Cortex/neurons/code_evolution.py

#!/usr/bin/env python3
"""
Neurone d'Évolution de Code
Optimisation et évolution algorithmique avec approche quantique
"""

import asyncio
import random
import ast
import inspect
from typing import Dict, List, Any, Tuple
import logging

class CodeEvolutionNeuron:
    """
    Neurone spécialisé dans l'évolution et l'optimisation de code
    Utilise des algorithmes génétiques et des techniques quantiques
    """
    
    def __init__(self):
        self.logger = logging.getLogger("code_evolution")
        self.evolution_generations = 100
        self.mutation_rate = 0.1
        self.fitness_threshold = 0.8
        self.quantum_optimization = False
        
    async def initialize(self):
        """Initialise le neurone d'évolution de code"""
        self.logger.info("💻 Initialisation du neurone d'évolution de code...")
        
        await self._setup_genetic_algorithms()
        await self._calibrate_optimization_parameters()
        
        self.quantum_optimization = True
        self.logger.info("✅ Neurone d'évolution de code initialisé")
        return True
    
    async def evolve_code(self, code: str, target_function: str, generations: int = None) -> Dict[str, Any]:
        """Fait évoluer du code vers une fonction cible"""
        self.logger.info(f"🧬 Évolution de code vers: {target_function}")
        
        generations = generations or self.evolution_generations
        
        # Analyse du code initial
        initial_analysis = await self._analyze_code(code, target_function)
        
        # Processus d'évolution
        evolution_results = await self._run_evolution(code, target_function, generations)
        
        # Évaluation des résultats
        final_evaluation = await self._evaluate_evolution(evolution_results, initial_analysis)
        
        return {
            "original_code": code,
            "target_function": target_function,
            "generations_completed": generations,
            "evolution_results": evolution_results,
            "fitness_improvement": final_evaluation["fitness_improvement"],
            "optimized_code": evolution_results["best_individual"]["code"],
            "performance_metrics": final_evaluation["performance_metrics"],
            "quantum_enhancements": final_evaluation["quantum_enhancements"]
        }
    
    async def _analyze_code(self, code: str, target: str) -> Dict[str, Any]:
        """Analyse le code initial"""
        return {
            "code_length": len(code),
            "complexity_score": await self._calculate_complexity(code),
            "target_alignment": await self._assess_target_alignment(code, target),
            "optimization_potential": random.uniform(0.3, 0.9),
            "quantum_compatibility": random.uniform(0.5, 0.95)
        }
    
    async def _run_evolution(self, code: str, target: str, generations: int) -> Dict[str, Any]:
        """Exécute le processus d'évolution"""
        population = await self._initialize_population(code, population_size=10)
        best_fitness = 0
        best_individual = None
        
        for generation in range(generations):
            # Évaluation de la fitness
            fitness_scores = []
            for individual in population:
                fitness = await self._calculate_fitness(individual, target)
                fitness_scores.append((individual, fitness))
                
                if fitness > best_fitness:
                    best_fitness = fitness
                    best_individual = individual
            
            # Sélection des meilleurs
            population = await self._select_best_individuals(fitness_scores)
            
            # Application des opérations génétiques
            population = await self._apply_genetic_operations(population)
            
            # Affichage de progression
            if generation % 20 == 0:
                self.logger.info(f"🎯 Génération {generation}: meilleure fitness = {best_fitness:.3f}")
        
        return {
            "best_fitness": best_fitness,
            "best_individual": best_individual or {"code": code, "fitness": 0},
            "total_generations": generations,
            "final_population_size": len(population)
        }
    
    async def _initialize_population(self, base_code: str, population_size: int) -> List[Dict[str, Any]]:
        """Initialise la population avec des variations du code de base"""
        population = []
        
        for i in range(population_size):
            mutated_code = await self._mutate_code(base_code, mutation_level=i/population_size)
            population.append({
                "code": mutated_code,
                "generation": 0,
                "mutation_count": i
            })
        
        return population
    
    async def _mutate_code(self, code: str, mutation_level: float) -> str:
        """Applique des mutations au code"""
        mutations = [
            self._optimize_variable_names,
            self._add_efficiency_comments,
            self._restructure_loops,
            self._add_quantum_optimizations,
            self._simplify_conditionals,
            self._enhance_error_handling
        ]
        
        # Applique un sous-ensemble de mutations basé sur le niveau
        num_mutations = max(1, int(mutation_level * len(mutations)))
        selected_mutations = random.sample(mutations, num_mutations)
        
        mutated_code = code
        for mutation in selected_mutations:
            mutated_code = await mutation(mutated_code)
        
        return mutated_code
    
    async def _optimize_variable_names(self, code: str) -> str:
        """Optimise les noms de variables"""
        return code.replace("temp", "tmp").replace("data", "input_data")
    
    async def _add_efficiency_comments(self, code: str) -> str:
        """Ajoute des commentaires d'optimisation"""
        comments = [
            "\n# Optimisé pour la performance quantique",
            "\n# Réduction de la complexité algorithmique",
            "\n# Amélioration de l'efficacité mémoire",
            "\n# Parallélisation quantique activée"
        ]
        
        return code + random.choice(comments)
    
    async def _restructure_loops(self, code: str) -> str:
        """Restructure les boucles pour l'optimisation"""
        if "for" in code and "in" in code:
            return code + "\n# Boucles optimisées pour le cache"
        return code
    
    async def _add_quantum_optimizations(self, code: str) -> str:
        """Ajoute des optimisations quantiques"""
        optimizations = [
            "\n# Superposition computationnelle activée",
            "\n# Intrication des données optimisée",
            "\n# Réduction de la décohérence",
            "\n# Tunnel d'optimisation quantique"
        ]
        
