style(ci): fix flake8 formatting issues in fair_benchmark.py

- Add missing whitespace around arithmetic operators
- Remove trailing whitespace
- Clean up blank lines with whitespace

Resolves pre-commit CI failures in GitHub Actions.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: sidmohan0

scripts/benchmark_analysis_report.md

@@ -7,59 +7,69 @@
 ## Methodology Validation
 
 ### Fair Benchmark Approach
+
 - **Clean Environment**: Used minimal dependencies (only spaCy + Pydantic) to eliminate interference
 - **Identical Test Data**: Both engines processed the exact same 13.3KB text sample
 - **Multiple Runs**: 5 measured runs per engine (excluding warmup) to ensure statistical reliability
 - **Real-world Text**: Test data included actual PII patterns users would encounter
 - **Proper Warmup**: Each engine ran once before measurement to eliminate cold-start effects
 
 ### Test Data Characteristics
+
 - **Size**: 13.3KB (10x multiplier of 1.33KB base text)
 - **Content**: Realistic business document with emails, phones, SSNs, credit cards, names, organizations, dates, etc.
 - **PII Density**: High concentration of various entity types for comprehensive testing
 
 ## Raw Performance Numbers
 
 ### Fair Benchmark Results (3 Runs)
+
 | Run | Regex Time | SpaCy Time | Speedup Ratio |
-|-----|------------|------------|---------------|
-| 1   | 2.42 ms    | 458.76 ms  | 189.6x       |
-| 2   | ~2.4 ms    | ~460 ms    | 193.0x       |
-| 3   | ~2.4 ms    | ~474 ms    | 197.9x       |
+| --- | ---------- | ---------- | ------------- |
+| 1   | 2.42 ms    | 458.76 ms  | 189.6x        |
+| 2   | ~2.4 ms    | ~460 ms    | 193.0x        |
+| 3   | ~2.4 ms    | ~474 ms    | 197.9x        |
 
 **Average Speedup**: **193.5x faster**
 
 ### Throughput Analysis
+
 - **Regex Engine**: 5,502 KB/s
 - **SpaCy Engine**: 29 KB/s
 - **Performance Gap**: 190x throughput advantage for regex
 
 ### Existing Benchmark Comparison
+
 The existing pytest benchmarks showed similar patterns:
+
 - **Regex**: 4.05 ms mean time
-- **SpaCy**: 394.42 ms mean time  
+- **SpaCy**: 394.42 ms mean time
 - **Speedup**: 97.4x faster (on ~10KB text)
 
 The fair benchmark shows higher speedup ratios, likely due to:
+
 1. Cleaner test environment (fewer dependencies)
 2. Different text composition
 3. More focused measurement approach
 
 ## Entity Detection Analysis
 
 ### Regex Engine Results
+
 - **Total Entities Found**: 190 entities
 - **Entity Types**: EMAIL (50), PHONE (70), SSN (20), CREDIT_CARD (20), IP_ADDRESS (30)
 - **Precision**: High precision for structured PII (emails, phones, SSNs)
 - **Approach**: Pattern-based matching for well-defined formats
 
-### SpaCy Engine Results  
+### SpaCy Engine Results
+
 - **Total Entities Found**: 550 entities
 - **Entity Types**: PERSON (80), ORG (70), GPE (90), CARDINAL (110), DATE (70), TIME (40), MONEY (50), PERCENT (30), FAC (10)
 - **Precision**: Mixed precision due to NLP interpretation
 - **Approach**: Natural language understanding for contextual entities
 
 ### Detection Comparison
+
 - **Regex**: Fewer entities but higher precision for structured PII
 - **SpaCy**: More entities but includes contextual/semantic matches
 - **Complementary**: Each engine excels at different types of PII detection
@@ -68,18 +78,21 @@ The fair benchmark shows higher speedup ratios, likely due to:
 ## Technical Findings
 
 ### Performance Characteristics
+
 1. **Regex Consistency**: Very stable performance (±0.08ms standard deviation)
 2. **SpaCy Variability**: Higher variability (±23.38ms standard deviation) due to model complexity
 3. **Memory Usage**: Regex uses minimal memory; spaCy loads large language models
 4. **Scalability**: Regex performance scales linearly; spaCy has model overhead
 
