"""
Quality Metrics Module

This module provides comprehensive quality metrics calculation for the Semantica
framework, enabling quantitative assessment of knowledge graph quality across
multiple dimensions.

Key Features:
    - Overall quality score calculation
    - Entity quality metrics
    - Relationship quality metrics
    - Completeness metrics (entity, relationship, property)
    - Consistency metrics (logical, temporal, hierarchical)

Main Classes:
    - QualityMetrics: Overall quality metrics calculator
    - CompletenessMetrics: Completeness metrics calculator
    - ConsistencyMetrics: Consistency metrics calculator

Example Usage:
    >>> from semantica.kg_qa import QualityMetrics
    >>> metrics = QualityMetrics()
    >>> score = metrics.calculate_overall_score(knowledge_graph)
    >>> entity_score = metrics.calculate_entity_quality(entities)

Author: Semantica Contributors
License: MIT
"""

from typing import Any, Dict, List, Optional
from dataclasses import dataclass

from ..utils.logging import get_logger
from ..utils.progress_tracker import get_progress_tracker


@dataclass
class QualityScore:
    """
    Quality score dataclass.
    
    This dataclass represents a comprehensive quality score for a knowledge graph,
    containing scores for different quality dimensions and optional metadata.
    
    Attributes:
        overall: Overall quality score (0.0 to 1.0)
        completeness: Completeness score (0.0 to 1.0)
        consistency: Consistency score (0.0 to 1.0)
        accuracy: Accuracy score (0.0 to 1.0)
        metadata: Additional metadata dictionary (optional)
    """
    
    overall: float
    completeness: float
    consistency: float
    accuracy: float
    metadata: Optional[Dict[str, Any]] = None


class QualityMetrics:
    """
    Quality metrics calculator.
    
    This class provides overall quality metrics calculation for knowledge graphs,
    aggregating entity quality, relationship quality, and consistency into
    comprehensive quality scores.
    
    Features:
        - Overall quality score calculation
        - Entity quality assessment
        - Relationship quality assessment
        - Weighted aggregation of metrics
    
    Example Usage:
        >>> metrics = QualityMetrics()
        >>> score = metrics.calculate_overall_score(knowledge_graph)
        >>> entity_score = metrics.calculate_entity_quality(entities)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize quality metrics calculator.
        
        Sets up the calculator with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("quality_metrics")
        self.config = kwargs
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.logger.debug("Quality metrics calculator initialized")
    
    def calculate_overall_score(
        self,
        knowledge_graph: Any
    ) -> float:
        """
        Calculate overall quality score.
        
        This method calculates an overall quality score by aggregating entity
        quality and consistency metrics using weighted averaging (60% completeness,
        40% consistency).
        
        Args:
            knowledge_graph: Knowledge graph instance (object with entities and
                           relationships, or dict with "entities" and "relationships")
            
        Returns:
            float: Overall quality score between 0.0 and 1.0 (higher is better)
        """
            self.progress_tracker.update_tracking(tracking_id, message="Calculating entity quality...")
            completeness = self.calculate_entity_quality(knowledge_graph)
            self.progress_tracker.update_tracking(tracking_id, message="Calculating consistency...")
            consistency = self._calculate_consistency(knowledge_graph)
            
            self.progress_tracker.update_tracking(tracking_id, message="Aggregating scores...")
            # Weighted average
            overall = (0.6 * completeness) + (0.4 * consistency)
            
            result = min(1.0, max(0.0, overall))
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                               message=f"Overall quality score: {result:.2f}")
            return result
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def calculate_entity_quality(
        self,
        entities: List[Dict[str, Any]]
    ) -> float:
        """
        Calculate entity quality score.
        
        This method calculates a quality score for entities based on the presence
        of required fields (ID and type). Each entity is scored, and the average
        is returned.
        
