run

run.py

@@ -3,16 +3,17 @@ import pandas as pd
 from time import time as t
 
 
-def run(show_plots=False):
+def run(show_plots=False, verbose=False):
     # names = [name_ for name_ in os.listdir("./problems") if "tsp" in name_]
     names = ["eil76.tsp", "kroA100.tsp"]
     methods = Solver_TSP.available_methods.keys()
     results = []
     index = []
     for name in names:
-        print("\n\n#############################")
         filename = f"problems/{name}"
         instance = Instance(filename)
+        if verbose:
+            print("\n\n#############################")
             instance.print_info()
         if show_plots:
             instance.plot_data()
@@ -20,12 +21,15 @@ def run(show_plots=False):
         for method in methods:
             solver = Solver_TSP(method)
             start = t()
-            solver(instance, return_value=False)
+            solver(instance, return_value=False, verbose=verbose)
             end = t()
 
+            if verbose:
                 print(f"the total length for the solution found is {solver.found_length}",
                       f"while the optimal length is {instance.best_sol}",
-                  f"the gap is {solver.gap} %", sep="\n")
+                      f"the gap is {solver.gap}%",
+                      f"the solution is found in {np.round(end - start, 5)} seconds", sep="\n")
+
             index.append((name, method))
             results.append([solver.found_length, instance.best_sol, solver.gap, end - start])
 
@@ -43,4 +47,4 @@ def run(show_plots=False):
 
 
 if __name__ == '__main__':
-    run(show_plots=True).to_csv("./results.csv")
+    run(show_plots=True, verbose=True).to_csv("./results.csv")

src/TSP_solver.py

@@ -8,7 +8,6 @@ else:
     from AI2019.src.utils import *
 
 
-
 class Solver_TSP:
 
     solution: ndarray