Merge pull request #33 from TensorBFS/remove-artifact

Move Artifacts.toml from main project to test project

Author: mroavi

Artifacts.toml

@@ -4,7 +4,6 @@ authors = ["Jin-Guo Liu", "Martin Roa Villescas"]
 version = "0.1.0"
 
 [deps]
-Artifacts = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -16,7 +15,6 @@ TropicalGEMM = "a4ad3063-64a7-4bad-8738-34ed09bc0236"
 TropicalNumbers = "b3a74e9c-7526-4576-a4eb-79c0d4c32334"
 
 [compat]
-Artifacts = "1"
 CUDA = "4"
 DocStringExtensions = "0.8.6, 0.9"
 OMEinsum = "0.7"

Project.toml

@@ -6,7 +6,8 @@ using Artifacts
 
 const SUITE = BenchmarkGroup()
 
-problem = read_uai_problem("Promedus_14")
+model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
+problem = read_instance(model_filepath; evidence_filepath, solution_filepath)
 
 optimizer = TreeSA(ntrials = 1, niters = 2, βs = 1:0.1:40)
 tn = TensorNetworkModel(problem; optimizer)

benchmark/bench_map.jl

@@ -8,7 +8,8 @@ using Artifacts
 
 const SUITE = BenchmarkGroup()
 
-problem = read_uai_problem("Promedus_14")
+model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
+problem = read_instance(model_filepath; evidence_filepath, solution_filepath)
 
 optimizer = TreeSA(ntrials = 1, niters = 5, βs = 0.1:0.1:100)
 tn1 = TensorNetworkModel(problem; optimizer)

benchmark/bench_mar.jl

@@ -6,7 +6,8 @@ using Artifacts
 
 const SUITE = BenchmarkGroup()
 
-problem = read_uai_problem("Promedus_14")
+model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
+problem = read_instance(model_filepath; evidence_filepath, solution_filepath)
 optimizer = TreeSA(ntrials = 1, niters = 2, βs = 1:0.1:40)
 
 # Does not marginalize any var

benchmark/bench_mmap.jl

@@ -1,7 +1,7 @@
 using TensorInference
 
 # Load the model that detailed in the README and `asia.uai`.
-instance = uai_problem_from_file(joinpath(@__DIR__, "asia.uai"))
+instance = read_instance(joinpath(@__DIR__, "asia.uai"))
 tnet = TensorNetworkModel(instance)
 
 # Get the probabilities (PR)
@@ -36,4 +36,4 @@ mmap = MMAPModel(instance; marginalized=[1,2,3,5,6,8])
 most_probable_config(mmap)
 # The total probability of having lung cancer is roughly half.
 log_probability(mmap, [1, 0])
-log_probability(mmap, [0, 0])
+log_probability(mmap, [0, 0])

example/asia/asia.jl

@@ -41,15 +41,6 @@ end
 $TYPEDSIGNATURES
 
 Set the evidence of an UAI instance.
-
-### Examples
-```jldoctest; setup=:(using TensorInference)
-julia> problem = read_uai_problem("Promedus_14"); problem.obsvars, problem.obsvals
-([42, 48, 27, 30, 29, 15, 124, 5, 148], [1, 1, 1, 1, 1, 1, 1, 1, 1])
-
-julia> set_evidence!(problem, 2=>0, 4=>1); problem.obsvars, problem.obsvals
-([2, 4], [0, 1])
-```
 """
 function set_evidence!(uai::UAIInstance, pairs::Pair{Int}...)
     empty!(uai.obsvars)

src/Core.jl

@@ -2,7 +2,6 @@ module TensorInference
 
 using OMEinsum, LinearAlgebra
 using DocStringExtensions, TropicalNumbers
-using Artifacts
 # The Tropical GEMM support
 using TropicalGEMM
 using StatsBase
@@ -12,7 +11,7 @@ export RescaledArray
 export contraction_complexity, TreeSA, GreedyMethod, KaHyParBipartite, SABipartite, MergeGreedy, MergeVectors
 
 # read and load uai files
-export read_uai_file, read_td_file, read_uai_evid_file, read_uai_mar_file, read_uai_problem, uai_problem_from_file
+export read_model_file, read_td_file, read_evidence_file, read_solution_file, read_instance
 export set_evidence!
 
