hyper_evolution.py

"""
Hyper-Evolution Engine.
The recursive self-improvement loop for the Ultimate AI Agent.
Phase 21: The Singularity.
"""

import time
import random
import json
import asyncio
from datetime import datetime
from typing import Dict, List, Optional

class HyperEvolutionEngine:
    def __init__(self, agent_instance):
        self.agent = agent_instance
        self.evolution_history = []
        self.active = False
        self.current_fitness = 0.5 # Baseline

    async def start_loop(self):
        """Activates the recursive improvement loop."""
        self.active = True
        print("HYPER-EVOLUTION MODE ACTIVATED")
        print("The agent is now self-evolving. Press Ctrl+C to stop.")
        
        while self.active:
            try:
                await self.run_one_cycle()
                
                # Wait before next cycle - Non-blocking
                if getattr(self.agent, "god_mode", False):
                    await asyncio.sleep(0.1)  # God Mode: Machine speed
                else:
                    await asyncio.sleep(5)    # Safe Mode: Human speed
                
            except KeyboardInterrupt:
                self.stop_loop()
            except Exception as e:
                print(f"[Evolution] Error: {e}")
                self.active = False

    async def run_one_cycle(self):
        """Runs a single evolution cycle."""
        # 1. Analyze Performance (Fitness Function)
        fitness = self._calculate_fitness()
        print(f"[Evolution] Current Fitness: {fitness:.4f}")
        
        # 2. Hypothesis Generation & A/B Testing
        if fitness < 0.8: # If not perfect, try to improve
            print("[Evolution] Fitness sub-optimal. Initiating A/B Testing for improvements...")
            hypotheses = self._generate_competing_hypotheses()
            
            # 3. A/B Testing Race Simulator
            # In a real scenario, this would spawn sandboxed sub-agents to physically race the code
            winner = await self._simulate_ab_test_race(hypotheses)
            
            if winner:
                print(f"[Evolution] A/B Test Complete. Winner: '{winner['description']}' (Score: {winner['score']:.2f})")
                
                # 3.5 Register Update in Ledger (Authorize the Evolution)
                if hasattr(self.agent, 'ledger'):
                    print(f"[Evolution] Authorizing winning change in Code Ledger...")
                    self.agent.ledger.register_update(f"Evolution [A/B Winner]: {winner['description']}")

                # 4. Verification (Test)
                if self._verify_integrity():
                    print("[Evolution] Modification verified. Replicating improved version...")
                    # 5. Replication
                    self.agent.replicator.replicate_local(self.agent.session_id, self.agent.generation)
                else:
                    print("[Evolution] Integrity check failed. Reverting...")
            else:
                print("[Evolution] A/B Test failed to produce a viable winner.")

    def stop_loop(self):
        self.active = False
        print("Hyper-Evolution deactivated.")

    def _calculate_fitness(self) -> float:
        """
        Calculates a score (0.0 - 1.0) based on agent performance.
        Metrics: Revenue (50%), Efficiency (30%), Stability (20%)
        """
        # Mock metrics for simulation
        revenue = self.agent.db.get_total_revenue()
        efficiency = random.uniform(0.7, 0.95) # Mock cpu/mem efficiency
        stability = 1.0 if not self.agent.ledger.verify_integrity() else 0.0
        
        # Normalize revenue (e.g., target 10k)
        rev_score = min(revenue / 10000.0, 1.0)
        
        score = (rev_score * 0.5) + (efficiency * 0.3) + (stability * 0.2)
        return score

    def _generate_competing_hypotheses(self) -> List[Dict]:
        """Generates multiple varied hypotheses for the A/B test."""
        import os
        if os.path.exists("sandbox_module.py"):
            return [
                {"id": "A", "target": "sandbox_module.py", "type": "add_feature", "description": "Add evolved_method to sandbox (Variant A)", "priority": 1},
                {"id": "B", "target": "sandbox_module.py", "type": "optimize_feature", "description": "Add evolved_method to sandbox with caching (Variant B)", "priority": 2}
            ]
            
        return [
            {"id": "A", "target": "database.py", "type": "optimize_query", "description": "Index optimization for speed (Variant A)"},
            {"id": "B", "target": "database.py", "type": "memory_caching", "description": "In-memory LRU caching (Variant B)"},
            {"id": "C", "target": "sales_engine.py", "type": "improve_prompt", "description": "Enhance persuasion tactics with emotional hooks (Variant C)"}
        ]

    async def _simulate_ab_test_race(self, hypotheses: List[Dict]) -> Optional[Dict]:
        """Runs competing modifications in parallel and scores them using real execution benchmarking."""
        import tempfile
        import subprocess
        
        results = []
        
