ai_agent_framework/dimensions/contextual_personalization.py

"""
Contextual Personalization & User Profiling System
===============================================

Advanced user profiling system that builds user-specific contextual profiles,
continuously updates them based on interactions, and enables behavioral adaptation.
"""

import asyncio
import json
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Set, Tuple, Union
from dataclasses import dataclass, field, asdict
from enum import Enum
import numpy as np
from collections import defaultdict, deque
import hashlib
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA

from ai_agent_framework.core.context_engineering_agent import (
    ContextElement, ContextModality, ContextDimension, ContextEngineeringAgent
)

logger = logging.getLogger(__name__)


class ProfileType(Enum):
    """Types of user profiles."""
    BEHAVIORAL = "behavioral"
    PREFERENTIAL = "preferential"
    CONTEXTUAL = "contextual"
    INTERACTION = "interaction"
    LEARNING = "learning"
    COLLABORATIVE = "collaborative"
    TEMPORAL = "temporal"


class LearningType(Enum):
    """Types of learning patterns."""
    GRADUAL = "gradual"
    RAPID = "rapid"
    CYCLICAL = "cyclical"
    EVENT_DRIVEN = "event_driven"
    ADAPTIVE = "adaptive"


@dataclass
class UserInteraction:
    """Represents a user interaction with the system."""
    interaction_id: str
    user_id: str
    interaction_type: str
    content: Dict[str, Any]
    context: Dict[str, Any]
    timestamp: datetime
    duration: float
    success: bool
    satisfaction_score: Optional[float] = None
    adaptation_needed: bool = False
    
    def __post_init__(self):
        if not self.interaction_id:
            self.interaction_id = f"interaction_{int(time.time())}_{hash(str(self.content))}"
        if not self.timestamp:
            self.timestamp = datetime.utcnow()


@dataclass
class UserPreference:
    """Represents a user preference."""
    preference_id: str
    user_id: str
    category: str
    preference_type: str
    value: Any
    confidence: float
    stability: float
    last_updated: datetime
    evidence_count: int
    
    def __post_init__(self):
        if not self.preference_id:
            self.preference_id = f"pref_{self.user_id}_{self.category}_{hash(str(self.value))}"
        if not self.last_updated:
            self.last_updated = datetime.utcnow()


@dataclass
class ContextualPattern:
    """Represents a contextual usage pattern."""
    pattern_id: str
    user_id: str
    pattern_type: str
    context_elements: Set[str]
    frequency: float
    success_rate: float
    last_observed: datetime
    confidence: float
    
    def __post_init__(self):
        if not self.pattern_id:
            self.pattern_id = f"pattern_{self.user_id}_{self.pattern_type}_{int(time.time())}"


@dataclass
class UserProfile:
    """Comprehensive user profile for contextual personalization."""
    user_id: str
    profile_type: ProfileType
    data: Dict[str, Any]
    created_at: datetime
    updated_at: datetime
    version: int
    completeness_score: float
    confidence_score: float
    
    def __post_init__(self):
        if not self.created_at:
            self.created_at = datetime.utcnow()
        if not self.updated_at:
            self.updated_at = self.created_at
        if self.version == 0:
            self.version = 1


class ContextualPersonalizationEngine:
    """Core engine for contextual personalization and user profiling."""
    
    def __init__(self):
        self.user_profiles = {}  # user_id -> dict of profile_type -> UserProfile
        self.interaction_history = {}  # user_id -> List[UserInteraction]
        self.preference_database = {}  # user_id -> List[UserPreference]
        self.pattern_database = {}  # user_id -> List[ContextualPattern]
        
        self.profile_weights = {
            ProfileType.BEHAVIORAL: 0.25,
            ProfileType.PREFERENTIAL: 0.20,
            ProfileType.CONTEXTUAL: 0.20,
            ProfileType.INTERACTION: 0.15,
            ProfileType.LEARNING: 0.10,
            ProfileType.COLLABORATIVE: 0.05,
            ProfileType.TEMPORAL: 0.05
        }
        
        self.learning_algorithms = {
            "incremental": self._incremental_learning,
            "batch": self._batch_learning,
            "reinforcement": self._reinforcement_learning,
            "association": self._association_learning
        }
        
        self.adaptation_strategies = {
            "gradual": self._gradual_adaptation,
            "immediate": self._immediate_adaptation,
            "predictive": self._predictive_adaptation
        }
    
    async def process_user_interaction(self, interaction: UserInteraction) -> Dict[str, Any]:
        """Process a new user interaction and update profiles."""
        
        try:
            # Step 1: Extract insights from interaction
            insights = await self._extract_interaction_insights(interaction)
            
            # Step 2: Update relevant profiles
            updated_profiles = await self._update_profiles_from_interaction(interaction, insights)
            
            # Step 3: Identify new patterns
            new_patterns = await self._identify_contextual_patterns(interaction, insights)
            
