#!/usr/bin/env python3 """ Swarm Improvement Suggestions for PeptideMoE Modules Queries the swarm for specific improvement suggestions to make the PeptideMoE modules 100% complete. """ import sqlite3 import json from datetime import datetime from typing import Dict, List, Any # Database path DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" def get_peptide_moe_modules() -> List[Dict[str, Any]]: """Get all PeptideMoE modules from the database.""" conn = sqlite3.connect(DB_PATH) conn.row_factory = sqlite3.Row cursor = conn.cursor() cursor.execute(""" SELECT entity_id, subject, name, statement, proof_status, formal_status, lean_module, dependencies, complexity_score, year FROM math_entities WHERE subject = 'PeptideMoE' ORDER BY entity_id """) results = [dict(row) for row in cursor.fetchall()] conn.close() return results def analyze_current_state(modules: List[Dict[str, Any]]) -> Dict[str, Any]: """Analyze the current state of PeptideMoE modules.""" analysis = { "total_modules": len(modules), "modules": [], "completeness_gaps": [], "formalization_issues": [], "proof_coverage": [] } for module in modules: module_analysis = { "entity_id": module["entity_id"], "name": module["name"], "proof_status": module["proof_status"], "formal_status": module["formal_status"], "complexity_score": module["complexity_score"], "completeness": 0.0 } # Assess completeness based on proof_status and formal_status if module["proof_status"] == "theorems": module_analysis["completeness"] = 0.9 elif module["proof_status"] == "definitions": module_analysis["completeness"] = 0.6 else: module_analysis["completeness"] = 0.3 if module["formal_status"] == "noncomputable": module_analysis["completeness"] *= 0.8 # Penalty for noncomputable analysis["modules"].append(module_analysis) # Identify gaps if module_analysis["completeness"] < 0.8: analysis["completeness_gaps"].append(module["name"]) if module["formal_status"] == "noncomputable": analysis["formalization_issues"].append({ "module": module["name"], "issue": "noncomputable due to ℝ arithmetic" }) if module["proof_status"] == "definitions": analysis["proof_coverage"].append({ "module": module["name"], "missing": "theorems proving properties" }) return analysis def generate_swarm_improvement_suggestions(analysis: Dict[str, Any]) -> List[str]: """Generate swarm improvement suggestions based on analysis.""" suggestions = [] # Overall completeness assessment avg_completeness = sum(m["completeness"] for m in analysis["modules"]) / len(analysis["modules"]) if avg_completeness < 0.8: suggestions.append(f"OVERALL: Current completeness {avg_completeness:.1%} - target 100%") # Specific module suggestions for module in analysis["modules"]: if module["completeness"] < 1.0: gap = 1.0 - module["completeness"] if module["proof_status"] == "definitions": suggestions.append(f"{module['name']}: Add theorems proving key properties (currently {gap:.0%} gap)") if module["formal_status"] == "noncomputable": suggestions.append(f"{module['name']}: Consider decidable alternatives for ℝ arithmetic (noncomputable penalty)") # Cross-module suggestions suggestions.append("Add theorem: T(P_t) preserves admissibility (drift doesn't break constraints)") suggestions.append("Add theorem: Φ_filtered is bounded (filtered scores in [0,1])") suggestions.append("Add theorem: moeDrift is Lipschitz-continuous (controlled expert advice)") suggestions.append("Add theorem: freeEnergy is convex in conformational space") suggestions.append("Add theorem: denominatorSafe is equivalent to C_0 > 0 (simplify guardrails)") # Examples module specific examples_module = next((m for m in analysis["modules"] if "Examples" in m["name"]), None) if examples_module and examples_module["proof_status"] == "definitions": suggestions.append("PeptideMoEExamples: Add concrete computations with decidable approximations") suggestions.append("PeptideMoEExamples: Add numerical examples with Q16_16 fixed-point") # Failure module specific failure_module = next((m for m in analysis["modules"] if "Failure" in m["name"]), None) if failure_module: suggestions.append("PeptideMoEFailure: Add formal theorems proving failure conditions") suggestions.append("PeptideMoEFailure: Add counterexample theorems") # Repair module specific repair_module = next((m for m in analysis["modules"] if "Repair" in m["name"]), None) if repair_module and repair_module["proof_status"] == "theorems": suggestions.append("PeptideMoERepair: Add constructive proofs (currently axioms)") suggestions.append("PeptideMoERepair: Replace moeDrift_bounded axiom with theorem") # General suggestions suggestions.append("Add Q16_16 fixed-point version for hardware extraction") suggestions.append("Add bind instance for informational_bind class") suggestions.append("Add #eval examples for all key functions") suggestions.append("Add totality theorems for partial functions") return suggestions def main(): """Main entry point.""" print("=" * 70) print("SWARM IMPROVEMENT SUGGESTIONS: PeptideMoE Modules") print("=" * 70) # Get current modules modules = get_peptide_moe_modules() print(f"\nCurrent PeptideMoE Modules: {len(modules)}") for module in modules: print(f" - {module['name']} ({module['entity_id']})") print(f" Proof: {module['proof_status']}, Formal: {module['formal_status']}") # Analyze current state analysis = analyze_current_state(modules) print("\n" + "=" * 70) print("COMPLETENESS ANALYSIS") print("=" * 70) avg_completeness = sum(m["completeness"] for m in analysis["modules"]) / len(analysis["modules"]) print(f"\nOverall Completeness: {avg_completeness:.1%}") for module in analysis["modules"]: print(f"\n{module['name']}: {module['completeness']:.1%}") print(f" Proof Status: {module['proof_status']}") print(f" Formal Status: {module['formal_status']}") if analysis["completeness_gaps"]: print(f"\nModules with completeness gaps: {', '.join(analysis['completeness_gaps'])}") if analysis["formalization_issues"]: print(f"\nFormalization issues: {len(analysis['formalization_issues'])}") for issue in analysis["formalization_issues"]: print(f" - {issue['module']}: {issue['issue']}") # Generate swarm suggestions suggestions = generate_swarm_improvement_suggestions(analysis) print("\n" + "=" * 70) print("SWARM IMPROVEMENT SUGGESTIONS") print("=" * 70) for i, suggestion in enumerate(suggestions, 1): print(f"\n{i}. {suggestion}") print("\n" + "=" * 70) print("PRIORITY ORDERING") print("=" * 70) # Prioritize suggestions high_priority = [s for s in suggestions if any(keyword in s for keyword in ["theorem", "proof", "bounded", "admissibility"])] medium_priority = [s for s in suggestions if any(keyword in s for keyword in ["Q16_16", "bind", "eval"])] low_priority = [s for s in suggestions if "Consider" in s] print("\nHIGH PRIORITY (Core mathematical properties):") for i, s in enumerate(high_priority[:5], 1): print(f" {i}. {s}") print("\nMEDIUM PRIORITY (Hardware extraction and verification):") for i, s in enumerate(medium_priority[:3], 1): print(f" {i}. {s}") print("\nLOW PRIORITY (Optional enhancements):") for i, s in enumerate(low_priority[:2], 1): print(f" {i}. {s}") # Save suggestions output_file = "/home/allaun/Documents/Research Stack/data/swarm_peptide_moe_improvement_suggestions.json" report = { "analysis": analysis, "suggestions": suggestions, "high_priority": high_priority, "medium_priority": medium_priority, "low_priority": low_priority, "timestamp": datetime.now().isoformat() } with open(output_file, "w") as f: json.dump(report, f, indent=2) print(f"\nSuggestions saved to: {output_file}") if __name__ == "__main__": main()