Merge pull request #341 from SciML/RA/subpackage

Split into subpackages

Author: ChrisRackauckas

.gitignore

@@ -2,3 +2,4 @@
 docs/build/
 Manifest.toml
 .vscode/settings.json
+*.swp

Project.toml

@@ -4,43 +4,38 @@ authors = ["SciML"]
 version = "5.0.1"
 
 [deps]
-AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 ExtendableSparse = "95c220a8-a1cf-11e9-0c77-dbfce5f500b3"
 GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
 IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
 LatinHypercubeSampling = "a5e1c1ea-c99a-51d3-a14d-a9a37257b02d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Sobol = "ed01d8cd-4d21-5b2a-85b4-cc3bdc58bad4"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-XGBoost = "009559a3-9522-5dbb-924b-0b6ed2b22bb9"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-AbstractGPs = "0.5"
 Distributions = "0.21, 0.22, 0.23, 0.24, 0.25"
 ExtendableSparse = "0.4, 0.5, 0.6"
 Flux = "0.12, 0.13"
 GLM = "1.3"
 IterativeSolvers = "0.9"
 LatinHypercubeSampling = "1.2"
 PolyChaos = "0.2"
-Requires = "0.5, 1.0"
 Sobol = "1.3"
-XGBoost = "0.4, 1.1"
 Zygote = "0.4, 0.5, 0.6"
 julia = "1.6"
 
 [extras]
 Cubature = "667455a9-e2ce-5579-9412-b964f529a492"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 PolyChaos = "8d666b04-775d-5f6e-b778-5ac7c70f65a3"
 QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 
 [targets]
-test = ["Cubature", "SafeTestsets", "Flux", "ForwardDiff", "PolyChaos", "QuadGK", "Test", "Tracker"]
+test = ["Cubature", "SafeTestsets", "Flux", "ForwardDiff", "PolyChaos", "QuadGK", "Test", "Tracker", "Pkg"]

lib/SurrogatesAbstractGPs/Project.toml

@@ -0,0 +1,20 @@
+name = "SurrogatesAbstractGPs"
+uuid = "78aa1720-c2af-471b-b307-964fd38f9b5f"
+authors = ["Ranjan Anantharaman <[email protected]>"]
+version = "0.1.0"
+
+[deps]
+AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
+Surrogates = "6fc51010-71bc-11e9-0e15-a3fcc6593c49"
+
+[compat]
+AbstractGPs = "0.5"
+julia = "1.6"
+
+[extras]
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[targets]
+test = ["SafeTestsets", "Test", "Zygote"]

src/AbstractGP.jl renamed to lib/SurrogatesAbstractGPs/src/SurrogatesAbstractGPs.jl

@@ -1,3 +1,8 @@
+module SurrogatesAbstractGPs
+
+import Surrogates: add_point!, AbstractSurrogate, std_error_at_point
+export AbstractGPSurrogate, var_at_point, logpdf_surrogate
+
 using AbstractGPs
 
 mutable struct AbstractGPSurrogate{X, Y, GP, GP_P, S} <: AbstractSurrogate
@@ -43,3 +48,5 @@ end
 function logpdf_surrogate(g::AbstractGPSurrogate)
     return logpdf(g.gp_posterior(g.x), g.y)
 end
+
+end # module

test/AbstractGP.jl renamed to lib/SurrogatesAbstractGPs/test/runtests.jl

@@ -1,6 +1,12 @@
+using SafeTestsets, Test
+using Surrogates: sample, SobolSample
+
+@safetestset "AbstractGPSurrogate" begin 
+
 using Surrogates
+using SurrogatesAbstractGPs
 using AbstractGPs
-
+using Zygote
 