        Args:
            entities: List of entity dictionaries
            
        Returns:
            float: Entity quality score between 0.0 and 1.0 (average across all entities)
        """
        if not entities:
            return 0.0
        
        # Calculate quality based on entity completeness
        scores = []
        for entity in entities:
            # Check required fields
            has_id = "id" in entity or "uri" in entity
            has_type = "type" in entity
            
            score = 0.0
            if has_id:
                score += 0.5
            if has_type:
                score += 0.5
            
            scores.append(score)
        
        return sum(scores) / len(scores) if scores else 0.0
    
    def calculate_relationship_quality(
        self,
        relationships: List[Dict[str, Any]]
    ) -> float:
        """
        Calculate relationship quality score.
        
        This method calculates a quality score for relationships based on the
        presence of required fields (source/subject, target/object, type/predicate).
        Each relationship is scored, and the average is returned.
        
        Args:
            relationships: List of relationship dictionaries
            
        Returns:
            float: Relationship quality score between 0.0 and 1.0 (average across all relationships)
        """
        if not relationships:
            return 0.0
        
        scores = []
        for rel in relationships:
            # Check required fields
            has_source = "source" in rel or "subject" in rel
            has_target = "target" in rel or "object" in rel
            has_type = "type" in rel or "predicate" in rel
            
            score = 0.0
            if has_source:
                score += 0.33
            if has_target:
                score += 0.33
            if has_type:
                score += 0.34
            
            scores.append(score)
        
        return sum(scores) / len(scores) if scores else 0.0
    
    def _calculate_consistency(self, knowledge_graph: Any) -> float:
        """
        Calculate consistency score (simplified).
        
        This is a placeholder method. In practice, this would check for logical
        inconsistencies in the knowledge graph.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            float: Consistency score between 0.0 and 1.0 (placeholder: 0.8)
        """
        # In practice, this would check for logical inconsistencies
        return 0.8  # Placeholder


class CompletenessMetrics:
    """
    Completeness metrics calculator.
    
    This class provides completeness metrics calculation for knowledge graphs,
    assessing whether entities, relationships, and properties meet schema
    requirements for completeness.
    
    Features:
        - Entity completeness calculation
        - Relationship completeness calculation
        - Property completeness calculation
        - Schema-based validation
    
    Example Usage:
        >>> metrics = CompletenessMetrics()
        >>> score = metrics.calculate_entity_completeness(entities, schema)
        >>> rel_score = metrics.calculate_relationship_completeness(relationships, schema)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize completeness metrics calculator.
        
        Sets up the calculator with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("completeness_metrics")
        self.config = kwargs
        
        self.logger.debug("Completeness metrics calculator initialized")
    
    def calculate_entity_completeness(
        self,
        entities: List[Dict[str, Any]],
        schema: Dict[str, Any]
    ) -> float:
        """
        Calculate entity completeness.
        
        This method calculates completeness scores for entities by checking
        whether they have all required properties as defined in the schema.
        Returns the average completeness score across all entities.
        
        Args:
            entities: List of entity dictionaries
            schema: Schema definition containing constraints with required_props
                   for each entity type
            
        Returns:
            float: Completeness score between 0.0 and 1.0 (average across entities)
        """
        if not entities:
            return 0.0
        
        constraints = schema.get("constraints", {})
        scores = []
        
        for entity in entities:
            entity_type = entity.get("type")
            if not entity_type:
                scores.append(0.0)
                continue
            
            constraint = constraints.get(entity_type, {})
            required_props = constraint.get("required_props", [])
            
            if not required_props:
                scores.append(1.0)
                continue
            
            # Count how many required properties are present
            present_props = sum(1 for prop in required_props if prop in entity)
            completeness = present_props / len(required_props) if required_props else 1.0
            
            scores.append(completeness)
        
        return sum(scores) / len(scores) if scores else 0.0
    
    def calculate_property_completeness(
        self,
        properties: Dict[str, Any],
        schema: Dict[str, Any]
    ) -> float:
        """
        Calculate property completeness.
        
        This method calculates completeness scores for properties by checking
        whether entity types have all required properties as defined in the schema.
        