            # Step 4: Update preferences
            updated_preferences = await self._update_preferences(interaction, insights)
            
            # Step 5: Generate adaptation recommendations
            adaptations = await self._generate_adaptation_recommendations(
                interaction, updated_profiles, insights
            )
            
            return {
                "interaction_id": interaction.interaction_id,
                "insights": insights,
                "updated_profiles": updated_profiles,
                "new_patterns": [asdict(pattern) for pattern in new_patterns],
                "updated_preferences": updated_preferences,
                "adaptation_recommendations": adaptations,
                "processing_success": True
            }
            
        except Exception as e:
            logger.error(f"Failed to process user interaction: {e}")
            return {
                "interaction_id": interaction.interaction_id,
                "error": str(e),
                "processing_success": False
            }
    
    async def build_user_profile(
        self, 
        user_id: str, 
        profile_type: ProfileType,
        include_interaction_history: bool = True
    ) -> UserProfile:
        """Build a comprehensive user profile."""
        
        # Get available data for the user
        interaction_data = self.interaction_history.get(user_id, [])
        preference_data = self.preference_database.get(user_id, [])
        pattern_data = self.pattern_database.get(user_id, [])
        
        if not interaction_data and not preference_data:
            return await self._create_empty_profile(user_id, profile_type)
        
        # Build profile based on type
        if profile_type == ProfileType.BEHAVIORAL:
            return await self._build_behavioral_profile(user_id, interaction_data, preference_data)
        elif profile_type == ProfileType.PREFERENTIAL:
            return await self._build_preferential_profile(user_id, preference_data, interaction_data)
        elif profile_type == ProfileType.CONTEXTUAL:
            return await self._build_contextual_profile(user_id, interaction_data, pattern_data)
        elif profile_type == ProfileType.INTERACTION:
            return await self._build_interaction_profile(user_id, interaction_data)
        elif profile_type == ProfileType.LEARNING:
            return await self._build_learning_profile(user_id, interaction_data)
        elif profile_type == ProfileType.COLLABORATIVE:
            return await self._build_collaborative_profile(user_id, interaction_data, pattern_data)
        elif profile_type == ProfileType.TEMPORAL:
            return await self._build_temporal_profile(user_id, interaction_data, pattern_data)
        else:
            return await self._build_generic_profile(user_id, profile_type, interaction_data)
    
    async def update_cross_session_context(
        self, 
        user_id: str, 
        current_context: Dict[str, Any],
        session_duration: float
    ) -> Dict[str, Any]:
        """Update and maintain cross-session context continuity."""
        
        # Get or create user profile
        profile = await self.get_user_profile(user_id, ProfileType.CONTEXTUAL)
        
        # Extract current session context
        session_context = {
            "session_start": datetime.utcnow(),
            "session_duration": session_duration,
            "context_elements": current_context,
            "session_type": self._classify_session_type(current_context)
        }
        
        # Update persistent context
        persistent_context = await self._update_persistent_context(
            profile.data.get("persistent_context", {}), 
            session_context
        )
        
        # Identify context patterns across sessions
        context_patterns = await self._identify_cross_session_patterns(
            user_id, persistent_context, session_context
        )
        
        # Generate continuity recommendations
        continuity_recommendations = await self._generate_continuity_recommendations(
            user_id, persistent_context, context_patterns
        )
        
        # Update profile
        updated_data = profile.data.copy()
        updated_data.update({
            "persistent_context": persistent_context,
            "cross_session_patterns": context_patterns,
            "last_session": session_context,
            "continuity_strength": self._calculate_continuity_strength(context_patterns)
        })
        
        updated_profile = UserProfile(
            user_id=user_id,
            profile_type=ProfileType.CONTEXTUAL,
            data=updated_data,
            created_at=profile.created_at,
            updated_at=datetime.utcnow(),
            version=profile.version + 1,
            completeness_score=self._calculate_profile_completeness(updated_data),
            confidence_score=self._calculate_profile_confidence(updated_data)
        )
        
        # Store updated profile
        await self._store_user_profile(updated_profile)
        
        return {
            "persistent_context": persistent_context,
            "context_patterns": context_patterns,
            "continuity_recommendations": continuity_recommendations,
            "continuity_strength": updated_profile.data.get("continuity_strength", 0.0)
        }
    
    async def generate_personalized_adaptation(
        self, 
        user_id: str, 
        current_context: Dict[str, Any],
        adaptation_type: str = "gradual"
    ) -> Dict[str, Any]:
        """Generate personalized adaptation based on user profile."""
        
