Fix dependencies

Project.toml

@@ -12,9 +12,7 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 InteratomicPotentials = "a9efe35a-c65d-452d-b8a8-82646cd5cb04"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
-OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"

examples/Ar/Project.toml

@@ -0,0 +1,7 @@
+[deps]
+AtomsBase = "a963bdd2-2df7-4f54-a1ee-49d51e6be12a"
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+InteratomicPotentials = "a9efe35a-c65d-452d-b8a8-82646cd5cb04"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+UnitfulAtomic = "a7773ee8-282e-5fa2-be4e-bd808c38a91a"

examples/Ar/plot-lennard-jones-ar.jl

@@ -1,8 +1,7 @@
 push!(Base.LOAD_PATH, "../../")
-using AtomsBase
+
 using Unitful, UnitfulAtomic
-using InteratomicPotentials
-using PotentialLearning
+using AtomsBase, InteratomicPotentials, PotentialLearning
 using LinearAlgebra, CairoMakie
 
 # Load dataset: Lennard-Jones + Argon

examples/DFT-subsampling/Project.toml

@@ -0,0 +1,16 @@
+[deps]
+AtomsBase = "a963bdd2-2df7-4f54-a1ee-49d51e6be12a"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Determinantal = "2673d5e8-682c-11e9-2dfd-471b09c6c819"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+InteratomicPotentials = "a9efe35a-c65d-452d-b8a8-82646cd5cb04"
+InvertedIndices = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
+JLD = "4138dd39-2aa7-5051-a626-17a0bb65d9c8"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+UnitfulAtomic = "a7773ee8-282e-5fa2-be4e-bd808c38a91a"

examples/DFT-subsampling/subsampling_dpp.jl

@@ -1,15 +1,11 @@
 push!(Base.LOAD_PATH, "../../")
 
-using PotentialLearning
-using LinearAlgebra, Random, Statistics, StatsBase, Distributions
-using AtomsBase, Unitful, UnitfulAtomic
-using InteratomicPotentials
-using Determinantal
+using LinearAlgebra, Random, InvertedIndices
+using Statistics, StatsBase, Distributions, Determinantal
+using Unitful, UnitfulAtomic
+using AtomsBase, InteratomicPotentials, PotentialLearning
+using CSV, JLD, DataFrames
 using CairoMakie
-using InvertedIndices
-using CSV
-using JLD
-using DataFrames
 
 include("subsampling_utils.jl")
 
@@ -60,7 +56,7 @@ ds = DataSet(confs .+ e_descr .+ f_descr)
 ndata = length(ds)
 
 # Compute cross validation error from training ---------------------------------
-batch_size = [80, 40, 20]
+batch_size = [80, 40]
 sel_ind = Dict{Int64, Vector}()
 cond_num = Dict{Int64, Vector}()
 

examples/Na/Project.toml

@@ -0,0 +1,7 @@
+[deps]
+AtomsBase = "a963bdd2-2df7-4f54-a1ee-49d51e6be12a"
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+InteratomicPotentials = "a9efe35a-c65d-452d-b8a8-82646cd5cb04"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+UnitfulAtomic = "a7773ee8-282e-5fa2-be4e-bd808c38a91a"

examples/Na/fit-dpp-ace-na.jl

@@ -1,12 +1,8 @@
 push!(Base.LOAD_PATH, "../../")
 
-using AtomsBase
-using InteratomicPotentials
-using PotentialLearning
 using Unitful, UnitfulAtomic
-using LinearAlgebra
-using CairoMakie
-#using JLD
+using AtomsBase, InteratomicPotentials, PotentialLearning
+using LinearAlgebra, CairoMakie
 
 # Load dataset
 confs, thermo = load_data("data/liquify_sodium.yaml", YAML(:Na, u"eV", u"Å"))

examples/README.md

@@ -7,7 +7,7 @@ $ cd PotentialLearning.jl/examples/Na
 
 Open Julia REPL, activate ```Project.toml``` file in folder ```examples```, and chose the number of threads. E.g.
 ```bash
-$ julia --project=.. --threads=4
+$ julia --project=. --threads=4
 ```
 