        Args:
            properties: Properties dictionary (mapping entity types to property dictionaries)
            schema: Schema definition containing constraints with required_props
            
        Returns:
            float: Completeness score between 0.0 and 1.0 (average across entity types)
        """
        constraints = schema.get("constraints", {})
        scores = []
        
        for entity_type, constraint in constraints.items():
            required_props = constraint.get("required_props", [])
            
            if entity_type in properties:
                entity_props = properties[entity_type]
                present_props = sum(1 for prop in required_props if prop in entity_props)
                completeness = present_props / len(required_props) if required_props else 1.0
                scores.append(completeness)
        
        return sum(scores) / len(scores) if scores else 1.0
    
    def calculate_relationship_completeness(
        self,
        relationships: List[Dict[str, Any]],
        schema: Dict[str, Any]
    ) -> float:
        """
        Calculate relationship completeness.
        
        This method calculates completeness scores for relationships by checking
        whether they have all required fields (source/subject, target/object,
        type/predicate). Returns the average completeness score.
        
        Args:
            relationships: List of relationship dictionaries
            schema: Schema definition (currently unused, reserved for future
                   relationship-specific constraints)
            
        Returns:
            float: Completeness score between 0.0 and 1.0 (average across relationships)
        """
        if not relationships:
            return 0.0
        
        # Check if relationships have required fields
        scores = []
        for rel in relationships:
            has_source = "source" in rel or "subject" in rel
            has_target = "target" in rel or "object" in rel
            has_type = "type" in rel or "predicate" in rel
            
            completeness = (has_source + has_target + has_type) / 3.0
            scores.append(completeness)
        
        return sum(scores) / len(scores) if scores else 0.0


class ConsistencyMetrics:
    """
    Consistency metrics calculator.
    
    This class provides consistency metrics calculation for knowledge graphs,
    assessing logical, temporal, and hierarchical consistency.
    
    Features:
        - Logical consistency calculation
        - Temporal consistency calculation
        - Hierarchical consistency calculation
    
    Example Usage:
        >>> metrics = ConsistencyMetrics()
        >>> logical_score = metrics.calculate_logical_consistency(knowledge_graph)
        >>> temporal_score = metrics.calculate_temporal_consistency(knowledge_graph)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize consistency metrics calculator.
        
        Sets up the calculator with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("consistency_metrics")
        self.config = kwargs
        
        self.logger.debug("Consistency metrics calculator initialized")
    
    def calculate_logical_consistency(
        self,
        knowledge_graph: Any
    ) -> float:
        """
        Calculate logical consistency.
        
        This method calculates a logical consistency score by checking for
        logical contradictions, conflicting relationships, and inconsistent
        property values. Currently returns a placeholder value.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            float: Logical consistency score between 0.0 and 1.0 (placeholder: 0.9)
        """
        # In practice, this would use a reasoner
        # For now, return a placeholder
        return 0.9
    
    def calculate_temporal_consistency(
        self,
        knowledge_graph: Any
    ) -> float:
        """
        Calculate temporal consistency.
        
        This method calculates a temporal consistency score by checking for
        temporal contradictions, invalid time ranges, and conflicting
        temporal relationships. Currently returns a placeholder value.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            float: Temporal consistency score between 0.0 and 1.0 (placeholder: 0.85)
        """
        # Check for temporal contradictions
        return 0.85
    
    def calculate_hierarchical_consistency(
        self,
        knowledge_graph: Any
    ) -> float:
        """
        Calculate hierarchical consistency.
        
        This method calculates a hierarchical consistency score by checking for
        hierarchical contradictions such as circular inheritance, invalid
        parent-child relationships, and conflicting hierarchical structures.
        Currently returns a placeholder value.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            float: Hierarchical consistency score between 0.0 and 1.0 (placeholder: 0.9)
        """
        # Check for hierarchical contradictions (e.g., circular inheritance)
        return 0.9

