reasoning/analogical.py

"""Analogical reasoning strategy implementation."""

import logging
from typing import Dict, Any, List, Optional, Set, Tuple, Callable
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, StrategyResult

class AnalogicalLevel(Enum):
    """Levels of analogical similarity."""
    SURFACE = "surface"
    STRUCTURAL = "structural"
    SEMANTIC = "semantic"
    FUNCTIONAL = "functional"
    CAUSAL = "causal"
    ABSTRACT = "abstract"

class MappingType(Enum):
    """Types of analogical mappings."""
    DIRECT = "direct"
    TRANSFORMED = "transformed"
    COMPOSITE = "composite"
    ABSTRACT = "abstract"
    METAPHORICAL = "metaphorical"
    HYBRID = "hybrid"

@dataclass
class AnalogicalPattern:
    """Represents a pattern for analogical matching."""
    id: str
    level: AnalogicalLevel
    features: Dict[str, Any]
    relations: List[Tuple[str, str, str]]  # (entity1, relation, entity2)
    constraints: List[str]
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

@dataclass
class AnalogicalMapping:
    """Represents a mapping between source and target domains."""
    id: str
    type: MappingType
    source_elements: Dict[str, Any]
    target_elements: Dict[str, Any]
    correspondences: List[Tuple[str, str, float]]  # (source, target, strength)
    transformations: List[Dict[str, Any]]
    confidence: float
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

@dataclass
class AnalogicalSolution:
    """Represents a solution derived through analogical reasoning."""
    id: str
    source_analogy: str
    mapping: AnalogicalMapping
    adaptation: Dict[str, Any]
    inference: Dict[str, Any]
    confidence: float
    validation: Dict[str, Any]
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

class AnalogicalStrategy(ReasoningStrategy):
    """Advanced analogical reasoning that:
    1. Identifies relevant analogies
    2. Maps relationships
    3. Transfers knowledge
    4. Validates mappings
    5. Refines analogies
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize analogical reasoning."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.min_similarity = self.config.get('min_similarity', 0.6)
        self.max_candidates = self.config.get('max_candidates', 5)
        self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7)
        
        # Knowledge base
        self.patterns: Dict[str, AnalogicalPattern] = {}
        self.mappings: Dict[str, AnalogicalMapping] = {}
        self.solutions: Dict[str, AnalogicalSolution] = {}
        
        # Performance metrics
        self.performance_metrics = {
            'pattern_matches': 0,
            'successful_mappings': 0,
            'failed_mappings': 0,
            'adaptation_success_rate': 0.0,
            'avg_solution_confidence': 0.0,
            'pattern_distribution': defaultdict(int),
            'mapping_distribution': defaultdict(int),
            'total_patterns_used': 0,
            'total_mappings_created': 0,
            'total_solutions_generated': 0
        }
    
    async def reason(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> StrategyResult:
        """
        Apply analogical reasoning to analyze the query.
        
        Args:
            query: The query to reason about
            context: Additional context and parameters
            
        Returns:
            StrategyResult containing the reasoning output and metadata
        """
        try:
            # Extract patterns
            patterns = await self._extract_patterns(query, context)
            self.performance_metrics['total_patterns_used'] = len(patterns)
            
            # Find matches
            matches = await self._find_matches(patterns, context)
            self.performance_metrics['pattern_matches'] = len(matches)
            
            # Create mappings
            mappings = await self._create_mappings(matches, context)
            self.performance_metrics['total_mappings_created'] = len(mappings)
            
            # Generate solutions
            solutions = await self._generate_solutions(mappings, context)
            self.performance_metrics['total_solutions_generated'] = len(solutions)
            
            # Select best solution
            best_solution = await self._select_best_solution(solutions, context)
            
            if best_solution:
                # Update knowledge base
                self._update_knowledge(patterns, mappings, best_solution)
                
