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Cache WVS model E-vectors; impute collapsed questions for projection

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Cache model E-vectors keyed by the exact question block, so iterating on the zone
style re-projects + re-draws without re-hitting the API (~20 min of calls). A
question a model didn't answer coherently (NaN) is imputed with the human average
at projection so one collapse doesn't drop the whole model dot.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-07-04 20:54:56 +08:00
co-authored by Claudypoo
parent 44c1fa0595
commit ea796dee9a
1 changed files with 33 additions and 14 deletions
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scripts/wvs_map.py
+33 -14
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@@ -14,8 +14,10 @@ from __future__ import annotations
import argparse
import ast
import json
import re
from collections import Counter
from pathlib import Path
import dotenv
import numpy as np
@@ -124,6 +126,8 @@ def main() -> None:
ap.add_argument("--max-think-tokens", type=int, default=64)
ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
ap.add_argument("--out", default="/tmp/claude-1000/wvs_map.png")
ap.add_argument("--cache", default="/tmp/claude-1000/wvs_model_vectors.json",
help="cache model E-vectors so re-plotting (new zone style) skips the API calls")
args = ap.parse_args()
recs = load_wvs(args.n_opts)
@@ -134,27 +138,42 @@ def main() -> None:
P, Vt, var, mu, Pc = maps.ipsative_pca(Hfrac)
instr = build_instrument(block, args.n_opts)
models: dict[str, np.ndarray] = {} # model name -> projected 2D point
# cache keyed by the exact question block: same block -> reuse E-vectors, only re-projecting +
# re-drawing (so iterating on the zone style costs no API calls).
sig = f"{args.n_opts}:{len(block)}:{hash(tuple(r['q'] for r in block)) & 0xffffffff}"
cpath = Path(args.cache)
cache = json.loads(cpath.read_text()).get(sig, {}) if cpath.exists() else {}
vecs: dict[str, np.ndarray] = {k: np.array(v) for k, v in cache.items()}
if args.local_model:
tok = AutoTokenizer.from_pretrained(args.local_model)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
tok.padding_side = "left"
lm = AutoModelForCausalLM.from_pretrained(args.local_model, dtype=torch.bfloat16).to(args.device).eval()
rows = read_items(lm, tok, instr, instr.items, resolve_answer_ids(tok, instr.answer_space),
max_think_tokens=args.max_think_tokens, batch_size=16, verbose_first=True)
v = model_vector(rows, len(block), args.n_opts)
models[args.local_model.split("/")[-1] + " (lp)"] = ((v @ Pc) - mu) @ Vt[:2].T
key = args.local_model.split("/")[-1] + " (lp)"
if key not in vecs:
tok = AutoTokenizer.from_pretrained(args.local_model)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
tok.padding_side = "left"
lm = AutoModelForCausalLM.from_pretrained(args.local_model, dtype=torch.bfloat16).to(args.device).eval()
rows = read_items(lm, tok, instr, instr.items, resolve_answer_ids(tok, instr.answer_space),
max_think_tokens=args.max_think_tokens, batch_size=16, verbose_first=True)
vecs[key] = model_vector(rows, len(block), args.n_opts)
for m in args.api_models:
rows = read_items_sampled(m, instr, instr.items, n_samples=args.api_samples, verbose_first=True)
v = model_vector(rows, len(block), args.n_opts)
models[m.split("/")[-1] + " (sampled)"] = ((v @ Pc) - mu) @ Vt[:2].T
key = m.split("/")[-1] + " (sampled)"
if key not in vecs:
rows = read_items_sampled(m, instr, instr.items, n_samples=args.api_samples, verbose_first=True)
vecs[key] = model_vector(rows, len(block), args.n_opts)
cpath.parent.mkdir(parents=True, exist_ok=True)
allc = json.loads(cpath.read_text()) if cpath.exists() else {}
allc[sig] = {k: v.tolist() for k, v in vecs.items()}
cpath.write_text(json.dumps(allc))
hmean = Hfrac.mean(0) # a question the model didn't answer coherently -> the human average there
models = {k: ((np.where(np.isnan(v), hmean, v) @ Pc) - mu) @ Vt[:2].T for k, v in vecs.items()}
zones, emph = zones_for(countries)
fig, ax = plt.subplots(figsize=(10, 8))
ax.set_facecolor("#faf8f2")
ax.grid(True, color="#eceadf", lw=0.3, zorder=0)
maps.draw_zone_ellipses(ax, P, countries, zones)
maps.draw_zone_regions(ax, P, countries, zones)
ax.scatter(P[:, 0], P[:, 1], s=22, c=maps.C_HUM, alpha=0.7, edgecolors="white", linewidths=0.4, zorder=3)
for i, c in enumerate(countries):
is_e = c in emph