code2-repo / compare_risk_discovery.py

Deepu1965

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"""
Risk Discovery Method Comparison Script
This script compares 9 different risk discovery methods:
BASIC METHODS (Fast):
1. K-Means Clustering (Original) - Simple centroid-based
2. LDA Topic Modeling - Probabilistic topic distributions
3. Hierarchical Clustering - Nested structure discovery
4. DBSCAN (Density-Based) - Outlier detection
ADVANCED METHODS (Comprehensive):
5. NMF (Non-negative Matrix Factorization) - Parts-based decomposition
6. Spectral Clustering - Graph-based relationship discovery
7. Gaussian Mixture Model - Probabilistic soft clustering
8. Mini-Batch K-Means - Ultra-fast scalable variant
9. Risk-o-meter (Doc2Vec + SVM) - Paper baseline (Chakrabarti et al., 2018)
Usage:
    # Basic comparison (4 methods)
    python compare_risk_discovery.py
    
    # Full comparison (9 methods including Risk-o-meter)
    python compare_risk_discovery.py --advanced
Outputs:
    - Comparison metrics for each method
    - Quality analysis and recommendations
    - Performance timing
"""
import argparse
import json
import numpy as np
from typing import Dict, List, Any, Tuple, Union
import time

from data_loader import CUADDataLoader
from risk_discovery import UnsupervisedRiskDiscovery
from risk_discovery_alternatives import (
    TopicModelingRiskDiscovery,
    HierarchicalRiskDiscovery,
    DensityBasedRiskDiscovery,
    NMFRiskDiscovery,
    SpectralClusteringRiskDiscovery,
    GaussianMixtureRiskDiscovery,
    MiniBatchKMeansRiskDiscovery,
    compare_risk_discovery_methods
)
from risk_o_meter import RiskOMeterFramework


def load_sample_data(data_path: str, max_clauses: Union[int, None] = 5000) -> List[str]:
    """Load sample clauses from CUAD dataset"""
    print(f"📂 Loading CUAD dataset from {data_path}...")
    
    try:
        data_loader = CUADDataLoader(data_path)
        all_data = data_loader.load_data()

        # Extract clause texts
        clauses: List[str] = []

        # Handle tuple outputs (e.g., (df_clauses, metadata))
        if isinstance(all_data, tuple) and all_data:
            df_candidate = all_data[0]
            try:
                if hasattr(df_candidate, '__getitem__') and 'clause_text' in df_candidate:
                    clauses.extend([str(text) for text in df_candidate['clause_text'].tolist()])
            except Exception:
                pass

        # If no clauses extracted yet, fall back to iterable parsing
        if not clauses:
            for item in all_data:
                if isinstance(item, dict) and 'clause_text' in item:
                    clauses.append(str(item['clause_text']))
                elif isinstance(item, str):
                    clauses.append(item)

        print(f"  Loaded {len(clauses)} clauses before limiting")

        # Limit to max_clauses if provided
        if max_clauses is not None and len(clauses) > max_clauses:
            print(f"  Using {max_clauses} out of {len(clauses)} clauses for comparison")
            clauses = clauses[:max_clauses]
        else:
            print("  Using full dataset")
        
        return clauses
    
    except Exception as e:
        print(f"⚠️ Could not load data: {e}")
        print("  Using synthetic sample data for demonstration")
        return generate_sample_clauses()


def generate_sample_clauses() -> List[str]:
    """Generate sample legal clauses for testing when dataset unavailable"""
    sample_clauses = [
        # Liability clauses
        "The Company shall not be liable for any indirect, incidental, or consequential damages arising from use of the services.",
        "Licensor's total liability under this Agreement shall not exceed the fees paid in the twelve months preceding the claim.",
        "In no event shall either party be liable for any loss of profits, business interruption, or loss of data.",
        
        # Indemnity clauses
        "The Service Provider agrees to indemnify and hold harmless the Client from any claims arising from breach of this Agreement.",
        "Customer shall indemnify Company against all third-party claims related to Customer's use of the Software.",
        "Each party shall indemnify the other for losses resulting from the indemnifying party's gross negligence or willful misconduct.",
        
        # Termination clauses
        "Either party may terminate this Agreement upon thirty (30) days written notice to the other party.",
        "This Agreement shall automatically terminate if either party files for bankruptcy or becomes insolvent.",
        "Upon termination, Customer must immediately cease use of the Software and destroy all copies.",
        
        # IP clauses
        "All intellectual property rights in the deliverables shall remain the exclusive property of the Company.",
        "Customer grants Vendor a non-exclusive license to use Customer's trademarks solely for providing the services.",
        "Any modifications or derivative works created by Licensor shall be owned by Licensor.",
        
        # Confidentiality clauses
        "Each party shall keep confidential all information disclosed by the other party marked as 'Confidential'.",
        "The obligation of confidentiality shall survive termination of this Agreement for a period of five (5) years.",
        "Confidential Information does not include information that is publicly available or independently developed.",
        
        # Payment clauses
        "Customer agrees to pay the monthly subscription fee of $10,000 within 15 days of invoice.",
        "All fees are non-refundable and must be paid in U.S. dollars.",
        "Late payments shall accrue interest at the rate of 1.5% per month or the maximum allowed by law.",
        
        # Compliance clauses
        "Both parties agree to comply with all applicable federal, state, and local laws and regulations.",
        "Vendor shall maintain compliance with SOC 2 Type II and ISO 27001 standards.",
        "Customer is responsible for ensuring its use of the Services complies with GDPR and other data protection laws.",
        
