File size: 3,286 Bytes
8c10cf2 | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | # GameBuilder Evaluation Scripts
This directory contains evaluation scripts for analyzing the GameBuilder project results across different LLM models.
## Scripts
### 1. analyze_retry_patterns.py
Analyzes RETRY patterns in GameBuilder project execution paths.
**Features:**
- Extracts error information from execution paths
- Counts RETRY attempts and locations
- Calculates error rates and RETRY rates per model
- Generates detailed statistics by agent and node type
**Output:**
- `retry_analysis.json` - Detailed JSON results
- `retry_summary.csv` - Summary statistics
- `error_by_agent.csv` - Error statistics by agent
**Usage:**
```bash
python3 analyze_retry_patterns.py
```
### 2. evaluate_success.py
Calculates success rates for each model in GameBuilder tasks.
**Features:**
- Analyzes validation results from test sessions
- Computes success/failure rates per model
- Tracks individual session outcomes
**Output:**
- `success-finish_detailed_results.json` - Detailed results
- `success-finish_rate.csv` - Success rate summary
**Usage:**
```bash
python3 evaluate_success.py
```
### 3. evaluate_trajectory.py
Evaluates trajectory metrics for GameBuilder project.
**Features:**
- Parses execution paths and extracts trajectories
- Evaluates 6 trajectory metrics:
- Exact match
- In-order match
- Any-order match
- Precision
- Recall
- Single-tool use
- Calculates path diversity and entropy
**Output:**
- CSV file with evaluation results
**Usage:**
```bash
python3 evaluate_trajectory.py --config reference_trajectory.yaml --output evaluation_results.csv
```
**Arguments:**
- `--config` - Reference trajectory config file (YAML format)
- `--base-dir` - RESULTS directory path (defaults to two levels up)
- `--output` - Output CSV file path
- `--format` - Output format (csv only)
## Configuration
### reference_trajectory.yaml
Defines the reference trajectory (ground truth) for evaluation.
**Key sections:**
- `project_name` - Project identifier (GameBuilder)
- `extract_types` - Node types to extract (SPAN, Chain, AGENT, LLM, Tool)
- `reference_trajectory` - Expected execution sequence
- `target_tools` - Tools to track for single-tool use metric (Python Code Validator)
- `models` - List of models to evaluate
## Project Overview
GameBuilder is a code generation project where:
1. **Senior Software Engineer** generates game code
2. **Software Quality Control Engineer** reviews and validates the code
3. **Chief Software Quality Control Engineer** performs final evaluation
The reference trajectory represents the ideal execution path with:
- 5 LLM calls (1 for code generation, 2 for review, 2 for evaluation)
- 2 tool calls (Python Code Validator used by both QA agents)
## 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 automatically process test results from the configured base directory
- Results are saved in the same directory as the scripts
- Execution paths are parsed from `execution_path.md` files in test session directories
- Validation results are read from `validation_result.json` files
- The Python Code Validator tool is the primary tool tracked for this project
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