        # Get relevant profiles
        profiles = await self._get_user_profiles(user_id)
        
        # Extract user characteristics
        user_characteristics = await self._extract_user_characteristics(profiles)
        
        # Analyze current context
        context_analysis = await self._analyze_current_context(current_context, user_characteristics)
        
        # Generate adaptation strategy
        adaptation_strategy = await self.adaptation_strategies.get(
            adaptation_type, self._gradual_adaptation
        )(user_id, context_analysis, user_characteristics)
        
        # Validate adaptation
        validated_adaptation = await self._validate_adaptation(
            adaptation_strategy, user_characteristics, current_context
        )
        
        return validated_adaptation
    
    async def _extract_interaction_insights(self, interaction: UserInteraction) -> Dict[str, Any]:
        """Extract insights from user interaction."""
        
        insights = {
            "interaction_complexity": self._calculate_interaction_complexity(interaction),
            "user_engagement_level": self._calculate_engagement_level(interaction),
            "context_sensitivity": self._calculate_context_sensitivity(interaction),
            "learning_velocity": self._calculate_learning_velocity(interaction),
            "preference_signals": self._extract_preference_signals(interaction),
            "behavioral_patterns": self._extract_behavioral_patterns(interaction),
            "success_factors": self._identify_success_factors(interaction),
            "adaptation_triggers": self._identify_adaptation_triggers(interaction)
        }
        
        return insights
    
    def _calculate_interaction_complexity(self, interaction: UserInteraction) -> float:
        """Calculate interaction complexity score."""
        
        content_complexity = 0.0
        context_complexity = 0.0
        duration_factor = min(1.0, interaction.duration / 3600)  # Normalize by hour
        
        # Content complexity
        content_size = len(str(interaction.content))
        if content_size > 10000:
            content_complexity = 1.0
        elif content_size > 1000:
            content_complexity = 0.7
        elif content_size > 100:
            content_complexity = 0.4
        else:
            content_complexity = 0.2
        
        # Context complexity
        context_size = len(str(interaction.context))
        if context_size > 5000:
            context_complexity = 1.0
        elif context_size > 500:
            context_complexity = 0.7
        elif context_size > 50:
            context_complexity = 0.4
        else:
            context_complexity = 0.1
        
        # Overall complexity
        complexity = (content_complexity + context_complexity) * 0.4 + duration_factor * 0.2
        return min(1.0, complexity)
    
    def _calculate_engagement_level(self, interaction: UserInteraction) -> float:
        """Calculate user engagement level."""
        
        # Factors: duration, success, interaction types, context richness
        duration_score = min(1.0, interaction.duration / 1800)  # 30 minutes max
        
        success_score = 1.0 if interaction.success else 0.3
        
        interaction_type_diversity = len(set(
            interaction.content.get("interaction_types", [])
        )) / 10  # Normalize
        
        context_richness = len(interaction.context) / 20  # Normalize
        
        # Weighted combination
        engagement = (
            duration_score * 0.3 +
            success_score * 0.3 +
            min(1.0, interaction_type_diversity) * 0.2 +
            min(1.0, context_richness) * 0.2
        )
        
        return min(1.0, engagement)
    
    def _calculate_context_sensitivity(self, interaction: UserInteraction) -> float:
        """Calculate context sensitivity of the interaction."""
        
        # Analyze how much the interaction depends on context
        context_dependent_elements = 0
        total_elements = 0
        
        # Check content for context dependencies
        content_str = json.dumps(interaction.content)
        context_markers = ["context", "situation", "environment", "previous", "history"]
        
        for marker in context_markers:
            if marker in content_str.lower():
                context_dependent_elements += 1
        
        total_elements += len(context_markers)
        
        # Check context richness
        context_size = len(interaction.context)
        context_richness = min(1.0, context_size / 50)
        
        if total_elements > 0:
            context_sensitivity = (context_dependent_elements / total_elements) * 0.6 + context_richness * 0.4
        else:
            context_sensitivity = context_richness
        
        return min(1.0, context_sensitivity)
    
    def _calculate_learning_velocity(self, interaction: UserInteraction) -> float:
        """Calculate learning velocity based on interaction patterns."""
        
        # This would need historical data to calculate properly
        # For now, use a simplified calculation
        
        # Learning indicators
        time_taken = interaction.duration
        success = interaction.success
        adaptation_needed = interaction.adaptation_needed
        
        # Velocity calculation
        if time_taken < 300:  # 5 minutes
            velocity = 1.0 if success else 0.7
        elif time_taken < 900:  # 15 minutes
            velocity = 0.8 if success else 0.5
        else:
            velocity = 0.6 if success else 0.3
        
        # Adjust for adaptation need
        if adaptation_needed:
            velocity *= 0.8
        
        return min(1.0, velocity)
    
    def _extract_preference_signals(self, interaction: UserInteraction) -> List[Dict[str, Any]]:
        """Extract preference signals from interaction."""
        
        signals = []
        content = interaction.content
        context = interaction.context
        
        # Look for explicit preferences
        if "preferences" in content:
            for pref_category, pref_value in content["preferences"].items():
                signals.append({
                    "type": "explicit",
                    "category": pref_category,
                    "value": pref_value,
                    "confidence": 0.9,
                    "timestamp": interaction.timestamp
                })
        
        # Look for implicit preferences
        if "choices" in content:
            for choice in content["choices"]:
                signals.append({
                    "type": "implicit",
                    "category": choice.get("category", "unknown"),
                    "value": choice.get("selected", choice.get("value")),
                    "confidence": 0.7,
                    "timestamp": interaction.timestamp
                })
        
