Bump QMC to 0.3 and fix tests

.github/workflows/Documentation.yml

@@ -16,7 +16,15 @@ jobs:
         with:
           version: '1'
       - name: Install dependencies
-        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
+        run: julia --project=docs/ -e 'using Pkg; 
+                                      Pkg.develop(PackageSpec(path=pwd()));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesAbstractGPs")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesFlux")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesMOE")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesPolyChaos")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesRandomForest")));
+                                      Pkg.develop(PackageSpec(path=joinpath(pwd(), "lib", "SurrogatesSVM")));
+                                      Pkg.instantiate()'
       - name: Build and deploy
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token

Project.toml

@@ -21,10 +21,10 @@ Flux = "0.12, 0.13"
 GLM = "1.3"
 IterativeSolvers = "0.9"
 PolyChaos = "0.2"
-QuasiMonteCarlo = "=0.2.16"
+QuasiMonteCarlo = "0.3"
 Statistics = "1"
 Zygote = "0.4, 0.5, 0.6"
-julia = "1.6"
+julia = "1.9"
 
 [extras]
 Cubature = "667455a9-e2ce-5579-9412-b964f529a492"

docs/src/InverseDistance.md

@@ -15,15 +15,15 @@ default()
 
 ### Sampling
 
-We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `LowDiscrepancySample()` to the `sample` function.
+We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `HaltonSample()` to the `sample` function.
 
 ```@example Inverse_Distance1D
 f(x) = sin(x) + sin(x)^2 + sin(x)^3
 
 n_samples = 25
 lower_bound = 0.0
 upper_bound = 10.0
-x = sample(n_samples, lower_bound, upper_bound, LowDiscrepancySample(;base=2))
+x = sample(n_samples, lower_bound, upper_bound, HaltonSample())
 y = f.(x)
 
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), legend=:top)

docs/src/ackley.md

@@ -58,7 +58,7 @@ The fit looks good. Let's now see if we are able to find the minimum value using
 optimization methods:
 
 ```@example ackley
-surrogate_optimize(ackley,DYCORS(),lb,ub,my_rad,UniformSample())
+surrogate_optimize(ackley,DYCORS(),lb,ub,my_rad,RandomSample())
 scatter(x, y, label="Sampled points", xlims=(lb, ub), ylims=(0, 30), legend=:top)
 plot!(xs, ackley.(xs), label="True function", legend=:top)
 plot!(xs, my_rad.(xs), label="Radial basis optimized", legend=:top)

docs/src/gekpls.md

@@ -80,7 +80,7 @@ This next example demonstrates how this can be accomplished.
     y = sphere_function.(x)
     g = GEKPLS(x, y, grads, n_comp, delta_x, lb, ub, extra_points, initial_theta)
     x_point, minima = surrogate_optimize(sphere_function, SRBF(), lb, ub, g,
-                                         UniformSample(); maxiters = 20,
+                                         RandomSample(); maxiters = 20,
                                          num_new_samples = 20, needs_gradient = true)
     println(minima)
 

docs/src/index.md

@@ -107,7 +107,7 @@ my_lobachevsky = LobachevskySurrogate(x,y,lb,ub,alpha=alpha,n=n)
 value = my_lobachevsky(5.0)
 
 #Adding more data points
-surrogate_optimize(f,SRBF(),lb,ub,my_lobachevsky,UniformSample())
+surrogate_optimize(f,SRBF(),lb,ub,my_lobachevsky,RandomSample())
 
 #New approximation
 value = my_lobachevsky(5.0)

docs/src/moe.md

@@ -92,7 +92,7 @@ end
 lb = [-1.0, -1.0]
 ub = [1.0, 1.0]
 n = 150
-x = sample(n, lb, ub, SobolSample())
+x = sample(n, lb, ub, RandomSample())
 y = discont_NDIM.(x)
 x_test = sample(10, lb, ub, GoldenSample())
 