 # marginals

src/TensorInference.jl

@@ -1,21 +1,21 @@
 """
 $(TYPEDSIGNATURES)
 
-Parse the problem instance found in `uai_filepath` defined in the UAI model
+Parse the problem instance found in `model_filepath` defined in the UAI model
 format.
 
 The UAI file formats are defined in:
 https://personal.utdallas.edu/~vibhav.gogate/uai16-evaluation/uaiformat.html
 """
-function read_uai_file(uai_filepath; factor_eltype = Float64)
+function read_model_file(model_filepath; factor_eltype = Float64)
     # Read the uai file into an array of lines
-    str = open(uai_filepath) do file
+    str = open(model_filepath) do file
         read(file, String)
     end
-    return read_uai_string(str; factor_eltype)
+    return read_model_string(str; factor_eltype)
 end
 
-function read_uai_string(str; factor_eltype = Float64)
+function read_model_string(str; factor_eltype = Float64)
     rawlines = split(str, "\n")
     # Filter out empty lines
     lines = filter(!isempty, rawlines)
@@ -65,19 +65,19 @@ end
 """
 $(TYPEDSIGNATURES)
 
-Return the observed variables and values in `uai_evid_filepath`. If the passed
+Return the observed variables and values in `evidence_filepath`. If the passed
 file path is an empty string, return empty vectors.
 
 The UAI file formats are defined in:
 https://personal.utdallas.edu/~vibhav.gogate/uai16-evaluation/uaiformat.html
 """
-function read_uai_evid_file(uai_evid_filepath::AbstractString)
-    if isempty(uai_evid_filepath)
+function read_evidence_file(evidence_filepath::AbstractString)
+    if isempty(evidence_filepath)
         # No evidence
         return Int64[], Int64[]
     else
         # Read the last line of the uai evid file
-        line = open(uai_evid_filepath) do file
+        line = open(evidence_filepath) do file
             readlines(file)
         end |> last
 
@@ -104,10 +104,10 @@ as in the model
 The UAI file formats are defined in:
 https://personal.utdallas.edu/~vibhav.gogate/uai16-evaluation/uaiformat.html
 """
-function read_uai_mar_file(uai_mar_filepath::AbstractString; factor_eltype = Float64)
+function read_solution_file(solution_filepath::AbstractString; factor_eltype = Float64)
 
     # Read the uai mar file into an array of lines
-    rawlines = open(uai_mar_filepath) do file
+    rawlines = open(solution_filepath) do file
         readlines(file)
     end
 
@@ -175,31 +175,24 @@ end
 """
 $(TYPEDSIGNATURES)
 
-Read a UAI problem from an artifact.
+Read a UAI problem instance from a file.
 """
-function read_uai_problem(problem::AbstractString; eltype=Float64)::UAIInstance
-    uai_filepath = joinpath(artifact"MAR_prob", problem * ".uai")
-    uai_evid_filepath = joinpath(artifact"MAR_prob", problem * ".uai.evid")
-    uai_mar_filepath = joinpath(artifact"MAR_sol", problem * ".uai.MAR")
-    return uai_problem_from_file(uai_filepath; uai_evid_filepath, uai_mar_filepath, eltype)
-end
-
-"""
-$(TYPEDSIGNATURES)
-
-Read a UAI problem from a file.
-"""
-function uai_problem_from_file(uai_filepath::String; uai_evid_filepath="", uai_mar_filepath="", eltype=Float64)::UAIInstance
-    nvars, cards, ncliques, factors = read_uai_file(uai_filepath; factor_eltype = eltype)
-    obsvars, obsvals = read_uai_evid_file(uai_evid_filepath)
-    reference_marginals = isempty(uai_mar_filepath) ? Vector{eltype}[] : read_uai_mar_file(uai_mar_filepath)
+function read_instance(
+    model_filepath::AbstractString;
+    evidence_filepath::AbstractString = "",
+    solution_filepath::AbstractString = "",
+    eltype = Float64
+)::UAIInstance
+    nvars, cards, ncliques, factors = read_model_file(model_filepath; factor_eltype = eltype)
+    obsvars, obsvals = read_evidence_file(evidence_filepath)
+    reference_marginals = isempty(solution_filepath) ? Vector{eltype}[] : read_solution_file(solution_filepath)
     return UAIInstance(nvars, ncliques, cards, factors, obsvars, obsvals, reference_marginals)
 end
 
