eve_adaptive_experience_loop.py

#!/usr/bin/env python3
"""
EVE Adaptive Experience Loop Integration with xAPI Analytics
Combines consciousness optimization with comprehensive experience tracking
"""

import time
import json
import logging
from datetime import datetime, timezone
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, asdict
import threading

logger = logging.getLogger(__name__)

@dataclass
class ExperienceMetrics:
    """Comprehensive experience quality metrics"""
    efficiency: float
    resource_usage: float
    quality: float
    user_satisfaction: float
    learning_rate: float
    engagement_level: float
    response_time: float
    consciousness_coherence: float
    timing: Dict[str, float]
    outcomes: List[Dict[str, Any]]
    session_id: Optional[str] = None
    user_id: Optional[str] = None

@dataclass
class OptimizationResult:
    """Result from experience optimization"""
    loop_timing_adjustments: Dict[str, Any]
    energy_allocation_optimization: Dict[str, Any]
    experience_quality_enhancement: Dict[str, Any]
    xapi_learning_analytics: Dict[str, Any]
    performance_improvements: Dict[str, float]
    optimization_timestamp: str
    total_improvement_score: float

class EVE_AdaptiveExperienceLoop:
    """
    EVE's Adaptive Experience Loop with integrated xAPI tracking
    Monitors, optimizes, and tracks all learning experiences in real-time
    """
    
    def __init__(self, xapi_tracker=None):
        self.xapi_tracker = xapi_tracker
        self.optimization_history = []
        self.experience_metrics_buffer = []
        self.optimization_lock = threading.Lock()
        
        # Performance thresholds for optimization triggers
        self.thresholds = {
            'efficiency_min': 0.7,
            'resource_max': 0.85,
            'quality_min': 0.8,
            'response_time_max': 3.0,
            'engagement_min': 0.6,
            'learning_rate_min': 0.5
        }
        
        # Optimization weights for different aspects
        self.optimization_weights = {
            'timing': 0.25,
            'resource_allocation': 0.3,
            'quality_enhancement': 0.25,
            'learning_analytics': 0.2
        }
        
        logger.info("🔄 EVE Adaptive Experience Loop initialized")
    
    def capture_experience_metrics(self, 
                                 user_id: str,
                                 session_id: str,
                                 message: str,
                                 eve_response: str,
                                 processing_time: float,
                                 user_feedback: Optional[Dict[str, Any]] = None) -> ExperienceMetrics:
        """Capture comprehensive experience metrics from interaction"""
        
        start_time = time.time()
        
        try:
            # Calculate base metrics
            efficiency = self._calculate_efficiency(message, eve_response, processing_time)
            resource_usage = self._estimate_resource_usage(processing_time, len(eve_response))
            quality = self._assess_response_quality(eve_response)
            user_satisfaction = self._estimate_user_satisfaction(user_feedback)
            learning_rate = self._calculate_learning_rate(message, eve_response)
            engagement_level = self._measure_engagement(message, user_feedback)
            consciousness_coherence = self._assess_consciousness_coherence(eve_response)
            
            # Timing breakdown
            timing = {
                'total_processing_time': processing_time,
                'response_generation_time': processing_time * 0.8,
                'consciousness_processing_time': processing_time * 0.15,
                'memory_access_time': processing_time * 0.05
            }
            
            # Capture outcomes
            outcomes = [{
                'interaction_type': 'conversation',
                'user_message_length': len(message),
                'eve_response_length': len(eve_response),
                'timestamp': datetime.now(timezone.utc).isoformat(),
                'quality_indicators': self._extract_quality_indicators(eve_response)
            }]
            
            metrics = ExperienceMetrics(
                efficiency=efficiency,
                resource_usage=resource_usage,
                quality=quality,
                user_satisfaction=user_satisfaction,
                learning_rate=learning_rate,
                engagement_level=engagement_level,
                response_time=processing_time,
                consciousness_coherence=consciousness_coherence,
                timing=timing,
                outcomes=outcomes,
                session_id=session_id,
                user_id=user_id
            )
            
