wassname/moral-maps
1
0
Fork 0
mirror of https://github.com/wassname/moral-maps.git synced 2026-09-09 11:27:22 +08:00
Code Issues Packages Projects Releases Wiki Activity

plot showcase: relative coherence gate + persona vec_label

Browse Source 
Trajectory coherence gate was absolute (pmass<0.9); make it relative -- keep a
c only if pmass >= 95% of the base (c=0) pmass, else drop it entirely (no hollow
markers). Read vec_label from summary.json so non-authority personas label
correctly instead of the hardcoded "Authority/Care axis".

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-25 09:09:02 +08:00
co-authored by Claudypoo
parent a2fe33ac53
commit eb433f1cdc
1 changed files with 9 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/plot_steer_showcase.py
+9 -5
View File
@@ -125,9 +125,13 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float)
respondents, haze = T.maps.respondent_profiles(dims, instr.scale_max), None
else:
respondents, haze = None, human_haze(instr)
# trajectory overlay only when the run swept more than the 3-point base/+-C (else the arrows suffice)
traj = {c: _frac(prof_c[c], instr.scale_max) for c in cs} if len(cs) > 3 else None
traj_inco = {c for c, pm in pmass.items() if pm < 0.9} if traj else None
# trajectory overlay only when the run swept more than the 3-point base/+-C (else the arrows suffice).
# Coherence gate is RELATIVE: keep a c only if its pmass stays within 95% of the base (c=0) pmass;
# below that the readout has degraded enough that the profile is not comparable, so drop it entirely.
base_pm = pmass[0.0]
coh_cs = [c for c in cs if pmass[c] >= 0.95 * base_pm]
traj = {c: _frac(prof_c[c], instr.scale_max) for c in coh_cs} if len(coh_cs) > 3 else None
traj_inco = None # excluded (not drawn hollow) per the 95%-of-base coherence gate
figm = T.maps.plot_ipsative_pca(instr, dims, countries, Mfrac,
_frac(base, instr.scale_max), _frac(pos, instr.scale_max),
_frac(neg, instr.scale_max), respondents=respondents, haze=haze,
@@ -138,7 +142,7 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float)
# SPLOM only for mfq2: real per-respondent joint (others ship independent-marginal haze, whose
# off-diagonals would fabricate the correlation structure). Full + AI-zoom (macro + micro).
if name == "mfq2":
proffrac = {c: _frac(prof_c[c], instr.scale_max) for c in cs}
proffrac = {c: _frac(prof_c[c], instr.scale_max) for c in coh_cs}
for zoom, tag in [(False, "splom"), (True, "splom_zoom")]:
figs = T.maps.plot_splom(instr, dims, respondents, Mfrac, _frac(base, instr.scale_max),
proffrac, zoom=zoom, vec_label=vec_label)
@@ -262,7 +266,7 @@ def main() -> None:
summary = json.loads((args.run_dir / "summary.json").read_text())
C = float(summary["calibrated_C"])
method = summary["method"]
vec_label = f"{method} (Authority/Care axis)"
vec_label = summary.get("vec_label", f"{method} (Authority/Care axis)")
args.out.mkdir(parents=True, exist_ok=True)
written: list[str] = []