utils/scoring.py

import numpy as np
import logging

logger = logging.getLogger(__name__)

def calculate_final_score(
    quality_score: float,
    aesthetics_score: float,
    prompt_score: float,
    ai_detection_score: float,
    has_prompt: bool = True
) -> float:
    """
    Calculate weighted composite score for image evaluation
    
    Args:
        quality_score: Technical image quality (0-10)
        aesthetics_score: Visual appeal score (0-10)
        prompt_score: Prompt adherence score (0-10)
        ai_detection_score: AI generation probability (0-1)
        has_prompt: Whether prompt metadata is available
        
    Returns:
        Final composite score (0-10)
    """
    try:
        # Validate input scores
        quality_score = max(0.0, min(10.0, quality_score))
        aesthetics_score = max(0.0, min(10.0, aesthetics_score))
        prompt_score = max(0.0, min(10.0, prompt_score))
        ai_detection_score = max(0.0, min(1.0, ai_detection_score))
        
        if has_prompt:
            # Standard weights when prompt is available
            weights = {
                'quality': 0.25,      # 25% - Technical quality
                'aesthetics': 0.35,   # 35% - Visual appeal (highest weight)
                'prompt': 0.25,       # 25% - Prompt following
                'ai_detection': 0.15  # 15% - AI detection (inverted)
            }
            
            # Calculate weighted score
            score = (
                quality_score * weights['quality'] +
                aesthetics_score * weights['aesthetics'] +
                prompt_score * weights['prompt'] +
                (1 - ai_detection_score) * weights['ai_detection']
            )
        else:
            # Redistribute prompt weight when no prompt available
            weights = {
                'quality': 0.375,     # 25% + 12.5% from prompt
                'aesthetics': 0.475,  # 35% + 12.5% from prompt
                'ai_detection': 0.15  # 15% - AI detection (inverted)
            }
            
            # Calculate weighted score without prompt
            score = (
                quality_score * weights['quality'] +
                aesthetics_score * weights['aesthetics'] +
                (1 - ai_detection_score) * weights['ai_detection']
            )
        
        # Ensure score is in valid range
        final_score = max(0.0, min(10.0, score))
        
        logger.debug(f"Score calculation - Quality: {quality_score:.2f}, "
                    f"Aesthetics: {aesthetics_score:.2f}, Prompt: {prompt_score:.2f}, "
                    f"AI Detection: {ai_detection_score:.3f}, Has Prompt: {has_prompt}, "
                    f"Final: {final_score:.2f}")
        
        return final_score
        
    except Exception as e:
        logger.error(f"Error calculating final score: {str(e)}")
        return 5.0  # Default neutral score

def calculate_category_rankings(scores_list: list, category: str) -> list:
    """
    Calculate rankings for a specific category
    
    Args:
        scores_list: List of score dictionaries
        category: Category to rank by ('quality_score', 'aesthetics_score', etc.)
        
    Returns:
        List of rankings (1-based)
    """
    try:
        if not scores_list or category not in scores_list[0]:
            return [1] * len(scores_list)
        
        # Extract scores for the category
        category_scores = [item[category] for item in scores_list]
        
        # Calculate rankings (higher score = better rank)
        rankings = []
        for i, score in enumerate(category_scores):
            rank = 1
            for j, other_score in enumerate(category_scores):
                if other_score > score:
                    rank += 1
            rankings.append(rank)
        
        return rankings
        
    except Exception as e:
        logger.error(f"Error calculating category rankings: {str(e)}")
        return list(range(1, len(scores_list) + 1))

def normalize_scores(scores: list, target_range: tuple = (0, 10)) -> list:
    """
    Normalize a list of scores to a target range
    
    Args:
        scores: List of numerical scores
        target_range: Tuple of (min, max) for target range
        
    Returns:
        List of normalized scores
    """
    try:
        if not scores:
            return []
        
        min_score = min(scores)
        max_score = max(scores)
        
        # Avoid division by zero
        if max_score == min_score:
            return [target_range[1]] * len(scores)
        
        target_min, target_max = target_range
        target_span = target_max - target_min
        score_span = max_score - min_score
        
        normalized = []
        for score in scores:
            normalized_score = target_min + (score - min_score) * target_span / score_span
            normalized.append(max(target_min, min(target_max, normalized_score)))
        
        return normalized
        
    except Exception as e:
        logger.error(f"Error normalizing scores: {str(e)}")
        return scores

def calculate_confidence_intervals(scores: list, confidence_level: float = 0.95) -> dict:
    """
    Calculate confidence intervals for a list of scores
    
    Args:
        scores: List of numerical scores
        confidence_level: Confidence level (0-1)
        
    Returns:
        Dictionary with mean, std, lower_bound, upper_bound
    """
    try:
        if not scores:
            return {'mean': 0, 'std': 0, 'lower_bound': 0, 'upper_bound': 0}
        
        mean_score = np.mean(scores)
        std_score = np.std(scores)
        
        # Calculate confidence interval using t-distribution
        from scipy import stats
        n = len(scores)
        t_value = stats.t.ppf((1 + confidence_level) / 2, n - 1)
        margin_error = t_value * std_score / np.sqrt(n)
        
        return {
            'mean': float(mean_score),
            'std': float(std_score),
            'lower_bound': float(mean_score - margin_error),
            'upper_bound': float(mean_score + margin_error)
        }
        
    except Exception as e:
        logger.error(f"Error calculating confidence intervals: {str(e)}")
        return {'mean': 0, 'std': 0, 'lower_bound': 0, 'upper_bound': 0}

def detect_outliers(scores: list, method: str = 'iqr') -> list:
    """
    Detect outliers in a list of scores
    