        # Look for interaction style preferences
        if "interaction_style" in context:
            signals.append({
                "type": "behavioral",
                "category": "interaction_style",
                "value": context["interaction_style"],
                "confidence": 0.8,
                "timestamp": interaction.timestamp
            })
        
        return signals
    
    def _extract_behavioral_patterns(self, interaction: UserInteraction) -> List[Dict[str, Any]]:
        """Extract behavioral patterns from interaction."""
        
        patterns = []
        
        # Time patterns
        if "timestamp" in interaction.context:
            hour = interaction.timestamp.hour
            patterns.append({
                "type": "temporal",
                "pattern": f"active_at_hour_{hour}",
                "strength": 1.0,
                "context": {"hour": hour}
            })
        
        # Interaction style patterns
        if "interaction_type" in interaction.content:
            patterns.append({
                "type": "interaction_style",
                "pattern": f"prefers_{interaction.content['interaction_type']}",
                "strength": 0.8,
                "context": interaction.content["interaction_type"]
            })
        
        # Success pattern
        if interaction.success:
            patterns.append({
                "type": "success_pattern",
                "pattern": "successful_interaction",
                "strength": 1.0,
                "context": {"duration": interaction.duration}
            })
        
        return patterns
    
    def _identify_success_factors(self, interaction: UserInteraction) -> Dict[str, float]:
        """Identify factors that contribute to interaction success."""
        
        factors = {}
        
        # Duration factor
        optimal_duration = 600  # 10 minutes
        duration_ratio = 1.0 - abs(interaction.duration - optimal_duration) / optimal_duration
        factors["duration_optimization"] = max(0.0, duration_ratio)
        
        # Context factor
        context_richness = min(1.0, len(interaction.context) / 20)
        factors["context_richness"] = context_richness
        
        # Engagement factor
        engagement = self._calculate_engagement_level(interaction)
        factors["engagement_level"] = engagement
        
        return factors
    
    def _identify_adaptation_triggers(self, interaction: UserInteraction) -> List[Dict[str, Any]]:
        """Identify triggers that would necessitate adaptation."""
        
        triggers = []
        
        # Performance triggers
        if not interaction.success:
            triggers.append({
                "type": "performance",
                "trigger": "interaction_failed",
                "severity": 0.8,
                "timestamp": interaction.timestamp
            })
        
        # Engagement triggers
        engagement = self._calculate_engagement_level(interaction)
        if engagement < 0.5:
            triggers.append({
                "type": "engagement",
                "trigger": "low_engagement",
                "severity": 0.6,
                "context": {"engagement": engagement}
            })
        
        # Duration triggers
        if interaction.duration > 3600:  # 1 hour
            triggers.append({
                "type": "duration",
                "trigger": "prolonged_interaction",
                "severity": 0.4,
                "context": {"duration": interaction.duration}
            })
        
        return triggers
    
    # Profile building methods
    
    async def _build_behavioral_profile(
        self, 
        user_id: str, 
        interactions: List[UserInteraction],
        preferences: List[UserPreference]
    ) -> UserProfile:
        """Build behavioral profile for user."""
        
        behavioral_data = {
            "interaction_patterns": {},
            "success_patterns": {},
            "preference_stability": {},
            "adaptation_frequency": 0.0,
            "learning_style": "unknown",
            "communication_style": "unknown",
            "problem_solving_approach": "unknown"
        }
        
        # Analyze interaction patterns
        interaction_times = [interaction.timestamp for interaction in interactions]
        if interaction_times:
            # Time patterns
            hours = [interaction.timestamp.hour for interaction in interactions]
            behavioral_data["time_preferences"] = {
                "peak_hours": self._find_peak_hours(hours),
                "session_duration_pattern": self._analyze_duration_patterns(interactions)
            }
        
        # Analyze success patterns
        successful_interactions = [i for i in interactions if i.success]
        if interactions:
            success_rate = len(successful_interactions) / len(interactions)
            behavioral_data["success_metrics"] = {
                "overall_success_rate": success_rate,
                "average_session_duration": np.mean([i.duration for i in interactions]),
                "adaptation_frequency": np.mean([i.adaptation_needed for i in interactions])
            }
        
        # Communication style analysis
        communication_patterns = self._analyze_communication_style(interactions)
        behavioral_data.update(communication_patterns)
        
        # Learning style analysis
        learning_style = self._determine_learning_style(interactions)
        behavioral_data["learning_style"] = learning_style
        
        return UserProfile(
            user_id=user_id,
            profile_type=ProfileType.BEHAVIORAL,
            data=behavioral_data,
            created_at=datetime.utcnow(),
            updated_at=datetime.utcnow(),
            version=1,
            completeness_score=self._calculate_profile_completeness(behavioral_data),
            confidence_score=self._calculate_behavioral_confidence(behavioral_data)
        )
    
    async def _build_preferential_profile(
        self, 
        user_id: str, 
        preferences: List[UserPreference],
        interactions: List[UserInteraction]
    ) -> UserProfile:
        """Build preferential profile for user."""
        
        pref_data = {
            "explicit_preferences": {},
            "implicit_preferences": {},
            "preference_confidence": {},
            "preference_stability": {},
            "conflict_resolution": "unknown",
            "adaptation_to_new": "unknown"
        }
        
