Flux constraints JuMP #14

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Merged

andrewrosemberg merged 75 commits into main from ar/flux_constraints

Feb 1, 2024

.gitignore

-Original file line number
+Diff line change
@@ Expand Up / @@ -8,3 +8,13 @@ Manifest.toml @@
     *.arrow
     *.m
     *.csv
+    .DS_Store
+    *.out
+    *.sbatch
+    examples/unitcommitment/app/*
+    *.edu
+    examples/unitcommitment/wandb/*
+    *.png
+    *.jls
+    *.jlso
+    *.jld2

LocalPreferences.toml

-Original file line number
+Diff line change
@@ -0,0 +1,3 @@
+    [CUDA_Runtime_jll]
+    __clear__ = ["local"]
+    version = "12.1"

Project.toml

-Original file line number
+Diff line change
@@ Expand Up / @@ -11,9 +11,12 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" @@
     JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
     LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
     MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
+    NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
     Nonconvex = "01bcebdf-4d21-426d-b5c4-6132c1619978"
+    Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
     ParametricOptInterface = "0ce4ce61-57bf-432b-a095-efac525d185e"
     Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+    Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
     UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
     Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
@@ Expand All / @@ -22,21 +25,23 @@ Arrow = "2" @@
     CSV = "0.10"
     Dualization = "0.5"
     JuMP = "1"
-    ParametricOptInterface = "0.5"
+    ParametricOptInterface = "0.7"
+    Zygote = "^0.6.68"
     julia = "1.6"
     [extras]
     AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
+    CUDA_Runtime_jll = "76a88914-d11a-5bdc-97e0-2f5a05c973a2"
     Clarabel = "61c947e1-3e6d-4ee4-985a-eec8c727bd6e"
     DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
     DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
     HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
     Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
+    MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
     NonconvexNLopt = "b43a31b8-ff9b-442d-8e31-c163daa8ab75"
     PGLib = "07a8691f-3d11-4330-951b-3c50f98338be"
     PowerModels = "c36e90e8-916a-50a6-bd94-075b64ef4655"
     Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-    MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
     [targets]
     test = ["Test", "DelimitedFiles", "PGLib", "HiGHS", "PowerModels", "DataFrames", "Clarabel", "Ipopt", "NonconvexNLopt", "MLJ"]

README.md

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -21,7 +21,7 @@ The user needs to first define a problem iterator:
  
    # The problem to iterate over

    model = Model(() -> POI.Optimizer(HiGHS.Optimizer()))

    @variable(model, x)

    p = @variable(model, p in POI.Parameter(1.0)) # The parameter (defined using POI)

    p = @variable(model, p in MOI.Parameter(1.0)) # The parameter (defined using POI)

    @constraint(model, cons, x + p >= 3)

    @objective(model, Min, 2x)

    @@ -35,7 +35,7 @@ problem_iterator = ProblemIterator(parameter_values)
  
    The parameter values of the problem iterator can be saved by simply:

    ```julia

    save(problem_iterator, "input_file.csv", CSVFile)

    save(problem_iterator, "input_file", CSVFile)

    ```

    Which creates the following CSV:

    @@ -64,7 +64,7 @@ Then chose what values to record:
  
    recorder = Recorder{CSVFile}("output_file.csv", primal_variables=[x], dual_variables=[cons])

    # Finally solve all problems described by the iterator

    solve_batch(model, problem_iterator, recorder)

    solve_batch(problem_iterator, recorder)

    ```

    Which creates the following CSV:

examples/flux/HuggingFaceDatasets.jl

-Original file line number
+Diff line change
@@ -0,0 +1,39 @@
+    using PyCall
+    using Conda
+    # Conda.add("huggingface_hub")
+    huggingface_hub = pyimport("huggingface_hub")
+    huggingface_hub.login(token=ENV["HUGGINGFACE_TOKEN"])
+    function download_dataset(organization, dataset, case_name, io_type; formulation="", cache_dir="./data/")
+        dataset_url = joinpath(organization, dataset)
+        if io_type ∉ ["input", "output"]
+            throw(ArgumentError("io_type must be 'input' or 'output'."))
+        end
+        if io_type == "input"
+            data_path = joinpath(case_name, "input")
+        else
+            if formulation == ""
+                throw(ArgumentError("Formulation must be specified for 'output' data."))
+            end
+            data_path = joinpath(case_name, "output", formulation)
+        end
+        # Fetch the dataset from the provided URL
+        huggingface_hub.snapshot_download(dataset_url, allow_patterns=["$data_path/*.arrow"], local_dir=cache_dir, repo_type="dataset", local_dir_use_symlinks=false)
+        return nothing
+    end
+    cache_dir="./examples/powermodels/data/"
+    organization = "L2O"
+    dataset = "pglib_opf_solves"
+    case_name = "pglib_opf_case300_ieee"
+    formulation = "DCPPowerModel"
+    io_type = "input"
+    download_dataset(organization, dataset, case_name, io_type; cache_dir=cache_dir)
+    io_type = "output"
+    download_dataset(organization, dataset, case_name, io_type; formulation=formulation , cache_dir=cache_dir)

