"""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)