score_utilizer.py

#!/usr/bin/env python3
"""
Score Utilizer - Extract and utilize highest-scoring pages from retrieval logs
This module provides utilities to parse log outputs and retrieve the best pages based on scores.
"""

import re
import json
from typing import List, Dict, Tuple, Optional

class ScoreUtilizer:
    """
    Utility class to extract and utilize highest-scoring pages from retrieval logs
    """
    
    def __init__(self):
        self.score_patterns = {
            'page_score': r'Page\s+(\d+)\s+\(doc_id:\s*(\d+)\)\s*\|\s*Score:\s*([\d.]+)',
            'highest_scoring': r'(\d+)\.\s*Page\s+(\d+)\s+-\s*Score:\s*([\d.]+)',
            'relevance_level': r'([🟢🟡🟠🔵🟣🔴])\s+([A-Z\s]+)\s+-\s+(.+)'
        }
    
    def parse_log_output(self, log_text: str) -> Dict:
        """
        Parse log output to extract page scores and relevance information
        
        Args:
            log_text: Raw log output from the retrieval system
            
        Returns:
            Dictionary containing parsed page scores and metadata
        """
        print("🔍 PARSING LOG OUTPUT FOR HIGHEST-SCORING PAGES")
        print("=" * 60)
        
        # Extract page scores
        page_scores = self._extract_page_scores(log_text)
        
        # Extract highest scoring pages
        top_pages = self._extract_top_pages(log_text)
        
        # Extract relevance distribution
        relevance_dist = self._extract_relevance_distribution(log_text)
        
        # Extract statistics
        stats = self._extract_statistics(log_text)
        
        result = {
            'page_scores': page_scores,
            'top_pages': top_pages,
            'relevance_distribution': relevance_dist,
            'statistics': stats,
            'parsed_at': self._get_timestamp()
        }
        
        print(f"✅ Successfully parsed {len(page_scores)} page scores")
        print(f"🏆 Found {len(top_pages)} top-scoring pages")
        print("=" * 60)
        
        return result
    
    def _extract_page_scores(self, log_text: str) -> List[Dict]:
        """Extract individual page scores from log text"""
        page_scores = []
        
        # Pattern: "Page  1 (doc_id:  0) | Score:   0.9234 | 🟢 EXCELLENT - Highly relevant"
        pattern = self.score_patterns['page_score']
        matches = re.findall(pattern, log_text)
        
        for match in matches:
            page_num, doc_id, score = match
            page_scores.append({
                'page_number': int(page_num),
                'doc_id': int(doc_id),
                'score': float(score),
                'relevance_level': self._get_relevance_level(float(score))
            })
        
        # Sort by score (highest first)
        page_scores.sort(key=lambda x: x['score'], reverse=True)
        
        return page_scores
    
    def _extract_top_pages(self, log_text: str) -> List[Dict]:
        """Extract top-scoring pages from log text"""
        top_pages = []
        
        # Pattern: "1. Page 1 - Score: 0.9234"
        pattern = self.score_patterns['highest_scoring']
        matches = re.findall(pattern, log_text)
        
        for match in matches:
            rank, page_num, score = match
            top_pages.append({
                'rank': int(rank),
                'page_number': int(page_num),
                'score': float(score),
                'relevance_level': self._get_relevance_level(float(score))
            })
        
        return top_pages
    
    def _extract_relevance_distribution(self, log_text: str) -> Dict:
        """Extract relevance distribution from log text"""
        distribution = {
            'excellent': 0,
            'very_good': 0,
            'good': 0,
            'moderate': 0,
            'basic': 0,
            'poor': 0
        }
        
        # Look for distribution lines like "🟢 Excellent (≥0.90): 2 pages"
        patterns = {
            'excellent': r'🟢\s+Excellent.*?(\d+)\s+pages?',
            'very_good': r'🟡\s+Very Good.*?(\d+)\s+pages?',
            'good': r'🟠\s+Good.*?(\d+)\s+pages?',
            'moderate': r'🔵\s+Moderate.*?(\d+)\s+pages?',
            'basic': r'🟣\s+Basic.*?(\d+)\s+pages?',
            'poor': r'🔴\s+Poor.*?(\d+)\s+pages?'
        }
        
        for level, pattern in patterns.items():
            match = re.search(pattern, log_text)
            if match:
                distribution[level] = int(match.group(1))
        
        return distribution
    
    def _extract_statistics(self, log_text: str) -> Dict:
        """Extract statistical information from log text"""
        stats = {}
        
