experiments/learnability/exp_pattern_prover/eval_pattern_prover.py

#!/usr/bin/env python3
"""
Evaluation script for pattern-based baseline prover.
Evaluates on extrinsic propositional logic problemset.
"""

import os
import json
import sys
from typing import Optional
from concurrent.futures import ProcessPoolExecutor, as_completed
from functools import partial

import problems
from learning.exp_pattern_prover.pattern_prover import PatternProver, PatternProverConfig
from proofsearch import format_blocks_with_indent

try:
    from tqdm import tqdm
except ImportError:
    # Fallback if tqdm not available
    def tqdm(iterable, **kwargs):
        return iterable


def _evaluate_single_problem(args):
    """Worker function to evaluate a single problem (for multiprocessing)."""
    problem, problemset_id, max_depth, max_iterations, verbose, log_dir = args
    
    try:
        # Load problemset (each worker needs its own copy)
        problemset = problems.load_problemset(problemset_id)
        
        # Create log file path if logging is enabled
        log_file = None
        if log_dir:
            os.makedirs(log_dir, exist_ok=True)
            log_file = os.path.join(log_dir, f"{problem}_log.jsonl")
        
        # Create pattern prover
        config = PatternProverConfig(
            max_depth=max_depth,
            max_iterations=max_iterations,
            verbose=verbose,
            log_file=log_file
        )
        prover = PatternProver(config)
        
        # Initialize and solve
        state = problemset.initialize_problem(problem)
        result = prover.proof_search(problem, state)
        
        # Extract proof if successful
        proof_text = None
        if result.success:
            try:
                # Get solution actions first to verify structure
                solution_actions = result.root.get_solution_actions()
                if solution_actions is None or (isinstance(solution_actions, list) and len(solution_actions) == 0):
                    proof_text = "<proof reconstruction failed: No solution actions found>"
                else:
                    # Make a copy since reconstruct_proof modifies the list
                    actions_copy = [a for a in solution_actions] if isinstance(solution_actions, list) else solution_actions
                    proof_text = format_blocks_with_indent(result.root.reconstruct_proof())
            except Exception as e:
                import traceback
                proof_text = f"<proof reconstruction failed: {e}>\n{traceback.format_exc()}"
        
        return {
            'problem': problem,
            'success': result.success,
            'iterations': result.iterations,
            'proof': proof_text,
            'error': None
        }
    except Exception as e:
        import traceback
        return {
            'problem': problem,
            'success': False,
            'iterations': 0,
            'proof': None,
            'error': f"{str(e)}\n{traceback.format_exc()}"
        }


def evaluate_pattern_prover(
    problemset_id: str = 'extrinsic-propositional-logic',
    max_problems: Optional[int] = None,
    max_depth: int = 30,
    max_iterations: int = 5000,
    verbose: bool = False,
    output_dir: str = '.',
    num_workers: Optional[int] = None,
    log_dir: Optional[str] = None,
):
    """
    Evaluate pattern prover on a problemset.
    
    Args:
        problemset_id: ID of problemset to evaluate on
        max_problems: Maximum number of problems to evaluate (None = all)
        max_depth: Maximum search depth
        max_iterations: Maximum iterations
        verbose: Print verbose output
        output_dir: Directory to save results
        num_workers: Number of parallel workers (None = use all CPU cores)
    """
    # Load problemset to get problem list
    print(f"Loading problemset: {problemset_id}")
    problemset = problems.load_problemset(problemset_id)
    print(f"Loaded {len(problemset)} problems")
    
    # Get problems to evaluate
    problem_names = problemset.problem_names()
    if max_problems is not None:
        problem_names = problem_names[:max_problems]
    
    # Determine number of workers
    if num_workers is None:
        import multiprocessing
        num_workers = multiprocessing.cpu_count()
    
    print(f"Evaluating {len(problem_names)} problems using {num_workers} workers...")
    print()
    
    # Set up log directory
    if log_dir is None:
        log_dir = os.path.join(output_dir, "logs")
    
    # Prepare arguments for workers
    worker_args = [
        (problem, problemset_id, max_depth, max_iterations, verbose, log_dir)
        for problem in problem_names
    ]
    