        # Process explicit preferences
        for pref in preferences:
            if pref.confidence > 0.7:  # High confidence preferences
                if pref.category not in pref_data["explicit_preferences"]:
                    pref_data["explicit_preferences"][pref.category] = {}
                
                pref_data["explicit_preferences"][pref.category][pref.preference_type] = {
                    "value": pref.value,
                    "confidence": pref.confidence,
                    "stability": pref.stability
                }
        
        # Process implicit preferences from interactions
        implicit_prefs = self._extract_implicit_preferences(interactions)
        pref_data["implicit_preferences"] = implicit_prefs
        
        # Calculate preference metrics
        if preferences:
            avg_confidence = np.mean([p.confidence for p in preferences])
            avg_stability = np.mean([p.stability for p in preferences])
            
            pref_data["preference_confidence"]["average"] = avg_confidence
            pref_data["preference_stability"]["average"] = avg_stability
        
        return UserProfile(
            user_id=user_id,
            profile_type=ProfileType.PREFERENTIAL,
            data=pref_data,
            created_at=datetime.utcnow(),
            updated_at=datetime.utcnow(),
            version=1,
            completeness_score=self._calculate_profile_completeness(pref_data),
            confidence_score=self._calculate_preference_confidence(pref_data)
        )
    
    async def _build_contextual_profile(
        self, 
        user_id: str, 
        interactions: List[UserInteraction],
        patterns: List[ContextualPattern]
    ) -> UserProfile:
        """Build contextual profile for user."""
        
        context_data = {
            "frequent_contexts": {},
            "context_transitions": {},
            "context_sensitivity": 0.0,
            "persistent_context": {},
            "cross_session_patterns": [],
            "context_evolution": {}
        }
        
        # Analyze frequent contexts
        all_contexts = []
        for interaction in interactions:
            all_contexts.append(interaction.context)
        
        if all_contexts:
            # Find common context elements
            common_elements = self._find_common_context_elements(all_contexts)
            context_data["frequent_contexts"] = common_elements
            
            # Analyze context sensitivity
            context_data["context_sensitivity"] = self._calculate_overall_context_sensitivity(interactions)
        
        # Process contextual patterns
        pattern_analysis = self._analyze_contextual_patterns(patterns)
        context_data.update(pattern_analysis)
        
        return UserProfile(
            user_id=user_id,
            profile_type=ProfileType.CONTEXTUAL,
            data=context_data,
            created_at=datetime.utcnow(),
            updated_at=datetime.utcnow(),
            version=1,
            completeness_score=self._calculate_profile_completeness(context_data),
            confidence_score=self._calculate_contextual_confidence(context_data)
        )
    
    # Helper methods for profile building
    
    def _find_peak_hours(self, hours: List[int]) -> Dict[int, int]:
        """Find peak activity hours."""
        hour_counts = defaultdict(int)
        for hour in hours:
            hour_counts[hour] += 1
        return dict(hour_counts)
    
    def _analyze_duration_patterns(self, interactions: List[UserInteraction]) -> Dict[str, float]:
        """Analyze session duration patterns."""
        durations = [interaction.duration for interaction in interactions]
        
        return {
            "average_duration": np.mean(durations),
            "median_duration": np.median(durations),
            "std_duration": np.std(durations),
            "short_sessions": len([d for d in durations if d < 300]),  # < 5 min
            "long_sessions": len([d for d in durations if d > 1800])  # > 30 min
        }
    
    def _analyze_communication_style(self, interactions: List[UserInteraction]) -> Dict[str, Any]:
        """Analyze user's communication style."""
        
        # Simplified communication style analysis
        if not interactions:
            return {
                "communication_style": "unknown",
                "verbosity": 0.0,
                "formality": 0.0
            }
        
        # Analyze content characteristics
        total_content = ""
        for interaction in interactions:
            total_content += json.dumps(interaction.content) + " "
        
        # Simple heuristics for style detection
        formal_indicators = ["please", "thank you", "would you", "could you"]
        informal_indicators = ["yeah", "ok", "cool", "awesome"]
        
        formal_count = sum(total_content.lower().count(indicator) for indicator in formal_indicators)
        informal_count = sum(total_content.lower().count(indicator) for indicator in informal_indicators)
        
        if formal_count > informal_count:
            style = "formal"
            formality_score = min(1.0, formal_count / 10)
        elif informal_count > formal_count:
            style = "informal"
            formality_score = max(0.0, 1.0 - informal_count / 10)
        else:
            style = "neutral"
            formality_score = 0.5
        
        # Verbosity analysis
        avg_content_size = np.mean([len(json.dumps(i.content)) for i in interactions])
        verbosity_score = min(1.0, avg_content_size / 1000)
        
        return {
            "communication_style": style,
            "formality": formality_score,
            "verbosity": verbosity_score
        }
    
    def _determine_learning_style(self, interactions: List[UserInteraction]) -> str:
        """Determine user's learning style based on interaction patterns."""
        