examples/flux/flux_forecaster_script.jl

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -3,26 +3,32 @@ TestEnv.activate()
  
    using Arrow

    using CSV

    using MLJFlux

    using Flux

    using MLJ

    using DataFrames

    using PowerModels

    using L2O

    # Paths

    case_name = "pglib_opf_case300_ieee" # pglib_opf_case300_ieee # pglib_opf_case5_pjm

    network_formulation = SOCWRConicPowerModel # SOCWRConicPowerModel # DCPPowerModel

    network_formulation = DCPPowerModel # SOCWRConicPowerModel # DCPPowerModel

    filetype = ArrowFile # ArrowFile # CSVFile

    path_dataset = joinpath(pwd(), "examples", "powermodels", "data")

    case_file_path = joinpath(path_dataset, case_name, string(network_formulation))

    case_file_path = joinpath(path_dataset, case_name)

    case_file_path_output = joinpath(case_file_path, "output", string(network_formulation))

    case_file_path_input = joinpath(case_file_path, "input")

    # Load input and output data tables

    iter_files = readdir(joinpath(case_file_path))

    iter_files = filter(x -> occursin(string(filetype), x), iter_files)

    iter_files_in = readdir(joinpath(case_file_path_input))

    iter_files_in = filter(x -> occursin(string(filetype), x), iter_files_in)

    file_ins = [

        joinpath(case_file_path, file) for file in iter_files if occursin("input", file)

        joinpath(case_file_path_input, file) for file in iter_files_in if occursin("input", file)

    ]

    iter_files_out = readdir(joinpath(case_file_path_output))

    iter_files_out = filter(x -> occursin(string(filetype), x), iter_files_out)

    file_outs = [

        joinpath(case_file_path, file) for file in iter_files if occursin("output", file)

        joinpath(case_file_path_output, file) for file in iter_files_out if occursin("output", file)

    ]

    batch_ids = [split(split(file, "_")[end], ".")[1] for file in file_ins]

examples/powermodels/pglib_datagen.jl

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -127,6 +127,7 @@ function pm_primal_builder!(
  
                    set_dual_variable!(recorder, real_balance)

                end

            end

            set_model!(recorder)

            return model, parameters, variable_refs

        end

    @@ -185,13 +186,13 @@ function generate_dataset_pglib(
  
            [network_data["load"]["$l"]["pd"] for l in 1:num_loads],

            [network_data["load"]["$l"]["qd"] for l in 1:num_loads],

        )

        p = load_parameter_factory(model, 1:num_inputs; load_set=POI.Parameter.(original_load))

        p = load_parameter_factory(model, 1:num_inputs; load_set=MOI.Parameter.(original_load))

        # Build model and Recorder

        file = joinpath(

            data_sim_dir, case_name * "_" * string(network_formulation) * "_output_" * batch_id

        )

        recorder = Recorder{filetype}(file; filterfn=filterfn)

        recorder = Recorder{filetype}(file; filterfn=filterfn,model=model)

        pm_primal_builder!(

            model, p, network_data, network_formulation; recorder=recorder, record_duals=true

        )

    @@ -250,7 +251,7 @@ function generate_worst_case_dataset_Nonconvex(
  
            [l["qd"] for l in values(network_data["load"])],

        )

        p = load_parameter_factory(

            model, 1:(num_loads * 2); load_set=POI.Parameter.(original_load)

            model, 1:(num_loads * 2); load_set=MOI.Parameter.(original_load)

        )

        # Define batch id

    @@ -265,7 +266,7 @@ function generate_worst_case_dataset_Nonconvex(
  
            data_sim_dir, case_name * "_" * string(network_formulation) * "_output_" * batch_id

        )

        recorder = Recorder{filetype}(

            file_output; filename_input=file_input, primal_variables=[], dual_variables=[]

            file_output; filename_input=file_input, primal_variables=[], dual_variables=[], model=model

        )

        # Build model

    @@ -372,7 +373,7 @@ function generate_worst_case_dataset(
  
            data_sim_dir, case_name * "_" * string(network_formulation) * "_output_" * batch_id

        )

        recorder = Recorder{filetype}(

            file_output; filename_input=file_input, primal_variables=[], dual_variables=[]

            file_output; filename_input=file_input, primal_variables=[], dual_variables=[], model=JuMP.Model() # dummy model

        )

        # Solve all problems and record solutions

examples/unitcommitment/Project.toml

-Original file line number
+Diff line change
@@ -0,0 +1,14 @@
+    [deps]
+    Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
+    CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+    DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+    Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
+    JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+    L2O = "e1d8bfa7-c465-446a-84b9-451470f6e76c"
+    LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+    Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+    ParametricOptInterface = "0ce4ce61-57bf-432b-a095-efac525d185e"
+    SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+    Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+    UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
+    UnitCommitment = "64606440-39ea-11e9-0f29-3303a1d3d877"

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