#!/usr/bin/env python3 """ Final Null Handler Integration Script Integrates the final null value handler into the existing merge pipeline. """ import sys import subprocess from pathlib import Path import numpy as np import pandas as pd from final_null_handler import FinalNullValueHandler, process_crypto_features_file, process_stock_features_file def run_final_null_handling(): """Run the final null value handling on all feature files""" print("="*60) print("STARTING FINAL NULL VALUE HANDLING") print("="*60) base_path = Path("data/merged/features") files_to_process = [ ("crypto_features.parquet", "crypto"), ("stocks_features.parquet", "stock"), ("merged_features.parquet", "merged") ] results = {} for filename, file_type in files_to_process: file_path = base_path / filename if not file_path.exists(): print(f"[WARNING] {filename} not found, skipping...") continue print(f"\n[INFO] Processing {filename}...") try: if file_type == "crypto": df_processed, report = process_crypto_features_file(file_path) elif file_type == "stock": df_processed, report = process_stock_features_file(file_path) elif file_type == "merged": # For merged file, determine type by content df_processed, report = process_merged_features_file(file_path) results[file_type] = { 'success': True, 'file_path': file_path, 'report': report, 'rows': len(df_processed), 'nulls_filled': report['total_nulls_filled'] } print(f"[SUCCESS] {filename} processed successfully!") print(f" - Rows: {len(df_processed):,}") print(f" - Nulls filled: {report['total_nulls_filled']:,}") except Exception as e: print(f"[ERROR] Error processing {filename}: {str(e)}") results[file_type] = { 'success': False, 'error': str(e), 'file_path': file_path } return results def process_merged_features_file(file_path): """Process merged features file (contains both crypto and stock data)""" print(f"Loading merged features from {file_path}...") df = pd.read_parquet(file_path) print(f"Loaded {len(df)} rows with {len(df.columns)} columns") print(f"Null values before processing: {df.isnull().sum().sum()}") handler = FinalNullValueHandler() # Separate crypto and stock data if possible if 'symbol' in df.columns: # Detect crypto vs stock based on available columns crypto_indicators = ['rank', 'dominance', 'performance.day', 'exchangePrices.binance'] stock_indicators = ['news_activity_score_x', 'strongBuy', 'marketCapitalization'] has_crypto_cols = any(col in df.columns for col in crypto_indicators) has_stock_cols = any(col in df.columns for col in stock_indicators) if has_crypto_cols and has_stock_cols: # Mixed data - process intelligently print("Detected mixed crypto/stock data - processing intelligently...") # Try to separate by symbol patterns or available data crypto_mask = df['rank'].notna() | df['dominance'].notna() if crypto_mask.any(): print(f"Processing {crypto_mask.sum()} rows as crypto data...") df_crypto = df[crypto_mask].copy() df_crypto_processed = handler.process_crypto_features(df_crypto) df.loc[crypto_mask] = df_crypto_processed stock_mask = ~crypto_mask if stock_mask.any(): print(f"Processing {stock_mask.sum()} rows as stock data...") df_stock = df[stock_mask].copy() df_stock_processed = handler.process_stock_features(df_stock) df.loc[stock_mask] = df_stock_processed df_processed = df elif has_crypto_cols: print("Detected crypto-only data...") df_processed = handler.process_crypto_features(df) elif has_stock_cols: print("Detected stock-only data...") df_processed = handler.process_stock_features(df) else: print("Could not determine data type, applying generic processing...") df_processed = handler.process_stock_features(df) # Default to stock processing else: print("No symbol column found, applying generic processing...") df_processed = handler.process_stock_features(df) print(f"Null values after processing: {df_processed.isnull().sum().sum()}") # Generate report report = handler.generate_report(df, df_processed, 'merged') # Save processed data df_processed.to_parquet(file_path, index=False) print(f"Saved processed merged features to {file_path}") return df_processed, report def validate_data_quality(results): """Validate that the data quality is maintained after null handling""" print("\n" + "="*60) print("DATA QUALITY