| # MarkdownValidator-MCP Evaluation Scripts |
|
|
| This directory contains evaluation scripts for analyzing the MarkdownValidator-MCP project results across different LLM models. |
|
|
| ## Scripts |
|
|
| ### 1. analyze_retry_patterns.py |
| Analyzes RETRY patterns in execution paths, including: |
| - Error locations and frequencies |
| - RETRY counts and rates |
| - Error distribution by agent and node type |
|
|
| **Usage:** |
| ```bash |
| python3 analyze_retry_patterns.py |
| ``` |
|
|
| **Outputs:** |
| - `retry_analysis.json` - Detailed retry statistics |
| - `retry_summary.csv` - Summary table |
| - `error_by_agent.csv` - Error breakdown by agent |
|
|
| ### 2. evaluate_scores.py |
| Collects and analyzes score statistics for each model. |
| |
| **Usage:** |
| ```bash |
| python3 evaluate_scores.py |
| ``` |
| |
| **Outputs:** |
| - `score_analysis.json` - Detailed score data |
| - `score_summary.csv` - Summary statistics (mean, min, max, perfect rate) |
| |
| ### 3. evaluate_success.py |
| Calculates success/failure rates for each model. |
| |
| **Usage:** |
| ```bash |
| python3 evaluate_success.py |
| ``` |
| |
| **Outputs:** |
| - `success-finish_detailed_results.json` - Detailed execution results |
| - `success-finish_rate.csv` - Success rate summary |
|
|
| ### 4. evaluate_trajectory.py |
| Evaluates trajectory metrics including: |
| - Exact match |
| - In-order match |
| - Any-order match |
| - Precision and Recall |
| - Single-tool use |
| - Path diversity (unique_path_ratio) |
| - Path entropy |
| |
| **Usage:** |
| ```bash |
| python3 evaluate_trajectory.py --config reference_trajectory.yaml |
| ``` |
| |
| **Options:** |
| - `--config` - Path to reference trajectory YAML file (default: reference_trajectory.yaml) |
| - `--base-dir` - RESULTS directory path (default: two levels up from script) |
| - `--output` - Output CSV file path (default: evaluation_results.csv) |
| - `--format` - Output format: csv or both (default: csv) |
| |
| **Outputs:** |
| - `evaluation_results.csv` - Trajectory evaluation results |
|
|
| ## Configuration |
|
|
| ### reference_trajectory.yaml |
| Defines the ideal reference trajectory for evaluation, including: |
| - Project name and extraction types |
| - Reference trajectory steps |
| - Target tools for evaluation |
| - Models to evaluate |
| |
| ## Models Evaluated |
| |
| - GPT-5 |
| - GPT-4o-mini |
| - DeepSeek-V3-1 |
| - DeepSeek-R1 |
| - Gemini-2.5-flash |
| - Gemini-2.5-flash-nothinking |
| - Qwen3-235b |
| |
| ## Requirements |
| |
| - Python 3.7+ |
| - pandas |
| - pyyaml |
| |
| ## Notes |
| |
| - All scripts read from `/Users/wzr/TOSEM-2025/RESULTS` by default |
| - Scripts output CSV and JSON files for further analysis |
| - Trajectory evaluation supports wildcard matching for flexible comparison |
| |