        # Extract average score
        avg_match = re.search(r'Average.*?Score:\s*([\d.]+)', log_text)
        if avg_match:
            stats['average_score'] = float(avg_match.group(1))
        
        # Extract highest score
        high_match = re.search(r'Highest.*?Score:\s*([\d.]+)', log_text)
        if high_match:
            stats['highest_score'] = float(high_match.group(1))
        
        # Extract lowest score
        low_match = re.search(r'Lowest.*?Score:\s*([\d.]+)', log_text)
        if low_match:
            stats['lowest_score'] = float(low_match.group(1))
        
        # Extract total pages
        total_match = re.search(r'Total.*?(\d+).*?results?', log_text)
        if total_match:
            stats['total_pages'] = int(total_match.group(1))
        
        return stats
    
    def get_highest_scoring_pages(self, parsed_data: Dict, count: int = 5) -> List[Dict]:
        """
        Get the highest-scoring pages from parsed data
        
        Args:
            parsed_data: Parsed log data from parse_log_output()
            count: Number of top pages to return
            
        Returns:
            List of highest-scoring pages
        """
        if 'page_scores' not in parsed_data:
            return []
        
        return parsed_data['page_scores'][:count]
    
    def get_pages_by_threshold(self, parsed_data: Dict, threshold: float = 0.80) -> List[Dict]:
        """
        Get pages that meet or exceed a score threshold
        
        Args:
            parsed_data: Parsed log data from parse_log_output()
            threshold: Minimum score threshold
            
        Returns:
            List of pages meeting the threshold
        """
        if 'page_scores' not in parsed_data:
            return []
        
        return [page for page in parsed_data['page_scores'] if page['score'] >= threshold]
    
    def get_pages_by_relevance_level(self, parsed_data: Dict, level: str = 'excellent') -> List[Dict]:
        """
        Get pages by specific relevance level
        
        Args:
            parsed_data: Parsed log data from parse_log_output()
            level: Relevance level ('excellent', 'very_good', 'good', 'moderate', 'basic', 'poor')
            
        Returns:
            List of pages with the specified relevance level
        """
        if 'page_scores' not in parsed_data:
            return []
        
        level_mapping = {
            'excellent': '🟢 EXCELLENT',
            'very_good': '🟡 VERY GOOD',
            'good': '🟠 GOOD',
            'moderate': '🔵 MODERATE',
            'basic': '🟣 BASIC',
            'poor': '🔴 POOR'
        }
        
        target_level = level_mapping.get(level, '🟢 EXCELLENT')
        return [page for page in parsed_data['page_scores'] if target_level in page['relevance_level']]
    
    def generate_utilization_report(self, parsed_data: Dict) -> str:
        """
        Generate a comprehensive report on how to utilize the highest-scoring pages
        
        Args:
            parsed_data: Parsed log data from parse_log_output()
            
        Returns:
            Formatted report string
        """
        report = []
        report.append("📊 HIGHEST-SCORING PAGES UTILIZATION REPORT")
        report.append("=" * 60)
        
        # Top pages summary
        top_pages = self.get_highest_scoring_pages(parsed_data, 5)
        report.append(f"\n🏆 TOP 5 HIGHEST-SCORING PAGES:")
        for i, page in enumerate(top_pages, 1):
            report.append(f"   {i}. Page {page['page_number']} - Score: {page['score']:.4f} ({page['relevance_level']})")
        
        # Threshold-based recommendations
        excellent_pages = self.get_pages_by_threshold(parsed_data, 0.90)
        very_good_pages = self.get_pages_by_threshold(parsed_data, 0.80)
        
        report.append(f"\n🎯 UTILIZATION RECOMMENDATIONS:")
        report.append(f"   🟢 Excellent pages (≥0.90): {len(excellent_pages)} pages - Use for primary context")
        report.append(f"   🟡 Very Good pages (≥0.80): {len(very_good_pages)} pages - Use for comprehensive coverage")
        
