# verification_metrics.py
"""
Verification metrics calculator service.

Provides methods for calculating accuracy, confusion matrices, and error patterns
from verification records.
"""

from typing import Dict, List, Any
from src.core.verification_models import VerificationRecord


class VerificationMetricsCalculator:
    """Calculates performance metrics from verification records."""

    @staticmethod
    def calculate_accuracy(records: List[VerificationRecord]) -> float:
        """
        Calculate overall accuracy from verification records.
        
        Accuracy = (correct_count / total_count) * 100
        
        Args:
            records: List of verification records
            
        Returns:
            Accuracy as a percentage (0-100), or 0 if no records
        """
        if not records:
            return 0.0
        
        correct_count = sum(1 for record in records if record.is_correct)
        return (correct_count / len(records)) * 100

    @staticmethod
    def calculate_accuracy_by_type(
        records: List[VerificationRecord],
    ) -> Dict[str, float]:
        """
        Calculate accuracy for each classification type.
        
        For each type (green, yellow, red), calculates:
        accuracy = (correct_count_for_type / total_count_for_type) * 100
        
        Args:
            records: List of verification records
            
        Returns:
            Dictionary with keys "green", "yellow", "red" and accuracy percentages
        """
        accuracy_by_type = {}
        
        for classification_type in ["green", "yellow", "red"]:
            type_records = [
                r for r in records
                if r.classifier_decision == classification_type
            ]
            
            if type_records:
                correct_count = sum(1 for r in type_records if r.is_correct)
                accuracy_by_type[classification_type] = (
                    correct_count / len(type_records) * 100
                )
            else:
                accuracy_by_type[classification_type] = 0.0
        
        return accuracy_by_type

    @staticmethod
    def calculate_confusion_matrix(
        records: List[VerificationRecord],
    ) -> Dict[str, Dict[str, int]]:
        """
        Generate a confusion matrix from verification records.
        
        The confusion matrix shows:
        - Rows: classifier decisions (what the classifier said)
        - Columns: ground truth labels (what the verifier said)
        - Values: count of records in each cell
        
        Args:
            records: List of verification records
            
        Returns:
            Dictionary with structure:
            {
                "green": {"green": count, "yellow": count, "red": count},
                "yellow": {"green": count, "yellow": count, "red": count},
                "red": {"green": count, "yellow": count, "red": count},
            }
        """
        # Initialize matrix with zeros
        matrix = {
            "green": {"green": 0, "yellow": 0, "red": 0},
            "yellow": {"green": 0, "yellow": 0, "red": 0},
            "red": {"green": 0, "yellow": 0, "red": 0},
        }
        
        # Populate matrix
        for record in records:
            classifier_decision = record.classifier_decision
            ground_truth = record.ground_truth_label
            matrix[classifier_decision][ground_truth] += 1
        
        return matrix

    @staticmethod
    def generate_error_patterns(
        records: List[VerificationRecord],
    ) -> List[str]:
        """
        Detect common error patterns from verification records.
        
        Identifies patterns like:
        - "Often misclassifies YELLOW as GREEN"
        - "Frequently misses RED indicators"
        
        Args:
            records: List of verification records
            
        Returns:
            List of error pattern descriptions
        """
        if not records:
            return []
        
        patterns = []
        
        # Get confusion matrix
        matrix = VerificationMetricsCalculator.calculate_confusion_matrix(records)
        
        # Analyze each classification type
        for classifier_type in ["green", "yellow", "red"]:
            type_records = [
                r for r in records
                if r.classifier_decision == classifier_type
            ]
            
            if not type_records:
                continue
            
            # Find most common misclassification
            misclassifications = {}
            for record in type_records:
                if not record.is_correct:
                    ground_truth = record.ground_truth_label
                    misclassifications[ground_truth] = (
                        misclassifications.get(ground_truth, 0) + 1
                    )
            
            if misclassifications:
                most_common_wrong = max(
                    misclassifications.items(), key=lambda x: x[1]
                )
                wrong_type, wrong_count = most_common_wrong
                
                # Calculate percentage of misclassifications
                error_rate = (wrong_count / len(type_records)) * 100
                
                if error_rate >= 20:  # Only report if >= 20% error rate
                    pattern = (
                        f"Often misclassifies {classifier_type.upper()} "
                        f"as {wrong_type.upper()} ({error_rate:.0f}% of {classifier_type.upper()} cases)"
                    )
                    patterns.append(pattern)
        
        # Analyze missed classifications (false negatives)
        for ground_truth_type in ["green", "yellow", "red"]:
            # Find records where classifier missed this type
            missed = [
                r for r in records
                if r.ground_truth_label == ground_truth_type
                and r.classifier_decision != ground_truth_type
            ]
            
            if missed:
                missed_rate = (len(missed) / len(records)) * 100
                
                if missed_rate >= 10:  # Only report if >= 10% miss rate
                    pattern = (
                        f"Frequently misses {ground_truth_type.upper()} indicators "
                        f"({missed_rate:.0f}% of all messages)"
                    )
                    patterns.append(pattern)
        
        return patterns

    @staticmethod
    def get_metrics_summary(records: List[VerificationRecord]) -> Dict[str, Any]:
        """
        Get a comprehensive summary of all metrics.
        
        Args:
            records: List of verification records
            
        Returns:
            Dictionary containing all calculated metrics
        """
        if not records:
            return {
                "total_records": 0,
                "correct_count": 0,
                "incorrect_count": 0,
                "accuracy": 0.0,
                "accuracy_by_type": {"green": 0.0, "yellow": 0.0, "red": 0.0},
                "confusion_matrix": {
                    "green": {"green": 0, "yellow": 0, "red": 0},
                    "yellow": {"green": 0, "yellow": 0, "red": 0},
                    "red": {"green": 0, "yellow": 0, "red": 0},
                },
                "error_patterns": [],
            }
        
        correct_count = sum(1 for r in records if r.is_correct)
        
        return {
            "total_records": len(records),
            "correct_count": correct_count,
            "incorrect_count": len(records) - correct_count,
            "accuracy": VerificationMetricsCalculator.calculate_accuracy(records),
            "accuracy_by_type": (
                VerificationMetricsCalculator.calculate_accuracy_by_type(records)
            ),
            "confusion_matrix": (
                VerificationMetricsCalculator.calculate_confusion_matrix(records)
            ),
            "error_patterns": (
                VerificationMetricsCalculator.generate_error_patterns(records)
            ),
        }