 Type ```]``` in Julia REPL, and then run ```instantiate```.

examples/aHfO2/build_dpp.jl

@@ -1,12 +1,10 @@
 push!(Base.LOAD_PATH, "../../")
 
-using LinearAlgebra, Random, Statistics, StatsBase, Distributions
-using AtomsBase, Unitful, UnitfulAtomic
-using InteratomicPotentials
-using CairoMakie
-using JLD
-using Determinantal
-using PotentialLearning
+using LinearAlgebra, Random
+using Statistics, StatsBase, Distributions, Determinantal
+using Unitful, UnitfulAtomic
+using AtomsBase, InteratomicPotentials, PotentialLearning
+using JLD, CairoMakie
 
 #################### Importing Data ###################
 # Import Raw Data

examples/aHfO2/build_multiple_dpp.jl

@@ -1,12 +1,10 @@
 push!(Base.LOAD_PATH, "../../")
 
-using LinearAlgebra, Random, Statistics, StatsBase, Distributions
-using AtomsBase, Unitful, UnitfulAtomic
-using InteratomicPotentials, InteratomicBasisPotentials
-using CairoMakie
-using JLD
-using Determinantal
-using PotentialLearning
+using LinearAlgebra, Random
+using Statistics, StatsBase, Distributions, Determinantal
+using Unitful, UnitfulAtomic
+using AtomsBase, InteratomicPotentials, PotentialLearning
+using JLD, CairoMakie
 
 #################### Importing Data ###################
 # Import Raw Data

src/Learning/linear-learn.jl

@@ -56,7 +56,7 @@ function learn!(
     x0 = [log(lp.σe[1]), log(lp.σf[1]), lp.β...]
     p = [AtAe, Atbe, AtAf, Atbf, α]
     prob = Optimization.OptimizationProblem(g, x0, p)
-    sol = Optimization.solve(prob, Optim.BFGS())
+    sol = Optimization.solve(prob, OptimizationOptimisers.Adam(), maxiters = 1000)
     lp.σe .= exp(sol.u[1])
     lp.σf .= exp(sol.u[2])
     lp.β  .= sol.u[3:end]

src/PotentialLearning.jl

@@ -9,8 +9,6 @@ using OrderedCollections
 using Flux
 using Zygote
 using Optimization
-using Optim
-using OptimizationOptimJL
 using OptimizationOptimisers
 using Printf
 

src/interface.jl

@@ -12,7 +12,7 @@ using LinearAlgebra
 using StaticArrays
 using Statistics
 using Optimization
-using OptimizationOptimJL
+using OptimizationOptimisers
 using UnitfulAtomic
 using Unitful
 using Flux

test/Project.toml

@@ -4,10 +4,7 @@ Determinantal = "2673d5e8-682c-11e9-2dfd-471b09c6c819"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 InteratomicPotentials = "a9efe35a-c65d-452d-b8a8-82646cd5cb04"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
-OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
-OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
@@ -19,6 +16,3 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 UnitfulAtomic = "a7773ee8-282e-5fa2-be4e-bd808c38a91a"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
-
-[compat]
-julia = "1.9"

test/dimension_reduction/dimension_reduction_tests.jl

@@ -26,16 +26,16 @@ pca = PCA(num_dim)
 @test typeof(as) <: DimensionReducer
 @test typeof(pca) <: DimensionReducer
 
-λ_as, W_as = fit(ds, as)
+λ_as, W_as = PotentialLearning.fit(ds, as)
 @test typeof(λ_as) <: Vector{Float64}
 @test typeof(W_as) <: Matrix{Float64}
 @test size(W_as, 1) == d 
 @test size(W_as, 2) == num_dim
 
-λ_pca, W_pca = fit(ds, pca)
+λ_pca, W_pca = PotentialLearning.fit(ds, pca)
 @test typeof(λ_pca) <: Vector{Float64}
 @test typeof(W_pca) <: Matrix{Float64}
 @test size(W_pca, 1) == d
 @test size(W_pca, 2) == num_dim
 
-@test all(λ_as .≈ λ_pca)
+@test all(λ_as .≈ λ_pca)