space/neurosymbolic.py

"""Advanced neurosymbolic reasoning combining neural and symbolic approaches."""

import logging
from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from collections import defaultdict

from .base import ReasoningStrategy

@dataclass
class NeuralFeature:
    """Neural features extracted from data."""
    name: str
    values: np.ndarray
    importance: float
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class SymbolicRule:
    """Symbolic rule with conditions and confidence."""
    name: str
    conditions: List[str]
    conclusion: str
    confidence: float
    metadata: Dict[str, Any] = field(default_factory=dict)

class NeurosymbolicReasoning(ReasoningStrategy):
    """
    Advanced neurosymbolic reasoning that:
    1. Extracts neural features
    2. Generates symbolic rules
    3. Combines approaches
    4. Handles uncertainty
    5. Provides interpretable results
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize neurosymbolic reasoning."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
        
        # Neurosymbolic specific parameters
        self.feature_threshold = self.config.get('feature_threshold', 0.1)
        self.rule_confidence_threshold = self.config.get('rule_confidence', 0.7)
        self.max_rules = self.config.get('max_rules', 10)
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Apply neurosymbolic reasoning to combine neural and symbolic approaches.
        
        Args:
            query: The input query to reason about
            context: Additional context and parameters
            
        Returns:
            Dict containing reasoning results and confidence scores
        """
        try:
            # Extract neural features
            features = await self._extract_features(query, context)
            
            # Generate symbolic rules
            rules = await self._generate_rules(features, context)
            
            # Combine approaches
            combined = await self._combine_approaches(features, rules, context)
            
            # Generate analysis
            analysis = await self._generate_analysis(combined, context)
            
            return {
                'answer': self._format_analysis(analysis),
                'confidence': self._calculate_confidence(combined),
                'features': features,
                'rules': rules,
                'combined': combined,
                'analysis': analysis
            }
            
        except Exception as e:
            logging.error(f"Neurosymbolic reasoning failed: {str(e)}")
            return {
                'error': f"Neurosymbolic reasoning failed: {str(e)}",
                'confidence': 0.0
            }
    
    async def _extract_features(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> List[NeuralFeature]:
        """Extract neural features from input."""
        features = []
        
        # Extract key terms
        terms = query.lower().split()
        
        # Process each term
        for term in terms:
            # Simple feature extraction for now
            values = np.random.randn(10)  # Placeholder for real feature extraction
            importance = np.abs(values).mean()
            
            if importance > self.feature_threshold:
                features.append(NeuralFeature(
                    name=term,
                    values=values,
                    importance=importance,
                    metadata={'source': 'term_extraction'}
                ))
        
        # Sort by importance
        features.sort(key=lambda x: x.importance, reverse=True)
        
        return features
    
    async def _generate_rules(
        self,
        features: List[NeuralFeature],
        context: Dict[str, Any]
    ) -> List[SymbolicRule]:
        """Generate symbolic rules from features."""
        rules = []
        
        # Process feature combinations
        for i, feature1 in enumerate(features):
            for j, feature2 in enumerate(features[i+1:], i+1):
                # Calculate correlation
                correlation = np.corrcoef(feature1.values, feature2.values)[0, 1]
                
                if abs(correlation) > self.rule_confidence_threshold:
                    # Create rule based on correlation
                    if correlation > 0:
                        condition = f"{feature1.name} AND {feature2.name}"
                        conclusion = "positively_correlated"
                    else:
                        condition = f"{feature1.name} XOR {feature2.name}"
                        conclusion = "negatively_correlated"
                    
                    rules.append(SymbolicRule(
                        name=f"rule_{len(rules)}",
                        conditions=[condition],
                        conclusion=conclusion,
                        confidence=abs(correlation),
                        metadata={
                            'features': [feature1.name, feature2.name],
                            'correlation': correlation
                        }
                    ))
                
                if len(rules) >= self.max_rules:
                    break
            
            if len(rules) >= self.max_rules:
                break
        
        return rules
    
    async def _combine_approaches(
        self,
        features: List[NeuralFeature],
        rules: List[SymbolicRule],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Combine neural and symbolic approaches."""
        combined = {
            'neural_weights': {},
            'symbolic_weights': {},
            'combined_scores': {}
        }
        
