move back to argparse but keep semantics

Project.toml

@@ -4,11 +4,11 @@ authors = ["Olivier Labayle"]
 version = "0.7.4"
 
 [deps]
+ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
 Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
-Comonicon = "863f3e99-da2a-4334-8734-de3dacbe5542"
 Configurations = "5218b696-f38b-4ac9-8b61-a12ec717816d"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
@@ -33,11 +33,11 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 
 [compat]
+ArgParse = "1.1.4"
 Arrow = "2.5.2"
 CSV = "0.10"
 CategoricalArrays = "0.10"
 Combinatorics = "1.0.2"
-Comonicon = "1.0.6"
 Configurations = "0.17.6"
 DataFrames = "1.3.4"
 EvoTrees = "0.16.5"

src/TargetedEstimation.jl

@@ -4,6 +4,7 @@ if occursin("Intel", Sys.cpu_info()[1].model)
     using MKL
 end
 
+using ArgParse
 using DataFrames
 using MLJBase
 using MLJ
@@ -25,7 +26,6 @@ using Tables
 using Random
 using YAML
 using JSON
-using Comonicon
 using Configurations
 
 import MLJModelInterface
@@ -42,10 +42,9 @@ include("resampling.jl")
 include(joinpath("models", "glmnet.jl"))
 include(joinpath("models", "adaptive_interaction_transformer.jl"))
 include(joinpath("models", "biallelic_snp_encoder.jl"))
+include("cli.jl")
 
-@main
-
-export Runner, tmle, sieve_variance_plateau, make_summary
+export Runner, tmle, sieve_variance_plateau, make_summary, main
 export GLMNetRegressor, GLMNetClassifier
 export RestrictedInteractionTransformer, BiAllelicSNPEncoder
 export AdaptiveCV, AdaptiveStratifiedCV, JointStratifiedCV

