llm-memory-editability / docs /development-artifacts /hebbian-future-v1 /source /scripts /analyze_hebbian_future.py
| #!/usr/bin/env python3 | |
| """Descriptive source margins, perturbations, and paired rehearsal effects.""" | |
| from __future__ import annotations | |
| import csv | |
| import itertools | |
| import numpy as np | |
| from llm_memory_editability.hebbian_future import ART, CONFIG, RESULTS, read, write | |
| def main(): | |
| cfg = read(CONFIG)["chains"] | |
| rows, world_effects, groups = [], [], [] | |
| for directory in sorted((RESULTS / "chains").glob("w*-r*-d*")): | |
| if not (directory / "complete.json").exists(): | |
| continue | |
| meta = read(directory / "complete.json") | |
| key = {k: meta[k] for k in ("world", "rho", "depth")} | |
| parent = read(directory / "learning.json")[-1] | |
| stages = [("parent", parent)] | |
| stages += [ | |
| (arm["arm"], arm["trajectory"][-1]) for arm in read(directory / "rehearsals.json") | |
| ] | |
| for arm, row in stages: | |
| record = {**key, "arm": arm, **row["summary"]} | |
| for kind in ("member", "root", "home", "composite"): | |
| margins = np.asarray(row["rows"][kind]["margin"]) | |
| record[kind + "_margin_median"] = float(np.median(margins)) | |
| record[kind + "_margin_q10"] = float(np.quantile(margins, 0.1)) | |
| rows.append(record) | |
| for arm in cfg["rehearsal_arms"]: | |
| final = next( | |
| r for r in rows if all(r[k] == v for k, v in key.items()) and r["arm"] == arm | |
| ) | |
| for metric in ( | |
| "held_composite", | |
| "held_common_conflict_composite", | |
| "held_two_step", | |
| "member_accuracy", | |
| "root_accuracy", | |
| "home_accuracy", | |
| ): | |
| world_effects.append( | |
| { | |
| **key, | |
| "arm": arm, | |
| "metric": metric, | |
| "change_from_parent": final[metric] - parent["summary"][metric], | |
| } | |
| ) | |
| metrics = [ | |
| "held_composite", | |
| "held_common_conflict_composite", | |
| "held_two_step", | |
| "member_accuracy", | |
| "root_accuracy", | |
| "home_accuracy", | |
| "member_margin_median", | |
| "root_margin_median", | |
| "home_margin_median", | |
| ] | |
| for depth, rho, arm in itertools.product( | |
| cfg["layers"], cfg["correlations"], ["parent", *cfg["rehearsal_arms"]] | |
| ): | |
| part = [r for r in rows if r["depth"] == depth and r["rho"] == rho and r["arm"] == arm] | |
| if part: | |
| groups.append( | |
| { | |
| "depth": depth, | |
| "rho": rho, | |
| "arm": arm, | |
| "worlds": len(part), | |
| **{k: float(np.mean([r[k] for r in part])) for k in metrics}, | |
| } | |
| ) | |
| for name, values in [ | |
| ("rehearsal-detail.csv", rows), | |
| ("rehearsal-effects.csv", world_effects), | |
| ("rehearsal-aggregates.csv", groups), | |
| ]: | |
| with (ART / name).open("w") as handle: | |
| writer = csv.DictWriter(handle, fieldnames=list(values[0])) | |
| writer.writeheader() | |
| writer.writerows(values) | |
| write( | |
| ART / "rehearsal-summary.json", | |
| { | |
| "aggregates": groups, | |
| "effects": world_effects, | |
| "interpretation": ( | |
| "Raw margins are descriptive within model/arm, not scale invariant robustness. " | |
| "Use perturbation curves separately. Rehearsal changes parameters broadly, " | |
| "not margin alone." | |
| ), | |
| }, | |
| ) | |
| if __name__ == "__main__": | |
| main() | |