    Args:
        scores: List of numerical scores
        method: Method to use ('iqr', 'zscore', 'modified_zscore')
        
    Returns:
        List of boolean values indicating outliers
    """
    try:
        if not scores or len(scores) < 3:
            return [False] * len(scores)
        
        scores_array = np.array(scores)
        
        if method == 'iqr':
            # Interquartile Range method
            q1 = np.percentile(scores_array, 25)
            q3 = np.percentile(scores_array, 75)
            iqr = q3 - q1
            lower_bound = q1 - 1.5 * iqr
            upper_bound = q3 + 1.5 * iqr
            outliers = (scores_array < lower_bound) | (scores_array > upper_bound)
            
        elif method == 'zscore':
            # Z-score method
            z_scores = np.abs(stats.zscore(scores_array))
            outliers = z_scores > 2.5
            
        elif method == 'modified_zscore':
            # Modified Z-score method (more robust)
            median = np.median(scores_array)
            mad = np.median(np.abs(scores_array - median))
            modified_z_scores = 0.6745 * (scores_array - median) / mad
            outliers = np.abs(modified_z_scores) > 3.5
            
        else:
            outliers = [False] * len(scores)
        
        return outliers.tolist()
        
    except Exception as e:
        logger.error(f"Error detecting outliers: {str(e)}")
        return [False] * len(scores)

def calculate_score_distribution(scores: list) -> dict:
    """
    Calculate distribution statistics for scores
    
    Args:
        scores: List of numerical scores
        
    Returns:
        Dictionary with distribution statistics
    """
    try:
        if not scores:
            return {}
        
        scores_array = np.array(scores)
        
        distribution = {
            'count': len(scores),
            'mean': float(np.mean(scores_array)),
            'median': float(np.median(scores_array)),
            'std': float(np.std(scores_array)),
            'min': float(np.min(scores_array)),
            'max': float(np.max(scores_array)),
            'q1': float(np.percentile(scores_array, 25)),
            'q3': float(np.percentile(scores_array, 75)),
            'skewness': float(stats.skew(scores_array)),
            'kurtosis': float(stats.kurtosis(scores_array))
        }
        
        return distribution
        
    except Exception as e:
        logger.error(f"Error calculating score distribution: {str(e)}")
        return {}

def apply_score_adjustments(
    scores: dict,
    adjustments: dict = None
) -> dict:
    """
    Apply custom score adjustments based on specific criteria
    
    Args:
        scores: Dictionary of scores
        adjustments: Dictionary of adjustment parameters
        
    Returns:
        Dictionary of adjusted scores
    """
    try:
        if adjustments is None:
            adjustments = {}
        
        adjusted_scores = scores.copy()
        
        # Apply anime mode adjustments
        if adjustments.get('anime_mode', False):
            # Boost aesthetics score for anime images
            if 'aesthetics_score' in adjusted_scores:
                adjusted_scores['aesthetics_score'] *= 1.1
                adjusted_scores['aesthetics_score'] = min(10.0, adjusted_scores['aesthetics_score'])
        
        # Apply quality penalties for low resolution
        if adjustments.get('penalize_low_resolution', True):
            width = adjustments.get('width', 1024)
            height = adjustments.get('height', 1024)
            total_pixels = width * height
            
            if total_pixels < 262144:  # Less than 512x512
                penalty = 0.8
                if 'quality_score' in adjusted_scores:
                    adjusted_scores['quality_score'] *= penalty
        
        # Apply prompt complexity adjustments
        prompt_length = adjustments.get('prompt_length', 0)
        if prompt_length > 0 and 'prompt_score' in adjusted_scores:
            if prompt_length > 100:  # Very long prompts are harder to follow
                adjusted_scores['prompt_score'] *= 0.95
            elif prompt_length < 10:  # Very short prompts are easier
                adjusted_scores['prompt_score'] *= 1.05
                adjusted_scores['prompt_score'] = min(10.0, adjusted_scores['prompt_score'])
        
        return adjusted_scores
        
    except Exception as e:
        logger.error(f"Error applying score adjustments: {str(e)}")
        return scores

def generate_score_summary(results_list: list) -> dict:
    """
    Generate summary statistics for a batch of evaluation results
    
    Args:
        results_list: List of result dictionaries
        
    Returns:
        Dictionary with summary statistics
    """
    try:
        if not results_list:
            return {}
        
        # Extract scores by category
        categories = ['quality_score', 'aesthetics_score', 'prompt_score', 'ai_detection_score', 'final_score']
        summary = {}
        
        for category in categories:
            if category in results_list[0]:
                scores = [result[category] for result in results_list if category in result]
                if scores:
                    summary[category] = calculate_score_distribution(scores)
        
        # Calculate overall statistics
        final_scores = [result['final_score'] for result in results_list if 'final_score' in result]
        if final_scores:
            summary['overall'] = {
                'total_images': len(results_list),
                'average_score': np.mean(final_scores),
                'best_score': max(final_scores),
                'worst_score': min(final_scores),
                'score_range': max(final_scores) - min(final_scores),
                'images_with_prompts': sum(1 for r in results_list if r.get('has_prompt', False))
            }
        
        return summary
        
    except Exception as e:
        logger.error(f"Error generating score summary: {str(e)}")
        return {}