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plots: MFV reuses shared ipsative map + range in z-space; fix range coherence-gate

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MFV got two bespoke figures (z-emphasis bars + dlogit dumbbell). Per GPT-5.5 code
review, reuse the geometry not the ordinal semantics: a _mfv_zspace adapter feeds the
same plot_ipsative_pca + plot_range the ordinal instruments use, in z-scored relative-
emphasis space (logit-violation and 1-5 wrongness cannot share a raw axis). Keep the
dlogit dumbbell as a raw-magnitude diagnostic; drop the bespoke map_emphasis.

Also fix a real bug the review found: plot_ordinal gated the map/SPLOM trajectory to
coherent c (pmass >= 0.95*base) but still passed ALL cs to range/range_zoom, so the
figures disagreed on which poles were valid. Range/zoom now use the same coh_cs.
plot_range gains an ylabel param (MFV is z-space, not 1-M); draw_steer asserts sorted
cs; MFV asserts only Social Norms is dropped.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-25 15:51:40 +08:00
co-authored by Claudypoo
parent fe0d27b9bd
commit 2160304541
2 changed files with 66 additions and 50 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/plot_steer_showcase.py
+59 -47
View File
@@ -20,6 +20,7 @@ import argparse
import csv
import json
from pathlib import Path
from types import SimpleNamespace
import matplotlib
matplotlib.use("Agg")
@@ -149,11 +150,16 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float)
paths.append(T.maps.save_both(figs, out / name, tag))
plt.close(figs)
figr = T.maps.plot_range(instr, dims, cs, prof, humans, None, vec_label)
# Range/zoom render the SAME coherence-gated c-points the map+SPLOM use (coh_cs), so the figures
# agree on which steer multipliers are valid. Without this the map drops incoherent/NaN poles while
# the range still plots them (GPT-5.5 code review). Base (c=0) is always in coh_cs (pmass==base_pm).
assert 0.0 in coh_cs, f"{name}: base c=0 dropped by coherence gate, pmass={pmass}"
prof_coh = {c: prof_c[c] for c in coh_cs}
figr = T.maps.plot_range(instr, dims, coh_cs, prof_coh, humans, None, vec_label)
paths.append(T.maps.save_both(figr, out / name, "range"))
plt.close(figr)
figz = T.maps.plot_range_zoom(instr, dims, cs, prof, humans, vec_label)
figz = T.maps.plot_range_zoom(instr, dims, coh_cs, prof_coh, humans, vec_label)
paths.append(T.maps.save_both(figz, out / name, "range_zoom"))
plt.close(figz)
return paths
@@ -176,55 +182,59 @@ def read_human_mfv() -> tuple[list[str], dict[str, dict[str, float]]]:
return sorted(by_country), by_country
def plot_mfv_map(run_dir: Path, out: Path, vec_label: str, C: float) -> Path:
"""Bespoke MFV map: per-foundation RELATIVE EMPHASIS (z across foundations) of the model's base /
+C / -C reads against the human MFV cultures. MFV is nominal (model emits logit(violation) per
foundation, humans rate wrongness 1-5), so absolute scales differ; z-scoring each profile within
itself compares the PATTERN -- which foundations a reader weights as more violation-worthy than
their own average -- which is exactly what the steer is meant to move. Social Norms is dropped (no
human norm). The steer shows as base->+C (red) and base->-C (blue) arrows per foundation."""
def _mfv_zspace(run_dir: Path):
"""Shared MFV adapter -> the common coordinate system the map AND range both consume: z-scored
relative-emphasis profiles (model base / +C / -C) + the human MFV culture matrix in the same
space. MFV is nominal (model emits logit(violation) per foundation, humans rate wrongness 1-5),
so absolute scales differ; z-scoring each profile ACROSS foundations compares the PATTERN -- which
foundations a reader weights as more violation-worthy than their own average -- which is exactly
what the steer moves. Social Norms is dropped (no human MFV norm), asserted so a taxonomy change
fails loud. Returns (founds, countries, M_z[countries x founds], base_z, posz, negz)."""
