V0.13.0 examples (#147)

* example updates * rm jupyter content * modified tests, added GaussianAdjoint * rm available sensitivity test * ... * fixing FMIImport 1.0.6 * fixed juliacon23 example * longer training for tests * SciML code formatting * minor modification to test net #2
ThummeTo · Sep 11, 2024 · 32ee3db · 32ee3db
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diff --git a/.github/workflows/Formatter.yml b/.github/workflows/Formatter.yml
@@ -0,0 +1,14 @@
+name: Format suggestions
+on:
+  pull_request:
+    # this argument is not required if you don't use the `suggestion-label` input
+    types: [ opened, reopened, synchronize, labeled, unlabeled ]
+jobs:
+  code-style:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: julia-actions/julia-format@v3
+        with:
+          version: '1' # Set `version` to '1.0.54' if you need to use JuliaFormatter.jl v1.0.54 (default: '1')
+          suggestion-label: 'format-suggest' # leave this unset or empty to show suggestions for all PRs
+
diff --git a/Project.toml b/Project.toml
@@ -1,33 +1,33 @@
-name = "FMIFlux"
-uuid = "fabad875-0d53-4e47-9446-963b74cae21f"
-version = "0.13.0"
-
-[deps]
-Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
-DifferentiableEigen = "73a20539-4e65-4dcb-a56d-dc20f210a01b"
-FMIImport = "9fcbc62e-52a0-44e9-a616-1359a0008194"
-FMISensitivity = "3e748fe5-cd7f-4615-8419-3159287187d2"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-ThreadPools = "b189fb0b-2eb5-4ed4-bc0c-d34c51242431"
-
-[weakdeps]
-JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
-
-[extensions]
-JLD2Ext = ["JLD2"]
-
-[compat]
-Colors = "0.12"
-DifferentiableEigen = "0.2.0"
-FMIImport = "1.0.0"
-FMISensitivity = "0.2.0"
-Flux = "0.9 - 0.14"
-Optim = "1.6"
-OrdinaryDiffEq = "6.0"
-Statistics = "1"
-ThreadPools = "2.1"
-julia = "1.6"
+name = "FMIFlux"
+uuid = "fabad875-0d53-4e47-9446-963b74cae21f"
+version = "0.13.0"
+
+[deps]
+Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
+DifferentiableEigen = "73a20539-4e65-4dcb-a56d-dc20f210a01b"
+FMIImport = "9fcbc62e-52a0-44e9-a616-1359a0008194"
+FMISensitivity = "3e748fe5-cd7f-4615-8419-3159287187d2"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+ThreadPools = "b189fb0b-2eb5-4ed4-bc0c-d34c51242431"
+
+[weakdeps]
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+
+[extensions]
+JLD2Ext = ["JLD2"]
+
+[compat]
+Colors = "0.12"
+DifferentiableEigen = "0.2.0"
+FMIImport = "1.0.6"
+FMISensitivity = "0.2.0"
+Flux = "0.9 - 0.14"
+Optim = "1.6"
+OrdinaryDiffEq = "6.0"
+Statistics = "1"
+ThreadPools = "2.1"
+julia = "1.6"
diff --git a/README.md b/README.md
@@ -17,7 +17,7 @@ You can evaluate FMUs inside of your loss function.
 [![Run PkgEval](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Eval.yml/badge.svg)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Eval.yml)
 [![Coverage](https://codecov.io/gh/ThummeTo/FMIFlux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/ThummeTo/FMIFlux.jl)
 [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
-[![FMIFlux Downloads](https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/FMIFlux)](https://pkgs.genieframework.com?packages=FMIFlux)
+[![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
 ## How can I use FMIFlux.jl?
 

diff --git a/docs/make.jl b/docs/make.jl
@@ -3,36 +3,38 @@
 # Licensed under the MIT license. See LICENSE file in the project root for details.
 #
 
