validate_optimization.py

#!/usr/bin/env python3
"""
Validation script to compare optimized vs original mapper output
Compares the following columns: 出力_科目, 出力_中科目, 出力_標準名称, 出力_項目名, 出力_標準単位
"""

import pandas as pd
import numpy as np
from typing import List, Dict, Tuple
import os
import sys
from datetime import datetime

# Add the meisai-check-ai directory to Python path
sys.path.append(os.path.join(os.path.dirname(__file__), 'meisai-check-ai'))

class OptimizationValidator:
    def __init__(self, original_file_path: str):
        """
        Initialize validator with original output file
        
        Args:
            original_file_path: Path to outputData_original.csv
        """
        self.original_file_path = original_file_path
        self.comparison_columns = [
            '出力_科目', 
            '出力_中科目', 
            '出力_標準名称', 
            '出力_項目名', 
            '出力_標準単位'
        ]
        
    def load_original_data(self) -> pd.DataFrame:
        """Load original output data"""
        try:
            df_original = pd.read_csv(self.original_file_path)
            print(f"✓ Loaded original data: {len(df_original)} rows")
            return df_original
        except Exception as e:
            print(f"✗ Error loading original data: {e}")
            raise
    
    def compare_dataframes(self, df_original: pd.DataFrame, df_optimized: pd.DataFrame) -> Dict:
        """
        Compare original vs optimized dataframes
        
        Returns:
            Dict with comparison results
        """
        results = {
            'total_rows': len(df_original),
            'columns_compared': self.comparison_columns,
            'differences': {},
            'summary': {}
        }
        
        # Check if dataframes have same length
        if len(df_original) != len(df_optimized):
            results['length_mismatch'] = {
                'original': len(df_original),
                'optimized': len(df_optimized)
            }
            print(f"⚠ Warning: Different number of rows - Original: {len(df_original)}, Optimized: {len(df_optimized)}")
        
        # Compare each column
        for col in self.comparison_columns:
            if col not in df_original.columns:
                results['differences'][col] = f"Column not found in original data"
                continue
                
            if col not in df_optimized.columns:
                results['differences'][col] = f"Column not found in optimized data"
                continue
            
            # Fill NaN values with empty string for comparison
            original_values = df_original[col].fillna('')
            optimized_values = df_optimized[col].fillna('')
            
            # Compare values
            differences = original_values != optimized_values
            diff_count = differences.sum()
            
            results['differences'][col] = {
                'total_differences': int(diff_count),
                'accuracy_percentage': round((1 - diff_count / len(df_original)) * 100, 2),
                'different_indices': differences[differences].index.tolist()[:10]  # Show first 10 different indices
            }
            
            if diff_count > 0:
                print(f"⚠ {col}: {diff_count} differences ({results['differences'][col]['accuracy_percentage']}% accuracy)")
            else:
                print(f"✓ {col}: Perfect match (100% accuracy)")
        
        # Overall summary
        total_differences = sum([results['differences'][col]['total_differences'] 
                               for col in self.comparison_columns 
                               if isinstance(results['differences'][col], dict)])
        
        overall_accuracy = round((1 - total_differences / (len(df_original) * len(self.comparison_columns))) * 100, 2)
        
        results['summary'] = {
            'total_differences': total_differences,
            'overall_accuracy': overall_accuracy,
            'perfect_match': total_differences == 0
        }
        
        return results
    
    def generate_difference_report(self, df_original: pd.DataFrame, df_optimized: pd.DataFrame, 
                                 output_file: str = None) -> str:
        """
        Generate detailed difference report
        
        Args:
            df_original: Original dataframe
            df_optimized: Optimized dataframe
            output_file: Optional output file path
            
        Returns:
            Report string
        """
        report_lines = []
        report_lines.append("=" * 80)
        report_lines.append(f"OPTIMIZATION VALIDATION REPORT")
        report_lines.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        report_lines.append("=" * 80)
        
        # Basic info
        report_lines.append(f"Original data rows: {len(df_original)}")
        report_lines.append(f"Optimized data rows: {len(df_optimized)}")
        report_lines.append(f"Columns compared: {', '.join(self.comparison_columns)}")
        report_lines.append("")
        
        # Compare each column
        for col in self.comparison_columns:
            if col not in df_original.columns or col not in df_optimized.columns:
                report_lines.append(f"❌ {col}: Column missing")
                continue
                
            original_values = df_original[col].fillna('')
            optimized_values = df_optimized[col].fillna('')
            
            differences = original_values != optimized_values
            diff_count = differences.sum()
            accuracy = round((1 - diff_count / len(df_original)) * 100, 2)
            
            status = "✅" if diff_count == 0 else "⚠️"
            report_lines.append(f"{status} {col}: {diff_count} differences ({accuracy}% accuracy)")
            
            if diff_count > 0:
                # Show some examples of differences
                diff_indices = differences[differences].index[:5]
                report_lines.append(f"   Sample differences (first 5):")
                for idx in diff_indices:
                    orig_val = str(original_values.iloc[idx])[:50]
                    opt_val = str(optimized_values.iloc[idx])[:50]
                    report_lines.append(f"   Row {idx}: '{orig_val}' → '{opt_val}'")
                report_lines.append("")
        
