Add Benchmark Comparison and Visualization System

[codegen-sh]

📊 Benchmark Analytics Enhancement

This PR implements comprehensive benchmark analysis and comparison tools to help users make informed decisions about provider selection and performance optimization.

🚀 Features Added

Core Analysis Modules

• Data Parser (benchmarks/analysis/data_parser.py): Load and process benchmark results from JSON/MD files
• Comparator (benchmarks/analysis/comparator.py): Compare providers, analyze trends, detect regressions
• Visualizer (benchmarks/analysis/visualizer.py): Generate charts, graphs, and interactive dashboards
• Reporter (benchmarks/analysis/reporter.py): Create comprehensive reports in HTML/Markdown/PDF
• Configuration (benchmarks/analysis/config.py): Manage analysis settings and preferences

CLI Commands

New grainchain analysis command group with:

• compare - Compare performance between providers
• trends - Analyze performance trends over time
• report - Generate comprehensive analysis reports
• regressions - Detect performance regressions automatically
• recommend - Get data-driven provider recommendations
• dashboard - Create performance dashboards

Analysis Capabilities

• Provider Comparison: Statistical comparison with improvements/regressions
• Trend Analysis: Time-series analysis with trend direction and strength
• Regression Detection: Automatic detection with configurable thresholds
• Interactive Dashboards: Plotly-based interactive visualizations
• Comprehensive Reports: Multi-format reporting with charts
• Provider Recommendations: Use-case specific recommendations (general, speed, reliability)

📈 Usage Examples

# Compare two providers
grainchain analysis compare --provider1 local --provider2 e2b --chart

# Analyze trends
grainchain analysis trends --provider local --days 30 --interactive

# Generate report
grainchain analysis report --format html --include-charts

# Detect regressions
grainchain analysis regressions --threshold 0.1

# Get recommendations
grainchain analysis recommend --use-case reliability

🔧 Configuration

• Analysis settings in benchmarks/configs/analysis.json
• Configurable time ranges, thresholds, chart styles
• Provider display names and colors
• Metric weights for scoring

📚 Documentation

• Analysis Guide: docs/analysis_guide.md - Comprehensive documentation
• Examples: examples/analysis_examples.py - Usage examples and patterns
• Updated README: Enhanced benchmarks/README.md and BENCHMARKING.md

🧪 Testing

• Comprehensive test suite with fixtures
• Unit tests for all analysis modules
• Integration tests for full workflow
• Sample benchmark data for testing

📁 File Structure

benchmarks/analysis/          # Analysis modules
├── __init__.py
├── data_parser.py           # Data loading and parsing
├── comparator.py            # Provider comparison and trends
├── visualizer.py            # Charts and dashboards
├── reporter.py              # Report generation
├── config.py                # Configuration management
└── models.py                # Data models

benchmarks/configs/
└── analysis.json            # Analysis configuration

docs/
└── analysis_guide.md        # Comprehensive documentation

examples/
└── analysis_examples.py     # Usage examples

tests/
├── test_analysis.py         # Core analysis tests
├── test_comparison.py       # Comparison tests
└── fixtures/
    └── sample_benchmark_data.json  # Test data

🎯 Impact

This addresses CG-18634 by providing:

• ✅ Tools to compare benchmark results across providers
• ✅ Performance trend analysis over time
• ✅ Visual reports and dashboards
• ✅ Data-driven provider recommendations
• ✅ Integration with existing benchmark workflow

🔍 Key Benefits

1. Informed Decision Making: Data-driven provider selection
2. Performance Monitoring: Track improvements and regressions
3. Visual Insights: Charts and dashboards for easy understanding
4. Automated Analysis: Regression detection and recommendations
5. Comprehensive Reporting: Professional reports for stakeholders

🚀 Next Steps

After merge, users can:

1. Run existing benchmarks to generate data
2. Use analysis commands to compare providers
3. Generate reports for performance insights
4. Set up automated regression monitoring
5. Make informed provider choices based on data

This enhancement significantly improves the value of the benchmarking system by making the data actionable and insights accessible.

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