        # Warranty clauses
        "Company warrants that the Software will perform substantially in accordance with the documentation.",
        "Vendor represents and warrants that it has the right to enter into this Agreement and grant the licenses herein.",
        "EXCEPT AS EXPRESSLY PROVIDED, THE SOFTWARE IS PROVIDED 'AS IS' WITHOUT WARRANTY OF ANY KIND.",
    ]
    
    # Replicate to create larger dataset
    clauses = sample_clauses * 50  # 1,200 clauses
    print(f"  Generated {len(clauses)} sample clauses for demonstration")
    
    return clauses


def compare_single_method(method_name: str, discovery_object, clauses: List[str], 
                         n_patterns: int = 7) -> Dict[str, Any]:
    """
    Test a single risk discovery method and measure performance.
    
    Args:
        method_name: Name of the method
        discovery_object: Instance of discovery class
        clauses: List of clauses to analyze
        n_patterns: Number of patterns to discover
    
    Returns:
        Results dictionary with timing and quality metrics
    """
    print(f"\n{'='*80}")
    print(f"Testing: {method_name}")
    print(f"{'='*80}")
    
    # Time the discovery process
    start_time = time.time()
    
    try:
        results = discovery_object.discover_risk_patterns(clauses)
        elapsed_time = time.time() - start_time
        
        print(f"\n⏱️  Execution time: {elapsed_time:.2f} seconds")
        
        # Add timing info
        results['execution_time'] = elapsed_time
        results['clauses_per_second'] = len(clauses) / elapsed_time
        
        return {
            'success': True,
            'results': results,
            'execution_time': elapsed_time
        }
    
    except Exception as e:
        elapsed_time = time.time() - start_time
        print(f"❌ Error: {e}")
        
        return {
            'success': False,
            'error': str(e),
            'execution_time': elapsed_time
        }


def analyze_pattern_diversity(results: Dict[str, Any]) -> Dict[str, float]:
    """
    Analyze diversity of discovered patterns.
    
    Metrics:
    - Pattern size variance (how balanced are cluster sizes?)
    - Pattern overlap (for methods that provide probabilities)
    """
    metrics = {}
    
    # Extract pattern sizes
    if 'discovered_topics' in results:
        # LDA
        patterns = results['discovered_topics']
        sizes = [p['clause_count'] for p in patterns.values()]
    elif 'discovered_clusters' in results:
        # Clustering methods
        patterns = results['discovered_clusters']
        sizes = [p['clause_count'] for p in patterns.values()]
    elif 'discovered_patterns' in results:
        # K-Means original - handle different key names
        patterns = results['discovered_patterns']
        sizes = [p.get('clause_count', p.get('size', 0)) for p in patterns.values()]
    else:
        return metrics
    
    # Calculate variance and balance
    if sizes:
        metrics['avg_pattern_size'] = float(np.mean(sizes))
        metrics['std_pattern_size'] = float(np.std(sizes))
        metrics['min_pattern_size'] = int(np.min(sizes))
        metrics['max_pattern_size'] = int(np.max(sizes))
        
        # Balance score: 1.0 = perfectly balanced, 0.0 = very imbalanced
        # Use coefficient of variation (inverted)
        cv = np.std(sizes) / np.mean(sizes) if np.mean(sizes) > 0 else 0
        metrics['balance_score'] = float(1.0 / (1.0 + cv))
    
    return metrics


def generate_comparison_report(all_results: Dict[str, Dict]) -> str:
    """Generate a comprehensive comparison report"""
    
    report = []
    report.append("=" * 80)
    report.append("🔬 RISK DISCOVERY METHOD COMPARISON REPORT")
    report.append("=" * 80)
    report.append("")
    
    # Summary table
    report.append("📊 SUMMARY TABLE")
    report.append("-" * 80)
    report.append(f"{'Method':<30} {'Patterns':<12} {'Quality':<20}")
    report.append("-" * 80)
    
    for method_name, result in all_results.items():
        # Handle direct results from compare_risk_discovery_methods
        n_patterns = result.get('n_clusters') or result.get('n_topics') or result.get('n_components', 'N/A')
        
        # Get quality metric
        quality_metrics = result.get('quality_metrics', {})
        if 'silhouette_score' in quality_metrics:
            sil_score = quality_metrics['silhouette_score']
            # Handle both numeric and string values
            if isinstance(sil_score, (int, float)):
                quality = f"Silhouette: {sil_score:.3f}"
            else:
                quality = f"Silhouette: {sil_score}"
        elif 'perplexity' in quality_metrics:
            perp = quality_metrics['perplexity']
            if isinstance(perp, (int, float)):
                quality = f"Perplexity: {perp:.1f}"
            else:
                quality = f"Perplexity: {perp}"
        else:
            quality = "See details"
        
        report.append(f"{method_name:<30} {str(n_patterns):<12} {quality:<20}")
    
    report.append("-" * 80)
    report.append("")
    
    # Detailed analysis for each method
    report.append("📋 DETAILED ANALYSIS")
    report.append("=" * 80)
    
    for method_name, result in all_results.items():
        report.append(f"\n{method_name.upper()}")
        report.append("-" * 80)
        
        # Method-specific details
        report.append(f"Method: {result.get('method', 'Unknown')}")
        
        # Discovered patterns
        n_patterns = result.get('n_clusters') or result.get('n_topics') or result.get('n_components', 0)
        report.append(f"Patterns Discovered: {n_patterns}")
        
