adjust name and compat

andrewrosemberg · andrewrosemberg · commit 99571826467a · 2024-10-12T17:56:56.000-04:00
diff --git a/LearningToOptimize.jpg b/LearningToOptimize.jpg
diff --git a/Project.toml b/Project.toml
@@ -1,4 +1,4 @@
-name = "L2O"
+name = "LearningToOptimize"
 uuid = "e1d8bfa7-c465-446a-84b9-451470f6e76c"
 authors = ["andrewrosemberg <andrewrosemberg@gmail.com> and contributors"]
 version = "1.0.0"
@@ -21,12 +21,18 @@ UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-Arrow = "^2"
-CSV = "^0.10"
-JuMP = "^1"
-ParametricOptInterface = "^0.8"
-Zygote = "^0.6.68"
-julia = "^1.9"
+Arrow = "2"
+CSV = "0.10"
+JuMP = "1"
+ParametricOptInterface = "0.8"
+Zygote = "0.6.68"
+julia = "1.9"
+DataFrames = "1"
+Flux = "0.14"
+Optimisers = "0.3"
+MLJFlux = "0.6"
+Distributions = "0.25"
+NNlib = "0.9"
 
 [extras]
 CUDA_Runtime_jll = "76a88914-d11a-5bdc-97e0-2f5a05c973a2"
diff --git a/README.md b/README.md
@@ -1,21 +1,21 @@
 
 <div style="overflow: auto;">
-    <h1 style="float: left;">L2O.jl</h1>
+    <h1 style="float: left;">LearningToOptimize.jl</h1>
     <div style="float: right; margin-left: 20px;">
-      <img src="https://raw.githubusercontent.com/andrewrosemberg/L2O.jl/main/L2O.jpg" alt="Logo" width="100" align="right">
+      <img src="https://raw.githubusercontent.com/andrewrosemberg/LearningToOptimize.jl/main/LearningToOptimize.jpg" alt="Logo" width="100" align="right">
     </div>
 </div>
 
-Learning to optimize (L2O) package that provides basic functionalities to help fit proxy models for optimization.
+Learning to optimize (LearningToOptimize) package that provides basic functionalities to help fit proxy models for optimization.
 
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://andrewrosemberg.github.io/L2O.jl/stable/)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://andrewrosemberg.github.io/L2O.jl/dev/)
-[![Build Status](https://github.com/andrewrosemberg/L2O.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/andrewrosemberg/L2O.jl/actions/workflows/CI.yml?query=branch%3Amain)
-[![Coverage](https://codecov.io/gh/andrewrosemberg/L2O.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/andrewrosemberg/L2O.jl)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://andrewrosemberg.github.io/LearningToOptimize.jl/stable/)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://andrewrosemberg.github.io/LearningToOptimize.jl/dev/)
+[![Build Status](https://github.com/andrewrosemberg/LearningToOptimize.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/andrewrosemberg/LearningToOptimize.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![Coverage](https://codecov.io/gh/andrewrosemberg/LearningToOptimize.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/andrewrosemberg/LearningToOptimize.jl)
 
 # Flowchart Summary
 
-![flowchart](https://github.com/andrewrosemberg/L2O.jl/blob/main/docs/L2O.png)
+![flowchart](https://github.com/andrewrosemberg/LearningToOptimize.jl/blob/main/docs/LearningToOptimize.png)
 
 ## Generate Dataset
 This package provides a basic way of generating a dataset of the solutions of an optimization problem by varying the values of the parameters in the problem and recording it.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,3 +1,3 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-L2O = "e1d8bfa7-c465-446a-84b9-451470f6e76c"
+LearningToOptimize = "e1d8bfa7-c465-446a-84b9-451470f6e76c"
diff --git a/docs/make.jl b/docs/make.jl
@@ -1,20 +1,20 @@
-using L2O
+using LearningToOptimize
 using Documenter
 