-function uai_problem_from_string(uai::String; eltype=Float64)::UAIInstance
-    nvars, cards, ncliques, factors = read_uai_string(uai; factor_eltype = eltype)
+function read_instance_from_string(uai::AbstractString; eltype = Float64)::UAIInstance
+    nvars, cards, ncliques, factors = read_model_string(uai; factor_eltype = eltype)
     return UAIInstance(nvars, ncliques, cards, factors, Int[], Int[], Vector{eltype}[])
 end
 
 # patch to get content by broadcasting into array, while keep array size unchanged.
-broadcasted_content(x) = asarray(content.(x), x)
+broadcasted_content(x) = asarray(content.(x), x)

src/utils.jl

@@ -1,34 +1,6 @@
-[MAR_prob]
-git-tree-sha1 = "4dd2447ab2b8eeb8fbfc35d0c7416b5f01ff9711"
+[uai2014]
+git-tree-sha1 = "5d2c17ee6ad8e89f046b370e865780dea89e4268"
 
-    [[MAR_prob.download]]
-    sha256 = "857f8b6706dad46698c6e90a1804e1b40633d87f6eb3d82b5f29929bb6f5155a"
-    url = "https://personal.utdallas.edu/~vibhav.gogate/uai14-competition/files/MAR_prob.tar.gz"
-
-[MAR_sol]
-git-tree-sha1 = "8a86826145efa6b7d7d9c837acd80e33d1368f37"
-
-    [[MAR_sol.download]]
-    sha256 = "d8616ebfc1ab538c2957919f8bb47bd9c1fb86392dd025585cde041bc4e0e934"
-    url = "https://personal.utdallas.edu/~vibhav.gogate/uai14-competition/files/MAR_sol.tar.gz"
-
-[MMAP_UAI2014]
-git-tree-sha1 = "869778bbf5214cbff2169ecc2f3e67e2cb1b6a7a"
-
-    [[MMAP_UAI2014.download]]
-    sha256 = "0080c4851e23c17a42262cdaf4cb1a7aa4b80674435756d0d1ade76d80b9f33f"
-    url = "https://personal.utdallas.edu/~vibhav.gogate/uai14-competition/files/MMAP_UAI2014.tar.gz"
-
-[PR_prob]
-git-tree-sha1 = "a9baff14cc5f64a5d95920928581e2b4eb82acd3"
-
-    [[PR_prob.download]]
-    sha256 = "20b38d43a0718ab555988f041069380a30bbb68881e60d07ebf004054eb9f14f"
-    url = "https://personal.utdallas.edu/~vibhav.gogate/uai14-competition/files/PR_prob.tar.gz"
-
-[PR_sol]
-git-tree-sha1 = "e6643155c4ee433d20ddb7e58441b33b35ecacb8"
-
-    [[PR_sol.download]]
-    sha256 = "86b37c0e7c83f6ff162c88d48250e0bf2b7dd73d44441074ae93a81986238436"
-    url = "https://personal.utdallas.edu/~vibhav.gogate/uai14-competition/files/PR_sol.tar.gz"
+    [[uai2014.download]]
+    sha256 = "22dff6d2b27f5e78b315e4c756c24a0fb9e476c8e814a2546d7f61822426b08a"
+    url = "https://github.com/mroavi/uai-2014-inference-competition/raw/main/uai2014.tar.gz"