"""
Comprehensive Deepchecks Test Runner - Original vs Cleaned Data Comparison

This script runs all Deepchecks tests on both original and cleaned data,
allowing for direct comparison of data quality improvements after cleaning.

Usage:
    python tests/deepchecks/run_all_tests_comparison.py

Output:
    - Generates reports for both original and cleaned data
    - Creates comparison summary
    - Saves all results in reports/deepchecks/
"""

import sys
from pathlib import Path

# Add project root to path
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))

from test_data_integrity import (
    run_data_integrity_suite,
    run_custom_integrity_checks,
    analyze_data_statistics
)
from test_train_test_validation import (
    run_train_test_validation_suite,
    run_custom_train_test_checks,
    compare_distributions,
    validate_split_quality
)


def print_section_header(title):
    """Print a formatted section header."""
    print("\n" + "="*80)
    print(f"  {title}")
    print("="*80 + "\n")


def run_all_tests_for_data(use_cleaned=False):
    """
    Run all Deepchecks tests for either original or cleaned data.
    
    Args:
        use_cleaned: If True, test cleaned data; otherwise test original data
    """
    data_type = "CLEANED" if use_cleaned else "ORIGINAL"
    
    print("\n" + "#"*80)
    print(f"#  RUNNING ALL DEEPCHECKS TESTS - {data_type} DATA")
    print("#"*80)
    
    # 1. Dataset Statistics
    print_section_header(f"1. DATASET STATISTICS - {data_type}")
    analyze_data_statistics(use_cleaned=use_cleaned)
    
    # 2. Data Integrity Suite
    print_section_header(f"2. DATA INTEGRITY SUITE - {data_type}")
    integrity_result = run_data_integrity_suite(save_output=True, use_cleaned=use_cleaned)
    
    # 3. Custom Integrity Checks
    print_section_header(f"3. CUSTOM INTEGRITY CHECKS - {data_type}")
    custom_integrity_results = run_custom_integrity_checks(save_output=True, use_cleaned=use_cleaned)
    
    # 4. Distribution Comparison
    print_section_header(f"4. TRAIN-TEST DISTRIBUTION COMPARISON - {data_type}")
    compare_distributions(use_cleaned=use_cleaned)
    
    # 5. Split Quality Validation
    print_section_header(f"5. TRAIN-TEST SPLIT QUALITY - {data_type}")
    validate_split_quality(use_cleaned=use_cleaned)
    
    # 6. Train-Test Validation Suite
    print_section_header(f"6. TRAIN-TEST VALIDATION SUITE - {data_type}")
    validation_result = run_train_test_validation_suite(save_output=True, use_cleaned=use_cleaned)
    
    # 7. Custom Train-Test Checks
    print_section_header(f"7. CUSTOM TRAIN-TEST CHECKS - {data_type}")
    custom_validation_results = run_custom_train_test_checks(save_output=True, use_cleaned=use_cleaned)
    
    return {
        "integrity_suite": integrity_result,
        "custom_integrity": custom_integrity_results,
        "validation_suite": validation_result,
        "custom_validation": custom_validation_results
    }


def main():
    """Main function to run all tests and generate comparison."""
    print("\n" + "*"*80)
    print("*  DEEPCHECKS COMPREHENSIVE TEST SUITE")
    print("*  Original vs Cleaned Data Comparison")
    print("*"*80)
    
    print("\nThis script will run all Deepchecks tests on both:")
    print("  1. Original data (before cleaning)")
    print("  2. Cleaned data (after data_cleaning.py)")
    print("\nThis allows direct comparison of data quality improvements.\n")
    
    # Check if cleaned data exists
    from hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR
    tfidf_dir = PROCESSED_DATA_DIR / "tfidf"
    
    cleaned_files_exist = all([
        (tfidf_dir / "features_tfidf_clean.npy").exists(),
        (tfidf_dir / "labels_tfidf_clean.npy").exists(),
        (tfidf_dir / "X_test_clean.npy").exists(),
        (tfidf_dir / "Y_test_clean.npy").exists()
    ])
    
    if not cleaned_files_exist:
        print("⚠️  WARNING: Cleaned data files not found!")
        print("   Please run data_cleaning.py first:")
        print("   python -m hopcroft_skill_classification_tool_competition.data_cleaning")
        print("\n   Continuing with original data only...\n")
        
        # Run tests only on original data
        print_section_header("TESTING ORIGINAL DATA ONLY")
        original_results = run_all_tests_for_data(use_cleaned=False)
        
    else:
        # Run tests on both original and cleaned data
        print("✓ Cleaned data files found")
        print("  Running tests on both original and cleaned data...\n")
        
        # Test original data
        original_results = run_all_tests_for_data(use_cleaned=False)
        
        # Test cleaned data
        cleaned_results = run_all_tests_for_data(use_cleaned=True)
        
        # Print comparison summary
        print("\n" + "="*80)
        print("  COMPARISON SUMMARY")
        print("="*80)
        print("\nOriginal vs Cleaned Data:")
        print("  - Original data tests saved with '_original' suffix")
        print("  - Cleaned data tests saved with '_clean' suffix")
        print("\nExpected improvements in cleaned data:")
        print("  ✓ No duplicates (0.0%)")
        print("  ✓ No label conflicts (0.0%)")
        print("  ✓ No data leakage (0 samples)")
        print("  ✓ Proper stratified split (80/20)")
        print("  ✓ Clean train/test separation")
    
    # Final summary
    print("\n" + "="*80)
    print("  ALL TESTS COMPLETED")
    print("="*80)
    print("\nReports saved in: reports/deepchecks/")
    print("\nFiles generated:")
    print("  Original data:")
    print("    - data_integrity_suite_results_original.json")
    print("    - train_test_validation_suite_results_original.json")
    
    if cleaned_files_exist:
        print("\n  Cleaned data:")
        print("    - data_integrity_suite_results_clean.json")
        print("    - train_test_validation_suite_results_clean.json")
    
    print("\nNext steps:")
    if not cleaned_files_exist:
        print("  1. Run data_cleaning.py to generate cleaned data")
        print("  2. Re-run this script to compare original vs cleaned")
    else:
        print("  1. Review JSON reports in reports/deepchecks/")
        print("  2. Compare original vs cleaned results")
        print("  3. Use cleaned data for model training")
    
    print("\n" + "="*80 + "\n")


if __name__ == "__main__":
    main()