Attempt to handle Issue #53 (#54)

* Setup test that should be satisfied when bug is solved

* fixup! Fix wrong usage of parametrize and skip test

* fix: Lin-Kernighan solver handles problems with few nodes

* fix: type hint

* feat: verbose flag when solving with brute force

---------

Co-authored-by: Luan <[email protected]>

python_tsp/heuristics/lin_kernighan.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 
+from python_tsp.exact import solve_tsp_brute_force
 from python_tsp.utils import setup_initial_solution
 
 
@@ -138,6 +139,33 @@ def _print_message(
  print(msg)
 
 
+def _solve_tsp_brute_force(
+ distance_matrix: np.ndarray,
+ log_file: Optional[str] = None,
+ verbose: bool = False,
+) -> Tuple[List[int], float]:
+ x, fx = solve_tsp_brute_force(distance_matrix)
+ x = x or []
+
+ log_file_handler = (
+ open(log_file, "w", encoding="utf-8") if log_file else None
+ )
+ msg = (
+ "Few nodes to use Lin-Kernighan heuristics, "
+ "using Brute Force instead. "
+ )
+ if not x:
+ msg += "No solution found."
+ else:
+ msg += f"Found value: {fx}"
+ _print_message(msg, verbose, log_file_handler)
+
+ if log_file_handler:
+ log_file_handler.close()
+
+ return x, fx
+
+
 def solve_tsp_lin_kernighan(
  distance_matrix: np.ndarray,
  x0: Optional[List[int]] = None,
@@ -173,10 +201,13 @@ def solve_tsp_lin_kernighan(
  Éric D. Taillard, "Design of Heuristic Algorithms for Hard Optimization,"
  Chapter 5, Section 5.3.2.1: Lin-Kernighan Neighborhood, Springer, 2023.
  """
+ num_vertices = distance_matrix.shape[0]
+ if num_vertices < 4:
+ return _solve_tsp_brute_force(distance_matrix, log_file, verbose)
+
  hamiltonian_cycle, hamiltonian_cycle_distance = setup_initial_solution(
  distance_matrix=distance_matrix, x0=x0
  )
- num_vertices = distance_matrix.shape[0]
  vertices = list(range(num_vertices))
  iteration = 0
  improvement = True

tests/heuristics/test_lin_kernighan.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pytest
 
 from python_tsp.heuristics import solve_tsp_lin_kernighan
@@ -6,12 +7,12 @@
  distance_matrix1,
  distance_matrix2,
  distance_matrix3,
- optimal_permutation1,
- optimal_permutation2,
- optimal_permutation3,
  optimal_distance1,
  optimal_distance2,
  optimal_distance3,
+ optimal_permutation1,
+ optimal_permutation2,
+ optimal_permutation3,
 )
 
 
@@ -82,3 +83,18 @@ def test_lin_kernighan_log_file_is_created_if_required(tmp_path):
 
  assert log_file.exists()
  assert "Current value" in log_file.read_text()
+
+
+@pytest.mark.parametrize(
+ "distance_matrix, xopt, fopt",
+ [
+ (np.array([[0, 5], [1, 0]]), [0, 1], 6),
+ (np.array([[0, 1], [1, 0]]), [0, 1], 2),
+ ],
+)
+def test_lin_kernighan_handles_few_node_problems(distance_matrix, xopt, fopt):
+ """It should handle problems with less than 4 nodes."""
+ x, fx = solve_tsp_lin_kernighan(distance_matrix=distance_matrix)
+
+ assert x == xopt
+ assert fx == fopt