refactor examples

yangeorget · Sep 24, 2024 · 3fa8b38 · 3fa8b38
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diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -6,8 +6,8 @@
 copyright = '2024, Yan Georget'
 author = 'Yan Georget'
 
-release = '0.8'
-version = '0.8.1'
+release = '0.9'
+version = '0.9.0'
 
 # -- General configuration
 

diff --git a/docs/source/installation.rst b/docs/source/installation.rst
@@ -67,13 +67,13 @@ Let's find all solutions to the `12-queens problem <https://www.csplib.org/Probl
 
 .. code-block:: bash
 
-   NUMBA_CACHE_DIR=.numba/cache PYTHONPATH=. python tests/examples/test_queens.py -n 12
+   NUMBA_CACHE_DIR=.numba/cache PYTHONPATH=. python nucs/examples/queens/queens_problem.py -n 12
 
 Let's find the optimal solution to the `Golomb ruler problem <https://www.csplib.org/Problems/prob006>`_ with 10 marks:
 
 .. code-block:: bash
 
-   NUMBA_CACHE_DIR=.numba/cache PYTHONPATH=. python tests/examples/test_golomb.py -n 10
+   NUMBA_CACHE_DIR=.numba/cache PYTHONPATH=. python nucs/examples/golomb/golomb_problem.py -n 10
 
 
 

diff --git a/nucs/examples/alpha/__init__.py b/nucs/examples/alpha/__init__.py
diff --git a/nucs/examples/alpha_problem.py → nucs/examples/alpha/alpha_problem.py b/nucs/examples/alpha_problem.py → nucs/examples/alpha/alpha_problem.py
@@ -1,7 +1,11 @@
+from pprint import pprint
 from typing import List
 
 from nucs.problems.problem import Problem
 from nucs.propagators.propagators import ALG_AFFINE_EQ, ALG_ALLDIFFERENT
+from nucs.solvers.backtrack_solver import BacktrackSolver
+from nucs.solvers.heuristics import min_value_dom_heuristic, smallest_domain_var_heuristic
+from nucs.statistics import get_statistics
 
 A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z = tuple(range(26))
 
@@ -81,3 +85,13 @@ def pretty_print_solution(self, solution: List[int]) -> None:
                 "Z": solution[Z],
             }
         )
+
+
+if __name__ == "__main__":
+    problem = AlphaProblem()
+    solver = BacktrackSolver(
+        problem, var_heuristic=smallest_domain_var_heuristic, dom_heuristic=min_value_dom_heuristic
+    )
+    solutions = solver.find_all()
+    pprint(get_statistics(solver.statistics))
+    print(solutions[0])
diff --git a/nucs/examples/bibd/__init__.py b/nucs/examples/bibd/__init__.py
diff --git a/nucs/examples/bibd_problem.py → nucs/examples/bibd/bibd_problem.py b/nucs/examples/bibd_problem.py → nucs/examples/bibd/bibd_problem.py
@@ -1,5 +1,9 @@
+from pprint import pprint
+
 from nucs.problems.problem import Problem
 from nucs.propagators.propagators import ALG_EXACTLY_EQ, ALG_LEXICOGRAPHIC_LEQ, ALG_MIN_EQ
+from nucs.solvers.backtrack_solver import BacktrackSolver
+from nucs.statistics import get_statistics
 
 
 class BIBDProblem(Problem):
@@ -36,3 +40,10 @@ def __init__(self, v: int, b: int, r: int, k: int, l: int):
         # lexleq on columns
         for j in range(0, b - 1):
             self.add_propagator((list(range(j, v * b, b)) + list(range(j + 1, v * b, b)), ALG_LEXICOGRAPHIC_LEQ, []))
+
+
+if __name__ == "__main__":
+    problem = BIBDProblem(8, 14, 7, 4, 3)
+    solver = BacktrackSolver(problem)
+    solver.solve_all()
+    pprint(get_statistics(solver.statistics))
diff --git a/nucs/examples/donald/__init__.py b/nucs/examples/donald/__init__.py
diff --git a/nucs/examples/donald_problem.py → nucs/examples/donald/donald_problem.py b/nucs/examples/donald_problem.py → nucs/examples/donald/donald_problem.py
@@ -1,7 +1,11 @@
+from pprint import pprint
 from typing import List
 
 from nucs.problems.problem import Problem
 from nucs.propagators.propagators import ALG_AFFINE_EQ, ALG_ALLDIFFERENT
+from nucs.solvers.backtrack_solver import BacktrackSolver
+from nucs.solvers.heuristics import min_value_dom_heuristic, smallest_domain_var_heuristic
+from nucs.statistics import get_statistics
 
