test(SurrogatesPolyChaos): update tests

lib/SurrogatesPolyChaos/test/runtests.jl

@@ -3,117 +3,62 @@ using SafeTestsets
 @safetestset "PolynomialChaosSurrogates" begin
     using Surrogates
     using PolyChaos
-    using Surrogates: sample, SobolSample
     using SurrogatesPolyChaos
     using Zygote
 
-    #1D
-    n = 20
-    lb = 0.0
-    ub = 4.0
-    f = x -> 2 * x
-    x = sample(n, lb, ub, SobolSample())
-    y = f.(x)
-
-    my_pce = PolynomialChaosSurrogate(x, y, lb, ub)
-    val = my_pce(2.0)
-    add_point!(my_pce, 3.0, 6.0)
-    my_pce_changed = PolynomialChaosSurrogate(x, y, lb, ub, op = Uniform01OrthoPoly(1))
-
-    #ND
-    n = 60
-    lb = [0.0, 0.0]
-    ub = [5.0, 5.0]
-    f = x -> x[1] * x[2]
-    x = sample(n, lb, ub, SobolSample())
-    y = f.(x)
-
-    my_pce = PolynomialChaosSurrogate(x, y, lb, ub)
-    val = my_pce((2.0, 2.0))
-    add_point!(my_pce, (2.0, 3.0), 6.0)
-
-    op1 = Uniform01OrthoPoly(1)
-    op2 = Beta01OrthoPoly(2, 2, 1.2)
-    ops = [op1, op2]
-    multi_poly = MultiOrthoPoly(ops, min(1, 2))
-    my_pce_changed = PolynomialChaosSurrogate(x, y, lb, ub, op = multi_poly)
-
-    # Surrogate optimization test
-    lb = 0.0
-    ub = 15.0
-    p = 1.99
-    a = 2
-    b = 6
-    objective_function = x -> 2 * x + 1
-    x = sample(20, lb, ub, SobolSample())
-    y = objective_function.(x)
-    my_poly1d = PolynomialChaosSurrogate(x, y, lb, ub)
-    @test_broken surrogate_optimize(objective_function, SRBF(), a, b, my_poly1d,
-        LowDiscrepancySample(; base = 2))
+    @testset "1D" begin
+        n = 20
+        lb = [0.0]
+        ub = [4.0]
+        f = x -> 2 * x
+        x = sample(n, lb, ub, SobolSample())
+        y = f.(x)
+        my_pce = PolynomialChaosSurrogate(x, y, lb, ub)
+        val = my_pce(2.0)
+        update!(my_pce, [3.0], [6.0])
+        my_pce_changed = PolynomialChaosSurrogate(x, y, lb, ub; op = Uniform01OrthoPoly(1))
+    end
 
-    lb = [0.0, 0.0]
-    ub = [10.0, 10.0]
-    obj_ND = x -> log(x[1]) * exp(x[2])
-    x = sample(40, lb, ub, RandomSample())
-    y = obj_ND.(x)
-    my_polyND = PolynomialChaosSurrogate(x, y, lb, ub)
-    surrogate_optimize(obj_ND, SRBF(), lb, ub, my_polyND, SobolSample(), maxiters = 15)
+    @testset "ND" begin
+        n = 60
+        lb = [0.0, 0.0]
+        ub = [5.0, 5.0]
+        f = x -> x[1] * x[2]
+        x = collect.(sample(n, lb, ub, SobolSample()))
+        y = f.(x)
 
-    # AD Compatibility
+        my_pce = PolynomialChaosSurrogate(x, y, lb, ub)
+        val = my_pce([2.0, 2.0])
+        update!(my_pce, [[2.0, 3.0]], [6.0])
 
-    lb = 0.0
-    ub = 3.0
-    n = 10
-    x = sample(n, lb, ub, SobolSample())
-    f = x -> x^2
-    y = f.(x)
+        op1 = Uniform01OrthoPoly(1)
+        op2 = Beta01OrthoPoly(2, 2, 1.2)
+        ops = [op1, op2]
+        multi_poly = MultiOrthoPoly(ops, min(1, 2))
+        my_pce_changed = PolynomialChaosSurrogate(x, y, lb, ub, op = multi_poly)
+    end
 
-    # #Polynomialchaos
-    @testset "Polynomial Chaos" begin
+    @testset "Derivative" begin
+        lb = 0.0
+        ub = 3.0
         f = x -> x^2
         n = 50
-        x = sample(n, lb, ub, SobolSample())
+        x = collect(sample(n, lb, ub, SobolSample()))
         y = f.(x)
         my_poli = PolynomialChaosSurrogate(x, y, lb, ub)
         g = x -> my_poli'(x)
         g(3.0)
     end
 
-    # #PolynomialChaos
-    @testset "Polynomial Chaos ND" begin
+    @testset "Gradient" begin
         n = 50
         lb = [0.0, 0.0]
         ub = [10.0, 10.0]
-        x = sample(n, lb, ub, SobolSample())
+        x = collect.(sample(n, lb, ub, SobolSample()))
         f = x -> x[1] * x[2]
         y = f.(x)
         my_poli_ND = PolynomialChaosSurrogate(x, y, lb, ub)
-        g = x -> Zygote.gradient(my_poli_ND, x)
-        g((1.0, 1.0))
-
-        n = 10
-        d = 2
-        lb = [0.0, 0.0]
-        ub = [5.0, 5.0]
-        x = sample(n, lb, ub, SobolSample())
-        f = x -> x[1]^2 + x[2]^2
-        y1 = f.(x)
-        grad1 = x -> 2 * x[1]
-        grad2 = x -> 2 * x[2]
-        function create_grads(n, d, grad1, grad2, y)
-            c = 0
-            y2 = zeros(eltype(y[1]), n * d)
-            for i in 1:n
-                y2[i + c] = grad1(x[i])
-                y2[i + c + 1] = grad2(x[i])
-                c = c + 1
-            end
-            return y2
-        end
-        y2 = create_grads(n, d, grad1, grad2, y)
-        y = vcat(y1, y2)
-        my_gek_ND = GEK(x, y, lb, ub)
-        g = x -> Zygote.gradient(my_gek_ND, x)
-        @test_broken g((2.0, 5.0)) #breaks after Zygote version 0.6.43
+        g = x -> Zygote.gradient(my_poli_ND, x)[1]
+        g([1.0, 1.0])
     end
 end