        # Overall summary
        total_comparisons = len(df_original) * len(self.comparison_columns)
        total_differences = sum([
            (df_original[col].fillna('') != df_optimized[col].fillna('')).sum()
            for col in self.comparison_columns
            if col in df_original.columns and col in df_optimized.columns
        ])
        
        overall_accuracy = round((1 - total_differences / total_comparisons) * 100, 2)
        
        report_lines.append("=" * 80)
        report_lines.append(f"OVERALL RESULTS:")
        report_lines.append(f"Total differences: {total_differences}")
        report_lines.append(f"Overall accuracy: {overall_accuracy}%")
        report_lines.append(f"Perfect match: {'Yes' if total_differences == 0 else 'No'}")
        report_lines.append("=" * 80)
        
        report_text = "\n".join(report_lines)
        
        if output_file:
            with open(output_file, 'w', encoding='utf-8') as f:
                f.write(report_text)
            print(f"📄 Report saved to: {output_file}")
        
        return report_text
    
    def validate_optimization(self, optimized_mapper_function, input_data: pd.DataFrame, 
                            report_file: str = None) -> bool:
        """
        Run full validation process
        
        Args:
            optimized_mapper_function: Function that takes input_data and returns optimized output
            input_data: Input dataframe to process
            report_file: Optional report file path
            
        Returns:
            True if validation passes (100% accuracy)
        """
        print("🔍 Starting optimization validation...")
        
        # Load original data
        df_original = self.load_original_data()
        
        # Run optimized mapper
        print("🚀 Running optimized mapper...")
        try:
            df_optimized = optimized_mapper_function(input_data)
            print(f"✓ Optimized processing completed: {len(df_optimized)} rows")
        except Exception as e:
            print(f"✗ Error in optimized processing: {e}")
            return False
        
        # Compare results
        print("📊 Comparing results...")
        results = self.compare_dataframes(df_original, df_optimized)
        
        # Generate report
        if report_file:
            self.generate_difference_report(df_original, df_optimized, report_file)
        
        # Print summary
        print("\n" + "="*50)
        print("🎯 VALIDATION SUMMARY")
        print("="*50)
        print(f"Overall accuracy: {results['summary']['overall_accuracy']}%")
        print(f"Perfect match: {'Yes' if results['summary']['perfect_match'] else 'No'}")
        print(f"Total differences: {results['summary']['total_differences']}")
        
        return results['summary']['perfect_match']

    def compare_two_files(self, optimized_file_path: str, report_file: str = None) -> bool:
        """
        Compare two CSV files directly
        
        Args:
            optimized_file_path: Path to optimized output CSV
            report_file: Optional report file path
            
        Returns:
            True if validation passes (100% accuracy)
        """
        print("🔍 Starting file comparison validation...")
        
        # Load original data
        df_original = self.load_original_data()
        
        # Load optimized data
        try:
            df_optimized = pd.read_csv(optimized_file_path)
            print(f"✓ Loaded optimized data: {len(df_optimized)} rows")
        except Exception as e:
            print(f"✗ Error loading optimized data: {e}")
            return False
        
        # Compare results
        print("📊 Comparing results...")
        results = self.compare_dataframes(df_original, df_optimized)
        
        # Generate report
        if report_file:
            self.generate_difference_report(df_original, df_optimized, report_file)
        
        # Print summary
        print("\n" + "="*50)
        print("🎯 VALIDATION SUMMARY")
        print("="*50)
        print(f"Overall accuracy: {results['summary']['overall_accuracy']}%")
        print(f"Perfect match: {'Yes' if results['summary']['perfect_match'] else 'No'}")
        print(f"Total differences: {results['summary']['total_differences']}")
        
        return results['summary']['perfect_match']

def main():
    """Example usage"""
    # Example paths - update these according to your setup
    original_file = "data/outputData_original.csv"
    input_file = "data/outputData_api.csv"
    
    if not os.path.exists(original_file):
        print(f"❌ Original file not found: {original_file}")
        print("Please ensure outputData_original.csv exists in the current directory")
        return
    
    # Initialize validator
    validator = OptimizationValidator(original_file)
    
    # Example of how to use with your mapper
    def example_optimized_mapper(input_data):
        # This is where you would call your optimized mapper
        # For now, return a copy of input_data as example
        df_result = input_data.copy()
        
        # Add expected output columns with dummy data for demo
        df_result['出力_科目'] = df_result.get('科目', '')
        df_result['出力_中科目'] = df_result.get('中科目', '')
        df_result['出力_標準名称'] = df_result.get('名称', '')
        df_result['出力_項目名'] = df_result.get('名称', '')
        df_result['出力_標準単位'] = df_result.get('単位', '')
        
        return df_result
    
    # Load input data
    if os.path.exists(input_file):
        input_data = pd.read_csv(input_file)
        
        # Run validation
        is_valid = validator.validate_optimization(
            example_optimized_mapper, 
            input_data, 
            "optimization_validation_report.txt"
        )
        
        if is_valid:
            print("🎉 Validation PASSED! Optimization maintains accuracy.")
        else:
            print("❌ Validation FAILED! Check the report for details.")
    else:
        print(f"❌ Input file not found: {input_file}")
        print("You can also compare two CSV files directly:")
        print("validator.compare_two_files('optimized_output.csv', 'report.txt')")

if __name__ == "__main__":
    main()