"""
Event-Centric Audience Segmentation Service
Author: AI Generated
Created: 2025-11-24 (Refactored for event-centric)
Purpose: Cluster users per event and save to EventAudienceSegment
"""

import numpy as np
from typing import List, Dict, Tuple
from datetime import datetime
from sklearn.cluster import KMeans
from bson import ObjectId

from database import db
from config import settings
from models.event_models import EventAudienceSegment, MarketingContent
from services.data_aggregation import UserDataAggregator
from services.preprocessing import DataCleaner
from services.monitoring import monitor
from services.model_registry import registry


class SegmentationService:
    """
    Event-centric user segmentation via K-Means clustering.
    """
    
    def __init__(self, event_code: str, n_clusters: int = None):
        """
        Initialize segmentation for a specific event.
        
        Args:
            event_code: Event identifier
            n_clusters: Number of segments (default: from settings)
        """
        self.event_code = event_code
        self.n_clusters = n_clusters or settings.N_CLUSTERS
        self.aggregator = UserDataAggregator(event_code)
        self.data_cleaner = DataCleaner()
        self.kmeans = None
        self.scaler = None
        self.feature_names = []
    
    def prepare_feature_matrix(self, user_data: List[Dict]) -> Tuple[np.ndarray, List[str]]:
        """
        Convert aggregated user data into feature matrix.
        
        Uses hybrid approach:
        - Event-specific: ticket_count, spend, is_follower
        - Global: RFM (user's overall power)
        
        Returns: (feature_matrix, user_ids)
        """
        feature_matrix = []
        user_ids = []
        
        for user in user_data:
            # Event-specific features
            event_ticket_count = user.get('event_ticket_count', 0)
            event_total_spend = user.get('event_total_spend', 0)
            is_follower = user.get('is_follower', 0)
            
            # Global RFM (user power)
            global_recency = user.get('global_recency', 999999)
            global_frequency = user.get('global_frequency', 0)
            global_monetary = user.get('global_monetary', 0)
            
            # Combine features
            features = [
                event_ticket_count,
                event_total_spend,
                is_follower,
                global_recency,
                global_frequency,
                global_monetary
            ]
            
            feature_matrix.append(features)
            user_ids.append(str(user['user_id']))
        
        # Store feature names
        self.feature_names = [
            'event_tickets',
            'event_spend',
            'is_follower',
            'global_recency',
            'global_frequency',
            'global_monetary'
        ]
        
        return np.array(feature_matrix), user_ids
    
    def fit_clustering(self, feature_matrix: np.ndarray) -> Tuple[KMeans, List[int]]:
        """
        Fit K-Means with preprocessing.
        """
        # Clean and normalize
        normalized_features, valid_indices = self.data_cleaner.clean_user_features(feature_matrix)
        
        # Save scaler for later use
        self.scaler = self.data_cleaner.scaler
        
        print(f"🔄 Fitting K-Means with {self.n_clusters} clusters...")
        self.kmeans = KMeans(
            n_clusters=self.n_clusters,
            random_state=settings.RANDOM_STATE,
            n_init=10
        )
        self.kmeans.fit(normalized_features)
        
        print(f"✓ Clustering complete. Inertia: {self.kmeans.inertia_:.2f}")
        
        return self.kmeans, valid_indices
    
    def interpret_cluster(self, cluster_id: int) -> Dict:
        """
        Interpret cluster characteristics.
        """
        centroid = self.kmeans.cluster_centers_[cluster_id]
        centroid_original = self.scaler.inverse_transform([centroid])[0]
        
        interpretation = {}
        for i, feature_name in enumerate(self.feature_names):
            interpretation[feature_name] = float(centroid_original[i])
        
        # Generate segment name
        event_spend = interpretation.get('event_spend', 0)
        event_tickets = interpretation.get('event_tickets', 0)
        global_monetary = interpretation.get('global_monetary', 0)
        is_follower = interpretation.get('is_follower', 0)
        
        segment_name = self._generate_segment_name(
            event_spend, event_tickets, global_monetary, is_follower
        )
        
        return {
            "segment_name": segment_name,
            "criteria": interpretation,
            "cluster_id": cluster_id
        }
    
    def _generate_segment_name(
        self,
        event_spend: float,
        event_tickets: float,
        global_monetary: float,
        is_follower: float
    ) -> str:
        """Generate Vietnamese segment name."""
        