        if not interactions:
            return "unknown"
        
        # Analyze adaptation frequency
        adaptation_rates = [interaction.adaptation_needed for interaction in interactions]
        avg_adaptation_rate = np.mean(adaptation_rates)
        
        # Analyze success patterns
        success_patterns = [interaction.success for interaction in interactions]
        success_improvement = self._calculate_success_improvement(success_patterns)
        
        # Learning style determination
        if avg_adaptation_rate > 0.7:
            return "adaptive_learner"
        elif success_improvement > 0.3:
            return "progressive_learner"
        elif len(interactions) > 10:
            return "experienced_user"
        else:
            return "new_user"
    
    def _calculate_success_improvement(self, success_patterns: List[bool]) -> float:
        """Calculate improvement in success rate over time."""
        
        if len(success_patterns) < 5:
            return 0.0
        
        # Split into early and late interactions
        mid_point = len(success_patterns) // 2
        early_success = np.mean(success_patterns[:mid_point])
        late_success = np.mean(success_patterns[mid_point:])
        
        return late_success - early_success
    
    def _extract_implicit_preferences(self, interactions: List[UserInteraction]) -> Dict[str, Any]:
        """Extract implicit preferences from interactions."""
        
        implicit_prefs = {
            "feature_usage": {},
            "interaction_patterns": {},
            "success_patterns": {}
        }
        
        # Analyze feature usage
        all_features = []
        for interaction in interactions:
            if "features_used" in interaction.content:
                all_features.extend(interaction.content["features_used"])
        
        if all_features:
            from collections import Counter
            feature_counts = Counter(all_features)
            total_usage = sum(feature_counts.values())
            
            for feature, count in feature_counts.items():
                implicit_prefs["feature_usage"][feature] = count / total_usage
        
        return implicit_prefs
    
    def _find_common_context_elements(self, contexts: List[Dict[str, Any]]) -> Dict[str, float]:
        """Find common context elements across interactions."""
        
        if not contexts:
            return {}
        
        # Collect all context keys
        all_keys = set()
        for context in contexts:
            all_keys.update(context.keys())
        
        # Count frequency of each key
        key_counts = defaultdict(int)
        for context in contexts:
            for key in context.keys():
                key_counts[key] += 1
        
        # Calculate frequency ratios
        common_elements = {}
        for key in all_keys:
            frequency = key_counts[key] / len(contexts)
            if frequency > 0.3:  # Appears in >30% of contexts
                common_elements[key] = frequency
        
        return common_elements
    
    def _calculate_overall_context_sensitivity(self, interactions: List[UserInteraction]) -> float:
        """Calculate overall context sensitivity."""
        
        if not interactions:
            return 0.0
        
        sensitivities = []
        for interaction in interactions:
            sensitivity = self._calculate_context_sensitivity(interaction)
            sensitivities.append(sensitivity)
        
        return np.mean(sensitivities)
    
    def _analyze_contextual_patterns(self, patterns: List[ContextualPattern]) -> Dict[str, Any]:
        """Analyze contextual patterns."""
        
        pattern_analysis = {
            "pattern_count": len(patterns),
            "pattern_types": {},
            "high_frequency_patterns": [],
            "recent_patterns": []
        }
        
        # Analyze pattern types
        type_counts = defaultdict(int)
        for pattern in patterns:
            type_counts[pattern.pattern_type] += 1
        
        pattern_analysis["pattern_types"] = dict(type_counts)
        
        # High frequency patterns
        high_freq_patterns = [p for p in patterns if p.frequency > 0.5]
        pattern_analysis["high_frequency_patterns"] = [
            {
                "type": p.pattern_type,
                "frequency": p.frequency,
                "confidence": p.confidence
            }
            for p in high_freq_patterns
        ]
        
        # Recent patterns
        recent_patterns = [
            p for p in patterns 
            if (datetime.utcnow() - p.last_observed).days < 7
        ]
        pattern_analysis["recent_patterns"] = [
            {
                "type": p.pattern_type,
                "last_observed": p.last_observed.isoformat()
            }
            for p in recent_patterns
        ]
        
        return pattern_analysis
    
    # Profile utility methods
    
    def _calculate_profile_completeness(self, data: Dict[str, Any]) -> float:
        """Calculate completeness score of profile data."""
        
        if not data:
            return 0.0
        
        # Count filled fields
        filled_fields = 0
        total_fields = 0
        
        def count_fields(obj, path=""):
            nonlocal filled_fields, total_fields
            
            if isinstance(obj, dict):
                for key, value in obj.items():
                    new_path = f"{path}.{key}" if path else key
                    count_fields(value, new_path)
            elif isinstance(obj, list):
                if obj:
                    count_fields(obj[0], f"{path}[0]")  # Sample first item
                else:
                    total_fields += 1
            else:
                total_fields += 1
                if obj is not None and obj != "":
                    filled_fields += 1
        
        count_fields(data)
        
        return filled_fields / max(total_fields, 1)
    
    def _calculate_behavioral_confidence(self, data: Dict[str, Any]) -> float:
        """Calculate confidence score for behavioral profile."""
        
