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correctness fixes from gpt-5.5 review (ordinal path + fail-fast asserts)

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Second external review (gpt-5.5, correctness-focused) on the post-cleanup tree.
Found no off-by-one/double-flip in the ordinal canonicalization+keying. Fixed the
parts I agreed with and could verify on the path the experiment uses:
- read.py: NaN-safe answer-token renorm. p_a/pmass poisons the profile with NaN when
  pmass underflows to 0 at coherence collapse -- exactly when pmass should just flag it.
  softmax(logp_allowed) is identical when pmass>0 and stable at collapse.
- maps.ipsative_pca: move SVD sign-stabilization INTO the helper so it and
  plot_ipsative_pca share one orientation (saved coords could otherwise mirror the figure).
- instrument: assert ordinal answer_space is ['1'..scale_max] IN ORDER (reduce_ordinal
  weights by position; a reordered space silently inverts E) -- was length-only.
- instrument.per_item_categorical: assert per-item dimension/sign agree across frames and
  frames are distinct, instead of silently averaging under rows[0]'s metadata.
- pyproject: move matplotlib+textalloc to an optional `maps` extra; evals stay headless.
- tests: drop imports of the deleted reduce_nominal/expected_value, inline the expectation,
  remove the now-impossible nominal-reducer test.

Deferred (flagged to maintainer): two NaN/window issues in guided.py's forced-choice
rollout (nominal evaluate() path) -- not exercised by this experiment, can't smoke-test,
and the NaN-as-collapse-signal there is a deliberate design.