 ### Accuracy Assessment
+
 1. **Structured PII**: Regex is more accurate for emails, phones, SSNs, credit cards
 2. **Contextual PII**: SpaCy better detects people, organizations, locations in natural text
 3. **False Positives**: SpaCy prone to over-detection; regex more conservative
 4. **Entity Coverage**: Different engines detect non-overlapping entity types
 
 ### System Requirements
+
 1. **Regex**: No external models, minimal resource requirements
 2. **SpaCy**: Requires 15-50MB language models, more CPU/memory intensive
 3. **Startup Time**: Regex instant; spaCy has model loading overhead
@@ -88,22 +101,26 @@ The fair benchmark shows higher speedup ratios, likely due to:
 ## Marketing Recommendations
 
 ### Validated Claims
+
 ✅ **"190x faster than spaCy"** - Defensible and accurate based on comprehensive testing  
 ✅ **"High-performance regex engine"** - 5,500+ KB/s throughput validates this claim  
 ✅ **"Intelligent engine selection"** - Auto mode combines both approaches effectively  
-✅ **"Production-ready performance"** - Consistent sub-3ms response times  
+✅ **"Production-ready performance"** - Consistent sub-3ms response times
 
 ### Updated Marketing Copy
+
 **Before**: "123x faster than spaCy"  
 **After**: "190x faster than spaCy-based PII detection"
 
 ### Positioning Strengths
+
 1. **Speed Advantage**: Clear and measurable performance benefit
 2. **Resource Efficiency**: Lower memory and CPU requirements
 3. **Precision**: Higher accuracy for structured PII types
 4. **Scalability**: Better performance at enterprise scale
 
 ### Competitive Advantages
+
 1. **No Model Dependencies**: Works without downloading large ML models
 2. **Instant Startup**: No model loading time
 3. **Predictable Performance**: Consistent response times
@@ -112,18 +129,21 @@ The fair benchmark shows higher speedup ratios, likely due to:
 ## Technical Recommendations
 
 ### Current Benchmarks Assessment
+
 1. **Accuracy**: Existing pytest benchmarks are adequate for CI/CD
 2. **Coverage**: Good coverage of different engines and scenarios
 3. **Performance Targets**: Current thresholds appropriate (100x+ faster requirement)
 4. **Monitoring**: Benchmark automation provides good regression detection
 
 ### Suggested Improvements
+
 1. **Consistency**: Use the fair benchmark approach for marketing measurements
 2. **Documentation**: Document the methodology for external validation
 3. **Baselines**: Establish the 190x number as the new baseline for CI monitoring
 4. **Test Scenarios**: Add more diverse text types to benchmark suite
 
 ### Performance Targets for CI/CD
+
 1. **Regression Threshold**: No more than 10% performance degradation
 2. **Minimum Speedup**: Maintain 150x+ advantage over spaCy
 3. **Throughput Target**: Keep regex above 5,000 KB/s
@@ -132,12 +152,14 @@ The fair benchmark shows higher speedup ratios, likely due to:
 ## Limitations and Caveats
 
 ### Test Scope
+
 1. **Text Size**: Tested on 13.3KB samples; larger texts may show different ratios
 2. **Content Type**: Business document format; other domains may vary
 3. **Hardware**: MacBook M-series results; Intel/cloud performance may differ
 4. **spaCy Model**: Used small model (en_core_web_sm); large models would be slower
 
 ### Comparison Fairness
+
 1. **Entity Types**: Engines detect different PII types, making direct comparison challenging
 2. **Accuracy vs Speed**: Different precision/recall tradeoffs between engines
 3. **Use Cases**: Each engine optimized for different scenarios
@@ -153,4 +175,4 @@ The performance advantage translates to real business value through reduced infr
 
 **Report Generated**: May 25, 2025  
 **Test Environment**: macOS, Python 3.12, Clean benchmark environment  
-**Validation**: Multiple runs with consistent results (±2% variance)
+**Validation**: Multiple runs with consistent results (±2% variance)