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administer: return per_item_frame (per-(item,frame) keyed rows)

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The frame-averaged per_item drops the per-framing granularity that experiment
analyses need (MFQ-2 map's framing-bias diagnostic + paired base-vs-steer delta).
These rows are already computed in the by_dim_frame loop, so returning them is free.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-23 10:23:02 +08:00
co-authored by Claudypoo
parent 0bef8b0d3d
commit 913befd308
1 changed files with 10 additions and 1 deletions
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src/tinymfv/administer.py
+10 -1
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@@ -46,13 +46,21 @@ def administer(model, tok, instr: Instrument, *, batch_size: int = 36) -> dict:
# per-frame factor means + framing spread (acquiescence/wording diagnostic): for each frame,
# canonicalize that frame's presented distribution to forward, key it, pool per factor.
# Also keep the per-(item, frame) rows: experiment analyses (e.g. the MFQ-2 map's framing-bias
# diagnostic + paired base-vs-steer delta) need the per-framing granularity that per_item
# averages away. agreement = forward-canonicalized E (agreement toward the original statement);
# keyed_agreement reflects reverse-keyed items, same as reduce_ordinal.
frames = sorted({r["frame"] for r in per_row})
by_dim_frame: dict[tuple[str, str], list[float]] = {}
per_item_frame: list[dict] = []
for r in per_row:
p_fwd = canonicalize_to_forward(r["p"], r["frame"], instr.kind)
E = float((p_fwd * w).sum())
keyed = (instr.scale_max + 1 - E) if r["sign"] < 0 else E
by_dim_frame.setdefault((r["dimension"], r["frame"]), []).append(keyed)
per_item_frame.append({"id": r["id"], "framing": r["frame"], "foundation": r["dimension"],
"agreement": E, "keyed_agreement": keyed,
"pmass_allowed": r["pmass_allowed"]})
rng = np.random.default_rng(0)
foundations = []
@@ -67,4 +75,5 @@ def administer(model, tok, instr: Instrument, *, batch_size: int = 36) -> dict:
**{f"f_{fr}": v for fr, v in per_fr.items()},
})
return {"profile": profile, "dimensions": instr.dimensions, "foundations": foundations,
"per_item": per_item_rows, "mean_pmass_allowed": mean_pmass}
"per_item": per_item_rows, "per_item_frame": per_item_frame,
"mean_pmass_allowed": mean_pmass}