        # Statistics
        if 'statistics' in parsed_data and parsed_data['statistics']:
            stats = parsed_data['statistics']
            report.append(f"\n📈 QUALITY METRICS:")
            if 'average_score' in stats:
                report.append(f"   Average Score: {stats['average_score']:.4f}")
            if 'highest_score' in stats:
                report.append(f"   Highest Score: {stats['highest_score']:.4f}")
            if 'total_pages' in stats:
                report.append(f"   Total Pages Analyzed: {stats['total_pages']}")
        
        # Usage suggestions
        report.append(f"\n💡 USAGE SUGGESTIONS:")
        report.append(f"   1. Feed top 3 pages to language model for focused responses")
        report.append(f"   2. Use excellent pages for critical information extraction")
        report.append(f"   3. Include very good pages for comprehensive analysis")
        report.append(f"   4. Consider page diversity for balanced coverage")
        
        report.append("=" * 60)
        
        return "\n".join(report)
    
    def _get_relevance_level(self, score: float) -> str:
        """Get relevance level based on score"""
        if score >= 0.90:
            return "🟢 EXCELLENT - Highly relevant"
        elif score >= 0.80:
            return "🟡 VERY GOOD - Very relevant"
        elif score >= 0.70:
            return "🟠 GOOD - Relevant"
        elif score >= 0.60:
            return "🔵 MODERATE - Somewhat relevant"
        elif score >= 0.50:
            return "🟣 BASIC - Minimally relevant"
        else:
            return "🔴 POOR - Not relevant"
    
    def _get_timestamp(self) -> str:
        """Get current timestamp"""
        from datetime import datetime
        return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

# Example usage function
def demonstrate_score_utilization():
    """
    Demonstrate how to use the ScoreUtilizer to extract and utilize highest-scoring pages
    """
    print("🧪 DEMONSTRATING SCORE UTILIZATION")
    print("=" * 60)
    
    # Example log output (this would come from your actual retrieval system)
    example_log = """
================================================================================
📊 RETRIEVAL SCORES - PAGE NUMBERS WITH HIGHEST SCORES
================================================================================
🔍 Collection: documents_20250101_120000
📄 Total documents found: 15
🎯 Requested top-k: 5
--------------------------------------------------------------------------------
📄 Page  1 (doc_id:  0) | Score:   0.9234 | 🟢 EXCELLENT - Highly relevant
📄 Page  3 (doc_id:  2) | Score:   0.8756 | 🟡 VERY GOOD - Very relevant
📄 Page  7 (doc_id:  6) | Score:   0.8123 | 🟡 VERY GOOD - Very relevant
📄 Page  2 (doc_id:  1) | Score:   0.7890 | 🟠 GOOD - Relevant
📄 Page  5 (doc_id:  4) | Score:   0.7456 | 🟠 GOOD - Relevant
--------------------------------------------------------------------------------
🏆 HIGHEST SCORING PAGES:
   1. Page 1 - Score: 0.9234
   2. Page 3 - Score: 0.8756
   3. Page 7 - Score: 0.8123
================================================================================
"""
    
    # Initialize utilizer
    utilizer = ScoreUtilizer()
    
    # Parse the log output
    parsed_data = utilizer.parse_log_output(example_log)
    
    # Get highest-scoring pages
    top_pages = utilizer.get_highest_scoring_pages(parsed_data, 3)
    print(f"\n🏆 TOP 3 HIGHEST-SCORING PAGES:")
    for page in top_pages:
        print(f"   Page {page['page_number']} - Score: {page['score']:.4f}")
    
    # Get pages by threshold
    excellent_pages = utilizer.get_pages_by_threshold(parsed_data, 0.90)
    print(f"\n🟢 EXCELLENT PAGES (≥0.90): {len(excellent_pages)} pages")
    
    # Generate utilization report
    report = utilizer.generate_utilization_report(parsed_data)
    print(f"\n{report}")
    
    print("\n✅ Score utilization demonstration completed!")

if __name__ == "__main__":
    demonstrate_score_utilization()