@@ -110,7 +110,6 @@ rbf = RadialBasis(x, y, lb, ub)
 rbf_pred_vals = rbf.(x_test)
 rbf_rmse = rmse(true_vals, rbf_pred_vals)
 println(rbf_rmse > moe_rmse)
-
 ```
 
 ### Usage Notes - Example With Other Surrogates

docs/src/parallel.md

@@ -17,24 +17,24 @@ To ensure that points of interest returned by `potential_optimal_points` are suf
 
 The following strategies are available for virtual point selection for all optimization algorithms:
 
-- "Minimum Constant Liar (CLmin)":
+- "Minimum Constant Liar (MinimumConstantLiar)":
   - The virtual point is assigned using the lowest known value of the merit function across all evaluated points.
-- "Mean Constant Liar (CLmean)":
+- "Mean Constant Liar (MeanConstantLiar)":
   - The virtual point is assigned using the mean of the merit function across all evaluated points.
-- "Maximum Constant Liar (CLmax)":
+- "Maximum Constant Liar (MaximumConstantLiar)":
   - The virtual point is assigned using the great known value of the merit function across all evaluated points.
 
 For Kriging surrogates, specifically, the above and follow strategies are available:  
 
-- "Kriging Believer (KB)":
+- "Kriging Believer (KrigingBeliever):
   - The virtual point is assigned using the mean of the Kriging surrogate at the virtual point.
-- "Kriging Believer Upper Bound (KBUB)":
+- "Kriging Believer Upper Bound (KrigingBelieverUpperBound)":
   - The virtual point is assigned using 3$\sigma$ above the mean of the Kriging surrogate at the virtual point.
-- "Kriging Believer Lower Bound (KBLB)":
+- "Kriging Believer Lower Bound (KrigingBelieverLowerBound)":
   - The virtual point is assigned using 3$\sigma$ below the mean of the Kriging surrogate at the virtual point.
 
 
-In general, CLmin and KBLB tend to favor exploitation while CLmax and KBUB tend to favor exploration. CLmean and KB tend to be a compromise between the two.
+In general, MinimumConstantLiar and KrigingBelieverLowerBound tend to favor exploitation while MaximumConstantLiar and KrigingBelieverUpperBound tend to favor exploration. MeanConstantLiar and KrigingBeliever tend to be a compromise between the two.
 
 ## Examples
 
@@ -50,7 +50,7 @@ y = f.(x)
 my_k = Kriging(x, y, lb, ub)
 
 for _ in 1:10
-    new_x, eis = potential_optimal_points(EI(), lb, ub, my_k, SobolSample(), 3, CLmean!)
+    new_x, eis = potential_optimal_points(EI(), MeanConstantLiar(), lb, ub, my_k, SobolSample(), 3)
     add_point!(my_k, new_x, f.(new_x))
 end
 ```

docs/src/polychaos.md

@@ -9,7 +9,7 @@ we are trying to fit. Under the hood, PolyChaos.jl has been used.
 It is possible to specify a type of polynomial for each dimension of the problem.
 ### Sampling
 