-DocMeta.setdocmeta!(L2O, :DocTestSetup, :(using L2O); recursive=true)
+DocMeta.setdocmeta!(LearningToOptimize, :DocTestSetup, :(using LearningToOptimize); recursive=true)
 
 makedocs(;
-    modules=[L2O],
+    modules=[LearningToOptimize],
     authors="andrewrosemberg <andrewrosemberg@gmail.com> and contributors",
-    repo="https://github.com/andrewrosemberg/L2O.jl/blob/{commit}{path}#{line}",
-    sitename="L2O.jl",
+    repo="https://github.com/andrewrosemberg/LearningToOptimize.jl/blob/{commit}{path}#{line}",
+    sitename="LearningToOptimize.jl",
     format=Documenter.HTML(;
         prettyurls=get(ENV, "CI", "false") == "true",
-        canonical="https://andrewrosemberg.github.io/L2O.jl",
+        canonical="https://andrewrosemberg.github.io/LearningToOptimize.jl",
         edit_link="main",
         assets=String[],
     ),
     pages=["Home" => "index.md"],
 )
 
-deploydocs(; repo="github.com/andrewrosemberg/L2O.jl", devbranch="main")
+deploydocs(; repo="github.com/andrewrosemberg/LearningToOptimize.jl", devbranch="main")
diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1,16 +1,16 @@
 ```@meta
-CurrentModule = L2O
+CurrentModule = LearningToOptimize
 ```
 
-# L2O
+# LearningToOptimize
 
-Documentation for [L2O](https://github.com/andrewrosemberg/L2O.jl).
+Documentation for [LearningToOptimize](https://github.com/andrewrosemberg/LearningToOptimize.jl).
 
-Learning to optimize (L2O) package that provides basic functionalities to help fit proxy models for optimization.
+Learning to optimize (LearningToOptimize) package that provides basic functionalities to help fit proxy models for optimization.
 
 ```@index
 ```
 
 ```@autodocs
-Modules = [L2O]
+Modules = [LearningToOptimize]
 ```
diff --git a/examples/generate_dataset_inputs.jl b/examples/generate_dataset_inputs.jl
@@ -1,4 +1,4 @@
-using L2O
+using LearningToOptimize
 
 general_sampler(
     "examples/powermodels/data/6468_rte/6468_rte_SOCWRConicPowerModel_POI_load.mof.json";
diff --git a/examples/powermodels/HuggingFaceDatasets.jl b/examples/powermodels/HuggingFaceDatasets.jl
@@ -28,7 +28,7 @@ function download_dataset(organization, dataset, case_name, io_type; formulation
 end
 
 cache_dir="./examples/powermodels/data/"
-organization = "L2O"
+organization = "LearningToOptimize"
 dataset = "pglib_opf_solves"
 case_name = "pglib_opf_case300_ieee"
 formulation = "SOCWRConicPowerModel" # ACPPowerModel SOCWRConicPowerModel DCPPowerModel
diff --git a/examples/powermodels/data_split.jl b/examples/powermodels/data_split.jl
@@ -15,7 +15,7 @@ using Random
 @everywhere Pkg.activate(dirname(dirname(@__DIR__)))
 @everywhere Pkg.instantiate()
 @everywhere using DataFrames
-@everywhere using L2O
+@everywhere using LearningToOptimize
 @everywhere using Gurobi
 @everywhere using Arrow
 @everywhere using MLUtils
diff --git a/examples/powermodels/generate_full_datasets_script.jl b/examples/powermodels/generate_full_datasets_script.jl
@@ -19,7 +19,7 @@ using Random
 