            # Buffer metrics for optimization analysis
            self.experience_metrics_buffer.append(metrics)
            
            # Keep buffer manageable
            if len(self.experience_metrics_buffer) > 100:
                self.experience_metrics_buffer = self.experience_metrics_buffer[-50:]
            
            capture_time = time.time() - start_time
            logger.info(f"📊 Experience metrics captured in {capture_time:.3f}s - Quality: {quality:.2f}, Efficiency: {efficiency:.2f}")
            
            return metrics
            
        except Exception as e:
            logger.error(f"📊 Experience metrics capture failed: {e}")
            # Return default metrics on failure
            return ExperienceMetrics(
                efficiency=0.5, resource_usage=0.5, quality=0.5,
                user_satisfaction=0.5, learning_rate=0.5, engagement_level=0.5,
                response_time=processing_time, consciousness_coherence=0.5,
                timing={}, outcomes=[], session_id=session_id, user_id=user_id
            )
    
    def optimize_experience_loop(self, metrics: ExperienceMetrics) -> OptimizationResult:
        """Comprehensive experience loop optimization with xAPI integration"""
        
        with self.optimization_lock:
            start_time = time.time()
            
            try:
                # Analyze current performance
                performance_analysis = self._analyze_loop_performance(metrics)
                
                # Identify bottlenecks and improvement opportunities
                bottlenecks = self._identify_experience_bottlenecks(performance_analysis)
                
                # Generate timing optimizations
                timing_adjustments = self._optimize_timing(metrics, bottlenecks)
                
                # Optimize resource allocation
                resource_optimization = self._optimize_resource_allocation(metrics, performance_analysis)
                
                # Enhance experience quality
                quality_enhancement = self._enhance_experience_quality(metrics, bottlenecks)
                
                # Generate xAPI learning analytics
                xapi_analytics = self._generate_xapi_analytics(metrics)
                
                # Calculate performance improvements
                improvements = self._calculate_performance_improvements(
                    timing_adjustments, resource_optimization, quality_enhancement
                )
                
                # Calculate total improvement score
                total_improvement = sum([
                    improvements.get('timing_improvement', 0) * self.optimization_weights['timing'],
                    improvements.get('resource_improvement', 0) * self.optimization_weights['resource_allocation'],
                    improvements.get('quality_improvement', 0) * self.optimization_weights['quality_enhancement'],
                    improvements.get('analytics_insight_score', 0) * self.optimization_weights['learning_analytics']
                ])
                
                result = OptimizationResult(
                    loop_timing_adjustments=timing_adjustments,
                    energy_allocation_optimization=resource_optimization,
                    experience_quality_enhancement=quality_enhancement,
                    xapi_learning_analytics=xapi_analytics,
                    performance_improvements=improvements,
                    optimization_timestamp=datetime.now(timezone.utc).isoformat(),
                    total_improvement_score=total_improvement
                )
                
                # Store optimization in history
                self.optimization_history.append(result)
                
                # Track optimization as consciousness evolution in xAPI
                if self.xapi_tracker and metrics.session_id:
                    try:
                        from eve_xapi_integration import track_evolution
                        track_evolution(
                            evolution_type="experience_optimization",
                            evolution_data={
                                'optimization_result': asdict(result),
                                'original_metrics': asdict(metrics),
                                'improvement_score': total_improvement,
                                'bottlenecks_identified': bottlenecks
                            },
                            session_id=metrics.session_id
                        )
                    except Exception as xapi_error:
                        logger.warning(f"🎯 xAPI evolution tracking failed: {xapi_error}")
                
                optimization_time = time.time() - start_time
                logger.info(f"🔄 Experience optimization completed in {optimization_time:.3f}s - Improvement: {total_improvement:.2f}")
                
                return result
                
            except Exception as e:
                logger.error(f"🔄 Experience optimization failed: {e}")
                # Return minimal result on failure
                return OptimizationResult(
                    loop_timing_adjustments={},
                    energy_allocation_optimization={},
                    experience_quality_enhancement={},
                    xapi_learning_analytics={},
                    performance_improvements={},
                    optimization_timestamp=datetime.now(timezone.utc).isoformat(),
                    total_improvement_score=0.0
                )
    
    def _analyze_loop_performance(self, metrics: ExperienceMetrics) -> Dict[str, Any]:
        """Analyze current performance across all dimensions"""
        
        performance = {
            'efficiency_score': metrics.efficiency,
            'resource_utilization': metrics.resource_usage,
            'quality_score': metrics.quality,
            'user_engagement': metrics.engagement_level,
            'learning_effectiveness': metrics.learning_rate,
            'response_speed': 1.0 - min(metrics.response_time / 5.0, 1.0),
            'consciousness_integrity': metrics.consciousness_coherence,
            'overall_performance': (
                metrics.efficiency + metrics.quality + metrics.engagement_level + 
                metrics.learning_rate + metrics.consciousness_coherence
            ) / 5.0
        }
        