-We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `LowDiscrepancySample()` to the `sample` function.
+We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `HaltonSample()` to the `sample` function.
 
 ```@example polychaos
 using Surrogates
@@ -20,7 +20,7 @@ default()
 n = 20
 lower_bound = 1.0
 upper_bound = 6.0
-x = sample(n,lower_bound,upper_bound,LowDiscrepancySample(2))
+x = sample(n,lower_bound,upper_bound,HaltonSample())
 f = x -> log(x)*x + sin(x)
 y = f.(x)
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), legend=:top)

docs/src/radials.md

@@ -141,7 +141,7 @@ This is why its size changes.
 size(xys)
 ```
 ```@example RadialBasisSurrogateND
-surrogate_optimize(booth, SRBF(), lower_bound, upper_bound, radial_basis, UniformSample(), maxiters=50)
+surrogate_optimize(booth, SRBF(), lower_bound, upper_bound, radial_basis, RandomSample(), maxiters=50)
 ```
 ```@example RadialBasisSurrogateND
 size(xys)

docs/src/samples.md

@@ -17,7 +17,7 @@ sample(n,lb,ub,S::GridSample)
 
 * Uniform sample
 ```
-sample(n,lb,ub,::UniformSample)
+sample(n,lb,ub,::RandomSample)
 ```
 
 * Sobol sample
@@ -32,8 +32,7 @@ sample(n,lb,ub,::LatinHypercubeSample)
 
 * Low Discrepancy sample
 ```
-LowDiscrepancySample{T}
-sample(n,lb,ub,S::LowDiscrepancySample)
+sample(n,lb,ub,S::HaltonSample)
 ```
 
 * Sample on section

docs/src/secondorderpoly.md

@@ -18,7 +18,7 @@ f = x -> 3*sin(x) + 10/x
 lb = 3.0
 ub = 6.0
 n = 10
-x = sample(n,lb,ub,LowDiscrepancySample(2))
+x = sample(n,lb,ub,HaltonSample())
 y = f.(x)
 scatter(x, y, label="Sampled points", xlims=(lb, ub))
 plot!(f, label="True function", xlims=(lb, ub))

docs/src/tutorials.md

@@ -43,7 +43,7 @@ using Surrogates
 f = x -> exp(x)*x^2+x^3
 lb = 0.0
 ub = 10.0
-x = sample(50,lb,ub,UniformSample())
+x = sample(50,lb,ub,RandomSample())
 y = f.(x)
 p = 1.9
 my_krig = Kriging(x,y,lb,ub,p=p)
@@ -58,7 +58,7 @@ std_err = std_error_at_point(my_krig,5.4)
 Let's now optimize the Kriging surrogate using Lower confidence bound method, this is just a one-liner:
 