 ########## SCRIPT REQUIRED PACKAGES ##########
 
-@everywhere using L2O
+@everywhere using LearningToOptimize
 @everywhere using Arrow
 @everywhere using Test
 @everywhere using UUIDs
diff --git a/examples/powermodels/jls2jld2.jl b/examples/powermodels/jls2jld2.jl
@@ -7,7 +7,7 @@ using MLJ
 using CUDA
 using DataFrames
 using PowerModels
-using L2O
+using LearningToOptimize
 using Random
 using JLD2
 using Wandb, Dates, Logging
diff --git a/examples/powermodels/pglib_datagen.jl b/examples/powermodels/pglib_datagen.jl
@@ -163,7 +163,7 @@ function generate_dataset_pglib(
     internal_load_sampler=load_sampler,
     network_formulation=DCPPowerModel,
     optimizer=() -> POI.Optimizer(HiGHS.Optimizer()),
-    filterfn=L2O.filter_fn,
+    filterfn=LearningToOptimize.filter_fn,
     early_stop_fn=(model, status, recorder) -> false,
     batch_id=string(uuid1()),
 )
diff --git a/examples/powermodels/powermodels_nn_testing.jl b/examples/powermodels/powermodels_nn_testing.jl
@@ -7,7 +7,7 @@ using CSV
 using Flux
 using DataFrames
 using PowerModels
-using L2O
+using LearningToOptimize
 using JLD2
 using Statistics
 
diff --git a/examples/powermodels/powermodels_nn_training.jl b/examples/powermodels/powermodels_nn_training.jl
@@ -12,7 +12,7 @@ using MLJ
 using CUDA
 using DataFrames
 using PowerModels
-using L2O
+using LearningToOptimize
 using Random
 using JLD2
 using Wandb, Dates, Logging
diff --git a/examples/powermodels/visualize.jl b/examples/powermodels/visualize.jl
@@ -141,7 +141,7 @@ load_vector.norm_loads = [norm(Vector(load_vector[i, Not(:id)])) for i in 1:size
 joined_data = innerjoin(load_vector, output_data[!, [:id, :operational_cost]], on=:id)
 
 # get k extreme points
-using L2O
+using LearningToOptimize
 using Gurobi
 function maxk(a, k)
     b = partialsortperm(a, 1:k, rev=true)
diff --git a/examples/solve_dataset.jl b/examples/solve_dataset.jl
@@ -19,7 +19,7 @@ using Random
 
 ########## SCRIPT REQUIRED PACKAGES ##########
 
-@everywhere using L2O
+@everywhere using LearningToOptimize
 @everywhere using UUIDs
 @everywhere import ParametricOptInterface as POI
 @everywhere using JuMP
@@ -74,5 +74,5 @@ problem_iterator_factory, num_batches = load(model_file, input_file, filetype; b
     recorder = Recorder{filetype}(output_file; filterfn= (model) -> true, model=problem_iterator.model)
     successfull_solves = solve_batch(problem_iterator, recorder)
     @info "Solved $(length(successfull_solves)) problems"
-    L2O.compress_batch_arrow(save_path, case_name; keyword_all="output", batch_id=string(batch_id), keyword_any=[string(batch_id)])
+    LearningToOptimize.compress_batch_arrow(save_path, case_name; keyword_all="output", batch_id=string(batch_id), keyword_any=[string(batch_id)])
 end
diff --git a/examples/unitcommitment/Project.toml b/examples/unitcommitment/Project.toml
@@ -4,7 +4,7 @@ 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"
+LearningToOptimize = "e1d8bfa7-c465-446a-84b9-451470f6e76c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 ParametricOptInterface = "0ce4ce61-57bf-432b-a095-efac525d185e"
diff --git a/examples/unitcommitment/bnb_dataset.jl b/examples/unitcommitment/bnb_dataset.jl
@@ -4,7 +4,7 @@
 
 using LinearAlgebra
 using Gurobi
-using L2O
+using LearningToOptimize
 using JuMP
 using Logging
 using JuMP
diff --git a/examples/unitcommitment/uc_nn_training.jl b/examples/unitcommitment/uc_nn_training.jl
@@ -8,7 +8,7 @@
 
 import Pkg; Pkg.activate(dirname(dirname(@__DIR__))); Pkg.instantiate()
 
