Merge pull request #11 from JuliaAI/dev

Bump MLJTuning version for upstream reproducibility fix

Author: lhnguyen-vn

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJParticleSwarmOptimization"
 uuid = "17a086e9-ed03-4f30-ab88-8b63f0f6126c"
 authors = ["Long Nguyen <[email protected]> and contributors"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -13,5 +13,5 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 [compat]
 Distributions = "0.25"
 MLJBase = "0.18"
-MLJTuning = "0.6"
+MLJTuning = "0.6.11"
 julia = "1.4"

test/Project.toml

@@ -1,5 +1,6 @@
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"

test/runtests.jl

@@ -1,12 +1,17 @@
-using MLJParticleSwarmOptimization
-using Random
-using Test
-using ComputationalResources
-using Distributions
-using EvoTrees
-using MLJBase
-using MLJTuning
-using StableRNGs
+using Distributed
+addprocs(2)
+
+@everywhere begin
+    using MLJParticleSwarmOptimization
+    using Random
+    using Test
+    using ComputationalResources
+    using Distributions
+    using EvoTrees
+    using MLJBase
+    using MLJTuning
+    using StableRNGs
+end
 
 const PSO = MLJParticleSwarmOptimization
 

test/strategies/adaptive.jl

@@ -91,10 +91,9 @@ for acceleration in (CPU1(), CPUProcesses(), CPUThreads())
         baseline_self_tuning_tree = TunedModel(
             model=tree,
             tuning=RandomSearch(rng=StableRNG(1234)),
-            # tuning=ParticleSwarm(n_particles=3, rng=rng),
             resampling=CV(nfolds=5, rng=StableRNG(8888)),
             range=[r1, r2],
-            measure=(ŷ, y) -> mean(abs.(ŷ .- y)),
+            measure=mae,
             n=15,
             acceleration=acceleration
         )
@@ -108,7 +107,7 @@ for acceleration in (CPU1(), CPUProcesses(), CPUThreads())
             tuning=AdaptiveParticleSwarm(rng=StableRNG(1234)),
             resampling=CV(nfolds=5, rng=StableRNG(8888)),
             range=[r1, r2],
-            measure=(ŷ, y) -> mean(abs.(ŷ .- y)),
+            measure=mae,
             n=15,
             acceleration=acceleration
         )

test/strategies/basic.jl

@@ -30,10 +30,9 @@ for acceleration in (CPU1(), CPUProcesses(), CPUThreads())
         baseline_self_tuning_tree = TunedModel(
             model=tree,
             tuning=RandomSearch(rng=StableRNG(1234)),
-            # tuning=ParticleSwarm(n_particles=3, rng=rng),
             resampling=CV(nfolds=5, rng=StableRNG(8888)),
             range=[r1, r2],
-            measure=(ŷ, y) -> mean(abs.(ŷ .- y)),
+            measure=mae,
             n=15,
             acceleration=acceleration
         )
@@ -47,7 +46,7 @@ for acceleration in (CPU1(), CPUProcesses(), CPUThreads())
             tuning=ParticleSwarm(rng=StableRNG(1234)),
             resampling=CV(nfolds=5, rng=StableRNG(8888)),
             range=[r1, r2],
-            measure=(ŷ, y) -> mean(abs.(ŷ .- y)),
+            measure=mae,
             n=15,
             acceleration=acceleration
         )