| # Jev Skills code |
|
|
| Standalone Python (3.10+). Reading parquet needs `pandas` and `pyarrow`; rebuilding `rules7.py` also needs `huggingface_hub`. |
| No `jevswarm` imports. |
|
|
| | file | what | |
| |---|---| |
| | `score.py` | the scorer (`load_items`, `score`, `evaluate`, oracle / uniform baselines) | |
| | `spatial_bench.py` | builds JevSpatial v1 (`games_spatial/*` 1-5), a few seconds, no network | |
| | `play_bench.py` | closed-loop game simulations (Snake, Dino, Breakout, Whac-A-Mole) driven by any decider: `--decider python --decider-target module:factory`, `--decider http --url ...` (any server speaking the System One wire format, `POST /v1/systemone` with `{"model", "state", "questions"}`), `oracle`, `random` | |
| | `rules7.py` | builds Jev Rules-7 (downloads the pinned ShARC dev and patent test files), uses `drop_keys.json` (rule texts excluded from the ShARC selection) | |
| | `browser_bench.py` | builds `agents/browser` | |
| | `bench_common.py`, `rows.py` | helpers; `rows.py` maps generator skills to `category/skill` and licence ids | |
| | `docs/` | notes on JevSpatial and Rules-7 | |
|
|
| ## Rebuild the items |
|
|
| ```bash |
| python spatial_bench.py --out out/ # jevspatial_v1_items.json.gz (2,040 items) |
| python rules7.py --out out/ # rules7_v1_items.json.gz (3,440 items; needs network) |
| python -c "import browser_bench as b; items, info = b.build(0); print(len(items))" # 600 items |
| ``` |
|
|
| The output is deterministic and byte-identical to the published rows for these three parts (checked when the dataset was |
| built). The other skills (tools, agent_action, chess, small_games, safety, judging, grounding) are shipped as data only; |
| their sources and pinned revisions are in the dataset card. |
|
|
| ## Score a model |
|
|
| ```python |
| import score |
| |
| def decide(state, questions): |
| # call your model; return one answer per question key |
| return {k: ({"noul": 0.5} if q["type"] == "noul" else |
| {"probabilities": {o: 1 / len(q["criteria"]) for o in q["criteria"]}}) |
| for k, q in questions.items()} |
| |
| items = score.load_items("../data/rules.parquet") # or parquet / jsonl / json.gz |
| print(score.evaluate(decide, items)["pooled_by_category"]) |
| ``` |
|
|
| For `score` questions return probabilities keyed by the level strings in `criteria` (or their indices). |
| `python score.py ../data/rules.parquet --baseline oracle` prints 1.0 for every skill. |
|
|
| ## Closed-loop games with your own decider |
|
|
| `play_bench.py` plays Snake, Dino, Breakout and Whac-A-Mole with any decider. A Python hook, `my_decider.py`: |
|
|
| ```python |
| def make_decider(): |
| def decide(state, questions): # uniform guesser |
| return {k: {"noul": 0.5} for k in questions} |
| return decide |
| ``` |
|
|
| `python play_bench.py --decider python --decider-target my_decider:make_decider --decisions 100`. The factory may also return an |
| object with a `.decide(state, questions)` method. Any server speaking the System One wire format works with |
| `--decider http --url http://HOST:PORT` (it posts `{"model", "state", "questions"}` to `/v1/systemone`); `--decider oracle` |
| (always the best move) and `--decider random` are references. |
|
|