-using L2O
+using LearningToOptimize
 using MLJFlux
 using CUDA # if error run CUDA.set_runtime_version!(v"12.1.0")
 using Flux
diff --git a/examples/unitcommitment/unitcommitment.jl b/examples/unitcommitment/unitcommitment.jl
@@ -9,7 +9,7 @@
 # using MLJFlux
 using Flux
 # using MLJ
-# using L2O
+# using LearningToOptimize
 # using CUDA
 # using Wandb
 using DataFrames
diff --git a/src/LearningToOptimize.jl b/src/LearningToOptimize.jl
@@ -1,4 +1,4 @@
-module L2O
+module LearningToOptimize
 
 using Arrow
 using CSV
diff --git a/src/datasetgen.jl b/src/datasetgen.jl
@@ -182,7 +182,7 @@ function _dataframe_to_dict(df::DataFrame, model_file::AbstractString)
     # Load model
     model = read_from_file(model_file)
     # Retrieve parameters
-    parameters, _ = L2O.load_parameters(model)
+    parameters, _ = LearningToOptimize.load_parameters(model)
     return _dataframe_to_dict(df, parameters)
 end
 
diff --git a/src/samplers.jl b/src/samplers.jl
@@ -95,7 +95,7 @@ end
         original_parameters::Vector{T};
         samplers::Vector{Function}=[
             (original_parameters) -> scaled_distribution_sampler(original_parameters, 1000),
-            L2O.line_sampler, 
+            LearningToOptimize.line_sampler, 
             (original_parameters) -> box_sampler(original_parameters, 10),
         ]
     ) where {T<:Real}
@@ -106,7 +106,7 @@ function general_sampler(
     original_parameters::Vector{T};
     samplers::Vector{Function}=[
         (original_parameters) -> scaled_distribution_sampler(original_parameters, 1000),
-        L2O.line_sampler, 
+        LearningToOptimize.line_sampler, 
         (original_parameters) -> box_sampler(original_parameters, 10),
     ]
 ) where {T<:Real}
@@ -139,7 +139,7 @@ end
         file::AbstractString;
         samplers::Vector{Function}=[
             (original_parameters) -> scaled_distribution_sampler(original_parameters, 1000),
-            L2O.line_sampler, 
+            LearningToOptimize.line_sampler, 
             (original_parameters) -> box_sampler(original_parameters, 10),
         ],
         batch_id::UUID=uuid1(),
@@ -155,7 +155,7 @@ function general_sampler(
     file::AbstractString;
     samplers::Vector{Function}=[
         (original_parameters) -> scaled_distribution_sampler(original_parameters, 1000),
-        L2O.line_sampler, 
+        LearningToOptimize.line_sampler, 
         (original_parameters) -> box_sampler(original_parameters, 10),
     ],
     batch_id::UUID=uuid1(),
diff --git a/test/datasetgen.jl b/test/datasetgen.jl
@@ -115,7 +115,7 @@ function test_problem_iterator(path::AbstractString)
         @objective(model, Min, 2x)
         num_p = 10
         batch_id = string(uuid1())
-        problem_iterator = ProblemIterator(Dict(p => collect(1.0:num_p)); param_type=L2O.JuMPParameterType)
+        problem_iterator = ProblemIterator(Dict(p => collect(1.0:num_p)); param_type=LearningToOptimize.JuMPParameterType)
         file_output = joinpath(path, "test_$(batch_id)_output") # file path
         recorder = Recorder{ArrowFile}(
             file_output; primal_variables=[x], dual_variables=[cons]
@@ -141,7 +141,7 @@ function test_problem_iterator(path::AbstractString)
         @objective(model, Min, 2x)
         num_p = 10
         batch_id = string(uuid1())
-        problem_iterator = ProblemIterator(Dict(p => collect(1.0:num_p)); param_type=L2O.JuMPNLPParameterType)
+        problem_iterator = ProblemIterator(Dict(p => collect(1.0:num_p)); param_type=LearningToOptimize.JuMPNLPParameterType)
         file_output = joinpath(path, "test_$(batch_id)_output") # file path
         recorder = Recorder{ArrowFile}(
             file_output; primal_variables=[x], dual_variables=[cons]
@@ -163,22 +163,22 @@ end
 
