient(AD, A) do a
+                testfunction(ta_row, a, 2)
+            end
+            gx ≈ ga
         end
-        @test_broken ga = gradient(AD, A) do a
-            testfunction(ta_col, a, 1)
+        @test_broken let
+            gx = gradient(AD, X) do x
+                testfunction(tx_col, x, 1)
+            end
+            ga = gradient(AD, A) do a
+                testfunction(ta_col, a, 1)
+            end
+            gx ≈ ga
         end
-        @test_broken ga = gradient(AD, A) do a
-            testfunction(ta_row, a, B, 2)
+        @test_broken let
+            gx = gradient(AD, X) do x
+                testfunction(tx_row, x, Y, 2)
+            end
+            ga = gradient(AD, A) do a
+                testfunction(ta_row, a, B, 2)
+            end
+            gx ≈ ga
         end
-        @test_broken ga = gradient(AD, A) do a
-            testfunction(ta_col, a, C, 1)
+        @test_broken let
+            gx = gradient(AD, X) do x
+                testfunction(tx_col, x, Z, 1)
+            end
+            ga = gradient(AD, A) do a
+                testfunction(ta_col, a, C, 1)
+            end
+            gx ≈ ga
         end
     end