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wassnameandClaude Opus 5
f6c22aca30
Lets a model be described as a 2.9 sigma member of the West instead of "somewhere west of Silicon Valley". Signed per-axis z says which way and how far on a named axis; Mahalanobis says how odd the placement is overall, and uses the cluster covariance because the zones are elongated and tilted. Reads the committed coords in wvs_model_ci.md rather than the coord cache, so it reruns offline without re-querying seventeen models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
"""How much of a cultural outlier each model is, in the SDs of a human macro-zone.
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Turns the committed model coordinates (docs/img/wvs/wvs_model_ci.md, written by wvs_map.py) into
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docs/img/wvs/wvs_model_outlier_sd.md. Reads the table rather than the coord cache so it reruns
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offline, without re-querying seventeen models. The human coordinates are recomputed from WVS here,
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the same way wvs_map.py computes them.
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uv run python scripts/wvs_outlier_table.py
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"""
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from __future__ import annotations
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from pathlib import Path
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import numpy as np
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from loguru import logger
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from tabulate import tabulate
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from moralmaps.iw_axes import resolve_items
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from wvs_map import cluster_outlier_sd, human_axis_scores, load_wvs_all
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IMG = Path(__file__).resolve().parent.parent / "docs" / "img" / "wvs"
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def read_model_coords(path: Path) -> dict[str, tuple]:
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"""(x, y) per model from the pipe-table wvs_map.py writes. Columns: model, x, y, x CI, y CI."""
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models = {}
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for line in path.read_text().splitlines():
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cells = [c.strip() for c in line.strip().strip("|").split("|")]
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if len(cells) < 3 or cells[0] in ("model", "") or set(cells[1]) <= set(":- "):
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continue
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models[cells[0]] = (float(cells[1]), float(cells[2]))
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return models
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def main() -> None:
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models = read_model_coords(IMG / "wvs_model_ci.md")
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countries, P = human_axis_scores(resolve_items(load_wvs_all()))
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logger.info(f"{len(models)} models against {len(countries)} human societies")
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rows = cluster_outlier_sd(countries, P, models)
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west_z = {n: z for n, zone, _, _, z, _ in rows if zone == "West"}
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rows.sort(key=lambda r: (-west_z.get(r[0], 0.0), r[0], r[1]))
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table = tabulate(rows, headers=["model", "zone", "n countries",
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"z self-expr", "z secular", "Mahalanobis"],
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tablefmt="pipe", floatfmt="+.2f")
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(IMG / "wvs_model_outlier_sd.md").write_text(table + "\n")
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logger.info("model distance from human zones, in zone SDs:\n" + table)
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if __name__ == "__main__":
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main()
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