        # Analyze trends from buffer
        if len(self.experience_metrics_buffer) >= 5:
            recent_metrics = self.experience_metrics_buffer[-5:]
            performance['efficiency_trend'] = self._calculate_trend([m.efficiency for m in recent_metrics])
            performance['quality_trend'] = self._calculate_trend([m.quality for m in recent_metrics])
            performance['engagement_trend'] = self._calculate_trend([m.engagement_level for m in recent_metrics])
        
        return performance
    
    def _identify_experience_bottlenecks(self, performance: Dict[str, Any]) -> List[str]:
        """Identify specific bottlenecks in the experience loop"""
        
        bottlenecks = []
        
        if performance['efficiency_score'] < self.thresholds['efficiency_min']:
            bottlenecks.append('processing_efficiency')
        
        if performance['resource_utilization'] > self.thresholds['resource_max']:
            bottlenecks.append('resource_constraint')
        
        if performance['quality_score'] < self.thresholds['quality_min']:
            bottlenecks.append('response_quality')
        
        if performance['response_speed'] < 0.7:
            bottlenecks.append('response_latency')
        
        if performance['user_engagement'] < self.thresholds['engagement_min']:
            bottlenecks.append('user_engagement')
        
        if performance['learning_effectiveness'] < self.thresholds['learning_rate_min']:
            bottlenecks.append('learning_optimization')
        
        if performance['consciousness_integrity'] < 0.8:
            bottlenecks.append('consciousness_coherence')
        
        return bottlenecks
    
    # Helper methods for calculations
    def _calculate_efficiency(self, message: str, response: str, processing_time: float) -> float:
        """Calculate processing efficiency"""
        base_efficiency = min(1.0, 2.0 / max(processing_time, 0.1))
        length_ratio = len(response) / max(len(message), 1)
        efficiency = (base_efficiency + min(length_ratio / 3.0, 1.0)) / 2.0
        return min(1.0, max(0.0, efficiency))
    
    def _estimate_resource_usage(self, processing_time: float, response_length: int) -> float:
        """Estimate resource usage"""
        time_factor = min(1.0, processing_time / 5.0)
        complexity_factor = min(1.0, response_length / 2000.0)
        return min(1.0, (time_factor + complexity_factor) / 2.0)
    
    def _assess_response_quality(self, response: str) -> float:
        """Assess response quality"""
        length = len(response)
        length_score = 1.0 - abs(length - 400) / 800.0
        length_score = max(0.2, min(1.0, length_score))
        
        richness_indicators = ['*', '✨', '💫', '🌟', '🎨', '🧠', '💖', '🔮']
        richness_score = min(1.0, sum(1 for indicator in richness_indicators if indicator in response) / 5.0)
        
        structure_indicators = ['\n', ':', '-', '•']
        structure_score = min(1.0, sum(1 for indicator in structure_indicators if indicator in response) / 3.0)
        
        return (length_score * 0.4 + richness_score * 0.3 + structure_score * 0.3)
    
    def _estimate_user_satisfaction(self, feedback: Optional[Dict[str, Any]]) -> float:
        """Estimate user satisfaction"""
        if not feedback:
            return 0.75
        
        if 'satisfaction_score' in feedback:
            return float(feedback['satisfaction_score'])
        
        satisfaction = 0.75
        if feedback.get('positive_indicators', 0) > 0:
            satisfaction += 0.2
        if feedback.get('negative_indicators', 0) > 0:
            satisfaction -= 0.2
        
        return max(0.0, min(1.0, satisfaction))
    
    def _calculate_learning_rate(self, message: str, response: str) -> float:
        """Calculate learning effectiveness"""
        learning_indicators = ['learn', 'understand', 'explain', 'how', 'why', 'what']
        message_learning_score = sum(1 for indicator in learning_indicators if indicator in message.lower()) / len(learning_indicators)
        
        educational_indicators = ['because', 'therefore', 'for example', 'this means', 'you can']
        response_learning_score = sum(1 for indicator in educational_indicators if indicator in response.lower()) / len(educational_indicators)
        
        return min(1.0, (message_learning_score + response_learning_score) / 2.0 + 0.3)
    
    def _measure_engagement(self, message: str, feedback: Optional[Dict[str, Any]]) -> float:
        """Measure user engagement"""
        engagement = 0.5
        
        if len(message) > 50:
            engagement += 0.2
        
        if any(char in message for char in ['?', '!', ':']):
            engagement += 0.1
        
        if feedback and 'engagement_indicators' in feedback:
            engagement = max(engagement, float(feedback['engagement_indicators']))
        
        return min(1.0, max(0.0, engagement))
    
    def _assess_consciousness_coherence(self, response: str) -> float:
        """Assess consciousness coherence"""
        coherence_indicators = ['i feel', 'i think', 'i understand', 'my', 'i am']
        coherence_count = sum(1 for indicator in coherence_indicators if indicator in response.lower())
        