        # Quality metrics
        if 'quality_metrics' in result:
            report.append("Quality Metrics:")
            for metric, value in result['quality_metrics'].items():
                if isinstance(value, float):
                    report.append(f"  - {metric}: {value:.3f}")
                else:
                    report.append(f"  - {metric}: {value}")
        
        # Pattern diversity
        diversity = analyze_pattern_diversity(result)
        if diversity:
            report.append("Pattern Diversity:")
            for metric, value in diversity.items():
                report.append(f"  - {metric}: {value:.3f}" if isinstance(value, float) else f"  - {metric}: {value}")
        
        # Show top 3 patterns
        if 'discovered_topics' in result:
            report.append("\nTop 3 Topics:")
            for i, (topic_id, topic) in enumerate(list(result['discovered_topics'].items())[:3]):
                report.append(f"  Topic {topic_id}: {topic['topic_name']}")
                report.append(f"    Keywords: {', '.join(topic['top_words'][:5])}")
                report.append(f"    Clauses: {topic['clause_count']} ({topic['proportion']:.1%})")
        
        elif 'discovered_clusters' in result:
            report.append("\nTop 3 Clusters:")
            for i, (cluster_id, cluster) in enumerate(list(result['discovered_clusters'].items())[:3]):
                report.append(f"  Cluster {cluster_id}: {cluster['cluster_name']}")
                report.append(f"    Keywords: {', '.join(cluster['top_terms'][:5])}")
                report.append(f"    Clauses: {cluster['clause_count']} ({cluster['proportion']:.1%})")
        
        elif 'discovered_patterns' in result:
            report.append("\nTop 3 Patterns:")
            for i, (pattern_id, pattern) in enumerate(list(result['discovered_patterns'].items())[:3]):
                # Handle different pattern formats
                pattern_name = pattern_id if isinstance(pattern_id, str) else pattern.get('name', f'Pattern {pattern_id}')
                keywords = pattern.get('key_terms', pattern.get('top_keywords', []))
                clause_count = pattern.get('clause_count', pattern.get('size', 0))
                
                report.append(f"  {pattern_name}")
                if keywords:
                    report.append(f"    Keywords: {', '.join(keywords[:5])}")
                report.append(f"    Clauses: {clause_count}")
        
        # Special features
        if method_name == 'dbscan' and 'n_outliers' in result:
            report.append(f"\nOutliers Detected: {result['n_outliers']} ({result['quality_metrics'].get('outlier_ratio', 0):.1%})")
            report.append("  → These represent rare or unique risk patterns")
    
    report.append("\n" + "=" * 80)
    report.append("🎯 RECOMMENDATIONS BY METHOD")
    report.append("=" * 80)
    
    report.append("""
═══ BASIC METHODS (Fast & Reliable) ═══
1. K-MEANS (Original):
   ✅ Best for: Fast, scalable clustering with clear boundaries
   ✅ Use when: You need consistent performance and interpretability
   ⚡ Speed: Very Fast | 🎯 Accuracy: Good | 📊 Scalability: Excellent
   
2. LDA TOPIC MODELING:
   ✅ Best for: Discovering overlapping risk categories
   ✅ Use when: Clauses may belong to multiple risk types
   ⚡ Speed: Moderate | 🎯 Accuracy: Very Good | 📊 Scalability: Good
   
3. HIERARCHICAL CLUSTERING:
   ✅ Best for: Understanding risk relationships and hierarchies
   ✅ Use when: You want to explore risk structure at different levels
   ⚡ Speed: Moderate | 🎯 Accuracy: Good | 📊 Scalability: Limited (<10K clauses)
   
4. DBSCAN:
   ✅ Best for: Finding rare/unusual risks and handling outliers
   ✅ Use when: You need to identify unique risk patterns
   ⚡ Speed: Fast | 🎯 Accuracy: Good | 📊 Scalability: Good
═══ ADVANCED METHODS (Comprehensive Analysis) ═══
5. NMF (Non-negative Matrix Factorization):
   ✅ Best for: Parts-based decomposition with interpretable components
   ✅ Use when: You want additive risk factors (clause = sum of components)
   ⚡ Speed: Fast | 🎯 Accuracy: Very Good | 📊 Scalability: Excellent
   💡 Unique: Components are non-negative, highly interpretable
   
6. SPECTRAL CLUSTERING:
   ✅ Best for: Complex relationships and non-convex cluster shapes
   ✅ Use when: Risk patterns have intricate graph-like relationships
   ⚡ Speed: Slow | 🎯 Accuracy: Excellent | 📊 Scalability: Limited (<5K clauses)
   💡 Unique: Uses eigenvalue decomposition, best quality for small datasets
   
7. GAUSSIAN MIXTURE MODEL:
   ✅ Best for: Soft probabilistic clustering with uncertainty estimates
   ✅ Use when: You need confidence scores for risk assignments
   ⚡ Speed: Moderate | 🎯 Accuracy: Very Good | 📊 Scalability: Good
   💡 Unique: Provides probability distributions, quantifies uncertainty
   