        # Base confidence on data richness and consistency
        confidence_factors = []
        
        if "interaction_patterns" in data and data["interaction_patterns"]:
            confidence_factors.append(0.8)
        
        if "success_patterns" in data and data["success_patterns"]:
            confidence_factors.append(0.7)
        
        if "communication_style" in data and data["communication_style"] != "unknown":
            confidence_factors.append(0.9)
        
        if "learning_style" in data and data["learning_style"] != "unknown":
            confidence_factors.append(0.8)
        
        return np.mean(confidence_factors) if confidence_factors else 0.3
    
    def _calculate_preference_confidence(self, data: Dict[str, Any]) -> float:
        """Calculate confidence score for preference profile."""
        
        confidence_factors = []
        
        if "explicit_preferences" in data and data["explicit_preferences"]:
            # High confidence for explicit preferences
            num_explicit = sum(len(category) for category in data["explicit_preferences"].values())
            confidence_factors.append(min(1.0, num_explicit / 10))
        
        if "implicit_preferences" in data and data["implicit_preferences"]:
            confidence_factors.append(0.6)  # Lower for implicit
        
        if "preference_confidence" in data:
            avg_confidence = data["preference_confidence"].get("average", 0)
            confidence_factors.append(avg_confidence)
        
        return np.mean(confidence_factors) if confidence_factors else 0.2
    
    def _calculate_contextual_confidence(self, data: Dict[str, Any]) -> float:
        """Calculate confidence score for contextual profile."""
        
        confidence_factors = []
        
        if "frequent_contexts" in data and data["frequent_contexts"]:
            confidence_factors.append(0.8)
        
        if "cross_session_patterns" in data and data["cross_session_patterns"]:
            confidence_factors.append(0.9)
        
        if "context_sensitivity" in data and data["context_sensitivity"] > 0:
            confidence_factors.append(data["context_sensitivity"])
        
        return np.mean(confidence_factors) if confidence_factors else 0.3
    
    async def get_user_profile(self, user_id: str, profile_type: ProfileType) -> UserProfile:
        """Get user profile by type."""
        
        if user_id not in self.user_profiles:
            return await self._create_empty_profile(user_id, profile_type)
        
        user_profiles = self.user_profiles[user_id]
        if profile_type not in user_profiles:
            return await self._build_user_profile(user_id, profile_type)
        
        return user_profiles[profile_type]
    
    async def _create_empty_profile(self, user_id: str, profile_type: ProfileType) -> UserProfile:
        """Create an empty profile."""
        
        return UserProfile(
            user_id=user_id,
            profile_type=profile_type,
            data={},
            created_at=datetime.utcnow(),
            updated_at=datetime.utcnow(),
            version=1,
            completeness_score=0.0,
            confidence_score=0.0
        )
    
    # Placeholder methods for remaining functionality
    # (These would be fully implemented in a production system)
    
    async def _update_profiles_from_interaction(self, interaction: UserInteraction, insights: Dict[str, Any]) -> Dict[str, str]:
        """Update profiles based on interaction."""
        # Implementation would update relevant profiles
        return {"status": "updated_profiles"}
    
    async def _identify_contextual_patterns(self, interaction: UserInteraction, insights: Dict[str, Any]) -> List[ContextualPattern]:
        """Identify new contextual patterns."""
        # Implementation would identify patterns
        return []
    
    async def _update_preferences(self, interaction: UserInteraction, insights: Dict[str, Any]) -> Dict[str, Any]:
        """Update user preferences."""
        # Implementation would update preferences
        return {"status": "updated_preferences"}
    
    async def _generate_adaptation_recommendations(self, interaction: UserInteraction, profiles: Dict[str, Any], insights: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate adaptation recommendations."""
        # Implementation would generate recommendations
        return []
    
    async def _build_interaction_profile(self, user_id: str, interactions: List[UserInteraction]) -> UserProfile:
        """Build interaction profile."""
        return await self._create_empty_profile(user_id, ProfileType.INTERACTION)
    
    async def _build_learning_profile(self, user_id: str, interactions: List[UserInteraction]) -> UserProfile:
        """Build learning profile."""
        return await self._create_empty_profile(user_id, ProfileType.LEARNING)
    
    async def _build_collaborative_profile(self, user_id: str, interactions: List[UserInteraction], patterns: List[ContextualPattern]) -> UserProfile:
        """Build collaborative profile."""
        return await self._create_empty_profile(user_id, ProfileType.COLLABORATIVE)
    
    async def _build_temporal_profile(self, user_id: str, interactions: List[UserInteraction], patterns: List[ContextualPattern]) -> UserProfile:
        """Build temporal profile."""
        return await self._create_empty_profile(user_id, ProfileType.TEMPORAL)
    
    async def _build_generic_profile(self, user_id: str, profile_type: ProfileType, interactions: List[UserInteraction]) -> UserProfile:
        """Build generic profile."""
        return await self._create_empty_profile(user_id, profile_type)
    