 A, B, D, E, G, L, N, O, R, T = tuple(range(10))
 
@@ -40,3 +44,13 @@ def pretty_print_solution(self, solution: List[int]) -> None:
                 "T": solution[T],
             }
         )
+
+
+if __name__ == "__main__":
+    problem = DonaldProblem()
+    solver = BacktrackSolver(
+        problem, var_heuristic=smallest_domain_var_heuristic, dom_heuristic=min_value_dom_heuristic
+    )
+    for solution in solver.solve():
+        problem.pretty_print_solution(solution)
+    pprint(get_statistics(solver.statistics))
diff --git a/nucs/examples/golomb/__init__.py b/nucs/examples/golomb/__init__.py
diff --git a/nucs/examples/golomb_problem.py → nucs/examples/golomb/golomb_problem.py b/nucs/examples/golomb_problem.py → nucs/examples/golomb/golomb_problem.py
@@ -1,11 +1,16 @@
+import argparse
+from pprint import pprint
+
 import numpy as np
 from numpy.typing import NDArray
 
 from nucs.constants import MAX, MIN
 from nucs.problems.problem import Problem
 from nucs.propagators.propagators import ALG_AFFINE_EQ, ALG_AFFINE_LEQ, ALG_ALLDIFFERENT
+from nucs.solvers.backtrack_solver import BacktrackSolver
 from nucs.solvers.consistency_algorithms import bound_consistency_algorithm
 from nucs.solvers.heuristics import first_not_instantiated_var_heuristic
+from nucs.statistics import get_statistics
 
 GOLOMB_LENGTHS = [0, 0, 1, 3, 6, 11, 17, 25, 34, 44, 55, 72, 85, 106, 127]
 