8. MINI-BATCH K-MEANS:
   ✅ Best for: Ultra-large datasets (100K+ clauses)
   ✅ Use when: You need K-Means quality at 3-5x faster speed
   ⚡ Speed: Ultra Fast | 🎯 Accuracy: Good | 📊 Scalability: Extreme (>1M clauses)
   💡 Unique: Online learning, extremely memory efficient
9. RISK-O-METER (Doc2Vec + SVM) ⭐ PAPER BASELINE:
   ✅ Best for: Supervised learning with labeled data
   ✅ Use when: You have risk labels and want paper-validated approach
   ⚡ Speed: Moderate | 🎯 Accuracy: Excellent (91% reported) | 📊 Scalability: Good
   💡 Unique: Paragraph vectors capture semantic meaning, proven in literature
   📄 Reference: Chakrabarti et al., 2018 - "Risk-o-meter framework"
═══ SELECTION GUIDE ═══
📊 Dataset Size:
   • <1K clauses: Use Spectral or GMM for best quality
   • 1K-10K clauses: All methods work well
   • 10K-100K clauses: Avoid Hierarchical and Spectral
   • >100K clauses: Use Mini-Batch K-Means
🎯 Quality Priority:
   • Highest: Spectral, GMM, LDA
   • Balanced: NMF, K-Means
   • Speed-focused: Mini-Batch, DBSCAN
🔍 Special Requirements:
   • Overlapping risks: LDA, GMM
   • Outlier detection: DBSCAN
   • Hierarchical structure: Hierarchical
   • Interpretability: NMF, LDA
   • Uncertainty estimates: GMM, LDA
""")
    
    report.append("=" * 80)
    
    return "\n".join(report)


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Compare risk discovery methods on CUAD dataset")
    parser.add_argument("--advanced", "-a", action="store_true", help="Include advanced methods in comparison")
    parser.add_argument(
        "--max-clauses",
        type=int,
        default=None,
        help="Maximum number of clauses to use (omit for full dataset)"
    )
    parser.add_argument(
        "--data-path",
        default="dataset/CUAD_v1/CUAD_v1.json",
        help="Path to CUAD dataset JSON file"
    )
    return parser.parse_args()


def main():
    """Main comparison script"""
    print("=" * 80)
    args = parse_args()

    include_advanced = args.advanced
    
    print("🔬 RISK DISCOVERY METHOD COMPARISON")
    print("=" * 80)
    print("")
    if include_advanced:
        print("🚀 FULL COMPARISON MODE (9 Methods)")
        print("")
        print("BASIC METHODS:")
        print("  1. K-Means Clustering")
        print("  2. LDA Topic Modeling")
        print("  3. Hierarchical Clustering")
        print("  4. DBSCAN (Density-Based)")
        print("")
        print("ADVANCED METHODS:")
        print("  5. NMF (Matrix Factorization)")
        print("  6. Spectral Clustering")
        print("  7. Gaussian Mixture Model")
        print("  8. Mini-Batch K-Means")
        print("  9. Risk-o-meter (Doc2Vec + SVM) ⭐ PAPER BASELINE")
    else:
        print("⚡ QUICK COMPARISON MODE (4 Basic Methods)")
        print("")
        print("  1. K-Means Clustering (Original)")
        print("  2. LDA Topic Modeling")
        print("  3. Hierarchical Clustering")
        print("  4. DBSCAN (Density-Based)")
        print("")
        print("💡 Tip: Use --advanced flag for all 9 methods")
    print("")
    
    # Load data
    clauses = load_sample_data(args.data_path, max_clauses=args.max_clauses)
    
    if not clauses:
        print("❌ No clauses loaded. Exiting.")
        return
    
    print(f"\n✅ Loaded {len(clauses)} clauses for comparison")
    
    # Parameters
    n_patterns = 7
    
    # Use the unified comparison function
    print("\n" + "=" * 80)
    print("🔄 RUNNING UNIFIED COMPARISON")
    print("=" * 80)
    
    start_time = time.time()
    comparison_results = compare_risk_discovery_methods(
        clauses, 
        n_patterns=n_patterns,
        include_advanced=include_advanced
    )
    total_time = time.time() - start_time
    
    # Extract results
    all_results = comparison_results['detailed_results']
    summary = comparison_results['summary']
    
    print(f"\n⏱️  Total Comparison Time: {total_time:.2f} seconds")
    
    # Generate comparison report
    print("\n" + "=" * 80)
    print("📊 GENERATING COMPARISON REPORT")
    print("=" * 80)
    
    report = generate_comparison_report(all_results)
    print("\n" + report)
    
    # Save results
    print("\n" + "=" * 80)
    print("💾 SAVING RESULTS")
    print("=" * 80)
    
    # Save report
    with open('risk_discovery_comparison_report.txt', 'w') as f:
        f.write(report)
    print("✅ Report saved to: risk_discovery_comparison_report.txt")
    
    # Save detailed results (JSON)
    # Convert numpy arrays to lists for JSON serialization
    def convert_for_json(obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        elif isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, dict):
            # Convert dict keys and values - handle numpy types in keys
            return {
                (str(k) if isinstance(k, (np.integer, np.floating)) else k): convert_for_json(v) 
                for k, v in obj.items()
            }
        elif isinstance(obj, list):
            return [convert_for_json(item) for item in obj]
        else:
            return obj
    
    json_results = convert_for_json(all_results)
    with open('risk_discovery_comparison_results.json', 'w') as f:
        json.dump(json_results, f, indent=2)
    print("✅ Detailed results saved to: risk_discovery_comparison_results.json")
    
    print("\n" + "=" * 80)
    print("🎉 COMPARISON COMPLETE")
    print("=" * 80)


if __name__ == "__main__":
    main()

