#!/usr/bin/env python3 """ run_experiments.py Reproducible experiments for the "Sudoku the Hard Way" p-adic regression solver. Default behaviour is *fast*: puzzles are produced by carving random solved grids down to a given clue count (no uniqueness guarantee). This keeps runtimes sane. Use --unique to enforce unique solutions (slower, but closer to "real" Sudoku). """ from __future__ import annotations import argparse import csv import time from pathlib import Path import random import statistics try: import matplotlib.pyplot as plt # type: ignore except ImportError: # pragma: no cover plt = None from padic_sudoku_regression import ( generate_unique_puzzle, random_solved_grid, grid_to_string, parse_puzzle, solve_stepwise_swap, solve_greedy_descent_swap, solve_greedy_local_edit_best, solve_greedy_local_edit_first, solve_zubarev_local_edit, solve_zubarev_walk, pretty, ) def carve_fast(solved, clues: int, rng: random.Random): puzzle = solved[:] positions = list(range(81)) rng.shuffle(positions) to_remove = 81 - clues for pos in positions[:to_remove]: puzzle[pos] = 0 return puzzle def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--outdir", type=str, default=str(Path(__file__).resolve().parent.parent / "outputs")) ap.add_argument("--seed", type=int, default=0) ap.add_argument("--n", type=int, default=12, help="puzzles per clue count") ap.add_argument("--clues", type=str, default="36,30,26", help="comma-separated clue counts") ap.add_argument("--max-steps", type=int, default=200000) ap.add_argument("--restarts", type=int, default=30) ap.add_argument( "--method", type=str, default="stepwise", choices=["stepwise", "greedy", "zubarev", "local-best", "local-first", "local-zubarev"], ) ap.add_argument("--beta0", type=float, default=0.5, help="Zubarev walk: initial beta (inverse temperature).") ap.add_argument("--beta1", type=float, default=6.0, help="Zubarev walk: final beta (ignored if schedule=constant).") ap.add_argument("--beta-schedule", type=str, default="linear", choices=["constant", "linear", "exp"]) ap.add_argument("--unique", action="store_true", help="enforce uniqueness (slower)") args = ap.parse_args() outdir = Path(args.outdir) outdir.mkdir(parents=True, exist_ok=True) clue_counts = [int(x.strip()) for x in args.clues.split(",") if x.strip()] rng = random.Random(args.seed) results_rows = [] # We'll capture a trace for the first puzzle in the middle clue count (if available). trace_target = clue_counts[len(clue_counts)//2] trace_saved = False t0 = time.time() for clues in clue_counts: for j in range(args.n): seed = rng.randrange(1_000_000_000) if args.unique: puzzle = generate_unique_puzzle(clues=clues, seed=seed) else: solved = random_solved_grid(random.Random(seed)) puzzle = carve_fast(solved, clues=clues, rng=random.Random(seed ^ 0xDEADBEEF)) puzzle_str = grid_to_string(puzzle) record_trace = (not trace_saved) and (clues == trace_target) and (j == 0) if args.method == "stepwise": res = solve_stepwise_swap( puzzle, seed=seed ^ 0xA5A5A5A5, max_steps=args.max_steps, restarts=args.restarts, record_trace=record_trace, trace_every=200, ) elif args.method == "greedy": res = solve_greedy_descent_swap( puzzle, seed=seed ^ 0xA5A5A5A5, max_steps=args.max_steps, restarts=args.restarts, record_trace=record_trace, trace_every=200, ) elif args.method == "local-best": res = solve_greedy_local_edit_best( puzzle, seed=seed ^ 0xA5A5A5A5, max_steps=args.max_steps, restarts=args.restarts, record_trace=record_trace, trace_every=200, ) elif args.method == "local-first": res = solve_greedy_local_edit_first( puzzle, seed=seed ^ 0xA5A5A5A5, max_steps=args.max_steps, restarts=args.restarts, record_trace=record_trace, trace_every=200, ) elif args.method == "local-zubarev": res = solve_zubarev_local_edit( puzzle, seed=seed ^ 0xA5A5A5A5, max_steps=args.max_steps, restarts=args.restarts, beta0=args.beta0, beta1=args.beta1, beta_schedule=args.beta_schedule, record_trace=record_trace, trace_every=200, ) else: res = solve_zubarev_walk( puzzle, seed=seed ^ 0xA5A5A5A5, max_steps=args.max_steps, restarts=args.restarts, beta0=args.beta0, beta1=args.beta1, beta_schedule=args.beta_schedule, record_trace=record_trace, trace_every=200, ) results_rows.append({ "clues": clues, "puzzle_seed": seed, "solve_seed": seed ^ 0xA5A5A5A5, "unique_enforced": int(args.unique), "method": args.method, "objective": res.objective_label, "puzzle": puzzle_str, "solved": int(res.solved), "steps": res.steps, "restarts_used": res.restarts, "seconds": res.seconds, "final_conflicts": res.final_conflicts, }) if record_trace and res.trace is not None: # Save trace plot if plt is None: print("Note: matplotlib not installed; skipping loss_curve plot.") else: fig = plt.figure() x_values = res.trace_steps if res.trace_steps is not None else [200 * k for k in range(len(res.trace))] plt.plot(x_values, res.trace) plt.xlabel("Iteration (approx.)") plt.ylabel(f"{res.objective_label} conflict pairs") plt.title(f"Loss trajectory (clues={clues}, seed={seed})") fig.tight_layout() png_path = outdir / "loss_curve.png" pdf_path = outdir / "loss_curve.pdf" fig.savefig(png_path, dpi=200) fig.savefig(pdf_path) plt.close(fig) # also save the puzzle and (if solved) its solution (outdir / "trace_puzzle.txt").write_text(pretty(parse_puzzle(puzzle_str))) if res.solved: (outdir / "trace_solution.txt").write_text(pretty(res.grid)) trace_saved = True # Write CSV csv_path = outdir / "experiment_results.csv" with csv_path.open("w", newline="") as f: w = csv.DictWriter(f, fieldnames=list(results_rows[0].keys())) w.writeheader() for row in results_rows: w.writerow(row) # Summaries summary_lines = [] summary_lines.append(f"Total runs: {len(results_rows)}") summary_lines.append(f"Unique enforced: {args.unique}") for clues in clue_counts: subset = [r for r in results_rows if r["clues"] == clues] solved = [r for r in subset if r["solved"] == 1] summary_lines.append(f"\nClues={clues}: solved {len(solved)}/{len(subset)}") if solved: steps = [r["steps"] for r in solved] secs = [r["seconds"] for r in solved] summary_lines.append(f" median steps: {int(statistics.median(steps))}") summary_lines.append(f" median seconds: {statistics.median(secs):.3f}") (outdir / "summary.txt").write_text("\n".join(summary_lines)) dt = time.time() - t0 print("Wrote", csv_path) print("Wall time:", f"{dt:.2f}s") print((outdir / "summary.txt").read_text()) if __name__ == "__main__": main()