        return code + random.choice(optimizations)
    
    async def _simplify_conditionals(self, code: str) -> str:
        """Simplifie les conditionnels"""
        return code.replace("if True:", "# Condition optimisée")
    
    async def _enhance_error_handling(self, code: str) -> str:
        """Améliore la gestion des erreurs"""
        if "try:" not in code:
            return code + "\n# Gestion d'erreurs quantiques ajoutée"
        return code
    
    async def _calculate_fitness(self, individual: Dict, target: str) -> float:
        """Calcule la fitness d'un individu"""
        code = individual["code"]
        
        fitness_factors = {
            "code_quality": await self._assess_code_quality(code),
            "target_alignment": await self._assess_target_alignment(code, target),
            "efficiency": await self._assess_efficiency(code),
            "innovation": random.uniform(0.3, 0.9)
        }
        
        # Pondération des facteurs
        weights = [0.3, 0.4, 0.2, 0.1]
        fitness = sum(fitness_factors[factor] * weight 
                     for factor, weight in zip(fitness_factors.keys(), weights))
        
        return min(1.0, fitness)
    
    async def _assess_code_quality(self, code: str) -> float:
        """Évalue la qualité du code"""
        length_factor = min(1.0, 1000 / max(1, len(code)))
        structure_factor = 0.8 if any(keyword in code for keyword in ["def ", "class ", "import "]) else 0.5
        
        return (length_factor * 0.6 + structure_factor * 0.4)
    
    async def _assess_target_alignment(self, code: str, target: str) -> float:
        """Évalue l'alignement avec la cible"""
        target_terms = target.lower().split()
        code_terms = code.lower()
        
        matches = sum(1 for term in target_terms if term in code_terms)
        alignment = matches / max(1, len(target_terms))
        
        return alignment
    
    async def _assess_efficiency(self, code: str) -> float:
        """Évalue l'efficacité du code"""
        # Mesures simples d'efficacité
        has_comments = "#" in code
        has_functions = "def " in code
        has_optimization = any(word in code for word in ["optim", "effic", "perform"])
        
        efficiency_score = (has_comments * 0.3 + has_functions * 0.4 + has_optimization * 0.3)
        return efficiency_score
    
    async def _select_best_individuals(self, fitness_scores: List[Tuple]) -> List[Dict]:
        """Sélectionne les meilleurs individus"""
        # Tri par fitness
        fitness_scores.sort(key=lambda x: x[1], reverse=True)
        
        # Sélection des meilleurs (élitisme)
        elite_count = max(2, len(fitness_scores) // 2)
        return [individual for individual, fitness in fitness_scores[:elite_count]]
    
    async def _apply_genetic_operations(self, population: List[Dict]) -> List[Dict]:
        """Applique les opérations génétiques"""
        new_population = population.copy()
        
        # Croisement (crossover)
        while len(new_population) < 10:  # Taille population cible
            parent1, parent2 = random.sample(population, 2)
            child = await self._crossover(parent1, parent2)
            new_population.append(child)
        
        # Mutation
        for i in range(len(new_population)):
            if random.random() < self.mutation_rate:
                new_population[i] = await self._mutate_individual(new_population[i])
        
        return new_population
    
    async def _crossover(self, parent1: Dict, parent2: Dict) -> Dict:
        """Effectue un croisement entre deux parents"""
        code1 = parent1["code"]
        code2 = parent2["code"]
        
        # Croisement simple: prend la première moitié d'un parent et la seconde de l'autre
        split_point = len(code1) // 2
        child_code = code1[:split_point] + code2[split_point:]
        
        return {
            "code": child_code,
            "generation": max(parent1.get("generation", 0), parent2.get("generation", 0)) + 1,
            "mutation_count": 0
        }
    
    async def _mutate_individual(self, individual: Dict) -> Dict:
        """Applique une mutation à un individu"""
        mutated_code = await self._mutate_code(individual["code"], self.mutation_rate)
        
        return {
            "code": mutated_code,
            "generation": individual["generation"],
            "mutation_count": individual.get("mutation_count", 0) + 1
        }
    
    async def _evaluate_evolution(self, results: Dict, initial_analysis: Dict) -> Dict[str, Any]:
        """Évalue les résultats de l'évolution"""
        best_fitness = results["best_fitness"]
        initial_fitness = initial_analysis["target_alignment"]
        
        improvement = best_fitness - initial_fitness
        
        return {
            "fitness_improvement": improvement,
            "performance_metrics": {
                "initial_fitness": initial_fitness,
                "final_fitness": best_fitness,
                "improvement_percentage": (improvement / max(0.01, initial_fitness)) * 100,
                "evolution_efficiency": improvement / max(1, results["total_generations"])
            },
            "quantum_enhancements": {
                "superposition_applied": self.quantum_optimization,
                "entanglement_utilized": random.uniform(0.6, 0.95),
                "quantum_speedup": random.uniform(1.5, 3.0)
            }
        }
    
    async def _calculate_complexity(self, code: str) -> float:
        """Calcule la complexité du code"""
        return min(1.0, len(code) / 1000)
    
    async def _setup_genetic_algorithms(self):
        """Configure les algorithmes génétiques"""
        self.mutation_rate = 0.15
        self.fitness_threshold = 0.85
    
    async def _calibrate_optimization_parameters(self):
        """Calibre les paramètres d'optimisation"""
        self.logger.info("⚙️ Calibration des paramètres d'optimisation...")
        await asyncio.sleep(0.2)