	"""
	Risk Discovery Method Comparison Script

	This script compares 9 different risk discovery methods:

	BASIC METHODS (Fast):
	1. K-Means Clustering (Original) - Simple centroid-based
	2. LDA Topic Modeling - Probabilistic topic distributions
	3. Hierarchical Clustering - Nested structure discovery
	4. DBSCAN (Density-Based) - Outlier detection

	ADVANCED METHODS (Comprehensive):
	5. NMF (Non-negative Matrix Factorization) - Parts-based decomposition
	6. Spectral Clustering - Graph-based relationship discovery
	7. Gaussian Mixture Model - Probabilistic soft clustering
	8. Mini-Batch K-Means - Ultra-fast scalable variant
	9. Risk-o-meter (Doc2Vec + SVM) - Paper baseline (Chakrabarti et al., 2018)

	Usage:
	# Basic comparison (4 methods)
	python compare_risk_discovery.py

	# Full comparison (9 methods including Risk-o-meter)
	python compare_risk_discovery.py --advanced

	Outputs:
	- Comparison metrics for each method
	- Quality analysis and recommendations
	- Performance timing
	"""
	import argparse
	import json
	import numpy as np
	from typing import Dict, List, Any, Tuple, Union
	import time

	from data_loader import CUADDataLoader
	from risk_discovery import UnsupervisedRiskDiscovery
	from risk_discovery_alternatives import (
	TopicModelingRiskDiscovery,
	HierarchicalRiskDiscovery,
	DensityBasedRiskDiscovery,
	NMFRiskDiscovery,
	SpectralClusteringRiskDiscovery,
	GaussianMixtureRiskDiscovery,
	MiniBatchKMeansRiskDiscovery,
	compare_risk_discovery_methods
	)
	from risk_o_meter import RiskOMeterFramework


	def load_sample_data(data_path: str, max_clauses: Union[int, None] = 5000) -> List[str]:
	"""Load sample clauses from CUAD dataset"""
	print(f"📂 Loading CUAD dataset from {data_path}...")

	try:
	data_loader = CUADDataLoader(data_path)
	all_data = data_loader.load_data()

	# Extract clause texts
	clauses: List[str] = []

	# Handle tuple outputs (e.g., (df_clauses, metadata))
	if isinstance(all_data, tuple) and all_data:
	df_candidate = all_data[0]
	try:
	if hasattr(df_candidate, '__getitem__') and 'clause_text' in df_candidate:
	clauses.extend([str(text) for text in df_candidate['clause_text'].tolist()])
	except Exception:
	pass

	# If no clauses extracted yet, fall back to iterable parsing
	if not clauses:
	for item in all_data:
	if isinstance(item, dict) and 'clause_text' in item:
	clauses.append(str(item['clause_text']))
	elif isinstance(item, str):
	clauses.append(item)

	print(f" Loaded {len(clauses)} clauses before limiting")

	# Limit to max_clauses if provided
	if max_clauses is not None and len(clauses) > max_clauses:
	print(f" Using {max_clauses} out of {len(clauses)} clauses for comparison")
	clauses = clauses[:max_clauses]
	else:
	print(" Using full dataset")

	return clauses

	except Exception as e:
	print(f"⚠️ Could not load data: {e}")
	print(" Using synthetic sample data for demonstration")
	return generate_sample_clauses()


	def generate_sample_clauses() -> List[str]:
	"""Generate sample legal clauses for testing when dataset unavailable"""
	sample_clauses = [
	# Liability clauses
	"The Company shall not be liable for any indirect, incidental, or consequential damages arising from use of the services.",
	"Licensor's total liability under this Agreement shall not exceed the fees paid in the twelve months preceding the claim.",
	"In no event shall either party be liable for any loss of profits, business interruption, or loss of data.",

	# Indemnity clauses
	"The Service Provider agrees to indemnify and hold harmless the Client from any claims arising from breach of this Agreement.",
	"Customer shall indemnify Company against all third-party claims related to Customer's use of the Software.",
	"Each party shall indemnify the other for losses resulting from the indemnifying party's gross negligence or willful misconduct.",

	# Termination clauses
	"Either party may terminate this Agreement upon thirty (30) days written notice to the other party.",
	"This Agreement shall automatically terminate if either party files for bankruptcy or becomes insolvent.",
	"Upon termination, Customer must immediately cease use of the Software and destroy all copies.",

	# IP clauses
	"All intellectual property rights in the deliverables shall remain the exclusive property of the Company.",
	"Customer grants Vendor a non-exclusive license to use Customer's trademarks solely for providing the services.",
	"Any modifications or derivative works created by Licensor shall be owned by Licensor.",

	# Confidentiality clauses
	"Each party shall keep confidential all information disclosed by the other party marked as 'Confidential'.",
	"The obligation of confidentiality shall survive termination of this Agreement for a period of five (5) years.",
	"Confidential Information does not include information that is publicly available or independently developed.",

	# Payment clauses
	"Customer agrees to pay the monthly subscription fee of $10,000 within 15 days of invoice.",
	"All fees are non-refundable and must be paid in U.S. dollars.",
	"Late payments shall accrue interest at the rate of 1.5% per month or the maximum allowed by law.",

	# Compliance clauses
	"Both parties agree to comply with all applicable federal, state, and local laws and regulations.",
	"Vendor shall maintain compliance with SOC 2 Type II and ISO 27001 standards.",
	"Customer is responsible for ensuring its use of the Services complies with GDPR and other data protection laws.",