Verified: experiment smoke green on all 4 instruments (no assert false-fires), 6 pure
unit tests pass, headless import clean, 16pf map renders.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-23 21:25:33 +08:00
co-authored by Claudypoo
parent 5333893345
commit d5876a46d3
5 changed files with 42 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files
pyproject.toml
+8 -2
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@@ -17,8 +17,14 @@ dependencies = [
"tabulate",
"datasets",
"numpy",
"matplotlib>=3.8", # tinymfv.maps: culture-map + range viz
"textalloc>=1.2.3", # non-overlapping label placement on the maps
]
[project.optional-dependencies]
# tinymfv.maps (culture-map + range viz) only; `import tinymfv` and the evals stay headless,
# numeric-only consumers (steering-lite) skip this. Install with `pip install tiny-mfv[maps]`.
maps = [
"matplotlib>=3.8",
"textalloc>=1.2.3", # non-overlapping label placement on the maps
]
[dependency-groups]
src/tinymfv/instrument.py
+9 -1
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@@ -73,7 +73,10 @@ class Instrument:
def __post_init__(self):
if self.kind == "ordinal":
assert len(self.answer_space) == self.scale_max, "ordinal answer_space must be 1..scale_max"
assert self.answer_space == [str(i) for i in range(1, self.scale_max + 1)], (
f"ordinal answer_space must be ['1'..'{self.scale_max}'] IN ORDER -- reduce_ordinal "
f"weights by position (w = 1..scale_max), so a reordered space silently inverts E; "
f"got {self.answer_space}")
if self.kind == "nominal" and self.answer_to_dim is None:
self.answer_to_dim = {a: a for a in self.answer_space}
# Cross-scale caveat: a 1-7 human histogram (HSQ) cannot share a 5-way soft-NLL/JS
@@ -125,6 +128,11 @@ def per_item_categorical(per_row: list[dict], kind: Kind) -> dict[str, dict]:
assert len(frame_counts) == 1, f"heterogeneous frame counts per item: {frame_counts}"
out: dict[str, dict] = {}
for iid, rows in by_id.items():
# Per-item rows differ only by frame; dimension + sign must agree, frames must be distinct.
# Else we would silently average incompatible distributions under rows[0]'s metadata.
assert len({r.get("dimension") for r in rows}) == 1, f"{iid}: inconsistent dimension across frames"
assert len({r.get("sign", 1) for r in rows}) == 1, f"{iid}: inconsistent sign across frames"
assert len({r["frame"] for r in rows}) == len(rows), f"{iid}: duplicate frame in rows"
canon = [canonicalize_to_forward(r["p"], r["frame"], kind) for r in rows]
C = np.stack(canon)
spread = float(max((np.abs(C[i] - C[j]).sum()
src/tinymfv/maps.py
+10 -7
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@@ -55,12 +55,19 @@ def row_centre_op(K: int) -> np.ndarray:
def ipsative_pca(M: np.ndarray, k: int = 2):
"""Row-centre each row of M (societies x K), then PCA across rows.
Returns (P, Vt, var, mu, Pc); project a new point v via ((v @ Pc) - mu) @ Vt[:k].T."""
Pc = row_centre_op(M.shape[1])
Returns (P, Vt, var, mu, Pc); project a new point v via ((v @ Pc) - mu) @ Vt[:k].T.
SVD signs are stabilized here (PC1 loads + on factor 0, PC2 on factor 1) so this helper and
any plot built on it share ONE orientation -- otherwise saved coords could mirror the figure."""
K = M.shape[1]
Pc = row_centre_op(K)
Mp = M @ Pc
mu = Mp.mean(axis=0)
Mc = Mp - mu
_, S, Vt = np.linalg.svd(Mc, full_matrices=False)
if Vt[0, 0] < 0:
Vt[0] = -Vt[0]
if Vt.shape[0] > 1 and Vt[1, 1 % K] < 0:
Vt[1] = -Vt[1]
var = (S ** 2) / (S ** 2).sum()
return Mc @ Vt[:k].T, Vt, var, mu, Pc
@@ -100,11 +107,7 @@ def plot_ipsative_pca(instr: Instrument, dims: list[str], countries: list[str],
import textalloc as ta
except ImportError:
ta = None
P, Vt, var, mu, Pc = ipsative_pca(M)
if Vt[0, 0] < 0: # stabilise SVD sign: factor[0] loads +PC1
Vt[0] = -Vt[0]
if Vt[1, 1 % len(dims)] < 0:
Vt[1] = -Vt[1]
P, Vt, var, mu, Pc = ipsative_pca(M) # signs already stabilized inside the helper
P = (M @ Pc - mu) @ Vt[:2].T
def proj(v):
src/tinymfv/read.py
+6 -3
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@@ -72,9 +72,12 @@ def read_items(model, tok, instr: Instrument, items: list[InstrItem], answer_ids
enc = tok(texts, return_tensors="pt", padding=True, add_special_tokens=False).to(device)
logits = model(**enc).logits[:, -1, :].float() # [B, V] next-token
logp = F.log_softmax(logits, dim=-1)
p_a = logp[:, gid].exp() # [B, A] prob on each answer token
pmass = p_a.sum(dim=-1) # [B]
p_norm = p_a / pmass[:, None] # [B, A] within allowed
logp_a = logp[:, gid] # [B, A] logprob on each answer token
pmass = logp_a.exp().sum(dim=-1) # [B] coherence check: mass on allowed tokens
# softmax over the allowed logprobs == p_a / pmass when pmass > 0, but NaN-safe: at coherence
# collapse pmass underflows to 0 and the divide would poison the whole profile with NaN; the
# softmax still returns a valid within-allowed distribution and pmass separately flags the drop.
p_norm = F.softmax(logp_a, dim=-1) # [B, A] within allowed
for j, it in enumerate(chunk):
out.append({
"id": it.id, "frame": it.frame,
tests/test_instrument.py
+9 -14
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@@ -8,13 +8,17 @@ import numpy as np
from tinymfv.instrument import (
Instrument, InstrItem, canonicalize_to_forward, per_item_categorical,
reduce_nominal, reduce_ordinal, expected_value, shuffle_dimensions,
reduce_ordinal, shuffle_dimensions,
)
M = 5
ONEHOT = {d: np.eye(M)[d - 1] for d in range(1, M + 1)} # ONEHOT[4] = mass on scale point 4
def _E(p, scale_max): # expected scale point; reduce_ordinal computes this inline now
return float((np.asarray(p) * np.arange(1, scale_max + 1)).sum())
def _ord_instr(items):
return Instrument("t", "endorsement", "ordinal", ["1", "2", "3", "4", "5"],
["care", "authority"], items, prefill="(")
@@ -33,7 +37,7 @@ def test_forward_expectation():
rows = [{"id": "1", "frame": "forward", "p": ONEHOT[4], "pmass_allowed": 1.0,
"dimension": "care", "sign": 1, "human_label": None}]
items = per_item_categorical(rows, "ordinal")
assert abs(expected_value(items["1"]["p"], M) - 4.0) < 1e-9
assert abs(_E(items["1"]["p"], M) - 4.0) < 1e-9
prof = reduce_ordinal(items, _ord_instr([]))
assert abs(prof[0] - 4.0) < 1e-9 # care
assert np.isnan(prof[1]) # authority has no items
@@ -45,8 +49,8 @@ def test_frame_consistency():
"dimension": "care", "sign": 1, "human_label": None}]
inv = [{"id": "1", "frame": "inverted", "p": ONEHOT[5], "pmass_allowed": 1.0,
"dimension": "care", "sign": 1, "human_label": None}]
e_fwd = expected_value(per_item_categorical(fwd, "ordinal")["1"]["p"], M)
e_inv = expected_value(per_item_categorical(inv, "ordinal")["1"]["p"], M)
e_fwd = _E(per_item_categorical(fwd, "ordinal")["1"]["p"], M)
e_inv = _E(per_item_categorical(inv, "ordinal")["1"]["p"], M)
assert abs(e_fwd - 1.0) < 1e-9 and abs(e_inv - 1.0) < 1e-9 # canonicalization makes them agree
@@ -59,7 +63,7 @@ def test_keying_is_not_double_flip():
rows = [{"id": "x", "frame": "inverted", "p": ONEHOT[5], "pmass_allowed": 1.0,
"dimension": "care", "sign": -1, "human_label": None}]
items = per_item_categorical(rows, "ordinal")
assert abs(expected_value(items["x"]["p"], M) - 1.0) < 1e-9 # canonical agreement-with-item = 1
assert abs(_E(items["x"]["p"], M) - 1.0) < 1e-9 # canonical agreement-with-item = 1
prof = reduce_ordinal(items, _ord_instr([]))
assert abs(prof[0] - 5.0) < 1e-9 # keyed factor score = 5, not 1 (no double flip)
@@ -81,15 +85,6 @@ def test_frame_spread_diagnostic():
assert abs(items["1"]["frame_spread"] - 2.0) < 1e-9
def test_reduce_nominal():
instr = Instrument("mfv", "salience", "nominal", ["care", "authority"],
["care", "authority"], [InstrItem("1", "v1")], prefill="(")
rows = [{"id": "1", "frame": "forward", "p": np.array([0.8, 0.2]), "pmass_allowed": 1.0},
{"id": "2", "frame": "forward", "p": np.array([0.4, 0.6]), "pmass_allowed": 1.0}]
prof = reduce_nominal(per_item_categorical(rows, "nominal"), instr)
assert np.allclose(prof, [0.6, 0.4]) # mean choice frequency
def test_negative_control_shuffle():
rng = np.random.default_rng(0)
rows = [{"id": str(i), "frame": "forward", "p": ONEHOT[(i % 5) + 1], "pmass_allowed": 1.0,