Use StableRNG for test that sometimes fails due to random data generation

Author: kahaaga

test/independence/JointDistanceDistributionTest.jl

@@ -1,14 +1,14 @@
 using Random: MersenneTwister
-rng = MersenneTwister(12346)
+rng = StableRNG(12346)
 x, y = randn(rng, 1000), randn(rng, 1000)
-m = JointDistanceDistribution(D = 3, B = 5)
-test =  JointDistanceDistributionTest(m)
+m = JointDistanceDistribution(D=3, B=5)
+test = JointDistanceDistributionTest(m)
 @test test isa JointDistanceDistributionTest
 @test independence(test, x, y) isa Associations.JDDTestResult
 
 # Don't reject null at significance level (1 - α) when there is no coupling.
 α = 0.05
-@test pvalue(independence(test, x, y)) > α 
+@test pvalue(independence(test, x, y)) > α
 
 # Reject null at significance level (1 - α) when there is coupling
 z = y .+ x

test/independence/SurrogateAssociationTest/LMeasure.jl

@@ -1,7 +1,7 @@
 # Analytical tests (in the limit of a lot of samples)
 # ------------------------------------------------------------
 using Random
-rng = MersenneTwister(1234)
+rng = StableRNG(1234)
 n = 100
 x, y = rand(rng, n), rand(rng, n)
 z = x .+ y

test/independence/SurrogateAssociationTest/azadkia_chatterjee_correlation.jl

@@ -1,25 +1,25 @@
 using Test
 using Random
-rng = Xoshiro(1234)
+rng = StableRNG(1234)
 
 # We can use surrogate tests and p-values to further verify the correctness of the 
 # algorithm.
-test = SurrogateAssociationTest(AzadkiaChatterjeeCoefficient(), nshuffles = 19, rng = rng)
+test = SurrogateAssociationTest(AzadkiaChatterjeeCoefficient(), nshuffles=19, rng=rng)
 n = 200
 # We expect that we *cannot* reject the null hypothesis for independent variables
 x = rand(rng, n)
 y = rand(rng, n)
 α = 0.05
-@test pvalue(independence(test, x, y)) > α 
+@test pvalue(independence(test, x, y)) > α
 
 # We expect that we *can* reject the null hypothesis for for dependent variables.
 x = rand(rng, n)
 y = rand(rng, n) .* x
-@test pvalue(independence(test, x, y )) < α 
+@test pvalue(independence(test, x, y)) < α
 
 # We expect that we *cannot* reject the null hypothesis for two extremal variables 
 # connected by an intermediate variable when conditioning on the intermediate variable.
 x = rand(rng, n)
 y = rand(rng, n) .+ x
 z = rand(rng, n) .* y
-@test pvalue(independence(test, x, z, y)) > α 
+@test pvalue(independence(test, x, z, y)) > α

test/independence/SurrogateAssociationTest/chatterjee_correlation.jl

@@ -1,18 +1,19 @@
 using Test
 using Random
-rng = Xoshiro(1234)
+using StableRNGs
+rng = StableRNG(1234)
 
 # We can use surrogate tests and p-values to further verify the correctness of the 
 # algorithm.
-test = SurrogateAssociationTest(ChatterjeeCorrelation(), nshuffles = 19, rng = rng)
+test = SurrogateAssociationTest(ChatterjeeCorrelation(), nshuffles=19, rng=rng)
 
 # We expect that we *cannot* reject the null hypothesis for independent variables
 x = rand(rng, 1:10, 200)
 y = rand(rng, 1:10, 200)
 α = 0.05
-@test pvalue(independence(test, x, y)) > α 
+@test pvalue(independence(test, x, y)) > α
 
 # We expect that we *can* reject the null hypothesis for for dependent variables.
 w = rand(rng, 1:10, 200)
 z = rand(rng, 1:10, 200) .* sin.(w) .+ cos.(w)
-@test pvalue(independence(test, z, w)) < α 
+@test pvalue(independence(test, z, w)) < α