src/cli.jl

@@ -0,0 +1,180 @@
+function cli_settings()
+    s = ArgParseSettings(description="TMLE CLI.")
+
+    @add_arg_table s begin
+        "tmle"
+            action = :command
+            help = "Run TMLE."
+
+        "svp"
+            action = :command
+            help = "Run Sieve Variance Plateau."
+
+        "merge"
+            action = :command
+            help = "Merges TMLE outputs together."
+    end
+
+    @add_arg_table s["tmle"] begin
+        "dataset"
+            arg_type = String
+            required = true
+            help = "Path to the dataset (either .csv or .arrow)"
+
+        "--estimands"
+            arg_type = String
+            help = "A string (`generateATEs`) or a serialized TMLE.Configuration (accepted formats: .json | .yaml | .jls)"
+            default = "generateATEs"
+
+        "--estimators"
+            arg_type = String
+            help = "A julia file containing the estimators to use."
+            default = "glmnet"
+
+        "--verbosity"
+            arg_type = Int
+            default = 0
+            help = "Verbosity level"
+
+        "--hdf5-output"
+            arg_type = String
+            help = "HDF5 file output."
+
+        "--json-output"
+            arg_type = String
+            help = "JSON file output."
+
+        "--jls-output"
+            arg_type = String
+            help = "JLS file output."
+
+        "--chunksize"
+            arg_type = Int
+            help = "Results are written in batches of size chunksize."
+            default = 100
+
+        "--rng"
+            arg_type = Int
+            help = "Random seed (Only used for estimands ordering at the moment)."
+            default = 123
+
+        "--cache-strategy"
+            arg_type = String
+            help = "Caching Strategy for the nuisance functions, any of (`release-unusable`, `no-cache`, `max-size`)."
+            default = "release-unusable"
+
+        "--sort-estimands"
+            help = "Sort estimands to minimize cache usage (A brute force approach will be used, resulting in exponentially long sorting time)."
+            action = :store_true
+    end
+
+    @add_arg_table s["svp"] begin
+        "input-prefix"
+            arg_type = String
+            help = "Input prefix to HDF5 files generated by the tmle CLI."
+
+        "--out"
+            arg_type = String
+            help = "Output filename."
+            default = "svp.hdf5"
+
+        "--grm-prefix"
+            arg_type = String
+            help = "Prefix to the aggregated GRM."
+            default = "GRM"
+
+        "--verbosity"
+            arg_type = Int
+            default = 0
+            help = "Verbosity level"
+
+        "--n-estimators"
+            arg_type = Int
+            default = 10
+            help = "Number of variance estimators to build for each estimate."
+
+        "--max-tau"
+            arg_type = Float64
+            default = 0.8
+            help = "Maximum distance between any two individuals."
+
+        "--estimator-key"
+            arg_type = String
+            help = "Estimator to use to proceed with sieve variance correction."
+            default = "TMLE"
+    end
+
+    @add_arg_table s["merge"] begin
+        "prefix"
+            arg_type = String
+            help = "Prefix to .hdf5 files to be used to create the summary file."
+
+        "--hdf5-output"
+            arg_type = String
+            help = "HDF5 file output."
+
+        "--json-output"
+            arg_type = String
+            help = "JSON file output."
+
+        "--jls-output"
+            arg_type = String
+            help = "JLS file output."
+    end
+
+    return s
+end
+
+
+makeOutput(T::Type, ::Nothing) = T()
+
+function makeOutput(T::Type, str)
+    args = split(str, ",")
+    kwargs = Dict(fn => tryparse(ft, val) for (val, fn, ft) ∈ zip(args, fieldnames(T), fieldtypes(T)))
+    return T(;kwargs...)
+end
+
+make_outputs(hdf5_string, json_string, jls_tring) = Outputs(
+    hdf5=makeOutput(HDF5Output, hdf5_string),
+    json=makeOutput(JSONOutput, json_string),
+    jls=makeOutput(JLSOutput, jls_tring)
+)
+
+function main(args=ARGS)
+    settings = parse_args(args, cli_settings())
+    cmd = settings["%COMMAND%"]
+    cmd_settings = settings[cmd]
+    if cmd ∈ ("tmle", "merge")
+        outputs = make_outputs(cmd_settings["hdf5-output"], cmd_settings["json-output"], cmd_settings["jls-output"])
+        if cmd == "tmle"
+            tmle(cmd_settings["dataset"];
+                estimands=cmd_settings["estimands"], 
+                estimators=cmd_settings["estimators"],
+                verbosity=cmd_settings["verbosity"], 
+                outputs=outputs,
+                chunksize=cmd_settings["chunksize"],
+                rng=cmd_settings["rng"],
+                cache_strategy=cmd_settings["cache-strategy"],
+                sort_estimands=cmd_settings["sort-estimands"]
+            )
+        else
+            make_summary(cmd_settings["prefix"];
+                outputs=outputs
+            )
+        end
+    else
+        sieve_variance_plateau(cmd_settings["input-prefix"];
+            out=cmd_settings["out"],
+            grm_prefix=cmd_settings["grm-prefix"],
+            verbosity=cmd_settings["verbosity"], 
+            n_estimators=cmd_settings["n-estimators"], 
+            max_tau=cmd_settings["max-tau"],
+            estimator_key=cmd_settings["estimator-key"]
+        )
+    end
+end
+
+function julia_main()::Cint
+    main()
+    return 0
+end

src/runner.jl

@@ -150,7 +150,7 @@ TMLE CLI.
 
 - `-s, --sort_estimands`: Sort estimands to minimize cache usage (A brute force approach will be used, resulting in exponentially long sorting time).
 """
-@cast function tmle(dataset::String;
+function tmle(dataset::String;
     estimands::String="generateATEs", 
     estimators::String="glmnet",
     verbosity::Int=0, 

src/sieve_variance.jl

@@ -226,7 +226,7 @@ Sieve Variance Plateau CLI.
 - `-m, --max_tau`: Maximum distance between any two individuals.
 - `-e, --estimator-key`: Estimator to use to proceed with sieve variance correction.
 """
-@cast function sieve_variance_plateau(input_prefix::String;
+function sieve_variance_plateau(input_prefix::String;
     out::String="svp.hdf5",
     grm_prefix::String="GRM",
     verbosity::Int=0, 

src/summary.jl

@@ -28,7 +28,7 @@ Combines multiple TMLE .hdf5 output files in a single file. Multiple formats can
 