@@ -114,3 +119,15 @@ def golomb_consistency_algorithm(statistics: NDArray, problem: GolombProblem) ->
                 problem.set_min_value(var_idx, new_min)
     # then apply BC
     return bound_consistency_algorithm(statistics, problem)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-n", type=int, default=10)
+    args = parser.parse_args()
+    problem = GolombProblem(args.n)
+    solver = BacktrackSolver(problem, consistency_algorithm=golomb_consistency_algorithm)
+    solution = solver.minimize(problem.length_idx)
+    pprint(get_statistics(solver.statistics))
+    print(solution)
+    print(solution[problem.length_idx])  # type: ignore
diff --git a/nucs/examples/knapsack/__init__.py b/nucs/examples/knapsack/__init__.py
diff --git a/nucs/examples/knapsack/knapsack_problem.py b/nucs/examples/knapsack/knapsack_problem.py
@@ -0,0 +1,35 @@
+from pprint import pprint
+from typing import List
+
+from nucs.problems.problem import Problem
+from nucs.propagators.propagators import ALG_AFFINE_EQ, ALG_AFFINE_LEQ
+from nucs.solvers.backtrack_solver import BacktrackSolver
+from nucs.solvers.heuristics import first_not_instantiated_var_heuristic, max_value_dom_heuristic
+from nucs.statistics import get_statistics
+
+
+class KnapsackProblem(Problem):
+    """
+    CSPLIB problem #133 - https://www.csplib.org/Problems/prob133/
+    """
+
+    def __init__(self, weights: List[int], volumes: List[int], capacity: int) -> None:
+        n = len(weights)
+        super().__init__([(0, 1)] * n + [(0, sum(weights))])
+        self.add_propagator((list(range(n)), ALG_AFFINE_LEQ, [*volumes, capacity]))
+        self.add_propagator((list(range(n + 1)), ALG_AFFINE_EQ, [*weights, -1, 0]))
+        self.weight = n
+
+
+if __name__ == "__main__":
+    problem = KnapsackProblem(
+        [40, 40, 38, 38, 36, 36, 34, 34, 32, 32, 30, 30, 28, 28, 26, 26, 24, 24, 22, 22],
+        [40, 40, 38, 38, 36, 36, 34, 34, 32, 32, 30, 30, 28, 28, 26, 26, 24, 24, 22, 22],
+        55,
+    )
+    solver = BacktrackSolver(
+        problem, var_heuristic=first_not_instantiated_var_heuristic, dom_heuristic=max_value_dom_heuristic
+    )
+    solution = solver.maximize(problem.weight)
+    pprint(get_statistics(solver.statistics))
+    print(solution)
diff --git a/nucs/examples/knapsack_problem.py b/nucs/examples/knapsack_problem.py
diff --git a/nucs/examples/magic_sequence/__init__.py b/nucs/examples/magic_sequence/__init__.py
diff --git a/nucs/examples/magic_sequence_problem.py → .../magic_sequence/magic_sequence_problem.py b/nucs/examples/magic_sequence_problem.py → .../magic_sequence/magic_sequence_problem.py
diff --git a/nucs/examples/magic_square/__init__.py b/nucs/examples/magic_square/__init__.py
diff --git a/nucs/examples/magic_square_problem.py → ...ples/magic_square/magic_square_problem.py b/nucs/examples/magic_square_problem.py → ...ples/magic_square/magic_square_problem.py
diff --git a/nucs/examples/quasigroup/__init__.py b/nucs/examples/quasigroup/__init__.py
diff --git a/nucs/examples/quasigroup_problem.py → ...examples/quasigroup/quasigroup_problem.py b/nucs/examples/quasigroup_problem.py → ...examples/quasigroup/quasigroup_problem.py
@@ -1,5 +1,11 @@
+import argparse
+from pprint import pprint
+
 from nucs.problems.latin_square_problem import M_COLOR, M_COLUMN, M_ROW, LatinSquareRCProblem
 from nucs.propagators.propagators import ALG_ELEMENT_LIV
+from nucs.solvers.backtrack_solver import BacktrackSolver
+from nucs.solvers.heuristics import min_value_dom_heuristic, smallest_domain_var_heuristic
+from nucs.statistics import get_statistics
 
 
 class QuasigroupProblem(LatinSquareRCProblem):
@@ -40,3 +46,15 @@ def __init__(self, n: int):
                         ),
                         0,
                     )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-n", type=int, default=10)
+    args = parser.parse_args()
+    problem = Quasigroup5Problem(args.n)
+    solver = BacktrackSolver(
+        problem, var_heuristic=smallest_domain_var_heuristic, dom_heuristic=min_value_dom_heuristic
+    )
+    solver.solve_all()
+    pprint(get_statistics(solver.statistics))
diff --git a/nucs/examples/queens/__init__.py b/nucs/examples/queens/__init__.py
diff --git a/nucs/examples/queens_problem.py → nucs/examples/queens/queens_problem.py b/nucs/examples/queens_problem.py → nucs/examples/queens/queens_problem.py
@@ -1,5 +1,10 @@
+import argparse
+from pprint import pprint
+
 from nucs.problems.problem import Problem
 from nucs.propagators.propagators import ALG_ALLDIFFERENT
+from nucs.solvers.backtrack_solver import BacktrackSolver
+from nucs.statistics import get_statistics
 