-import Pkg; Pkg.develop(path=joinpath(@__DIR__,"../../FMIFlux.jl"))
+import Pkg;
+Pkg.develop(path = joinpath(@__DIR__, "../../FMIFlux.jl"));
 using Documenter, FMIFlux
 using Documenter: GitHubActions
 
-makedocs(sitename="FMIFlux.jl",
-        format = Documenter.HTML(
-            collapselevel = 1,
-            sidebar_sitename = false,
-            edit_link = nothing,
-            size_threshold_ignore = [joinpath("examples","juliacon_2023.md")]
-        ),
-        warnonly=true,
-        pages= Any[
-            "Introduction" => "index.md"
-            "Examples" => [
-                    "Overview" => "examples/overview.md"
-                    "Simple CS-NeuralFMU" => "examples/simple_hybrid_CS.md"
-                    "Simple ME-NeuralFMU" => "examples/simple_hybrid_ME.md"
-                    "Growing Horizon ME-NeuralFMU" => "examples/growing_horizon_ME.md"
-                    "JuliaCon 2023" => "examples/juliacon_2023.md"
-                    "MDPI 2022" => "examples/mdpi_2022.md"
-                    "Modelica Conference 2021" => "examples/modelica_conference_2021.md"
-                    "Pluto Workshops" => "examples/workshops.md"
-            ]
-            "FAQ" => "faq.md"
-            "Library Functions" => "library.md"
-            "Related Publication" => "related.md"
-            "Contents" => "contents.md"
-            ]
-        )
+makedocs(
+    sitename = "FMIFlux.jl",
+    format = Documenter.HTML(
+        collapselevel = 1,
+        sidebar_sitename = false,
+        edit_link = nothing,
+        size_threshold_ignore = [joinpath("examples", "juliacon_2023.md")],
+    ),
+    warnonly = true,
+    pages = Any[
+        "Introduction" => "index.md"
+        "Examples" => [
+            "Overview" => "examples/overview.md"
+            "Simple CS-NeuralFMU" => "examples/simple_hybrid_CS.md"
+            "Simple ME-NeuralFMU" => "examples/simple_hybrid_ME.md"
+            "Growing Horizon ME-NeuralFMU" => "examples/growing_horizon_ME.md"
+            "JuliaCon 2023" => "examples/juliacon_2023.md"
+            "MDPI 2022" => "examples/mdpi_2022.md"
+            "Modelica Conference 2021" => "examples/modelica_conference_2021.md"
+            "Pluto Workshops" => "examples/workshops.md"
+        ]
+        "FAQ" => "faq.md"
+        "Library Functions" => "library.md"
+        "Related Publication" => "related.md"
+        "Contents" => "contents.md"
+    ],
+)
 
 function deployConfig()
     github_repository = get(ENV, "GITHUB_REPOSITORY", "")
@@ -44,4 +46,8 @@ function deployConfig()
     return GitHubActions(github_repository, github_event_name, github_ref)
 end
 