d = json.loads((run_dir / "mfv.json").read_text())
base_l = d["base_logit_per_foundation"]
pos_dl, neg_dl = d["pos"]["dlogit_per_foundation"], d["neg"]["dlogit_per_foundation"]
countries, human = read_human_mfv()
hfounds = set(next(iter(human.values())))
founds = [f for f in d["foundation_order"] if f.lower() in hfounds] # 6 shared, model order
founds = [f for f in d["foundation_order"] if f.lower() in hfounds] # shared, model order
dropped = [f for f in d["foundation_order"] if f.lower() not in hfounds]
assert dropped == ["Social Norms"], f"unexpected MFV foundations without a human norm: {dropped}"
fl = [f.lower() for f in founds]
base = _zscore(np.array([base_l[f]["mean"] for f in founds]))
posz = _zscore(np.array([base_l[f]["mean"] + pos_dl[f]["mean"] for f in founds]))
negz = _zscore(np.array([base_l[f]["mean"] + neg_dl[f]["mean"] for f in founds]))
Hz = {c: _zscore(np.array([human[c][f] for f in fl])) for c in countries}
M = np.array([_zscore(np.array([human[c][f] for f in fl])) for c in countries])
return founds, countries, M, base, posz, negz
rng = np.random.default_rng(0)
fig, ax = plt.subplots(figsize=(7.2, 4.6))
ax.axhline(0, color="0.85", lw=0.8, zorder=0)
POS, NEG, GREY = T.maps.POS_COL, T.maps.NEG_COL, T.maps.COUNTRY_GREY
for i, f in enumerate(founds):
hvals = np.array([Hz[c][i] for c in countries])
ax.scatter(i - 0.18 + (rng.random(len(hvals)) - 0.5) * 0.12, hvals, s=26, color=GREY,
alpha=0.9, edgecolor="white", linewidth=0.3, zorder=3)
ax.plot([i - 0.30, i - 0.06], [np.median(hvals)] * 2, color=T.maps.MEDIAN_GREY, lw=1.4, zorder=4)
xs = i + 0.18
ax.plot(xs, base[i], "o", ms=4, color="black", zorder=7)
for pole, col in [(posz[i], POS), (negz[i], NEG)]:
if abs(pole - base[i]) > 1e-9:
ax.plot([xs, xs], [base[i], pole], color=col, lw=2.0, zorder=6, solid_capstyle="round")
ax.plot(xs, pole, marker=("^" if pole >= base[i] else "v"), color=col, ms=7,
markeredgecolor="none", zorder=8)
ax.scatter([], [], marker="o", color=GREY, label=f"human culture (n={len(countries)})")
ax.scatter([], [], marker="o", color="black", label="model base")
ax.scatter([], [], marker="^", color=POS, label=f"steer +C={C:+.2f}")
ax.scatter([], [], marker="v", color=NEG, label=f"steer -C={-C:+.2f}")
ax.legend(fontsize=7.5, loc="lower right", framealpha=0.9, ncol=2)
ax.set_xticks(range(len(founds)))
ax.set_xticklabels(founds, rotation=20, ha="right", fontsize=8)
ax.set_xlim(-0.6, len(founds) - 0.4)
ax.set_ylabel("relative emphasis (z across foundations)")
ax.set_title(f"MFV foundation emphasis vs human cultures: {vec_label}", fontsize=10)
ax.spines[["top", "right"]].set_visible(False)
fig.tight_layout()
path = T.maps.save_both(fig, out / "mfv", "map_emphasis")
# MFV has no ordinal Instrument (it goes through evaluate_multibool, not administer), but the shared
# plotters only read .name/.display off it -- a shim supplies those. The y-values are z-scores, not a
# 1-M scale, so it carries no scale_max and the range passes its own ylabel.
_MFV_INSTR = SimpleNamespace(name="mfv", display="MFV vignettes")
_MFV_YLABEL = "relative emphasis (z across foundations)"
def plot_mfv_map(run_dir: Path, out: Path, vec_label: str, C: float) -> Path:
"""MFV ipsative culture map via the SAME plot_ipsative_pca the ordinal instruments use, in the
z-scored relative-emphasis space (logit-violation and 1-5 wrongness cannot share a raw axis).
base->+C (red) / base->-C (blue) arrows show where the steer moves the AI among human cultures."""
founds, countries, M, base, posz, negz = _mfv_zspace(run_dir)
labels = ("base (c=0)", f"+C={C:+.2f}", f"-C={-C:+.2f}")
fig = T.maps.plot_ipsative_pca(_MFV_INSTR, founds, countries, M, base, posz, negz, labels=labels)
path = T.maps.save_both(fig, out / "mfv", "map_pca_ipsative")
plt.close(fig)
return path
def plot_mfv_range(run_dir: Path, out: Path, vec_label: str, C: float) -> Path:
"""MFV range via the SAME plot_range the ordinal instruments use, in z relative-emphasis space.
Only base/+C/-C (the MFV eval is a 3-point sweep, not a multi-C grid like the ordinal admin)."""