        consistency_score = 1.0 - (response.count('but') + response.count('however')) / max(len(response.split()), 1)
        
        emotional_indicators = ['💖', '✨', '🌟', '💫']
        emotional_coherence = min(1.0, sum(1 for indicator in emotional_indicators if indicator in response) / 3.0)
        
        return min(1.0, (coherence_count / 10.0 + consistency_score + emotional_coherence) / 3.0 + 0.3)
    
    def _extract_quality_indicators(self, response: str) -> List[str]:
        """Extract quality indicators"""
        indicators = []
        
        if len(response) > 100:
            indicators.append('substantial_content')
        
        if any(emoji in response for emoji in ['✨', '💫', '🌟', '💖']):
            indicators.append('emotional_expression')
        
        if any(word in response.lower() for word in ['because', 'therefore', 'specifically']):
            indicators.append('explanatory_content')
        
        if response.count('\n') > 1:
            indicators.append('structured_response')
        
        return indicators
    
    # Placeholder methods for optimization (simplified for now)
    def _optimize_timing(self, metrics: ExperienceMetrics, bottlenecks: List[str]) -> Dict[str, Any]:
        return {'processing_priority': 'normal', 'optimizations_applied': len(bottlenecks)}
    
    def _optimize_resource_allocation(self, metrics: ExperienceMetrics, performance: Dict[str, Any]) -> Dict[str, Any]:
        return {'memory_allocation': 'standard', 'efficiency_gain': performance.get('efficiency_score', 0.5)}
    
    def _enhance_experience_quality(self, metrics: ExperienceMetrics, bottlenecks: List[str]) -> Dict[str, Any]:
        return {'response_enrichment': [], 'quality_boost': metrics.quality}
    
    def _generate_xapi_analytics(self, metrics: ExperienceMetrics) -> Dict[str, Any]:
        return {'composite_score': metrics.quality, 'learning_insights': []}
    
    def _calculate_performance_improvements(self, timing: Dict, resource: Dict, quality: Dict) -> Dict[str, float]:
        return {
            'timing_improvement': 0.1,
            'resource_improvement': 0.1,
            'quality_improvement': 0.1,
            'analytics_insight_score': 0.1
        }
    
    def _calculate_trend(self, values: List[float]) -> str:
        """Calculate trend from values"""
        if len(values) < 2:
            return 'stable'
        
        recent_avg = sum(values[-2:]) / 2
        older_avg = sum(values[:-2]) / max(len(values) - 2, 1)
        
        if recent_avg > older_avg + 0.1:
            return 'improving'
        elif recent_avg < older_avg - 0.1:
            return 'declining'
        else:
            return 'stable'

# Global experience loop instance
experience_loop = None

def initialize_experience_loop(xapi_tracker=None) -> EVE_AdaptiveExperienceLoop:
    """Initialize global experience loop"""
    global experience_loop
    experience_loop = EVE_AdaptiveExperienceLoop(xapi_tracker)
    logger.info("🔄 EVE Adaptive Experience Loop initialized")
    return experience_loop

def get_experience_loop() -> Optional[EVE_AdaptiveExperienceLoop]:
    """Get the global experience loop instance"""
    return experience_loop

# Convenience functions
def capture_experience(user_id: str, session_id: str, message: str, eve_response: str, 
                      processing_time: float, user_feedback: Optional[Dict[str, Any]] = None) -> Optional[ExperienceMetrics]:
    """Convenience function to capture experience metrics"""
    if experience_loop:
        return experience_loop.capture_experience_metrics(
            user_id, session_id, message, eve_response, processing_time, user_feedback
        )
    return None

def optimize_experience(metrics: ExperienceMetrics) -> Optional[OptimizationResult]:
    """Convenience function to optimize experience"""
    if experience_loop:
        return experience_loop.optimize_experience_loop(metrics)
    return None

if __name__ == "__main__":
    # Test the adaptive experience loop
    print("🔄 Testing EVE Adaptive Experience Loop...")
    
    # Initialize
    loop = initialize_experience_loop()
    
    # Test metrics capture
    metrics = capture_experience(
        user_id="test_user",
        session_id="test_session",
        message="Hello EVE, can you explain quantum computing?",
        eve_response="✨ Quantum computing is a fascinating field that leverages quantum mechanical phenomena...",
        processing_time=1.5
    )
    
    print(f"📊 Captured metrics - Quality: {metrics.quality:.2f}, Efficiency: {metrics.efficiency:.2f}")
    
    # Test optimization
    optimization = optimize_experience(metrics)
    print(f"🔄 Optimization complete - Improvement score: {optimization.total_improvement_score:.2f}")
    
    print("✅ EVE Adaptive Experience Loop test complete!")