        # Calculate neural weights
        total_importance = sum(f.importance for f in features)
        if total_importance > 0:
            combined['neural_weights'] = {
                f.name: f.importance / total_importance
                for f in features
            }
        
        # Calculate symbolic weights
        total_confidence = sum(r.confidence for r in rules)
        if total_confidence > 0:
            combined['symbolic_weights'] = {
                r.name: r.confidence / total_confidence
                for r in rules
            }
        
        # Combine scores
        all_elements = set(
            list(combined['neural_weights'].keys()) +
            list(combined['symbolic_weights'].keys())
        )
        
        for element in all_elements:
            neural_score = combined['neural_weights'].get(element, 0)
            symbolic_score = combined['symbolic_weights'].get(element, 0)
            
            # Simple weighted average
            combined['combined_scores'][element] = (
                neural_score * 0.6 +  # Favor neural slightly
                symbolic_score * 0.4
            )
        
        return combined
    
    async def _generate_analysis(
        self,
        combined: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate neurosymbolic analysis."""
        # Sort elements by combined score
        ranked_elements = sorted(
            combined['combined_scores'].items(),
            key=lambda x: x[1],
            reverse=True
        )
        
        # Calculate statistics
        scores = list(combined['combined_scores'].values())
        mean = np.mean(scores) if scores else 0
        std = np.std(scores) if scores else 0
        
        # Calculate entropy
        entropy = -sum(
            s * np.log2(s) if s > 0 else 0
            for s in combined['combined_scores'].values()
        )
        
        return {
            'top_element': ranked_elements[0][0] if ranked_elements else '',
            'score': ranked_elements[0][1] if ranked_elements else 0,
            'alternatives': [
                {'name': name, 'score': score}
                for name, score in ranked_elements[1:]
            ],
            'statistics': {
                'mean': mean,
                'std': std,
                'entropy': entropy
            }
        }
    
    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
        """Format analysis into readable text."""
        sections = []
        
        # Top element
        if analysis['top_element']:
            sections.append(
                f"Most significant element: {analysis['top_element']} "
                f"(score: {analysis['score']:.2%})"
            )
        
        # Alternative elements
        if analysis['alternatives']:
            sections.append("\nAlternative elements:")
            for alt in analysis['alternatives']:
                sections.append(
                    f"- {alt['name']}: {alt['score']:.2%}"
                )
        
        # Statistics
        stats = analysis['statistics']
        sections.append("\nAnalysis statistics:")
        sections.append(f"- Mean score: {stats['mean']:.2%}")
        sections.append(f"- Standard deviation: {stats['std']:.2%}")
        sections.append(f"- Information entropy: {stats['entropy']:.2f} bits")
        
        return "\n".join(sections)
    
    def _calculate_confidence(self, combined: Dict[str, Any]) -> float:
        """Calculate overall confidence score."""
        if not combined['combined_scores']:
            return 0.0
        
        # Base confidence
        confidence = 0.5
        
        # Get scores
        scores = list(combined['combined_scores'].values())
        
        # Strong leading score increases confidence
        max_score = max(scores)
        if max_score > 0.8:
            confidence += 0.3
        elif max_score > 0.6:
            confidence += 0.2
        elif max_score > 0.4:
            confidence += 0.1
        
        # Low entropy (clear distinction) increases confidence
        entropy = -sum(s * np.log2(s) if s > 0 else 0 for s in scores)
        max_entropy = -np.log2(1/len(scores))  # Maximum possible entropy
        
        if entropy < 0.3 * max_entropy:
            confidence += 0.2
        elif entropy < 0.6 * max_entropy:
            confidence += 0.1
        
        return min(confidence, 1.0)