 
 class QueensProblem(Problem):
@@ -18,3 +23,13 @@ def __init__(self, n: int):
         self.add_propagator((list(range(n)), ALG_ALLDIFFERENT, []))
         self.add_propagator((list(range(n, 2 * n)), ALG_ALLDIFFERENT, []))
         self.add_propagator((list(range(2 * n, 3 * n)), ALG_ALLDIFFERENT, []))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-n", type=int, default=12)
+    args = parser.parse_args()
+    problem = QueensProblem(args.n)
+    solver = BacktrackSolver(problem)
+    solver.solve_all()
+    pprint(get_statistics(solver.statistics))
diff --git a/nucs/examples/schur_lemma/__init__.py b/nucs/examples/schur_lemma/__init__.py
diff --git a/nucs/examples/schur_lemma_problem.py → ...amples/schur_lemma/schur_lemma_problem.py b/nucs/examples/schur_lemma_problem.py → ...amples/schur_lemma/schur_lemma_problem.py
diff --git a/nucs/examples/sudoku/__init__.py b/nucs/examples/sudoku/__init__.py
diff --git a/nucs/examples/sudoku_problem.py → nucs/examples/sudoku/sudoku_problem.py b/nucs/examples/sudoku_problem.py → nucs/examples/sudoku/sudoku_problem.py
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name="NUCS"
-version="0.8.1"
+version="0.9.0"
 authors = [
   { name="Yan Georget", email="[email protected]" },
 ]

diff --git a/tests/examples/test_alpha.py b/tests/examples/test_alpha.py
@@ -1,9 +1,7 @@
-from pprint import pprint
-
-from nucs.examples.alpha_problem import AlphaProblem
+from nucs.examples.alpha.alpha_problem import AlphaProblem
 from nucs.solvers.backtrack_solver import BacktrackSolver
 from nucs.solvers.heuristics import min_value_dom_heuristic, smallest_domain_var_heuristic
-from nucs.statistics import STATS_SOLVER_SOLUTION_NB, get_statistics
+from nucs.statistics import STATS_SOLVER_SOLUTION_NB
 
 
 class TestAlpha:
@@ -42,13 +40,3 @@ def test_alpha(self) -> None:
             14,
             18,
         ]
-
-
-if __name__ == "__main__":
-    problem = AlphaProblem()
-    solver = BacktrackSolver(
-        problem, var_heuristic=smallest_domain_var_heuristic, dom_heuristic=min_value_dom_heuristic
-    )
-    solutions = solver.find_all()
-    pprint(get_statistics(solver.statistics))
-    print(solutions[0])
diff --git a/tests/examples/test_bibd.py b/tests/examples/test_bibd.py
@@ -1,8 +1,6 @@
-from pprint import pprint
-
-from nucs.examples.bibd_problem import BIBDProblem
+from nucs.examples.bibd.bibd_problem import BIBDProblem
 from nucs.solvers.backtrack_solver import BacktrackSolver
-from nucs.statistics import STATS_SOLVER_SOLUTION_NB, get_statistics
+from nucs.statistics import STATS_SOLVER_SOLUTION_NB
 
 
 class TestBIBD:
@@ -17,10 +15,3 @@ def test_8_14_7_4_3(self) -> None:
         solver = BacktrackSolver(problem)
         solver.solve_all()
         assert solver.statistics[STATS_SOLVER_SOLUTION_NB] == 92
-
-
-if __name__ == "__main__":
-    problem = BIBDProblem(8, 14, 7, 4, 3)
-    solver = BacktrackSolver(problem)
-    solver.solve_all()
-    pprint(get_statistics(solver.statistics))
diff --git a/tests/examples/test_donald.py b/tests/examples/test_donald.py
@@ -1,9 +1,7 @@
-from pprint import pprint
-
-from nucs.examples.donald_problem import DonaldProblem
+from nucs.examples.donald.donald_problem import DonaldProblem
 from nucs.solvers.backtrack_solver import BacktrackSolver
 from nucs.solvers.heuristics import min_value_dom_heuristic, smallest_domain_var_heuristic
-from nucs.statistics import STATS_SOLVER_SOLUTION_NB, get_statistics
+from nucs.statistics import STATS_SOLVER_SOLUTION_NB
 
