from ..core.base import BaseModel
import numpy as np
import pandas as pd
from typing import Dict, List, Optional, Union, Tuple, Callable
from scipy.optimize import minimize, differential_evolution, NonlinearConstraint
import optuna
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel
import tensorflow as tf
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.core.problem import Problem
from pymoo.factory import get_sampling, get_crossover, get_mutation
from pymoo.optimize import minimize as pymoo_minimize

class AdvancedMarketingOptimizer(BaseModel):
    """
    Gelişmiş Pazarlama Optimizasyonu Modeli
    
    Özellikler:
    - Çoklu hedef optimizasyonu
    - Bayesian optimizasyon
    - Kısıtlı optimizasyon
    - Bütçe optimizasyonu
    - Kampanya optimizasyonu
    """
    
    def __init__(self,
                 method: str = 'bayesian',
                 n_objectives: int = 1,
                 constraints: Optional[Dict] = None,
                 random_state: int = 42):
        super().__init__()
        self.method = method
        self.n_objectives = n_objectives
        self.constraints = constraints or {}
        self.random_state = random_state
        
        # Optimizasyon bileşenleri
        self.gp_model = None
        self.study = None
        self.best_solution = None
        self.pareto_front = None
        
        self._initialize_components()
        
    def _initialize_components(self):
        """Optimizasyon bileşenlerini başlat"""
        if self.method == 'bayesian':
            kernel = ConstantKernel(1.0) * RBF([1.0] * self.n_objectives)
            self.gp_model = GaussianProcessRegressor(
                kernel=kernel,
                random_state=self.random_state
            )
            
    def _create_multi_objective_problem(self,
                                      objective_functions: List[Callable],
                                      bounds: List[Tuple],
                                      constraints: List[Dict]) -> Problem:
        """Çoklu hedef optimizasyon problemi oluştur"""
        class MarketingProblem(Problem):
            def __init__(self, obj_funcs, bounds, constraints):
                super().__init__(
                    n_var=len(bounds),
                    n_obj=len(obj_funcs),
                    n_constr=len(constraints),
                    xl=np.array([b[0] for b in bounds]),
                    xu=np.array([b[1] for b in bounds])
                )
                self.obj_funcs = obj_funcs
                self.constraints = constraints
                
            def _evaluate(self, x, out, *args, **kwargs):
                # Hedef fonksiyonları hesapla
                f = np.column_stack([obj(x) for obj in self.obj_funcs])
                
                # Kısıtlamaları hesapla
                if self.constraints:
                    g = np.column_stack([
                        constr['fun'](x) for constr in self.constraints
                    ])
                    out["G"] = g
                    
                out["F"] = f
                
        return MarketingProblem(objective_functions, bounds, constraints)
        
    def _bayesian_optimization(self,
                             objective: Callable,
                             bounds: List[Tuple],
                             n_trials: int = 100) -> Dict:
        """Bayesian optimizasyon"""
        X_samples = []
        y_samples = []
        
        def acquisition_function(x, gp, best_value):
            mean, std = gp.predict(x.reshape(1, -1), return_std=True)
            z = (mean - best_value) / std
            return -(mean + 1.96 * std)  # Upper Confidence Bound
            
        for _ in range(n_trials):
            if len(X_samples) > 0:
                # GP modeli güncelle
                self.gp_model.fit(np.array(X_samples), np.array(y_samples))
                
                # Acquisition fonksiyonu ile yeni nokta seç
                best_value = min(y_samples)
                res = differential_evolution(
                    lambda x: acquisition_function(x, self.gp_model, best_value),
                    bounds
                )
                x_next = res.x
            else:
                # İlk noktayı random seç
                x_next = np.random.uniform(
                    [b[0] for b in bounds],
                    [b[1] for b in bounds]
                )
                
            # Yeni noktayı değerlendir
            y_next = objective(x_next)
            
            X_samples.append(x_next)
            y_samples.append(y_next)
            
        best_idx = np.argmin(y_samples)
        return {
            'x': X_samples[best_idx],
            'fun': y_samples[best_idx],
            'x_samples': X_samples,
            'y_samples': y_samples
        }
        
    def optimize_budget_allocation(self,
                                 channels: List[str],
                                 total_budget: float,
                                 objective_function: Callable,
                                 channel_constraints: Optional[Dict] = None) -> Dict:
        """
        Bütçe dağılımı optimizasyonu
        
        Parameters:
        -----------
        channels: Kanal listesi
        total_budget: Toplam bütçe
        objective_function: Hedef fonksiyon
        channel_constraints: Kanal bazlı kısıtlar
        
        Returns:
        --------
        Dict: Optimal bütçe dağılımı
        """
        n_channels = len(channels)
        
        # Kısıtları oluştur
        constraints = [
            {'type': 'eq', 'fun': lambda x: np.sum(x) - total_budget}
        ]
        
        if channel_constraints:
            for channel, (min_budget, max_budget) in channel_constraints.items():
                idx = channels.index(channel)
                if min_budget is not None:
                    constraints.append(
                        {'type': 'ineq', 'fun': lambda x, i=idx: x[i] - min_budget}
                    )
                if max_budget is not None:
                    constraints.append(
                        {'type': 'ineq', 'fun': lambda x, i=idx: max_budget - x[i]}
                    )
                    
