mirror of
https://github.com/allaunthefox/Research-Stack.git
synced 2026-07-31 03:05:21 +00:00
225 lines
8.7 KiB
Python
225 lines
8.7 KiB
Python
#!/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()
|