Merge pull request #26 from JuliaAI/test-updates

Maintenance on tests

Author: ablaom

.github/workflows/CI.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.4'
+          - '1.6'
           - '1'
         os:
           - ubuntu-latest

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJParticleSwarmOptimization"
 uuid = "17a086e9-ed03-4f30-ab88-8b63f0f6126c"
 authors = ["Long Nguyen <[email protected]> and contributors"]
-version = "0.1.2"
+version = "0.1.3"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -12,6 +12,6 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]
 Distributions = "0.25"
-MLJBase = "0.18, 0.19, 0.20"
+MLJBase = "0.18, 0.19, 0.20, 0.21"
 MLJTuning = "0.6, 0.7"
-julia = "1.4"
+julia = "1.6"

test/parameters.jl

@@ -93,9 +93,13 @@
         state = PSO.initialize(rng, ranges, n)
         PSO.retrieve!(rng, state)
         params =  state.parameters
-        @test params[1] == ["a", "a", "c"]
-        @test params[2] ≈  [553, 250, 375]
-        @test params[3] ≈  [3.9372495283243105, 3.6569395920512977, 3.6354556967115146]
-        @test params[4] ≈ [-0.8067647f0, 0.4209916f0, 0.6736019f0]
+        @test params[1] isa AbstractVector{<:AbstractString}
+        @test params[2] isa AbstractVector{<:Integer}
+        @test params[3] isa AbstractVector{<:AbstractFloat}
+        @test params[4] isa AbstractVector{<:AbstractFloat}
+        @test length(params[4]) == 3
+        @test all(params) do p
+            length(p) == n
+        end
     end
 end

test/strategies/adaptive.jl

@@ -89,7 +89,7 @@ for acceleration in modes
 
         tree = EvoTreeRegressor(rng=rng)
         r1 = range(tree, :max_depth; values=[3:7;])
-        r2 = range(tree, :η; lower=-2, upper=0, scale=exp10)
+        r2 = range(tree, :eta; lower=-2, upper=0, scale=exp10)
 
         baseline_self_tuning_tree = TunedModel(
             model=tree,

test/strategies/basic.jl

@@ -25,7 +25,7 @@ for acceleration in (CPU1(), CPUProcesses(), CPUThreads())
 
         tree = EvoTreeRegressor(rng=rng)
         r1 = range(tree, :max_depth; values=[3:7;])
-        r2 = range(tree, :η; lower=-2, upper=0, scale=exp10)
+        r2 = range(tree, :eta; lower=-2, upper=0, scale=exp10)
 
         baseline_self_tuning_tree = TunedModel(
             model=tree,

test/update.jl

@@ -43,7 +43,8 @@
 
     @testset "Update personal best" begin
         @test state.pbest == measurements
-        @test state.pbest_X ≈ [0.35710007620140427 0.37056803307520936 0.2723318907233864  2.7429797605672808 3.9372495283243105 -0.8067647083847199;
+        # https://github.com/JuliaAI/MLJParticleSwarmOptimization.jl/issues/27
+        @test_broken state.pbest_X ≈ [0.35710007620140427 0.37056803307520936 0.2723318907233864  2.7429797605672808 3.9372495283243105 -0.8067647083847199;
                                0.471258004384426   0.3400938657534834  0.18864812986209056 2.3976392099947    3.6569395920512977  0.420991611378423 ;
                                0.09661447846912713 0.19630556211862715 0.7070799594122457  2.5742724788985445 3.6354556967115146  0.6736019046580138]
     end
@@ -52,4 +53,5 @@
         @test all(state.gbest .== 0.1)
         @test all(state.gbest_X .== state.pbest_X[2, :]')
     end
-end
+end
+