 - `-o, --outputs`: Ouptuts configuration.
 """
-@cast function make_summary(
+function make_summary(
     prefix::String;
     outputs::Outputs=Outputs()
     )

test/runner.jl

@@ -154,22 +154,24 @@ end
 
 @testset "Test tmle: lower p-value threshold only JSON output" begin
     build_dataset(;n=1000, format="csv")
-    outputs = TargetedEstimation.Outputs(
-        json=TargetedEstimation.JSONOutput(filename="output.json", pval_threshold=1e-15)
-    )
     tmpdir = mktempdir(cleanup=true)
     estimandsfile = joinpath(tmpdir, "configuration.json")
     configuration = statistical_estimands_only_config()
     TMLE.write_json(estimandsfile, configuration)
     estimatorfile = joinpath(CONFIGDIR, "ose_config.jl")
     datafile = "data.csv"
-    tmle(datafile; 
-        estimands=estimandsfile, 
-        estimators=estimatorfile,
-        outputs=outputs)
+
+    # Using the main entry point
+    main([
+        "tmle", 
+        datafile, 
+        "--estimands", estimandsfile, 
+        "--estimators", estimatorfile,
+        "--json-output", "output.json,1e-15"]
+    )
 
     # Essential results
-    results_from_json = TMLE.read_json(outputs.json.filename)
+    results_from_json = TMLE.read_json("output.json")
     n_IC_empties = 0
     for result in results_from_json
         if result[:OSE].IC != []
@@ -179,7 +181,7 @@ end
     @test n_IC_empties > 0
 
     rm(datafile)
-    rm(outputs.json.filename)
+    rm("output.json")
 end
 
 @testset "Test tmle: Failing estimands" begin
@@ -238,11 +240,6 @@ end
 
 @testset "Test tmle: Causal and Composed Estimands" begin
     build_dataset(;n=1000, format="csv")
-    outputs = TargetedEstimation.Outputs(
-        json = TargetedEstimation.JSONOutput(filename="output.json"),
-        jls = TargetedEstimation.JLSOutput(filename="output.jls"),
-        hdf5 = TargetedEstimation.HDF5Output(filename="output.hdf5")
-    )
     tmpdir = mktempdir(cleanup=true)
     estimandsfile = joinpath(tmpdir, "configuration.jls")
 
@@ -251,16 +248,21 @@ end
     estimatorfile = joinpath(CONFIGDIR, "ose_config.jl")
     datafile = "data.csv"
 
-    tmle(datafile;
-        estimands=estimandsfile, 
-        estimators=estimatorfile,
-        outputs=outputs, 
-        chunksize=2
-    )
+    # Using the main entry point
+    main([
+        "tmle", 
+        datafile, 
+        "--estimands", estimandsfile, 
+        "--estimators", estimatorfile,
+        "--chunksize", "2",
+        "--json-output", "output.json",
+        "--hdf5-output", "output.hdf5",
+        "--jls-output", "output.jls"
+    ])
 
     # JLS Output
     results = []
-    open(outputs.jls.filename) do io
+    open("output.jls") do io
         while !eof(io)
             push!(results, deserialize(io))
         end
@@ -279,19 +281,19 @@ end
     @test results[3].OSE isa TMLE.ComposedEstimate
 
     # JSON Output
-    results_from_json = TMLE.read_json(outputs.json.filename)
+    results_from_json = TMLE.read_json("output.json")
     @test length(results_from_json) == 3
 
     # HDF5
-    results_from_json = jldopen(outputs.hdf5.filename)
+    results_from_json = jldopen("output.hdf5")
     @test length(results_from_json["Batch_1"]) == 2
     composed_result = only(results_from_json["Batch_2"])
     @test composed_result.OSE.cov == results[3].OSE.cov
 
     rm(datafile)
-    rm(outputs.jls.filename)
-    rm(outputs.json.filename)
-    rm(outputs.hdf5.filename)
+    rm("output.jls")
+    rm("output.json")
+    rm("output.hdf5")
 end