-deploydocs(repo = "github.com/ThummeTo/FMIFlux.jl.git", devbranch = "main", deploy_config = deployConfig())
+deploydocs(
+    repo = "github.com/ThummeTo/FMIFlux.jl.git",
+    devbranch = "main",
+    deploy_config = deployConfig(),
+)
diff --git a/examples/jupyter-src/.gitignore b/examples/jupyter-src/.gitignore
@@ -1,2 +1,3 @@
 params/
-*.png
+*.png
+*.gif
diff --git a/examples/jupyter-src/juliacon_2023.ipynb b/examples/jupyter-src/juliacon_2023.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
     "using FMIFlux       # for NeuralFMUs\n",
     "using FMIZoo        # a collection of demo models, including the VLDM\n",
     "using FMIFlux.Flux  # Machine Learning in Julia\n",
-    "using DifferentialEquations # for picking a NeuralFMU solver\n",
+    "using DifferentialEquations: Tsit5 # for picking a NeuralFMU solver\n",
     "\n",
     "import JLD2         # data format for saving/loading parameters\n",
     "\n",
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -408,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -552,7 +552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -737,7 +737,7 @@
     "    params = FMIFlux.params(neuralFMU)\n",
     "\n",
     "    # initialize the scheduler, keywords are passed to the NeuralFMU\n",
-    "    initialize!(scheduler; parameters=data.params, p=params[1], showProgress=showProgress)\n",
+    "    FMIFlux.initialize!(scheduler; parameters=data.params, p=params[1], showProgress=showProgress)\n",
     "    \n",
     "    # initialize Adam optimizer with our hyperparameters\n",
     "    optim = Adam(ETA, (BETA1, BETA2))\n",
@@ -747,16 +747,16 @@
     "                   neuralFMU,                       # the neural FMU including the parameters to train\n",
     "                   Iterators.repeated((), steps),   # an iterator repeating `steps` times\n",
     "                   optim;                           # the optimizer to train\n",
-    "                   gradient=:ForwardDiff,           # currently, only ForwardDiff leads to good results for multi-event systems\n",
-    "                   chunk_size=32,                   # ForwardDiff chunk_size (=number of parameter estimations per run)\n",
-    "                   cb=() -> update!(scheduler),     # update the scheduler after every step \n",
+    "                   gradient=:ReverseDiff,           # ForwardDiff leads to good results for multi-event systems\n",
+    "                   chunk_size=32,                   # ForwardDiff chunk_size (=number of parameter estimations per run) - only if ForwardDiff is used\n",
+    "                   cb=() -> FMIFlux.update!(scheduler),     # update the scheduler after every step \n",
     "                   proceed_on_assert=true)          # proceed, even if assertions are thrown, with the next step\n",
     "    \n",
     "    # the default execution mode\n",
     "    singleInstanceMode(fmu, false)\n",
     "\n",
     "    # save our result parameters\n",
-    "    fmiSaveParameters(neuralFMU, joinpath(@__DIR__, \"params\", \"$(ind).jld2\"))\n",
+    "    FMIFlux.saveParameters(neuralFMU, joinpath(@__DIR__, \"params\", \"$(ind).jld2\"))\n",
     "    \n",
     "    # simulate the NeuralFMU on a validation trajectory\n",
     "    resultNFMU = neuralFMU(x0, (data_validation.consumption_t[1], data_validation.consumption_t[end]); parameters=data_validation.params, showProgress=showProgress, maxiters=1e7, saveat=data_validation.consumption_t)\n",
@@ -817,11 +817,11 @@
     "neuralFMU = build_NFMU(fmu)\n",
     "\n",
     "# load parameters from hyperparameter optimization\n",
-    "loadParameters(neuralFMU, joinpath(@__DIR__, \"juliacon_2023.jld2\"))\n",
+    "FMIFlux.loadParameters(neuralFMU, joinpath(@__DIR__, \"juliacon_2023.jld2\"))\n",
     "\n",
     "# simulate and plot the NeuralFMU\n",
-    "resultNFMU =   neuralFMU(x0,  (tStart, tStop); parameters=data.params, showProgress=showProgress, saveat=tSave) \n",
-    "resultFMU  = fmiSimulate(fmu, (tStart, tStop); parameters=data.params, showProgress=showProgress, saveat=tSave) \n",
+    "resultNFMU = neuralFMU(x0,  (tStart, tStop); parameters=data.params, showProgress=showProgress, saveat=tSave) \n",
+    "resultFMU  =  simulate(fmu, (tStart, tStop); parameters=data.params, showProgress=showProgress, saveat=tSave) \n",
     "\n",
     "# plot the NeuralFMU, original FMU and data (cumulative consumption)\n",
     "fig = plot(resultNFMU; stateIndices=6:6, stateEvents=false, timeEvents=false, label=\"NeuralFMU\", ylabel=\"cumulative consumption [m/s]\")\n",
@@ -929,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -957,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [

diff --git a/examples/jupyter-src/juliacon_2023_distributedhyperopt.jl b/examples/jupyter-src/juliacon_2023_distributedhyperopt.jl
@@ -13,34 +13,48 @@ using DistributedHyperOpt   # add via `add "https://github.com/ThummeTo/Distribu
 # ENV["JULIA_DEBUG"] = "DistributedHyperOpt"
 
 nprocs()
-workers = addprocs(5) 
+workers = addprocs(5)
 @everywhere include(joinpath(@__DIR__, "workshop_module.jl"))
 
