Merge pull request #37 from JuliaAI/dev

For a 0.4.1 release

Author: ablaom

.github/codecov.yml

@@ -0,0 +1,9 @@
+coverage:
+  status:
+    project:
+      default:
+        threshold: 0.5%
+        removed_code_behavior: fully_covered_patch
+    patch:
+      default:
+        target: 80%

.github/workflows/CI-nightly.yml

@@ -47,6 +47,5 @@ jobs:
       - uses: julia-actions/julia-runtest@latest
       - uses: julia-actions/julia-uploadcodecov@v0.1
         continue-on-error: true
-      - uses: julia-actions/julia-uploadcoveralls@v0.1
-        continue-on-error: true
+
 

.github/workflows/CI.yml

@@ -1,7 +1,7 @@
 name = "MLJEnsembles"
 uuid = "50ed68f4-41fd-4504-931a-ed422449fee0"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.4.0"
+version = "0.4.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"

Project.toml

@@ -408,31 +408,56 @@ function _fit(res::CPUProcesses, func, verbosity, stuff)
     end
 end
 
+# Create thread safe version of RNGs.
+# Random._GLOBAL_RNG() and Random.default_rng() are threadsafe by default_rng
+# as they have thread local state from julia >=1.3<=1.6 and task local state Julia >=1.7   
+threadsafe_rng(rng::typeof(Random.default_rng())) = rng
+threadsafe_rng(rng::Random._GLOBAL_RNG) = rng
+threadsafe_rng(rng) = deepcopy(rng)
+
 function _fit(res::CPUThreads, func, verbosity, stuff)
     atom, n, n_patterns, n_train, rng, progress_meter, args = stuff
     if verbosity > 0
         println("Ensemble-building in parallel on $(Threads.nthreads()) threads.")
     end
+    
     nthreads = Threads.nthreads()
+    
+    if nthreads == 1
+        return  _fit(CPU1(), func, verbosity, stuff)
+    end
+
     chunk_size = div(n, nthreads)
     left_over = mod(n, nthreads)
     resvec = Vector(undef, nthreads) # FIXME: Make this type-stable?
 
-    Threads.@threads for i = 1:nthreads
-        resvec[i] = if i != nworkers()
-            func(atom, 0, chunk_size, n_patterns, n_train, rng, progress_meter, args...)
-        else
-            func(
-                atom,
-                0,
-                chunk_size + left_over,
-                n_patterns,
-                n_train,
-                rng,
-                progress_meter,
-                args...,
+    @sync begin
+        for i in 1:nthreads-1
+            Threads.@spawn(
+                resvec[i] = func(
+                    atom,
+                    0,
+                    chunk_size,
+                    n_patterns,
+                    n_train,
+                    threadsafe_rng(rng),
+                    progress_meter,
+                    args...
+                )
             )
         end
+        Threads.@spawn(
+            resvec[nthreads] = func(
+                    atom,
+                    0,
+                    chunk_size + left_over,
+                    n_patterns,
+                    n_train,
+                    threadsafe_rng(rng),
+                    progress_meter,
+                    args...
+            )
+        )
     end
 
     return reduce(_reducer, resvec)

src/ensembles.jl

@@ -256,8 +256,23 @@ end
     @test length(ensemble.fitresult.ensemble) == 5
 
     @test !isnan(predict(ensemble, MLJEnsembles.selectrows(X, test))[1])
+
+    # tests using integer rngs (see issue 27)
+    X_, y_ = @load_iris
+    atom = KNNClassifier(K = 7)
+    ensemble_model = EnsembleModel(
+        atom;
+        bagging_fraction=0.6,
+        rng=123,
+        out_of_bag_measure = [log_loss, brier_score]
+    )
+    ensemble = machine(ensemble_model, X_, y_)
+    fit!(ensemble)
+    @test length(ensemble.fitresult.ensemble) == ensemble_model.n
+
 end
 
+
 end
 
 true

test/ensembles.jl

@@ -58,7 +58,50 @@ end
     @test predict(smach, X) == predict(mach, X)
 
     rm(filename)
+end
 
+# define a supervised model with ephemeral `fitresult`, but which overcomes this by
+# overloading `save`/`restore`:
+thing = []
+struct EphemeralRegressor <: Deterministic end
+function MLJBase.fit(::EphemeralRegressor, verbosity, X, y)
+    # if I serialize/deserialized `thing` then `id` below changes:
+    id = objectid(thing)
+    fitresult = (thing, id, mean(y))
+    return fitresult, nothing, NamedTuple()
+end
+function MLJBase.predict(::EphemeralRegressor, fitresult, X)
+    thing, id, μ = fitresult
+    return id == objectid(thing) ? fill(μ, nrows(X)) :
+        throw(ErrorException("dead fitresult"))
+end
+MLJBase.target_scitype(::Type{<:EphemeralRegressor}) = AbstractVector{Continuous}
+function MLJBase.save(::EphemeralRegressor, fitresult)
+    thing, _, μ = fitresult
+    return (thing, μ)
+end
+function MLJBase.restore(::EphemeralRegressor, serialized_fitresult)
+    thing, μ = serialized_fitresult
+    id = objectid(thing)
+    return (thing, id, μ)
+end
+
+@testset "serialization for atomic models with non-persistent fitresults" begin
+    # https://github.com/alan-turing-institute/MLJ.jl/issues/1099
+    X, y = (; x = rand(10)), fill(42.0, 3)
+    ensemble = EnsembleModel(
+        EphemeralRegressor(),
+        bagging_fraction=0.7,
+        n=2,
+    )
+    mach = machine(ensemble, X, y)
+    fit!(mach, verbosity=0)
+    io = IOBuffer()
+    MLJBase.save(io, mach)
+    seekstart(io)
+    mach2 = machine(io)
+    close(io)
+    @test MLJBase.predict(mach2, (; x = rand(2))) ≈ fill(42.0, 2)
 end
 
 end