                # Update metrics
                self._update_metrics(patterns, mappings, solutions, best_solution)
                
                # Build reasoning trace
                reasoning_trace = self._build_reasoning_trace(
                    patterns, matches, mappings, solutions, best_solution
                )
                
                return StrategyResult(
                    strategy_type="analogical",
                    success=True,
                    answer=best_solution.inference.get('conclusion'),
                    confidence=best_solution.confidence,
                    reasoning_trace=reasoning_trace,
                    metadata={
                        'source_analogy': best_solution.source_analogy,
                        'mapping_type': best_solution.mapping.type.value,
                        'adaptation_details': best_solution.adaptation,
                        'validation_results': best_solution.validation
                    },
                    performance_metrics=self.performance_metrics
                )
            
            return StrategyResult(
                strategy_type="analogical",
                success=False,
                answer=None,
                confidence=0.0,
                reasoning_trace=[{
                    'step': 'error',
                    'error': 'No valid solution found',
                    'timestamp': datetime.now().isoformat()
                }],
                metadata={'error': 'No valid solution found'},
                performance_metrics=self.performance_metrics
            )
            
        except Exception as e:
            logging.error(f"Analogical reasoning error: {str(e)}")
            return StrategyResult(
                strategy_type="analogical",
                success=False,
                answer=None,
                confidence=0.0,
                reasoning_trace=[{
                    'step': 'error',
                    'error': str(e),
                    'timestamp': datetime.now().isoformat()
                }],
                metadata={'error': str(e)},
                performance_metrics=self.performance_metrics
            )
    
    async def _extract_patterns(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> List[AnalogicalPattern]:
        """Extract patterns from query for analogical matching."""
        # This is a placeholder implementation
        # In practice, this would use more sophisticated pattern extraction
        pattern = AnalogicalPattern(
            id=f"pattern_{len(self.patterns)}",
            level=AnalogicalLevel.SURFACE,
            features={'query': query},
            relations=[],
            constraints=[],
            metadata={'context': context}
        )
        return [pattern]
    
    async def _find_matches(
        self,
        patterns: List[AnalogicalPattern],
        context: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Find matching patterns in knowledge base."""
        matches = []
        
        for pattern in patterns:
            # Example matching logic
            similarity = np.random.random()  # Placeholder
            if similarity >= self.min_similarity:
                matches.append({
                    'pattern': pattern,
                    'similarity': similarity,
                    'features': pattern.features
                })
        
        return sorted(
            matches,
            key=lambda x: x['similarity'],
            reverse=True
        )[:self.max_candidates]
    
    async def _create_mappings(
        self,
        matches: List[Dict[str, Any]],
        context: Dict[str, Any]
    ) -> List[AnalogicalMapping]:
        """Create mappings between source and target domains."""
        mappings = []
        
        for match in matches:
            mapping = AnalogicalMapping(
                id=f"mapping_{len(self.mappings)}",
                type=MappingType.DIRECT,
                source_elements=match['features'],
                target_elements=context,
                correspondences=[],
                transformations=[],
                confidence=match['similarity']
            )
            mappings.append(mapping)
        
        return mappings
    
    async def _generate_solutions(
        self,
        mappings: List[AnalogicalMapping],
        context: Dict[str, Any]
    ) -> List[AnalogicalSolution]:
        """Generate solutions through analogical transfer."""
        solutions = []
        
        for mapping in mappings:
            if mapping.confidence >= self.adaptation_threshold:
                solution = AnalogicalSolution(
                    id=f"solution_{len(self.solutions)}",
                    source_analogy=str(mapping.source_elements),
                    mapping=mapping,
                    adaptation={'applied_rules': []},
                    inference={'conclusion': 'Analogical solution'},
                    confidence=mapping.confidence,
                    validation={'checks_passed': True},
                    metadata={'context': context}
                )
                solutions.append(solution)
        
        return solutions
    
    async def _select_best_solution(
        self,
        solutions: List[AnalogicalSolution],
        context: Dict[str, Any]
    ) -> Optional[AnalogicalSolution]:
        """Select the best solution based on multiple criteria."""
        if not solutions:
            return None
            
        # Sort by confidence and return best
        return max(solutions, key=lambda x: x.confidence)
    
    def _update_knowledge(
        self,
        patterns: List[AnalogicalPattern],
        mappings: List[AnalogicalMapping],
        solution: AnalogicalSolution
    ) -> None:
        """Update knowledge base with new patterns and successful mappings."""
        # Store new patterns
        for pattern in patterns:
            self.patterns[pattern.id] = pattern
            
        # Store successful mappings
        for mapping in mappings:
            if mapping.confidence >= self.min_confidence:
                self.mappings[mapping.id] = mapping
        
        # Store successful solution
        self.solutions[solution.id] = solution
    
    def _update_metrics(
        self,
        patterns: List[AnalogicalPattern],
        mappings: List[AnalogicalMapping],
        solutions: List[AnalogicalSolution],
        best_solution: AnalogicalSolution
    ) -> None:
        """Update performance metrics."""
        # Update pattern distribution
        for pattern in patterns:
            self.performance_metrics['pattern_distribution'][pattern.level] += 1
        