 # creating paths for log files (logs), parameter sets (params) and hyperparameter plots (plots) 
-for dir ∈ ("logs", "params", "plots") 
+for dir ∈ ("logs", "params", "plots")
     path = joinpath(@__DIR__, dir)
     @info "Creating (if not already) path: $(path)"
     mkpath(path)
-end 
-
-beta1 = 1.0 .- exp10.(LinRange(-4,-1,4))
-beta2 = 1.0 .- exp10.(LinRange(-6,-1,6))
-
-sampler = DistributedHyperOpt.Hyperband(;R=81, η=3, ressourceScale=1.0/81.0*NODE_Training.data.cumconsumption_t[end])
-optimization = DistributedHyperOpt.Optimization(NODE_Training.train!, 
-                                             DistributedHyperOpt.Parameter("eta", (1e-5, 1e-2); type=:Log, samples=7, round_digits=5),  
-                                             DistributedHyperOpt.Parameter("beta1", beta1), 
-                                             DistributedHyperOpt.Parameter("beta2", beta2), 
-                                             DistributedHyperOpt.Parameter("batchDur", (0.5, 20.0); samples=40, round_digits=1), 
-                                             DistributedHyperOpt.Parameter("lastWeight", (0.1, 1.0); samples=10, round_digits=1),
-                                             DistributedHyperOpt.Parameter("schedulerID", [:Random, :Sequential, :LossAccumulation]),
-                                             DistributedHyperOpt.Parameter("loss", [:MSE, :MAE]) )
-DistributedHyperOpt.optimize(optimization; 
-                          sampler=sampler, 
-                          plot=true, 
-                          plot_ressources=true,
-                          save_plot=joinpath(@__DIR__, "plots", "hyperoptim.png"),
-                          redirect_worker_io_dir=joinpath(@__DIR__, "logs"))
-
-Plots.plot(optimization; size=(1024, 1024), ressources=true)
+end
+
+beta1 = 1.0 .- exp10.(LinRange(-4, -1, 4))
+beta2 = 1.0 .- exp10.(LinRange(-6, -1, 6))
+
+sampler = DistributedHyperOpt.Hyperband(;
+    R = 81,
+    η = 3,
+    ressourceScale = 1.0 / 81.0 * NODE_Training.data.cumconsumption_t[end],
+)
+optimization = DistributedHyperOpt.Optimization(
+    NODE_Training.train!,
+    DistributedHyperOpt.Parameter(
+        "eta",
+        (1e-5, 1e-2);
+        type = :Log,
+        samples = 7,
+        round_digits = 5,
+    ),
+    DistributedHyperOpt.Parameter("beta1", beta1),
+    DistributedHyperOpt.Parameter("beta2", beta2),
+    DistributedHyperOpt.Parameter("batchDur", (0.5, 20.0); samples = 40, round_digits = 1),
+    DistributedHyperOpt.Parameter("lastWeight", (0.1, 1.0); samples = 10, round_digits = 1),
+    DistributedHyperOpt.Parameter("schedulerID", [:Random, :Sequential, :LossAccumulation]),
+    DistributedHyperOpt.Parameter("loss", [:MSE, :MAE]),
+)
+DistributedHyperOpt.optimize(
+    optimization;
+    sampler = sampler,
+    plot = true,
+    plot_ressources = true,
+    save_plot = joinpath(@__DIR__, "plots", "hyperoptim.png"),
+    redirect_worker_io_dir = joinpath(@__DIR__, "logs"),
+)
+
+Plots.plot(optimization; size = (1024, 1024), ressources = true)
 minimum, minimizer, ressource = DistributedHyperOpt.results(optimization)