        # High spenders on this event
        if event_spend > 500000 and event_tickets > 2:
            return "Khách Hàng VIP Sự Kiện"
        
        # Bought tickets but moderate spend
        elif event_tickets > 0 and event_spend > 100000:
            return "Khách Hàng Tích Cực"
        
        # Only followers, no tickets yet
        elif is_follower > 0.5 and event_tickets == 0:
            return "Người Theo Dõi Tiềm Năng"
        
        # High global value but low event engagement
        elif global_monetary > 1000000 and event_spend < 100000:
            return "Khách Hàng Chưa Khai Phá"
        
        # Low event engagement
        else:
            return "Khách Hàng Ít Tương Tác"
    
    def save_segments_to_db(
        self,
        cluster_interpretations: List[Dict],
        user_ids: List[str],
        labels: np.ndarray
    ) -> List[ObjectId]:
        """
        Save to EventAudienceSegment collection.
        """
        print("🔄 Saving event segments to database...")
        
        segment_ids = []
        
        for cluster_info in cluster_interpretations:
            cluster_id = cluster_info['cluster_id']
            
            # Get user_ids in this cluster
            cluster_user_indices = np.where(labels == cluster_id)[0]
            cluster_user_ids = [ObjectId(user_ids[i]) for i in cluster_user_indices]
            
            segment = EventAudienceSegment(
                event_code=self.event_code,
                segment_name=cluster_info['segment_name'],
                segment_type=cluster_info['segment_name'],  # Can categorize further
                user_count=len(cluster_user_ids),
                user_ids=cluster_user_ids,
                criteria=cluster_info['criteria'],
                marketing_content=None,  # Will be generated by GenAI
                created_at=datetime.utcnow(),
                last_updated=datetime.utcnow()
            )
            
            result = db.event_audience_segments.insert_one(
                segment.dict(by_alias=True, exclude={'id'})
            )
            segment_ids.append(result.inserted_id)
            
            print(f"  ✓ '{segment.segment_name}': {len(cluster_user_ids)} users")
        
        return segment_ids
    
    def run_segmentation(self) -> List[ObjectId]:
        """
        Execute event-centric segmentation pipeline.
        """
        import time
        start_time = time.time()
        
        print("=" * 60)
        print(f"🚀 Segmenting Event: {self.event_code}")
        print("=" * 60)
        
        try:
            # Step 1: Aggregate event users
            user_data = self.aggregator.aggregate_user_features()
            
            if len(user_data) < self.n_clusters:
                print(f"⚠ Not enough users ({len(user_data)}) for {self.n_clusters} clusters")
                return []
            
            # Step 2: Prepare features
            feature_matrix, user_ids = self.prepare_feature_matrix(user_data)
            print(f"✓ Feature matrix: {feature_matrix.shape}")
            
            # Step 3: Clustering
            self.kmeans, valid_indices = self.fit_clustering(feature_matrix)
            user_ids = [user_ids[i] for i in valid_indices]
            
            # Step 4: Get labels
            normalized_features = self.scaler.transform(feature_matrix[valid_indices])
            labels = self.kmeans.labels_
            
            # Step 5: Interpret clusters
            cluster_interpretations = [
                self.interpret_cluster(i) for i in range(self.n_clusters)
            ]
            
            # Step 6: Save to EventAudienceSegment
            segment_ids = self.save_segments_to_db(
                cluster_interpretations,
                user_ids,
                labels
            )
            
            # Step 7: Save model
            metadata = {
                "event_code": self.event_code,
                "n_clusters": self.n_clusters,
                "n_users": len(user_ids),
                "inertia": float(self.kmeans.inertia_)
            }
            registry.save_model(
                self.kmeans,
                f"kmeans_{self.event_code}",
                metadata
            )
            
            # Step 8: Monitoring
            execution_time = time.time() - start_time
            metrics = {
                "event_code": self.event_code,
                "n_users": len(user_ids),
                "n_segments": self.n_clusters,
                "inertia": float(self.kmeans.inertia_),
                "execution_time": execution_time,
                "centroids": self.kmeans.cluster_centers_.tolist()
            }
            monitor.log_segmentation_run(metrics)
            
            print("=" * 60)
            print("✅ Segmentation Complete!")
            print(f"⏱️  Time: {execution_time:.2f}s")
            print("=" * 60)
            
            return segment_ids
        
        except Exception as e:
            monitor.log_error("segmentation", e, {
                "event_code": self.event_code,
                "n_clusters": self.n_clusters
            })
            raise