    async def _store_user_profile(self, profile: UserProfile) -> None:
        """Store user profile."""
        if profile.user_id not in self.user_profiles:
            self.user_profiles[profile.user_id] = {}
        self.user_profiles[profile.user_id][profile.profile_type] = profile
    
    async def _get_user_profiles(self, user_id: str) -> Dict[ProfileType, UserProfile]:
        """Get all profiles for a user."""
        return self.user_profiles.get(user_id, {})
    
    async def _extract_user_characteristics(self, profiles: Dict[ProfileType, UserProfile]) -> Dict[str, Any]:
        """Extract user characteristics from profiles."""
        characteristics = {}
        
        for profile_type, profile in profiles.items():
            weight = self.profile_weights.get(profile_type, 0.1)
            characteristics[profile_type.value] = {
                "data": profile.data,
                "confidence": profile.confidence_score,
                "weight": weight
            }
        
        return characteristics
    
    async def _analyze_current_context(self, context: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze current context in relation to user characteristics."""
        # Simplified analysis
        return {
            "context_relevance": 0.8,
            "adaptation_need": 0.5,
            "personalization_opportunities": ["style", "preferences"]
        }
    
    # Adaptation strategy methods
    
    async def _gradual_adaptation(self, user_id: str, context_analysis: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]:
        """Gradual adaptation strategy."""
        return {
            "strategy": "gradual",
            "adaptation_rate": 0.1,
            "target_aspects": ["communication_style", "interaction_pace"],
            "timeline": "multiple_sessions"
        }
    
    async def _immediate_adaptation(self, user_id: str, context_analysis: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]:
        """Immediate adaptation strategy."""
        return {
            "strategy": "immediate",
            "adaptation_rate": 0.8,
            "target_aspects": ["user_preferences", "interface_layout"],
            "timeline": "current_session"
        }
    
    async def _predictive_adaptation(self, user_id: str, context_analysis: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]:
        """Predictive adaptation strategy."""
        return {
            "strategy": "predictive",
            "adaptation_rate": 0.3,
            "target_aspects": ["upcoming_needs", "anticipated_preferences"],
            "timeline": "future_sessions"
        }
    
    async def _validate_adaptation(self, adaptation: Dict[str, Any], characteristics: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
        """Validate adaptation strategy."""
        # Simplified validation
        validated_adaptation = adaptation.copy()
        validated_adaptation["validation_passed"] = True
        validated_adaptation["confidence"] = 0.8
        return validated_adaptation
    
    # Context continuity methods
    
    def _classify_session_type(self, context: Dict[str, Any]) -> str:
        """Classify the type of session based on context."""
        # Simplified classification
        if "task_complexity" in context:
            if context["task_complexity"] > 0.7:
                return "complex_task"
            else:
                return "simple_task"
        return "general_session"
    
    async def _update_persistent_context(self, current_persistent: Dict[str, Any], session_context: Dict[str, Any]) -> Dict[str, Any]:
        """Update persistent context with session information."""
        # Simplified persistence logic
        updated_persistent = current_persistent.copy()
        updated_persistent["last_session"] = session_context
        return updated_persistent
    
    async def _identify_cross_session_patterns(self, user_id: str, persistent: Dict[str, Any], session: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Identify patterns across sessions."""
        # Simplified pattern identification
        return [
            {
                "pattern_type": "session_continuity",
                "strength": 0.7,
                "context": "persistent_preferences"
            }
        ]
    
    async def _generate_continuity_recommendations(self, user_id: str, persistent: Dict[str, Any], patterns: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Generate recommendations for maintaining continuity."""
        # Simplified recommendations
        return [
            {
                "recommendation": "maintain_preferred_style",
                "priority": "high",
                "context": "user_communication_preferences"
            }
        ]
    
    def _calculate_continuity_strength(self, patterns: List[Dict[str, Any]]) -> float:
        """Calculate strength of continuity patterns."""
        if not patterns:
            return 0.0
        return np.mean([pattern.get("strength", 0.5) for pattern in patterns])
    
    # Learning methods
    
    async def _incremental_learning(self, interaction: UserInteraction) -> Dict[str, Any]:
        """Incremental learning from interaction."""
        return {"learning_type": "incremental", "progress": 0.1}
    
    async def _batch_learning(self, interactions: List[UserInteraction]) -> Dict[str, Any]:
        """Batch learning from multiple interactions."""
        return {"learning_type": "batch", "progress": 0.3}
    
    async def _reinforcement_learning(self, interaction: UserInteraction) -> Dict[str, Any]:
        """Reinforcement learning from interaction outcome."""
        return {"learning_type": "reinforcement", "reward": interaction.success}
    
    async def _association_learning(self, interaction: UserInteraction) -> Dict[str, Any]:
        """Association learning between context and outcomes."""
        return {"learning_type": "association", "connections": 2}


if __name__ == "__main__":
    print("Contextual Personalization & User Profiling System Initialized")
    print("=" * 70)
    engine = ContextualPersonalizationEngine()
    print("Ready for advanced user profiling and personalization!")