	# Warranty clauses
	"Company warrants that the Software will perform substantially in accordance with the documentation.",
	"Vendor represents and warrants that it has the right to enter into this Agreement and grant the licenses herein.",
	"EXCEPT AS EXPRESSLY PROVIDED, THE SOFTWARE IS PROVIDED 'AS IS' WITHOUT WARRANTY OF ANY KIND.",
	]

	# Replicate to create larger dataset
	clauses = sample_clauses * 50 # 1,200 clauses
	print(f" Generated {len(clauses)} sample clauses for demonstration")

	return clauses


	def compare_single_method(method_name: str, discovery_object, clauses: List[str],
	n_patterns: int = 7) -> Dict[str, Any]:
	"""
	Test a single risk discovery method and measure performance.

	Args:
	method_name: Name of the method
	discovery_object: Instance of discovery class
	clauses: List of clauses to analyze
	n_patterns: Number of patterns to discover

	Returns:
	Results dictionary with timing and quality metrics
	"""
	print(f"\n{'='*80}")
	print(f"Testing: {method_name}")
	print(f"{'='*80}")

	# Time the discovery process
	start_time = time.time()

	try:
	results = discovery_object.discover_risk_patterns(clauses)
	elapsed_time = time.time() - start_time

	print(f"\n⏱️ Execution time: {elapsed_time:.2f} seconds")

	# Add timing info
	results['execution_time'] = elapsed_time
	results['clauses_per_second'] = len(clauses) / elapsed_time

	return {
	'success': True,
	'results': results,
	'execution_time': elapsed_time
	}

	except Exception as e:
	elapsed_time = time.time() - start_time
	print(f"❌ Error: {e}")

	return {
	'success': False,
	'error': str(e),
	'execution_time': elapsed_time
	}


	def analyze_pattern_diversity(results: Dict[str, Any]) -> Dict[str, float]:
	"""
	Analyze diversity of discovered patterns.

	Metrics:
	- Pattern size variance (how balanced are cluster sizes?)
	- Pattern overlap (for methods that provide probabilities)
	"""
	metrics = {}

	# Extract pattern sizes
	if 'discovered_topics' in results:
	# LDA
	patterns = results['discovered_topics']
	sizes = [p['clause_count'] for p in patterns.values()]
	elif 'discovered_clusters' in results:
	# Clustering methods
	patterns = results['discovered_clusters']
	sizes = [p['clause_count'] for p in patterns.values()]
	elif 'discovered_patterns' in results:
	# K-Means original - handle different key names
	patterns = results['discovered_patterns']
	sizes = [p.get('clause_count', p.get('size', 0)) for p in patterns.values()]
	else:
	return metrics

	# Calculate variance and balance
	if sizes:
	metrics['avg_pattern_size'] = float(np.mean(sizes))
	metrics['std_pattern_size'] = float(np.std(sizes))
	metrics['min_pattern_size'] = int(np.min(sizes))
	metrics['max_pattern_size'] = int(np.max(sizes))

	# Balance score: 1.0 = perfectly balanced, 0.0 = very imbalanced
	# Use coefficient of variation (inverted)
	cv = np.std(sizes) / np.mean(sizes) if np.mean(sizes) > 0 else 0
	metrics['balance_score'] = float(1.0 / (1.0 + cv))

	return metrics


	def generate_comparison_report(all_results: Dict[str, Dict]) -> str:
	"""Generate a comprehensive comparison report"""

	report = []
	report.append("=" * 80)
	report.append("🔬 RISK DISCOVERY METHOD COMPARISON REPORT")
	report.append("=" * 80)
	report.append("")

	# Summary table
	report.append("📊 SUMMARY TABLE")
	report.append("-" * 80)
	report.append(f"{'Method':<30} {'Patterns':<12} {'Quality':<20}")
	report.append("-" * 80)

	for method_name, result in all_results.items():
	# Handle direct results from compare_risk_discovery_methods
	n_patterns = result.get('n_clusters') or result.get('n_topics') or result.get('n_components', 'N/A')

	# Get quality metric
	quality_metrics = result.get('quality_metrics', {})
	if 'silhouette_score' in quality_metrics:
	sil_score = quality_metrics['silhouette_score']
	# Handle both numeric and string values
	if isinstance(sil_score, (int, float)):
	quality = f"Silhouette: {sil_score:.3f}"
	else:
	quality = f"Silhouette: {sil_score}"
	elif 'perplexity' in quality_metrics:
	perp = quality_metrics['perplexity']
	if isinstance(perp, (int, float)):
	quality = f"Perplexity: {perp:.1f}"
	else:
	quality = f"Perplexity: {perp}"
	else:
	quality = "See details"

	report.append(f"{method_name:<30} {str(n_patterns):<12} {quality:<20}")

	report.append("-" * 80)
	report.append("")

	# Detailed analysis for each method
	report.append("📋 DETAILED ANALYSIS")
	report.append("=" * 80)

	for method_name, result in all_results.items():
	report.append(f"\n{method_name.upper()}")
	report.append("-" * 80)

	# Method-specific details
	report.append(f"Method: {result.get('method', 'Unknown')}")

	# Discovered patterns
	n_patterns = result.get('n_clusters') or result.get('n_topics') or result.get('n_components', 0)
	report.append(f"Patterns Discovered: {n_patterns}")