        # Bounds
        bounds = [(0, total_budget)] * n_channels
        
        # Optimizasyon
        if self.method == 'bayesian':
            result = self._bayesian_optimization(
                objective_function,
                bounds
            )
        else:
            result = minimize(
                objective_function,
                x0=np.ones(n_channels) * total_budget / n_channels,
                bounds=bounds,
                constraints=constraints,
                method='SLSQP'
            )
            
        # Sonuçları formatla
        allocation = {
            channel: budget for channel, budget in zip(channels, result['x'])
        }
        
        return {
            'allocation': allocation,
            'objective_value': -result['fun'],
            'success': True if self.method == 'bayesian' else result.success
        }
        
    def optimize_campaign_parameters(self,
                                   objective_functions: List[Callable],
                                   parameter_bounds: Dict[str, Tuple],
                                   constraints: Optional[List[Dict]] = None) -> Dict:
        """
        Kampanya parametrelerini optimize et
        
        Parameters:
        -----------
        objective_functions: Hedef fonksiyonlar listesi
        parameter_bounds: Parametre sınırları
        constraints: Kısıtlar
        
        Returns:
        --------
        Dict: Optimal kampanya parametreleri
        """
        if self.n_objectives > 1:
            # Çoklu hedef optimizasyonu
            problem = self._create_multi_objective_problem(
                objective_functions,
                list(parameter_bounds.values()),
                constraints or []
            )
            
            algorithm = NSGA2(
                pop_size=100,
                sampling=get_sampling("real_random"),
                crossover=get_crossover("real_sbx", prob=0.9),
                mutation=get_mutation("real_pm", prob=0.1),
                eliminate_duplicates=True
            )
            
            results = pymoo_minimize(
                problem,
                algorithm,
                ('n_gen', 100),
                seed=self.random_state,
                verbose=False
            )
            
            self.pareto_front = results.F
            optimal_solutions = results.X
            
            return {
                'pareto_optimal_solutions': [
                    dict(zip(parameter_bounds.keys(), solution))
                    for solution in optimal_solutions
                ],
                'objective_values': results.F,
                'success': results.success
            }
            
        else:
            # Tek hedef optimizasyonu
            def combined_objective(x):
                return objective_functions[0](dict(zip(parameter_bounds.keys(), x)))
                
            if self.method == 'bayesian':
                result = self._bayesian_optimization(
                    combined_objective,
                    list(parameter_bounds.values())
                )
            else:
                result = minimize(
                    combined_objective,
                    x0=np.mean(list(parameter_bounds.values()), axis=1),
                    bounds=list(parameter_bounds.values()),
                    constraints=constraints,
                    method='SLSQP'
                )
                
            optimal_params = dict(zip(parameter_bounds.keys(), result['x']))
            
            return {
                'optimal_parameters': optimal_params,
                'objective_value': -result['fun'],
                'success': True if self.method == 'bayesian' else result.success
            }
            
    def optimize_timing(self,
                       performance_function: Callable,
                       time_windows: List[Tuple[float, float]],
                       frequency_constraints: Optional[Dict] = None) -> Dict:
        """
        Kampanya zamanlama optimizasyonu
        
        Parameters:
        -----------
        performance_function: Performans fonksiyonu
        time_windows: Zaman pencereleri
        frequency_constraints: Frekans kısıtları
        
        Returns:
        --------
        Dict: Optimal zamanlama
        """
        def objective(x):
            return -performance_function(x)
            
        constraints = []
        if frequency_constraints:
            for constraint_type, value in frequency_constraints.items():
                if constraint_type == 'min_gap':
                    constraints.append(
                        NonlinearConstraint(
                            lambda x: np.min(np.diff(x)),
                            value,
                            np.inf
                        )
                    )
                elif constraint_type == 'max_frequency':
                    constraints.append(
                        NonlinearConstraint(
                            lambda x: len(x),
                            0,
                            value
                        )
                    )
                    
        result = minimize(
            objective,
            x0=np.mean(time_windows, axis=1),
            bounds=time_windows,
            constraints=constraints,
            method='SLSQP'
        )
        
        return {
            'optimal_timing': result.x,
            'performance_value': -result.fun,
            'success': result.success
        }
        
    def get_optimization_insights(self) -> Dict:
        """Optimizasyon içgörüleri"""
        insights = {}
        
        if self.method == 'bayesian' and self.gp_model is not None:
            insights['acquisition_function_values'] = self.gp_model.predict(
                np.array(self.best_solution['x_samples'])
            )
            
        if self.pareto_front is not None:
            insights['pareto_front'] = self.pareto_front
            insights['pareto_front_analysis'] = {
                'spread': np.max(self.pareto_front, axis=0) - np.min(self.pareto_front, axis=0),
                'density': len(self.pareto_front) / np.prod(
                    np.max(self.pareto_front, axis=0) - np.min(self.pareto_front, axis=0)
                )
            }
            
        return insights 