 ```@example kriging
-surrogate_optimize(f,LCBS(),lb,ub,my_krig,UniformSample(); maxiters = 10, num_new_samples = 10)
+surrogate_optimize(f,LCBS(),lb,ub,my_krig,RandomSample(); maxiters = 10, num_new_samples = 10)
 ```
 
 Surrogate optimization methods have two purposes: they both sample the space in unknown regions and look for the minima at the same time.

lib/SurrogatesAbstractGPs/test/runtests.jl

@@ -87,7 +87,7 @@ using Surrogates: sample, SobolSample
         a = 2
         b = 6
         my_k_EI1 = AbstractGPSurrogate(x, y)
-        surrogate_optimize(objective_function, EI(), a, b, my_k_EI1, UniformSample(),
+        surrogate_optimize(objective_function, EI(), a, b, my_k_EI1, RandomSample(),
                            maxiters = 200, num_new_samples = 155)
     end
 
@@ -99,7 +99,7 @@ using Surrogates: sample, SobolSample
         lb = [1.0, 1.0]
         ub = [6.0, 6.0]
         my_k_E1N = AbstractGPSurrogate(x, y)
-        surrogate_optimize(objective_function_ND, EI(), lb, ub, my_k_E1N, UniformSample())
+        surrogate_optimize(objective_function_ND, EI(), lb, ub, my_k_E1N, RandomSample())
     end
 
     @testset "check working of logpdf_surrogate 1D" begin

lib/SurrogatesFlux/src/SurrogatesFlux.jl

@@ -4,7 +4,6 @@ import Surrogates: add_point!, AbstractSurrogate, _check_dimension
 export NeuralSurrogate
 
 using Flux
-using Flux: @epochs
 
 mutable struct NeuralSurrogate{X, Y, M, L, O, P, N, A, U} <: AbstractSurrogate
     x::X
@@ -32,7 +31,9 @@ function NeuralSurrogate(x, y, lb, ub; model = Chain(Dense(length(x[1]), 1), fir
     X = vec.(collect.(x))
     data = zip(X, y)
     ps = Flux.params(model)
-    @epochs n_echos Flux.train!(loss, ps, data, opt)
+    for epoch in 1:n_echos
+        Flux.train!(loss, ps, data, opt)
+    end
     return NeuralSurrogate(x, y, model, loss, opt, ps, n_echos, lb, ub)
 end
 
@@ -58,7 +59,9 @@ function add_point!(my_n::NeuralSurrogate, x_new, y_new)
     end
     X = vec.(collect.(my_n.x))
     data = zip(X, my_n.y)
-    @epochs my_n.n_echos Flux.train!(my_n.loss, my_n.ps, data, my_n.opt)
+    for epoch in 1:my_n.n_echos
+        Flux.train!(my_n.loss, my_n.ps, data, my_n.opt)
+    end
     nothing
 end
 

lib/SurrogatesFlux/test/runtests.jl

@@ -4,7 +4,6 @@ using SafeTestsets
     using Surrogates
     using Surrogates: SobolSample
     using Flux
-    using Flux: @epochs
     using SurrogatesFlux
     using LinearAlgebra
     using Zygote

lib/SurrogatesMOE/test/runtests.jl

@@ -78,7 +78,7 @@ end
     n = 150
     x = sample(n, lb, ub, SobolSample())
     y = discont_NDIM.(x)
-    x_test = sample(10, lb, ub, GoldenSample())
+    x_test = sample(9, lb, ub, GoldenSample())
 
     expert_types = [
         KrigingStructure(p = [1.0, 1.0], theta = [1.0, 1.0]),
@@ -116,7 +116,7 @@ end
     lb = [-1.0, -1.0]
     ub = [1.0, 1.0]
     n = 120
-    x = sample(n, lb, ub, UniformSample())
+    x = sample(n, lb, ub, RandomSample())
     y = discont_NDIM.(x)
     x_test = sample(10, lb, ub, GoldenSample())
 
@@ -184,7 +184,7 @@ end
     lb = [-1.0, -1.0]
     ub = [1.0, 1.0]
     n = 110
-    x = sample(n, lb, ub, UniformSample())
+    x = sample(n, lb, ub, RandomSample())
     y = discont_NDIM.(x)
     expert_types = [InverseDistanceStructure(p = 1.0),
         RadialBasisStructure(radial_function = linearRadial(), scale_factor = 1.0,

lib/SurrogatesPolyChaos/test/runtests.jl

@@ -54,7 +54,7 @@ using SafeTestsets
     lb = [0.0, 0.0]
     ub = [10.0, 10.0]
     obj_ND = x -> log(x[1]) * exp(x[2])
-    x = sample(40, lb, ub, UniformSample())
+    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)

lib/SurrogatesSVM/test/runtests.jl

@@ -20,7 +20,7 @@ using SafeTestsets
     obj_N = x -> x[1]^2 * x[2]
     lb = [0.0, 0.0]
     ub = [10.0, 10.0]
-    x = sample(100, lb, ub, UniformSample())
+    x = sample(100, lb, ub, RandomSample())
     y = obj_N.(x)
     my_svm_ND = SVMSurrogate(x, y, lb, ub)
     val = my_svm_ND((5.0, 1.2))

src/Optimization.jl

@@ -110,18 +110,8 @@ function surrogate_optimize(obj::Function, ::SRBF, lb, ub, surr::AbstractSurroga
 
             new_lb = incumbent_x .- 3 * scale * norm(incumbent_x .- lb)
             new_ub = incumbent_x .+ 3 * scale * norm(incumbent_x .- ub)
-
-            @inbounds for i in 1:length(new_lb)
-                if new_lb[i] < lb[i]
-                    new_lb = collect(new_lb)
-                    new_lb[i] = lb[i]
-                end
-                if new_ub[i] > ub[i]
-                    new_ub = collect(new_ub)
-                    new_ub[i] = ub[i]
-                end
-            end
-
+            new_lb = vec(max.(new_lb, lb))
+            new_ub = vec(min.(new_ub, ub))
             new_sample = sample(num_new_samples, new_lb, new_ub, sample_type)
             s = zeros(eltype(surr.x[1]), num_new_samples)
             for j in 1:num_new_samples
@@ -2126,7 +2116,7 @@ end
 
 function section_sampler_returner(sample_type::SectionSample, surrn_x, surrn_y,
                                   lb, ub, surrn)
-    d_fixed = QuasiMonteCarlo.fixed_dimensions(sample_type)
+    d_fixed = fixed_dimensions(sample_type)
     @assert length(surrn_y) == size(surrn_x)[1]
     surrn_xy = [(surrn_x[y], surrn_y[y]) for y in 1:length(surrn_y)]
     section_surr1_xy = filter(xyz -> xyz[1][d_fixed] == Tuple(sample_type.x0[d_fixed]),