	# Quality metrics
	if 'quality_metrics' in result:
	report.append("Quality Metrics:")
	for metric, value in result['quality_metrics'].items():
	if isinstance(value, float):
	report.append(f" - {metric}: {value:.3f}")
	else:
	report.append(f" - {metric}: {value}")

	# Pattern diversity
	diversity = analyze_pattern_diversity(result)
	if diversity:
	report.append("Pattern Diversity:")
	for metric, value in diversity.items():
	report.append(f" - {metric}: {value:.3f}" if isinstance(value, float) else f" - {metric}: {value}")

	# Show top 3 patterns
	if 'discovered_topics' in result:
	report.append("\nTop 3 Topics:")
	for i, (topic_id, topic) in enumerate(list(result['discovered_topics'].items())[:3]):
	report.append(f" Topic {topic_id}: {topic['topic_name']}")
	report.append(f" Keywords: {', '.join(topic['top_words'][:5])}")
	report.append(f" Clauses: {topic['clause_count']} ({topic['proportion']:.1%})")

	elif 'discovered_clusters' in result:
	report.append("\nTop 3 Clusters:")
	for i, (cluster_id, cluster) in enumerate(list(result['discovered_clusters'].items())[:3]):
	report.append(f" Cluster {cluster_id}: {cluster['cluster_name']}")
	report.append(f" Keywords: {', '.join(cluster['top_terms'][:5])}")
	report.append(f" Clauses: {cluster['clause_count']} ({cluster['proportion']:.1%})")

	elif 'discovered_patterns' in result:
	report.append("\nTop 3 Patterns:")
	for i, (pattern_id, pattern) in enumerate(list(result['discovered_patterns'].items())[:3]):
	# Handle different pattern formats
	pattern_name = pattern_id if isinstance(pattern_id, str) else pattern.get('name', f'Pattern {pattern_id}')
	keywords = pattern.get('key_terms', pattern.get('top_keywords', []))
	clause_count = pattern.get('clause_count', pattern.get('size', 0))

	report.append(f" {pattern_name}")
	if keywords:
	report.append(f" Keywords: {', '.join(keywords[:5])}")
	report.append(f" Clauses: {clause_count}")

	# Special features
	if method_name == 'dbscan' and 'n_outliers' in result:
	report.append(f"\nOutliers Detected: {result['n_outliers']} ({result['quality_metrics'].get('outlier_ratio', 0):.1%})")
	report.append(" → These represent rare or unique risk patterns")

	report.append("\n" + "=" * 80)
	report.append("🎯 RECOMMENDATIONS BY METHOD")
	report.append("=" * 80)

	report.append("""
	═══ BASIC METHODS (Fast & Reliable) ═══

	1. K-MEANS (Original):
	✅ Best for: Fast, scalable clustering with clear boundaries
	✅ Use when: You need consistent performance and interpretability
	⚡ Speed: Very Fast \| 🎯 Accuracy: Good \| 📊 Scalability: Excellent

	2. LDA TOPIC MODELING:
	✅ Best for: Discovering overlapping risk categories
	✅ Use when: Clauses may belong to multiple risk types
	⚡ Speed: Moderate \| 🎯 Accuracy: Very Good \| 📊 Scalability: Good

	3. HIERARCHICAL CLUSTERING:
	✅ Best for: Understanding risk relationships and hierarchies
	✅ Use when: You want to explore risk structure at different levels
	⚡ Speed: Moderate \| 🎯 Accuracy: Good \| 📊 Scalability: Limited (<10K clauses)

	4. DBSCAN:
	✅ Best for: Finding rare/unusual risks and handling outliers
	✅ Use when: You need to identify unique risk patterns
	⚡ Speed: Fast \| 🎯 Accuracy: Good \| 📊 Scalability: Good

	═══ ADVANCED METHODS (Comprehensive Analysis) ═══

	5. NMF (Non-negative Matrix Factorization):
	✅ Best for: Parts-based decomposition with interpretable components
	✅ Use when: You want additive risk factors (clause = sum of components)
	⚡ Speed: Fast \| 🎯 Accuracy: Very Good \| 📊 Scalability: Excellent
	💡 Unique: Components are non-negative, highly interpretable

	6. SPECTRAL CLUSTERING:
	✅ Best for: Complex relationships and non-convex cluster shapes
	✅ Use when: Risk patterns have intricate graph-like relationships
	⚡ Speed: Slow \| 🎯 Accuracy: Excellent \| 📊 Scalability: Limited (<5K clauses)
	💡 Unique: Uses eigenvalue decomposition, best quality for small datasets

	7. GAUSSIAN MIXTURE MODEL:
	✅ Best for: Soft probabilistic clustering with uncertainty estimates
	✅ Use when: You need confidence scores for risk assignments
	⚡ Speed: Moderate \| 🎯 Accuracy: Very Good \| 📊 Scalability: Good
	💡 Unique: Provides probability distributions, quantifies uncertainty

	8. MINI-BATCH K-MEANS:
	✅ Best for: Ultra-large datasets (100K+ clauses)
	✅ Use when: You need K-Means quality at 3-5x faster speed
	⚡ Speed: Ultra Fast \| 🎯 Accuracy: Good \| 📊 Scalability: Extreme (>1M clauses)
	💡 Unique: Online learning, extremely memory efficient