        # Update mapping distribution
        for mapping in mappings:
            self.performance_metrics['mapping_distribution'][mapping.type] += 1
            if mapping.confidence >= self.min_confidence:
                self.performance_metrics['successful_mappings'] += 1
            else:
                self.performance_metrics['failed_mappings'] += 1
        
        # Calculate adaptation success rate
        total_adaptations = len(solutions)
        successful_adaptations = sum(
            1 for s in solutions
            if s.confidence >= self.adaptation_threshold
        )
        self.performance_metrics['adaptation_success_rate'] = (
            successful_adaptations / total_adaptations
            if total_adaptations > 0 else 0.0
        )
        
        # Calculate average solution confidence
        self.performance_metrics['avg_solution_confidence'] = (
            sum(s.confidence for s in solutions) / len(solutions)
            if solutions else 0.0
        )
    
    def _build_reasoning_trace(
        self,
        patterns: List[AnalogicalPattern],
        matches: List[Dict[str, Any]],
        mappings: List[AnalogicalMapping],
        solutions: List[AnalogicalSolution],
        best_solution: AnalogicalSolution
    ) -> List[Dict[str, Any]]:
        """Build the reasoning trace for the solution."""
        trace = []
        
        # Pattern extraction step
        trace.append({
            'step': 'pattern_extraction',
            'patterns': [self._pattern_to_dict(p) for p in patterns],
            'timestamp': datetime.now().isoformat()
        })
        
        # Pattern matching step
        trace.append({
            'step': 'pattern_matching',
            'matches': matches,
            'timestamp': datetime.now().isoformat()
        })
        
        # Mapping creation step
        trace.append({
            'step': 'mapping_creation',
            'mappings': [self._mapping_to_dict(m) for m in mappings],
            'timestamp': datetime.now().isoformat()
        })
        
        # Solution generation step
        trace.append({
            'step': 'solution_generation',
            'solutions': [self._solution_to_dict(s) for s in solutions],
            'timestamp': datetime.now().isoformat()
        })
        
        # Best solution selection step
        trace.append({
            'step': 'solution_selection',
            'selected_solution': self._solution_to_dict(best_solution),
            'timestamp': datetime.now().isoformat()
        })
        
        return trace
    
    def _pattern_to_dict(self, pattern: AnalogicalPattern) -> Dict[str, Any]:
        """Convert pattern to dictionary for serialization."""
        return {
            'id': pattern.id,
            'level': pattern.level.value,
            'features': pattern.features,
            'relations': pattern.relations,
            'constraints': pattern.constraints,
            'metadata': pattern.metadata,
            'timestamp': pattern.timestamp
        }
    
    def _mapping_to_dict(self, mapping: AnalogicalMapping) -> Dict[str, Any]:
        """Convert mapping to dictionary for serialization."""
        return {
            'id': mapping.id,
            'type': mapping.type.value,
            'source_elements': mapping.source_elements,
            'target_elements': mapping.target_elements,
            'correspondences': mapping.correspondences,
            'transformations': mapping.transformations,
            'confidence': mapping.confidence,
            'timestamp': mapping.timestamp
        }
    
    def _solution_to_dict(self, solution: AnalogicalSolution) -> Dict[str, Any]:
        """Convert solution to dictionary for serialization."""
        return {
            'id': solution.id,
            'source_analogy': solution.source_analogy,
            'mapping': self._mapping_to_dict(solution.mapping),
            'adaptation': solution.adaptation,
            'inference': solution.inference,
            'confidence': solution.confidence,
            'validation': solution.validation,
            'metadata': solution.metadata,
            'timestamp': solution.timestamp
        }
    
    def clear_knowledge_base(self) -> None:
        """Clear the knowledge base."""
        self.patterns.clear()
        self.mappings.clear()
        self.solutions.clear()
        
        # Reset performance metrics
        self.performance_metrics.update({
            'pattern_matches': 0,
            'successful_mappings': 0,
            'failed_mappings': 0,
            'adaptation_success_rate': 0.0,
            'avg_solution_confidence': 0.0,
            'pattern_distribution': defaultdict(int),
            'mapping_distribution': defaultdict(int),
            'total_patterns_used': 0,
            'total_mappings_created': 0,
            'total_solutions_generated': 0
        })