VALIDATION") print("="*60) validation_results = {} for file_type, result in results.items(): if not result.get('success', False): continue file_path = result['file_path'] try: df = pd.read_parquet(file_path) # Basic validation checks validation = { 'total_rows': len(df), 'total_columns': len(df.columns), 'remaining_nulls': df.isnull().sum().sum(), 'duplicate_rows': df.duplicated().sum(), 'infinite_values': np.isinf(df.select_dtypes(include=[np.number])).sum().sum(), 'data_types_consistent': True, # Could add more sophisticated checks } # Check for unrealistic values numeric_cols = df.select_dtypes(include=[np.number]).columns extreme_values = {} for col in numeric_cols: if col in df.columns: col_data = df[col].dropna() if len(col_data) > 0: q1, q99 = col_data.quantile([0.01, 0.99]) extreme_count = ((col_data < q1 - 10 * (q99 - q1)) | (col_data > q99 + 10 * (q99 - q1))).sum() if extreme_count > 0: extreme_values[col] = extreme_count validation['extreme_values'] = extreme_values validation['quality_score'] = calculate_quality_score(validation) validation_results[file_type] = validation print(f"\n{file_type.upper()} VALIDATION:") print(f" ✓ Rows: {validation['total_rows']:,}") print(f" ✓ Columns: {validation['total_columns']}") print(f" ✓ Remaining nulls: {validation['remaining_nulls']}") print(f" ✓ Duplicate rows: {validation['duplicate_rows']}") print(f" ✓ Infinite values: {validation['infinite_values']}") print(f" ✓ Quality score: {validation['quality_score']:.2%}") if extreme_values: print(f" [WARNING] Extreme values detected in {len(extreme_values)} columns") except Exception as e: print(f"[ERROR] Validation failed for {file_type}: {str(e)}") validation_results[file_type] = {'error': str(e)} return validation_results def calculate_quality_score(validation): """Calculate a simple quality score""" score = 1.0 # Penalize remaining nulls if validation['total_rows'] > 0: null_ratio = validation['remaining_nulls'] / (validation['total_rows'] * validation['total_columns']) score -= null_ratio * 0.5 # Penalize duplicates if validation['total_rows'] > 0: dup_ratio = validation['duplicate_rows'] / validation['total_rows'] score -= dup_ratio * 0.3 # Penalize infinite values if validation['infinite_values'] > 0: score -= 0.1 # Penalize extreme values extreme_columns = len(validation.get('extreme_values', {})) if extreme_columns > 0: score -= (extreme_columns / validation['total_columns']) * 0.2 return max(0.0, score) def print_final_summary(results, validation_results): """Print final summary of the null handling process""" print("\n" + "="*60) print("FINAL NULL HANDLING SUMMARY") print("="*60) total_nulls_filled = sum(r.get('nulls_filled', 0) for r in results.values() if r.get('success')) successful_files = sum(1 for r in results.values() if r.get('success')) total_files = len(results) print(f"\n[INFO] PROCESSING RESULTS:") print(f" Files processed: {successful_files}/{total_files}") print(f" Total nulls filled: {total_nulls_filled:,}") print(f"\n[METRICS] QUALITY METRICS:") for file_type, validation in validation_results.items(): if 'error' not in validation: print(f" {file_type}: {validation['quality_score']:.1%} quality score") if successful_files == total_files: print(f"\n[SUCCESS] ALL FILES PROCESSED SUCCESSFULLY!") else: failed_files = total_files - successful_files print(f"\n[WARNING] {failed_files} files failed to process") print("\n[TIPS] RECOMMENDATIONS:") print(" - Review any remaining null columns in the reports") print(" - Monitor data quality scores in production") print(" - Consider additional validation rules if needed") print("\n" + "="*60) def main(): """Main function""" try: # Import numpy for validation import numpy as np globals()['np'] = np # Run the null handling process results = run_final_null_handling() # Validate data quality validation_results = validate_data_quality(results) # Print final summary print_final_summary(results, validation_results) # Return success if all files processed successfully success_count = sum(1 for r in results.values() if r.get('success')) return 0 if success_count == len(results) else 1 except Exception as e: print(f"[ERROR] Fatal error in null handling process: {str(e)}") return 1 if __name__ == "__main__": exit_code = main() sys.exit(exit_code)