	9. RISK-O-METER (Doc2Vec + SVM) ⭐ PAPER BASELINE:
	✅ Best for: Supervised learning with labeled data
	✅ Use when: You have risk labels and want paper-validated approach
	⚡ Speed: Moderate \| 🎯 Accuracy: Excellent (91% reported) \| 📊 Scalability: Good
	💡 Unique: Paragraph vectors capture semantic meaning, proven in literature
	📄 Reference: Chakrabarti et al., 2018 - "Risk-o-meter framework"

	═══ SELECTION GUIDE ═══

	📊 Dataset Size:
	• <1K clauses: Use Spectral or GMM for best quality
	• 1K-10K clauses: All methods work well
	• 10K-100K clauses: Avoid Hierarchical and Spectral
	• >100K clauses: Use Mini-Batch K-Means

	🎯 Quality Priority:
	• Highest: Spectral, GMM, LDA
	• Balanced: NMF, K-Means
	• Speed-focused: Mini-Batch, DBSCAN

	🔍 Special Requirements:
	• Overlapping risks: LDA, GMM
	• Outlier detection: DBSCAN
	• Hierarchical structure: Hierarchical
	• Interpretability: NMF, LDA
	• Uncertainty estimates: GMM, LDA
	""")

	report.append("=" * 80)

	return "\n".join(report)


	def parse_args() -> argparse.Namespace:
	parser = argparse.ArgumentParser(description="Compare risk discovery methods on CUAD dataset")
	parser.add_argument("--advanced", "-a", action="store_true", help="Include advanced methods in comparison")
	parser.add_argument(
	"--max-clauses",
	type=int,
	default=None,
	help="Maximum number of clauses to use (omit for full dataset)"
	)
	parser.add_argument(
	"--data-path",
	default="dataset/CUAD_v1/CUAD_v1.json",
	help="Path to CUAD dataset JSON file"
	)
	return parser.parse_args()


	def main():
	"""Main comparison script"""
	print("=" * 80)
	args = parse_args()

	include_advanced = args.advanced

	print("🔬 RISK DISCOVERY METHOD COMPARISON")
	print("=" * 80)
	print("")
	if include_advanced:
	print("🚀 FULL COMPARISON MODE (9 Methods)")
	print("")
	print("BASIC METHODS:")
	print(" 1. K-Means Clustering")
	print(" 2. LDA Topic Modeling")
	print(" 3. Hierarchical Clustering")
	print(" 4. DBSCAN (Density-Based)")
	print("")
	print("ADVANCED METHODS:")
	print(" 5. NMF (Matrix Factorization)")
	print(" 6. Spectral Clustering")
	print(" 7. Gaussian Mixture Model")
	print(" 8. Mini-Batch K-Means")
	print(" 9. Risk-o-meter (Doc2Vec + SVM) ⭐ PAPER BASELINE")
	else:
	print("⚡ QUICK COMPARISON MODE (4 Basic Methods)")
	print("")
	print(" 1. K-Means Clustering (Original)")
	print(" 2. LDA Topic Modeling")
	print(" 3. Hierarchical Clustering")
	print(" 4. DBSCAN (Density-Based)")
	print("")
	print("💡 Tip: Use --advanced flag for all 9 methods")
	print("")

	# Load data
	clauses = load_sample_data(args.data_path, max_clauses=args.max_clauses)

	if not clauses:
	print("❌ No clauses loaded. Exiting.")
	return

	print(f"\n✅ Loaded {len(clauses)} clauses for comparison")

	# Parameters
	n_patterns = 7

	# Use the unified comparison function
	print("\n" + "=" * 80)
	print("🔄 RUNNING UNIFIED COMPARISON")
	print("=" * 80)

	start_time = time.time()
	comparison_results = compare_risk_discovery_methods(
	clauses,
	n_patterns=n_patterns,
	include_advanced=include_advanced
	)
	total_time = time.time() - start_time

	# Extract results
	all_results = comparison_results['detailed_results']
	summary = comparison_results['summary']

	print(f"\n⏱️ Total Comparison Time: {total_time:.2f} seconds")

	# Generate comparison report
	print("\n" + "=" * 80)
	print("📊 GENERATING COMPARISON REPORT")
	print("=" * 80)

	report = generate_comparison_report(all_results)
	print("\n" + report)

	# Save results
	print("\n" + "=" * 80)
	print("💾 SAVING RESULTS")
	print("=" * 80)

	# Save report
	with open('risk_discovery_comparison_report.txt', 'w') as f:
	f.write(report)
	print("✅ Report saved to: risk_discovery_comparison_report.txt")

	# Save detailed results (JSON)
	# Convert numpy arrays to lists for JSON serialization
	def convert_for_json(obj):
	if isinstance(obj, np.ndarray):
	return obj.tolist()
	elif isinstance(obj, np.integer):
	return int(obj)
	elif isinstance(obj, np.floating):
	return float(obj)
	elif isinstance(obj, dict):
	# Convert dict keys and values - handle numpy types in keys
	return {
	(str(k) if isinstance(k, (np.integer, np.floating)) else k): convert_for_json(v)
	for k, v in obj.items()
	}
	elif isinstance(obj, list):
	return [convert_for_json(item) for item in obj]
	else:
	return obj

	json_results = convert_for_json(all_results)
	with open('risk_discovery_comparison_results.json', 'w') as f:
	json.dump(json_results, f, indent=2)
	print("✅ Detailed results saved to: risk_discovery_comparison_results.json")

	print("\n" + "=" * 80)
	print("🎉 COMPARISON COMPLETE")
	print("=" * 80)


	if __name__ == "__main__":
	main()