Merge pull request #298 from ClapeyronThermo/HANNA_docs_tests

Hanna Docs

.github/workflows/CI_ClapeyronHANNA.yml

@@ -0,0 +1,60 @@
+name: CI (ClapeyronHANNA)
+
+on:
+  pull_request:
+    branches:
+      - master
+    paths:
+      - "lib/ClapeyronHANNA/**"
+      - ".github/workflows/CI_ClapeyronHANNA.yml"
+  push:
+    branches:
+      - master
+
+concurrency:
+  # Skip intermediate builds: always.
+  # Cancel intermediate builds: only if it is a pull request build.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+
+jobs:
+  test:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        version:
+          - "1"
+        os:
+          - ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: ${{ matrix.version }}
+      - uses: actions/cache@v4
+        env:
+          cache-name: cache-artifacts
+        with:
+          path: ~/.julia/artifacts
+          key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
+          restore-keys: |
+            ${{ runner.os }}-test-${{ env.cache-name }}-
+            ${{ runner.os }}-test-
+            ${{ runner.os }}-
+      - name: "Install Dependencies and Run Tests"
+        run: |
+          import Pkg
+          Pkg.Registry.update()
+          Pkg.instantiate()
+          Pkg.test(; coverage=true)
+        shell: julia --color=yes --code-coverage=user --depwarn=yes --project=lib/ClapeyronHANNA {0}
+      - uses: julia-actions/julia-processcoverage@v1
+        with:
+          directories: lib/ClapeyronHANNA/src
+      - uses: codecov/codecov-action@v4
+        with:
+          file: lcov.info
+          token: ${{ secrets.CODECOV_TOKEN }}
+          verbose: true
+          fail_ci_if_error: true

.github/workflows/Documentation.yml

@@ -0,0 +1,23 @@
+name: Documentation
+on:
+  - push
+# needed to allow julia-actions/cache to delete old caches that it has created
+permissions:
+  actions: write
+  contents: read
+concurrency:
+  # Skip intermediate builds: always.
+  # Cancel intermediate builds: always.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+jobs:
+  docs:
+    name: Documentation
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-docdeploy@latest
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}

.github/workflows/ci.yml

@@ -1,7 +1,15 @@
 name: CI
 on:
-  - push
-  - pull_request
+  pull_request:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+      - 'examples/**'
+  push:
+    paths-ignore:
+      - 'docs/**'
+      - 'examples/**'
 # needed to allow julia-actions/cache to delete old caches that it has created
 permissions:
   actions: write
@@ -61,13 +69,3 @@ jobs:
         with:
           file: lcov.info
           token: ${{ secrets.CODECOV_TOKEN }}
-  docs:
-    name: Documentation
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: julia-actions/julia-buildpkg@latest
-      - uses: julia-actions/julia-docdeploy@latest
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}

docs/Project.toml

@@ -1,6 +1,7 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Clapeyron = "7c7805af-46cc-48c9-995b-ed0ed2dc909a"
+ClapeyronHANNA = "3f3167c7-7596-4199-a8f8-b0e6cfdc2746"
 
 [compat]
 Documenter = "1"

docs/make.jl

@@ -1,6 +1,5 @@
 push!(LOAD_PATH,"../src/")
-using Documenter,Clapeyron
-
+using Documenter,Clapeyron,ClapeyronHANNA
 makedocs(sitename = "Clapeyron.jl",
 format = Documenter.HTML(
     # Use clean URLs, unless built as a "local" build