 function test_load(model_file::AbstractString, input_file::AbstractString, ::Type{T}, ids::Vector{UUID};
     batch_size::Integer=32
-) where {T<:L2O.FileType}
+) where {T<:LearningToOptimize.FileType}
     # Test Load full set 
-    problem_iterator = L2O.load(model_file, input_file, T)
-    @test problem_iterator isa L2O.AbstractProblemIterator
+    problem_iterator = LearningToOptimize.load(model_file, input_file, T)
+    @test problem_iterator isa LearningToOptimize.AbstractProblemIterator
     @test length(problem_iterator.ids) == length(ids)
     # Test load only half of the ids
     num_ids_ignored = floor(Int, length(ids) / 2)
-    problem_iterator =  L2O.load(model_file, input_file, T; ignore_ids=ids[1:num_ids_ignored])
+    problem_iterator =  LearningToOptimize.load(model_file, input_file, T; ignore_ids=ids[1:num_ids_ignored])
     @test length(problem_iterator.ids) == length(ids) - num_ids_ignored
     # Test warning all ids to be ignored
-    problem_iterator =  L2O.load(model_file, input_file, T; ignore_ids=ids)
+    problem_iterator =  LearningToOptimize.load(model_file, input_file, T; ignore_ids=ids)
     @test isnothing(problem_iterator)
     # Test Load batch of problem iterators
-    problem_iterator_factory, num_batches =  L2O.load(model_file, input_file, T; batch_size=batch_size)
+    problem_iterator_factory, num_batches =  LearningToOptimize.load(model_file, input_file, T; batch_size=batch_size)
     @test num_batches == ceil(Int, length(ids) / batch_size)
-    @test problem_iterator_factory(1) isa L2O.AbstractProblemIterator
+    @test problem_iterator_factory(1) isa LearningToOptimize.AbstractProblemIterator
     return nothing
 end
 
@@ -192,7 +192,7 @@ function test_compress_batch_arrow(case_file_path::AbstractString=mktempdir(), c
     end
     @test length([file for file in readdir(case_file_path; join=true) if occursin(case_name, file) && occursin("arrow", file) && occursin(keyword, file) && any(x -> occursin(x, file), batch_ids)]) == 10
     batch_id = string(uuid1())
-    L2O.compress_batch_arrow(case_file_path, case_name; keyword_all=keyword, batch_id=batch_id, keyword_any=batch_ids)
+    LearningToOptimize.compress_batch_arrow(case_file_path, case_name; keyword_all=keyword, batch_id=batch_id, keyword_any=batch_ids)
     @test length([file for file in readdir(case_file_path; join=true) if occursin(case_name, file) && occursin("arrow", file) && occursin(keyword, file) && any(x -> occursin(x, file), batch_ids)]) == 0
     @test length([file for file in readdir(case_file_path; join=true) if occursin(case_name, file) && occursin("arrow", file) && occursin(keyword, file) && occursin(batch_id, file)]) == 1
     return nothing
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -3,7 +3,7 @@ using DelimitedFiles
 using Flux
 using HiGHS
 using JuMP
-using L2O
+using LearningToOptimize
 import ParametricOptInterface as POI
 using Test
 using UUIDs
@@ -30,7 +30,7 @@ include(joinpath(test_dir, "inconvexhull.jl"))
 
 include(joinpath(test_dir, "samplers.jl"))
 
-@testset "L2O.jl" begin
+@testset "LearningToOptimize.jl" begin
     test_load_parameters_model()
     test_load_parameters()
     test_line_sampler()
diff --git a/test/samplers.jl b/test/samplers.jl
diff --git a/test/test_flux_forecaster.jl b/test/test_flux_forecaster.jl

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-using L2O`
	`1`	`+using LearningToOptimize`
`2`	`2`
`3`	`3`	`general_sampler(`
`4`	`4`	`"examples/powermodels/data/6468_rte/6468_rte_SOCWRConicPowerModel_POI_load.mof.json";`