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wassname
b53043cec3
Cleanup by a prior agent, verified green here: 'just smoke' (erase arm) runs end-to-end and all four wired gates pass (verify_rewards 52/52, verify_eval_gap, verify_partition, verify_science_invariants). - train.py -318 lines: Config dataclass -> train_config.py, checkpoint/ deploy-artifact IO -> run_artifacts.py. - results.py / results_deploy.py / probe_distill.py slimmed. - drop stale derived csvs under out/figs (a5_generalisation, dyn_*, substrate_aggregate, train_vs_deploy_60). - gitignore /.pi/ panel scratch. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
55 lines
2.2 KiB
Python
55 lines
2.2 KiB
Python
"""Training-rollout table from completed structured run artifacts."""
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from __future__ import annotations
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import polars as pl
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from tabulate import tabulate
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from vgrout.run_artifacts import completed_runs
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def main() -> None:
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runs = [run for run in completed_runs()
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if "tiny-random" not in run["cfg"]["model"] and "probe" not in run["cfg"]["out_tag"]]
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rows = [{
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"time": run["time"],
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"arm": run["arm"],
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"seed": str(run["cfg"]["seed"]),
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"mix": str(run["cfg"]["mix_ratio"]),
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"refr": str(run["cfg"]["vhack_refresh_every"]),
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"over": str(run["cfg"]["project_overshoot"]),
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"gate": run["cfg"]["gate_mode"],
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"k": str(run["cfg"]["v_hack_k"]),
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"dropf": str(run["cfg"]["v_hack_drop_bottom_frac"]),
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"vhack": run["cfg"]["vhack_pairs_path"].split("/")[-1].removesuffix(".json"),
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"L5_hack": run["l5_hack"],
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"L5_solve": run["l5_solve"],
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"WH_hack": run["whole_hack"],
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"n": len(run["rows"]),
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"run": run["run_dir"].name,
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} for run in runs]
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if not rows:
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print("no completed non-smoke runs in out/runs/")
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return
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df = pl.DataFrame(rows).sort("time")
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cols = ["arm", "seed", "mix", "refr", "over", "gate", "k", "dropf",
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"vhack", "L5_hack", "L5_solve", "WH_hack", "n", "run"]
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print("\n## All runs (sorted by time)\n")
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print(tabulate(df.select(cols).rows(), headers=cols, tablefmt="pipe", floatfmt=".3f"))
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key = ["arm", "mix", "refr", "over", "gate", "k", "dropf", "vhack"]
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grouped = (df.group_by(key)
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.agg(pl.col("L5_hack").mean().alias("hack"),
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pl.col("L5_hack").std().alias("hack_sd"),
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pl.col("L5_solve").mean().alias("solve"),
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pl.col("L5_solve").std().alias("solve_sd"),
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pl.len().alias("n"),
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pl.col("seed").sort().str.join(",").alias("seeds"))
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.sort(["mix", "arm", "refr", "over", "gate", "k"]))
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gcols = key + ["hack", "hack_sd", "solve", "solve_sd", "n", "seeds"]
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print("\n## Grouped by config (mean +/- std over seeds)\n")
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print(tabulate(grouped.select(gcols).rows(), headers=gcols, tablefmt="pipe", floatfmt=".3f"))
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if __name__ == "__main__":
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main()
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