    # Evaluate in parallel
    results = {
        "results": [],
        "num_proved": 0,
        "num_total": len(problem_names),
        "solved_problems": []
    }
    
    if num_workers == 1:
        # Sequential execution (useful for debugging)
        for args in worker_args:
            result = _evaluate_single_problem(args)
            results["results"].append(result)
            if result['success']:
                results["num_proved"] += 1
                results["solved_problems"].append(result['problem'])
            if verbose:
                status = "✓" if result['success'] else "✗"
                print(f"{status} {result['problem']}: {result['iterations']} iterations")
    else:
        # Parallel execution
        with ProcessPoolExecutor(max_workers=num_workers) as executor:
            futures = {
                executor.submit(_evaluate_single_problem, args): args[0]
                for args in worker_args
            }
            
            for future in tqdm(as_completed(futures), total=len(futures), desc="Evaluating"):
                problem_name = futures[future]
                try:
                    result = future.result()
                    results["results"].append(result)
                    if result['success']:
                        results["num_proved"] += 1
                        results["solved_problems"].append(result['problem'])
                    if verbose:
                        status = "✓" if result['success'] else "✗"
                        print(f"{status} {result['problem']}: {result['iterations']} iterations")
                except Exception as e:
                    print(f"Error evaluating {problem_name}: {e}")
                    results["results"].append({
                        'problem': problem_name,
                        'success': False,
                        'iterations': 0,
                        'proof': None,
                        'error': str(e)
                    })
    
    # Sort results by problem name for consistency
    results["results"].sort(key=lambda x: x['problem'])
    
    # Print summary
    print()
    print("=" * 60)
    print("SUMMARY")
    print("=" * 60)
    print(f"Solved: {results['num_proved']}/{results['num_total']}")
    if results['num_total'] > 0:
        print(f"Pass rate: {results['num_proved']/results['num_total']*100:.1f}%")
    print()
    
    if results['solved_problems']:
        print("Solved problems:")
        for p in sorted(results['solved_problems']):
            print(f"  - {p}")
        print()
    
    # Save results
    os.makedirs(output_dir, exist_ok=True)
    output_file = os.path.join(output_dir, "pattern_prover_results.json")
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=2)
    print(f"Results saved to: {output_file}")
    
    return results


def main():
    """Main entry point."""
    import argparse
    
    parser = argparse.ArgumentParser(description="Evaluate pattern-based prover")
    parser.add_argument(
        '--problemset',
        type=str,
        default='extrinsic-propositional-logic',
        help='Problemset ID (default: extrinsic-propositional-logic)'
    )
    parser.add_argument(
        '--max-problems',
        type=int,
        default=None,
        help='Maximum number of problems to evaluate (default: all)'
    )
    parser.add_argument(
        '--max-depth',
        type=int,
        default=30,
        help='Maximum search depth (default: 30)'
    )
    parser.add_argument(
        '--max-iterations',
        type=int,
        default=5000,
        help='Maximum iterations (default: 5000)'
    )
    parser.add_argument(
        '--verbose',
        action='store_true',
        help='Print verbose output'
    )
    parser.add_argument(
        '--output-dir',
        type=str,
        default='.',
        help='Output directory for results (default: current directory)'
    )
    parser.add_argument(
        '--num-workers',
        type=int,
        default=None,
        help='Number of parallel workers (default: use all CPU cores)'
    )
    parser.add_argument(
        '--log-dir',
        type=str,
        default=None,
        help='Directory for detailed logs (default: output_dir/logs)'
    )
    
    args = parser.parse_args()
    
    evaluate_pattern_prover(
        problemset_id=args.problemset,
        max_problems=args.max_problems,
        max_depth=args.max_depth,
        max_iterations=args.max_iterations,
        verbose=args.verbose,
        output_dir=args.output_dir,
        num_workers=args.num_workers,
        log_dir=args.log_dir
    )


if __name__ == '__main__':
    main()


#command to run:
# python eval_pattern_prover.py --problemset extrinsic-95--output-dir results/pattern_prover --num-workers 4 --log-dir results/pattern_prover_95