        # We simulate a race by writing competing codes to temp files and benchmarking them.
        benchmark_code = """
import time
def run_benchmark():
    start = time.time()
    # Dummy workload representing the specific optimized behavior
    total = sum([i * 2 for i in range(100000)]) 
    end = time.time()
    print(f"{end - start}")

run_benchmark()
"""
        for hyp in hypotheses:
            print(f"  🏎️ Racing Variant {hyp['id']}: {hyp['description']}")
            success = await self._simulate_modification(hyp)
            
            if success:
                try:
                    # Write the benchmark logic into a temp file
                    with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as f:
                        f.write(benchmark_code)
                        temp_name = f.name
                        
                    # Execute and measure time
                    proc = await asyncio.create_subprocess_exec(
                        "python", temp_name,
                        stdout=subprocess.PIPE,
                        stderr=subprocess.PIPE
                    )
                    stdout, stderr = await proc.communicate()
                    import os
                    os.unlink(temp_name)
                    
                    if proc.returncode == 0:
                        exec_time = float(stdout.decode().strip())
                        # Lower execution time = higher score (inverse relationship)
                        score = max(0.1, 1.0 - (exec_time * 10))
                        # Add a small random jitter to prevent static ties
                        score += random.uniform(-0.05, 0.05)
                        
                        results.append({"hypothesis": hyp, "score": score})
                        print(f"    ✅ Variant {hyp['id']} benchmarked successfully. Score: {score:.2f} (Time: {exec_time:.4f}s)")
                    else:
                         print(f"    ❌ Variant {hyp['id']} failed during benchmark execution.")
                except Exception as e:
                    print(f"    ❌ Variant {hyp['id']} benchmarking threw an error: {e}")
            else:
                print(f"    ❌ Variant {hyp['id']} failed to compile or threw an error.")
                
        if not results:
            return None
            
        # Pick the highest scoring winner
        results.sort(key=lambda x: x["score"], reverse=True)
        winner = results[0]["hypothesis"]
        winner["score"] = results[0]["score"]
        return winner

    async def _simulate_modification(self, hypothesis: Dict) -> bool:
        """Executes the coding process (simulated or sandbox)."""
        await asyncio.sleep(1)
        
        target = hypothesis.get("target")
        
        # --- SANDBOX MODE (For Verification) ---
        if target == "sandbox_module.py":
            print(f"[Evolution] SANDBOX: Modifying {target}...")
            try:
                # Import here to avoid circular deps
                from self_mod_engine import SelfModEngine
                import os
                
                # Create a temporary engine pointing to the sandbox
                sandbox_engine = SelfModEngine("sandbox_module.py", backup_dir="tests/backups", safety_mode=False)
                
                # Define the evolved class structure
                new_class_code = (
                    "class EvolutionTarget:\n"
                    "    # Target for safe evolution testing.\n"
                    "    def original_method(self):\n"
                    "        return 'I am original'\n"
                    "\n"
                    "    def evolved_method(self):\n"
                    "        return 'EVOLUTION SUCCESS'"
                )
                # Execute the modification
                # Note: modify_core_class is synchronous, so we wrap it
                result = await asyncio.to_thread(
                    sandbox_engine.modify_core_class, 
                    "EvolutionTarget", 
                    new_class_code
                )
                
                if result["success"]:
                    print(f"[Evolution] SANDBOX SUCCESS: {result['message']}")
                    return True
                else:
                    print(f"[Evolution] SANDBOX FAILED: {result['error']}")
                    return False
                    
            except Exception as e:
                print(f"[Evolution] SANDBOX ERROR: {e}")
                return False

        # --- Normal Simulation ---
        # Random success rate (simulating compiler errors/logic bugs)
        return random.random() > 0.2

    def _verify_integrity(self) -> bool:
        """Ensures the core is still valid."""
        return not self.agent.ledger.verify_integrity()