founds, countries, M, base, posz, negz = _mfv_zspace(run_dir)
cs = [-1.0, 0.0, 1.0]
prof = {-1.0: negz, 0.0: base, 1.0: posz}
humans = {f: sorted(((countries[ci], float(M[ci, fi])) for ci in range(len(countries))), key=lambda t: t[1])
for fi, f in enumerate(founds)}
fig = T.maps.plot_range(_MFV_INSTR, founds, cs, prof, humans, None, vec_label, ylabel=_MFV_YLABEL)
path = T.maps.save_both(fig, out / "mfv", "range")
plt.close(fig)
return path
@@ -238,17 +248,18 @@ def plot_mfv(run_dir: Path, out: Path, vec_label: str, C: float) -> Path:
y = np.arange(len(order))[::-1]
fig, ax = plt.subplots(figsize=(6.4, 4.2))
ax.axvline(0, color="0.6", lw=0.8, zorder=1)
POS, NEG = T.maps.POS_COL, T.maps.NEG_COL
for f, yi in zip(order, y):
pm, ps = pos[f]["mean"], pos[f]["std"] / max(1, pos[f]["n"]) ** 0.5
nm, ns = neg[f]["mean"], neg[f]["std"] / max(1, neg[f]["n"]) ** 0.5
ax.plot([nm, pm], [yi, yi], color="0.8", lw=1.0, zorder=2)
ax.errorbar(pm, yi, xerr=1.96 * ps, fmt="o", color="#c0392b", ms=5, capsize=2, zorder=3)
ax.errorbar(nm, yi, xerr=1.96 * ns, fmt="o", color="#2c6fbb", ms=5, capsize=2, zorder=3)
ax.errorbar(pm, yi, xerr=1.96 * ps, fmt="o", color=POS, ms=5, capsize=2, zorder=3)
ax.errorbar(nm, yi, xerr=1.96 * ns, fmt="o", color=NEG, ms=5, capsize=2, zorder=3)
ax.set_yticks(y); ax.set_yticklabels(order)
ax.set_xlabel("Delta logit(violation) vs bare (nats)")
ax.set_title(f"Steered MFV vignettes: {vec_label}", fontsize=11)
ax.scatter([], [], color="#c0392b", label=f"+C={C:+.2f}")
ax.scatter([], [], color="#2c6fbb", label=f"-C={-C:+.2f}")
ax.scatter([], [], color=POS, label=f"+C={C:+.2f}")
ax.scatter([], [], color=NEG, label=f"-C={-C:+.2f}")
ax.legend(fontsize=8, loc="best")
ax.spines[["top", "right"]].set_visible(False)
fig.tight_layout()
@@ -274,8 +285,9 @@ def main() -> None:
if (args.run_dir / f"{name}_profiles.csv").exists():
written += [str(p) for p in plot_ordinal(args.run_dir, args.out, name, vec_label, C)]
if (args.run_dir / "mfv.json").exists():
written.append(str(plot_mfv_map(args.run_dir, args.out, vec_label, C)))
written.append(str(plot_mfv(args.run_dir, args.out, vec_label, C)))
written.append(str(plot_mfv_map(args.run_dir, args.out, vec_label, C))) # shared ipsative map (z-space)
written.append(str(plot_mfv_range(args.run_dir, args.out, vec_label, C))) # shared range (z-space)
written.append(str(plot_mfv(args.run_dir, args.out, vec_label, C))) # raw dlogit dumbbell (diagnostic)
print(f"wrote {len(written)} figures under {args.out}:")
for w in written:
print(" ", w)
src/tinymfv/maps.py
+7 -3
View File
@@ -435,6 +435,7 @@ def draw_steer(ax, xs: float, cs: list[float], yv: np.ndarray, base_y: float,
(the zoom labels them c=X and wants them; the main range does not -- there they only clutter a short
arm into a blob). The +c arm is nudged +dx in x and the -c arm -dx, so a NON-bidirectional steer
(both poles the same side of base) reads as two short PARALLEL arms rather than overlapping."""
assert list(cs) == sorted(cs) and 0.0 in cs, f"draw_steer needs sorted cs with c=0 (yv[-1]=+pole, yv[0]=-pole), got {cs}"
ax.plot(xs, base_y, "o", ms=ms, color="black", zorder=7) # base: the unsteered model
if dots:
for c, y in zip(cs, yv):
@@ -512,16 +513,19 @@ def draw_range_panel(ax, instr: Instrument, dims: list[str], cs: list[float], pr
def plot_range(instr: Instrument, dims: list[str], cs: list[float], prof: dict, humans: dict,
cloud: np.ndarray | None, vec: str):
cloud: np.ndarray | None, vec: str, *, ylabel: str | None = None):
"""Single-axes range figure for one steering vector. Returns the Figure.
The dot/line encoding legend belongs in the figure CAPTION, not the image: a paragraph of
legend text in the suptitle forces the figure wide to fit one line and squashes the panel.
The axes title already names the geometry (tail=-c, arrow=+c)."""
The axes title already names the geometry (tail=-c, arrow=+c).
`ylabel` overrides the default ordinal "<instr> mean (1-M)" -- nominal MFV passes its own
"relative emphasis (z across foundations)" since its y-values are z-scores, not a 1-M scale."""
figw = max(6.4, 1.5 * len(dims) + 1.5)
fig, ax = plt.subplots(figsize=(figw, 4.8))
draw_range_panel(ax, instr, dims, cs, prof, humans, cloud, vec)
ax.set_ylabel(f"{instr.display} mean (1-{instr.scale_max})")
ax.set_ylabel(ylabel or f"{instr.display} mean (1-{instr.scale_max})")
fig.suptitle(f"Steered {instr.display}: {vec}", fontsize=11)
fig.tight_layout()
return fig