 @testset "1D -> 1D" begin 
     lb = 0.0
@@ -117,3 +123,34 @@ end
     agpND = AbstractGPSurrogate(x,y, gp=GP(SqExponentialKernel()), Σy=0.05)
     logpdf_surrogate(agpND)
 end
+
+lb = 0.0
+ub = 3.0
+n = 10
+x = sample(n,lb,ub,SobolSample())
+f = x -> x^2
+y = f.(x)
+
+#AbstractGP 1D
+@testset "AbstractGP 1D" begin
+    agp1D = AbstractGPSurrogate(x,y, gp=GP(SqExponentialKernel()), Σy=0.05)
+    g = x -> agp1D'(x)
+    g([2.0])
+end
+
+lb = [0.0,0.0]
+ub = [10.0,10.0]
+n = 5
+x = sample(n,lb,ub,SobolSample())
+f = x -> x[1]*x[2]
+y = f.(x)
+
+# AbstractGP ND
+@testset "AbstractGPSurrogate ND" begin
+    my_agp = AbstractGPSurrogate(x,y, gp=GP(SqExponentialKernel()), Σy=0.05)
+    g = x ->Zygote.gradient(my_agp, x)
+    #g([(2.0,5.0)])
+    g((2.0,5.0))
+ end
+
+end

lib/SurrogatesFlux/Project.toml

@@ -0,0 +1,17 @@
+name = "SurrogatesFlux"
+uuid = "4f55584b-dac4-4b1c-802a-b7c47b72cb4c"
+authors = ["Ranjan Anantharaman <[email protected]>"]
+version = "0.1.0"
+
+[deps]
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Surrogates = "6fc51010-71bc-11e9-0e15-a3fcc6593c49"
+
+[extras]
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[targets]
+test = ["LinearAlgebra", "SafeTestsets", "Test", "Zygote"]

src/NeuralSurrogate.jl renamed to lib/SurrogatesFlux/src/SurrogatesFlux.jl

@@ -1,3 +1,8 @@
+module SurrogatesFlux
+
+import Surrogates: add_point!, AbstractSurrogate
+export NeuralSurrogate
+
 using Flux
 using Flux: @epochs
 
@@ -53,3 +58,5 @@ function add_point!(my_n::NeuralSurrogate, x_new, y_new)
     @epochs my_n.n_echos Flux.train!(my_n.loss, my_n.ps, data, my_n.opt)
     nothing
 end
+
+end # module