docs/src/eos/activity.md

@@ -43,7 +43,7 @@ Clapeyron.tcPRWilsonRes
 Clapeyron.COSMOSAC02
 Clapeyron.COSMOSAC10
 Clapeyron.COSMOSACdsp
-Clapeyron.HANNA
+ClapeyronHANNA.HANNA
 ```
 
 

lib/ClapeyronHANNA/Project.toml

@@ -1,20 +1,22 @@
 name = "ClapeyronHANNA"
 uuid = "3f3167c7-7596-4199-a8f8-b0e6cfdc2746"
 authors = ["Sebastian Schmitt <[email protected]>"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 Clapeyron = "7c7805af-46cc-48c9-995b-ed0ed2dc909a"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Transformers = "21ca0261-441d-5938-ace7-c90938fde4d4"
 
 [compat]
 CSV = "0.10"
 Clapeyron = "0.6"
 Flux = "0.14"
 JLD2 = "0.5"
+LinearAlgebra = "1"
 Transformers = "0.3"
 julia = "1.6"
 

lib/ClapeyronHANNA/src/ClapeyronHANNA.jl

@@ -8,6 +8,7 @@ using Clapeyron: getparams, init_puremodel, Rgas
 import Clapeyron: default_locations
 using Flux, Transformers.HuggingFace, Transformers.TextEncoders
 using CSV, JLD2
+using LinearAlgebra
 
 include("HANNA.jl")
 
@@ -47,7 +48,9 @@ silu(x) = @. x/(1+exp(-x))
 
 # Cosine similarity
 function cosine_similarity(x1,x2;eps=1e-8)
-    res = sum(x1.*x2)./(max.(sqrt.(sum(x1.^2)),eps).*max.(sqrt.(sum(x2.^2)),eps))
+    ∑x1 = sqrt(dot(x1,x1))
+    ∑x2 = sqrt(dot(x2,x2))
+    res = dot(x1,x2)/(max(∑x1,eps*one(∑x1))*max(∑x2,eps*one(∑x2)))
 end
 
-end
+end #module

lib/ClapeyronHANNA/src/HANNA.jl

@@ -2,13 +2,24 @@
 struct HANNAParam <: EoSParam
     smiles::SingleParam{String}
     emb_scaled::Vector{Vector{Float64}}
-    T_scaler::Function
+    T_scaler::Function #TODO:maybe parametrize this?
     theta::Dense
     alpha::Chain
     phi::Chain
     Mw::SingleParam{Float64}
 end
 
+function Clapeyron.split_model(param::HANNAParam,splitter)
+    return [Clapeyron.each_split_model(param,i) for i ∈ splitter]
+end 
+
+function Clapeyron.each_split_model(param::HANNAParam,i)
+    Mw = Clapeyron.each_split_model(param.Mw,i)
+    smiles = Clapeyron.each_split_model(param.smiles,i)
+    emb_scaled = Clapeyron.each_split_model(param.emb_scaled,i)
+    return HANNAParam(smiles,emb_scaled,param.T_scaler,param.theta,param.alpha,param.phi,Mw)
+end 
+
 abstract type HANNAModel <: ActivityModel end
 
 struct HANNA{c<:EoSModel} <: HANNAModel
@@ -59,7 +70,7 @@ HANNA
 default_locations(::Type{HANNA}) = ["properties/identifiers.csv", "properties/molarmass.csv"]
 
 function HANNA(components;
-        puremodel = nothing,
+        puremodel = BasicIdeal,
         userlocations = String[],
         pure_userlocations = String[],
         verbose = false)
@@ -107,13 +118,7 @@ function HANNA(components;
     )
     phi[1].bias .+= b1_ϕ
     phi[2].bias .+= b2_ϕ
-
-    if isnothing(puremodel)
-        _puremodel = init_puremodel(BasicIdeal(),components,pure_userlocations,false)
-    else
-        _puremodel = init_puremodel(puremodel,components,pure_userlocations,verbose)
-    end
-
+    _puremodel = init_puremodel(puremodel,components,pure_userlocations,verbose)
     params = HANNAParam(params["canonicalsmiles"],emb_scaled,T_scaler,theta,alpha,phi,params["Mw"])
     references = String["10.48550/arXiv.2407.18011"]
 
@@ -125,20 +130,25 @@ function C.excess_gibbs_free_energy(model::HANNAModel,p,T,z)
 
     # Scale input (T and embs)
     T_s = model.params.T_scaler(T)
-
     # Fine tuning of the component embeddings
     θ_i = model.params.theta.(model.params.emb_scaled)
-
-    # Calculate cosine similarity and distance between the two components
-    cosine_sim = cosine_similarity(θ_i[1],θ_i[2])
-    cosine_dist = 1.0-cosine_sim
-
-    # Concatenate embeddings with T and x
-    c_i = vcat.(T_s,x,θ_i)
-    α_i = model.params.alpha.(c_i)
-    c_mix = sum(α_i)
-    gE_NN = model.params.phi(c_mix)[1]
-
-    # Apply cosine similarity adjustment
-    return gE_NN * prod(x) * cosine_dist * Rgas(model) * T * sum(z)
+    gE = zero(Base.promote_eltype(T_s,x))
+    n = length(model)
+    for i in 1:n 
+        for j in (i+1):n
+            # Calculate cosine similarity and distance between the two components
+            cosine_sim_ij = cosine_similarity(θ_i[i],θ_i[j])
+            cosine_dist_ij = 1.0 - cosine_sim_ij
+
+            # Concatenate embeddings with T and x
+            c_i = vcat.(T_s,[x[i],x[j]],[θ_i[i],θ_i[j]])
+            α_i = model.params.alpha.(c_i)
+            c_mix = sum(α_i)
+            gE_NN = model.params.phi(c_mix)[1]
+
+            # Apply cosine similarity adjustment
+            gE += x[i]*x[j]*gE_NN*cosine_dist_ij
+        end
+    end
+    return gE * Rgas(model) * T * sum(z)
 end