Datasets:
| # Research Spec: GNN Architecture Search for Code Complexity Prediction | |
| # | |
| # Automated search across 5 GNN architectures and hyperparameters | |
| # for predicting Ruby method complexity from AST structure. | |
| # | |
| # Launch: | |
| # ratiocinator research specs/gnn_complexity.yaml | |
| # | |
| # The dataset (22K Ruby methods) is in the repo branch. | |
| # No external data staging needed. | |
| # What to research | |
| topic: "Comparing GNN architectures (GCN, SAGE, GAT, GIN, GraphConv) for predicting Ruby code complexity from Abstract Syntax Trees. The baseline uses GraphSAGE with hidden_dim=64, 3 layers, achieving MAE 4.77 on 22K Ruby methods." | |
| goal_metric: val_mae | |
| maximize: false | |
| # Target codebase | |
| repo_url: https://github.com/timlawrenz/jubilant-palm-tree.git | |
| repo_branch: experiment/ratiocinator-gnn-study | |
| runner_script: scripts/run_complexity_arm.sh | |
| # Infrastructure — models are tiny (~50K params), training is fast | |
| hardware: | |
| gpu: "RTX 4090" | |
| num_gpus: 1 | |
| min_cpu_ram_gb: 32 | |
| min_inet_down: 1000.0 | |
| min_cuda_version: 12.0 | |
| max_dph: 0.40 | |
| disk_gb: 50.0 | |
| image: pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime | |
| data: | |
| source: none # Dataset is in the repo branch | |
| deps: | |
| pre_install: | |
| - "apt-get update -qq && apt-get install -y -qq git-lfs > /dev/null 2>&1 || true" | |
| - "cd /workspace/experiment && git lfs install && git lfs pull" | |
| - "pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.7.0+cu128.html" | |
| - "pip install pandas tqdm sentence-transformers nltk scikit-learn numpy" | |
| requirements: requirements.txt | |
| exclude_from_requirements: | |
| - torch | |
| - torchvision | |
| - torch_geometric | |
| verify: "python -c \"import torch_geometric; print(f'PyG {torch_geometric.__version__}')\"" | |
| metrics: | |
| protocol: json_line | |
| json_prefix: "METRICS:" | |
| # Budget — small models, fast training (~2-5 min per arm) | |
| max_iterations: 3 | |
| max_dollars: 15.00 | |
| train_timeout_s: 1200 | |
| download_timeout_s: 600 | |
| # Output | |
| paper_title: "What Graph Neural Networks Can and Cannot Learn About Code: A Systematic Empirical Study on Ruby AST Analysis" | |