 
 class TestDonald:
@@ -15,13 +13,3 @@ def test_donald(self) -> None:
         solutions = solver.find_all()
         assert solver.statistics[STATS_SOLVER_SOLUTION_NB] == 1
         assert solutions[0] == [4, 3, 5, 9, 1, 8, 6, 2, 7, 0]
-
-
-if __name__ == "__main__":
-    problem = DonaldProblem()
-    solver = BacktrackSolver(
-        problem, var_heuristic=smallest_domain_var_heuristic, dom_heuristic=min_value_dom_heuristic
-    )
-    for solution in solver.solve():
-        problem.pretty_print_solution(solution)
-    pprint(get_statistics(solver.statistics))
diff --git a/tests/examples/test_golomb.py b/tests/examples/test_golomb.py
@@ -1,12 +1,8 @@
-import argparse
-from pprint import pprint
-
 import pytest
 
 from nucs.constants import MIN
-from nucs.examples.golomb_problem import GolombProblem, golomb_consistency_algorithm, index, init_domains
+from nucs.examples.golomb.golomb_problem import GolombProblem, golomb_consistency_algorithm, index, init_domains
 from nucs.solvers.backtrack_solver import BacktrackSolver
-from nucs.statistics import get_statistics
 
 
 class TestGolomb:
@@ -28,15 +24,3 @@ def test_golomb(self, mark_nb: int, solution_nb: int) -> None:
         solution = solver.minimize(problem.length_idx)
         assert solution
         assert solution[problem.length_idx] == solution_nb
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-n", type=int, default=10)
-    args = parser.parse_args()
-    problem = GolombProblem(args.n)
-    solver = BacktrackSolver(problem, consistency_algorithm=golomb_consistency_algorithm)
-    solution = solver.minimize(problem.length_idx)
-    pprint(get_statistics(solver.statistics))
-    print(solution)
-    print(solution[problem.length_idx])  # type: ignore
diff --git a/tests/examples/test_knapsack.py b/tests/examples/test_knapsack.py
@@ -1,9 +1,6 @@
-from pprint import pprint
-
-from nucs.examples.knapsack_problem import KnapsackProblem
+from nucs.examples.knapsack.knapsack_problem import KnapsackProblem
 from nucs.solvers.backtrack_solver import BacktrackSolver
 from nucs.solvers.heuristics import first_not_instantiated_var_heuristic, max_value_dom_heuristic
-from nucs.statistics import get_statistics
 
 
 class TestKnapsack:
@@ -19,17 +16,3 @@ def test_knapsack(self) -> None:
         solution = solver.maximize(problem.weight)
         assert solution
         assert solution[problem.weight] == 54
-
-
-if __name__ == "__main__":
-    problem = KnapsackProblem(
-        [40, 40, 38, 38, 36, 36, 34, 34, 32, 32, 30, 30, 28, 28, 26, 26, 24, 24, 22, 22],
-        [40, 40, 38, 38, 36, 36, 34, 34, 32, 32, 30, 30, 28, 28, 26, 26, 24, 24, 22, 22],
-        55,
-    )
-    solver = BacktrackSolver(
-        problem, var_heuristic=first_not_instantiated_var_heuristic, dom_heuristic=max_value_dom_heuristic
-    )
-    solution = solver.maximize(problem.weight)
-    pprint(get_statistics(solver.statistics))
-    print(solution)
diff --git a/tests/examples/test_magic_sequence.py b/tests/examples/test_magic_sequence.py
@@ -1,6 +1,6 @@
 import pytest
 
-from nucs.examples.magic_sequence_problem import MagicSequenceProblem
+from nucs.examples.magic_sequence.magic_sequence_problem import MagicSequenceProblem
 from nucs.solvers.backtrack_solver import BacktrackSolver
 from nucs.solvers.heuristics import last_not_instantiated_var_heuristic, min_value_dom_heuristic
 from nucs.statistics import STATS_SOLVER_SOLUTION_NB

diff --git a/tests/examples/test_magic_square.py b/tests/examples/test_magic_square.py
@@ -1,6 +1,6 @@
 import pytest
 
-from nucs.examples.magic_square_problem import MagicSquareProblem
+from nucs.examples.magic_square.magic_square_problem import MagicSquareProblem
 from nucs.solvers.backtrack_solver import BacktrackSolver
 from nucs.solvers.heuristics import max_value_dom_heuristic, smallest_domain_var_heuristic
 from nucs.statistics import STATS_SOLVER_SOLUTION_NB