Merge pull request #36 from TensorBFS/solution-files

Support reading solution files for the MAP, MMAP and PR tasks

Author: mroavi

src/Core.jl

@@ -24,7 +24,7 @@ $(TYPEDEF)
 
 * `obsvars` is a vector of observed variables,
 * `obsvals` is a vector of observed values,
-* `reference_marginals` is a vector of marginal probabilities.
+* `reference_solution` is a vector with the reference solution.
 """
 struct UAIInstance{ET, FT <: Factor{ET}}
     nvars::Int
@@ -34,7 +34,7 @@ struct UAIInstance{ET, FT <: Factor{ET}}
 
     obsvars::Vector{Int}
     obsvals::Vector{Int}
-    reference_marginals::Vector{Vector{ET}}
+    reference_solution::Union{Vector{Vector{ET}}, Vector{Int}}
 end
 
 """

src/utils.jl

@@ -72,29 +72,41 @@ The UAI file formats are defined in:
 https://personal.utdallas.edu/~vibhav.gogate/uai16-evaluation/uaiformat.html
 """
 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(evidence_filepath) do file
-            readlines(file)
-        end |> last
-
-        # Extract number of observed vars, and their id together with their corresponding value
-        nobsvars, rest = split(line) |> x -> parse.(Int, x) |> x -> (x[1], x[2:end])
-        observations = reshape(rest, 2, :)
-
-        # Convert to 1-based indexing
-        obsvars = observations[1, :] .+ 1
-        obsvals = observations[2, :]
-
-        @assert nobsvars == length(obsvars)
-    end
+
+    isempty(evidence_filepath) && return Int64[], Int64[] # no evidence
+
+    # Read the last line of the uai evid file
+    line = open(evidence_filepath) do file
+        readlines(file)
+    end |> last
+
+    # Extract number of observed vars, and their id together with their corresponding value
+    nobsvars, rest = split(line) |> x -> parse.(Int, x) |> x -> (x[1], x[2:end])
+    observations = reshape(rest, 2, :)
+
+    # Convert to 1-based indexing
+    obsvars = observations[1, :] .+ 1
+    obsvals = observations[2, :]
+
+    @assert nobsvars == length(obsvars)
 
     return obsvars, obsvals
 end
 
+function read_solution_file(solution_filepath::AbstractString; factor_eltype = Float64)
+    result = Vector{factor_eltype}[]
+    extension = splitext(solution_filepath)[2]
+    if extension == ".MAR"
+        return read_mar_solution_file(solution_filepath; factor_eltype)
+    elseif extension == ".MAP" || extension == ".MMAP" || extension == ".PR"
+      # Return the last line of the file as a vector of integers
+      result = open(solution_filepath) do file
+          readlines(file)
+      end |> last |> split |> x -> parse.(Int, x)
+    end
+    return result
+end
+
 """
 $(TYPEDSIGNATURES)
 
@@ -104,7 +116,7 @@ as in the model
 The UAI file formats are defined in:
 https://personal.utdallas.edu/~vibhav.gogate/uai16-evaluation/uaiformat.html
 """
-function read_solution_file(solution_filepath::AbstractString; factor_eltype = Float64)
+function read_mar_solution_file(solution_filepath::AbstractString; factor_eltype = Float64)
 
     # Read the uai mar file into an array of lines
     rawlines = open(solution_filepath) do file
@@ -185,8 +197,8 @@ function read_instance(
 )::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)
+    reference_solution = isempty(solution_filepath) ? Vector{eltype}[] : read_solution_file(solution_filepath)
+    return UAIInstance(nvars, ncliques, cards, factors, obsvars, obsvals, reference_solution)
 end
 
 function read_instance_from_string(uai::AbstractString; eltype = Float64)::UAIInstance

test/cuda.jl

@@ -15,7 +15,7 @@ CUDA.allowscalar(false)
     # for dangling vertices, the output size is 1.
     npass = 0
     for i in 1:(instance.nvars)
-        npass += (length(marginals2[i]) == 1 && instance.reference_marginals[i] == [0.0, 1]) || isapprox(Array(marginals2[i]), instance.reference_marginals[i]; atol = 1e-6)
+        npass += (length(marginals2[i]) == 1 && instance.reference_solution[i] == [0.0, 1]) || isapprox(Array(marginals2[i]), instance.reference_solution[i]; atol = 1e-6)
     end
     @test npass == instance.nvars
 end

test/mar.jl

@@ -14,7 +14,7 @@ end
 @testset "cached, rescaled contract" begin
     model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths("Promedus_14", "MAR")
     instance = read_instance(model_filepath; evidence_filepath, solution_filepath)
-    ref_sol = instance.reference_marginals
+    ref_sol = instance.reference_solution
     optimizer = TreeSA(ntrials = 1, niters = 5, βs = 0.1:0.1:100)
     tn = TensorNetworkModel(instance; optimizer)
     p1 = probability(tn; usecuda = false, rescale = false)
@@ -68,7 +68,7 @@ end
                 @testset "$(problem_name)" begin
                     model_filepath, evidence_filepath, solution_filepath = get_instance_filepaths(problem_name, "MAR")
                     instance = read_instance(model_filepath; evidence_filepath, solution_filepath)
-                    ref_sol = instance.reference_marginals
+                    ref_sol = instance.reference_solution
                     obsvars = instance.obsvars
 
                     # does not optimize over open vertices