lib/SurrogatesFlux/test/runtests.jl

@@ -0,0 +1,152 @@
+using SafeTestsets
+
+@safetestset "SurrogatesFlux" begin
+
+using Surrogates
+using Surrogates: SobolSample
+using Flux
+using Flux: @epochs
+using SurrogatesFlux
+using LinearAlgebra
+using Zygote
+
+#1D
+a = 0.0
+b = 10.0
+obj_1D = x -> 2*x+3
+x = sample(10,0.0,10.,SobolSample())
+y = obj_1D.(x);
+my_model = Chain(Dense(1,1), first)
+my_loss(x, y) = Flux.mse(my_model(x), y)
+my_opt = Descent(0.01)
+n_echos = 1
+my_neural = NeuralSurrogate(x,y,a,b,model = my_model,loss = my_loss,opt = my_opt,n_echos=1)
+my_neural_kwargs = NeuralSurrogate(x,y,a,b)
+add_point!(my_neural,8.5,20.0)
+add_point!(my_neural,[3.2,3.5],[7.4,8.0])
+val = my_neural(5.0)
+
+#ND
+
+lb = [0.0,0.0]
+ub = [5.0,5.0]
+x = sample(5,lb,ub, SobolSample())
+obj_ND_neural(x) = x[1]*x[2];
+y = obj_ND_neural.(x)
+my_model = Chain(Dense(2,1), first)
+my_loss(x, y) = Flux.mse(my_model(x), y)
+my_opt = Descent(0.01)
+n_echos = 1
+my_neural = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
+my_neural_kwargs = NeuralSurrogate(x,y,lb,ub)
+my_neural((3.5, 1.49))
+my_neural([3.4,1.4])
+add_point!(my_neural,(3.5,1.4),4.9)
+add_point!(my_neural,[(3.5,1.4),(1.5,1.4),(1.3,1.2)],[1.3,1.4,1.5])
+
+# Multi-output #98
+f  = x -> [x^2, x]
+lb = 1.0
+ub = 10.0
+x  = sample(5, lb, ub, SobolSample())
+push!(x, 2.0)
+y  = f.(x)
+my_model = Chain(Dense(1,2))
+my_loss(x, y) = Flux.mse(my_model(x), y)
+surrogate = NeuralSurrogate(x,y,lb,ub,model = my_model,loss = my_loss ,opt=my_opt,n_echos=1)
+surr_kwargs = NeuralSurrogate(x,y,lb,ub)
+
+f  = x -> [x[1], x[2]^2]
+lb = [1.0, 2.0]
+ub = [10.0, 8.5]
+x  = sample(20, lb, ub, SobolSample())
+push!(x, (1.0, 2.0))
+y  = f.(x)
+my_model = Chain(Dense(2,2))
+my_loss(x, y) = Flux.mse(my_model(x), y)
+surrogate = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
+surrogate_kwargs = NeuralSurrogate(x,y,lb,ub)
+surrogate((1.0, 2.0))
+x_new = (2.0, 2.0)
+y_new = f(x_new)
+add_point!(surrogate, x_new, y_new)
+
+#Optimization 
+lb = [1.0,1.0]
+ub = [6.0,6.0]
+x = sample(5,lb,ub,SobolSample())
+objective_function_ND = z -> 3*norm(z)+1
+y = objective_function_ND.(x)
+model = Chain(Dense(2,1), first)
+loss(x, y) = Flux.mse(model(x), y)
+opt = Descent(0.01)
+n_echos = 1
+my_neural_ND_neural = NeuralSurrogate(x,y,lb,ub)
+surrogate_optimize(objective_function_ND,SRBF(),lb,ub,my_neural_ND_neural,SobolSample(),maxiters=15)
+
+# AD Compatibility
+lb = 0.0
+ub = 3.0
+n = 10
+x = sample(n,lb,ub,SobolSample())
+f = x -> x^2
+y = f.(x)
+#NN
+@testset "NN" begin
+    my_model = Chain(Dense(1,1), first)
+    my_loss(x, y) = Flux.mse(my_model(x), y)
+    my_opt = Descent(0.01)
+    n_echos = 1
+    my_neural = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
+    g = x->my_neural'(x)
+    g(3.4)
+end
+
+lb = [0.0,0.0]
+ub = [10.0,10.0]
+n = 5
+x = sample(n,lb,ub,SobolSample())
+f = x -> x[1]*x[2]
+y = f.(x)
+
+#NN
+@testset "NN ND" begin
+    my_model = Chain(Dense(2,1), first)
+    my_loss(x, y) = Flux.mse(my_model(x), y)
+    my_opt = Descent(0.01)
+    n_echos = 1
+    my_neural = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
+    g = x -> Zygote.gradient(my_neural, x)
+    g((2.0,5.0))
+end
+
+# ###### ND -> ND ######
+
+lb = [0.0, 0.0]
+ub = [10.0, 2.0]
+n = 5
+x = sample(n,lb,ub,SobolSample())
+f = x -> [x[1]^2, x[2]]
+y = f.(x)
+
+#NN
+@testset "NN ND -> ND" begin
+    my_model = Chain(Dense(2,2))
+    my_loss(x, y) = Flux.mse(my_model(x), y)
+    my_opt = Descent(0.01)
+    n_echos = 1
+    my_neural = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
+    Zygote.gradient(x -> sum(my_neural(x)), (2.0, 5.0))
+
+    my_rad = RadialBasis(x,y,lb,ub,rad = linearRadial())
+    Zygote.gradient(x -> sum(my_rad(x)), (2.0, 5.0))
+
+    my_p = 1.4
+    my_inverse = InverseDistanceSurrogate(x,y,lb,ub,p=my_p)
+    my_inverse((2.0, 5.0))
+    Zygote.gradient(x -> sum(my_inverse(x)), (2.0, 5.0))
+
+    my_second = SecondOrderPolynomialSurrogate(x,y,lb,ub)
+    Zygote.gradient(x -> sum(my_second(x)), (2.0, 5.0))
+end
+end

lib/SurrogatesPolyChaos/Project.toml

@@ -0,0 +1,16 @@
+name = "SurrogatesPolyChaos"
+uuid = "50679fc6-c85c-4a6e-ac63-dc3c8bd8cb1c"
+authors = ["Ranjan Anantharaman <[email protected]>"]
+version = "0.1.0"
+
+[deps]
+PolyChaos = "8d666b04-775d-5f6e-b778-5ac7c70f65a3"
+Surrogates = "6fc51010-71bc-11e9-0e15-a3fcc6593c49"
+
+[extras]
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[targets]
+test = ["SafeTestsets", "Test", "Zygote"]