Merge pull request #884 from JuliaAI/dev

For a 0.21.6 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.21.5"
+version = "0.21.6"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"

src/composition/learning_networks/signatures.jl

@@ -101,7 +101,7 @@ const DOC_NETWORK_INTERFACES =
 
     Here `yhat`, `Xsmall`, `W` and `loss_node` are nodes in the network.
 
-    The keys of the signature are always one of the following:
+    The keys of the learning network interface always one of the following:
 
     - The name of an operation, such as `:predict`, `:predict_mode`, `:transform`,
       `:inverse_transform`. See "Operation keys" below.

test/hyperparam/one_dimensional_range_methods.jl

@@ -8,6 +8,7 @@ using Statistics
 using StableRNGs
 
 rng = StableRNG(66600099)
+stable_rng() = StableRNG(123)
 
 const Dist = Distributions
 
@@ -182,17 +183,16 @@ end
                   origin=5, unit=1) # origin and unit not relevant here
         s = MLJBase.sampler(r, Dist.Normal())
 
-        Random.seed!(1);
-        v = rand(s, 1000)
+        rng = stable_rng()
+        v = rand(rng, s, 1000)
         @test all(x >= 0.2 for x in v)
         @test abs(minimum(v)/0.2 - 1) <= 0.02
 
-        rng = _default_rng(1);
+        rng = stable_rng()
         @test rand(rng, s, 1000) == v
 
         q = quantile(v, 0.0:0.1:1.0)
-        Random.seed!(1);
-        v2 = filter(x -> x>=0.2, rand(Dist.Normal(), 3000))[1:1000]
+        v2 = filter(x -> x>=0.2, rand(stable_rng(), Dist.Normal(), 3000))[1:1000]
         q2 = quantile(v2, 0.0:0.1:1.0)
         @test all(x -> x≈1.0, q ./ q2)
     end