Download analyses/tetradic_recompute_H.py from anonymousfileupload/divsim-code: direct link, hf CLI and curl.
- Browser
- Download file 2.28 kB
-
https://huggingface.co/datasets/anonymousfileupload/divsim-code/resolve/main/analyses/tetradic_recompute_H.py
- Command line
-
hf download hf://datasets/anonymousfileupload/divsim-code/analyses/tetradic_recompute_H.py
-
curl -L -o tetradic_recompute_H.py https://huggingface.co/datasets/anonymousfileupload/divsim-code/resolve/main/analyses/tetradic_recompute_H.py
2.28 kB
| """Recompute four-way H stats over all COMPLETE tetratic runs (no checkpoint file). | |
| Persistent: re-run anytime. Prints per-model incoming H + per-seed H->gpt-oss + counts.""" | |
| import sqlite3, json | |
| from collections import defaultdict | |
| from pathlib import Path | |
| import numpy as np | |
| HERE = Path(__file__).parent | |
| SHORT = {"Qwen/Qwen3-32B":"Qwen","openai/gpt-oss-20b":"gpt-oss", | |
| "mistralai/Magistral-Small-2509":"Mag","zai-org/GLM-4-32B-0414":"GLM-4"} | |
| perH = defaultdict(list); byseed = defaultdict(list); runs = [] | |
| for db in sorted(HERE.glob("tetra_s*_r*.db")): | |
| base = db.stem | |
| if (HERE / f"{base}_checkpoint.json").exists(): | |
| continue # incomplete | |
| mp = str(db).replace(".db", ".metadata.json") | |
| if not Path(mp).exists(): | |
| continue | |
| seed = base.split("_")[1] | |
| fam = {int(k)+1: SHORT.get(v["model"]) for k, v in json.load(open(mp))["agent_model_map"].items()} | |
| try: | |
| c = sqlite3.connect(db) | |
| posts = c.execute("SELECT post_id,user_id FROM post").fetchall() | |
| coms = c.execute("SELECT post_id,user_id FROM comment").fetchall(); c.close() | |
| except Exception: | |
| continue | |
| owner = {p: u for p, u in posts}; F = sorted(set(fam.values())) | |
| nf = {f: sum(1 for v in fam.values() if v == f) for f in F}; ntot = sum(nf.values()) | |
| edges = [(fam[cu], fam[owner[p]]) for p, cu in coms | |
| if owner.get(p) and owner[p] != cu and cu in fam and owner[p] in fam] | |
| tot = len(edges) | |
| if tot < 100: | |
| continue | |
| obs = defaultdict(int) | |
| for x, y in edges: obs[(x, y)] += 1 | |
| runs.append(base) | |
| for Y in F: | |
| h = np.mean([obs.get((X, Y), 0)/tot/((nf[X]/ntot)*((nf[Y])/(ntot-1)))-1 for X in F if X != Y]) | |
| perH[Y].append(h) | |
| if Y == "gpt-oss": byseed[seed].append(h) | |
| print(f"=== tetratic H over {len(runs)} complete four-way runs ===") | |
| print(f"runs: {sorted(runs)}") | |
| print(f"{'model':8s} {'meanH':>7} {'sd':>6} {'min':>7} {'max':>7} n") | |
| for m in ["gpt-oss", "Qwen", "GLM-4", "Mag"]: | |
| v = np.array(perH[m]) if perH[m] else np.array([np.nan]) | |
| print(f"{m:8s} {v.mean():+7.2f} {v.std():6.2f} {v.min():+7.2f} {v.max():+7.2f} {len(perH[m])}") | |
| print("H->gpt-oss by seed:") | |
| for s in sorted(byseed): | |
| v = byseed[s]; print(f" seed {s:5s}: {np.mean(v):+.2f} (n={len(v)})") | |