time improvement, julia script

Author: rostam

Algorithms/StarBicoloringVertexCoverNonReq.h

@@ -6,7 +6,6 @@
 #define PRECOL_STAR_BICOLORING_VERTEX_COVER_NON_REQ_M_H
 
 #include "ColoringAlgorithms.h"
-#include "isets.h"
 
 /**
  * \brief Compute a star bicoloring of a bipartite graph GraphInstance (this version
@@ -615,7 +614,7 @@ class StarBicoloringVertexCoverNonReq : public ColoringAlgorithms{
 
             IS.push_back((*di).first);
         }
-        //Color vertices in independent set with color 0
+        //Color vertices in an independent set with color 0
         for (vector<int>::iterator IS_it = IS.begin();
              IS_it != IS.end();
              ++IS_it) {

mybuild/run_colpack.py

@@ -15,8 +15,10 @@
 
 
 method_map = {
-    "D2RestrictedColumns": "DISTANCE_TWO",
-    "D2Columns": "DISTANCE_TWO"
+    "D2RestrictedColumns": "COLUMN_PARTIAL_DISTANCE_TWO",
+    "D2RestrictedRows": "ROW_PARTIAL_DISTANCE_TWO",
+    "D2Columns": "COLUMN_PARTIAL_DISTANCE_TWO",
+    "D2Rows": "ROW_PARTIAL_DISTANCE_TWO",
 }
 
 # Function to extract metrics from ColPack output

mybuild/test_time.jl

@@ -0,0 +1,64 @@
+using SparseMatrixColorings
+using MatrixMarket
+using SparseArrays
+using CSV
+using DataFrames
+
+function color_mtx_file(filepath::String)
+    println("\n==============================")
+    println("Loading matrix from: $filepath")
+    A = mmread(filepath)
+
+    println("Matrix size: ", size(A), ", nnz = ", nnz(A))
+
+    problem = ColoringProblem(; structure = :nonsymmetric, partition = :column)
+    algo    = GreedyColoringAlgorithm(; decompression = :direct)
+
+    # Measure time
+    t = @elapsed result = coloring(A, problem, algo)
+
+    ncolors = maximum(column_colors(result))
+
+    # Report results
+    println("Coloring took $(round(t, digits=4)) seconds")
+    println("Number of colors used: $ncolors")
+
+    return (rows=size(A,1), cols=size(A,2), nnz=nnz(A), time=t, ncolors=ncolors)
+end
+
+# Run from CSV and save results
+function run_from_csv(input_csv::String, output_csv::String)
+    df = CSV.read(input_csv, DataFrame)
+
+    results = DataFrame(
+        Matrix=String[],
+        Rows=Int[],
+        Cols=Int[],
+        NNZ=Int[],
+        TimeJulia=Float64[],
+        NumberOfColorsJulia=Int[]
+    )
+
+    for row in eachrow(df)
+        filepath = row.Matrix
+        try
+            stats = color_mtx_file(filepath)
+            push!(results, (
+                filepath,
+                stats.rows,
+                stats.cols,
+                stats.nnz,
+                stats.time,
+                stats.ncolors
+            ))
+        catch e
+            @warn "Failed to process $filepath" exception=(e, catch_backtrace())
+        end
+    end
+
+    CSV.write(output_csv, results)
+    println("\n✅ Results written to $output_csv")
+end
+
+# Example usage
+run_from_csv("input.csv", "Output/output_julia.csv")