File size: 3,795 Bytes
27f6252 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | #!/usr/bin/env python3
"""
Quick Data Analysis Runner
Runs the comprehensive data analysis and provides immediate recommendations.
"""
import sys
import argparse
from pathlib import Path
# Add current directory to path
sys.path.insert(0, str(Path(__file__).parent))
from analyze_training_data import TrainingDataAnalyzer
def main():
"""Run data analysis and provide recommendations"""
parser = argparse.ArgumentParser(description="Quick CVE Training Data Analysis")
parser.add_argument("--dataset", default="data/training_datasets/enhanced_training_dataset_with_mitigations.json",
help="Path to training dataset")
parser.add_argument("--sample-size", type=int, default=200,
help="Number of samples to analyze")
parser.add_argument("--no-viz", action="store_true",
help="Skip visualization generation")
args = parser.parse_args()
print("🔍 CVE Training Data Analysis")
print("=" * 50)
# Check if dataset exists
dataset_path = args.dataset
if not Path(dataset_path).exists():
print(f"❌ Dataset not found: {dataset_path}")
print("Please run dataset_preparation.py first to create the training dataset")
return False
# Run analysis
analyzer = TrainingDataAnalyzer(dataset_path)
print("📊 Running comprehensive data analysis...")
print("This will analyze:")
print(" - Instruction diversity")
print(" - Input diversity")
print(" - Output patterns")
print(" - Duplicate detection")
print(" - Data distribution")
print()
success = analyzer.run_full_analysis(sample_size=args.sample_size, generate_viz=not args.no_viz)
if success:
print("\n📁 Analysis results saved to 'data_analysis/' directory")
print("📊 Check 'data_analysis_visualization.png' for visualizations")
print("📝 Check 'data_analysis_report.md' for detailed report")
# Provide immediate recommendations
red_flag_count = sum(1 for results in analyzer.analysis_results.values()
if 'red_flag' in results and results['red_flag'])
print("\n🎯 IMMEDIATE RECOMMENDATIONS:")
print("=" * 50)
if red_flag_count > 2:
print("❌ CRITICAL: Multiple data quality issues detected!")
print(" → Fix data quality before training")
print(" → Consider data augmentation")
print(" → Remove duplicate examples")
print(" → Increase validation set size")
elif red_flag_count > 0:
print("⚠️ WARNING: Some data quality issues detected")
print(" → Use overfitting fixes in training pipeline")
print(" → Start with small subsets (--subset 10)")
print(" → Monitor training closely")
print(" → Consider data cleaning")
else:
print("✅ GOOD: Data quality looks acceptable")
print(" → Safe to proceed with training")
print(" → Use standard training settings")
print(" → Monitor for overfitting during training")
print("\n🚀 NEXT STEPS:")
print("1. Review the detailed report in data_analysis_report.md")
print("2. Run training test: python test_overfitting_fixes.py")
print("3. Start training: python src/training/fine_tuning_pipeline.py --subset 10")
return True
else:
print("❌ Data analysis failed!")
return False
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1) |